November 2014 - Academy of Managed Care Pharmacy

Transcription

J MCP
■ Journal of Mana g ed Care & Specialty Pharmac y ■ November 2014
Journal of
Managed
Care & specialty
Pharmacy
Volume 20
■ Number 11 ■ November 2014
■■  SPECIALty PHARMACY
■ Vol. 20, No. 11
Do Value Thresholds for Oncology Drugs Differ from Nononcology Drugs?
Yuna Hyo Jung Bae, PharmD, and C. Daniel Mullins, PhD
Predictors of Treatment Initiation with Tumor Necrosis Factor-α Inhibitors in Patients with Rheumatoid Arthritis
Rishi J. Desai, PhD; Jaya K. Rao, MD; Richard A. Hansen, PhD; Gang Fang, PhD; Matthew L. Maciejewski, PhD; and Joel F. Farley, PhD
■ Pa g es 1069-1146
■■  benefit management
Price Elasticity and Medication Use: Cost Sharing Across Multiple Clinical Conditions
Justin Gatwood, PhD, MPH; Teresa B. Gibson, PhD; Michael E. Chernew, PhD; Amanda M. Farr, MPH; Emily Vogtmann, PhD, MPH;
and A. Mark Fendrick, MD
North Carolina Medicaid Recipient Management Lock-In Program: The Pharmacist’s Perspective
S. Rose Werth, BA; Nidhi Sachdeva, MPH, CHES; Andrew W. Roberts, PharmD; Mariana Garrettson, MPH; Chris Ringwalt, PhD;
Leslie A. Moss, MHA, CHES; Theodore Pikoulas, PharmD, BCPP; and Asheley Cockrell Skinner, PhD
The Impact of Information Technology on Managed Care Pharmacy: Today and Tomorrow
Douglas Hillblom, PharmD; Anthony Schueth, MS; Scott M. Robertson, RPh, PharmD; Laura Topor, BA;
and Greg Low, RPh, BSPharm, PhD
Is There an Association Between the High-Risk Medication Star Ratings and Member Experience CMS Star Ratings Measures?
Sara C. Erickson, PharmD; R. Scott Leslie, MPH; and Bimal V. Patel, PharmD, MS
■■  online exclusive
A Case Study in Generic Drug Use: Should There Be Risk Adjustment in Incentive Payments for the Use of Generic Medications?
Surrey M. Walton, PhD; Christine Rash, PharmD; Bruce L. Lambert, PhD; and William L. Galanter, MD, PhD
Perceptions and Attitudes of Community Pharmacists Towards Generic Medicines
Suzanne S. Dunne, BSc (Hons), MSc; Bill Shannon, MD, FRCGP, MICGP; Walter Cullen, MD, MICGP, MRCGP;
and Colum P. Dunne, BSc (Hons), MBA, PhD
Journal of Managed Care & Specialty Pharmacy
A Peer-Reviewed Journal of the Academy of Managed Care Pharmacy ■ www.jmcp.org ■ www.amcp.org
Help AMCP Recognize
Managed Care
Pharmacy’s Leaders
Nominate Someone for an AMCP Award!
The AMCP Distinguished Service Award recognizes an AMCP member who has made exceptional
and sustained contributions to AMCP over at least a five year period. Last year’s winner was Marty
Mattei.
The AMCP Grassroots Advocacy Award recognizes an individual or group responsible for significant
activity around a grassroots cause. Last year’s winners were Caroline Atwood and Marshal Abdullah.
The Individual Contribution Award recognizes an AMCP member making a significant contribution
to the Academy by any means other than service as an AMCP Committee Member. Last year’s winner
was John Strezewski.
The Spirit of Volunteerism recognizes a current AMCP Committee Member who has demonstrated
exemplary and outstanding service to AMCP over the past year; and, has provided volunteer activities
that resulted in successful and/or high quality AMCP program, projects or services for its members.
Last year’s winner was Patrick Gleason.
JMCP Award for Excellence recognizes an article published in JMCP that represents the best
scholarly work in managed care pharmacy. The award presented in 2014 went to The Effect of
Hepatitis C Treatment Response on Medical Costs: A Longitudinal Analysis in an Integrated Care Setting.
And consider applying to become an AMCP Fellow! The Fellows program was established to
acknowledge sustained excellence in the pharmacy profession, grant recognition for exceptional
contributions, long term commitment and active participation in the Academy.
e Submit your nomination or download a Fellow application at www.amcp.org/awards
All nominations and completed Fellow applications are due December 16, 2014.
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COMPLETEDIABETESSOLUTIONS™
USM 2014-00021 Rev 03/14
AMCP Headquarters
100 North Pitt St., Suite 400, Alexandria, VA 22314
Tel.: 703.683.8416 • Fax: 703.683.8417
Editorial staff
Volume 20, No. 11
Editor-in-Chief
John Mackowiak, PhD
919.942.9903, [email protected]
C ONTENTS
Publisher
Edith A. Rosato, RPh, IOM, Chief Executive Officer
Academy of Managed Care Pharmacy
Assistant Editor
Laura E. Happe, PharmD, MPH
■ commentary
1073 The Impact of Information Technology on Managed Care Pharmacy:
Today and Tomorrow
Douglas Hillblom, PharmD; Anthony Schueth, MS; Scott M. Robertson, RPh, PharmD;
Laura Topor, BA; and Greg Low, RPh, BSPharm, PhD
■ BRIEF REPORT
1086 Do Value Thresholds for Oncology Drugs Differ from Nononcology Drugs?
Assistant Editor
Eleanor M. Perfetto, MS, PhD
Assistant Editor
Karen L. Rascati, PhD
Managing Editor
Jennifer A. Booker
Copy Editor
Carol Blumentritt
1093 A Case Study in Generic Drug Use: Should There Be Risk Adjustment in
Incentive Payments for the Use of Generic Medications?
Surrey M. Walton, PhD; Christine Rash, PharmD; Bruce L. Lambert, PhD;
and William L. Galanter, MD, PhD
Advertising
■ RESEARCH
Advertising for the Journal of Managed Care & Specialty
Pharmacy is accepted in accordance with the advertising policy of the Academy of Managed Care Pharmacy.
Graphic Designer
Margie C. Hunter
For advertising information, contact:
Derek Lundsten, VP, Business Development
American Medical Communications, Inc.
630 Madison Avenue, Manalapan, NJ 07726
Tel.: 973.713.2650
E-mail: [email protected]
1102 Price Elasticity and Medication Use: Cost Sharing Across Multiple
Clinical Conditions
Justin Gatwood, PhD, MPH; Teresa B. Gibson, PhD; Michael E. Chernew, PhD;
Amanda M. Farr, MPH; Emily Vogtmann, PhD, MPH; and A. Mark Fendrick, MD
1110 Predictors of Treatment Initiation with Tumor Necrosis Factor-α
Inhibitors in Patients with Rheumatoid Arthritis
Rishi J. Desai, PhD; Jaya K. Rao, MD; Richard A. Hansen, PhD;
Gang Fang, PhD; Matthew L. Maciejewski, PhD; and Joel F. Farley, PhD
editorial
Questions related to editorial content and submission
should be directed to JMCP Managing Editor Jennifer
Booker: [email protected]; 703.317.0725.
Manuscripts should be submitted electronically at
jmcp.msubmit.net.
subscriptions
Annual subscription rates: USA, individuals, institutions–$90; other countries–$120. Single copies cost
$15. Missing issues are replaced free of charge up to 6
months after date of issue. Send requests to AMCP
headquarters.
reprints
(continued on page 1073)
Journal of Managed Care & Specialty Pharmacy (ISSN 1944-706X) is published 12 times per year and is
the official publication of the Academy of Managed Care Pharmacy (AMCP), 100 North Pitt St., Suite 400,
Alexandria, VA 22314; 703.683.8416; 800.TAP.AMCP; 703.683.8417 (fax). The paper used by the Journal of
Managed Care & Specialty Pharmacy meets the requirements of ANSI/NISO Z39.48-1992 (Permanence of
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dues for AMCP include $90 allocated for the Journal of Managed Care & Specialty Pharmacy. Send address
changes to JMCP, 100 North Pitt St., Suite 400, Alexandria, VA 22314.
1070 Journal of Managed Care & Specialty Pharmacy
JMCP
November 2014
Vol. 20, No. 11
Information about commercial reprints and permission to
reuse material from JMCP may be found at amcp.org/
JMCP_Reprints_and_Permissions. Authors may order
reprints from the Sheridan Press; contact contact Tamara
Smith, [email protected], 800.352.2210
All articles published represent the opinions of the authors and
do not reflect the official policy or views of the Academy of
Managed Care Pharmacy or the authors’ institutions unless so
specified. Copyright © 2014, Academy of Managed Care
Pharmacy. All rights reserved. No part of this publication
may be reproduced or transmitted in any form or by any
means, electronic or mechanical, without written permission from the Academy of Managed Care Pharmacy.
www.amcp.org
Facts and Figures: Part D Drug Utilization
Provided by MedImpact Healthcare Systems, Inc.
Top 10 Drugs by Rx Claim Cost
79%
Top 10 Drugs by Rx Claim Volume
5.0% Sofosbuvir
3.3% Levothyroxine Sodium
3.6% Insulin Glargine
3.0% Lisinopril
1.8% Fluticasone/Salmeterol
2.9% Omeprazole
1.8% Rosuvastatin Calcium
2.7% Simvastatin
1.7% Adalimumab
21%
75%
1.6% Lenalidomide
25%
2.6% Atorvastatin Calcium
2.6% Amlodipine Besylate
1.5% Sitagliptin Phosphate
2.2% Metformin HCL
1.5% Aripiprazole
2.2% Hydrocodone BIT/Acetaminophen
1.5% Etanercept
1.8% Furosemide
1.4% Tiotropium Bromide
1.6% Losartan Potassium
Top 10 Drugs by Claim Cost: Prior Authorization and Step Therapy Trends by Brand
National Snapshot
% of Total Medicare Rx lives
(n=28,855,056)
100%
80%
60%
40%
20%
0%
Sovaldi
Lantus
Advair Diskus
Crestor
Humira
% of Lives with PA
Revlimid
Januvla
Abilify
Enbrel
Spiriva
% of Lives with ST
Highest . . . . . . . . . . . . . . . . . . . . . . . . . . . . Share Top-10 Rx Claim Cost . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lowest
Regional Variation Top 3 Drugs by Claim Cost
Prior Authorization
West
1. Enbrel
2. Humira
3. Sovaldi
Step Therapy
Northeast
1. Enbrel
2. Humira
3. Revlimid
Midwest
1. Enbrel
2. Humira
3. Revlimid
West
1. Januvia
2. Crestor
3. Abilify
Midwest
1. Januvia
2. Crestor
3. Advair Diskus
Northeast
1. Januvia
2. Crestor
3. Advair Diskus
South
1. Januvia
2. Crestor
3. Advair Diskus
South
1. Enbrel
2. Humira
3. Revlimid
Source: MedImpact August 2014 claims data and BusinessOne Technologies’ Maestro. Accessed 9/3/2014. Geographical regions as defined by U.S. Census Bureau.
Visit www.medimpact.com today
10181 Scripps Gateway Ct. San Diego, CA 92131
Phone: 800.788.2949
www.medimpact.com
www.amcp.org
Vol. 20, No. 11
November 2014
JMCP
Journal of Managed Care & Specialty Pharmacy 1071
Board of Directors
President: Dana Davis McCormick, RPh
President-Elect: Raulo S. Frear, PharmD
Past President: Kim A. Caldwell, RPh
Treasurer: H. Eric Cannon, PharmD, FAMCP
Chief Executive Officer: Edith A. Rosato, RPh, IOM
Director: Stanley E. Ferrell, RPh, MBA
Director: James T. Kenney, Jr., RPh, MBA
Director: Janeen McBride, RPh
Director: Lynn Nishida, RPh
Director: Gary M. Owens, MD
Volume 20, No. 11
C ONTENTS (continued)
Editorial advisory board
■ RESEARCH
1122 North Carolina Medicaid Recipient Management Lock-In Program:
The Pharmacist’s Perspective
S. Rose Werth, BA; Nidhi Sachdeva, MPH, CHES; Andrew W. Roberts, PharmD;
Mariana Garrettson, MPH; Chris Ringwalt, PhD; Leslie A. Moss, MHA, CHES;
Theodore Pikoulas, PharmD, BCPP; and Asheley Cockrell Skinner, PhD
1129 Is There an Association Between the High-Risk Medication Star Ratings
and Member Experience CMS Star Ratings Measures?
1138 Perceptions and Attitudes of Community Pharmacists Towards
Generic Medicines
Suzanne S. Dunne, BSc (Hons), MSc; Bill Shannon, MD, FRCGP, MICGP;
Walter Cullen, MD, MICGP, MRCGP; and Colum P. Dunne, BSc (Hons), MBA, PhD
■ departments
1071
Facts and Figures: Part D Drug Utilization,
MedImpact Healthcare Systems, Inc.
Editorial Mission
JMCP publishes peer-reviewed original research manuscripts, subject reviews, and other content
intended to advance the use of the scientific method, including the interpretation of research findings
in managed care pharmacy. JMCP is dedicated to improving the quality of care delivered to patients
served by managed care and specialty pharmacy by providing its readers with the results of scientific investigation and evaluation of clinical, health, service, and economic outcomes of pharmacy
services and pharmaceutical interventions, including formulary management. JMCP strives to engage
and serve professionals in pharmacy, medicine, nursing, and related fields to optimize the value of
pharmaceutical products and pharmacy services delivered to patients. JMCP employs extensive biasmanagement procedures intended to ensure the integrity and reliability of published work.
JMCP
November 2014
Vol. 20, No. 11
Committee Purpose: To advise and assist the editors
and staff in the solicitation and development of JMCP
content. For more information see: www.amcp.org/
eab. To volunteer to be a committee member, watch for
the call for AMCP volunteers every September.
Trent McLaughlin, BSPharm, Xcenda, Scottsdale,
AZ (chair)
Karen Worley, PhD, Humana, Inc., Cincinnati, OH
(chair)
Christopher Bell, MS, GlaxoSmithKline, Research
Triangle Park, NC
Gary Besinque, PharmD, FCSHP, Kaiser
Permanente, Downey, CA
Norman V. Carroll, BSPharm, PhD, Virginia
Commonwealth University, Richmond, VA
Mark Conklin, MS, PharmD, Pharmacy Quality
Solutions, Sewickley, PA
Bridget Flavin, PharmD, Regence, Portland, OR
Renee Rizzo Fleming, BSPharm, MBA,
PRN Managed Care Consulting Services, LLC,
East Amherst, NY
Patrick Gleason, PharmD, BCPS, FCCP, Prime
Therapeutics LLC, Minneapolis, MN
Todd A. Hood, MHA, PharmD, Celgene Corporation,
Cumming, GA
Mark Jackson, BSPharm, PIVINA Consulting, Inc.,
Windsor, ON
Donald Klepser, MBA, PhD, University of Nebraska
Medical Center, Omaha, NE
Stephen J. Kogut, BSPharm, MBA, PhD, University
of Rhode Island, Kingston, RI
Bradley C. Martin, PharmD, PhD, University of
Arkansas for Medical Sciences, Little Rock, AR
Uche Anadu Ndefo, BCPS, PharmD, Texas
Southern University, Houston, TX
Robert L. Ohsfeldt, PhD, Texas A&M Health
Science Center, College Station, TX
Gary M. Owens, MD, Gary Owens Associates,
Ocean View, DE
Cathlene Richmond, BCPS, BS, PharmD, Kaiser
Permanente, Oakland, CA
Mark Christopher Roebuck, MBA, PhD,
RxEconomics, Hunt Valley, MD
Jordana Schmier, MA, Exponent, Alexandria, VA
Marv Shepherd, BSPharm, MS, PhD, University of
Texas at Austin, Austin, TX
Jennifer Booker, Academy of Managed Care
Pharmacy, Alexandria, VA
John Mackowiak, PhD, Academy of Managed Care
Pharmacy, Alexandria, VA
Author Guidelines:
http://amcp.org/JMCP_AuthorGuidelines
Mission Statement:
http://amcp.org/JMCP_MissionStatement
www.amcp.org
c o mm e n tary
The Impact of Information Technology on Managed
Care Pharmacy: Today and Tomorrow
Douglas Hillblom, PharmD; Anthony Schueth, MS; Scott M. Robertson, RPh, PharmD;
Laura Topor, BA; and Greg Low, RPh, BSPharm, PhD
SUMMARY
Understanding the use of health information technology (HIT) and its implications is crucial for the future of managed care pharmacy. Information is the
cornerstone of providing and managing care, and the ability to exchange
data is easier and more complicated than ever before. In this commentary, a
subset of the Academy of Managed Care Pharmacy Healthcare Information
Technology Advisory Council addresses how HIT supports managed care today
and its anticipated evolution, with a focus on quality, patient safety, communication, and efficiency. Among the tools and functions reviewed are electronic
health records, electronic prescribing, health information exchange, electronic
prior authorization, pharmacists as care team members, formularies, prescription drug abuse, and policy levers to address these issues.
J Manag Care Pharm. 2014;20(11):1073-79
Copyright © 2014, Academy of Managed Care Pharmacy. All rights reserved.
I
n health care, there is a silent and often unrecognized dependency on health information technology (HIT). It underlies
every element of managed care pharmacy, from payers
managing a pharmacy benefit through the provision of medication therapy management (MTM) and innovative programs that
deliver quality pharmaceutical care to providers involved in
direct patient care. It is also redefining how key stakeholders in
medication management communicate with each other.
This commentary describes how managed care pharmacy
uses HIT today and how that will evolve in the near future.
It examines how information is shared among managed care
programs, providers, sponsors, government, and members,
facilitating more informed decision making and enhancing
productivity and efficiency. It also presents challenges and
opportunities for moving forward. These insights were identified by a subset of the Academy of Managed Care Pharmacy
(AMCP) Healthcare Information Technology Advisory Council,
whose role is to advise AMCP membership on the role of HIT
in managed care pharmacy and who believes that for HIT to
continue to serve managed care pharmacy, AMCP must be
involved in its development.
■■  HIT in Managed Care Pharmacy Today
Managed care pharmacy currently employs many essential HIT
tools including pharmacy claims processing, electronic prescribing (e-prescribing), computerized physician order entry
(CPOE), and electronic health records (EHRs).1
Pharmacy Claims Processing
Pharmacies began to make the technology leap from paper
claims to real-time claims adjudication with the National
www.amcp.org
Vol. 20, No. 11
Council for Prescription Drug Programs’ (NCPDP) creation
of the pharmacy universal claim form in 1977 and rollout, 11
years later, of the NCPDP Telecommunication Standard Version
1.0. Prospective drug utilization review (PDUR)—regulated
by Medicaid in January 1993—then enabled pharmacists to
receive real-time adverse drug event alerts when a prescription claim violates a pre-established criterion for appropriate
drug use. The current NCPDP Telecommunications Standard
(vD.0) supports over 20 transaction functions and facilitates
the exchange of over 4 billion real-time pharmacy claims with
such speed and simplicity that most participants do not realize
they are using sophisticated HIT.2 Unlike medical claims that
are still predominantly submitted in “batches” over the course
of days or even weeks, the vast majority of pharmacy claims
today are processed and adjudicated in a matter of few seconds
through data centers that maintain high availability, such as
those of Emdeon and RelayHealth—2 providers of health information exchange services.
E-prescribing and CPOE
The expanded influence of HIT is evident in many areas of
managed care today, with the expanded adoption of e-prescribing and medication orders that are increasingly integrated
in EHRs.3 Although the functionality to prescribe medications
electronically has been available for decades, it was primarily
in the acute care setting until 2006. While the ability to order
medications has continued to expand in hospitals, the ability to
electronically write and transmit prescriptions in the ambulatory setting is now mainstream and becoming the standard of
care. At its core, this function eliminates misinterpretation of
handwriting, reduces the need to rekey information and the
corresponding possibility of error, and improves the transmission process. Most e-prescribing and CPOE solutions have
incorporated formularies, providing complementary clinical
and economic decision support, which provide additional
quality and financial value. According to Surescripts, a leading network providing prescription transmission services,
there were over 1 billion prescriptions sent electronically in
2013. Nearly three-quarters (73%) of office-based physicians
are e-prescribing, and nearly all retail pharmacies are wired to
receive e-prescriptions, providing meaningful value to managed care pharmacy.4 Over a 7-year period (2005-2012), a
Michigan coalition of General Motors, Ford Motor Company,
Chrysler, Blue Cross Blue Shield of Michigan, the Health
Alliance Plan, Express Scripts, CVS Caremark, and Catamaran
found that e-prescribing saved stakeholders $119 million in
drug costs and $11 million from avoided hospitalizations.5
November 2014
JMCP
Electronic Health Records (EHRs)
An EHR is a systematic collection of electronic health information about an individual patient or population6 that is increasingly capable of being shared across different health care settings. EHRs may include a range of data, including demographics, medical history, medications and allergies, immunization
status, laboratory test results, radiology images, vital signs,
personal statistics such as age and weight, and billing information. Designed to represent data that accurately captures the
state of the patient at all times, it allows an entire patient history to be viewed without the need to track down the patient’s
previous medical records and assists in ensuring that data are
accessible, accurate, appropriate, and legible. In 2013, about
three-quarters of physicians had adopted EHRs due in part to
federal incentive programs.7 The effect of this level of adoption is promising for managed care. In a study conducted from
2004 to 2009, Kaiser found a statistically significant decrease
in emergency department (ED) visits (28.8 per 1,000) and hospitalizations among diabetes patients whose physicians used its
EHR.8 The next step in developing EHRs is to connect disparate
EHRs into health information exchanges (HIEs).
■■  Developing Areas for HIT in Managed Care Pharmacy
Advances in HIT are creating new opportunities for managed
care pharmacy to improve the efficiency and quality of health
care. HIT and HIEs are creating new roles and responsibilities for managed care pharmacists. HIT will facilitate critical
functions, such as obtaining prior authorization to ensure that
patients receive medications that are safe, appropriate, and
cost-effective. Further progress will enable use of HIT to identify and reverse the rise in prescription drug abuse. New forms
of HIT, such as telehealth and mobile health, will be adopted
by managed care pharmacists and patients and will reduce
costs, improve outcomes, and engage patients in their care.
Health Information Exchanges (HIEs)
HIEs will knit together unrelated information sources to provide health care professionals with a more comprehensive view
of a patient’s medical information. This is needed to connect
hospitals, providers, payers, and pharmacies to effectively and
efficiently collect and share clinical, pharmacy, and administrative data. HIE also provides the means to communicate
interventions performed by pharmacists, such as medication
counseling, to other providers and patients. There are nearly
300 HIEs in the United States that enable the electronic sharing
of health-related information. One-half of the nation’s hospitals
are now participating in a regional, state, or private HIE, and
71% plan to invest in technology to ensure their connectivity to
HIEs in the next 2 years.9 Furthermore, nearly one-half of the
nation’s physicians plan to join an HIE.
The benefits of managed care involvement in HIE are plentiful. They include preventing avoidable admissions; simplifying
and streamlining drug and medical authorizations; improving
quality of information and streamlined distribution of reporting
for quality improvement programs; reducing avoidable service
JMCP
November 2014
utilization and costs associated with ED visits; and simplifying
and streamlining the flow and presentation of information in
an EHR, thereby reducing the time spent interpreting data from
a variety of sources.10 There are several examples of payer participation in HIEs. A noteworthy example is in Nebraska, where
Coventry and Blue Cross Blue Shield of Nebraska (BCBSNE)
are participating in the Nebraska Health Information Initiative
(NEHII). NEHII’s area of focus is to lower costs, improve timeliness and reliability, and mitigate security risks associated with
the exchange of clinical data via facsimile and paper-based
methods for medical and drug authorization and care management activities.11 One of their programs provides external medication history to clinicians during the medication reconciliation
process at transitions of care. BCBSNE provided the seed money
and actively engaged in governance, business planning, and
construction of infrastructure. Health plans currently pay a permember-per-year fee to the HIE.
HIT-Enabled Pharmacist Involvement in Team Care
The rise of new integrated approaches to the delivery of care—
such as accountable care organizations (ACOs) and patientcentered medical homes—will create new roles and responsibilities for managed care pharmacists. The result: increased care
coordination facilitated by HIT and a robust HIE infrastructure.
For example, pharmacists will play key roles in the care
team, which will be facilitated by HIT. According to an AMCP
report, studies have demonstrated that pharmacists participating in team-based care models have made positive contributions to patient care and safe medication use with the help
of HIT.12 Recently, California passed legislation that would
expand this access to participation in care teams for pharmacists in an effort to help address shortages among other health
care professions.13 As a clinical expert working as part of an
interdisciplinary team, pharmacists can assess whether medication use is contributing to unwanted effects and can
help achieve desired outcomes from medication use. Ideally,
pharmacists will be considered “eligible providers” and thus
entitled to additional compensation if meaningful use metrics are achieved.14 The pharmacist’s role in patient-centered
team-based care can be enhanced by integration of prescription information with other sources of clinical information.15
These sources include EHRs, disease registries, and patients
themselves, who can access and provide data through Web
portals, personal health records, and mobile health applications. Electronic exchange of these data through a robust HIE
infrastructure will be essential.
The primary care team may be in the best position to
coordinate a patient’s care, but often it will need information
from other providers, including ambulatory providers. This
need for coordination and data sharing will lead to increased
reliance on EHRs and HIEs in managed care organizations—
for e-prescribing, patient care, and to prevent readmissions.
These capabilities will put managed care pharmacists in a better position to work with their patients and care team to make
informed decisions about medication therapy options.16
Vol. 20, No. 11
www.amcp.org
Pharmacists in managed care arrangements also will play a
vital role in their organizations by leveraging HIT to provide
patient care services and MTM. This includes comprehensive
medication reviews, medication reconciliation, drug utilization review, ordering and reviewing lab tests, immunizations,
drug-dosage adjustments, and identifying gaps in care.17 These
programs are essential to care coordination by improving medication adherence, managing where and when care is delivered,
and improving patient outcomes. Pharmacists have been providing MTM services for Medicare patients, and the model is being
expanded to include similar services for non-Medicare patients.
Some clinics are expanding the services to a commercial population, offering medication reconciliation postdischarge.
Electronic Prior Authorization
Prior authorization (PA) involves getting permission from the
patient’s health insurer before a medication therapy can begin.
It is a complex, multistep process, which many view as a necessary part of the health care system to help ensure that patients
receive medication therapies that are safe, appropriate, and
cost-effective. Going forward, PA will become an even bigger
part of managed care pharmacists’ workflow, and newer tools,
such as electronic prior authorization (ePA) submission, will be
an essential workplace tool. Factors influencing the adoption of
ePA include labor costs, workflow productivity and efficiency,
and enhanced performance metrics and quality measurements.
Plans are expected to require more PAs in general and for the
specialty medications needed by increasing numbers of the
elderly and chronically ill patients that will be in their panels.
Growth in spending on specialty drugs is far outpacing spending on traditional drugs, and many new specialty pharmaceuticals are in the pipeline. The 2013 Express Scripts Drug Trend
Report projects that specialty drug spending will jump by 67%
by 2015, and nearly half of all prescription drug sales will be
for specialty medications by 2016.18
In the future, managed care pharmacists increasingly will
be moving away from yesterday’s phone-fax-paper processes
toward ePA. At the point of prescribing, ePA will create value
for pharmacies by eliminating the responsibility of the pharmacist to facilitate the PA process. This will allow pharmacists
to focus on patients and revenue-generating activities. In addition, ePA will help PAs get approved more quickly, thus helping
to reduce abandoned prescriptions due to patient frustration
with the process, the number of rejected claims, and increase
patient satisfaction. HIT can also enhance the ability to comply with the U.S. Food and Drug Administration’s (FDA) Risk
Evaluation and Mitigation Strategies (REMS), which are needed
for many specialty medications.19 REMS are structured plans to
manage specific risks of drugs that are effective but associated
with known or potential risks, such as death or injury. REMS
help the FDA, drug manufacturers, and prescribers make sure
that the benefits of such drugs outweigh their risks.
The changes in PA will be facilitated by the availability of
ePA standard transactions. NCPDP has developed a new ePA
standard transaction for products covered by patients’ phar-
www.amcp.org
Vol. 20, No. 11
macy benefits. Based on SCRIPT—named by the Medicare
Drug Improvement and Modernization Act as the standard
for e-prescribing—ePA is envisioned to occur during the
e-prescribing process. The new transaction allows the provider
to request a PA question set from the payer, return the answers,
and receive a response, all electronically (potentially in near
real time). Questions may be customized, depending on the
patient and the medication involved, and clinical attachments,
such as subsets of the medical record, also are supported. With
the widespread adoption of EHR technology, much of the information to be exchanged can be system-driven, reducing the
burden of manually entering and reviewing the data.
In addition, ePA will be required increasingly by states.
They have recognized the value of an ePA process and are moving forward by statute and regulation. For example, Minnesota
Laws, Chapter 336, Sec. 5, requires that “no later than January
1, 2016, drug prior authorization requests must be accessible
and submitted by health care providers, and accepted by group
purchasers, electronically through secure electronic transmissions. Facsimile shall not be considered electronic transmission.”20 In the short term, many states are considering use of
a standardized PA form, which can be digitized later as standards and interoperability adoption increase.
Get Ready for e-Health
Telehealth and mobile health (m-health) are rapidly picking
up traction in today’s health care environment.21,22 Growth
in these areas will be partially driven by the expansion of
the number of insured individuals and the limited number of
health care providers available to meet the need. The impact
will increasingly be felt by managed care and managed care
pharmacy as ways to enable patient visits, consultations,
medication adherence, and remote monitoring. This is especially important for the elderly and chronically ill—key populations for many managed care organizations, such as Medicaid
managed care and Medicare ACOs. Telehealth patients in the
United States are expected to rise nearly 6-fold by 2017, and
telehealth revenue is set to rise to $707.9 million in 2017.23
The impact is already being felt through lower costs and fewer
hospitalizations and readmissions.24 A study conducted for
California and its Medicaid program, Medi-Cal, concluded that
telemedicine used for “home monitoring for chronic diseases
[such as] heart failure and diabetes . . . has the potential to produce savings to the Medi-Cal program of as much as several
hundred million dollars annually.”25 It reported a 42% reduction in costs related to heart failure care and a 9% reduction in
costs related to diabetes care.
At the same time, m-health applications (i.e., “apps”) are
skyrocketing—mostly because apps and mobile devices
empower consumers to take charge of their own treatment
and create more effective communications with providers and
pharmacists, which will help bring down costs.26 There are
already 100,000 health applications available in app stores, and
the top 10 m-health applications are expected to generate up to
4 million free and 300,000 paid daily downloads.27
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JMCP
What does this mean for managed care pharmacy? Both
telehealth and m-health hold opportunities to provide highquality pharmacy services and follow-up care as well as engage
patients. For example, telehealth consultations may be carried
out by managed care pharmacists through clinical software in
the cloud.28 This kind of interaction may be especially useful in
rural areas and for homebound, chronically ill patients. These
technologies can help lower costs by facilitating the delivery of
care and connecting people to their health care providers.29 As
plans increase their membership among a younger demographic,
they will also demand the same access to services via technology
as they are accustomed to receiving from other industries.
M-health apps can also help patients order medication refills
and inform the care process by electronically providing information on their regimens and clinical status. Patients can track
their own medication administration activity, similar to the
inpatient medication administration record, as well as tracking physical activity and nutrition information. M-health can
provide tools to help patients manage their care and improve
medication adherence.28 Similarly, m-health can be a tool for
managed care pharmacists to establish relationships with
patients and educate them on the importance of medication
adherence. Such technology-enabled interactions can make a
positive impact on patient outcomes.30 For example, alerts can
be pushed out to patients to schedule visits and lab tests and
to remind them to take particular medications or alert them to
potential interactions.
Pharmacists will be able to use m-health in their day-to-day
operations. Apps have been developed for drug references, clinical references, medical calculators, laboratory references, news
and continuing medical education, and productivity. Having
information at their fingertips can improve their relationships
with patients and other members of the care team.
Addressing Prescription Drug Abuse
Despite progress during the last several years, prescription
drug abuse and fraud remain significant problems for all
health plans and providers. The Centers for Disease Control
and Prevention has declared prescription drug abuse a national
epidemic that costs 20,000 lives and $72 billion dollars a year.31
More than 2.4 million people were considered opioid abusers in 2010. The number is growing, particularly among the
elderly. Statistics show that seniors account for an increasing
proportion of unintentional substance abusers, and they are a
major population served by managed care pharmacists.32
Increasingly, HIT is being used innovatively to address substance abuse and drug diversion. For example, e-prescribing
can help clinicians recognize substance abuse through medication history checks, which show controlled and noncontrolled
medications that were filled by the patient. E-prescribing
systems, as well as pharmacy systems, can also flag potentially
deadly prescription errors and drug interactions related to
opioid use, thus, preventing accidental deaths and overdoses.
Renewal request monitoring can help flag abuse and diversion.33 In addition, as of June 2013, 47 states have implemented
JMCP
November 2014
a prescription drug monitoring program (PDMP).34 The typical
PDMP program is voluntary for prescribers and consists of
electronic databases that collect, monitor, and analyze prescribing and dispensing data sent electronically from pharmacies and dispensing practitioners. Massachusetts, New York,
and Vermont, however, now require that prescribers check
their PDMP state databases prior to prescribing controlled
medications. New York was the first state to mandate that prescribers consult a PMDP prior to prescribing Schedule II, III,
and IV controlled substances.
Data Analytics
One of today’s biggest trends is “big data,” that is, enhanced
data analytics using new tools, more sophisticated analysis techniques, and the sharing of expanded datasets from
multiple sources beyond traditional claims data. To be sure,
pharmacy systems have been retrospectively mining their own
claims data for years. While that will be important, pharmacies
in the future will be looking to enhanced data analytics and
data sharing in 3 areas: improved patient care; better benefit
management; and fighting fraud, waste, and abuse.
One way to improve care and patient outcomes is to put
the results of data analytics into the hands of pharmacists and
other providers at the point of care. Data from EHRs—including those connected to an HIE with access to other EHRs and
claims data—will provide the basis for new work flows and
actionable information at the point of care. The result: better
care coordination by “pulling” the patient into the system before
conditions worsen, become even more costly, or potentially
adverse events occur, such as an emergency room visit or deadly
drug interactions. As an example, a pharmacy team associated
with the University of Massachusetts’ Medical School analyzed
data elements from various sources—including pharmacy
records and medical claims—to identify patterns that identify
prescriber outliers, evaluate member outcomes, and quantify
hospitalization rates.35 Results of the analyses also are used to
develop recommendations that improve patient outcomes and
reduce short- and long-term health care costs, such as listing
medication classes where generic usage can be increased, predicting the effect of formulary restrictions on cost savings, and
assessing diagnosis data specific to the member population.36
Analysis of data from multiple sources and platforms can
provide more immediately actionable information beyond what
can be determined from pharmacy claims analysis alone. In 1
mail order pharmacy, for example, hundreds of thousands of
customer service logs were analyzed. Results detected a spike
in calls between days 75 and 105 of some patients’ medication regimens. Looking closer, analysts found that the calls
correlated with refill dates, and they discovered that some
customers were calling for refills because their medications
were taken with variable dosages. To reduce the number of
lengthy customer service calls and expensive “emergency”
refills and rush orders, the pharmacy began asking patients
how many pills they had remaining at day 30 and day 60, so
that they could better predict when the medication would run
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www.amcp.org
out.37 Express Scripts is taking a big data approach to identify
consistent patterns and tailor the most effective interventions
for those patients most at risk for medication nonadherence.38
At the same time, electronic sharing of pharmacy claims
data, coupled with real-time submission by pharmacies, creates an opportunity for analytics to become an effective tool for
pharmacy benefit management. For example, utilization trends
can be quickly identified, which can lead to the development
of clinical strategies and follow-up services that create value for
clients.39 These might include formulary management, medication therapy management, and clinical decision support, all of
which are enabled by HIT.
Pharmacies also are using large-scale data analytics to identify individual cases of blatant abuse. Sophisticated algorithms
and other techniques can be employed to examine data from
multiple sources and highlight suspect activity or patterns
of abuse. Express Scripts’ analytical models helped identify
a husband-and-wife team that had obtained approximately
7,000 pills for controlled substances—a total worth of about
$150,000—by using 17 doctors and pharmacies in different
cities. They had signed multiple exclusivity contracts with doctors, stating they would only get narcotics prescriptions from
those doctors.40
■■  Challenges Presented by HIT in Managed Care
Despite the growing business imperative for the routine sharing of health information between multiple stakeholders in the
managed care environment, gaps and challenges remain.
Interoperability
Interoperability describes the extent to which systems and
devices can exchange data and interpret that shared data.41 In
terms of connectivity, nursing homes, home health providers,
and other postacute care and community-based providers lag
behind other sites of care. Increased interoperability also will
be challenging given the expected rise of Medicaid managed
care organizations, since 85% of Medicaid enrollees will be
in managed care organizations by 2020.42 Medicaid health IT
systems tend to lag behind those in the private sector, which
could potentially hinder the accessibility and sharing of necessary pharmacy and related clinical data as patients move in and
out of Medicaid managed care plans.
Standards and certified vendor solutions are needed. A
particular need is the development of standards and enhancements to manage specialty prescriptions electronically, which
NCPDP has begun to address. While nearly all retail and institutional pharmacies can receive and process e-prescriptions,
specialty pharmacies are behind because of the additional
information needed to support dispensing the medication (and
related supplies and services). The lack of e-prescription connectivity of specialty pharmacies has implications for costs,
quality, and patient safety. In fact, specialty medications are a
significant and growing part of care regimens and drug spend.
These costs in turn impact the financial success and risk sharing for ACOs and other managed care systems.
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Vol. 20, No. 11
In addition, recent privacy violations have caused justifiable
concerns about widespread sharing of protected health care
information (PHI). While safeguards for PHI are covered under
HIPAA, it will become increasingly important for covered entities to implement ongoing measures to help safeguard patient
information as clinical information exchange becomes pervasive.
Formulary Data Quality and System Support
Although e-prescribing is becoming widespread, there appears
to be limited use of its advanced features by physicians. These
include identifying potential drug interactions and obtaining
patient formulary information.43 Today, the availability and
usefulness of medication history and formulary data is limited,
which causes prescribers to question its reliability. Physicians
in 1 study reviewed the formulary data only occasionally.
Respondents observed that formulary information was inconsistently available, out-of-date, or inaccurate. Some respondents
expressed a desire to see additional information, such as a
patient’s actual copayment amounts, formulary alternatives for
off-formulary medications, or whether a medication requires
PA. More than two-thirds of the practices responding believed
that the volume of formulary-related pharmacy callbacks was
still burdensome because their e-prescribing solutions did not
alert them to the need for PAs after the e-prescriptions were
sent, resulting in inefficiencies for both parties.44
Alert Fatigue
Part of the challenge of increasing use of available information, including clinical decision support (CDS), is alert fatigue.
Alert fatigue is a well-known problem where a continuing large
volume of notifications lead the user to become insensitive to
the information presented.45 Some users or implementations
will forgo full activation of CDS, or set alert thresholds high, to
reduce the potential for alert fatigue. Balancing CDS information versus alert fatigue and patient safety is one of the conundrums of utilizing technology in health care.
■■  Policy Levers Are Key
The federal government is aware of these issues and, according
to the Office of the National Coordinator for HIT (ONC), will
use policy levers to help address them.46 One possibility is the
integration of HIT into federal payment policies. For example,
the use of interoperable EHR systems to share information could
eventually become part of reimbursement criteria in the Medicare
and Medicaid programs.47 HIT adoption has been furthered by
the Meaningful Use incentive program. While the incentives may
not be available in the future because of budgetary constraints,
requirements for HIT adoption and use are likely to continue.
Meaningful Use Stage 2 is heavy on HIE requirements, while
Stage 3 is expected to include requirements for advanced HIE and
increased clinical detail.48 At some point in the near future, the
federal government will be asked to issue guidance concerning
the use of NCPDP’s new ePA standard transaction.
To promote increased interoperability, the ONC is convening the HIT community to prioritize HIT challenges and
November 2014
JMCP
subsequently enable development and harmonization of related
standards, specifications, and implementation guidance.49
Moreover, the ONC has developed a national HIE strategy that
will help frame policy discussions and drive initiatives with
federal backing.
Efforts will continue by the private sector and public/private
initiatives to address connectivity and interoperability issues.
An example is the CommonWell Health Alliance, which is a
group of health care stakeholders that have banded together
to define and promote a national infrastructure for interoperability with common standards and policies for EHRs.50 Quest
Diagnostics and Surescripts recently announced an agreement
to pioneer the formation of an integrated service to make laboratory and prescription information broadly and easily accessible to prescribers. Standards development organizations will
continue to create new technical standards and specifications
for data exchange and interoperability.
On the state level, legislatures and boards of pharmacy are
expected to be addressing HIT-related issues. There are gaps in
e-prescribing of controlled substance (EPCS) laws and regulations at the state level that are likely to be addressed. As of the
publication of this article, EPCS is legal in 48 states and the
District of Columbia.4 Clarity and synergy in state laws would
eliminate a barrier to EPCS adoption with the added benefits
of improving prescribers’ workflows, protecting against drug
diversion, and increasing patient safety.
Change can be difficult. The successful transition to electronic systems and workflows must be understood, championed, and managed. Managed care organizations must support
development and use of HIT and HIE, including in their pharmacies. Pharmacists must be included as leaders championing
and motivating staff to participate in the change process and to
understand benefits in implementations and workflows.
■■  Conclusions
Managed care pharmacy professionals must understand the
value and effective use of HIT solutions, if they are to have
an impact on how the future develops. Those who do not
recognize and prepare for these changes will not only miss an
opportunity, but will be at a competitive disadvantage in the
marketplace. Managed care pharmacy professionals will look
to AMCP to innovate and champion the development of HIT
applications and disseminate their benefits—all of which are a
value-add to the membership.
As such, we urge AMCP to maintain the Healthcare
Information Technology Advisory Council’s charter, which
expired early in 2014. Without the assistance of the Advisory
Council, AMCP leaders and members will have to ferret out
such information on their own and try to interpret it in the
context of their own business models and solutions. This is a
difficult and daunting task, which can lead to costly mistakes
in strategic positioning, HIT investments, and patient care.
JMCP
November 2014
Authors
DOUGLAS HILLBLOM, PharmD, is Vice President, Professional
Practice and Pharmacy Policy, OptumRx, Sacramento, California;
ANTHONY SCHUETH, MS, is CEO and Managing Partner,
Point-of-Care Partners, LLC, Coral Springs, Florida; SCOTT M.
ROBERTSON, RPh, PharmD, is Principal Technology Consultant,
Kaiser Permanente, Pasadena, California; LAURA TOPOR, BA,
is President, Granada Health, Minnetonka, Minnesota; and GREG
LOW, RPh, BSPharm, PhD, is Program Director, MGPO Pharmacy,
Massachusetts General Hospital, Boston, Massachusetts.
AUTHOR CORRESPONDENCE: Douglas Hillblom, PharmD, Vice
President, Professional Practice and Pharmacy Policy, OptumRx, 8880
Cal Center Dr., Ste. 300, Sacramento, CA 95826. Tel.: 916.403.0703;
Fax: 954.212.0200; E-mail: [email protected].
DISCLOSURES
The authors are members of a subgroup of the AMCP Healthcare Information
Technology Advisory Council. They declare no other potential conflicts of interest.
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November 2014
JMCP
Introducing
Jardiance
®
(empagliflozin) tablets 10 mg/25 mg
JARDIANCE is an SGLT2 inhibitor for the
treatment of adults with type 2 diabetes,
in addition to diet and exercise
• Significant A1C reduction
• Once-daily oral dosing
• Additional benefit of weight loss*
* JARDIANCE is not indicated for weight loss. Weight change was a secondary endpoint in
clinical trials.1
INDICATION AND LIMITATION OF USE
JARDIANCE is indicated as an adjunct to diet and exercise to improve glycemic control in adults with
type 2 diabetes mellitus.
JARDIANCE is not recommended for patients with type 1 diabetes or for the treatment of diabetic
ketoacidosis.
IMPORTANT SAFETY INFORMATION
CONTRAINDICATIONS
JARDIANCE should not be used in patients with a history of serious hypersensitivity to JARDIANCE
or in patients with severe renal impairment, end-stage renal disease, or dialysis.
JARDIANCE is proven to significantly reduce A1C
In addition to lowering A1C, JARDIANCE significantly reduced weight†
JARDIANCE monotherapy vs placebo (24 weeks)
JARDIANCE is not indicated for
weight loss. Weight change was
a secondary endpoint.1
†
A1C REDUCTION
0.4
(24 weeks)
Mean baseline=7.9%
0.2
WEIGHT CHANGE
(N=228)
(24 weeks)
Mean baseline=172 lb
(N=224)
-0.2
Weight (%) change from baseline
(adjusted mean)
A1C (%) change from baseline
(adjusted mean)
0.0
(N=224)
0.1%
-0.4
-0.6
-0.8
-0.7
-1.0
■ JARDIANCE 10 mg
%
-0.8
%
p <0.0001 vs placebo‡
■ JARDIANCE 25 mg
(N=224)
0.0
‡
A1C reduction: Difference from placebo (adjusted mean) was
-0.7% and -0.9% for JARDIANCE 10 mg and 25 mg, respectively.
Weight change: Difference from placebo (adjusted mean) was
-2.5% and -2.8% for JARDIANCE 10 mg and 25 mg, respectively.
§
Study design: In a 24-week, double-blind, placebocontrolled study of 676 patients with type 2 diabetes
mellitus, the efficacy and safety of JARDIANCE 10
mg (N=224) and 25 mg (N=224) were evaluated vs
placebo (N=228). The primary endpoint was A1C
change from baseline.1
JARDIANCE 10 mg and 25 mg
significantly reduced systolic blood
pressure (SBP)II by -2.6 mm Hg
(placebo-adjusted, p=0.0231) and -3.4
mm Hg (placebo-corrected, p=0.0028),
respectively, at 24 weeks1¶
(N=224) (N=228)
-0.4%
-1.0
-2.0
-3.0
-4.0
-2.8%-3.2%
-5.0
■ Placebo
IMPORTANT SAFETY INFORMATION (continued)
p <0.0001 vs placebo§
JARDIANCE 10 mg
JARDIANCE 25 mg
Placebo
WARNINGS AND PRECAUTIONS
Hypotension
JARDIANCE causes intravascular volume contraction. Symptomatic
hypotension may occur after initiating JARDIANCE particularly
in patients with renal impairment, the elderly, in patients with low
systolic blood pressure, and in patients on diuretics. Before initiating
JARDIANCE, assess for volume contraction and correct volume status
if indicated. Monitor for signs and symptoms of hypotension after
initiating therapy.
Impairment in Renal Function
JARDIANCE increases serum creatinine and decreases eGFR. Renal
function should be evaluated prior to initiating JARDIANCE and
periodically thereafter. More frequent monitoring is recommended
with eGFR below 60 mL/min/1.73 m2. The risk of impaired renal
function with JARDIANCE is increased in elderly patients and patients
with moderate renal impairment. JARDIANCE should be discontinued
in patients with a persistent eGFR less than 45 mL/min/1.73 m2.
JARDIANCE is not indicated as antihypertensive
therapy. Blood pressure (BP) change was a
secondary endpoint.1
II
¶ SBP
mean baseline: 133.0 mm Hg, 129.9 mm Hg,
and 130.0 mm Hg for JARDIANCE 10 mg, 25 mg,
and placebo, respectively.1
Please see additional Important Safety
Information and Brief Summary of
full Prescribing Information on the
adjacent pages.
Learn more at www.Jardiance.com
75C, 63M, 63Y
5
4
3
75K
95
5
95
5
95
5
25
25
75
M+Y
25
50K
50C, 39M, 39Y
25K
25C, 16M, 16Y
25
25
25
75
25
25
25
25
M+Y
50
25
95
5
95
5
95
5
95
5
80K, 80C, 70M, 70Y
300%
4
96
4
96
4
4
96
96
1
2
C+Y
50
50
50
50
3
97
3
97
3
97
3
97
25
75
50
50
50
50
C+Y
50
95
96
96
4
96
4
96
4
97
3
97
3
97
3
75
50
25
97
98
99.5
99
1
0.5
2
98
99.5
99
1
0.5
2
98
99.5
99
1
0.5
2
98
99
99.5
25
50
75
C+M
75
75
75
75
GATF/SWOP Digital
Proofing Bar
2
0.5
1
99
99.5
98
2
0.5
1
99
99.5
98
2
1
0.5
99.5
98
2
99
1
0.5
98
99.5
99
25
50
75
C+M
75
75
75
JARPROFISI
8.2.14
JARPROFISI
8.2.14
75
Genital
Mycotic
Infections
WARNINGS
AND
PRECAUTIONS
(continued)
WARNINGS
AND
PRECAUTIONS
(continued)
JARDIANCE increases the risk for genital mycotic infections. Patients with a history of chronic or
Genital
Mycotic
Infections
Genital
Mycotic
Infections
recurrent genital mycotic infections were more likely to develop these infections. Monitor and treat
JARDIANCE
increases
the
risk
forfor
genital
mycotic
infections.
Patients
with
a history
ofof
chronic
oror
JARDIANCE
increases
the
risk
genital
mycotic
infections.
Patients
with
a history
chronic
as appropriate.
recurrent
genital
mycotic
infections
were
more
likely
toto
develop
these
infections.
Monitor
and
treat
recurrent
genital
mycotic
infections
were
more
likely
develop
these
infections.
Monitor
and
treat
asUrinary
appropriate.
as
appropriate.
Tract Infections
JARDIANCE increases the risk for urinary tract infections. Monitor and treat as appropriate.
Urinary
Tract
Infections
Urinary
Tract
Infections
JARDIANCE
increases
the
risk
forfor
urinary
tract
infections.
Monitor
and
treat
asas
appropriate.
JARDIANCE
increases
the
risk
urinary
tract
infections.
Monitor
and
treat
appropriate.
Increased Low-Density
Lipoprotein
Cholesterol
(LDL-C)
Increases in LDL-C can occur with JARDIANCE. Monitor and treat as appropriate.
Increased
Low-Density
Lipoprotein
Cholesterol
(LDL-C)
Increased
Low-Density
Lipoprotein
Cholesterol
(LDL-C)
Increases
in
LDL-C
can
occur
with
JARDIANCE.
Monitor
and
treat
asas
appropriate.
Increases in LDL-C
can occur with JARDIANCE. Monitor
and
treat
appropriate.
Macrovascular
Outcomes
There have been no clinical studies establishing conclusive evidence of macrovascular risk reduction
Macrovascular
Outcomes
Macrovascular
Outcomes
with JARDIANCE or any other antidiabetic drug.
There
have
been
nono
clinical
studies
establishing
conclusive
evidence
ofof
macrovascular
risk
reduction
There
have
been
clinical
studies
establishing
conclusive
evidence
macrovascular
risk
reduction
with
JARDIANCE
oror
any
other
antidiabetic
drug.
with
JARDIANCE
any
other
antidiabetic
drug.
ADVERSE REACTIONS
The most common adverse reactions (>5%) associated with placebo and JARDIANCE 10 mg and 25
ADVERSE
REACTIONS
ADVERSE
REACTIONS
mg were urinary tract infections (7.6%, 9.3%, 7.6%, respectively) and female genital mycotic infections
The
most
common
adverse
reactions
(>5%)
associated
with
placebo
and
JARDIANCE
1010
mgmg
and
2525
The
most
common
adverse
reactions
(>5%)
associated
with
placebo
and
JARDIANCE
and
(1.5%, 5.4%, 6.4%, respectively).
mgmg
were
urinary
tract
infections
(7.6%,
9.3%,
7.6%,
respectively)
and
female
genital
mycotic
infections
were
urinary
tract
infections
(7.6%,
9.3%,
7.6%,
respectively)
and
female
genital
mycotic
infections
(1.5%,
6.4%,
respectively).
(1.5%,
5.4%,
6.4%,
respectively).
When5.4%,
JARDIANCE
was
administered with insulin or sulfonylurea, the incidence of hypoglycemic events
was increased.
When
JARDIANCE
was
administered
with
insulin
oror
sulfonylurea,
the
incidence
ofof
hypoglycemic
events
When
JARDIANCE
was
administered
with
insulin
sulfonylurea,
the
incidence
hypoglycemic
events
was
increased.
was
increased.
DRUG INTERACTIONS
Coadministration of JARDIANCE with diuretics resulted in increased urine volume and frequency of
DRUG
INTERACTIONS
DRUG
INTERACTIONS
voids, which might enhance the potential for volume depletion.
Coadministration
ofof
JARDIANCE
with
diuretics
resulted
in in
increased
urine
volume
and
frequency
ofof
Coadministration
JARDIANCE
with
diuretics
resulted
increased
urine
volume
and
frequency
voids,
which
might
enhance
the
potential
forfor
volume
depletion.
voids,
which
might
enhance
the
potential
volume
depletion.
USE IN SPECIAL POPULATIONS
Pregnancy
USE
ININ
SPECIAL
POPULATIONS
USE
SPECIAL
POPULATIONS
There are no adequate and well-controlled studies of JARDIANCE in pregnant women. JARDIANCE
Pregnancy
Pregnancy
should be used during pregnancy only if the potential benefit justifies the potential risk to the fetus.
There
are
nono
adequate
and
well-controlled
studies
ofof
JARDIANCE
in in
pregnant
women.
JARDIANCE
There
are
adequate
and
well-controlled
studies
JARDIANCE
pregnant
women.
JARDIANCE
should
bebe
used
during
pregnancy
only
if the
potential
benefit
justifies
the
potential
risk
toto
the
fetus.
should
used
during
pregnancy
only
if the
potential
benefit
justifies
the
potential
risk
the
fetus.
Nursing
Mothers
It is not known if JARDIANCE is excreted in human milk. Because of the potential for serious adverse
Nursing
Mothers
Nursing
Mothers
reactions in nursing infants from JARDIANCE, discontinue nursing or discontinue JARDIANCE.
It is
not
known
if JARDIANCE
is is
excreted
in in
human
milk.
Because
ofof
the
potential
forfor
serious
adverse
It is
not
known
if JARDIANCE
excreted
human
milk.
Because
the
potential
serious
adverse
reactions
in
nursing
infants
from
JARDIANCE,
discontinue
nursing
or
discontinue
JARDIANCE.
reactions
in
nursing
infants
from
JARDIANCE,
discontinue
nursing
or
discontinue
JARDIANCE.
Geriatric Use
JARDIANCE is expected to have diminished efficacy in elderly patients with renal impairment. The
Geriatric
Use
Geriatric
Use
incidence of volume depletion-related adverse reactions and urinary tract infections increased in
JARDIANCE
is is
expected
toto
have
diminished
efficacy
in in
elderly
patients
with
renal
impairment.
The
JARDIANCE
expected
have
diminished
efficacy
elderly
patients
with
renal
impairment.
The
patients ≥ 75 years treated with JARDIANCE.
incidence
of
volume
depletion-related
adverse
reactions
and
urinary
tract
infections
increased
in
incidence of volume depletion-related adverse reactions and urinary tract infections increased in
JARPROFISI 8.2.14
patients
≥≥
7575
years
treated
with
JARDIANCE.
patients
years
treated
with
JARDIANCE.
0.5
WARNINGS AND
PRECAUTIONS
(continued)
IMPORTANT
SAFETY
INFORMATION
(continued)
IMPORTANT
SAFETY
INFORMATION
(continued)
In adults with type 2 diabetes,
In
adults with type 2demonstrated
diabetes,
JARDIANCE
similar A1C reduction vs glimepiride
with the additional
benefit of
significant
weight loss
JARDIANCE
demonstrated
similar
A1C reduction
vs* glimepiride
with the additional benefit of significant weight loss*
A1C REDUCTION IN A 52-WEEK INTERIM ANALYSIS†
Mean baseline=7.9%; 7.9%
A1C REDUCTION IN A 52-WEEK INTERIM ANALYSIS†
loss.
Weight change was a secondary endpoint.1
*JARDIANCE is not indicated for weight loss.
1 design: In a 104-week, doubleStudy
WEIGHT CHANGE
IN A 52-WEEK
ANALYSIS endpoint.
Weight
change
was aINTERIM
secondary
§
Mean baseline=7.9%; 7.9%
Mean baseline=182 lb; 183 lb
0.00
WEIGHT CHANGE IN A 52-WEEK INTERIM ANALYSIS§
3.0
-0.25
-0.25
-0.50
-0.50
-0.75
-0.75
4
Baseline
12
4
Baseline
12
28
40
52 Wk 52
40
52 p<0.0001
Wk 52
Weeks
28
Weeks
JARDIANCE 25 mg + metformin (N=693)
Glimepiride + metformin (N=700)
(mITT)
(mITT)
p<0.0001
WeightWeight
(% change
(% change
from baseline)
from baseline)
(adjusted
(adjusted
mean) mean)
‡
‡
A1C (%)A1C
mean
(%)change
mean change
from baseline
from baseline
(adjusted
(adjusted
mean) mean)
0.00
*JARDIANCE is not indicated for weight
Mean baseline=182 lb;
% 183 lb
2.0
3.0
1.0
2.0
0.0
1.0
-1.0
0.0
-2.0
-1.0
-3.0
-2.0
-4.0
-3.0
-5.0
-4.0
-5.0
2.0
2.0%
Difference
of 5.9%
or 10.8 lbII
Difference
of 5.9%
or 10.8 lbII
-3.9%
-3.9%
p<0.0001
II
II
p<0.0001
blind study of 1,545 patients with type
2
diabetes
mellitus,
the efficacy
of
Study
design:
In a 104-week,
doubleJARDIANCE
mg as
add-onwith
therapy
blind study of25
1,545
patients
type
to
metformin
(N=765)
evaluated
2 diabetes
mellitus,
thewas
efficacy
of vs
glimepiride
dose 2.7
mg)
JARDIANCE(mean
25 mgdaily
as add-on
therapy
added
to metformin
(N=780),
to metformin
(N=765)
was evaluated vs
administered
oncedaily
daily.dose 2.7 mg)
glimepiride (mean
†added to metformin (N=780),
Completers only.
‡administered once daily.
Mean change from baseline adjusted for baseline
A1C, geographical
Completers
only. region, and eGFR at baseline.
§
‡Modified intent-to-treat population (mITT). Last
Mean change from baseline adjusted for baseline
observation
on study
(LOCF)
used
to impute
A1C, geographical
region,
andwas
eGFR
at baseline.
§data missing at Week 52.
Modified intent-to-treat population (mITT). Last
#
SBP
mean baseline:
133.4
mmwas
Hg and
observation
on study
(LOCF)
used to impute
133.5
mm Hg at
forWeek
JARDIANCE
25 mg and
data missing
52.
1
#glimepiride, respectively.
SBP mean baseline: 133.4 mm Hg and
†
133.5 mm Hg for JARDIANCE 25 mg and
glimepiride, respectively.1
+ metformin (N=780)
¶
JARDIANCE
25 mg significantly reduced SBPGlimepiride
(-3.6 mm
Hg) vs an increase with glimepiride (2.2 mm Hg)
#
at 52 weeks; adjusted mean, p<0.0001
JARDIANCE 25 mg significantly reduced SBP¶ (-3.6 mm Hg) vs an increase with glimepiride (2.2 mm Hg)
¶
JARDIANCE
is not
indicated
as antihypertensive
at 52 weeks;
adjusted
mean,
p<0.0001# therapy. BP change was a secondary endpoint.1
¶
• The recommended
dose ofas
JARDIANCE
is 10 mg
once daily.
In patients
JARDIANCE
JARDIANCE
is not indicated
antihypertensive
therapy.
BP change
was atolerating
secondary
endpoint.1 10 mg, the dose may be
increased to 25 mg
• The recommended dose of JARDIANCE is 10 mg once daily. In patients tolerating
JARDIANCE 10 mg, the dose may be
1
• increased
Primary endpoint
to 25 mgwas A1C change from baseline after 52 weeks and 104 weeks. At 52 weeks, change from baseline
(adjusted mean) was -0.7% with both JARDIANCE and glimepiride. Data at 104 weeks are not yet available
• Primary endpoint was A1C change from baseline after 52 weeks and 104 weeks.1 At 52 weeks, change from baseline
(adjusted mean) was -0.7% with both JARDIANCE and glimepiride. Data at 104 weeks are not yet available
WARNINGS
AND
PRECAUTIONS
(continued) (continued)
IMPORTANT
SAFETY
INFORMATION
Hypoglycemia with Concomitant Use with Insulin and Insulin
WARNINGS AND PRECAUTIONS (continued)
Secretagogues
Hypoglycemia with Concomitant Use with Insulin and Insulin
Insulin and insulin secretagogues are known to cause hypoglycemia.
Secretagogues
The use of JARDIANCE with these agents can increase the risk of
Insulin and insulin secretagogues are known to cause hypoglycemia.
hypoglycemia. A lower dose of insulin or the insulin secretagogue
The use of JARDIANCE with these agents can increase the risk of
may be required to reduce the risk of hypoglycemia when used in
hypoglycemia. A lower dose of insulin or the insulin secretagogue
combination with JARDIANCE.
may be required to reduce the risk of hypoglycemia when used in
combination
with JARDIANCE.
Please see additional
Important Safety Information and Brief Summary
of full Prescribing Information on the adjacent pages.
Please see additional Important Safety Information and Brief Summary
Reference: 1. Data on file. Boehringer Ingelheim Pharmaceuticals, Inc. Ridgefield, CT. 2014.
of full Prescribing Information on the adjacent pages.
Reference: 1. Data on file. Boehringer Ingelheim Pharmaceuticals, Inc. Ridgefield, CT. 2014.
Copyright © 2014 Boehringer Ingelheim Pharmaceuticals, Inc. All rights reserved. (8/14) JAR622805MHC
Copyright © 2014 Boehringer Ingelheim Pharmaceuticals, Inc. All rights reserved. (8/14) JAR622805MHC
® ®
JARDIANCE
(empagliflozin)
tablets,
for for
oraloral
useuse
JARDIANCE
(empagliflozin)
tablets,
b
Female
genital
mycotic
infections
include
the following
adverse
reactions:
vulvovaginal
mycotic
infec-infecFemale
genital
mycotic
infections
include
the following
adverse
reactions:
vulvovaginal
mycotic
tion, tion,
vaginal
infection,
vulvitis,
vulvovaginal
candidiasis,
genital
infection,
genital
candidiasis,
genital
vaginal
infection,
vulvitis,
vulvovaginal
candidiasis,
genital
infection,
genital
candidiasis,
genital
infection
fungal,
genitourinary
tracttract
infection,
vulvovaginitis,
cervicitis,
urogenital
infection
fungal,
vaginiBRIEF
SUMMARY
OF PRESCRIBING
INFORMATION
infection
fungal,
genitourinary
infection,
vulvovaginitis,
cervicitis,
urogenital
infection
fungal,
vaginiBRIEF
SUMMARY
OF PRESCRIBING
INFORMATION
tis bacterial.
Percentages
calculated
with
the
number
of
female
subjects
in
each
group
as
denominator:
tis bacterial. Percentages calculated with the number of female subjects in each group as denominator:
Please
seesee
package
insert
for for
full full
Prescribing
Information.
Please
package
insert
Prescribing
Information.
placebo
(N=481),
JARDIANCE
10 mg
JARDIANCE
25 mg
placebo
(N=481),
JARDIANCE
10 (N=443),
mg (N=443),
JARDIANCE
25 (N=420).
mg (N=420).
c
c
adverse
eventevent
grouping,
including,
but not
to, polyuria,
pollakiuria,
and nocturia
Predefined
adverse
grouping,
including,
butlimited
not limited
to, polyuria,
pollakiuria,
and nocturia
INDICATIONS
ANDAND
USAGE:
JARDIANCE
is indicated
as an
to diet
and and
exercise
INDICATIONS
USAGE:
JARDIANCE
is indicated
as adjunct
an adjunct
to diet
exercisedPredefined
d
MaleMale
genital
mycotic
infections
include
the following
adverse
reactions:
balanoposthitis,
balanitis,
genital
mycotic
infections
include
the following
adverse
reactions:
balanoposthitis,
balanitis,
to improve
glycemic
control
in adults
withwith
typetype
2 diabetes
mellitus.
Limitation
of Use:
to improve
glycemic
control
in adults
2 diabetes
mellitus.
Limitation
of Use:genital
infections
fungal,
genitourinary
tract
infection,
balanitis
candida,
scrotal
abscess,
penile
infec-infecinfections fungal, genitourinary tract infection, balanitis candida, scrotal abscess,
penile
JARDIANCE
is not
recommended
for patients
withwith
typetype
1 diabetes
or for
treatment
of oftion.genital
JARDIANCE
is not
recommended
for patients
1 diabetes
or the
for the
treatment
Percentages
calculated
with with
the number
of male
subjects
in each
groupgroup
as denominator:
placebo
tion.
Percentages
calculated
the number
of male
subjects
in each
as denominator:
placebo
diabetic
ketoacidosis.
diabetic
ketoacidosis.
(N=514),
JARDIANCE
10 mg
JARDIANCE
25 mg
(N=514),
JARDIANCE
10 (N=556),
mg (N=556),
JARDIANCE
25 (N=557).
mg (N=557).
b
CONTRAINDICATIONS:
CONTRAINDICATIONS:
Thirst
(including
polydipsia)
waswas
reported
in 0%,
1.7%,
andand
1.5%
for placebo,
JARDIANCE
Thirst
(including
polydipsia)
reported
in 0%,
1.7%,
1.5%
for placebo,
JARDIANCE
• History
of serious
hypersensitivity
reaction
to JARDIANCE.
• History
of serious
hypersensitivity
reaction
to JARDIANCE.
10 10
mg,mg,
andand
JARDIANCE
25 25
mg,mg,
respectively.
Volume
Depletion:
JARDIANCE
causes
JARDIANCE
respectively.
Volume
Depletion:
JARDIANCE
causes
• Severe
renalrenal
impairment,
end-stage
renalrenal
disease,
or dialysis
[see[see
UseUse
in Specific
• Severe
impairment,
end-stage
disease,
or dialysis
in Specifican osmotic
diuresis,
which
maymay
leadlead
to intravascular
volume
contraction
andand
adverse
an osmotic
diuresis,
which
to intravascular
volume
contraction
adverse
Populations].
Populations].
reactions
related
to volume
depletion.
In the
poolpool
of five
placebo-controlled
clinical
tri- trireactions
related
to volume
depletion.
In the
of five
placebo-controlled
clinical
adverse
reactions
related
to volume
depletion
(e.g.,(e.g.,
blood
pressure
(ambulatory)
adverse
reactions
related
to volume
depletion
blood
pressure
(ambulatory)
WARNINGS
ANDAND
PRECAUTIONS:
Hypotension:
JARDIANCE
causes
intravascular
WARNINGS
PRECAUTIONS:
Hypotension:
JARDIANCE
causes
intravascularals, als,
blood
pressure
systolic
decreased,
dehydration,
hypotension,
hypovolemia,
decreased,
blood
pressure
systolic
decreased,
dehydration,
hypotension,
hypovolemia,
volume
contraction.
Symptomatic
hypotension
maymay
occur
afterafter
initiating
JARDIANCE
volume
contraction.
Symptomatic
hypotension
occur
initiating
JARDIANCEdecreased,
hypotension,
andand
syncope)
werewere
reported
by 0.3%,
0.5%,
andand
0.3%
of of
orthostatic
hypotension,
syncope)
reported
by 0.3%,
0.5%,
0.3%
[see[see
Adverse
Reactions]
particularly
in patients
withwith
renalrenal
impairment,
the the
elderly,
in inorthostatic
Adverse
Reactions]
particularly
in patients
impairment,
elderly,
treated
withwith
placebo,
JARDIANCE
10 mg,
andand
JARDIANCE
25 mg
respectively.
patients
treated
placebo,
JARDIANCE
10 mg,
JARDIANCE
25 mg
respectively.
patients
withwith
low low
systolic
blood
pressure,
andand
in patients
on diuretics.
Before
initiatpatients
systolic
blood
pressure,
in patients
on diuretics.
Before
initiat-patients
maymay
increase
the the
risk risk
of hypotension
in patients
at risk
for volume
contracJARDIANCE
increase
of hypotension
in patients
at risk
for volume
contracing ing
JARDIANCE,
assess
for volume
contraction
andand
correct
volume
status
if indicated.
JARDIANCE,
assess
for volume
contraction
correct
volume
status
if indicated.JARDIANCE
[see[see
Warnings
andand
Precautions
andand
UseUse
in Specific
Populations].
Increased
Urination:
Warnings
Precautions
in Specific
Populations].
Increased
Urination:
Monitor
for signs
andand
symptoms
of hypotension
afterafter
initiating
therapy
andand
increase
Monitor
for signs
symptoms
of hypotension
initiating
therapy
increasetion tion
poolpool
five five
placebo-controlled
clinical
trials,
adverse
reactions
of increased
urination
In the
placebo-controlled
clinical
trials,
adverse
reactions
of increased
urination
monitoring
in clinical
situations
where
volume
contraction
is expected
[see[see
UseUse
in inIn the
monitoring
in clinical
situations
where
volume
contraction
is expected
polyuria,
pollakiuria,
andand
nocturia)
occurred
moremore
frequently
on JARDIANCE
thanthan
polyuria,
pollakiuria,
nocturia)
occurred
frequently
on JARDIANCE
Specific
Populations].
Impairment
in Renal
Function:
JARDIANCE
increases
serum
Specific
Populations].
Impairment
in Renal
Function:
JARDIANCE
increases
serum(e.g.,(e.g.,
(see(see
Table
1). Specifically,
nocturia
waswas
reported
by 0.4%,
0.3%,
andand
0.8%
on placebo
Table
1). Specifically,
nocturia
reported
by 0.4%,
0.3%,
0.8%
creatinine
andand
decreases
eGFR
[see[see
Adverse
Reactions].
TheThe
risk risk
of impaired
renalrenalon placebo
creatinine
decreases
eGFR
Adverse
Reactions].
of impaired
treated
withwith
placebo,
JARDIANCE
10 mg,
andand
JARDIANCE
25 mg,
respecof patients
treated
placebo,
JARDIANCE
10 mg,
JARDIANCE
25 mg,
respecfunction
withwith
JARDIANCE
is increased
in elderly
patients
andand
patients
withwith
moderate
function
JARDIANCE
is increased
in elderly
patients
patients
moderateof patients
Impairment
in Renal
Function:
UseUse
of JARDIANCE
waswas
associated
withwith
increases
tively.
Impairment
in Renal
Function:
of JARDIANCE
associated
increases
renalrenal
impairment.
MoreMore
frequent
monitoring
of renal
function
is recommended
in these
impairment.
frequent
monitoring
of renal
function
is recommended
in thesetively.
creatinine
andand
decreases
in eGFR
(see(see
Table
2). Patients
withwith
moderate
renalrenal
in serum
creatinine
decreases
in eGFR
Table
2). Patients
moderate
patients
[see[see
UseUse
in Specific
Populations].
Renal
function
should
be evaluated
priorprior
to toin serum
patients
in Specific
Populations].
Renal
function
should
be evaluated
at baseline
hadhad
larger
mean
changes.
[see[see
Warnings
andand
Precautions
andand
impairment
at baseline
larger
mean
changes.
Warnings
Precautions
initiating
JARDIANCE
andand
periodically
thereafter.
Hypoglycemia
withwith
Concomitant
initiating
JARDIANCE
periodically
thereafter.
Hypoglycemia
Concomitantimpairment
in Specific
Populations].
in Specific
Populations].
UseUse
withwith
Insulin
andand
Insulin
Secretagogues:
Insulin
andand
insulin
secretagogues
are areUseUse
Insulin
Insulin
Secretagogues:
Insulin
insulin
secretagogues
known
to cause
hypoglycemia.
TheThe
risk risk
of hypoglycemia
is increased
when
JARDIANCE
known
to cause
hypoglycemia.
of hypoglycemia
is increased
when
JARDIANCETable
2: Changes
from
Baseline
in Serum
Creatinine
andand
eGFR
in the
PoolPool
of of
Table
2: Changes
from
Baseline
in Serum
Creatinine
eGFR
in the
is used
in combination
withwith
insulin
secretagogues
(e.g.,(e.g.,
sulfonylurea)
or insulin
[see[see
is used
in combination
insulin
secretagogues
sulfonylurea)
or insulin
FourFour
24-week
Placebo-Controlled
Studies
andand
Renal
Impairment
Study
24-week
Placebo-Controlled
Studies
Renal
Impairment
Study
Adverse
Reactions].
Therefore,
a lower
dosedose
of the
insulin
secretagogue
or insulin
Adverse
Reactions].
Therefore,
a lower
of the
insulin
secretagogue
or insulin
maymay
be required
to reduce
the the
risk risk
of hypoglycemia
when
usedused
in combination
withwith
be required
to reduce
of hypoglycemia
when
in combination
PoolPool
of 24-Week
Placebo-Controlled
of 24-Week
Placebo-Controlled
JARDIANCE.
Genital
Mycotic
Infections:
JARDIANCE
increases
the the
risk risk
for genital
JARDIANCE.
Genital
Mycotic
Infections:
JARDIANCE
increases
for genital
Studies
Studies
mycotic
infections
[see[see
Adverse
Reactions].
Patients
withwith
a history
of chronic
or recurmycotic
infections
Adverse
Reactions].
Patients
a history
of chronic
or recurPlacebo
Placebo JARDIANCE
JARDIANCE JARDIANCE
JARDIANCE
rentrent
genital
mycotic
infections
werewere
moremore
likelylikely
to develop
mycotic
genital
infections.
genital
mycotic
infections
to develop
mycotic
genital
infections.
10 mg
25 mg
10 mg
25 mg
Monitor
andand
treattreat
as appropriate.
Urinary
Tract
Infections:
JARDIANCE
increases
the the
Monitor
as appropriate.
Urinary
Tract
Infections:
JARDIANCE
increases
N N
825825
830830
822822
risk risk
for urinary
tracttract
infections
[see[see
Adverse
Reactions].
Monitor
andand
treattreat
as approprifor urinary
infections
Adverse
Reactions].
Monitor
as appropri- Baseline
Baseline
Creatinine (mg/dL)
0.840.84
0.850.85
0.850.85
ate.ate.
Increased
Low-Density
Lipoprotein
Cholesterol
(LDL-C):
Increases
in LDL-C
Increased
Low-Density
Lipoprotein
Cholesterol
(LDL-C):
Increases
in LDL-C Mean
Mean Creatinine (mg/dL)2 2
can can
occur
withwith
JARDIANCE
[see[see
Adverse
Reactions].
Monitor
andand
treattreat
as appropriate.
occur
JARDIANCE
Adverse
Reactions].
Monitor
as appropriate.
eGFR
(mL/min/1.73
m ) m ) 87.387.3
87.187.1
87.887.8
eGFR
(mL/min/1.73
Macrovascular
Outcomes:
There
havehave
beenbeen
no clinical
studies
establishing
conclusive
Macrovascular
Outcomes:
There
no clinical
studies
establishing
conclusive
N N
771771
797797
783783
evidence
of macrovascular
risk risk
reduction
withwith
JARDIANCE
or any
otherother
antidiabetic
drug.
evidence
of macrovascular
reduction
JARDIANCE
or any
antidiabetic
drug. Week
12 12
Week
Creatinine
(mg/dL)
0.00
0.02
0.01
Creatinine
(mg/dL)
0.00
0.02
0.01
Change
ADVERSE
REACTIONS:
TheThe
following
important
adverse
reactions
are are
described
below
ADVERSE
REACTIONS:
following
important
adverse
reactions
described
below Change
-1.3-1.3
-1.4-1.4
eGFR
(mL/min/1.73
m2) m2) -0.3-0.3
eGFR
(mL/min/1.73
andand
elsewhere
in the
labeling:
Hypotension
[see[see
Warnings
andand
Precautions];
Impairment
elsewhere
in the
labeling:
Hypotension
Warnings
Precautions];
Impairment
in Renal
Function
[see[see
Warnings
andand
Precautions];
Hypoglycemia
withwith
Concomitant
UseUse
N N
708708
769769
754754
in Renal
Function
Warnings
Precautions];
Hypoglycemia
Concomitant
24 24
Week
withwith
Insulin
andand
Insulin
Secretagogues
[see[see
Warnings
andand
Precautions];
Genital
Mycotic
Insulin
Insulin
Secretagogues
Warnings
Precautions];
Genital
Mycotic Week
Creatinine
(mg/dL)
0.00
0.01
0.01
Creatinine
(mg/dL)
0.00
0.01
0.01
Change
Infections
[see[see
Warnings
andand
Precautions];
Urinary
TractTract
Infections
[see[see
Warnings
andand Change
Infections
Warnings
Precautions];
Urinary
Infections
Warnings
-0.6-0.6
-1.4-1.4
eGFR
(mL/min/1.73
m2) m2) -0.3-0.3
eGFR
(mL/min/1.73
Precautions];
Increased
Low-Density
Lipoprotein
Cholesterol
(LDL-C)
[see[see
Warnings
Precautions];
Increased
Low-Density
Lipoprotein
Cholesterol
(LDL-C)
Warnings
a
a
Moderate
Renal
Impairment
Moderate
Renal
Impairment
andand
Precautions].
Clinical
Trials
Experience:
Because
clinical
trialstrials
are are
conducted
Precautions].
Clinical
Trials
Experience:
Because
clinical
conducted
under
widely
varying
conditions,
adverse
reaction
ratesrates
observed
in the
trialstrials
of aof a
under
widely
varying
conditions,
adverse
reaction
observed
in clinical
the clinical
Placebo
JARDIANCE
Placebo
JARDIANCE
drugdrug
cannot
be directly
compared
to rates
in the
clinical
trialstrials
of another
drugdrug
andand
maymay
cannot
be directly
compared
to rates
in the
clinical
of another
25 mg
25 mg
not not
reflect
the the
ratesrates
observed
in practice.
PoolPool
of Placebo-Controlled
TrialsTrials
evaluating
reflect
observed
in practice.
of Placebo-Controlled
evaluating
N N
187187
– –
187187
JARDIANCE
10 and
25 mg:
TheThe
datadata
in Table
1 are
derived
fromfrom
a pool
of four
24-week
JARDIANCE
10 and
25 mg:
in Table
1 are
derived
a pool
of four
24-week
Creatinine
(mg/dL)
1.491.49
– –
1.461.46
Baseline
Creatinine
(mg/dL)
placebo-controlled
trialstrials
andand
18-week
datadata
fromfrom
a placebo-controlled
trialtrial
withwith
insulin.
placebo-controlled
18-week
a placebo-controlled
insulin. Baseline
JARDIANCE
waswas
usedused
as monotherapy
in one
trialtrial
andand
as add-on
therapy
in four
trials.
eGFR
(mL/min/1.73
m2) m2) 44.344.3
– –
45.445.4
JARDIANCE
as monotherapy
in one
as add-on
therapy
in four
trials.
eGFR
(mL/min/1.73
These
datadata
reflect
exposure
of 1976
patients
to JARDIANCE
withwith
a mean
exposure
duraThese
reflect
exposure
of 1976
patients
to JARDIANCE
a mean
exposure
duraN N
176176
– –
179179
tion tion
of approximately
23 weeks.
Patients
received
placebo
(N=995),
JARDIANCE
10 mg
of approximately
23 weeks.
Patients
received
placebo
(N=995),
JARDIANCE
10 mg Week
12 12
Week
Creatinine
(mg/dL)
0.01
–
0.12
Creatinine
(mg/dL)
0.01
–
0.12
(N=999),
or JARDIANCE
25 mg
(N=977)
onceonce
daily.daily.
TheThe
mean
age age
of the
waswas Change
(N=999),
or JARDIANCE
25 mg
(N=977)
mean
of population
the population
Change
– –
-3.8-3.8
eGFR
(mL/min/1.73
m2) m2) 0.1 0.1
eGFR
(mL/min/1.73
56 years
andand
3% 3%
werewere
olderolder
thanthan
75 years
of age.
MoreMore
thanthan
half half
(55%)
of the
56 years
75 years
of age.
(55%)
of population
the population
waswas
male;
46%46%
werewere
White,
50%50%
werewere
Asian,
andand
3% 3%
werewere
Black
or African
American.
male;
White,
Asian,
Black
or African
American.
N N
170170
– –
171171
24 24
At baseline,
57%57%
of the
population
hadhad
diabetes
moremore
thanthan
5 years
andand
hadhad
a mean
Week
At baseline,
of the
population
diabetes
5 years
a mean Week
Creatinine
(mg/dL)
0.01
–
0.10
Creatinine
(mg/dL)
0.01
–
0.10
Change
hemoglobin
A1cA1c
(HbA1c)
of 8%.
Established
microvascular
complications
of diabetes
hemoglobin
(HbA1c)
of 8%.
Established
microvascular
complications
of diabetes Change
eGFR
(mL/min/1.73
m2) m2) 0.2 0.2
– –
-3.2-3.2
eGFR
(mL/min/1.73
at baseline
included
diabetic
nephropathy
(7%),
retinopathy
(8%),
or neuropathy
(16%).
at baseline
included
diabetic
nephropathy
(7%),
retinopathy
(8%),
or neuropathy
(16%).
Baseline
renalrenal
function
waswas
normal
or mildly
impaired
in 91%
of patients
andand
moderately
N N
164164
– –
162162
Baseline
function
normal
or mildly
impaired
in 91%
of patients
moderately
2
52 52
Week
impaired
in 9%
of patients
(mean
eGFR
86.886.8
mL/min/1.73
m2).mTable
1 shows
common
impaired
in 9%
of patients
(mean
eGFR
mL/min/1.73
). Table
1 shows
common Week
Creatinine
(mg/dL)
0.02
–
0.11
Creatinine
(mg/dL)
0.02
–
0.11
Change
adverse
reactions
(excluding
hypoglycemia)
associated
withwith
the the
use use
of JARDIANCE.
TheThe Change
adverse
reactions
(excluding
hypoglycemia)
associated
of JARDIANCE.
2
2
)
-0.3
–
-2.8
eGFR
(mL/min/1.73
m
)
-0.3
–
-2.8
eGFR
(mL/min/1.73
m
adverse
reactions
werewere
not present
at baseline,
occurred
moremore
commonly
on JARDIANCE
adverse
reactions
not present
at baseline,
occurred
commonly
on JARDIANCE
a
of patients
fromfrom
renalrenal
impairment
studystudy
with with
eGFReGFR
30 to30less
thanthan
60 mL/min/1.73
m2 m2
thanthan
on placebo
andand
occurred
in greater
thanthan
or equal
to 2%
of patients
treated
withwithaSubset
Subset
of patients
impairment
to less
60 mL/min/1.73
on placebo
occurred
in greater
or equal
to 2%
of patients
treated
JARDIANCE
10 mg
or JARDIANCE
25 mg.
JARDIANCE
10 mg
or JARDIANCE
25 mg.
Hypoglycemia:
TheThe
incidence
of hypoglycemia
by study
is shown
in Table
3. The
inci-inciHypoglycemia:
incidence
of hypoglycemia
by study
is shown
in Table
3. The
Table
1: Adverse
Reactions
Reported
in ≥2%
of Patients
Treated
withwith
JARDIANCE
Table
1: Adverse
Reactions
Reported
in ≥2%
of Patients
Treated
JARDIANCEdence
of hypoglycemia
increased
when
JARDIANCE
waswas
administered
withwith
insulin
or or
dence
of hypoglycemia
increased
when
JARDIANCE
administered
insulin
andand
Greater
than
Placebo
in
Pooled
Placebo-Controlled
Clinical
Studies
of
Greater than Placebo in Pooled Placebo-Controlled Clinical Studies ofsulfonylurea
[see[see
Warnings
andand
Precautions].
sulfonylurea
Warnings
Precautions].
JARDIANCE
Monotherapy
or
Combination
Therapy
JARDIANCE Monotherapy or Combination Therapy
a
a Severeb Hypoglycemic
Table
3: Incidence
of Overall
and
Events
in Controlled
Table
3: Incidence
of Overall
and Severeb Hypoglycemic
Events
in Controlled
Clinical
Studies
Clinical
Studies
Number
(%)(%)
of Patients
Number
of Patients
Placebo
JARDIANCE
10 mg
JARDIANCE
25 mg
Placebo
JARDIANCE
10 mg
JARDIANCE
25 mg
Monotherapy
Placebo
10 mg
JARDIANCE
25 mg
Monotherapy
PlaceboJARDIANCE
JARDIANCE
10 mg
JARDIANCE
25 mg
N=995
N=999
N=977
N=995
N=999
N=977
(24 (24
weeks)
(n=229)
(n=224)
(n=223)
weeks)
(n=229)
(n=224)
(n=223)
a
a
7.6%
9.3%
7.6%
Urinary
tracttract
infection
Urinary
infection
7.6%
9.3%
7.6%
b
b 1.5%
Overall
(%) (%)
0.4%
0.4%
0.4%
Overall
0.4%
0.4%
0.4%
Female
genital
mycotic
infections
5.4%
6.4%
Female
genital
mycotic
infections
1.5%
5.4%
6.4%
Upper
respiratory
tracttract
infection
3.1%
4.0%
Upper
respiratory
infection 3.8%
3.8%
3.1%
4.0%
Severe
(%) (%)
0% 0%
0% 0%
0% 0%
Severe
c
c
Increased
urination
1.0%
3.4%
3.2%
Increased
urination
1.0%
3.4%
3.2%
JARDIANCE
25 mg
JARDIANCE
10
mg
In
Combination
with
Placebo
+
JARDIANCE
25 mg
JARDIANCE
10
mg
In
Combination
with
Placebo
+
Dyslipidemia
3.4%
3.9%
2.9%
Dyslipidemia
3.4%
3.9%
2.9%
Metformin
(24 (24
weeks)
+ Metformin
+ Metformin + Metformin
Metformin
weeks) Metformin
Metformin + Metformin
Arthralgia
2.2%
2.4%
2.3%
Arthralgia
2.2%
2.4%
2.3%
(n=214)
(n=217)
(n=206)
(n=214)
(n=217)
(n=206)
d
d
MaleMale
genital
mycotic
infections
0.4%
3.1%
1.6%
genital
mycotic
infections
0.4%
3.1%
1.6%
Nausea
1.4%
2.3%
1.1%
Overall
(%)
0.5%
1.8%
1.4%
Nausea
1.4%
2.3%
1.1%
Overall (%)
0.5%
1.8%
1.4%
a
a
Predefined
adverse
eventevent
grouping,
including,
but not
to, urinary
tracttract
infection,
asymptomatic
Predefined
adverse
grouping,
including,
but limited
not limited
to, urinary
infection,
asymptomatic
Severe
(%) (%)
Severe
bacteriuria,
cystitis
bacteriuria,
cystitis
0% 0%
0% 0%
0% 0%
Secretagogues:
Coadministration
of empagliflozin
withwith
insulin
or insulin
secretagogues
Secretagogues:
Coadministration
of empagliflozin
insulin
or insulin
secretagogues
increases
the the
risk risk
for hypoglycemia
[see[see
Warnings
andand
Precautions].
Positive
Urine
increases
for hypoglycemia
Warnings
Precautions].
Positive
Urine
Test:
Monitoring
glycemic
control
withwith
urineurine
glucose
teststests
is not
Glucose
Test:
Monitoring
glycemic
control
glucose
is recommended
not recommended
25 mg
Placebo
10 mg
In Combination
withwith
JARDIANCE
25 mg Glucose
PlaceboJARDIANCE
JARDIANCE
10 mgJARDIANCE
In Combination
taking
SGLT2
inhibitors
as SGLT2
inhibitors
increase
urinary
glucose
excretion
in patients
taking
SGLT2
inhibitors
as SGLT2
inhibitors
increase
urinary
glucose
excretion
+ + in patients
+ + + Metformin
Metformin
+ Sulfonylurea
+ Metformin
(n=225) + Metformin
+ Metformin
Metformin
+ Sulfonylurea (n=225)
will will
leadlead
to positive
urineurine
glucose
tests.
UseUse
alternative
methods
to monitor
gly-glyto positive
glucose
tests.
alternative
methods
to monitor
Sulfonylurea
(24 (24
weeks)
Sulfonylurea andand
Sulfonylurea Sulfonylurea
weeks)
cemic
control.
Interference
withwith
1,5-anhydroglucitol
(1,5-AG)
Assay:
Monitoring
cemic
control.
Interference
1,5-anhydroglucitol
(1,5-AG)
Assay:
Monitoring
(n=217)
(n=224)
(n=217)
(n=224)
glycemic
control
withwith
1,5-AG
assay
is not
recommended
as measurements
of 1,5-AG
glycemic
control
1,5-AG
assay
is not
recommended
as measurements
of 1,5-AG
Overall
(%) (%)
8.4%
16.1%
11.5%
Overall
8.4%
16.1%
11.5%
are are
unreliable
in assessing
glycemic
control
in patients
taking
SGLT2
inhibitors.
UseUse
unreliable
in assessing
glycemic
control
in patients
taking
SGLT2
inhibitors.
alternative
methods
to monitor
glycemic
control.
alternative
methods
to monitor
glycemic
control.
Severe
(%) (%)
0% 0%
0% 0%
0% 0%
Severe
USEUSE
IN SPECIFIC
POPULATIONS:
Pregnancy:
Pregnancy
Category
C: There
are are
no no
IN SPECIFIC
POPULATIONS:
Pregnancy:
Pregnancy
Category
C: There
25 mg
Placebo
10 mg
In Combination
withwith
JARDIANCE
25 mg adequate
PlaceboJARDIANCE
JARDIANCE
10 mgJARDIANCE
In Combination
andand
well-controlled
studies
of JARDIANCE
in pregnant
women.
JARDIANCE
adequate
well-controlled
studies
of JARDIANCE
in pregnant
women.
JARDIANCE
(n=165)
Pioglitazone
+/- +/+ Pioglitazone should
(n=165) + Pioglitazone
+ Pioglitazone + Pioglitazone
Pioglitazone
be used
during
pregnancy
onlyonly
if theif the
potential
benefit
justifies
the the
potential
risk risk
should
be used
during
pregnancy
potential
benefit
justifies
potential
Metformin
+/- +/Metformin
Metformin
(24 (24
weeks)
Metformin to the
Metformin +/- +/Metformin
weeks)
fetus.
Based
on results
fromfrom
animal
studies,
empagliflozin
maymay
affect
renalrenal
develto the
fetus.
Based
on results
animal
studies,
empagliflozin
affect
devel(n=168)
(n=165)
(n=168)
(n=165)
opment
andand
maturation.
In studies
conducted
in rats,
empagliflozin
crosses
the the
placenta
opment
maturation.
In studies
conducted
in rats,
empagliflozin
crosses
placenta
andand
reaches
fetalfetal
tissues.
During
pregnancy,
consider
appropriate
alternative
therapies,
reaches
tissues.
During
pregnancy,
consider
appropriate
alternative
therapies,
Overall
(%) (%)
1.8%
1.2%
2.4%
Overall
1.8%
1.2%
2.4%
especially
during
the the
second
andand
thirdthird
trimesters.
Nursing
Mothers:
It is Itnot
known
if if
especially
during
second
trimesters.
Nursing
Mothers:
is not
known
Severe
(%) (%)
0% 0%
0% 0%
0% 0%
Severe
JARDIANCE
is excreted
in human
milk.milk.
Empagliflozin
is secreted
in the
milkmilk
of lactating
JARDIANCE
is excreted
in human
Empagliflozin
is secreted
in the
of lactating
ratsrats
reaching
levels
up to
higher
thanthan
thatthat
in maternal
plasma.
Since
human
reaching
levels
up5totimes
5 times
higher
in maternal
plasma.
Since
human
In Combination
withwith
Placebo
10 mg
JARDIANCE
25 mg
In Combination
PlaceboJARDIANCE
JARDIANCE
10 mg
JARDIANCE
25 mgkidney
maturation
occurs
in utero
andand
during
the the
firstfirst
2 years
of life
when
lactational
kidney
maturation
occurs
in utero
during
2 years
of life
when
lactational
c
Insulin
(18 (18
weeks
) c)
(n=170)
(n=169)
(n=155)
Insulin
weeks
(n=170)
(n=169)
(n=155)
exposure
maymay
occur,
therethere
maymay
be risk
to the
developing
human
kidney.
Because
many
exposure
occur,
be risk
to the
developing
human
kidney.
Because
many
drugs
are are
excreted
in human
milkmilk
andand
because
of the
potential
for serious
adverse
drugs
excreted
in human
because
of the
potential
for serious
adverse
Overall
(%) (%)
20.6%
19.5%
28.4%
Overall
20.6%
19.5%
28.4%
reactions
in nursing
infants
fromfrom
JARDIANCE,
a decision
should
be made
whether
to to
reactions
in nursing
infants
JARDIANCE,
a decision
should
be made
whether
Severe
(%) (%)
0% 0%
0% 0%
1.3%
Severe
1.3%
discontinue
nursing
or toordiscontinue
JARDIANCE,
taking
into into
account
the the
importance
discontinue
nursing
to discontinue
JARDIANCE,
taking
account
importance
of the
drugdrug
to the
mother.
Pediatric
Use:Use:
TheThe
safety
andand
effectiveness
of JARDIANCE
a
of the
to the
mother.
Pediatric
safety
effectiveness
of JARDIANCE
a
Overall
hypoglycemic
events:
plasma
or capillary
glucose
of less
thanthan
or equal
to 70tomg/dL
Overall
hypoglycemic
events:
plasma
or capillary
glucose
of less
or equal
70 mg/dL
b
b
in pediatric
patients
under
18 years
of age
havehave
not not
beenbeen
established.
Geriatric
Use:Use:
in pediatric
patients
under
18 years
of age
established.
Geriatric
Severe
hypoglycemic
events:
requiring
assistance
regardless
of blood
glucose
Severe
hypoglycemic
events:
requiring
assistance
regardless
of blood
glucose
c
c
No JARDIANCE
dosage
change
is recommended
based
on age.
A total
of 2721
(32%)
Insulin
dosedose
couldcould
not be
during
the initial
18 week
treatment
period
No JARDIANCE
dosage
change
is recommended
based
on age.
A total
of 2721
(32%)
Insulin
notadjusted
be adjusted
during
the initial
18 week
treatment
period
patients
treated
withwith
empagliflozin
werewere
65 years
of age
andand
older,
andand
491491
(6%)(6%)
werewere
patients
treated
empagliflozin
65 years
of age
older,
Genital
Mycotic
Infections:
In the
five five
placebo-controlled
clinical
trials,
the incidence
Genital
Mycotic
Infections:
In pool
the pool
placebo-controlled
clinical
trials,
the incidence75 years
of age
andand
older.
JARDIANCE
is expected
to have
diminished
efficacy
in elderly
75 years
of age
older.
JARDIANCE
is expected
to have
diminished
efficacy
in elderly
of genital
mycotic
infections
(e.g.,(e.g.,
vaginal
mycotic
infection,
vaginal
infection,
genital
of genital
mycotic
infections
vaginal
mycotic
infection,
vaginal
infection,
genitalpatients
withwith
renalrenal
impairment
[see[see
UseUse
in Specific
Populations].
TheThe
risk risk
of volume
patients
impairment
in Specific
Populations].
of volume
infection
fungal,
vulvovaginal
candidiasis,
andand
vulvitis)
waswas
increased
in patients
treated
infection
fungal,
vulvovaginal
candidiasis,
vulvitis)
increased
in patients
treateddepletion-related
adverse
reactions
increased
in patients
whowho
werewere
75 years
of age
depletion-related
adverse
reactions
increased
in patients
75 years
of age
withwith
JARDIANCE
compared
to placebo,
occurring
in 0.9%,
4.1%,
andand
3.7%
of patients
JARDIANCE
compared
to placebo,
occurring
in 0.9%,
4.1%,
3.7%
of patientsandand
olderolder
to 2.1%,
2.3%,
andand
4.4%
for placebo,
JARDIANCE
10 mg,
andand
JARDIANCE
to 2.1%,
2.3%,
4.4%
for placebo,
JARDIANCE
10 mg,
JARDIANCE
randomized
to placebo,
JARDIANCE
10 10
mg,mg,
andand
JARDIANCE
25 25
mg,mg,
respectively.
randomized
to placebo,
JARDIANCE
JARDIANCE
respectively.25 mg.
TheThe
risk risk
of urinary
tracttract
infections
increased
in patients
whowho
werewere
75 years
of age
25 mg.
of urinary
infections
increased
in patients
75 years
of age
Discontinuation
fromfrom
study
duedue
to genital
infection
occurred
in 0%
of placebo-treated
Discontinuation
study
to genital
infection
occurred
in 0%
of placebo-treatedandand
olderolder
to 10.5%,
15.7%,
andand
15.1%
in patients
randomized
to placebo,
JARDIANCE
to 10.5%,
15.7%,
15.1%
in patients
randomized
to placebo,
JARDIANCE
patients
andand
0.2%
of patients
treated
withwith
either
JARDIANCE
10 or1025ormg.
Genital
mycotic
patients
0.2%
of patients
treated
either
JARDIANCE
25 mg.
Genital
mycotic10 mg,
andand
JARDIANCE
25 mg,
respectively
[see[see
Warning
andand
Precautions
andand
Adverse
10 mg,
JARDIANCE
25 mg,
respectively
Warning
Precautions
Adverse
infections
occurred
moremore
frequently
in female
thanthan
malemale
patients
(see(see
Table
1). Phimosis
infections
occurred
frequently
in female
patients
Table
1). PhimosisReactions].
Renal
Impairment:
TheThe
efficacy
andand
safety
of JARDIANCE
werewere
eval-evalReactions].
Renal
Impairment:
efficacy
safety
of JARDIANCE
occurred
moremore
frequently
in male
patients
treated
withwith
JARDIANCE
10 mg
(less(less
thanthanuated in a study of patients with mild and moderate renal impairment. In this study,
occurred
frequently
in male
patients
treated
JARDIANCE
10 mg
uated in a study of patients with mild and moderate renal impairment. In this study,
0.1%)
andand
JARDIANCE
25 mg
(0.1%)
thanthan
placebo
(0%).
Urinary
TractTract
Infections:
In the
0.1%)
JARDIANCE
25 mg
(0.1%)
placebo
(0%).
Urinary
Infections:
In the195195
patients
exposed
to JARDIANCE
hadhad
an eGFR
between
60 and
90 mL/min/1.73
m2, m2,
patients
exposed
to JARDIANCE
an eGFR
between
60 and
90 mL/min/1.73
poolpool
five five
placebo-controlled
clinical
trials,
the
incidence
of
urinary
tract
infections
(e.g.,
placebo-controlled clinical trials, the incidence of urinary tract infections (e.g.,91 patients
exposed
to JARDIANCE
hadhad
an eGFR
between
45 and
60 mL/min/1.73
m2 m2
91 patients
exposed
to JARDIANCE
an eGFR
between
45 and
60 mL/min/1.73
urinary
tract
infection,
asymptomatic
bacteriuria,
and
cystitis)
was
increased
in
patients
urinary tract infection, asymptomatic bacteriuria, and cystitis) was increased in patientsand and
97 patients
exposed
to JARDIANCE
had had
an eGFR
between
30 and
45 mL/min/1.73
m2. m2.
97 patients
exposed
to JARDIANCE
an eGFR
between
30 and
45 mL/min/1.73
treated
withwith
JARDIANCE
compared
to placebo
(see(see
Table
1). Patients
withwith
a history
of ofThe glucose lowering benefit of JARDIANCE 25 mg decreased in patients with worstreated
JARDIANCE
compared
to placebo
Table
1). Patients
a history
The glucose lowering benefit of JARDIANCE 25 mg decreased in patients with worschronic
or recurrent
urinary
tracttract
infections
werewere
moremore
likelylikely
to experience
a urinary
tracttractening renal function. The risks of renal impairment [see Warnings and Precautions],
chronic
or recurrent
urinary
infections
to experience
a urinary
ening renal function. The risks of renal impairment [see Warnings and Precautions],
infection.
TheThe
raterate
of treatment
discontinuation
duedue
to urinary
tracttract
infections
waswas
0.1%,
infection.
of treatment
discontinuation
to urinary
infections
0.1%,volume
depletion
adverse
reactions
andand
urinary
tracttract
infection-related
adverse
reactions
volume
depletion
adverse
reactions
urinary
infection-related
adverse
reactions
0.2%,
andand
0.1%
for placebo,
JARDIANCE
10 mg,
andand
JARDIANCE
25 mg,
respectively.
0.2%,
0.1%
for placebo,
JARDIANCE
10 mg,
JARDIANCE
25 mg,
respectively.increased
withwith
worsening
renalrenal
function.
TheThe
efficacy
andand
safety
of JARDIANCE
havehave
increased
worsening
function.
efficacy
safety
of JARDIANCE
Urinary
tracttract
infections
occurred
moremore
frequently
in female
patients.
TheThe
incidence
of ofnot been established in patients with severe renal impairment, with ESRD, or receiving
Urinary
infections
occurred
frequently
in female
patients.
incidence
not been established in patients with severe renal impairment, with ESRD, or receiving
urinary
tracttract
infections
in female
patients
randomized
to placebo,
JARDIANCE
10 mg,
urinary
infections
in female
patients
randomized
to placebo,
JARDIANCE
10 mg,dialysis.
JARDIANCE
is not
expected
to be
in these
patient
populations
[see[see
dialysis.
JARDIANCE
is not
expected
to effective
be effective
in these
patient
populations
andand
JARDIANCE
25 mg
waswas
16.6%,
18.4%,
andand
17.0%,
respectively.
TheThe
incidence
JARDIANCE
25 mg
16.6%,
18.4%,
17.0%,
respectively.
incidenceContraindications
andand
Warnings
andand
Precautions].
Hepatic
Impairment:
JARDIANCE
Contraindications
Warnings
Precautions].
Hepatic
Impairment:
JARDIANCE
of urinary
tracttract
infections
in male
patients
randomized
to placebo,
JARDIANCE
10 mg,
of urinary
infections
in male
patients
randomized
to placebo,
JARDIANCE
10 mg,maymay
be used
in patients
withwith
hepatic
impairment.
be used
in patients
hepatic
impairment.
andand
JARDIANCE
25 mg
waswas
3.2%,
3.6%,
andand
4.1%,
respectively
[see[see
Warnings
andand
JARDIANCE
25 mg
3.2%,
3.6%,
4.1%,
respectively
Warnings
In the
of anofoverdose
withwith
JARDIANCE,
contact
the Poison
Control
OVERDOSAGE:
In event
the event
an overdose
JARDIANCE,
contact
the Poison
Control
Precautions
andand
UseUse
in Specific
Populations].
Laboratory
Tests:
Increase
in Low-Density
Precautions
in Specific
Populations].
Laboratory
Tests:
Increase
in Low-DensityOVERDOSAGE:
Employ
the the
usual
supportive
measures
(e.g.,(e.g.,
remove
unabsorbed
material
fromfrom
Center.
Employ
usual
supportive
measures
remove
unabsorbed
material
Lipoprotein
Cholesterol
(LDL-C):
Dose-related
increases
in low-density
lipoprotein
cho-cho-Center.
Lipoprotein
Cholesterol
(LDL-C):
Dose-related
increases
in low-density
lipoprotein
gastrointestinal
tract,tract,
employ
clinical
monitoring,
andand
institute
supportive
treatment)
gastrointestinal
employ
clinical
monitoring,
institute
supportive
treatment)
lesterol
(LDL-C)
werewere
observed
in patients
treated
withwith
JARDIANCE.
LDL-C
increased
lesterol
(LDL-C)
observed
in patients
treated
JARDIANCE.
LDL-C
increasedthe the
by the
patient’s
clinical
status.
Removal
of empagliflozin
by hemodialysis
has has
as dictated
by the
patient’s
clinical
status.
Removal
of empagliflozin
by hemodialysis
by 2.3%,
4.6%,
andand
6.5%
in patients
treated
withwith
placebo,
JARDIANCE
10 mg,
andandas dictated
by 2.3%,
4.6%,
6.5%
in patients
treated
placebo,
JARDIANCE
10 mg,
beenbeen
studied.
studied.
JARDIANCE
25 mg,
respectively
[see[see
Warnings
andand
Precautions].
TheThe
range
of mean
JARDIANCE
25 mg,
respectively
Warnings
Precautions].
range
of meannot not
baseline
LDL-C
levels
waswas
90.390.3
to 90.6
mg/dL
across
treatment
groups.
Increase
in inAdditional
baseline
LDL-C
levels
to 90.6
mg/dL
across
treatment
groups.
Increase
information
can can
be found
at www.hcp.jardiance.com
Additional
information
be found
at www.hcp.jardiance.com
Hematocrit:
In a In
pool
of four
placebo-controlled
studies,
median
hematocrit
decreased
by by
Hematocrit:
a pool
of four
placebo-controlled
studies,
median
hematocrit
decreased
1.3%
in placebo
andand
increased
by 2.8%
in JARDIANCE
10 mg
andand
2.8%
in JARDIANCE
© 2014
Boehringer
Ingelheim
International
GmbH
1.3%
in placebo
increased
by 2.8%
in JARDIANCE
10 mg
2.8%
in JARDIANCECopyright
Copyright
© 2014
Boehringer
Ingelheim
International
GmbH
25 mg
treated
patients.
At the
endend
of treatment,
0.6%,
2.7%,
andand
3.5%
of patients
withwithALLALL
RIGHTS
RESERVED
25 mg
treated
patients.
At the
of treatment,
0.6%,
2.7%,
3.5%
of patients
RIGHTS
RESERVED
hematocrits
initially
within
the the
reference
range
hadhad
values
above
the the
upper
limitlimit
of the
hematocrits
initially
within
reference
range
values
above
upper
of theJAR-BS-8/14
JAR582220PROF
JAR-BS-8/14
JAR582220PROF
reference
range
withwith
placebo,
JARDIANCE
10 mg,
andand
JARDIANCE
25 mg,
respectively.
reference
range
placebo,
JARDIANCE
10 mg,
JARDIANCE
25 mg,
respectively.
DRUG
INTERACTIONS:
Diuretics:
Coadministration
of
empagliflozin
with
diuretics
DRUG INTERACTIONS: Diuretics: Coadministration of empagliflozin with diuretics
resulted
in increased
urineurine
volume
andand
frequency
of voids,
which
might
enhance
the the
resulted
in increased
volume
frequency
of voids,
which
might
enhance
potential
for for
volume
depletion
[see[see
Warnings
andand
Precautions].
Insulin
or Insulin
potential
volume
depletion
Warnings
Precautions].
Insulin
or Insulin
Table
3 (cont’d)
Table
3 (cont’d)
br e i f r e p o rt
Do Value Thresholds for Oncology Drugs
Differ from Nononcology Drugs?
ABSTRACT
BACKGROUND: In the past decade, many oncologic drugs have been
approved that extend life and/or improve patients’ quality of life. However,
new cancer drugs are often associated with high price and increased medical spending. For example, in 2010, the average annual cost of care for
breast cancer in the final stage of disease was reported to be $94,284, and
the total estimated cost in the United States was $16.50 billion.
OBJECTIVE: To determine whether value threshold, as defined by the
incremental cost-effectiveness ratio (ICER), differed between oncology and
other therapeutic areas.
METHODS: The PubMed database was searched for articles published
between January 2003 and December 2013 with calculated ICER for therapeutic drug entities in a specific therapeutic area. The search term used
was “ICER” and “United States.” From 275 results, only those articles that
reported ICERs using quality-adjusted life-years (QALY) were included.
In addition, only those articles that used a U.S. payer perspective were
retained. Among those, nondrug therapy articles and review articles were
excluded. The mean ICER and value threshold for oncologic drugs and nononcologic drugs were evaluated for the analysis.
RESULTS: From 54 articles selected for analysis, 13 pertained to drugs in
oncology therapeutics, and the remaining 41 articles addressed ICER for
drugs in other therapeutic areas. The mean and median of ICERs calculated
for cancer-specific drug intervention was $138,582/QALY and $55,500/
QALY, respectively, compared with $49,913/QALY and $31,000/QALY,
respectively, for noncancer drugs. Among the cancer drugs, 45.0% had
ICERs below $50,000/QALY and 70.0% below $100,000/QALY. In comparison, 72.0% of noncancer drugs showed ICERs below $50,000/QALY, and
90.0% had ICERs below $100,000/QALY. When a specific threshold was
mentioned, it was in the range of $100,000-$150,000 in cancer drugs,
whereas drugs in other therapeutic areas used traditional threshold value
within the range of $50,000-$100,000.
CONCLUSIONS: The average ICER reported for cancer drugs was more than
2-fold greater than the average ICER for noncancer drugs. In general, articles that addressed the relative value of oncologic pharmaceuticals used
higher value thresholds and reported higher ICERs than articles evaluating
noncancer drugs.
What is already known about this subject
•Although the incremental cost-effectiveness ratio (ICER),
expressed as the cost per quality-adjusted life-year (QALY), has
long been used as a standard metric in cost-effectiveness analyses,
its use has been met with challenges both in the United States
and abroad.
JMCP
November 2014
•In the United States, the 2010 Patient Protection and Affordable
Care Act prohibits the Patient-Centered Outcomes Research
Institute (PCORI) from the use of cost/QALY ICER as a threshold
to make recommendations on what type of health care or intervention should be utilized.
•Other factors besides a drug’s ICER influence the drug formulary
decisions of insurers and third-party payers. Often, they need to
consider factors such as available resources, existence of alternatives, and/or anticipated impact of the new drug being considered
for the formulary.
What this study adds
•This review of ICERs in oncology and other therapeutic areas
documents wide variation in ICERs across disease states.
•Our systematic approach to make a side-by-side comparison of
ICERs of cancer drugs and noncancer drugs from the literature
within the past decade suggests that higher ICER thresholds for
anticancer agents may exist.
T
he U.S. Food and Drug Administration (FDA) approves
new anticancer drugs based on evidence for safety
and efficacy, which often is demonstrated by extending progression-free survival or overall survival by weeks to
months. Although research and development of new drugs
are imperative for continued improvement of cancer therapy,
many have questioned how sustainable it is for government
and third-party payers to continue paying for the increasingly
high price of contemporary cancer drugs for the incremental
benefit they bring to the patients. The cost of a 1-year supply of
these drugs typically reaches $100,000, and pricing for the new
incoming agents have been on an upward trend. For example,
in 2010, the average annual cost of care for breast cancer in
the final stage of disease was reported to be $94,284, and the
total estimated cost in the United States was $16.50 billion.1
In order to assess the question of whether a new cancer drug
holds adequate value for its price, cost-effectiveness analysis is
often performed, which aids the health care decision makers
with formulary listings or reimbursement policies.
The incremental cost-effectiveness ratio (ICER) is used by
many institutions to evaluate the value of a new drug in comparison with the established therapy from clinical efficacy and
cost perspectives. The ICER is often expressed in the unit of
Vol. 20, No. 11
www.amcp.org
cost per quality-adjusted life-years (QALY), which incorporates
components of quality of life as well as the duration to establish standardization in measuring health utility. It can be used
in evaluating how much additional value a new drug can add
compared with the current standard therapy at a measured
cost. This is often performed with a predetermined threshold
value that serves as the maximum ICER limit in deciding
whether a drug is cost-effective. There are many factors in addition to the ICER that influence drug formulary decisions by
institutions or third-party payers. The threshold value could be
set based on an institution’s financial budget, or it could also be
taken from previous decisions, which are often variable across
institutions and globally.2 Currently, there is no uniformity in
determination of a threshold across health care institutions,
and a lack of standard exists.3 In the United States, the use of
ICERs in assigning value in health outcomes has faced challenges, since the Patient Protection and Affordable Care Act
in 2010 prohibited use of cost per QALY as a threshold within
the research sponsored by the Patient-Centered Outcomes
Research Institute (PCORI). This prohibition was in response
to long-standing public concerns that the use of ICER as a
threshold would discriminate on the basis of age and disability.4 Despite these concerns, ICER is often used in health care
institutions and by third-party payers in private sectors and
other countries as a valuable tool in the health care decisionmaking process. The objective of this study was to determine
whether value threshold, as defined by the ICER, differed
between oncology and other therapeutic areas.
■■  Methods
Data Collection
The lead author conducted searches and pulled data, which
were reviewed by the coauthor. The PubMed electronic database was searched, using the search term “ICER” AND “United
States.” Results were restricted to articles in English. Time
frame was limited to the 11 years from January 1, 2003, to
December 31, 2013, and focus was on the treatments developed
during the recent advancement in cancer research. With these
criteria, we were able to obtain 275 articles from the search
results. Articles that addressed cost-effectiveness of nondrug
therapy were excluded. Articles that reported ICER of nonprescription drugs were also excluded from the list. Included
articles were those that assumed a U.S. payer perspective and
reported ICER in unit of dollar per QALY. If a study reported
ICERs from review of multiple independent studies, it was
excluded (Figure 1).
Analysis
All articles were sorted into either an oncology-related drug
group or a nononcology-related drug group. Drugs used for
treatment of cancer or reducing the risk of cancer were categorized into the oncology-related drug group. Individual
www.amcp.org
Vol. 20, No. 11
FIGURE 1
Article Selection Process for Analyses of
Average ICERs and Value Thresholds
Total number of articles from
PubMed database search
(N = 275)
Excluded:
Articles not containing
cost-effectiveness analysis (n = 54)
Articles with nondrug intervention
(n = 120)
Articles with nonprescription drug intervention
(n = 6)
Non-U.S. perspective
(n = 14)
Not third-party payer perspective
(n = 7)
Articles containing review of other analyses
(n = 9)
ICER is not shown in dollar per QALY
(n = 11)
Number of articles
after exclusions
(n = 54)
Articles pertaining to
cancer drugs with ICERs
(n = 13)
Articles that mentioned
a specific threshold
(n = 5)
Articles pertaining to
noncancer drugs with ICERs
(n = 41)
Articles that mentioned a
specific threshold
(n = 11)
ICER = incremental cost-effectiveness ratio; QALY = quality-adjusted life-year.
values of ICER were obtained from the articles and analyzed
for the mean and median in each group. If an article presented
multiple ICERs of 1 drug to several comparators, each ICER
was entered separately into the analyses. A similar method
of analysis was employed for threshold values stated for the
evaluation of ICER in some of the articles. The mean value
thresholds obtained from each group of articles were compared
for the analysis. If an article mentioned 1 value threshold and
November 2014
JMCP
TABLE 1
Reported ICERs and Value Threshholds for Oncologic Agents
Disease State
Prostate cancer 9
Colon cancer10
Stage IV lung cancer11
Cervical cancer12
Pancreatic cancer13
Breast cancer14
Breast cancer15
Ovarian cancer16
Drug
Comparison
Abiraterone
Placebo
Mitoxantrone
Placebo
Abiraterone
Mitoxantrone
FOLFOX
5-FU/LV
5-FU/LV
Observation group
Erlotinib
Platinum-containing chemotherapy
Gemcitabine/cisplatin
Cisplatin
Everolimus
Sunitinib
Denosumab
Zoledronic acid
Bevacizumab
Standard care
Carboplatin/paclitaxel and
Carboplatin/paclitaxel
additional paclitaxel cycle
Carboplatin/paclitaxel/bevacizumab
Carboplatin/paclitaxel
Peg-filgrastim
Filgrastim
Peg-filgrastim
Filgrastim
Tamoxifen
Standard care
ICER
($/QALY)
94,000
101,000
91,000
54,000
14,000
110,644
33,000
41,000
697,000
745,000
13,000
Value Threshold
($)
N/A
N/A
100,000
100,000
N/A
N/A
150,000
100,000
326,000
N/A
31,000
N/A
6,000
N/A
190,000 (low risk with uterus)
100,000
72,000 (low risk without uterus)
57,000 (high risk with uterus)
37,000 (high risk without uterus)
Adjuvant trastuzumab
Standard care
39,000
N/A
Breast cancer 20
Anastrozole
Tamoxifen
20,000
N/A
Breast cancer 21
Total: 13 articles
Total: 20 interventions
Mean: 138,582
Mean: 110,000
FOLFOX = leucovorin, fluorouracil, oxaliplatin; 5-FU = fluorouracil; ICER = incremental cost-effectiveness ratio; LV = leucovorin; N/A = not available; QALY = qualityadjusted life-year.
Breast cancer17
Non-Hodgkin lymphoma18
Breast cancer risk
reduction19
presented more than 1 ICER for a particular drug, it was
counted for each of the ICERs. We also assessed whether the
article compared the reported ICER with the value threshold
ICER. Because the prices of therapeutic agents, as well as
ICERs, are likely to be higher in more recent years, we examined ICER thresholds of the historical benchmark of $50,000/
QALY and the more contemporary benchmark of $100,000/
QALY.
Sixteen articles mentioned a specific value threshold for the
evaluation of ICER. Of those 16 value thresholds, 5 were from
the oncologic drug group, and 11 were from the nononcologic
drug group. In the oncologic drug group, the range of value
thresholds was $100,000-$150,000 with the mean of $110,000.
In comparison, the range of thresholds used in the nononcologic drug group was $50,000-$100,000, and the mean was
$68,181.
■■  Results
For the analysis, we included 13 articles that addressed cancer
treatment and 41 articles that related to treatment of other diseases or conditions. From these articles, we obtained 20 ICERs
that were related to cancer treatment and 50 ICERs that were
related to treatment of noncancer conditions.
The range of ICERs reported for oncologic agents was
$6,000-$745,000. The mean in this group was $138,582/QALY
and the median was $55,500/QALY (Table 1). Among these
values, 45.0% (9 of 20) were below $50,000/QALY, and 70.0%
(14 of 20) were less than $100,000/QALY. As for noncancerrelated drugs, the range of ICERs reported in the articles was
$-54,000-$332,309, and the mean and median were $49,913/
QALY and $31,000/QALY, respectively. In this group, 72.0%
(36 of 50) fell below $50,000/QALY, and 90.0% (45 of 50) were
below $100,000/QALY (Table 2).
■■  Discussion
The data showed that the mean ICER of oncologic drugs was
higher than the mean ICER of nononcologic drugs by more
than 2-fold. Among the articles that mentioned specific value
threshold in the analysis, all oncologic drugs were evaluated
in context of thresholds between $100,000-$150,000, whereas
the thresholds for nononcologic drugs were in the range of
$50,000-$100,000. The results confirmed that oncologic drugs
are often evaluated with value thresholds higher than the traditional range to adjust to high ICERs reported for these agents.
A high range of ICERs is also observed in some specialty
drugs, such as biologics—drugs made of biological rather than
chemical properties—along with many of the cancer therapy
drugs. These specialty biologic drugs are often approved for
life-threatening illnesses, such as multiple sclerosis, hemophilia, cancer, human immunodeficiency virus, and diabetes.
JMCP
November 2014
Vol. 20, No. 11
www.amcp.org
TABLE 2
Reported ICERs and Value Thresholds for Nononcologic Agents
Value
Threshold
ICER
($)
Disease State
Drug
Comparison
($/QALY)
Chemoprevention in Barrett’s Esophagus22
Aspirin + statin
Aspirin
158,000
100,000
Duloxetine
Naproxen
47,678
N/A
Osteoarthritis23
Boceprevir
Standard dual-therapy: peginterferon
29,184
50,000
Chronic hepatitis C 24
alpha and ribavirin
Telaprevir
44,247
Nitroglycerin
Standard care to all patients
34,000
N/A
Glaucoma 25
Neurogenic detrusor overactivity 26
OnabotulinumtoxinA
Best supportive care
24,000
N/A
Disease-modifying osteoarthritis drugs
Standard care
57,000
N/A
Knee osteoarthritis27
Duloxetine
Naproxen
59,473
N/A
Chronic low back pain 28
Generic-based antiretroviral therapy
No antiretroviral therapy
21,000
100,000
Human immunodeficiency virus29
Branded antiretroviral therapy
Generic-based antiretroviral therapy
114,000
Exenatide
Insulin glargine
15,000
N/A
Type 2 diabetes30
Guanfacine XR + stimulant
Stimulant monotherapy
31,000
50,000
ADHD31
Low molecular-weight heparin
No prophylaxis
90,893
N/A
Anticoagulation in cancer patients32
Rivaroxaban
Warfarin
27,000
100,000
Stroke prevention in atrial fibrillation 33
Fingolimod
IFN beta-1a
73,000
100,000
Multiple sclerosis34
Vaccine (1% colonization rate)
No vaccine
25,217
N/A
S. aureus vaccine in hemodialysis patients35
Schizophrenia 36
Olanzapine ODT
SOT
19,000
N/A
Olanzapine ODT
Risperidone SOT
39,000
Erythropoietin stimulating agents
Routine blood transfusions
873
N/A
End-stage renal disease37
Atorvastatin
Simvastatin
45,000
N/A
Hyperlipidemia 38
Ticagrelor
Genotype-driven treatment
10,000
50,000
Acute coronary syndrome39
Atazanavir – ritonavir
Lopinavir – ritonavir
26,000
50,000
Human immunodeficiency virus 40
Liraglutide
Exenatide
40,000
N/A
Type 2 diabetes 41
Darunavir – ritonavir
Lopinavir – ritonavir
23,000
N/A
Human immunodeficiency virus 42
Bevacizumab
Ranibizumab
-54,000
N/A
Macular degeneration43
Rosuvastatin (20-year horizon)
Placebo
10,000
N/A
Cardiovascular disease44
Rosuvastatin (10-year horizon)
44,000
Adalimumab
Etanercept
5,000
50,000
Psoriasis 45
Infliximab
Etanercept
293,000
Omalizumab
Usual care
172,000
N/A
Asthma46
2-dose influenza vaccine
No vaccine
6,787
50,000
Influenza during pregnancy47
Bisphosphonates
No bisphosphonates
87,853
N/A
Osteoporosis 48 (ages 75-79)
Clopidogrel/aspirin
Aspirin
36,000
N/A
Cardiovascular disease49
Continuous subcutaneous insulin injection
Multiple daily injection
16,000
N/A
Type 1 diabetes50
Pioglitazone
Rosiglitazone
20,000
N/A
Type 2 diabetes51
Urokinase
Alteplase
332,309
N/A
Peripheral artery disease52
Tipranavir – ritonavir
Protease inhibitor – ritonavir
56,000
N/A
Human immunodeficiency virus53
Olanzapine
No treatment
50,000
N/A
Alzheimer’s disease54
Exenatide
Standard care
35,000
N/A
Type 2 diabetes55
Celecoxib
NSAID
31,000
N/A
Osteoarthritis56
Aprepitant
Standard care
96,000
N/A
Chemotherapy-induced nausea and vomiting57
Exenatide
Insulin
13,000
N/A
Type 2 diabetes58
Duloxetine
Standard care
-342
N/A
Diabetic peripheral neuropathy59
-429
Pramipexole
Levodopa
42,000
N/A
Parkinson’s disease60
Boceprevir (response-guided therapy)
Peginterferon-ribavirin
30,200
50,000
Hepatitis C61
Boceprevir (fixed-duration 48 weeks)
Boceprevir (response-guided therapy)
91,500
HEPLISAV – diabetes patients
Engerix – B
12,613
N/A
Hepatitis B prophylaxis62
HEPLISAV – health care workers
Engerix – B
11,062
HEPLISAV – travelers
Engerix – B
5,564
Total: 41 articles
Total: 50 interventions
Mean:
Mean:
49,913
68,181
ADHD = attention deficit hyperactivity disorder; ICER = incremental cost-effectiveness ratio; IFN = interferon; NSAID = nonsteroidal anti-inflammatory drug; ODT = orally
disintegrating tablet; QALY = quality-adjusted life-year; SOT = standard oral tablet; XR = extended-release.
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Vol. 20, No. 11
November 2014
JMCP
This is one of the elements that keeps the prices of these drugs
just as high as cancer drugs. Their high prices are also derived
from the extended exclusivity protection of the patent for
biologic drugs that is separated from regular pharmaceuticals
that allowed the monopolistic pricing for these drugs. It is only
recently that the patent for some of the earlier biologics expired
allowing development of generic versions of these biologics,
often referred to as biosimilars.5 However, the regulation of
these products is expected to meet with challenges, since the
different manufacturing process of biopharmaceuticals may
affect the activity of the product. It is reasonable to expect the
prices of these drugs to become more affordable as the knowledge of the production technology for biosimilars becomes
more standardized in the future.
More cancer patients benefit from continued advancement
in cancer treatment research that allows patients to live longer.
However, there has yet to be a cure for cancer. Current therapy
includes drugs that delay cancer progression and extend overall survival as much as possible. Patients are treated with each
approved agent sequentially or in combination over the course
of the disease, since the effectiveness of the drugs is overcome
by resistance, which requires a change in therapy. This need to
continuously change treatment plans is the reason that prices
of cancer drugs remained high in the past. The use of 1 drug
did not invalidate the need for the other drug, creating a virtually monopolistic pricing scheme.6 When a new and improved
version of a drug becomes available, the older drug is often
viewed as substandard treatment and over time becomes an
obsolete option rather than used in establishing a competitive
pricing scheme.
Many argue that it is unsustainable for the current health
system to continue to pay for expensive cancer drugs that
provide modest incremental benefits in therapy. The current
task remains for society and payers to draw the line and decide
when a life-saving drug is “too expensive.” The FDA approved
Zaltrap (ziv-aflibercept) in 2012 for second-line treatment
of advanced colon cancer based on a phase III clinical trial
that showed ziv-aflibercept extended median overall survival
by 42 days. However, ziv-aflibercept received disapproval
for cost-effectiveness by Memorial Sloan-Kettering Cancer
Center in New York and the National Institute for Health and
Care Excellence (NICE) in the United Kingdom, based on the
conclusion that ziv-aflibercept was no more effective than
Avastin (bevacizumab), a similar drug already on the market,
but was twice as expensive—priced at $10,000 for a month
supply, compared with $5,000 a month for bevacizumab.
The reported ICER for ziv-aflibercept by NICE was between
$97,000-$102,656/QALY. This may also suggest that with persistent entry of similar cancer drugs into the market, the prices
will decline over time, and the ICER of cancer drugs will also
decrease in the future.7
JMCP
November 2014
Cancer drugs are not the only drugs that historically have
high prices. Drugs that treat serious illnesses also tend to enter
the market with prices in the higher ranges. Correspondingly,
we have observed drugs being compared with different
thresholds based on the seriousness of the disease they treat.
For example, lifestyle drugs are often compared with a lower
threshold, while life-saving drugs, such as orphan drugs, are
compared with a much higher threshold. This practice raises
the question of whether it is valid to have fixed $50,000/QALY
or $100,000/QALY thresholds across all payers, types of care,
and populations.8 Because of the variations among the types
of third-party payers in the United States, it is reasonable that
the threshold acceptance should also be based on those factors
unique to the insurer and the care given.
Limitations
First, the reported ICERs included in our analyses are not in
direct reference to what is being accepted in the real-world
formulary decisions. The value thresholds used in the reports
analysed have been chosen by the authors performing the
cost-effectiveness analyses, and while it may indicate a general
trend of higher value thresholds in oncology drugs, it is not
directly attributed by the actual value thresholds utilized by
insurers and third-party payers. Second, some of the articles
included in our analysis addressed the cost-effectiveness of an
old drug, for example, as an added therapy to standard care.
Finally, the comparator drug in the cost-effectiveness analyses
was not required to be the most appropriate standard therapy
for the disease state by the practice guidelines or the most costeffective choice in current practice. The reported ICERs can
vary significantly based on the value of the comparator drug.
The results of our analyses indicate that cancer drugs are associated with higher ICERs in comparison with ICERs reported
for noncancer drugs. On average, the ICER for cancer drugs
was more than 2-fold higher than other therapeutic areas,
with the majority of cancer and noncancer ICERs falling in the
$100,000-150,000 and $50,000-100,000 ranges, respectively.
Authors
YUNA HYO JUNG BAE, PharmD, is Postdoctoral Fellow in
Pharmacoeconomics and Health Outcomes Research, Western
University of Health and Sciences College of Pharmacy, Pomona,
California, and C. DANIEL MULLINS, PhD, is Professor,
Pharmaceutical Health Services Department, University of Maryland
School of Pharmacy, Baltimore, Maryland.
AUTHOR CORRESPONDENCE: Yuna Hyo Jung Bae, PharmD,
309 E. Second St., Pomona, CA 91766. E-mail: [email protected].
Vol. 20, No. 11
www.amcp.org
DISCLOSURES
There was no external funding for this research, and the authors have no
financial or other potential conflicts of interest regarding the subject of this
manuscript. Mullins receives grant support from Bayer and Amgen; payment
from Genentech for lecturing and speaking engagements; is a paid consultant
to Amgen, Bayer, BMS, Genentech, and Pfizer; currently has grants pending
with Bayer and Pfizer; and holds an advisory board membership with Bayer,
Pfizer, and Mundipharma.
Bae and Mullins contributed equally to concept and design of this study.
Bae collected the data, which were interpreted by both authors. The manuscript was written primarily by Bae, assisted by Mullins, and revised by both
authors.
13. Casciano R, Chulikavit M, Perrin A, Liu Z, Wang X, Garrison LP. Costeffectiveness of everolimus vs sunitinib in treating patients with advanced,
progressive pancreatic neuroendocrine tumors in the United States. J Med
Econ. 2012;15(Suppl 1):55-64.
14. Snedecor SJ, Carter JA, Kaura S, Botteman MF. Cost-effectiveness of
denosumab versus zoledronic acid in the management of skeletal metastases
secondary to breast cancer. Clin Ther. 2012;34(6):1334-49.
15. Montero AJ, Avancha K, Gluck S, Lopes G. A cost-benefit analysis
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ACKNOWLEDGMENTS
We would like to thank Lisa Blatt, MA, for her guidance and assistance with
the preparation of the manuscript.
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JMCP
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Vol. 20, No. 11
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br i e f r e p o rt
A Case Study in Generic Drug Use: Should There Be Risk Adjustment
in Incentive Payments for the Use of Generic Medications?
Surrey M. Walton, PhD; Christine Rash, PharmD; Bruce L. Lambert, PhD;
and William L. Galanter, MD, PhD
ABSTRACT
BACKGROUND: Encouraging generic drug use has reduced health care costs
for payers and consumers, but the availability of therapeutically interchangeable medications or generic medications of choice is not equal across disease
states. The extent to which systems of care are able to substitute with generics is not well understood.
OBJECTIVES: To (a) define and measure the maximum generic rate (MGR) of
currently prescribed drugs within an academic medical group in and (b) illustrate differences across drugs associated with selected underlying diseases.
METHODS: Prescription claims data were examined from an academic medical group in Chicago, Illinois. Based on pharmacologic and therapeutic criteria,
drugs were classified into 2 categories—potentially substitutable and not
potentially substitutable—based on whether the drugs are branded forms of
the same chemical entities that are available as generics or are therapeutically
interchangeable with other medications that have different chemical compositions but the same mechanisms of action and potential efficacy. A medication was considered potentially substitutable if it (a) did not have a narrow
therapeutic index as defined by the FDA; (b) did not belong to 1 of 6 protected
classes of drugs in the Medicare D provisions; (c) was substitutable with a
generic medication containing the same chemical entity; or (d) was therapeutically interchangeable with a therapeutically equivalent medication. MGR was
defined as the percentage of prescriptions that could potentially be prescribed
in generic form. This rate was examined overall and across drugs known to be
associated with illustrative diseases including hypertension, diabetes mellitus,
and obstructive lung diseases.
RESULTS: The MGR ranged from 100% for drugs used in hypertension to
26.7% for drugs used in obstructive lung diseases. The MGR was 83.6%.
CONCLUSIONS: Payers wishing to promote generic substitution should incorporate the potential for substitution of clinically appropriate generic medications as part of incentives for generic utilization to avoid unintended consequences of using a fixed target rate. A practical methodology for determining
an MGR is offered.
•Use of generic medications can reduce costs with potential savings
estimates ranging around $300 per patient without harming patients.
•Current incentives towards generic medication use tend to use a
flat rate not conditional on the existing use of drugs or underlying
patient population.
•This study provides a method for generating target generic rates
that control for the existing medications in use and, thereby, the
underlying patient population.
•This study provides novel estimates of the potential for generic substitution for groups of drugs associated with common conditions.
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Vol. 20, No. 11
E
ncouraging the use of generic drugs has helped reduce
costs and can continue to reduce health care costs for
payers and consumers with a recent simulation estimate suggesting savings of close to $300 per patient among
Medicare Part D enrollees.1-4 Recent opportunities and related
efforts to increase the use of generic drugs have been key driving factors moderating pharmacy cost trends over the past 5
years.5,6 Various health care payers have employed specific
payment initiatives to drive down costs by incentivizing providers to increase the percentage of generic drugs used in their
patient populations.4 Currently, payers often reward independent physician associations (IPAs), individual physicians, and
accountable care organizations (ACOs) that show improved
prescribing patterns in terms of approaching predetermined
benchmarks for generic prescribing. Benchmarks for generic
dispensing rates (GDR) are typically derived using formulas
created by insurance companies on an annual basis based on
past performance of “top performing” IPAs.7,8
One concern with strategies designed to achieve a flat-rate
benchmark for generic utilization is that available treatments
for some patient populations are branded only, such as insulin
products for insulin-requiring patients with diabetes mellitus and asthma medications in the form of inhaler devices.
Furthermore, the use of medical supplies (e.g., blood glucose
monitors, glucose test strips, glucose testing lancets) tend to
be branded only, and orders for such equipment often count
as prescriptions under some payers’ financial incentive measures. In addition, in certain clinical scenarios that require
medications with a narrow therapeutic index (NTI), switching
patients from brand to generic medication may be clinically
inappropriate and potentially harmful (e.g., switching a patient
on Tegretol tablets to carbamazepine tablets for management
of seizure disorder). Hence, the prevalence of conditions being
treated and pre-existing drug choices may have a significant
impact on the overall ability for an IPA, ACO, or individual
physician to achieve a particular percentage of generic use and
therefore to reach the benchmark for financial incentive. For
example, 1 study claimed that 87% of variation in generic prescription rates was due to differences in case-mix.9
To our knowledge, only 1 previous study has examined the
maximum potential cost savings as a result of generic substitution. Kunisawa et al. (2013) examined 9 million dispensing
records in Japan from January to March 2010 and defined
maximum potential quantity-based shares as the quantity of
generic drugs dispensed plus the quantity of branded drugs
that could have been replaced by generic drugs divided by the
quantity of all drugs dispensed.10 A “substitution index” was
November 2014
JMCP
defined as the proportion of potentially substitutable drugs
(brand-name medications that could have a generic alternative dispensed) that were actually prescribed as generic. Study
results showed a maximum potential quantity-based share of
50.1% and a maximum possible cost savings of 16.5% with
generic medications.10 Kunisawa et al. concluded that comparisons based on quantity-based share may misrepresent actual
medication use, and a substitution index accounting for maximum potential quantity-based share may be a fairer measure
and encourage more realistic goals for generic medication use.10
Payers commonly offer financial incentives to providers for
achieving benchmark rates of generic drug use.11,12 Providers
and payers should understand the extent to which patient case
mix affects the ability to prescribe generic medications. For
example, if an IPA sees a high volume of patients with insulindependent type 2 diabetes mellitus, they will be forced, due to
the nature of the disease state, to prescribe more brand-name
products for which therapeutic alternatives do not exist (i.e.,
glucometers, insulin, glucose testing strips, glucose testing
lancets, insulin syringes) compared with an IPA that has a low
volume of insulin-dependent type 2 diabetic patients. As previously noted, the ability to prescribe generic drugs depends
on the availability of generic alternatives, but the availability
of generic alternatives varies significantly as a function of case
mix (i.e., as a function of patient and disease-related factors).9
A clearer understanding of the availability of generic alternatives for a given population would allow providers and payers to negotiate more rational financial incentives for generic
prescribing. For incentives to be rational in the sense intended
here, incentives must be based on the potential for generic
substitution or therapeutic interchange within a medical group
or IPA. Given the potential for differences in the ability of
clinicians to substitute for alternatives (because of differences
in case mix), uniform benchmarks, common in contemporary
contracts between providers and payers, are inappropriate.
To date, insufficient research exists regarding the extent
of variance in opportunities for generic prescribing. No one
has quantified, for example, what proportion of drugs are
available as generic or have a therapeutic alternative among
medications used often in common medically treated chronic
conditions such as diabetes, hypertension, and chronic respiratory diseases such as chronic obstructive pulmonary disease
(COPD) and asthma. Examining variation in the availability of
alternative generic drugs would provide insight to payers who
create fixed GDR benchmarks for contracted IPAs, ACOs, and
individual physicians based on internally derived formulas.
We provide relatively simple mechanisms for quantifying the
extent of generic substitution possible based on commonly
available information in prescription claims data. Although
this is an inexact science, such information should allow incentive benchmarks to be adjusted, leveling the playing field and
providing equal opportunity for provider groups to achieve
benchmarks when generic alternatives are available. Rational
incentives should not penalize providers’ use of branded products when no clinically acceptable generic substitute or generic
therapeutic alternative is available to treat a patient’s condition.
JMCP
November 2014
The purpose of this study was to define and measure, based
on prescription claims data, the generic conversion potential
of medication therapy of a midsized academic medical group.
Medications that cannot be converted to generic alternative
were also characterized by the reason for the inability to produce a generic conversion.
■■  Methods
This study focused on identifying branded drugs being used
when there were available alternatives of clinically appropriate
generic drugs based on a retrospective analysis of prescription
claims data from January 1, 2012, through December 31, 2012.
Drugs in the claims data were classified based on an algorithm
for coding medications as substitutable or not, which is described
in further detail below. For illustrative purposes, drugs were
further examined across groups of drugs known to be used in
treating 4 common chronic diseases (see Table 1) as follows:
hypertension, diabetes mellitus, COPD, and asthma. The groups
of drugs were then characterized in terms of the proportion of
medications that could be generic, as well as the burden that
unavoidable branded medications for these diseases put on the
overall GDR. Due to the large overlap of identical inhaled treatment options between COPD and asthma (i.e., Advair Diskus is
a brand-only inhaled medication used for the treatment of COPD
and asthma), these 2 disease states were subsequently combined
and analyzed as 1 group. In addition, a descriptive measure of
potential generic use within a disease, called the maximum
generic rate (MGR), was constructed and analyzed.
The prescription data represented patients covered by a
single pharmacy benefit manager (PBM). The physician group
was composed of 3 large academic clinics: General Internal
Medicine, Family Medicine, and Pediatrics, along with multiple
community clinics including a federally qualified health center.
The patients were racially diverse with significant numbers
of African-American, Caucasian, and Hispanic patients. The
insurance was not a Medicare plan, so the age distribution was
younger than a cohort of all patients. Since all the patients were
covered by insurance, indigent patients were not represented in
the cohort. The analyses were based on the number of prescriptions for all medications or supplies.
Branded medications were coded as potentially substitutable
(having an available therapeutically equivalent generic or available therapeutic alternative) or not potentially generic (no available
therapeutically equivalent generic and no available therapeutic alternative that is nonbranded). The characterization was
performed by one of the authors, who is a general internist and
the chair of the medical center’s pharmacy and therapeutics
committee, and another author, who is a fellowship-trained
ambulatory care clinical pharmacist, using the following algorithm (see Figure 1):
1.Was the medication a branded form of the same chemical
entity that is available as a generic with the same delivery
system? If yes, the medication was considered a potentially
substitutable generic prescription. For instance, if Lipitor was
prescribed and filled as the brand name product, it is considered potentially substitutable, since atorvastatin calcium is an
available nonbranded, therapeutically equivalent medication.
Vol. 20, No. 11
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TABLE 1
Medications Assigned to COPD/Asthma, Hypertension, and Diabetes
H
acebutolol
H
alfuzosin
H
aliskiren/amlodipine/hydrochlorothiazide
H
aliskiren-amlodipine
H
aliskiren-hydrochlorothiazide
H
aliskiren-valsartan
H
amiloride-hydrochlorothiazide
H
amlodipine
H
amlodipine/hydrochlorothiazide/olmesartan
H
amlodipine/hydrochlorothiazide/valsartan
H
amlodipine-atorvastatin
H
amlodipine-benazepril
H
amlodipine-olmesartan
H
amlodipine-telmisartan
H
amlodipine-valsartan
H
atenolol
H
atenolol-chlorthalidone
H
azilsartan
H
azilsartan-chlorthalidone
H
benazepril
H
benazepril-hydrochlorothiazide
H
bendroflumethiazide
H
bendroflumethiazide-nadolol
H
betaxolol
H
bisoprolol
H
bisoprolol-hydrochlorothiazide
H
candesartan
H
candesartan-hydrochlorothiazide
H
captopril
H
captopril-hydrochlorothiazide
H
chlorothiazide
H
clonidine
H
diltiazem
H
doxazosin
H
enalapril
H
enalapril-hydrochlorothiazide
H
eprosartan
H
eprosartan-hydrochlorothiazide
H
felodipine
H
fosinopril
H
fosinopril-hydrochlorothiazide
H
hydrALAZINE
H
hydrALAZINE-hydrochlorothiazide
H
hydrochlorothiazide
H
hydrochlorothiazide-irbesartan
H
hydrochlorothiazide-lisinopril
H
hydrochlorothiazide-losartan
C/A = COPD/asthma; DM = diabetes mellitus; H = hypertension.
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
DM
DM
DM
DM
DM
DM
DM
hydrochlorothiazide-methyldopa
hydrochlorothiazide-metoprolol
hydrochlorothiazide-moexipril
hydrochlorothiazide-olmesartan
hydrochlorothiazide-propranolol
hydrochlorothiazide-quinapril
hydrochlorothiazide-spironolactone
hydrochlorothiazide-telmisartan
hydrochlorothiazide-triamterene
hydrochlorothiazide-valsartan
irbesartan
isradipine
labetalol
lisinopril
losartan
methyclothiazide
methyldopa
metoprolol
minoxidil
moexipril
nadolol
nicardipine
NIFEdipine
nisoldipine
olmesartan
perindopril
pindolol
prazosin
propranolol
quinapril
ramipril
telmisartan
terazosin
timolol
trandolapril
trandolapril-verapamil
triamterene
valsartan
verapamil
chlorpropamide
exenatide
glimepiride-pioglitazone
glimepiride-rosiglitazone
glipizide
glipizide-metformin
glyburide
2.Exceptions were made for NTI agents as defined in the
1988 NTI list from the U.S. Food and Drug Administration
(FDA).13 An example is Dilantin. We also used more recent
lists from Health Canada and the North Carolina Board
of Pharmacy to produce the final NTI list: aminophylline carbamazepine, cyclosporine, digoxin, ethosuximide
www.amcp.org
Vol. 20, No. 11
DM
DM
DM
DM
DM
DM
DM
DM
DM
DM
DM
DM
DM
DM
DM
DM
DM
DM
DM
DM
DM
DM
DM
DM
DM
DM
C/A
C/A
C/A
C/A
C/A
C/A
C/A
C/A
C/A
C/A
C/A
C/A
C/A
C/A
C/A
C/A
C/A
C/A
C/A
C/A
glyburide-metformin
insulin aspart
insulin aspart protamine
insulin aspart-insulin aspart protamine
insulin detemir
insulin glargine
insulin glulisine
insulin isophane
insulin isophane-insulin regular
insulin lispro
insulin lispro-insulin lispro protamine
insulin regular
linagliptin
linagliptin-metformin
metformin
metformin-pioglitazone
metformin-rosiglitazone
metformin-saxagliptin
metformin-sitagliptin
nateglinide
pioglitazone
repaglinide
rosiglitazone
saxagliptin
simvastatin-sitagliptin
sitagliptin
albuterol
albuterol-ipratropium
arformoterol
beclomethasone
budesonide
budesonide-formoterol
ciclesonide
flunisolide
fluticasone
fluticasone-salmeterol
formoterol
formoterol-mometasone
indacaterol
ipratropium
levalbuterol
mometasone
montelukast
pirbuterol
salmeterol
tiotropium
flecainide, levothyroxine sodium, lithium carbonate, phenytoin, primidone, procainamide hydrochloride, quinidine,
sirolimus, tacrolimus, theophylline, valproic acid, and
warfarin sodium.14,15 Medications that did not meet the first
criterion were considered not potentially substitutable if they
were in 1 of the 6 protected classes of drugs as defined in the
November 2014
JMCP
FIGURE 1
Flow Diagram of Drug Categorization with Illustrative Examples
Lipitor
Prozac
Coreg CR
Potential Generic
Crestor
Abilify
Dilantin
Brand
#1
A branded form of the same chemical entity available as a generic
with the same delivery system?
#2
A narrow therapeutic index medication?
#3
A member of the Medicare 6 classes of concern?
#4
A brand of the same chemical entity available as a generic with
clincally insignificant differences in delivery system?
#5
Branded member of a class that is thought to have class effect
benefits and minimal risk of harm in switching?
Medicare D provisions.17 These include immunosuppressant, antidepressant, antipsychotic, anticonvulsant, antiretroviral, and antineoplastic classes. For example, Abilify
does not have a generic and is an antipsychotic.
3.For medications that were not NTI, or in 1 of the 6 protected classes, it was next determined if the medication was
a branded form of the same chemical entity available as a
generic product with a delivery system that differed from the
branded drug in clinically insignificant ways, for example,
Coreg CR. A switch from a daily to a twice daily regimen was
considered acceptable (i.e., a therapeutic alternative), but conversion to a 3 or more times a day drug was not considered
acceptable due to the increased risk of nonadherence.17-19
4.For medications still remaining, the reviewer determined
if the medication was a branded member of a class that is
thought clinically to have class effect benefits and minimal
risk of harm in switching (i.e., a therapeutic alternative).
For example, the use of brand-only Crestor 10 mg for the
treatment of hyperlipidemia. This would be considered a
potentially generic prescription due to the availability of
generic atorvastatin calcium at higher, equipotent doses.
This algorithm required clinical judgment. Two independent coders coded each drug and had zero disagreements.
JMCP
November 2014
All medications and prescriptions were categorized for further analysis according to the reason for nonsubstitutability:
NTI; 6 protected classes; branded durable medical equipment,
supply, or device; and unnecessary branded formulation. For
illustrative purposes, medications were selected and categorized further as being predominantly indicated for diabetes
(DM), hypertension (HTN), or COPD/asthma (see Table 1).
MGR was defined as the highest proportion of generic medication use possible.
Maximum Generic Rate: 1 – not potentially substitutable rate
Another useful concept employed was the brand burden of
a disease. This is the not potentially substitutable rate for a specific disease or, more specifically, for the set of prescriptions
inferred to be used for the disease in our analysis. So for either
a disease or, in our case, a group of medications inferred to be
for a disease, we defined the following:
Brand Burden(disease) = 1 – Maximum Generic Rate(disease)
The brand burden was determined for the diseases HTN,
DM, and COPD/asthma.
■■  Results
There were 99,336 prescriptions or supplies filled during 1
year of observation, with 76.1% being generic. Table 2 shows
Vol. 20, No. 11
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TABLE 2
TABLE 3
Classification of Nongeneric
Medications
% of
%
Classification
N
Subtype
of Total
Potentially substitutable
7,466
31.4
Brand available as generic
4,320
57.9
18.2
Therapeutic alternative available
3,146
42.1
13.2
Not potentially generic
16,315
68.6
Narrow therapeutic index
487
3.0
2.0
2,320
14.2
9.8
Six protected classesa
No generic substitute or therapeutic
13,508
82.8
56.8
alternative generic available
Total
23,781
100.0
a Six protected classes include immunosuppressants, antidepressants, antipsychotics,
anticonvulsants, antiretrovirals, and antineoplastics.
the classification of the 23,781 nongeneric prescriptions into
5 mutually exclusive types. More than two-thirds (68.6%) of
the prescriptions were classified as not potentially generic. The
inclusion of medications from the 6 protected drug classes only
contributed to 9.8% of the nongeneric prescriptions, with the
inclusion of NTI medication being less at 2.0%. The majority
of branded products were prescribed in clinical contexts where
there was no generic alternative available. The majority of the
potentially generic prescriptions were found to be for branded
versions of generically available medications.
The distribution across disease categories of the brand-only
medications and durable medical equipment and supplies is
shown in Table 3. DM (30.9%) and COPD/asthma (23.1%)
accounted for more than half of the prescriptions that could not
be switched to a generic alternative. Roughly 1 in 8 of the prescriptions were for supplies used for the treatment of DM (e.g.,
syringes, test strips, etc.). The largest single category included
medications used to treat diseases other than DM or COPD/
asthma. This category contained no prescriptions for drugs
used to treat HTN. The 6 protected classes from Medicare Part
D, combined with NTI drugs, accounted for roughly 15% of
prescriptions that could not clinically appropriately be written
for generic alternatives.
Table 4 shows potential generic substitutability (generic,
potentially substitutable, or not potentially substitutable) overall and by the selected disease categories (HTN, DM, COPD/
asthma). Table 4 also includes the MGR overall and by selected
disease category. Potentially substitutable branded prescribing was highest for COPD/asthma at 14.7% and lowest for DM
at 3.0%. The average for all prescriptions was 7.5%. The not
potentially substitutable rate was 16.4% overall. The specific not
potentially substitutable rate for each disease, or brand burden,
was as high as 73% for COPD/asthma and 0% for HTN. The
MGRs ranged from 100% for HTN to 26.7% for COPD/asthma,
while the average for all prescriptions was 83.6%.
Generic substitution remains a valuable tool in improving efficiency in the health care system. However, our findings illus-
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Vol. 20, No. 11
Distribution of Branded-Only
Medications and DME by Disease Type
Percentage of Branded-Only
Drug Category
Medications or Equipment
DM
30.9% (13.3% DME, 17.6% medications)
COPD/asthma
23.1%
Medications for other diagnoses
31%
Six protected classes
11.6%
Narrow therapeutic index
2.9%
Non-DM DME
0.5%
COPD = chronic obstructive pulmonary disease; DM = diabetes mellitus;
DME = durable medical equipment.
trate the importance of considering the underlying distribution
of drugs, itself impacted by underlying case mix, in the formation of policy surrounding generic drug use. This is of particular concern if strong financial incentives are tied to achieving
fixed benchmarks. Failure to adjust can lead to perverse incentives that may encourage institutions either to select patients
whose drug therapy can be handled exclusively by generics
(e.g., HTN) and/or to provide less than optimal clinical care by
prescribing generic drugs when a branded alternative would be
the true drug of choice.
This project attempts to address the question of how to
set an optimal target for generic drug use. Here, we offer a
relatively low cost method inferred from the existing prescribing data. Adjustment along these lines at an institutional or
IPA level would promote feasible generic substitution but not
penalize groups that had a disadvantageous patient mix (e.g.,
groups with large numbers of patients with COPD/asthma
and/or DM) or incentivize inappropriate medication switching
when the drug is unavailable as a generic.
There are other ways to perform a case mix adjustment. For
example, one might use financial claims data to determine the
mix of diseases in a given cohort of patients. Each disease could
have an ideal MGR, and across all diseases, an overall weighted
MGR could be developed. Our data suggest sizable differences in
the maximum feasible generic prescribing rate by disease, with
asthma and other chronic obstructive lung diseases having an
MGR a quarter of that of HTN, 26.7% vs. 100%.
Although this method of case adjustment is what is often
done for length of stay, cost of care, and other markers, we
believe the better method is to determine, for each medication,
whether there was a potentially acceptable generic substitute
or therapeutic alternative available as a generic, as we have
done. In this manner, imperfect knowledge of the diseases and
the best MGR for each disease is not required. In addition, the
development of the MGR allows an assignment of each branded
medication as having a clinically acceptable generic substitute
or generic therapeutic alternative or not, something that can be
shared with prescribers.
Initially, making this determination for each medication is
not trivial, but once performed, maintenance of this database
would require changes only for newly available generic formulations, newly available medications and new evidence-based
November 2014
JMCP
TABLE 4
Distribution of Prescriptions by
Hypertension, Diabetes Mellitus,
COPD/Asthma, and Generic Status
COPD/
HTN
DM
Asthma
All
n (%)
n (%)
n (%)
N (%)
Generic
16,916
3,169
616
75,555
(94.9)
(37.3)
(12.0)
(76.1)
Potentially
903
259
756
7,466
substitutable
(5.1)
(3.1)
(14.7)
(7.5)
Not potentially
0
5,064
3,766
16,315
substitutable
(0)
(59.6)
(73.3)
(16.4)
Total sample size
17,819
8,492
5,138
99,336
100.0%
40.4%
26.7%
83.6%
Maximum generic rate a
a Maximum generic rate is the highest possible percentage of prescriptions for a
given disease that could have been written for generic alternatives.
COPD = chronic obstructive pulmonary disease; DM = diabetes mellitus;
HTN = hypertension.
indications, whether FDA approved or off-label. Further, as
electronic medical records capture better and better data, if
shared with PBMs, this type of model can always be improved.
One of the remarkable findings was the large differences in
the brand burden by disease. This is clearly related to factors
that will change over time, such as the guideline-based preferred
therapy for a disease and new evidence-based brand-only additions to therapy and generic availability. In addition, the particular order of medication given for a chronic disease in a patient
over time may affect the use of generic medications. Nonetheless,
the concept of a brand burden for a disease becomes clear when
looking at our 3 diseases of interest. In our cohort, HTN has no
brand burden. Treating HTN pharmacologically or taking new
patients with HTN into an IPA should not affect nongeneric
medication use if medications are selected carefully.
On the other hand, DM has a large brand burden. Moreover,
for patients on insulin, not only the insulin itself but also
the syringes, lancets, test strips, and glucometers produce
multiple branded prescriptions with no generic alternatives.
Consequently, a traditional fixed generic rate incentive would
produce a misaligned incentive not to use insulin. Ideally, there
should be no incentive in place that would lead prescribers to
consider delaying the start of insulin therapy. For diseases such
as asthma and COPD, most medical therapy is also presently
branded, and the brand burden we found was 73.3%. Here,
clinicians should not be disincentivized to diagnose lung disease or treat with branded inhalers, since this, in fact, would
represent the best clinical, evidence-based practice.
Using the concept of the MGR, the performance of an IPA,
clinician, or health system would be based on the difference
between their MGRs and their actual generic rates. In the
example analyzed in this article, the medical therapy for HTN
is not ideal. While the MGR is 100%, the actual generic rate is
94.9%, and there is still a 5% improvement that can be made
within drugs used to treat this common disease. Overall, the
MGR is 83.6%, and the actual generic rate is 76.1%, so it follows that the incentive should be to move from 76.1% to 83.6%
JMCP
November 2014
by better use of generic medications. Moreover, if the MGR
is used, there are no incentives to decrease the proportion of
patients with DM, asthma, and COPD or to cut down on insulin or inhaler use.
Limitations
The physician group used for this study is predominantly
urban within an academic teaching center. Prescribing decisions may not represent the norm for IPAs as a whole. Patient
population is also limited to a commercially insured health
maintenance organization population in a large urban setting. It is not likely representative of the average patient who
has medication therapy managed by a PBM, which now may
encompass Medicaid, Medicare, and commercial insurers.
There are inherent limitations in our coding of drugs based
solely on prescription claims data. First, there is potential for
error via the underlying assumptions that a medication is being
used for a specific indication without patient-level information. However, this might not be such a significant weakness,
since often a generic substitution exists, independent of the
exact indication. For instance, whether the use of a branded
angiotensin receptor blocker is for nephropathy, heart failure,
or HTN, an acceptable generic substitution is available. This is
likely more often the case than the presence of a generic substitution only being available for one indication but not another.
Again, in practical use of this method, periodic clinical updating of whether or not a drug has a generic substitute would be
helpful and recommended.
Second, in this analysis, we chose to use a somewhat arbitrary NTI list because the FDA has not produced a recent NTI
list, and there are many to choose from. Hence, we included a
medication such as levothyroxine as an NTI medication, but a
more aggressive PBM may have left this out. The same could be
said for the 6 protected classes used in Medicare D formularies. The exclusion of NTI and protected classes of medications
contributed to about 1 in 6 of the not potentially substitutable
prescriptions. Thus, it is clear that if others use our method
and produce an MGR for a disease or cohort of patients, the
results are likely to change some, but the MGR method will be
consistent across the prescribers and IPAs being managed for
a given PBM.
Third, the method requires clinical judgment, and we based
our results on consensus between only 2 experienced clinicians.
Although we recognize that this is imperfect in design and that
the number of clinicians involved in decision making was low,
we felt it was sufficient for the purpose of introducing the concept. Further, the general method used here is relatively easily
adapted to other settings, and if a PBM were to adopt this methodology, it could certainly choose to use more than 2 clinicians.
Substantial efficiencies can be gained by substituting generic
drugs for branded drugs, but this can only be done when the
drugs used for a particular condition are actually available in
clinically acceptable generic form. The availability of generic
alternatives varies substantially across prescriptions currently
Vol. 20, No. 11
www.amcp.org
being used. To illustrate, essentially all drugs for HTN are available as generic, or have a generic alternative, but most drugs
for obstructive lung disease do not have a generic alternative.
Hence, the maximum rate of generic substitution is determined
by the underlying patient population. Because incentives
ignore drug and disease-specific variation in the availability of
generic alternatives, continued use of uniform generic prescription incentive rates may encourage patient cherry-picking or
inappropriate prescribing. Rational incentives for generic drug
use should vary according to the underlying patient case-mix.
As a solution, we proposed using a per medication methodology and determining an MGR.
3. Balaban D, Dhalla I, Law M, Bell C. Private expenditures on brand
name prescription drugs after generic entry. Appl Health Econ Health Policy.
2013;11(5):523-29.
4. Gilman BH, Kautter J. Impact of multitiered copayments on the use and
cost of prescription drugs among Medicare beneficiaries. Health Serv Res.
2008;43(2):478-95.
5. Hartman M, Martin A, Nuccio O, Catlin A; National Health Expenditure
Accounts Team. Health spending growth at a historic low in 2008. Health
Aff (Millwood). 2010;29(1):147-55.
6. Martin AB, Hartman M, Whittle L, Catlin A; National Health Expenditure
Accounts Team. National health spending in 2012: rate of health spending
growth remained low for the fourth consecutive year. Health Aff (Millwood).
2014;33(1):67-77.
7. Share DA, Mason MH. Michigan’s Physician Group Incentive Program
offers a regional model for incremental ‘fee for value’ payment reform. Health
Aff (Millwood). 2012;31(9):1993-2001.
Authors
SURREY M. WALTON, PhD, is Associate Professor, Department
of Pharmacy Systems, Outcomes, and Policy, and CHRISTINE
RASH, PharmD, is Clinical Pharmacist and Internal Medicine/
Managed Care Clinical Assistant Professor, Department of
Pharmacy Practice, College of Pharmacy, University of Illinois
Chicago. BRUCE L. LAMBERT, PhD, is Professor, Department of
Communication Studies, and Director, Center for Education and
Research on Therapeutics, Northwestern University, Chicago, Illinois,
and WILLIAM L. GALANTER, MD, PHD, is Assistant Professor,
Departments of Medicine; Pharmacy Practice; and Pharmacy
Systems, Outcomes, and Policy, University of Illinois Chicago,
and Medical Director, Center for Education and Research on
Therapeutics, Northwestern University, Chicago, Illinois.
AUTHOR CORRESPONDENCE: Surrey M. Walton, PhD,
Department of Pharmacy Systems, Outcomes, and Policy, College of
Pharmacy, University of Illinois Chicago, 833 S. Wood St.,
(M/C 871) Rm. 287, Chicago IL 60612. Tel.: 312.413.2775;
E-mail: [email protected].
DISCLOSURES
None of the authors received any funding directly for this study; however,
Galanter and Lambert are supported by grant number U19HS021093 from
the Agency for Healthcare Research and Quality. The content is solely the
responsibility of the authors and does not necessarily represent the official
views of the Agency for Healthcare Research and Quality. The funders had
no role in study design, data collection and analysis, decision to publish, or
preparation of the manuscript.
Study concept and design were primarily contributed by Galanter,
Walton, and Lambert, with assistance from Rash. Rash and Galanter collected
the data, which was interpreted by Walton, Rash, and Galanter, with assistance from Lambert. The manuscript was written by Walton, Lambert, Rash,
and Galanter and revised by Rash, Galanter, Walton, and Lambert.
References
1. Duru OK, Ettner S, Turk N, et al. Potential savings associated with drug
substitution in Medicare Part D: the Translating Research into Action for
Diabetes (TRIAD) Study. J Gen Intern Med. 2014;2(1):230-36.
2. Aitken M, Berndt ER, Cutler DM. Prescription drug spending trends in
the United States: looking beyond the turning point. Health Aff (Millwood).
2009;28(1):w151-w160.
www.amcp.org
Vol. 20, No. 11
8. Conrad DA, Perry L. Quality-based financial incentives in health
care: can we improve quality by paying for it? Annu Rev Public Health.
2009;30(1):357-71.
9. Wosinska M, Huckman RS. Generic dispensing and substitution
in mail and retail pharmacies. Health Aff (Millwood). 2004;Suppl Web
Exclusives:W4-409-16.
10. Kunisawa S, Otsubo T, Lee J, Imanaka Y. Improving the assessment of prescribing: use of a ‘substitution index’. J Health Serv Res Policy.
2013;18(3):138-43.
11. Dylst P, Vulto A, Simoens S. Demand-side policies to encourage the use
of generic medicines: an overview. Expert Rev Pharmacoecon Outcomes Res.
2013;13(1):59-72.
12. Hoadley JF, Merrell K, Hargrave E, Summer L. In Medicare Part D plans,
low or zero copays and other features to encourage the use of generic statins
work, could save billions. Health Aff (Millwood). 2012;31(10):2266-75.
13. Center for Drug Evaluation and Research. Guidance for industry: bioavailability and bioequivalence studies for orally administered drug products—general considerations. March 2003. Available at: http://www.fda.gov/
downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/
ucm070124.pdf. Accessed September 15, 2014.
14. North Carolina Department of Health and Human Services. North
Carolina Medicaid Pharmacy Newsletter. Number 115, March 2004, page
6. Available at: http://www.ncdhhs.gov/dma/pharmnews/0304pharm.pdf.
15. Health Canada. Guidance document: comparative bioavailability standards: formulations used for systemic effects. Ottawa, Ontario. February
2012. Available at: http://www.hc-sc.gc.ca/dhp-mps/alt_formats/pdf/prodpharma/applic-demande/guide-ld/bio/gd_standards_ld_normes-eng.pdf.
16. U.S. Center for Medicare and Medicaid Services. Medicare prescription
drug benefit manual. Chapter 6: Part D drugs and formulary requirements.
2010. Available at: https://www.cms.gov/Medicare/Prescription-DrugCoverage/PrescriptionDrugCovContra/downloads/Chapter6.pdf. Accessed
September 15, 2014.
17. Wells KE, Peterson EL, Ahmedani BK, Williams LK. Real-world effects of
once vs greater daily inhaled corticosteroid dosing on medication adherence.
Ann Allergy Asthma Immunol. 2013;111(3):216-20.
18. Coleman CI, Roberts MS, Sobieraj DM, Lee S, Alam T, Kaur R. Effect of
dosing frequency on chronic cardiovascular disease medication adherence.
Curr Med Res Opin. 2012;28(5):669-80.
19. Coleman CL, Limone B, Sobieraj DM, et al. Dosing frequency and medication adherence in chronic disease. J Manag Care Pharm. 2012;18(7):52739. Available at: http://www.amcp.org/WorkArea/DownloadAsset.
aspx?id=15582.
November 2014
JMCP
ART-related diarrhea
Positively
in Control
As unique as ART-related diarrhea is to HIV patients,
so is its treatment. With its novel mechanism of action,
Fulyzaq® (crofelemer) is the only treatment proven
effective for the relief of ART-related diarrhea.1
In clinical studies, the most common adverse reactions
were upper respiratory tract infection, bronchitis, cough,
flatulence, and increased bilirubin.
Indication
FulyzAq® (crofelemer) is an antidiarrheal indicated for the symptomatic relief of noninfectious diarrhea in adult patients with
HIV/AIDS on antiretroviral therapy.
Important Safety Information about FULYZAQ
FulyzAq® (crofelemer) delayed-release tablets should not be used for the treatment of infectious diarrhea. Rule out infectious
etiologies of diarrhea before starting FulyzAq. If infectious etiologies are not considered, and FulyzAq is initiated based on a
presumptive diagnosis of noninfectious diarrhea, then there is a risk that patients with infectious etiologies will not receive the
appropriate treatments, and their disease may worsen.
Based on animal data, FulyzAq may cause fetal harm. Safety and effectiveness of FulyzAq have not been established in
patients less than 18 years of age.
In clinical studies, the most common adverse reactions (occurring in ≥3% of patients and at a rate greater than placebo) were
upper respiratory tract infection, bronchitis, cough, flatulence, and increased bilirubin.
Please see brief summary for FULYZAQ [FUHL-ih-zack] on the following page and complete Prescribing Information at
www.Fulyzaq.com.
Reference: 1. Fulyzaq [prescribing information]. Raleigh, NC: Salix Pharmaceuticals, Inc; 2013.
Ful2-0214
T:6.875”
The following is a brief summary only. See complete
Prescribing Information at www.Fulyzaq.com or
request complete Prescribing Information by calling
1-800-508-0024.
INDICATIONS AND USAGE
FULYZAQ is an anti-diarrheal indicated for the
symptomatic relief of non-infectious diarrhea in adult
patients with HIV/AIDS on anti-retroviral therapy.
CONTRAINDICATIONS
None.
WARNINGS AND PRECAUTIONS
Risks of Treatment in Patients with
Infectious Diarrhea
If infectious etiologies are not considered, and
FULYZAQ is initiated based on a presumptive
diagnosis of non-infectious diarrhea, then there is a
risk that patients with infectious etiologies will not
receive the appropriate treatments, and their
disease may worsen. Before starting FULYZAQ, rule
out infectious etiologies of diarrhea. FULYZAQ is not
indicated for the treatment of infectious diarrhea.
ADVERSE REACTIONS
Clinical Trials Experience
Table 1: Adverse Reactions Occurring
in at Least 2% of Patients in the 125 mg
Twice Daily Group
Adverse Reaction
Upper respiratory
tract infection
Bronchitis
Cough
Flatulence
Increased bilirubin
Nausea
Back pain
Arthralgia
Urinary tract infection
Nasopharyngitis
Musculoskeletal pain
Hemorrhoids
Giardiasis
Anxiety
Increased alanine
aminotransferase
Abdominal distension
*Twice daily
Crofelemer
125 mg BID*
N = 229
n (%)
Placebo
13 (5.7)
4 (1.5)
9 (3.9)
8 (3.5)
7 (3.1)
7 (3.1)
6 (2.6)
6 (2.6)
6 (2.6)
5 (2.2)
5 (2.2)
5 (2.2)
5 (2.2)
5 (2.2)
5 (2.2)
5 (2.2)
0
3 (1.1)
3 (1.1)
3 (1.1)
4 (1.5)
4 (1.5)
0
2 (0.7)
2 (0.7)
1 (0.4)
0
0
1 (0.4)
3 (1.1)
5 (2.2)
1 (0.4)
N = 274
n (%)
DRUG INTERACTIONS
Drug Interaction Potential
In vitro studies have shown that crofelemer has the
potential to inhibit cytochrome P450 isoenzyme 3A
and transporters MRP2 and OATP1A2 at
concentrations expected in the gut. Due to the
minimal absorption of crofelemer, it is unlikely to
inhibit cytochrome P450 isoenzymes 1A2, 2A6, 2B6,
2C9, 2C19, 2D6, 2E1 and CYP3A4 systemically.
Nelfinavir, Zidovudine, and Lamivudine
FULYZAQ administration did not have a clinically
relevant interaction with nelfinavir, zidovudine, or
lamivudine in a drug-drug interaction trial.
USE IN SPECIFIC POPULATIONS
Pregnancy
Pregnancy Category C
Reproduction studies performed with crofelemer
in rats at oral doses up to 177 times the
recommended daily human dose of 4.2 mg/kg
revealed no evidence of impaired fertility or harm
to the fetus. In pregnant rabbits, crofelemer at an
oral dose of about 96 times the recommended
daily human dose of 4.2 mg/kg, caused abortions
and resorptions of fetuses. However, it is not clear
whether these effects are related to the maternal
toxicity observed. A pre- and postnatal development
study performed with crofelemer in rats at oral
doses of up to 177 times the recommended daily
human dose of 4.2 mg/kg revealed no evidence of
adverse pre- and postnatal effects in offspring.
There are, however, no adequate, well-controlled
studies in pregnant women. Because animal
reproduction studies are not always predictive of
human response, this drug should be used during
pregnancy only if clearly needed.
and patients with baseline CD4 cell counts greater
than or equal to 404 cells/μL (N=289).
The safety profile of crofelemer was similar in
patients with baseline HIV viral loads less than
400 copies/mL (N = 412) and patients with
baseline HIV viral loads greater than or equal to
400 copies/mL (N = 278).
PATIENT COUNSELING
INFORMATION
Instruct patients that FULYZAQ tablets may be
taken with or without food.
Instruct patients that FULYZAQ tablets should
not be crushed or chewed. Tablets should be
swallowed whole.
You are encouraged to report negative side effects of
prescription drugs to the FDA. Visit www.fda.gov/medwatch
or call 1-800-FDA-1088.
To report adverse events, a product complaint, or for
additional information, call: 1-800-508-0024.
Rx Only
Manufactured by Patheon, Inc. for
Salix Pharmaceuticals, Inc.
8510 Colonnade Center Drive, Raleigh, NC 27615
www.salix.com
Copyright © Salix Pharmaceuticals, Inc.
US Patent Nos. 7,341,744 and 7,323,195.
FUL-RALAB36-022014
FULYZAQ is distributed by Salix Pharmaceuticals, Inc.
under license from Napo Pharmaceuticals, Inc.
The botanical drug substance of FULYZAQ is extracted from
Croton lechleri (the botanical raw material) that is harvested
from the wild in South America.
Nursing Mothers
It is not known whether crofelemer is excreted in
human milk. Because many drugs are excreted in
human milk and because of the potential for adverse
reactions in nursing infants from FULYZAQ, a
decision should be made whether to discontinue
nursing or to discontinue the drug, taking into
account the importance of the drug to the mother.
Pediatric Use
The safety and effectiveness of FULYZAQ have
not been established in pediatric patients less
than 18 years of age.
Geriatric Use
Clinical studies with crofelemer did not include
sufficient numbers of patients aged 65 and over to
determine whether they respond differently than
younger patients.
Use in Patients with Low CD4 Counts
and High Viral Loads
No dose modifications are recommended with
respect to CD4 cell count and HIV viral load,
based on the findings in subgroups of patients
defined by CD4 cell count and HIV viral load.
The safety profile of crofelemer was similar in
patients with baseline CD4 cell count less than
404 cells/μL (lower limit of normal range) (N=388)
CROF14CDNY2660_Fulyzaq_BS_6.875x9.875_r6.indd 1
CROF14CDNY2659_Journal_AD_Update_2014_A_Size_BS_r5.indd
1
3/10/14
3/6/14 12:25
3:06 PM
PM
T:9.875”
Because clinical trials are conducted under widely
varying conditions, adverse reaction rates observed
in the clinical trials of a drug cannot be directly
compared to rates in the clinical trials of another drug
and may not reflect the rates observed in practice.
A total of 696 HIV-positive patients in three placebocontrolled trials received FULYZAQ for a mean
duration of 78 days. Of the total population across the
three trials, 229 patients received a dose of 125 mg
twice a day for a mean duration of 141 days,
69 patients received a dose of 250 mg twice a day for
a mean duration of 139 days, 102 patients received a
dose of 250 mg four times a day for a mean duration
of 14 days, 54 patients received a dose of 500 mg
twice a day for a mean duration of 146 days, and
242 patients received a dose of 500 mg four times a
day for a mean duration of 14 days.
Adverse reactions for FULYZAQ that occurred in at
least 2% of patients and at a higher incidence than
placebo are provided in Table 1.
Adverse reactions that occurred in between 1%
and 2% of patients taking a 250 mg daily dose of
FULYZAQ were abdominal pain, acne, increased
aspartate aminotransferase, increased conjugated
bilirubin, increased unconjugated blood bilirubin,
constipation, depression, dermatitis, dizziness, dry
mouth, dyspepsia, gastroenteritis, herpes zoster,
nephrolithiasis, pain in extremity, pollakiuria,
procedural pain, seasonal allergy, sinusitis and
decreased white blood cell count.
Adverse reactions were similar in patients who
received doses greater than 250 mg daily.
RESEARCH
Price Elasticity and Medication Use: Cost Sharing
Across Multiple Clinical Conditions
Justin Gatwood, PhD, MPH; Teresa B. Gibson, PhD; Michael E. Chernew, PhD; Amanda M. Farr, MPH;
Emily Vogtmann, PhD, MPH; and A. Mark Fendrick, MD
ABSTRACT
BACKGROUND: To address the impact that out-of-pocket prices may have
on medication use, it is vital to understand how the demand for medications may be affected when patients are faced with changes in the price
to acquire treatment and how price responsiveness differs across medication classes.
OBJECTIVE: To examine the impact of cost-sharing changes on the demand
for 8 classes of prescription medications.
METHODS: This was a retrospective database analysis of 11,550,363 commercially insured enrollees within the 2005-2009 MarketScan Database.
Patient cost sharing, expressed as a price index for each medication
class, was the main explanatory variable to examine the price elasticity of
demand. Negative binomial fixed effect models were estimated to examine
medication fills. The elasticity estimates reflect how use changes over time
as a function of changes in copayments.
RESULTS: Model estimates revealed that price elasticity of demand ranged
from -0.015 to -0.157 within the 8 categories of medications (P < 0.01 for 7
of 8 categories). The price elasticity of demand for smoking deterrents was
largest (-0.157, P < 0.0001), while demand for antiplatelet agents was not
responsive to price (P > 0.05).
CONCLUSIONS: The price elasticity of demand varied considerably by medication class, suggesting that the influence of cost sharing on medication
use may be related to characteristics inherent to each medication class or
underlying condition.
•Cost sharing for prescription medications has risen in the past
decade, placing a larger share of the treatment cost on the
enrollee.
•In some medication classes, increases in consumer cost sharing
have been linked to lower levels of medication utilization.
•Price elasticity of demand varied considerably by medication
class among 8 categories of prescription medications.
•Results suggest the need for plan designers and practitioners to be
sensitive to changing levels of patient cost sharing when providing guidance on medication use.
JMCP
November 2014
B
etween 2000 and 2009, the average copayment for
generic (tier 1), preferred brand (tier 2), and nonpreferred
brand (tier 3) medications increased 25%, 80%, and 59%,
respectively.1 By 2010, the average drug copayment was $10 for
a generic prescription, $29 for preferred brand medications,
and $49 for nonpreferred brand medications.2 Many studies
have found that higher prescription drug cost-sharing amounts
are associated with lower levels of medication utilization. For
example, Goldman et al. (2004) found that doubling copayment amounts resulted in a 25%-45% decrease in the days
of medication supplied, depending on the drug class.3 Across
multiple studies, the price elasticity of demand for medications
(i.e., the percentage of change in the quantity demanded of a
good in response to a 1% change in its price) has been observed
to be inelastic and in a range from -0.032 to -0.60—every
percentage increase in a medication’s price would result in a
0.032%-0.60% decrease in the amount demanded (often fills
per person or the number of days with medication on hand).4-7
Moreover, without respect to medication class, price elasticity of demand for medications to treat chronic conditions was
shown to be -0.23 in Medicare patients.8 While such evidence
primarily emphasizes the effects that differential cost sharing
can have on medication use, it further serves to highlight differences between drug classes and conditions, suggesting that
price responsiveness varies by medication class.
The variation in price responsiveness may be rooted in the
basic determinants of demand for prescription drugs: a derived
demand based on the individual’s overall demand for health—
the ultimate good being “purchased.” We would expect to
see changes in its demand due to several factors, including
increases in its own price, as reflected in the level of cost sharing faced by the patient to purchase the medication, and the
availability and price of close substitutes or complements.9 In
the latter case, if a medication class has less expensive over-thecounter (OTC) substitutes, then the price elasticity of prescription alternatives are likely to be higher than other classes without OTC alternatives, so that when faced with a price increase,
the patient may substitute away from prescription medications.
Similarly, if a high percentage of generic medications exist
within the medication class, then patients may substitute away
from brand-name alternatives.
However, when considering the role of medication in the
production of health, we must consider the varied intention
Vol. 20, No. 11
www.amcp.org
inherent to different classes of medications and their related
effects. While numerous medications may be given to resolve
acute symptoms or disease, many are prescribed to manage
ongoing symptoms or as preventive measures intended to
reduce the impact of risk factors or symptoms known to be
associated with particular conditions. In the case of prevention, the benefit understood by the patient and the physician
may differ dramatically, impacting the resulting value placed
on the medication by the patient, who is the consumer.10 In
determining this value and making a purchasing decision,
the patient must then weigh the benefits and likelihood of
obtaining better health in the future against the present cost
of the medication; in essence, the patient is determining individual and immediate necessity for the prescribed treatment.
Observations to date incorporating the perception of necessity have demonstrated that there is mixed evidence on price
elasticities for medications considered to be essential and those
that are “discretionary,” with a few studies finding differences
in the price responsiveness between essential and discretionary
medications and others finding no clear pattern.3,5,7 Additional
evidence on price responsiveness across medication classes
is warranted to better understand how patients behave when
faced with differential cost sharing and how this pattern may
differ by medication class.
The purpose of this study was to further investigate the relationship between cost sharing and medication utilization patterns across multiple conditions among commercially insured
adults. Unlike most previous studies, we analyzed price effects
for each medication class across an enrolled population of 11.5
million adults, estimating the collective effects of price changes
on medication utilization within the entirety of enrollment.
This approach adopted a payer view of the impact of changes in
cost sharing, the net effect of which is a combination of behaviors: initiation, discontinuation, and adherence to a medication
class. This is in contrast to a patient cohort approach, which
focuses on discontinuation and adherence rates for those who
have already chosen to initiate a medication class. Results of
this study adds to the scant literature on differences in the
price elasticity of demand for medications across 8 classes of
pharmacotherapy for the treatment of a variety of conditions
and will inform efforts to determine cost sharing levels for
medication classes.
■■  Methods
Data Source
This analysis was based on data from the 2005-2009 Truven
Health MarketScan Commercial Claims and Encounters
Research Database. This database contains health insurance
claims for inpatient, outpatient, and outpatient prescription
drug services of millions of employees of over 100 medium
and large-sized firms in the United States. The data have been
statistically de-identified and conform to the Health Insurance
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Vol. 20, No. 11
Portability and Accountability Act of 1996 (HIPAA); neither
informed consent nor institutional review board (IRB) approval
were necessary.
Study Population
Enrollees were eligible for inclusion in the study if they were
between the ages of 18 and 64, were continuously enrolled for
at least 7 continuous calendar quarters between January 1,
2006, and September 30, 2009, and had no evidence of pregnancy throughout the study period. Enrollees were included
in any calendar quarter if they were enrolled throughout the
quarter.
Prescription Fills
Medication categories of interest included smoking deterrents,
nonsteroidal anti-inflammatory drugs or opioids (NSAIDs/
opioids), proton pump inhibitors (PPIs), anticonvulsants,
3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitors
(statins), bisphosphonates, thyroid hormone, and antiplatelet agents (see Appendix, available in online article). These
medications were chosen because they are commonly used
and because many are high-cost categories and are classes for
which cost-related nonadherence has been identified.
The number of prescription fills for each medication was
calculated per quarter throughout the study window based
on paid claims for each patient. The number of fills per claim
was based on the days supply field on the prescription drug
claims: a claim with days supply of 30 or less was considered
to be 1 fill, while claims with days supply greater than 30 were
standardized to a 30-day fill. For example, if a patient had one
12-day fill for a specific drug during the quarter, that patient
would have 1 prescription fill, whereas if a patient had one
90-day fill for a specific drug, that patient would have 3 prescription fills during the quarter.
Explanatory Variables
The main explanatory variable was patient cost sharing—for
each medication class, this was measured using a cost-sharing
index created for each employer/plan combination within the
database.11 The cost-sharing index was based on the average
cost-sharing amount (i.e., copayment, coinsurance) per prescription (standardized to a 30-day supply) in an employer/
plan for brand and generic drugs in each medication class. The
price index is a market basket approach providing an aggregate
measure of plan-level cost sharing weighted for utilization.
The index aggregated the brand and generic copayments using
weights developed from the overall proportion of utilization of
brand and generic drugs within each medication class during
the study time frame, with the weights for each class summing
to 1. For example, the weights for statins were 0.55 for brand
name and 0.45 for generic. Such aggregation of cost sharing
into levels for each employer/plan combination reduces any
November 2014
JMCP
effects of potential selection bias related to actual, individuallevel cost sharing and related choices.
Sociodemographic variables included age in years, gender,
urban or rural residence (based on metropolitan statistical
area), median household income (from the 2000 census files
based on ZIP codes), and U.S. census region (Northeast, North
Central, West, and South). The number of general practitioners
and specialists per capita were also included (based on county
of residence) from the Area Resource File. The relationship of
the enrolled to the employee (self, spouse, or dependent) was
indicated.
Two health status controls were also included. The Deyo
Charlson Comorbidity Index (CCI) was calculated in the year
prior to the index date—the date of first fill for each medication. This version of the CCI accounts for the effects of comorbid conditions in the analysis of claims data.12 The number of
psychiatric diagnostic groupings (PDG) were also measured
during the pre-index period of this study. PDG measures the
presence of psychiatric or substance dependent conditions.
There are 12 possible PDGs, which are aggregated from ICD9-CM (International Classification of Diseases, Ninth Revision,
Clinical Modification) diagnosis codes.13 Examples include
alcohol and other substance use disorders, depression, bipolar
disorder, post-traumatic stress disorders, and schizophrenia.
Analysis
As a descriptive analysis of each medication class, patient characteristics and quarterly utilization measures were calculated
in the quarter of first fill for each medication.
For a multivariable analysis of price elasticity, we created
a panel dataset with employer/plan as the cross sectional unit
and calendar quarter as the unit of time. In each employer/plan
combination, quarterly prescription fills and enrollee counts
were summarized, and average characteristics within the plan
(e.g., average age, percent female) were calculated. In these
plans, cost-sharing levels were assessed across all plan members. The key variable of interest, cost sharing, was measured
at the plan level, and we focused on the price responsiveness
to these cost-sharing changes. Such an analysis approximates
the payer view and includes nonusers of medications in the
denominator. Although we did not formally examine cost sharing and initiation of use of a medication class, this per enrollee
analysis included the impact of differential rates of initiation.
We estimated negative binomial fixed effect models with
a fixed effect for each employer/plan combination to account
for time invariant employer/plan-level characteristics. We also
included quarterly time dummy variables to account for common time trends and exposure-adjusted for enrollment counts
in the employer/plan.14
To estimate the impact of cost sharing on the demand for
medications, we utilized a fixed effects panel data measuring
changes in utilization as they related to changes in cost sharing
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November 2014
TABLE 1
Sample Characteristics
Total Enrollment
Characteristic
N = 11,550,464
42.03(12.69)
Age in years, mean (SD)
Female gender, %
53.1
Census region, %
Northeast
13.8
North Central
25.9
South
38.8
West
20.7
Urban, %
85.8
Employee relationship, %
Employee
58.9
Spouse
30.2
Child/other
10.9
Plan type, %
PPO
52.4
HMO
21.3
POS
14.2
Other
12.2
49.98(18.08)
Income (000s), mean (SD)a
2.96(1.36)
General practitioners per capita, mean (SD)
9.40(7.26)
Specialists per capita, mean (SD)
0.21(0.71)
Charlson Comorbidity Index, mean (SD)
0.14(0.46)
Psychiatric diagnostic grouping, mean (SD)
a Average median household income by ZIP code of residence.
HMO =health maintenance organization; POS = point of service; PPO = preferred
provider organization; SD = standard deviation.
in a given employer/plan from 1 year to the next. By estimating a unique intercept for each employer/plan, the fixed effect
design accounted for any time-invariant employer/plan characteristics that were correlated with cost sharing and medication
use. The model also accounted for time-variant characteristics
that might affect cost sharing over time equally across all
employer/plans by including a separate intercept for each quarter. Price responsiveness was measured by calculating the price
elasticity: the percentage of change in the utilization measure
(fills) with a 1% percentage of change in price.
■■  Results
Demographics
Across the entire population, the mean age was approximately
42 years, and slightly more than half of the included enrollees
were female. The majority of subjects were employees (versus
spouse or child), and a larger proportion resided in the southern United States, reflecting the underlying composition of
the convenience sample, and in an urban setting. Also, most
subjects were insured under a preferred provider organization,
and the average median income by ZIP code of residence was
approximately $50,000 (Table 1).
The demographic characteristics of individuals utilizing
each medication class varied: mean ages ranged from 45.5
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TABLE 2
Medication Use and Costs by Class
Sample Population
Fills
Medication Cost a
Drug Class
(n)
Mean (SD)
Mean (SD)
Statins
1,123,236
2.64(1.31)
204.14(158.40)
Bisphosphonates
200,404
2.70(1.30)
196.13(106.39)
Thyroid hormone
438,603
2.96(1.42)
31.29(25.20)
Antiplatelet agents
116,799
2.70(1.40)
330.83(187.35)
NSAIDs/opioids
8,249,187
1.65(1.32)
42.67(227.05)
PPIs
994,424
2.23(1.30)
267.72(210.51)
Anticonvulsants
555,401
2.21(1.66)
210.09(336.77)
Smoking deterrents
200,522
1.68(1.00)
172.81(111.20)
a Average spend by quarter.
NSAID = nonsteroidal anti-inflammatory drug; PPI = proton pump inhibitor; SD = standard deviation.
to 55.7, and patients were mostly female in all but 2 classes
(statins and antiplatelet agents). Average median household
income was similar across all drug classes, ranging from
$45,550 to $50,400. Aside from patients on antiplatelet agents,
comorbidity indices were relatively similar (not shown).
Medication Use and Cost
Patient cost sharing (the price index) varied from $6.92 for
thyroid hormones to $23.48 for antiplatelet agents. Average
spending (quarterly) for medications in each class ranged
considerably by drug class—those taking thyroid hormone
reported an average expenditure of $31.29, while those on
antiplatelet agents had an average expenditure of $330.38.
Quarterly spending generally increased with increasing comorbidity indices, while higher levels of generic medication use
were associated with lower cost sharing. The vast majority of
fills for antiplatelet agents (84.4%) and nearly all fills for smoking deterrents (95.3%) were for branded medications; however,
most fills for NSAIDs/opioids were for generic products (88.4%;
Table 2). Between the first and last quarter of the study period
for each medication class, the percentage of change in average cost sharing ranged from 1.1% (biphosphonates) to 70.9%
(smoking deterrents) for generic medications and from 2.0%
(NSAIDs/opioids) to 45.7% (smoking deterrents) for brand
medications.
Elasticity of Demand
Estimated price elasticity of demand per enrollee is shown
graphically in Figure 1. The effects on prescription drug fills at
a per enrollee level ranged from -0.015 to -0.157 (P < 0.01 for 7
of the 8 medication classes). Smoking deterrents were observed
to have the largest price responsiveness across all drug classes
(-0.157, P < 0.001) followed by PPIs and bisphosphonates;
NSAIDs/opioids were observed to be the least responsive to
price across all medication classes (-0.015, P < 0.05). Demand
for antiplatelet agents was not observed to be responsive to
price (P > 0.05).
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Vol. 20, No. 11
Price Index
($)
12.11
15.71
6.92
16.43
8.05
15.30
12.24
23.48
Generic Use
(%)
45.5
24.5
48.4
15.6
88.4
36.7
62.4
4.7
Relatively few studies have investigated the effect of differential cost sharing on price elasticity of demand for a variety of
classes of prescription medication. Our results suggest that
changes in the demand for particular medications, as evidenced by subsequent fills, can be expected when patients face
increases in their required cost sharing. While the magnitude
of effects varied by medication class, statistically significant
changes, with 1 exception, were seen for each of the included
medication classes, suggesting that differential cost sharing has
the potential to impact ongoing medication use regardless of
therapeutic class.
Compared with 2 recent analyses examining the impact
of cost sharing on the use of medications in several defined
therapeutic classes indicated to treat chronic conditions, the
values for price elasticity of demand in our study were noticeably smaller. Goldman et al. observed that across 8 therapeutic
classes, the decrease in medication use due to a doubling in
copayment (100% change in price) ranged from 25%-45%.3
Moreover, they reported findings that are double our own,
where a doubling in copayment was associated with an inelastic response for antiplatelets to a 15.7% decrease for smoking
deterrents. Unlike Goldman et al., we controlled for unobserved employer/plan traits by including employer/plan fixed
effects, so our elasticities reflect changes over time in employers as a function of how much they raised copayments without
relying on differences between employers. Some variation in
results may be due to the medication classes examined by each
study, since only 4 classes were represented in both analyses; however, wide disparities in the use of medications were
observed between classes included in both studies. Differences
may also be due to the benefit structure examined by each
study as the availability of medications for these conditions
and the number of lower-priced generic substitutes would have
changed considerably between the years of data used by each
investigation, allowing for more patients to remain on their
original therapies rather than making abrupt cost-related dis-
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JMCP
FIGURE 1
Price Elasticity of Demand by Drug Class
-0.2
-0.15
-0.1
-0.05
0
-0.015
-0.018
-0.032
-0.051
NSAIDs/opiodsa
Antiplatelets
Thyroid hormonea
Anticonvulsantsa
Statinsa
-0.064
Bisphosphonatesa
-0.066
-0.087
PPIa
Smoking deterrentsa
-0.157
Note: Bars represent price elasticity of demand for each drug class.
a P < 0.01, otherwise P > 0.05.
NSAID = nonsteroidal anti-inflammatory drug; PPI = proton pump inhibitor.
continuations. Considering the years of data used by Goldman
et al. (1997-2000) and our own study (2005-2009), significant
differences in market structure would be expected; therefore,
our study provides an updated snapshot of consumer reaction
to cost-sharing changes in a more recent market that likely
offered more generic and OTC options.
More recently, Landsman et al. (2005) published findings
on medication price elasticity of demand across 8 therapeutic
classes that similarly exceeded our own.6 Partial explanation
for these differences may be due to the limited overlap of studied medication classes between the 2 studies. However, disparities existed for estimates of the price elasticity of demand
when considering the 2 classes (statins and NSAIDs/opioids)
included in both investigations. Differences in results are also
likely due to the methods employed by each study. Our investigation focused on longitudinal changes in cost sharing over
time and the corresponding changes in utilization, including
plans in each quarter that did and did not change cost-sharing amounts, allowing for a contemporaneous comparison.
Comparatively, Landsman et al. examined 3 health plans with
a defined benefit change in prescription drug tier structure,
attributing the entire drop in demand after the price increase
to the price increase.6 Additionally, and similar to what was
mentioned previously, our study examined changes in price
elasticity from 2005 to 2009, providing an update on consumer
behavior and a snapshot of a more recent market from the
1999-2001 data employed by Landsman et al.
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November 2014
The current findings may provide guidance on how patients
are likely to alter their medication-taking behaviors when
changes are made to the prices they face for particular classes
of drugs. For medications with close OTC substitutes, we may
have expected to see more dramatic differences in use—such
as that observed with PPIs—as patients substitute away from
more expensive prescription products. Such a phenomenon
was seen in an earlier study where a doubling of copayments
led to a 45% and 44% reduction in days supplied for NSAIDs
and antihistamines, respectively.3 However, this would fail
to explain the relatively small change in NSAIDs/opioids we
observed, a category of medications for which some therapeutic equivalent exists over the counter. For this class of drugs,
this may be a reflection of the severity of the underlying disease or pain being addressed in our population, suggesting a
level of treatment that cannot be adequately managed by OTC
substitutes. In terms of benefit design, high value classes with
the largest amount of price responsiveness are those that may
cause the greatest concern. These classes may be the best candidates for payers to reduce cost-sharing amounts or to implement value-based approaches.
Limitations
Several limitations impacted the results of our study. The
analysis was driven by the use of administrative claims data
that is an indirect measure of medication use and may not
Vol. 20, No. 11
www.amcp.org
accurately reflect actual use patterns; however, since these data
are generated based on health care claims, they do reflect actual
purchasing patterns, which was the focus of this research. The
database is also limited in how health status may be measured
and accounted for, changes in which may lead to alterations in
medication use. Additionally, since multiple medications may
have been taken by those included, the resulting fill behavior
may be the result of changes in the total cost burden faced by
the patient rather than merely the consequence of change realized in each drug class. This may have affected some of the
reported values for price elasticity of demand. However, the
large sample size employed and range of medication classes
included in this analysis are strengths of this study, contributions beyond what has been performed previously in this area
of research. Also, this study was limited to only those individuals with commercial health coverage; therefore, results may not
be generalizable to patients with other insurance or without
coverage.
These results of this study suggest that variation in price
elasticity of demand exists between classes of medication in
response to changes in cost sharing. Considering these results,
payers should be wary of the potential effects that changes in
cost sharing may have on subsequent medication use, regardless of the role that each medication may play in the patient’s
treatment, since some deterrence is likely when cost sharing
increases. Of particular note are high value drug classes where
patients are highly responsive to price—consideration for the
lowering of copayments in these classes should be made to
avoid potential interruptions in therapy. Therefore, connecting
the patient with the present and future value of therapy at the
time of initiation and beyond should be encouraged to optimize treatment effectiveness.
DISCLOSURES
This project was funded by Pfizer, Inc. All opinions expressed are those of
the authors.
This study was a Best Poster Finalist at the International Society for
Pharmacoeconomics and Outcomes Research (ISPOR) 18th Annual International Meeting, New Orleans, LA, May 22, 2013.
Study concept and design were contributed by Gibson, Chernew,
Vogtmann, and Fendrick. Data were collected by Gibson, Farr, and Vogtmann,
and interpreted by Gibson, Vogtmann, and Gatwood, with assistance from
Chernew, Farr, and Fendrick. The manuscript was primarily written and
revised by Gatwood, with assistance from Gibson and the other authors.
REFERENCES
1. Kaiser Family Foundation, Health Research and Educational Trust.
Employer Health Benefits 2011 Summary of Findings. Menlo Park, CA: Kaiser
Family Foundation; 2011. Available at: http://www.nahu.org/meetings/
capitol/2012/attendees/jumpdrive/2011%20Employee%20Benefits%20
Survey%20by%20KFF.pdf. Accessed September 11, 2014.
2. Kaiser Family Foundation. Prescription drug trends. May 2010. Available
at: http://kaiserfamilyfoundation.files.wordpress.com/2013/01/3057-08.pdf.
3. Goldman DP, Joyce GF, Escarce JJ, et al. Pharmacy benefits and the use of
drugs by the chronically ill. JAMA. 2004;291(91):2344-50.
4. Smith DG. The effects of copayments and generic substitution on the use
and costs of prescription drugs. Inquiry. 1993;30(2):189-98.
5. Gibson TB, McLaughlin CG, Smith DG. A copayment increase for prescription drugs: the long-term and short-term effects on use and expenditures. Inquiry. 2005;42(3):293-310.
6. Landsman PB, Yu W, Liu X, Teutsch SM, Berger ML. Impact of 3-tier
pharmacy benefit design and increased consumer cost-sharing on drug utilization. Am J Manag Care. 2005;11(10):621-28.
7. Goldman DP, Joyce GF, Zheng Y. Prescription drug cost sharing: associations with medication and medical utilization and spending and health.
JAMA. 2007;298(1):61-69.
8. Gilman BH, Kautter J. Impact of multitiered copayments on the use and
cost of prescription drugs among Medicare beneficiaries. Health Serv Res.
2008;43(2):478-95.
9. Leibowitz A, Manning WG, Newhouse JP. The demand for prescription
drugs as a function of cost-sharing. Sco Sci Med. 1985;21(10):1063-69.
Authors
JUSTIN GATWOOD, PhD, MPH, is Assistant Professor, University
of Tennessee College of Pharmacy, Memphis, Tennessee. TERESA
B. GIBSON, PhD, is Vice President, and AMANDA M. FARR,
MPH, is Researcher, Truven Health Analytics, Ann Arbor, Michigan.
EMILY VOGTMANN, PhD, MPH, is Cancer Prevention Fellow,
National Cancer Institute, Bethesda, Maryland; MICHAEL E.
CHERNEW, PhD, is Professor, Harvard Medical School, Cambridge,
Massachusetts; and A. MARK FENDRICK, MD, is Professor,
University of Michigan Medical School, Ann Arbor, Michigan.
AUTHOR CORRESPONDENCE: Justin Gatwood, PhD, MPH,
University of Tennessee College of Pharmacy, 881 Madison Ave.,
Memphis, TN 38103. Tel.: 901.448.7215;
www.amcp.org
Vol. 20, No. 11
10. Dor A, Encinosa W. How does cost-sharing affect drug purchases?
Insurance regimes in the private market for prescription drugs. NBER
Working Paper No. 10738. September 2004. Available at: http://www.nber.
org/papers/w10738. Accessed September 11, 2014.
11. Chernew M, Gibson TB, Yu-Isenberg K, Sokol MC, Rosen AB,
Fendrick AM. Effects of increased patient cost sharing on socioeconomic
disparities in health care. J Gen Intern Med. 2008;23(8):1131-36.
12. Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity
index for use with ICD-9-CM administrative databases. J Clin Epid.
1992;45(6):613-19.
13. Ashcraft ML, Fries BE, Nerenz DR, et al. A psychiatric patient classification system: an alternative to diagnosis-related groups. Med Care.
1989;27(5):543.
14. Hilbe JM. Negative Binomial Regression. 2d ed. New York: Cambridge
University Press; 2011.
November 2014
JMCP
Appendix
Study Category
Proton pump
inhibitors
Biphosphonates
Statins
Anticonvulsants
Thyroid
hormone
Antiplatelet
agents
NSAIDs/opioids
Smoking
deterrents
Medication Classes Used to Determine Study Categories
Example Medications
Common Uses
Dexlansoprazole, Esomeprazole Magnesium, Lansoprazole, Lansoprazole/Naproxen, Gastroesophageal
Omeprazole, Omeprazole/Sodium Bicarbonate, Pantoprazole Sodium, Rabeprazole
reflux disorder
Sodium
Alendronate Sodium, Alendronate Sodium/Cholecalciferol, Ibandronate Sodium,
Osteoporosis
Risedronate Sodium, Risedronate Sodium/Calcium Carbonate, Tiludronate Disodium,
Etidronate Disodium, Pamidronate Disodium, Zoledronic Acid
Atorvastatin Calcium, Cerivastatin Sodium, Fluvastatin Sodium, Lovastatin,
Lovastatin/Niacin, Pravastatin Sodium, Rosuvastatin Calcium, Simvastatin,
Amlodipine Besylate/Atorvastatin Calcium, Ezetimibe/Simvastatin, Niacin/Lovastatin,
Niacin/Simvastatin
Carbamazepine, Clonazepam, Diazepam, Divalproex Sodium, Ethosuximide,
Ethotoin, Felbamate, Gabapentin, Lacosamide, Lamotrigine, Levetiracetam,
Mephenytoin, Methsuximide, Oxcarbazepine, Paramethadione, Phenacemide,
Phenobarbital, Phenobarbital Sodium, Phenytoin, Pregabalin, Primidone, Rufinamide,
Tiagabine HCl, Topiramate, Trimethadione, Valproate Sodium, Valproic Acid,
Vigabatrin, Zonisamide
Levothyroxine Sodium, Liothyronine Sodium, Liotrix
Dipyridamole, Cilostazol, Clopidogrel Bisulfate, Aspirin/Dipyridamole, Ticlopidine
HCl
Hyperlipidemia
Anxiety disorders,
insomnia, muscle
relaxant, epilepsy,
panic disorders
Market
Limited generic entry during study period; OTC substitutes in similar classes
Some generic substitutes
made available during the
study period; no in-class
OTC options
No OTC substitutes; some
generic medications
available
Numerous generic options
available; no OTC substitutes
Hypothyroidism
Generic substitutes available but no OTC options
Acute coronary syn- OTC medication available
drome, peripheral
but limited generic substiartery disease, stroke tutes available
Pain, inflammation, Numerous generic and
arthritis
OTC substitutes available
Alfentanil HCl, Buprenorphine HCl/Naloxone HCl, Butorphanol Tartrate, Celecoxib,
Codeinea, Diclofenac Sodium, Etodolac, Fenoprofen Calcium, Flurbiprofen,
Ibuprofen a, Indomethacin, Ketoprofen, Ketorolac Tromethamine, Meclofenamate
Sodium, Mefenamic Acid, Levomethadyl Acetate HCl, Levorphanol Tartrate,
Meloxicam, Meperidine HCl a, Methadone HCl, Morhine Sulfatea, Nalbuphine
HCl, Nabumetone, Naproxen, Oxaprozin, Pentazocine HCl, Piroxicam, Rofecoxib,
Sulindac, Tolmetin Sodium, Valdecoxib, Aspirin a, Salsalate, Propoxyphene HCl a,
Tapentadol HCl, Acetaminophen (including combination products), Fentanyl Citrate a,
Hydrocodone a, Hydromorphone a, Oxycodone a, Oxymorphone, Propoxyphene
Napsylate, Tramadol HCl a, Remifentanil HCl, Bromfenac Sodium
Bupropion HCl, Nicotine, Nicotine Polacrilex, Varenicline, Varenicline Tartrate
Smoking cessation
OTC medications widely
available but few generic
substitutes
a Including
available combination products.
HCl = hydrochloride; OTC = over the counter.
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RESEARCH
Predictors of Treatment Initiation with Tumor Necrosis
Factor-α Inhibitors in Patients with Rheumatoid Arthritis
Rishi J. Desai, PhD; Jaya K. Rao, MD; Richard A. Hansen, PhD; Gang Fang, PhD;
Matthew L. Maciejewski, PhD; and Joel F. Farley, PhD
ABSTRACT
BACKGROUND: Introduction of biologic disease-modifying antirheumatic
drugs (DMARDs) has revolutionized treatment in patients with rheumatoid arthritis (RA). However, due to substantially higher costs of biologics
compared with nonbiologics, patients with less insurance generosity may
have difficulty affording these agents, which may lead to potential access
disparities.
OBJECTIVE: To identify factors affecting treatment initiation with tumor
necrosis factor (TNF)-α inhibitor biologics in patients with RA.
METHODS: Health insurance claims data derived from Truven’s MarketScan
Commercial Claims and Encounters and Medicare Supplemental and
Coordination of Benefits (2007-2010) were used to conduct a retrospective cohort study. Two separate cohorts of RA patients were identified:
(1) monotherapy nonbiologic DMARD users and (2) combination therapy
nonbiologic DMARD users. The primary outcome was TNF-α inhibitor initiation 12 months following an index inpatient or outpatient RA visit during
2008-2009. Predictors were measured 12 months pre-index and grouped
into predisposing, enabling, or need factors based on Andersen’s Behavior
Model. Predisposing variables included age, sex, and geographic location;
enabling variables included insurance-related factors such as capitation,
payer type, and insurance generosity, which was defined using costsharing information from prescriptions filled by the patients in the previous
year; and need variables included disease-related factors such as severity
of RA, use of pain control medications, and presence of other comorbidities. Hierarchical logistic regression models were used to derive estimates
of the impact of individual predictors.
RESULTS: Initiation of TNF-α inhibitors was observed in 10.31% of the
monotherapy nonbiologic DMARD users (1,922 of 18,641) and 13.09% of
combination nonbiologic DMARD users (983 of 7,508). Among monotherapy
nonbiologic DMARD users, initiation with TNF-α inhibitors was associated
with the predisposing factors of age (OR = 0.98, 95% CI = 0.97-0.98 for
each year increase) and geographic region (Midwest vs. South OR = 0.83,
95% CI = 0.73-0.93; Northeast vs. South OR = 0.77, 95% CI = 0.64-0.92;
and West vs. South OR = 0.86, 95% CI = 0.74-0.99); enabling factors of
visit to rheumatologists (1 visit vs. no visit OR = 1.22, 95% CI = 1.01-1.46),
health insurance type (commercial vs. Medicare supplemental OR = 0.79,
95% CI = 0.66-0.95), and drug benefit generosity (above average vs.
poor OR = 1.16, 95% CI = 1.01-1.34 and most generous vs. poor OR = 1.21,
95% CI = 1.05-1.40); and need factors of RA severity (OR = 1.19, 95%
CI = 1.14-1.23 for each unit increase in a claims-based RA severity index
[CIRAS]), pre-index pain reliever use (steroids OR = 1.81, 95% CI = 1.622.02; nonselective nonsteroidal anti-inflammatory drugs [NSAID] OR = 1.17,
95% CI = 1.05-1.31; COX-2 inhibitors OR = 1.22, 95% CI = 1.05-1.41), and
comorbidities (OR = 0.94, 95% CI = 0.90-0.99 for each unit increase in
a comorbidity index). Treatment initiation with TNF-α inhibitors among
patients with combination therapy nonbiologic DMARDs use at baseline
JMCP
November 2014
was associated with age (OR = 0.98, 95% CI = 0.97-0.99 for each year
increase) and region (Midwest vs. South OR = 0.81, 95% CI = 0.68-0.96).
Stronger associations with some of the need factors were observed (CIRAS
OR = 1.28, 95% CI = 1.21-1.35 for each unit increase, steroids use OR = 2.05,
95% CI = 1.73-2.42, and nonselective NSAID use OR = 1.36, 95% CI = 1.171.58) in these patients compared with the monotherapy nonbiologic DMARD
users. However, unlike the monotherapy DMARD user group, the enabling
factors of health insurance type and drug benefit generosity were not found
to be associated with TNF-α inhibitor initiation among nonbiologic DMARD
combination therapy users.
CONCLUSIONS: Potential disparities in the initiation of TNF-α inhibitors
among RA patients on monotherapy DMARDs at baseline were noted among
older patients, patients in certain geographic region of the United States,
and patients with less generous prescription drug benefits. Although future
research should examine the impact of these disparities on health outcomes, payers should be aware of the potential for undertreatment among
these groups of RA patients when making formulary decisions.
•Biologic agents, indicated for the treatment of rheumatoid arthritis
(RA) in patients who do not respond adequately to nonbiologics
alone, are substantially more costly compared with nonbiologics.
•Prior studies from limited geographic regions of the United
States suggest that certain patient characteristics, including lower
income, minority race, and higher age, are negatively associated
with biologic treatment in RA.
•This is the largest study based on U.S. commercial and Medicare
population evaluating treatment predictors of tumor necrosis
factor (TNF)-α inhibitor biologics conducted using a nationally
representative sample of commercially insured RA patients.
•Among RA patients on monotherapy nonbiologics, insurance
generosity was found be a significant predictor of treatment
initiation with TNF-α inhibitor biologics. However, among RA
patients on combination therapy nonbiologics, the need for treatment, and not enabling characteristics such as insurance generosity, predicted treatment initiation with TNF-α inhibitor biologics.
This observation demonstrates potential disparities related to
patient cost sharing in the early stages of RA.
Vol. 20, No. 11
www.amcp.org
R
heumatoid arthritis (RA) is an autoimmune disease that
affects approximately 1.3 million adults in the United
States.1 RA is associated with substantial morbidity and mortality.2-4 Disease-modifying antirheumatic drugs
(DMARDs), which are generally classified into nonbiologics and biologics, form the mainstay of RA management.
Nonbiologic DMARDs include agents, such as methotrexate,
sulfasalazine, hydroxychloroquine, and leflunomide, that halt
disease progression by suppressing inflammation. In contrast,
biologic DMARDs target specific components of the immune
system, such as T cells, B cells, and cytokines (i.e., tumor
necrosis factor (TNF)-α and interleukins), that play an important role in the pathogenesis of RA. Currently there are 10
biologics approved for the indication of RA: 5 TNF-α inhibitors
(infliximab, etanercept, adalimumab, certolizumab, and golimumab), 2 interleukin inhibitors (tocilizumab and anakinra),
a T-cell activation inhibitor (abatacept), a CD-20 activity
blocker (rituximab), and a janus kinase inhibitor (tofacitinib).
Among the available biologics, TNF-α inhibitors are the most
commonly used agents, accounting for approximately 90% of
the total biologic use.5 According to the American College of
Rheumatology (ACR) recommendations, RA patients with low
or moderate disease activity without features of poor prognosis
should receive treatment with nonbiologic DMARDs, while RA
patients with moderate-to-high disease severity with features
of poor prognosis whose RA is not well controlled with nonbiologic DMARDs alone should receive treatment with biologic
DMARDs.6,7
Since all the biologics are only available as brands, they
are substantially more costly than nonbiologic DMARDs.
According to 1 estimate, the total direct costs for biologics
are approximately 5-fold greater than nonbiologic DMARDs.2
Given this cost, certain patient subgroups may have difficulty
affording treatment, including patients with low income, less
generous insurance coverage, and minority race. This is supported by studies that show lower biologic treatment initiation
in RA patients with older age, lower income, and minority
race.8-10 This disparity may lead to differences in such clinical outcomes as greater disease activity and lower remission
rates in these patient subgroups.11 Reduction in overall health
services utilization costs through sustained remission in RA is
well documented.12 Therefore, from a payer’s point of view, it is
very important to understand and address potential disparities
in the use of biologics among RA patients in order to control
future health care costs.
Although previous studies have examined treatment disparities in particular subgroups,8-10 none has used a comprehensive
model incorporating the variety of treatment determinants that
might predict TNF-α inhibitor use. This study used Andersen’s
Behavioral Model (ABM) of health services use to examine
TNF-α inhibitor use in a large cohort of commercially insured
RA patients from the United States.13 ABM is a theoretical
www.amcp.org
Vol. 20, No. 11
model that uses a combination of factors grouped into predisposing, enabling, and need factors in order to predict the use
of health care services. In addition, this study expanded on the
current literature, which includes studies conducted in limited
geographic regions of the United States,8-10 by evaluating factors influencing treatment initiation with TNF-α inhibitors in a
nationally representative sample of RA patients. We exclusively
focus on TNF-α inhibitor biologics because non-TNF biologics
are generally reserved for a select group of patients who either
fail to respond to a TNF-α inhibitor agent or are at an increased
risk of adverse events from TNF-α inhibitors.6
■■  Methods
Study Design and Data Source
A retrospective cohort study was designed to evaluate the predictors of TNF-α inhibitor treatment initiation in RA patients
who were aged 18 years and older using data from Truven’s
MarketScan Commercial Claims And Encounters (CCAE) and
Medicare Supplemental and Coordination of Benefits (COB)
for the period between January 1, 2007, to December 31, 2010.
These databases contain de-identified, person-specific health
data including clinical utilization, expenditures, insurance
enrollment/plan benefit, inpatient, outpatient, and prescription
information. The CCAE contains health care data for nearly 40
million individuals, encompassing employees, their spouses,
and their dependents. The COB contains the health care experiences of 3.8 million Medicare-eligible retirees with employersponsored Medicare supplemental plans.14 These patients have
coordination of benefits , meaning that in addition to Medicare
they have a private insurance plan paid for by their employers and therefore are not typical of the usual Medicare patient
population. The Medicare supplemental dataset provided by
Truven contains information on Medicare paid and supplemental insurance paid services.
Patient Identification and Exclusion Criteria
Diagnosis of RA was identified using the International
Classification of Diseases, Ninth Revision, Clinical Modification
(ICD-9-CM) diagnosis code of 714.0 on at least 2 outpatient
or 1 inpatient insurance claims between January 1, 2008, and
December 31, 2009. The date of the first claim was defined
as the index date. In order to ensure continuous availability
of health care data, we required patients to be continuously
enrolled in their health plans 12 months pre-index (defined
as the baseline period) and 12 months post-index (defined
as the follow-up period). Combining diagnosis codes with
DMARD prescription fills is known to result in a high positive
predictive value (> 85%) in identifying RA from administrative
claims.15 Therefore, to improve the specificity of our RA identification algorithm, we further required these patients to have
used at least 1 nonbiologic DMARD during the 12 months
baseline period.
November 2014
JMCP
FIGURE 1
Study Design
24 months
Continuous enrollment in the health plan
Baseline
outpatient/inpatient
visit with
rheumatoid arthritis
Prescription filled
for a TNF-α
inhibitor
Index date
January 1, 2007
12 months pre-index
• Required nonbiologic DMARD
monotherapy or combination
therapy use
• Excluded prevalent biologic
DMARD users
December 31, 2010
12 months follow-up
Identified initiation of a
TNF-α inhibitor
DMARD = disease-modifying antirheumatic drug; TNF=tumor necrosis factor.
Given our focus on TNF-α inhibitor initiation, we excluded
patients who used any biologic DMARDs during the 12-month
baseline period. To ensure RA-specific TNF-α inhibitor initiation, we excluded patients having psoriatic arthritis or Crohn’s
disease (inflammatory conditions for which TNF-α inhibitor
treatment is indicated). Further, to ensure that all the included
patients were eligible to receive TNF-α inhibitors during
follow-up, we excluded patients with a history of tuberculosis,
which is a contraindication to TNF-α inhibitor use.
Eligible RA patients were followed for 12 months beginning
from their index dates to examine initiation of TNF-α inhibitors (Figure 1). To contrast patient characteristics between nonbiologic DMARD users and TNF-α inhibitors users, patients
who either did not fill any DMARD prescription or initiated
treatment with non-TNF biologics prior to initiating TNF-α
inhibitors were excluded during follow-up. Additionally, in
order to compare RA patients at different stages of the disease
separately, we created 2 separate cohorts based on nonbiologic
DMARD use during the baseline period: (1) RA patients on
monotherapy nonbiologic DMARDs in the baseline period—
this cohort represented RA patients with mild-to-moderate
disease activity without features of poor prognosis—and (2)
RA patients on combination therapy nonbiologic DMARDs
in the baseline period—this cohort represented RA patients
with moderate-or-high disease activity with features of poor
prognosis.
JMCP
November 2014
Measures
Predictors of biologic treatment initiation were measured during the 12-month baseline period in both cohorts and grouped
according to the ABM for health services use.13 ABM posits
a process of health care use in which predisposing factors
influence the ability (measured through enabling factors) of
a person to obtain health care that, when adding the need for
treatment, predicts the use of health care services.
Predisposing Factors. Predisposing factors included the variables that may influence the likelihood of receiving health care
services. Predisposing factors of age and sex from the ABM
have successfully predicted some health care services use in RA
patients in the past.16 Therefore, we hypothesized that patient
demographic factors including age (as a continuous variable),
gender (male/female), geographic location (Northeast, Midwest,
West, and South), and urban/rural residence (as determined by
metropolitan statistical areas [MSA]) may be able to explain the
use of TNF-α inhibitors in this population.
Enabling Factors. Enabling factors included variables that
may influence a patient’s ability to secure health care services.
Because of the high cost of TNF-α inhibitors, based on the ABM
we hypothesized that RA patients with better means to secure
health care may initiate these agents more frequently. We
included the following factors as enabling variables to capture a
patient’s ability to secure health care services: visit to a rheumatologist as a categorical variable indicating no visit; 1 visit and
more than 1 visit in the baseline period; the year of a patient’s
Vol. 20, No. 11
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index visit as a binary variable, 2008 or 2009; health plan type
as a binary variable indicating capitated plan (included health
maintenance organization or capitated point-of-service plans)
or noncapitated plan (included basic major medical, comprehensive, exclusive and preferred provider organizations,
noncapitated point of service, consumer-driven health plan, or
high deductible health plan); type of insurance as a binary variable indicating either Medicare supplemental or commercial
insurance; and drug benefit generosity. Drug benefit generosity
was approximated by creating a “generosity index” using payment information from the prescriptions filled by patients in
the 12-month baseline period.17 This index was calculated as a
continuous variable in the range of 0-1 and was defined as the
proportion of total drug costs paid by the patient out of pocket
as copay or coinsurance. Based on this index, patients were
classified into quartiles of drug benefit generosity to facilitate
interpretation. The quartiles were termed as poor drug benefit
generosity (fourth quartile, > 33% cost shared by the patients),
average drug benefit generosity (third quartile, 20%-33% cost
shared by the patients), above average drug benefit generosity
(second quartile, 10%-20% cost shared by the patients), and
most generous drug benefit (lowest out-of-pocket costs, first
quartile, < 10% cost shared by the patients).
Need Factors. Need factors included health conditions of
patients that necessitate the utilization of health services. Since
TNF-α inhibitors are reserved for patients whose RA is not
well controlled with nonbiologic DMARDs,6 we hypothesized
that patients with more severe RA, as captured by the need
variables in the ABM, may initiate TNF-α inhibitors more frequently. In this set, we included a continuous measure approximating disease severity (claims-based index of RA severity
[CIRAS]) validated in a previous study.18 We also added indicators for baseline steroid use, nonsteroidal anti-inflammatory
drug (NSAIDs) use, and COX-2 inhibitor use based on at least
1 dispensing of these agents during the baseline period. The
comorbidity profile of patients, which was calculated as a continuous score based on the presence of 20 individual comorbid
conditions, was also included in this set.19
The outcome variable of interest was initiation of a TNF-α
inhibitor agent. We dichotomized TNF-α inhibitor initiation
as present or absent based on pharmacy or medical claims
indicating use of these agents during the 12-month period
following the index date. The following TNF-α inhibitors were
included in this study: adalimumab, certolizumab, etanercept,
golimumab, and infliximab. The use of these agents was identified using both the National Drug Code (NDC) numbers from
outpatient pharmacy files for filled prescriptions and J codes
using outpatient services files for injectable agents administered at physician offices. The following NDCs were used:
00074937402, 00074379901, 00074379902, 00074433902,
00074433906, 00074433907, and 54868482200 for adalimumab; 50474070062 and 50474071079 for certolizumab;
54868478200, 58406042534, 58406042541, 58406045501,
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Vol. 20, No. 11
58406045504, 58406043501, 58406043504, 58406044501,
and 58406044504 for etanercept; 57894007001 and
57894007002 for golimumab; and 57894003001 for infliximab. The following J codes were used: J0135 for adalimumab,
J0718 for certolizumab, J1438 for etanercept, and J1745 for
infliximab.
Statistical Analyses
Descriptive statistics were used to summarize patient characteristics among TNF-α inhibitor initiators and nonbiologic
DMARD users. For dichotomous and categorical variables,
the results were presented as numbers and proportions. For
continuous variables, the results were presented as mean
(± standard deviation). The patient factors were then compared
between TNF-α inhibitor initiators and nonbiologic DMARD
users with standardized differences.20 Standardized differences
were used to avoid statistically significant differences that have
limited clinical importance between our 2 groups owing to the
large sample size. A standardized difference of less than 10
suggests no correlation between the variable in question and
the treatment group.
To understand the impact of various predictors on the initiation of TNF-α inhibitors while controlling for other variables,
hierarchical logistic regression models were used in which
the predictors were entered in 3 sets. The dependent variable
in these models was a binary indicator for initiation of TNF-α
inhibitors. The independent variables were grouped in 3 categories based on ABM: predisposing, enabling, and need variables.
Predisposing variables were first included in the model followed
by enabling variables and then need variables for both cohorts.
Improvement in model fit was assessed using the Akaike information criterion (AIC) after addition of each set of variables.
The goodness-of-fit of the logistic regression models were tested
using Hosmer-Lemeshow tests. Linear equivalents of the logistic
regression analyses were used to derive variance inflation factors (VIF), which were used to check for collinearity among the
variables added to the model.21 All analyses were conducted
using SAS version 9.2 (SAS Institute, Cary, NC).
Sensitivity Analyses
In order to evaluate the robustness of our findings, we undertook 2 sets of sensitivity analyses. First, Medicare and commercial enrollees may have different patient characteristics
and coverage characteristics. Therefore, in order to evaluate
whether our results are sensitive to pooling these patients
and studying them as a single group, we fit logistic regression
models predicting initiation of TNF-α inhibitors in Medicare
and commercial enrollees separately in both cohorts. Second,
certain TNF-α inhibitors that are administered at physician
offices (most notably infliximab infusion) are likely to be
covered under medical benefits, while other agents that are
available as a self-injectable kit (e.g., etanercept) are more likely
to be covered under pharmacy benefits. Therefore, to check
whether our results apply to both physician-administered as
November 2014
JMCP
FIGURE 2
Study Sample Derivation
Patients identified as having RA between January 1, 2008, and December 31, 2009, who had 24 months
continuous enrollment in their health plans
Included n = 44,709
Total excluded n = 16,408
• Prevalent biologic use (15,246)
• History of Chrohn’s disease or psoriatic arthritis (1,094)
• History of tuberculosis (68)
RA patients on nonbiologic DMARD treatment
Included n = 28,201
Total excluded n = 2,052
• No DMARD prescriptions filled during follow-up (1,599)
• Initiated non-TNF-α inhibitor biologic during follow-up (453)
RA patients on nonbiologic DMARD treatment who continue nonbiologic DMARDs or initiate a TNF-α inhibitor
Included n = 26,149
Cohort 1
RA patients on monotherapy
nonbiologic DMARDs at baseline
(n = 18,641)
Cohort 2
RA patients on combination therapy
nonbiologic DMARDs at baseline
(n = 7,508)
DMARD = disease-modifying antirheumatic drug; RA = rheumatoid arthritis; TNF = tumor necrosis factor.
well as prescription TNF-α inhibitors, we fit separate logistic
regression models predicting initiation of both types of TNF-α
inhibitors in both cohorts.
■■  Results
Derivation of Study Cohorts
Figure 2 shows application of the inclusion and exclusion
criteria for this study. We identified 44,709 RA patients who
had at least 12 months pre-index and 12 months post-index
continuous enrollment in their health plans. After excluding
prevalent biologic users (15,246), patients ineligible for TNF-α
inhibitor initiation (68), patients with comorbid inflammatory conditions (1,094), patients with no DMARD use during
follow-up (1,599), and initiators of non-TNF-biologics (453),
a total of 26,149 patients met all our inclusion criteria. These
patients were then divided into 2 cohorts. Cohort 1, which
included RA patients on monotherapy nonbiologic DMARDs in
the baseline period, comprised 18,641 patients, and cohort 2,
which included RA patients on combination therapy nonbiologic DMARDs, comprised 7,508 patients.
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Patient Characteristics
A total of 1,922 patients (10.31%) among monotherapy nonbiologic users (cohort 1) and a total of 983 patients (13.09%) among
combination therapy nonbiologic users (cohort 2) initiated
treatment with a TNF-α inhibitor during the 12-month followup period. Table 1 compares the baseline characteristics of the
TNF-α inhibitor initiators with patients who continued treatment with nonbiologic DMARDs during the follow-up period.
Comparison of the predisposing variables suggested that the
TNF-α inhibitor initiators were younger in both cohorts (mean
age: 54 years vs. 62 years, standardized difference (SD) = 63.16
in cohort 1; 53 years vs. 60 years, SD = 57.18 in cohort 2). In
both cohorts, patients in the South initiated TNF-α inhibitors
more frequently, while patients in the Midwest initiated these
agents less frequently. For the enabling variables, a lower proportion of the TNF-α inhibitor initiators had not visited a rheumatologist in the prior year compared with noninitiators only
among monotherapy nonbiologic DMARD users (39.23% vs.
46.07%, SD = 13.87). The type of insurance was less frequently
Medicare among the TNF-α inhibitor initiators in both cohorts
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TABLE 1
Patient Characteristics of TNF-α Inhibitors Initiators and Noninitiators Stratified by Baseline DMARD
Treatment, 2007-2010
Cohort 1: Monotherapy Nonbiologic DMARD
Users at Baseline
(n=18,641)
TNF-α Inhibitor TNF-α Inhibitor
Initiators
Noninitiators
(n = 1,922)
(n = 16,719)
Cohort 2: Combination Therapy Nonbiologic
Users at Baseline
(n=7,508)
TNF-α Inhibitor TNF-α Inhibitor
Initiators
Noninitiators
(n = 983)
(n = 6,525)
Standardized
Standardized
Variable
n (%)
n (%)
n (%)
n (%)
Differencea
Differencea
Predisposing factors
Patient age, in years, mean (SD)
63.2
57.2
54(12.4) 62(13.5)
53(11.9) 60(12.6)
Female
5.5
7.4
1,477(76.9) 12,456(74.5)
783(79.7) 4,998(76.6)
Metropolitan statistical area
1,567(81.6) 13,596(81.3)
0.6
791(80.5) 5,295(81.2)
1.8
Region
Northeast
10.2
5.4
158(8.2) 1,882(11.3)
69(7.0) 552(8.4)
North Central
507(26.4) 5,372(32.1)
12.7
278(28.3) 2,326(35.6)
15.9
South
22.2
20.7
959(49.9) 6,509(38.9)
450(45.8) 2,328(35.7)
5.8
3.3
West
298(15.5) 2,956(17.7)
186(18.9) 1,319(20.2)
Enabling factors
Capitation
Noncapitated health plan
0.4
2.2
1,618(84.2) 14,101(84.3)
806(82.0) 5,404(82.8)
Capitated health plan
304(15.8) 2,618(15.7)
177(18.0) 1,121(17.2)
Visits to rheumatologists
No visit in the prior year
13.9
5.9
754(39.2) 7,703(46.1)
398(40.5) 2,833(43.4)
7.9
0.5
At least 1 visit in the prior year
183(9.5) 1,226(7.3)
54(5.5) 366(5.6)
More than 1 visit in the prior year
9.3
6.1
985(51.2) 7,790(46.6)
531(54.0) 3,326(50.9)
Calendar year of the index visit
2008
21.6
17.8
1,214(63.2) 8,788(52.6)
675(68.7) 3,928(60.2)
2009
708(36.8) 7,931(48.4)
301(31.3) 2,597(39.8)
Payer type
54.1
46.8
Commercial
1,647(85.7) 10,508(62.8)
847(86.2) 4,362(66.8)
Medicare
275(14.3) 6,211(37.1)
136(13.8) 2,163(33.1)
Drug benefit generosityb
Most generous
435(22.7) 4,177(25.1)
5.5
199(20.2) 1,706(26.2)
14.0
Better than average
472(24.7) 4,096(24.6)
0.1
263(26.7) 1,686(25.8)
2.0
485(25.3) 4,159(25.0)
262(26.6) 1,611(24.7)
Average
0.8
4.5
522(27.3) 4,218(25.3)
259(26.3) 1,518(23.3)
Below average
4.4
7.1
Need factors
6.0(1.9) 4.8(1.9)
12.7
6.1(1.9) 4.9(1.8)
15.8
CIRAS,c mean (SD)
Comedications of interest
COX-2 inhibitors
260(13.5) 1,893(11.3)
6.7
141(14.3) 777(11.9)
7.2
Nonsteroidal anti-inflammatory drugs
573(29.8) 3,630(21.7)
18.6
322(32.8) 1,462(22.4)
23.3
Steroids
1,391(72.4) 9,668(57.8)
30.9
774(78.7) 4,067(62.3)
36.6
0.3(1.1) 0.5(1.4)
12.2
0.4(1.1) 0.5(1.4)
10.6
Combined comorbidity score (CCS)d
Selected individual comorbid conditions from CCSd
Congestive heart failure
70(3.6) 1,035(6.2)
11.8
27(2.7) 420(6.4)
17.7
Any tumor
65(3.4) 1,254(7.5)
18.2
39(4.0) 469(7.2)
14.1
89(4.6) 1,391(8.3)
46(4.7) 502(7.7)
Cardiac arrhythmias
15.0
12.5
Hypertension
687(35.7) 6,976(41.7)
12.3
323(32.9) 2,644(40.5)
15.9
a A standardized difference of 10 (approximately equivalent to P < 0.05) indicates significant imbalance of a baseline covariate.
bDrug benefit generosity was classified according to the quartiles of a calculated generosity index. This index was calculated as a continuous variable and defined as the
proportion of total drug cost paid by the patient out of pocket. The quartiles were termed as poor drug benefit generosity (highest out-of-pocket costs, fourth quartile),
average drug benefit generosity (third quartile), above average drug benefit generosity (second quartile), and most generous drug benefit (lowest out-of-pocket costs, first
quartile).
cCIRAS: Claims-based index of rheumatoid arthritis severity, which ranged from 0.6 to 10.7, with higher values indicating more severe rheumatoid arthritis.
d A composite score indicating a patient’s comorbidity burden after taking into account 20 individual conditions. The score ranged from -2 to 13 in our cohorts, with greater
values indicating higher disease burden. From 20 individual conditions, only those with a standardized difference > 10 were shown.
DMARD = disease-modifying antirheumatic drug; SD = standard deviation; TNF = tumor necrosis factor.
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TABLE 2
Multivariate Predictors of Treatment
Initiation with TNF-α Inhibitors in
RA Patients, 2007-2010
Predicting Treatment Initiation with
TNF-α Inhibitors, OR (95% CI)
Cohort 1: Among
Cohort 2: Among
Monotherapy
Combination
Nonbiologic Users
Nonbiologic Users
at Baseline
at Baseline
Variable
Patient age
0.98 (0.97-0.98)
0.98 (0.97-0.99)
Gender
Female
1
1
Male
0.99 (0.88-1.11)
0.93 (0.78-1.11)
Metropolitan statistical area (MSA)
Non-MSA
1
1
MSA
1.09 (0.96-1.24)
0.95 (0.79-1.13)
Region
South
1
1
North Central
0.83 (0.73-0.94)
0.81 (0.68-0.96)
Northeast
0.77 (0.64-0.92)
0.84 (0.63-1.11)
West
0.86 (0.74-0.99)
0.94 (0.77-1.14)
Enabling factors
Capitation
1
1
Capitated plan
0.95 (0.83-1.09)
1.01 (0.84-1.22)
Visit to rheumatologist in the pre-index period
No visit
1
1
At least 1 visit
1.21 (1.01-1.45)
0.87 (0.64-1.20)
More than 1 visit
0.95 (0.85-1.06)
0.75 (0.64-0.89)
Calendar year of index visit
2008
1
1
2009
1.09 (0.97-1.22)
1.16 (0.98-1.38)
Insurance type
Commercial
1
1
Medicare
0.75 (0.62-0.91)
0.90 (0.68-1.19)
Drug benefit generositya
Poor
1
1
Average
1.03 (0.90-1.18)
0.97 (0.80-1.18)
Better than average
1.16 (1.01-1.33)
1.01 (0.83-1.23)
Most generous
1.20 (1.04-1.39)
0.89 (0.72-1.10)
Need factors
1.19 (1.14-1.23)
1.28 (1.21-1.35)
RA severity (CIRAS)b
Pain medication use
No steroid use
1
1
Steroid use
1.80 (1.62-2.01)
2.04 (1.73-2.41)
No COX-2 inhibitor use
1
1
COX-2 inhibitor use
1.22 (1.06-1.41)
1.22 (0.99-1.50)
No NSAID use
1
1
NSAID use
1.17 (1.05-1.31)
1.36 (1.17-1.59)
0.94 (0.90-0.98)
0.95 (0.90-1.01)
Combined comorbidity scorec
a Drug benefit generosity was classified according to the quartiles of a calculated generosity index. This index was calculated as a continuous variable and defined as the proportion of total drug cost paid by the patient out of pocket. The quartiles were termed as
poor drug benefit generosity (highest out-of-pocket costs, fourth quartile), average drug
benefit generosity (third quartile), above average drug benefit generosity (second quartile), and most generous drug benefit (lowest out-of-pocket costs, first quartile).
bCIRAS: Claims-based index of rheumatoid arthritis severity, which ranged from 0.6 to
10.7 with higher values indicating more severe rheumatoid arthritis.
c A composite score indicating a patient’s comorbidity burden after taking into account
20 individual conditions. The score ranged from -2 to 13 in our cohorts, with greater
values indicating higher disease burden.
CI = confidence interval; NSAID = nonsteroidal anti-inflammatory drug; OR = odds ratio;
TNF = tumor necrosis factor.
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(14.31% vs. 37.15%, SD = 54.13 in cohort 1; 13.84% vs. 33.15%,
SD = 46.79 in cohort 2). Among need variables, the severity of
RA was found to be significantly greater among TNF-α inhibitors in both cohorts (mean CIRAS 5.96 vs. 4.70, SD = 12.67 in
cohort 1; 6.12 vs. 4.89, SD = 15.85 in cohort 2). In both cohorts,
a higher proportion of patients used NSAIDs during the baseline period compared with the noninitiator groups (29.81% vs.
21.71%, SD = 18.60 in cohort 1; 32.76% vs. 22.41%, SD = 23.32
in cohort 2). Steroid use was also more frequent in the TNF-α
initiator groups in both cohorts (72.37% vs. 57.83%, SD = 30.88
in cohort 1; 78.74% vs. 62.33%, SD = 36.59 in cohort 2). Overall
comorbidity burden was lower in the TNF-α inhibitor initiator
group in both cohorts (mean score 0.30 vs. 0.46, SD = 12.19 in
cohort 1; 0.36 vs. 0.49, SD = 10.62 in cohort 2).
Predictors of TNF-α Inhibitor Initiation
The results of our multivariate models that evaluated the
influence of various predictors on treatment initiation with
TNF-α inhibitors are presented in Table 2. The goodnessof-fit for both models was found to be adequate (P for
Hosmer-Lemeshaw > 0.05), and no evidence for collinearity was
observed for the variables added to the model (VIF < 5 for all
the variables).
Among monotherapy nonbiologic users, the predisposing
variables of patient age and geographic region were found
to be significant predictors of TNF-α inhibitor initiation.
Each year increase in age reduced the odds of TNF-α inhibitor initiation by 2% (odds ratio [OR] = 0.98, 95% confidence
interval [CI] = 0.97-0.98). Patients in the Midwest, Northeast,
and West regions had significantly lower likelihood of treatment initiation with TNF-α inhibitors compared with patients
in the South (OR = 0.83, 95% CI = 0.73-0.93; OR = 0.77, 95%
CI = 0.64-0.92; and OR = 0.86, 95% CI = 0.74-0.99, respectively). Of the enabling variables, having Medicare supplemental insurance had lower likelihood of TNF-α inhibitor treatment initiation compared with having commercial insurance
(OR = 0.75, 95% CI = 0.62-0.91). On the other hand, patients
who visited their rheumatologists once in the prior year had
21% higher odds of initiating a TNF-α inhibitor compared with
those who did not visit their rheumatologists at all (OR = 1.21,
95% CI = 1.01-1.45). The drug benefit generosity variable also
significantly predicted treatment initiation with a TNF-α
inhibitor. Patients with better than average and the most generous drug benefit had 16% and 21% higher odds of initiating a
TNF-α inhibitor compared with patients with poor drug benefits (OR = 1.16, 95% CI = 1.01-1.33; OR = 1.20, 95% CI = 1.041.39). All the need variables were found to have an association
with TNF-α inhibitor initiation. With each unit increase in
RA severity measure (CIRAS), the odds of TNF-α inhibitor
initiation increased by 19% (OR = 1.19, 95% CI = 1.15-1.23).
Previous use of steroids raised the odds of TNF-α inhibitor
initiation by 80% (OR = 1.80, 95% CI = 1.62-2.01); previous use
of NSAIDs raised the odds by 17% (OR = 1.17, 95% CI = 1.05-
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1.31); and previous use of COX-2 inhibitors raised the odds by
22% (OR = 1.22, 95% CI = 1.06-1.41). Each unit decrease in the
combined comorbidity score lowered the odds of TNF-α inhibitor initiation by 6% (OR = 0.94, 95% CI = 0.90-0.99). Addition
of each sets of variables in the model improved the model fit
for this cohort (AIC for the model with predisposing factors
only = 11,667; predisposing + enabling factors = 11,640; and
predisposing + enabling + need factors = 11,386).
Some interesting contrasts were observed in the model
predicting TNF-α inhibitor initiation among combination
therapy nonbiologic users. Similar to monotherapy nonbiologic
users, higher age was significantly associated inversely with
TNF-α inhibitor initiation (OR = 0.98, 95% CI = 0.97-0.99), and
patients in the Midwest had lower odds of initiating treatment
with TNF-α inhibitors compared with patients in the South
(OR = 0.81, 95% CI = 0.68-0.96). However, none of the enabling
factors that predicted TNF-α inhibitor initiation in cohort 1
were found to be significantly associated with TNF-α inhibitor
initiation in this cohort. Surprisingly, having visited a rheumatologist more than once in the prior year reduced the odds of
TNF-α inhibitor initiation by 24%, compared with having no
visit (OR = 0.75, 95% CI = 0.64-0.89). A stronger association
between TNF-α inhibitor initiation and RA-related need factors, including CIRAS (OR = 1.28, 95% CI = 1.21-1.35), steroids
use (OR = 2.04, 95% CI = 1.73-2.41), and NSAID use (OR = 1.36,
95% CI = 1.17-1.59) was observed in this cohort. Consistent
with these observations, it was also noted that the addition
of enabling variables to the model did not improve the model
fit for this cohort, but addition of need variables improved
the model fit (AIC for the model with predisposing factors
only = 5,559; predisposing + enabling factors = 5,563; and predisposing + enabling + need factors = 5,364).
Sensitivity Analyses Findings
In our first sensitivity analysis where we fit separate models
predicting TNF-α inhibitors in commercial and Medicare
enrollees, no noticeable differences in trends were observed
compared with the main analysis in important explanatory
variables including age, drug benefit generosity, and RA-related
factors including CIRAS and steroid use (Appendix A, available
in online article). However, since the estimates in Medicare were
based on fewer TNF-α inhibitor initiations (275 in monotherapy
and 136 in combination therapy) compared with estimates in
commercial enrollees (1,647 in monotherapy and 847 in combination therapy), we observed estimates in Medicare enrollees
with wider CIs. Additionally, certain factors, including gender,
MSA, and visits to rheumatologists, were observed to have
estimates that were numerically inconsistent (meaning on different sides of the null value of 1.0) between the 2 data sources.
However, in all instances, the 95% CI for these estimates demonstrated considerable overlap between the 2 data sources.
In our second sensitivity analysis where we fit separate logistic regression models predicting initiation of physician-admin-
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istered and prescription-filled TNF-α inhibitors, the majority
of the findings were similar (Appendix B, available in online
article). However, the drug benefit generosity variable was a
stronger predictor of TNF-α inhibitors filled at a pharmacy,
while it did not predict the initiation of physician-administered
TNF-α inhibitors in the monotherapy cohort, unlike our main
analysis.
Findings from the current study provide insights into realworld treatment initiation patterns of TNF-α inhibitors in
patients with RA. One of the purposes of our study was to
examine potential disparities in treatment using a well-defined
conceptual model. As suggested by the ABM, under an equitable health care system, the use of health care services would
primarily be driven by need factors. However, we found significant variation in TNF-α inhibitor initiation across patients,
with predisposing factors—including age and geographic
region—as well as enabling factors—including visit to rheumatologists, drug benefit generosity, and insurance type—
playing a role in treatment initiation with TNF-α inhibitors.
This is potentially suggestive of inequitable access among RA
patients. A recent investigation observed that close to 50% of
RA patients did not receive care consistent with the 2008 ACR
guidelines.22 Our study identifies some of the potential factors
that may be contributing to this worrisome trend.
In order to better characterize factors influencing treatment
initiation with TNF-α inhibitors, we separately evaluated the
effects of various sets of predictors in 2 cohorts of patients
with different stages of RA, as suggested by either monotherapy
or combination therapy nonbiologic use during the baseline
period. We observed that in the cohort of monotherapy nonbiologic users, patients with certain demographics (younger
age or residence in the South) and with better means to secure
health care (care of rheumatologists or health plans with a
higher drug benefit generosity) had higher odds of initiating
treatment with TNF-α inhibitors. These results suggest that
during the early stages of the disease, potential disparities in
access to the costly TNF-α inhibitors may exist. Delay in initiation of timely TNF-α inhibitors may lead to higher probability
of radiographic progression and hence reduced quality of life
among these patients.23 Prior research has also demonstrated
that RA patients with multiple failed nonbiologic DMARDs
prior to initiating a TNF-α inhibitor have lower odds for treatment response with TNF-α inhibitors.24 This further emphasizes the importance of timely initiation of TNF-α inhibitors
in RA patients. Because of the substantially higher cost of
biologics, there is a potential for inequitable access in the use
of these agents among RA patients. The coverage of biologics
under a higher or specialty formulary tier of pharmacy benefits
has become increasingly common.25 Research suggests that
this practice has substantially increased the out-of-pocket costs
for biologics.25,26 Greater patient cost sharing has been known
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JMCP
to delay or reduce the odds of initiation of treatments in various disease conditions,27 including RA.28 In the current study,
we reported findings in line with these observations among
RA patients who were on monotherapy nonbiologic DMARDs
at baseline. Reduction in patient cost sharing may represent
a potential strategy for payers to increase the odds of timely
biologic initiation.
Among RA patients with moderate-or-high disease activity
with features of poor prognosis (as approximated by combination nonbiologic DMARDs use at baseline in cohort 2), we
observed that need factors mostly explained the initiation of
TNF-α inhibitors, and enabling factors, such as insurance generosity and insurance type, played little role. This finding suggests less potential for disparity in TNF-α inhibitor treatment
use among commercially insured patients with higher need
for treatment. Although our finding of having visited a rheumatologist more than once in the prior year resulting in lower
odds of TNF-α inhibitor initiation compared with no visit in
this cohort may seem counterintuitive at first glance, we postulate that this may reflect improved RA management under
the constant care of a rheumatologist, which may in turn result
in lower need for TNF-α inhibitor initiation in these patients.
It was interesting to note that TNF-α inhibitor initiators were
younger than patients not initiating these treatments. This is
consistent with prior studies that evaluated initiation of biologics specifically,8-10 as well as several studies that examined the
use of DMARDs as a class.29-31 This inverse association between
age and TNF-α inhibitor initiation may be attributed to several
factors. It is likely that older patients may be at a higher risk for
adverse events of TNF-α inhibitors, owing to a higher burden
of comorbid conditions and frailty. Although the literature suggests similar effectiveness of TNF-α inhibitors across different
age groups,32,33 our finding of their differential use based on age
is concerning because it may reflect less aggressive RA management and possibly uncontrolled RA in older patients. Another
factor leading to less aggressive treatment in older RA patients
may be physician preference.34 We also observed that patients
in the South were more likely to initiate treatment with TNF-α
inhibitors. Ours is the first study to document regional variations in the initiation of treatment with TNF-α inhibitors. The
regional variation we observed persisted after controlling for
other predisposing, enabling, and need factors. Prior research
has documented substantial regional variation in prescription
medication utilization among Medicare Part D enrollees in
the United States, and some of the factors contributing to the
geographic variation may include prescriber practice styles,
prescriber awareness, and patient preferences.35
The significant association of higher RA severity score and
pre-index pain medication use with TNF-α inhibitor treatment
initiation is an expected finding because these need variables
represent high RA activity. We also observed strong trends
towards lower likelihood of TNF-α inhibitor initiation among
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November 2014
patients with higher combined comorbidity scores. This finding may reflect the fact that TNF-α inhibitors are contraindicated in a variety of comorbid conditions, including congestive
heart failure, multiple sclerosis, and a variety of infections,
while nonbiologic DMARDs are not.36 Therefore, it is possible
that physicians may avoid TNF-α inhibitor treatment in RA
patients with a higher burden of comorbidities.
Our study has several unique strengths. First, this is the
largest study of its kind conducted in RA patients from all
over the United States, who are enrolled in commercial or
Medicare supplemental insurance, that provides estimates on
the influence of population characteristics on TNF-α inhibitor treatment initiation. Second, because of the availability of
diagnoses for various comorbid conditions within the claims,
we were able to exclude patients with contraindications and
risk-adjust our estimates based on the presence of various
comorbidities. Third, we carefully constructed 2 cohorts of
RA patients according to their disease progression based on
their baseline DMARD use and predicted initiation of TNF-α
inhibitors separately in each cohort. This approach ensured the
inclusion of homogenous groups of patients in each cohort and
provided insights into factors that were differentially associated
with TNF-α inhibitor initiation in each of the cohorts. Finally,
we conducted extensive sensitivity analyses to evaluate the
robustness of our findings.
Limitations
We also acknowledge several limitations of this study. As with
any other study using administrative claims, we were not
able to validate the diagnoses of the disease condition (RA).
To address this limitation, we used nonbiologic DMARD prescriptions in pharmacy claims along with ICD-9-CM codes
on inpatient or outpatient visit to identify RA. Combining
DMARD claims with diagnosis codes has been shown to result
in a high positive predictive value (86%) for identifying RA
in administrative claims in a prior validation study.15 Further,
the administrative claims contain very limited information on
clinical conditions of RA patients, such as disease activity and
swollen joint count. Therefore, we were not able to capture the
exact severity of RA in patients in our cohorts. However, as a
proxy, we used the validated claims-based index for getting an
approximation of RA severity.18 Next, because of the unavailability of information on important patient factors, including
race, education, income, and family medical cost burden, our
study cannot explain potential disparities in TNF-α inhibitor initiation owing to these factors. Additionally, claims data
may have incomplete information on certain variables. For
instance, provider type is coded as “unknown” on some physician visits. This may have artificially inflated the number with
zero visits to rheumatologists and deflated the number with
1 and more than 1 visit. As a result, the absolute percentage
reported in Table 1 may not represent a typical care pattern by
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rheumatologists of RA patients with commercial or Medicare
supplemental insurance. However, we do not expect the
amount of missing information to be related to the initiation
of TNF-inhibitors. Therefore, we postulate that our effect
estimates in Table 2 are not systematically biased due to this
problem. Next, an important factor leading to TNF-α inhibitor
initiation may be physician preference, independent of patient
need for treatment.37 Since we did not have information about
this important variable in our data, our study cannot explain
potential variability related to physician preferences. Finally,
the insurance claims data only represent employed individuals
and their dependents, and the Medicare supplemental data only
represent retirees whose insurance are paid by their employers,
which somewhat limits the generalizability of our study.
Potential disparities in the initiation of TNF-α inhibitors
among RA patients on monotherapy DMARDS at baseline were
noted among older patients, patients in certain geographic
regions of the United States, and patients with less generous
prescription drug benefits among commercial and Medicare
supplemental participants. Although future research should
examine the impact of these disparities on health outcomes,
payers should be aware of the potential for undertreatment
among these groups of RA patients when making formulary
decisions. Among patients on combination therapy DMARDs,
little impact of enabling factors, including drug benefit generosity and data type on the initiation of TNF-α inhibitors, was
observed. Future research using data that have detailed information on drug benefit structure of health plans of the patients
should be considered to confirm our findings.
Authors
RISHI J. DESAI, PhD, is Postdoctoral Research Fellow, Division
of Pharmacoepidemiology and Pharmacoeconomics, Department
of Medicine, Harvard Medical School and Brigham & Women’s
Hospital, Boston, Massachusetts. GANG FANG, PhD, is Assistant
Professor; and JOEL F. FARLEY, PhD, is Associate Professor,
Division of Pharmaceutical Outcomes and Policy, University of
North Carolina Eshelman School of Pharmacy, Chapel Hill, North
Carolina. JAYA K. RAO, MD, is Deputy Editor, Annals of Internal
Medicine, Philadelphia, Pennsylvania; RICHARD A. HANSEN,
PhD, is Professor, Department of Pharmacy Care Systems, Harrison
School of Pharmacy, Auburn University, Auburn, Alabama; and
MATTHEW L. MACIEJEWSKI, PhD, is Associate Professor,
Division of General Internal Medicine, Department of Medicine,
Duke University Medical Center, Durham, North Carolina.
AUTHOR CORRESPONDENCE: Joel F. Farley, PhD, Associate
Professor, UNC Eshelman School of Pharmacy, CB 7573 Kerr Hall,
Rm. 2201, Chapel Hill, NC 27599-7573. Tel.: 919.966.9973;
Fax: 919.966.8486; E-mail: [email protected].
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Vol. 20, No. 11
DISCLOSURES
This study was not supported by any external funding institution. Farley,
Maciejewski, and Hansen have received consulting support for unrelated projects from Daiichi Sankyo and Novartis Pharmaceuticals. Hansen has provided
expert testimony for Allergan. Rao reports owning stocks in Pfizer and Eli
Lilly. No other authors have any conflict of interest to report.
Study concept and design were primarily contributed by Desai and
Farley, with assistance from Rao, Hensen, Fang, and Maciejewski. Desai and
Farley collected the data, which were interpreted by Maciejewski, Hansen,
and Desai, assisted by Rao, Fang, and Farley. The manuscript was written
by Desai, Fang, Rao, and Hansen, assisted by Farley and Maciejewski, and
revised by Desai, Rao, Hansen, and Fang, assisted by Maciejewski and Farley.
References
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of arthritis and other rheumatic conditions in the United States. Part I.
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differences in the use of biologic disease-modifying antirheumatic drugs
among California Medicaid rheumatoid arthritis patients. Arthritis Care Res
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10. DeWitt EM, Lin L, Glick HA, Anstrom KJ, Schulman KA, Reed SD.
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13. Andersen RM. Revisiting the behavioral model and access to medical
care: does it matter? J Health Soc Behav. 1995;36(1):1-10.
14. Danielson E. Health research data for the real world: the MarketScan
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15. Kim SY, Servi A, Polinski JM, et al. Validation of rheumatoid arthritis
diagnoses in health care utilization data. Arthritis Res Ther. 2011;13(1):R32.
16. Jacobi CE, Triemstra M, Rupp I, Dinant HJ, Van Den Bos GA. Health care
utilization among rheumatoid arthritis patients referred to a rheumatology
center: unequal needs, unequal care? Arthritis Rheum. 2001;45(4):324-30.
17. Artz MB, Hadsall RS, Schondelmeyer SW. Impact of generosity level
of outpatient prescription drug coverage on prescription drug events and
expenditure among older persons. Am J Public Health. 2002;92(8):1257-63.
18. Ting G, Schneeweiss S, Scranton R, et al. Development of a health care
utilisation data-based index for rheumatoid arthritis severity: a preliminary
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19. Gagne JJ, Glynn RJ, Avorn J, Levin R, Schneeweiss S. A combined
comorbidity score predicted mortality in elderly patients better than existing
scores. J Clin Epidemiol. 2011;64(7):749-59.
20. Austin PC. Using the standardized difference to compare the prevalence
of a binary variable between two groups in observational research. Commun
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27. Solomon MD, Goldman DP, Joyce GF, Escarce JJ. Cost sharing and
the initiation of drug therapy for the chronically ill. Arch Intern Med.
2009;169(8):740-48.
28. Karaca-Mandic P, Joyce GF, Goldman DP, Laouri M. Cost sharing, family
health care burden, and the use of specialty drugs for rheumatoid arthritis.
Health Serv Res. 2010;45(5 Pt 1):1227-50.
29. Bonafede MM, Fox KM, Johnson BH, Watson C, Gandra SR. Factors
associated with the initiation of disease-modifying antirheumatic drugs in
newly diagnosed rheumatoid arthritis: a retrospective claims database study.
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30. Desai R, Agarwal S, Aparasu R. Drug utilization trends for arthritis and
other rheumtic conditions and impact of patients’ age on treatment choice.
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31. Tutuncu Z, Reed G, Kremer J, Kavanaugh A. Do patients with olderonset rheumatoid arthritis receive less aggressive treatment? Ann Rheum Dis.
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21. Allison PD. Logistic Regression Using the SAS System: Theory and
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32. Bathon JM, Fleischmann RM, Van der Heijde D, et al. Safety and efficacy
of etanercept treatment in elderly subjects with rheumatoid arthritis.
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22. Harrold LR, Harrington JT, Curtis JR, et al. Prescribing practices in a
U.S. cohort of rheumatoid arthritis patients before and after publication
of the American College of Rheumatology treatment recommendations.
33. Genevay S, Finckh A, Ciurea A, Chamot AM, Kyburz D, Gabay C.
Tolerance and effectiveness of anti-tumor necrosis factor alpha therapies in
elderly patients with rheumatoid arthritis: a population-based cohort study.
23. Keystone EC, Breedveld FC, van der Heijde D, et al. Long-term effect
of delaying combination therapy with tumor necrosis factor inhibitor in
patients with aggressive early rheumatoid arthritis: 10-year efficacy and
safety of adalimumab from the randomized controlled PREMIER trial with
open-label extension. J Rheumatol. 2014;41(1):5-14.
34. Fraenkel L, Rabidou N, Dhar R. Are rheumatologists’ treatment decisions
influenced by patients’ age? Rheumatology. 2006;45(12):1555-57.
24. Hyrich KL, Watson KD, Silman AJ, Symmons DP; British Society for
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British Society for Rheumatology Biologics Register. Rheumatology (Oxford).
2006;45(12):1558-65.
25. Goldman DP, Joyce GF, Lawless G, Crown WH, Willey V. Benefit design
and specialty drug use. Health Aff (Millwood). 2006;25(5):1319-31.
26. Polinski JM, Mohr PE, Johnson L. Impact of Medicare Part D on access
to and cost sharing for specialty biologic medications for beneficiaries with
rheumatoid arthritis. Arthritis Rheum. 2009;61(6):745-54.
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35. Munson J, Morden N, Goodman D, Valle N, Wennberg J. The Dartmouth
atlas of medicare prescription drug use. The Dartmouth Institute for Health
Policy & Clinical Practice. October 15, 2013. Available at: http://www.dartmouthatlas.org/downloads/reports/Prescription_Drug_Atlas_101513.pdf.
36. Saag KG, Teng GG, Patkar NM, et al. American College of Rheumatology
2008 recommendations for the use of nonbiologic and biologic diseasemodifying antirheumatic drugs in rheumatoid arthritis. Arthritis Rheum.
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Vol. 20, No. 11
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Appendix A
Multivariate Predictors of Treatment Initiation with TNF-α Inhibitors in RA Patients
by Data Source, 2007-2010
Medicare Data, OR (95% CI)
Among Monotherapy
Nonbiologic Users at
Baseline
Among Combination
Therapy Nonbiologic
Users at Baseline
Commercial Data, OR (95% CI)
Among Monotherapy
Baseline
Among Combination
Therapy Nonbiologic
Users at Baseline
Variables
Patient age
0.94(0.92-0.96)
0.97(0.94-1.00)
0.98(0.97-0.99)
0.99(0.98-0.99)
Gender
Female
1
1
1
1
Male
0.79(0.59-1.04)
0.78(0.51-1.17)
1.03(0.91-1.17)
0.96(0.80-1.17)
Non-MSA
1
1
1
1
MSA
0.84(0.61-1.15)
0.63(0.41-0.98)
1.15(1.00-1.32)
1.02(0.83-1.24)
Region
South
1
1
1
1
0.56(0.41-0.76)
0.70(0.46-1.08)
0.88(0.77-1.01)
0.84(0.70-1.02)
North Central
0.70(0.46-1.06)
1.12(0.61-2.06)
0.78(0.63-0.96)
0.76(0.55-1.04)
Northeast
0.98(0.69-1.38)
0.95(0.56-1.61)
0.82(0.70-0.96)
0.94(0.76-1.18)
West
Enabling factors
Capitation
1
1
1
1
Capitated plan
0.84(0.56-1.25)
0.77(0.43-1.37)
0.97(0.84-1.13)
1.08(0.88-1.32)
No visit
1
1
1
1
1.84(1.15-2.93)
1.48(0.67-3.26)
1.13(0.93-1.37)
0.81(0.57-1.14)
At least 1 visit
More than 1 visit
1.25(0.95-1.66)
1.07(0.72-1.58)
0.91(0.81-1.03)
0.72(0.60-0.86)
2008
1
1
1
1
0.88(0.59-1.13)
0.87(0.56-1.35)
1.05(0.92-1.29)
1.11(0.88-1.45)
2009
Poor
1
1
1
1
1.07(0.71-1.61)
0.93(0.50-1.73)
1.02(0.88-1.18)
0.98(0.80-1.21)
Average
Better than average
1.31(0.88-1.94)
1.07(0.59-1.94)
1.11(0.96-1.29)
1.00(0.81-1.23)
Most generous
1.37(0.93-2.02)
1.38(0.79-2.42)
1.16(0.99-1.36)
0.78(0.62-0.99)
Need factors
RA factors
1.17(1.05-1.30)
1.14(0.97-1.33)
1.19(1.15-1.24)
1.31(1.24-1.39)
CIRASb
No steroid use
1
1
1
1
Steroid use
1.66(1.26-2.19)
2.24(1.44-3.48)
1.82(1.62-2.05)
2.00(1.67-2.40)
No coxib use
1
1
1
1
Coxib use
0.98(0.68-1.41)
1.73(1.09-2.77)
1.27(1.08-1.49)
1.12(0.89-1.41)
No NSAID use
1
1
1
1
NSAID use
0.95(0.68-1.33)
1.26(0.80-1.99)
1.19(1.06-1.34)
1.37(1.17-1.62)
0.91(0.84-0.99)
0.95(0.85-1.06)
0.96(0.92-1.01)
0.96(0.90-1.03)
a Drug benefit generosity was classified according to the quartiles of a calculated generosity index. This index was calculated as a continuous variable and defined as the
quartile).
b CIRAS: Claims-based index of rheumatoid arthritis severity, which ranged from 0.6 to 10.7 with higher values indicating more severe rheumatoid arthritis.
c A composite score indicating a patient’s comorbidity burden after taking into account 20 individual conditions. The score ranged from -2 to 13 in our cohorts, with greater
CI = confidence interval; NSAID = nonsteroidal anti-inflammatory drug; OR = odds ratio; RA = rheumatoid arthritis; TNF = tumor necrosis factor.
1120a Journal of Managed Care & Specialty Pharmacy
JMCP
November 2014
Vol. 20, No. 11
www.amcp.org
Appendix B
Multivariate Predictors of Treatment Initiation with TNF-α Inhibitors in RA Patients
by the Source of Treatment Receipt, 2007-2010
Pharmacy-Filled TNF-α Inhibitors,
Physician-Administered TNF-α Inhibitors,
OR (95% CI)
Among Monotherapy
Baseline
OR (95% CI)
Among Combination
Therapy Nonbiologic
Users at Baseline
Among Monotherapy
Baseline
Among Combination
Therapy Nonbiologic
Users at Baseline
Variables
Patient age
0.97(0.97-0.98)
0.98(0.97-0.99)
1.00(0.98-1.01)
0.98(0.97-1.00)
Gender
Female
1
1
1
1
Male
1.02(0.9-1.16)
0.93(0.77-1.13)
0.91(0.72-1.15)
0.96(0.68-1.37)
Non-MSA
1
1
1
1
1.04(0.91-1.2)
0.92(0.75-1.11)
1.28(0.98-1.68)
1.08(0.73-1.60)
MSA
Region
South
1
1
1
1
0.92(0.81-1.06)
0.85(0.71-1.03)
0.58(0.45-0.74)
0.69(0.48-0.98)
North Central
0.84(0.68-1.03)
0.84(0.62-1.15)
0.58(0.40-0.84)
0.81(0.46-1.42)
Northeast
0.94(0.8-1.11)
1.00(0.80-1.25)
0.62(0.46-0.83)
0.77(0.50-1.18)
West
Enabling factors
Capitation
1
1
1
1
0.92(0.79-1.07)
0.90(0.73-1.11)
1.12(0.86-1.46)
1.55(1.09-2.20)
Capitated plan
No visit
1
1
1
1
1.13(0.92-1.39)
0.86(0.61-1.22)
1.44(1.02-2.02)
0.92(0.46-1.81)
At least 1 visit
More than 1 visit
0.96(0.85-1.09)
0.72(0.60-0.86)
0.90(0.71-1.13)
0.91(0.65-1.27)
2008
1
1
1
1
1.13(1.00-1.28)
1.15(0.95-1.39)
0.95(0.75-1.20)
1.24(0.87-1.75)
2009
Data source
Commercial
1
1
1
1
0.57(0.45-0.72)
0.75(0.54-1.03)
1.31(0.90-1.89)
1.53(0.89-2.65)
Medicare
Poor
1
1
1
1
Average
1.04(0.89-1.21)
0.93(0.75-1.15)
1.01(0.77-1.32)
1.20(0.80-1.78)
Better than average
1.18(1.01-1.38)
1.02(0.83-1.27)
1.06(0.80-1.40)
0.99(0.65-1.50)
Most generous
1.26(1.07-1.47)
0.89(0.71-1.12)
1.04(0.78-1.38)
0.90(0.58-1.41)
Need factors
RA factors
1.19(1.14-1.24)
1.29(1.21-1.37)
1.19(1.11-1.28)
1.25(1.11-1.39)
CIRASb
No steroid use
1
1
1
1
Steroid use
1.74(1.54-1.97)
2.05(1.70-2.46)
2.06(1.64-2.59)
2.04(1.43-2.92)
No coxib use
1
1
1
1
Coxib use
1.24(1.06-1.47)
1.09(0.86-1.38)
1.17(0.88-1.57)
1.76(1.20-2.58)
No NSAID use
1
1
1
1
NSAID use
1.19(1.05-1.34)
1.33(1.12-1.57)
1.12(0.90-1.40)
1.58(1.15-2.16)
0.96(0.91-1.00)
0.96(0.90-1.02)
0.90(0.83-0.98)
0.94(0.83-1.05)
a Drug benefit generosity was classified according to the quartiles of a calculated generosity index. This index was calculated as a continuous variable and defined as the
quartile).
b CIRAS: Claims-based index of rheumatoid arthritis severity, which ranged from 0.6 to 10.7 with higher values indicating more severe rheumatoid arthritis.
c A composite score indicating a patient’s comorbidity burden after taking into account 20 individual conditions. The score ranged from -2 to 13 in our cohorts, with greater
CI = confidence interval; NSAID = nonsteroidal anti-inflammatory drug; OR = odds ratio; RA = rheumatoid arthritis; TNF = tumor necrosis factor.
1120b Journal of Managed Care & Specialty Pharmacy
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November 2014
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RESEARCH
North Carolina Medicaid Recipient Management Lock-In Program:
The Pharmacist’s Perspective
S. Rose Werth, BA; Nidhi Sachdeva, MPH, CHES; Andrew W. Roberts, PharmD;
Mariana Garrettson, MPH; Chris Ringwalt, PhD; Leslie A. Moss, MHA, CHES;
Theodore Pikoulas, PharmD, BCPP; and Asheley Cockrell Skinner, PhD
ABSTRACT
BACKGROUND: The misuse and abuse of prescription opioids have become
an urgent health issue in North Carolina (NC), particularly among Medicaid
patients who suffer high rates of morbidity and mortality due to abuse
and overdose. The NC Division of Medical Assistance (DMA) implemented
a recipient management lock-in program, which limits identified patients
for a 12-month period to 1 prescriber and 1 pharmacy for benzodiazepine,
opiate, and certain anxiolytic prescriptions in order to prevent misuse and
reduce overutilization of Medicaid benefits.
OBJECTIVES: To (a) evaluate pharmacists’ perceptions of the implementation of the NC recipient management lock-in program (MLIP) and (b) determine how the beliefs and attitudes of pharmacists could promote or inhibit
its success.
METHODS: We conducted 12 structured phone interviews with NC pharmacists serving lock-in patients. Interview responses were analyzed through
construct analysis, which identified themes organized into 3 domains: organization and implementation, perceived effectiveness, and acceptability.
RESULTS: Most respondents reported a positive experience with the
program but expressed doubt concerning its impact on prescription
drug abuse. The program successfully utilized the pharmacist role as a
gatekeeper of controlled substances, and the procedures of the program
required no active effort on pharmacists’ part. However, respondents suggested that the DMA improve communication and outreach to address
pharmacists’ lack of knowledge about the program’s purpose and confusion over remediating problems that arise with lock-in patients. The DMA
should also address the ways in which the program can interfere with
access to health care and treatment, allow patients to see multiple physicians within the same clinic, and clarify procedures for patients whose
complex health issues require multiple specialists. CONCLUSIONS: Although possible improvements were identified, the NC
MLIP has strong potential for success as it utilizes pharmacists’ medication gate-keeping role, while minimizing the effort required for successful
implementation.
•Mortality among Medicaid recipients due to prescription drug
abuse is 5 times that of the general population.
•North Carolina Medicaid beneficiaries and providers had one of
the highest rates of potentially fraudulent purchases of controlled
substances in the the nation.
JMCP
November 2014
•North Carolina implemented a narcotic lock-in program to
address controlled substance misuse and reduce cost and utilization among Medicaid beneficiaries.
•While many respondents reported an overall positive experience
with the program, they doubted its impact on prescription drug
misuse and abuse.
•Educating pharmacists about the program’s purpose and policies as well as addressing potential barriers to patient care may
improve the program.
O
verdose and abuse of prescription drugs has become an
increasingly urgent health issue in the United States.
Most overdoses from prescribed medications result
from nonmedical uses, which refers to both the misuse and
abuse of prescription drugs.1 The national death rate due to
unintentional poisoning increased by 91% between 1999 and
2009 and by 213% in North Carolina (NC) during the same
period.2,3 Opioid analgesics, a class of controlled substances
(CS) that includes methadone, codeine, hydrocodone, oxycodone, and morphine, among others, are the driving factor
behind this increase.4 In the last decade, prescription painkillers have caused more deaths than either cocaine or heroin.5
The economic burden of prescription drug misuse and abuse
is estimated to contribute $25 billion to health care costs, $5
billion to the justice system, and $25.6 billion in workplace
losses annually.6
Mortality among Medicaid recipients due to prescription
drug abuse is 5 times that of the general population, and opioid
nonmedical users are more likely to be covered by Medicaid
than by any other insurance program.7,8 In 2009, the U.S.
Government Accountability Office reported that NC Medicaid
beneficiaries and providers had one of the highest rates of
potentially fraudulent and abusive purchases of CS of any state
in the nation.9
Efforts to prevent unintentional poisoning are reliant on
the effective monitoring of prescription CS. The NC Division
of Medical Assistance (DMA) implemented the NC Medicaid
Recipient Management Lock-In Program (NC MLIP) to address
issues of CS misuse and abuse in the NC Medicaid population.
Vol. 20, No. 11
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The goal of the program is to decrease the nonmedical use of
opiates, benzodiazepines, and some anxiolytics by Medicaid
recipients and to prevent recipient overutilization of Medicaid
benefits.10 The program operates by identifying NC Medicaid
recipients with 1 of the following criteria: (a) > 6 prescription claims for an opioid medication in 2 consecutive months;
(b) > 6 prescription claims for a benzodiazepine or controlled
anxiolytic medications in 2 consecutive months; or (c) > 3 prescribers of these same CS in 2 consecutive months.
Providers, NC Medicaid administrators, and administrators at Community Care of North Carolina (CCNC), the state’s
managed care organization for Medicaid enrollees, can also
nominate patients whom they think should be included in
the program based on a subjective assessment of their risk
behavior, although in practice, this option is rarely used.
Once identified, Medicaid administrators lock patients into
1 prescriber and 1 pharmacy so that the recipient can only
receive Medicaid-covered CS prescriptions from the providers
to whom they are restricted.10 The patients will be locked in
for a 1-year time period after which they will be removed from
the program if they no longer meet criteria.10 There are 2,000
to 3,000 patients enrolled at any given time. The MLIP has
already demonstrated effectiveness in an analysis conducted by
the DMA in 2012, which showed that the program saved $5.2
million over the first year and reduced the number of pain pills
and anti-anxiety medications prescribed to MLIP patients by
2.3 million in only 3 months.11
The beliefs and attitudes of providers carrying out health
interventions can either hinder or improve program effectiveness.12-16 Pharmacists, as the final intermediary between
patient and prescription, are in a position to promote the successful operation of the program and identify opportunities for
its improvement, provided they understand and support the
process and implementation. The purpose of this study was to
assess pharmacists’ attitudes towards the MLIP and their experiences implementing the program by conducting in-depth
structured interviews with NC pharmacists serving locked-in
Medicaid patients.
■■  Methods
Respondents and Setting
The sampling frame included all licensed pharmacists in NC.
The NC Board of Pharmacy sent a solicitation e-mail to all
licensed pharmacists, which included basic study information
and a link to an initial screening survey. This survey was used
to determine if respondents were practicing pharmacists with
locked-in patients and to gauge their willingness to participate
in phone interviews. From this screening, a list of 97 willing respondents was generated and prioritized according to
the highest number of MLIP patients served. Two project staff
members contacted and interviewed pharmacists from the prioritized list until the data were saturated to the point where no
www.amcp.org
Vol. 20, No. 11
new information or themes emerged. In-depth phone interviews
were conducted until thematic saturation was reached at a total
of 12 pharmacists. Respondents practiced in a range of settings,
including urban and rural pharmacies, independent and chain
pharmacies, and clinic and hospital outpatient pharmacies.
Structured Interviews
Structured phone interviews were conducted using an
Institutional Review Board (IRB)-approved topic guide (see
Appendix, available in online article). Interview questions were
intended to prompt pharmacists to report their experiences as
the sole dispenser to a locked-in patient, as well as their experiences with the program, with particular regard to its impact
on patient outcomes and access to health care. The interviews
lasted 15 to 45 minutes, depending on the length of the respondent’s answers and subsequent discussion. The interviews were
audio-recorded, with each respondent’s permission, to aid in
subsequent data analysis.
Data Management and Analysis
Data were drawn from detailed notes that were taken by the
interviewers; accuracy was confirmed using the audio recordings. Following thematic content analysis,17 each line or group
of lines was coded with an identifier. Two members of the
research team coded the first interview separately and created
identifiers as they appeared within the data. The identifiers and
codes were discussed and combined into 1 uniform codebook
with the assistance of a third senior researcher. The remaining
interviews were analyzed using the uniform codebook. If new
codes arose from subsequent interviews, they were discussed
and added to the codebook. The coded text was divided into
themes as they emerged within the analysis, with quotes and
sections from interviews selected to describe and exemplify
each theme. The themes were used to examine pharmacists’
perceptions of 3 domains: (1) program organization and implementation, (2) perceived effectiveness of the program, and
(3) the program’s acceptability to pharmacists and their patients.
This study was reviewed and approved by the University of
North Carolina at Chapel Hill IRB.
■■  Results
The screening survey yielded 97 respondents who met all selection criteria and were willing to participate in an interview.
Interviews were arranged sequentially, prioritizing respondents who served the highest number of lock-in patients.
Thematic saturation was reached with 12 in-depth structured
interviews. References to CS indicate only those targeted by the
NC MLIP: opiates, benzodiazepines, and certain anxiolytics.
Table 1 lists the 12 themes identified within the data, organizing each theme into 1 of the 3 domains.
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TABLE 1
Content Analysis, Domains, and Themes
Themes
Domains
Organization/
Implementation
Notification
Perceived
Effectiveness
Overall opinion
Selection process
Communication
Program impact
Impact on dispensing
patterns
Proposed changes
Understanding of
program intent
Problem remediation
Acceptability
Difficulty of
implementation
Impact on job roles
Unintended effects on
patients
Domain 1: Organization and Implementation
Notification. With the exception of 1 respondent, pharmacists
received or were aware of letters sent from Medicaid informing
them that their pharmacies had become the sole dispenser for
a patient enrolled in the MLIP. Pharmacists can refuse a patient
who has been locked-in, but none of the respondents had
used this option. However, many respondents also depended
on the claims process to alert them to locked-in patients. One
pharmacist commented: “I have seen letters that come in the mail
sometimes informing us of a patient that may or may not be locked
in, but we generally rely on the [claims] transmission process of the
Medicaid system.”
Respondents reported very few problems with the notification process and transition into the program. A few pharmacists reported patients claiming to be unaware of their lock-in
status, but the pharmacists were skeptical about these patients’
reports that they had not been formally notified.
Selection Process. The majority of respondents stated that
they did not believe any of their patients were mistakenly
enrolled into the program. On the contrary, many believed
that too few of their patients were locked in. One respondent
stated: “I’ve seen a lot that have been mistakenly not enrolled in the
program. . . . There’s a lot of people falling through the cracks.”
Only 3 other respondents described patients as unfit for the
program. One pharmacist stated that up to 75% of his lockedin patients did not belong in the MLIP and went further, saying: “I don’t know that we’ve ever had a scenario that I felt like the
patient had been appropriately locked in with a prescriber.”
Communication with Administrators and Managers. The
main form of communication between pharmacists and program officials was Medicaid phone channels. If pharmacists
needed to contact Medicaid administrators with questions
about the program or problems with patients or claims, they
had to call the Medicaid phone line and wait on hold to be
redirected to an administrator in charge of the program or leave
a message. Respondents expressed frustration about Medicaid’s
limited “bankers’ hours.” Many pharmacists reported waiting on hold for long periods of time or waiting for hours for a
return phone call.
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November 2014
Communications between the DMA and each of the
pharmacies did not flow through corresponding channels.
Respondents described different protocols within pharmacies
for handling messages and information from the state Medicaid
program. One pharmacist reported that only 1 person within
the pharmacy handled communications from Medicaid. A
respondent who had previously served as a pharmacy manager
mentioned that managers may not relay information about the
program to their technicians. The level of support for the program from pharmacy management varied considerably.
Understanding of Program Intent. Respondents received
mixed or incomplete messages about the goals and intentions
of the MLIP. Ten of 12 respondents stated the explicit desired
outcome that the program would ensure patients received their
CS prescriptions from 1 prescriber and 1 pharmacist. Other
perceived goals of the program included reduction of doctor
and pharmacy shopping, minimization of Medicaid costs, prevention of prescription drug misuse and abuse, and prevention
of adverse patient outcomes. However, 1 pharmacist expressed
frustration that “They don’t really tell us what the goal is. I don’t
know. I would guess cost, but it could be anything.”
Half of respondents were confused about the boundaries
between the MLIP and the NC Medicaid prescription capitation
program. The capitation program required patients who filled
11 prescriptions of any drug category within a certain time
period to opt-in to a locked-in single pharmacy if they needed
another prescription that same month. The capitation program
ended several months prior to our interviews. Respondents
called it the “over 11” or “opt-in” program, reporting that many
pharmacists use the terminology of the 2 programs interchangeably. Some respondents explained that their pharmacy
computers used the same indicators for MLIP patients as were
used for the previous program. A couple of respondents were
unaware that the MLIP was an entirely separate program.
Problem Remediation. Respondents experienced difficulty
when resolving problems they encountered with the program.
A few pharmacists mentioned that drug shortages in the past
year required patients to switch to a nonlock-in pharmacy
that stocked their prescriptions. Respondents labeled this a
“temporary unlock” or “secondary lock.” Two pharmacists
discussed their patients’ need for exceptions in the case of holidays, closings, or travel and their wish that they could provide
these patients with 3-day supplies in such situations. When
encountering issues with prescribers, a number of pharmacists
did not know of any readily available solutions, especially for
incidents occurring on weekends. In 1 case, a respondent’s
patient was discharged from an emergency department (ED)
and presented a CS prescription written by the ED doctor: “We
can’t [fill it]. There’s no work around. What the patient is required to
do is take the provider’s prescription and contact that provider that’s
been approved to rewrite the order. To me, if it’s a surgical patient,
that could be something that causes them to come back to the hospital. Especially if it’s on the weekend and the provider is not open ‘til
Monday. Really a lot of these hiccups come on the weekend.”
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Domain 2: Perceived Effectiveness
Overall Opinion. Ten of 12 respondents reported positive
experiences with the program but also volunteered flaws or
areas for improvement. One pharmacist viewed the program
as a temporary fix and said that many patients would “go back
to their old lifestyle” after only a year in the program. Those
who expressed the most positive opinions recognized the
extent of the prescription drug epidemic and appreciated the
program’s efforts to mitigate it. Of the pharmacists interviewed,
only 2 had explicitly negative evaluations of the program, with
1 calling it “one of the worst programs I’ve ever dealt with.”
These pharmacists believed that the MLIP was either ineffective or was actively harming their patients by restricting access
to care. The discussion under the next section, “Domain 3:
Acceptability” further addresses this effect.
come back out on a recurring basis at least a couple times a year just
to remind pharmacy providers that there are options for patients.”
One pharmacist proposed changes to address cash payments: “[I would like it] if they had a line that would allow health
care practitioners out there to submit things for potential abuse
situations or diversion situations. When a man comes in locked
into 1 store and his prescription is early and he offers to pay several
hundred dollars in cash to get the prescription early—that should be
reported somewhere.”
Other recommended changes included expanding the program to enroll more patients; managing patients using chronic
pain specialists; increasing pharmacist awareness and education about the program; locking patients into 1 clinic instead
of 1 unique provider; and improving patients’ transition out of
the program.
Program Impact. With regard to specific program outcomes,
4 pharmacists believed that it reduced doctor shopping; 4
believed that it reduced misuse, abuse, and diversion of drugs;
and 3 believed that it reduced Medicaid costs. Two other pharmacists stated that the program somewhat affected these outcomes but was not effective enough. Four respondents explicitly stated that it did not affect any abuse or misuse outcomes.
A major issue brought up by 8 of the 12 respondents was that
patients may circumvent the program by paying cash for their
prescriptions. In a few instances, cash payments were used for
prescriptions that respondents viewed as medically necessary
even though they had been rejected by Medicaid. However,
respondents primarily saw cash payments as a way for misusers and abusers to continue receiving excess prescriptions. One
pharmacist stated: “I have a feeling if people are doctor hopping
and shopping, they’ll just learn to go to a store they’ve never been to
and pay cash. I don’t know how effective [the program] is at keeping
controlled substances off the street that shouldn’t be there.”
Domain 3: Acceptability
Difficulty in Implemention. The MLIP program was not difficult for respondents to implement. Five of the 12 respondents
specifically mentioned that their roles were passive or did not
require effort outside of their normal duties.
Impact on Dispensing Patterns. Seven of the 12 pharmacists
stated that the program had not altered how they monitored
enrolled patients. The other 5 respondents treated lock-in
patients differently, refusing to fill certain prescriptions, checking the state’s Controlled Substance Reporting System before all
fills, and verifying prescriptions with physicians. Four respondents believed that the program had increased their monitoring
of all patients. One respondent particularly stressed this effect:
“I think the idea that knowing that somebody else is watching and
that there’s a certain level of accountability changes it for everyone. If
it’s successful it should change your thought process across the board.”
Proposed Changes. Respondents proposed a wide array of
changes or improvements to the program. They most frequently requested improved communication with Medicaid.
One respondent proposed changes to the Medicaid newsletter:
“I would like to see the [lock-in policies] in the Medicaid newsletter
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Impact on Job Roles. Respondents honed in on 3 different
roles they take on as pharmacists: as a caregiver, a gatekeeper,
and as a business person. Pharmacists must maintain a balance
between providing care to their patients and controlling the
flow of prescription drugs within a community. With regard
to controlling drug flow, 1 pharmacist described this tradeoff:
“There’s a wide spectrum. There’s some who feel like they need to be
the police. I don’t think that’s our role. I think we need to make the
prescribers aware and be aware ourselves. You have to have some
dialogue with the patients. If you just turn everyone away they’ll go
to multiple prescribers and pharmacies. They will learn how to work
the system.” Several pharmacists reported that the program had
made their roles easier. One pharmacist expressed gratitude:
“Really it makes my life a lot easier being the sole provider. I mean
really it does because that takes the question out. So if they’re only
seeing 1 doctor and 1 drug store then there’s that much more control.”
Aiding their role as caregivers, the program would alert respondents to when a patient needed counseling: “It really affirms in
a busy pharmacy world, where you might be filling hundreds of prescriptions, that you need to stop, take a pregnant pause for a minute,
and have a conversation.”
Other pharmacists mentioned that acting as the sole dispenser allows them to improve adherence and track disease
states. Many respondents saw the program as supportive of
their business, insofar as the patients locked into their pharmacies increase their patient volume and sales. When asked about
rejecting lock-in patients, 1 pharmacist replied: “I’ve never tried
not to be locked in [i.e., involved in the lock-in program] because
of course we’re trying to increase prescription sales.” For a few
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JMCP
respondents, focusing on the program’s potential to increase
volume was an “economic reality” faced by independent pharmacies, which often struggle to make a profit against large
chains.
Unintended Effects on Patients. Pharmacists were asked how
the program affected their patients’ access to care. Six respondents reported that the program could be a barrier to receiving pain treatment, mental health care, or general health care.
While 6 respondents stated that the program had no effect on
access, 3 of those 6 also described instances where patients
had difficulty receiving medications or care at other points in
the interview. Some of these problems arose from the type of
physician a patient was locked in with: “I think what happened
was a lot of patients were identified for the lock-in program because
they had received short supplies of opiate pain relievers—either they
were postsurgical, or they had an injury, an accident, or something.
And, then they were locked in with this short-term—sometimes it
was even an ED physician—and then they were locked out of their
psychiatrists. And, then it would go the opposite way, too . . . people
who had gotten locked in with their psychiatrists, but then their pain
doctor was locked out and couldn’t write their medicines.”
Other access issues stemmed from the availability of physicians: “They make an appointment for their next visit and the prescriber they’re locked in individually is not here. They see whoever is
available to be seen next. They’re not doctor shopping. They’re just
seeing whoever is there.”
Availability of Medicaid also hindered access: “Any of those
types of programs where stuff needs approval or assistance at the
Medicaid level, that’s a potential barrier in all of those cases. Not to
say those programs aren’t all good, but when you don’t have 24-hour,
7-days-a-week coverage for those types of things, that is a barrier for
some patients.”
One particularly frustrated respondent summed up this
issue as an inherent design flaw: “The program’s not designed to
keep the bad people away and the keep the good people getting what
they need. It’s just designed to keep the bad people away. It’s hard
to do both, but it can be done. . . . The way it was done you ended up
with a lot of people who probably would have benefited from being
locked into 1 pain provider but were adversely affected on other parts
of their treatment.”
The qualitative data ascertained through in-depth interviews
with NC MLIP pharmacists revealed a wide range of experiences with, and opinions about, the lock-in program. While
results may not be generalizable to all program pharmacists,
its findings can provide insight into the program’s strengths
and weaknesses.
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November 2014
Organization and Implementation
While respondents valued the ease of enrollment, many
believed that communication within the program was problematic. One pharmacist stated, “I think it would be a better
program if all pharmacists knew exactly what we were trying to do.”
The program was initially announced to pharmacies through
the DMA bulletin, while several subsequent bulletins issued
reminders.10,18,19 CCNC clinical pharmacists have also done
education and training with regards to the MLIP, especially
with community pharmacies throughout the state. The work
culture within some respondents’ pharmacies may have interfered with their ability to receive these messages. If individual
staff members handle Medicaid correspondence without communicating with their coworkers, or if pharmacy managers do
not discuss the program’s goals and features with their staff,
widespread understanding of the program cannot be achieved.
The issues that respondents faced with problem remediation
may be a result of their confusion about the program’s goals
and policies. Only 1 DMA bulletin mentioned how to handle
lock-in exceptions. In an emergency situation, a 4-day supply of a CS may be provided to patients by a pharmacy or
prescriber to which they are not locked in.10 The process for
switching lock-in providers or handling other complications is
not clearly available online nor was it described in any DMA
bulletins. Only 1 pharmacist mentioned this policy; others
merely expressed confusion. Respondents’ dissatisfaction with
Medicaid phone channels underscores program shortfalls in
communications.
Perceived Effectiveness
The interviewed pharmacists gave mixed responses when
asked whether the program had affected their CS dispensing
practices. Some pharmacists reported increased monitoring
of locked-in patients, but the majority stated that the program
had not affected how they dispensed CS.
While interviews revealed both positive and negative
aspects of the program, the recurring mention of cash payments for CS prescriptions outside of the program indicates
a substantial weakness. While the scale of this problem is
unknown, cash payments for CS prescriptions would undoubtedly counteract the programs’ impact, outside of reducing
Medicaid expenditures. Also, there has been some question
as to whether all pharmacists are upholding the integrity of
the program, with some possibly allowing patients to fill a CS
with cash after receiving a rejected prescription claim from
Medicaid. Fortunately, NC’s prescription drug monitoring
program, the Controlled Substances Reporting System, will
soon be upgraded to include the method of payment used for
all CS prescriptions in the system records. Awareness by pharmacists that a patient is enrolled in Medicaid should make it
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increasingly difficult for Medicaid patients to circumvent the
MLIP by paying cash.
Several respondents stated that a 12-month lock-in does
not allow sufficient time to establish long-term positive effects
on recipient behavior. This time period does allow for CCNC
clinical pharmacists and care managers to offer education to
the patient, connect the patient with any needed substance
abuse services, and provide more intensive care management
services.20,21 The MLIP also lacks a transition period at the end
of recipients’ lock-in. Patients are re-enrolled if they still meet
lock-in criteria. However, there is no transitory process or
provision of resources for those who no longer qualify, even if
their pharmacists believe they are still misusing or abusing CS.
Program Acceptability
Overall, program acceptability was high among study respondents, in large part because the automated Medicaid claims
system minimized pharmacists’ efforts in actively engaging the
MLIP. The program also allowed pharmacists to track patient
utilization behavior, prompted them to provide targeted patient
counseling, and ensured continued patronage. However, the
biggest concern to arise from these interviews was that the
program may impede patients’ access to care. For example,
patients who are locked in with a single provider, as opposed to
any provider within a given clinic, may be denied appropriate
treatment if their prescribers are unavailable.
The program may be particularly harmful to patients with
multiple diseases, such as chronic pain and psychiatric comorbidities, since these patients are more likely to see multiple
specialists. Particular problems may arise from the program’s
definition of CS. The definition applies to opioid analgesics,
which are used to treat acute and chronic pain, and to benzodiazepines and anxiolytics, which are most commonly used
to treat anxiety disorders. While there is significant correlation between chronic pain and mental health issues, opioid
analgesics are usually prescribed by different specialists than
benzodiazepines or anxiolytics.22 While allowing patients to
receive CS prescriptions from multiple providers may present
a health risk,23 locking them into 1 provider may not be the
most advantageous method to address complex health needs.
For this reason, the DMA may be requested to allow for up to
2 providers for a single patient in situations in which 2 prescribers are being utilized (i.e., benzodiazepine prescribed by
a psychiatrist and opioid medication prescribed by a pain specialist). It appears however, that pharmacists were not widely
aware of this policy.
The MLIP has only 1 set of enrollment criteria for all opiate, benzodiazepines, and controlled anxiolytic users, treating
them as a single, homogenous group. This strategy could lead
to inappropriate patient enrollment, targeting patients with
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Vol. 20, No. 11
broadly specified criteria. Multiple studies have illustrated that
prescription drug users comprise diverse subpopulations21-23
that include high school students; the elderly; street drug users;
chronic pain, cancer, and acute pain patients; and patients
receiving end-of-life care.24-26 The reason patients misuse CS
also vary, ranging from poor patient education and undertreatment of pain to dependence and addiction, diversion, and
desire for euphoria.24,26 The MLIP’s enrollment criteria may not
be effectively capturing these diverse subpopulations, although
the ultimate goal for each group of patients would be the same:
to reduce use and misuse of prescription medications, reduce
unintentional overdoses, and promote the safe and effective
use of these dangerous medications that can be addicting to
all patients.
Limitations
The foundational strength of this study was the use of structured interviews, allowing the pharmacists surveyed to express
a wide range of thoughts and opinions. The interview guide
ensured that researchers covered key topics with pharmacists,
while giving them the opportunity to freely express their
opinions and concerns and raise new and unforeseen topics
for discussion. The primary study limitation was the small
sample size, leading to a potential limited generalizability of
study results. Additionally, although we sent our request to all
members of the NC Board of Pharmacy, the sample consisted
primarily of outpatient community pharmacists.
This study revealed strengths and shortcomings of the NC
MLIP. The program holds promise for success as it utilizes
pharmacists’ medication gate-keeping role, while minimizing
the effort required from pharmacists for successful implementation. The program can also improve pharmacists’ awareness
of prescription drug misuse and abuse among their patients
and improve their ability to prevent and combat it. However,
more outreach is needed by the DMA in order to educate pharmacists about the MLIP’s policies and underlying purpose.
Improved communication between pharmacies and the DMA
is also necessary for program success. This is particularly true
when issues with locked-in patients arise (e.g., on weekends).
Resolving these issues may require new or improved methods
of communication. Examples of methods that may be beneficial include extended access to Medicaid phone channels; a
direct phone line for problems pharmacies face with locked-in
patients; or set protocols for them to follow when common
issues arise. The flexibility of the program should also be
enhanced, allowing patients to see multiple prescribers within
the same clinic. While the NC MLIP addresses an urgent health
issue within the NC Medicaid population, further refinements
have the potential to substantially enhance its impact.
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Authors
S. ROSE WERTH, BA, is Program Specialist, Partnership for a DrugFree NC, Durham, North Carolina. NIDHI SACHDEVA, MPH,
CHES, is Project Manager; MARIANA GARRETTSON, MPH, is
Research Scientist; and CHRIS RINGWALT, PhD, is Senior Scientist,
Injury Prevention Research Center, University of North Carolina at
Chapel Hill. ANDREW W. ROBERTS, PharmD, is PhD Candidate,
Division of Pharmaceutical Outcomes and Policy, UNC Eshelman
School of Pharmacy, and Postdoctoral Fellow, Cecil G. Sheps Center
for Health Services Research, University of North Carolina at
Chapel Hill; LESLIE A. MOSS, MHA, CHES, is Data Coordinator,
Division of Medical Assistance, North Carolina Department of
Health and Human Services, Raleigh; THEODORE PIKOULAS,
PharmD, BCPP, is Associate Director of Behavioral Health Pharmacy
Programs, Community Care of North Carolina, Raleigh; and
ASHELEY COCKRELL SKINNER, PhD, is Associate Professor of
Pediatrics, University of North Carolina at Chapel Hill.
AUTHOR CORRESPONDENCE: Asheley Cockrell Skinner, PhD,
University of North Carolina at Chapel Hill School of Medicine,
231 MacNider, 229B, CB 7225, Chapel Hill, NC 27599.
Tel.: 919.843.9941; E-mail: [email protected].
DISCLOSURES
This research was supported by Cooperative Agreement CDC U01 CE00216001, Building Interdisciplinary Research Careers in Women’s Health (BIRWCH)
Training Grant NIH K12 HD001441 (Skinner), CTSA Grant UL1TR000083,
the UNC Injury Prevention Research Center, and a National Research Service
Award Post-Doctoral Traineeship from the Agency for Health Care Research
and Quality sponsored by the Cecil G. Sheps Center for Health Services
Research, 5 T32 HS000032 (Roberts). The authors have no conflicts of interest to disclose.
Study concept and design was primarily contributed by Garrettson,
Ringwalt, and Skinner, with assistance from Sachdeva and Werth. Data collection was primarily the responsibility of Werth and Sachdeva, with assistance from Garrettson and Ringwalt, and Pikoulas and Werth interpreted the
data, with assistance from the rest of the authors. The manuscript was written
by Werth, Roberts, and Ringwalt, assisted by the rest of the authors, and revision was carried out by Moss and Skinner, assisted by the rest of the authors.
ACKNOWLEDGMENTS
The authors would like to thank the North Carolina Board of Pharmacy for
its assistance in recruitment.
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Appendix
Interview Guide
Provider Interview Guide
Hello, my name is _____________. I am with the Injury Prevention Research Center at UNC Chapel Hill. I am calling to follow up with the interview about the
Medicaid Lock-In Program that we scheduled for today. Is now still a good time for a 20- to 30-minute interview? (If not, reschedule.)
Great. Did you receive the fact sheet in the confirmation e-mail? I would like to review that briefly with you before we start. (Go over fact sheet.)
What questions, if any, do you have about the study or the fact sheet?
Please keep in mind that this interview is completely voluntary; there are no right or wrong answers—so please answer as honestly and best as you can; your name will not
be linked to your responses; and you have the option to stop the interview at any time.
CONFIRM CONSENT: Are you still interested in participating? Would it be all right to begin the interview now?
QUESTIONS
My first questions are about the processes involved in becoming a NC MLIP patient’s sole provider:
1.How do you get notified that you have been selected as a sole provider for an enrolled patient?
2.Can you decide whether to accept or not? If so,
a. How do you decide whether to accept a patient or not?
b.How do you communicate with Medicaid about your decision?
c. If you have declined to accept a patient, how did the Medicaid administrator respond?
3.Do you believe that any of the patients you serve have been mistakenly, or inappropriately, enrolled?
a. If so, what was the nature of the mistake?
4.What challenges, if any, have you had with any aspect of this process?
Now, I have several questions about your experience of treating patients who are enrolled in the NC MLIP:
5.We understand from the online survey that you have been the provider for XX patients in the MLIP, and you identified your overall experience with the
program as XX on a scale of 1 to 6. Is this still accurate?
a.(For people who have more than 1 patient locked in) What were the most common patient characteristics or circumstances that led to their initial
enrollment in the program?
6.Please describe your characterization of your experience as positive and/or negative. Could you tell me more about what makes you characterize your
experience that way?
7.If you were going to give advice to another physician/pharmacist who is accepting the role of sole provider, what would you say?
8.If you could make 1 change to the program that would make it easier for providers like you, what would that be?
My last questions are about your perceptions of the impact of this program on your patients and on you:
9.How does this program affect a patient’s access to pain care? (Probe: Does the program improve or impair patients’ effective pain management?)
10.How does this program affect a patient’s access to mental health care? (Probe: Does the program improve or impair patients’ mental illness management?)
11.How does this program affect a patient’s access to all other medical care, if at all?
12.How does this program affect a patient’s health?
13.How has this program changed the way that you prescribe/dispense controlled substances to patients who are enrolled in the program?
14.How has this program changed the way that you prescribe/dispense controlled substances to other patients?
15.What else about the implementation or impact of this program would you like to share?
1128a Journal of Managed Care & Specialty Pharmacy
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November 2014
Vol. 20, No. 11
www.amcp.org
RESEARCH
Is There an Association Between the High-Risk Medication Star
Ratings and Member Experience CMS Star Ratings Measures?
ABSTRACT
BACKGROUND: Methods to achieve high star ratings for the High-Risk
Medication (HRM) measure are thought to result in unintended consequences and to compromise several member experience measures that
ultimately put at risk the plan sponsor’s Medicare Part D Centers for
Medicare & Medicaid (CMS) star rating.
•Achieving high star ratings is associated with increased likelihood of new beneficiary enrollment and enrollment from existing
Medicare beneficiaries switching from lower-rated plans. CMS
quality bonus payments also provide increased revenue opportunities for plans with high star ratings.
OBJECTIVE: To determine if HRM scores are associated with relevant member experience measure scores.
METHODS: This is a cross-sectional analysis utilizing CMS 2013 and 2014
plan star ratings reports (2011 and 2012 benefit year data) for Medicare
Advantage prescription drug (MA-PD) plans and prescription drug plans
(PDPs). Medicare contracts with complete data for all measures of interest
in 2013 and 2014 star ratings reports were included (N = 443). Bivariate
linear regressions were performed for each of 2 independent variables:
(1) 2014 HRM score and (2) 2013 to 2014 change in HRM score. Dependent
variables were the 2014 scores for “Getting Needed Prescription Drugs,”
“Complaints about Drug Plan,” “Rating of Drug Plan,” and “Members
Choosing to Leave the Plan.”
RESULTS: The bivariate linear regressions demonstrated weak positive
associations between the 2014 HRM score and each of the 2014 member
experience measures that explained 0.5% to 4% (R 2 ) of variance of these
measures. The bivariate regressions for the 2013 to 2014 change in the
HRM score and 2014 member experience measures of interest demonstrated associations accounting for 1% to 8% of variance (R 2 ). The greatest
associations were observed between each independent variable and the
2014 “Getting Needed Prescription Drugs” score with correlation coefficients of 0.21 and 0.29.
CONCLUSIONS: HRM star ratings and change in HRM star ratings are
weakly correlated with member experience measures in concurrent measurement periods. Plan sponsors may be more aggressive in HRM utilization management, since it is unlikely to negatively impact CMS summary
star ratings.
•The Beers Criteria for potentially inappropriate medications
to be used in the elderly and, more recently, the updated
2012 American Geriatric Society (AGS) Beers Criteria, have
been adapted into the quality performance measure High-Risk
Medications (HRM) used in Centers for Medicare & Medicaid
(CMS) star ratings.
•CMS has placed an emphasis on the member experience with
several star rating measures related to member satisfaction that
have a greater total weight contribution to the overall summary
star rating than the individual HRM star rating measure.
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Vol. 20, No. 11
•The results of this study indicate that the HRM star rating measure score and its improvement, prior to the implementation of
the updated 2012 AGS Beers Criteria, have minimal influence on
the member experience star ratings measure scores.
•This information may be used by plan sponsors in making
informed decisions regarding HRM utilization management.
T
he late Mark H. Beers, MD, a geriatrician, is widely
known for his contribution to geriatric care through
the development of a list of potentially inappropriate
medications (PIMs) used by the elderly. Published in 1991, the
“Beers Criteria” of PIMs was developed to reduce inappropriate
medication use in nursing home patients.1 The 1997 update
expanded the criteria for application to all ambulatory older
persons.2 The last update under Dr. Beer’s auspices was published in 2003 and stood as the definitive criteria for nearly a
decade.3 The American Geriatric Society (AGS) sponsored the
2012 Beers Criteria update, which included many improvements, such as following the Institute of Medicine standard of
evidence and transparency for creating guidelines.4,5
The use of the Beers Criteria expands beyond individual
practitioners or institutions of care. The National Committee
for Quality Assurance first included a performance measure based on the Beers Criteria known as “Use of HighRisk Medications in the Elderly,” in the 2006 Healthcare
Effectiveness Data and Information Set (HEDIS), a tool for
health plans to measure care and service performance.6 The
High-Risk Medication (HRM) measure was subsequently
adapted and endorsed by the Pharmacy Quality Alliance
(PQA), endorsed by the National Quality Forum, and adopted
by the Centers for Medicare & Medicaid Services (CMS).
Consistent with HEDIS and PQA, CMS included the revised
2012 AGS Beers Criteria into the HRM measure specifications
for the 2015 star ratings for measuring HRM use in the 2013
benefit year. CMS includes the HRM measure in the Part D star
November 2014
JMCP
TABLE 1
Measure Label
High-risk medication
Getting needed
prescription drugs
Members’ rating of
drug plan
CMS Star Ratings Measures Included in Analysis
Measure Description
Percentage of plan members aged 65 years or older
who received 2 or more prescription fills for the
same drug with a high risk of serious side effects in
the elderly
Case-mix adjusted percentage of the best possible
score the plan earned on how easy it is for members
to get the prescription drugs they need using the
plan
Case-mix adjusted percentage of the best possible
score the plan earned from members who rated the
prescription drug plan
Complaints about the
drug plan
Data Source
Prescription drug event data files submitted by drug
plans
CAHPS survey questions:
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November 2014
1.5
In the last 6 months, how often was it easy to use your
health plan to get the medicines your doctor prescribed?
health plan to fill a prescription at a local pharmacy?
health plan to fill prescriptions by mail?
CAHPS survey question:
1.5
Using any number from 0 to 10, where 0 is the worst
prescription drug plan possible and 10 is the best prescription drug plan possible, what number would you
use to rate your prescription drug plan?
Complaints logged into Complaint Tracking Module
1.5
Total complaints received about the drug plan per
1,000 members adjusted by average contract enrollment
Members choosing to The percentage of plan members who chose to leave Disenrollment reason codes in Medicare’s enrollment
leave the plan
the plan during the benefit year (does not include
system
members who moved out of the service area)
CAHPS = Consumer Assessment of Healthcare Providers and Systems; CMS = Centers for Medicare & Medicaid Services.
ratings for Medicare Advantage prescription drug (MA-PD)
plans and stand-alone prescription drug plans (PDPs).
Star ratings summarize the performance of contracted
health and drug plans on various indicators of clinical quality, care access, customer service, and member satisfaction.
Contracts receive 1 to 5 stars, with 5 stars representing the
highest quality for each individual performance measure, as
well as for aggregate groupings of like measures referred to as
domains and an overall summary rating.
Star ratings are important to contracting plans for several
reasons. The Affordable Care Act of 2010 requires quality
bonus payments and rebates to be tied to star ratings.7 Also,
low-performing contracts risk exclusion from Medicare, since
past performance is a consideration in the approval of applications.8 Lastly, CMS star ratings are posted publicly to help
beneficiaries choose plans. An association has been recently
established between Medicare Advantage plan star ratings and
enrollment numbers.9 Reid et al. (2013) found that new enrollees were more likely to enroll in plans with higher star ratings,
and continuing enrollees were more likely to switch to plans
with higher star ratings.9
The HRM star ratings measure is 1 of 5 triple-weighted
medication-related patient safety measures that comprise
19% of the Part C rating or 54% of the Part D rating. CMS
has also placed an emphasis on the member experience with
several star rating measures related to member satisfaction.
The management of HRMs within the prescription benefit may
therefore be problematic for Medicare contracts. Low-touch
Weighting Value
3
1.5
interventions, such as educational mailings intended to influence prescribing behavior, are not likely to have an immediate
or dramatic effect. Requiring prior authorization approval for
HRMs or formulary exclusion of HRMs are likely to cause significant member disruption, negatively impacting the member
experience. HRMs are often older, generic medications that
members have been able to obtain easily prior to turning 65
and enrolling in a Medicare plan. Safe, low-cost alternatives are
not always available. Plans with poor HRM star ratings may be
hesitant to implement management strategies with the assumption that this may have an unintended negative impact on
member satisfaction scores, which have a greater total weight
contribution to the overall summary star rating. The purpose
of our analysis was to determine the relationship of the HRM
score and the change in HRM score to relevant member experience measures.
■■  Methods
Study Design
This is a cross-sectional analysis utilizing the 2 most recently
released CMS star ratings reports (i.e., the 2013 and 2014 star
ratings reports that use 2011 and 2012 benefit years, respectively). These reports can be found on the CMS website.10
Data
Each October, prior to open enrollment for the upcoming benefit year, the most recent star ratings results are available on the
CMS website. The report is named for the coming benefit year.
Vol. 20, No. 11
www.amcp.org
FIGURE 1
Medicare Contracts Included in 2013-2014 Star Ratings Reports and Included in Analysis
Medicare contracts included in
2013 star ratings report
(n = 650)
Medicare contracts included in both 2013
and 2014 star ratings reports
(n = 603)
Medicare contracts included in
2014 star ratings report
(n = 749)
Medicare contracts given overall Part D
rating in 2013 report
(n = 557)
rating in 2013 and 2014 reports
(n = 549)
rating in 2014 report
(n = 550)
Medicare contracts with complete
information for all 5 studied
measuresa in 2013 reports
(n = 490)
measuresa in 2013 and 2014 reports
(n = 443)
This is the sample for the primary
analysis
measuresa in 2014 reports
(n = 450)
Medicare contracts with a 2-star or
greater increase in HRM measure from
2013 to 2014 reports
(n = 81)
This is sample for the subanalysis
aContracts were excluded if “No data available,” “Not enough data available,” “Plan too new to be measured,” or “CMS identified issues with this plan’s data” was indicated for any of the following measures: High-Risk Medications, Complaints about Plan, Getting Needed Prescriptions, Rating of Drug Plan, and Members Leaving Plan.
HRM = high-risk medication.
For example, the star ratings report released in October 2013
is titled “2014 Medicare Part D Star Ratings Data,” since the
information is intended to help beneficiaries select plans for the
2014 benefit year. The 2014 star ratings report released in 2013
reflects the performance of each Medicare contract during
the 2012 benefit year. The majority of the data was collected
throughout the 2012 benefit year, although some measures,
such as those obtained from the Consumer Assessment of
Healthcare Providers and Systems (CAHPS) survey data, were
obtained in early 2013.
Each star rating measure has the contract’s calculated score
presented, known as the base level. Thresholds are established
to assign base-level measure scores a star rating based on a
5-star scale. These scores are rolled up to a domain star rating,
and the domain star ratings are rolled up to a Part D summary
rating and Part C summary rating (for MA-PD plans). In the
2014 star ratings for the 2012 benefit year, there were 15 Part
D star ratings measures and 36 Part C measures that roll up to
a total of 9 domains. The Part D score is the highest level for
a PDP. MA-PD plans receive an overall rating that summarizes
the 51 measures included in Part C and Part D measures.
The measures of interest for this analysis included the
Patient Safety measure HRM and all measures that were deter-
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Vol. 20, No. 11
mined to be potentially negatively impacted by formulary
restrictions. Four measures within the domains of “Member
Experience with the Drug Plan” and “Member Complaints,
Problems Getting Services, and Improvement in the Drug
Plan’s Performance” were included as follows: Getting Needed
Prescription Drugs; Members’ Rating of Drug Plan; Complaints
About the Drug Plan; and Members Choosing to Leave the Plan
(Table 1).
Inclusion Criteria
Medicare contracts with base-level detail and star ratings for
all measures of interest in both the 2013 and the 2014 star ratings reports were included. Contracts with “No data available,”
“Not enough data available,” “Plan too new to be measured,” or
“CMS identified issues with this plan’s data” in place of base
data or star ratings for the 5 measures of interest were excluded
(Figure 1).
High-Risk Medication Measure
(Explanatory or Independent Variable)
The HRM performance measure for 2013 and 2014 star ratings
(2011 and 2012 benefit year, respectively) is reported as the
percentage of members aged 65 years and older enrolled during
November 2014
JMCP
TABLE 2
2013 and 2014 CMS Star Ratings Base Measure Results for Measures of Interest
Measure Label
High-risk medication
Getting needed prescription drugs
Members’ rating of drug plan
Complaints about the drug plan a
Members choosing to leave the plan
Measure Detail
Data time frame
Base data range
Base mean (SD)
Data time frame
Base data range
Base mean (SD)
Data time frame
Base data range
Base mean (SD)
Data time frame
Base data range
Base mean (SD)
Data time frame
Base data range
Base mean (SD)
2013 CMS Star Ratings Report
January 1, 2011-December 31, 2011
2%-21%
8% (3%)
February 15, 2012-May 31, 2012
84%-97%
91% (2%)
February 15, 2012-May 31, 2012
77%-95%
85% (3%)
January 1, 2012-June 30, 2012
0-2.68
0.33 (0.28)
0%-48%
10% (7%)
2014 CMS Star Ratings Report
1%-18%
6% (3%)
February 15, 2013-May 31, 2013
85%-97%
91% (2%)
February 15, 2013-May 31, 2013
76%-96%
85% (3%)
January 1, 2013-June 30, 2013
0-3.44
0.28 (0.28)
0%-64%
11% (8%)
a Reported
as complaints per 1,000 members.
CMS = Centers for Medicare & Medicaid Services; SD = standard deviation.
the benefit year with at least 2 fills for a prescription medication that has a high risk for adverse events in the elderly. The
base score is calculated for members aged 65 years and older
from prescription drug event claims data files provided by drug
plans to CMS throughout the benefit year.
Patient Satisfaction Measures (Dependent Variables)
Getting Needed Prescription Drugs. Data for the Getting
Needed Prescription Drugs performance measure are acquired
through the CAHPS survey. Three questions from the CAHPS
survey are used to calculate the Getting Needed Prescription
Drugs base score:
• “In the last 6 months, how often was it easy to use your
health plan to get the medicines your doctor prescribed?”
health plan to fill a prescription at a local pharmacy?”
health plan to fill prescriptions by mail?”
The CAHPS survey is administered between February 15
through May 31 after the benefit year by CMS-approved vendors by mail and telephone to a random sampling of members.
The measure is reported as the percentage of the best possible
CAHPS survey score. It is a case-mix adjusted measure that
takes into account socioeconomic differences (such as age, education level, and dual eligibility) of enrollees across contracts.
Members’ Rating of Drug Plan. The Members’ Rating of Drug
Plan performance measure is also a case-mix adjusted percentage of the best possible CAHPS survey score. The measure is
based on the score of 1 question within the CAHPS survey:
“Using any number from 0 to 10, where 0 is the worst prescription drug plan possible and 10 is the best prescription drug
JMCP
November 2014
plan possible, what number would you use to rate your prescription drug plan?” The data are collected by CMS-appointed
vendors February 15 through May 31 after the benefit year.
Complaints about the Drug Plan. The Complaints about the
Drug Plan performance measure is reported as the number of
complaints received about the drug plan logged into the CMS
Complaint Tracking Module (CTM) per 1,000 members. The
CTM is a central repository for complaints received in either
central or regional offices or 1-800-MEDICARE. Historically,
CTM data from the first 6 months following the benefit year is
used for the performance measure.
Members Choosing to Leave the Plan. The Members Choosing
to Leave the Plan performance measure is reported as the percentage of members who chose to disenroll from the plan during the benefit year. This is calculated by taking the number of
members who chose to disenroll using disenrollment codes in
Medicare’s enrollment system, excluding members who disenrolled due to reasons beyond their control (e.g., relocation out
of the service area, disenrollment due to low-income subsidy
reassignments, or not meeting requirements for special needs
plans), and dividing the number by the number of members
enrolled at any time during the benefit year.
Statistical Analysis
Bivariate regressions were performed for 2 independent variables: (1) 2014 star ratings HRM base score (2012 benefit year)
and (2) the change in HRM base score from the 2013 to 2014
star ratings reports (2011 and 2012 benefit years; Table 2).
The dependent variables were the member experience base
scores in the 2013 and 2014 star ratings reports. Three of the 4
Vol. 20, No. 11
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TABLE 3
Bivariate Linear Regression for All Plans with Complete
Information for 5 Measures in 2013 and 2014 (n = 443)
High-Risk
Medication Measure
Member Experience Measure
(Independent Variable)
(Dependent Variable)
Beta Coefficient
0.139
2014 high-risk
2014 getting needed prescription drugs basea
medication base
0.066
2014 rating of drug plan basea
-1.067
2014 complaints about drug plan basea
-0.260
2014 members choosing to leave the plan basea
0.213
2013-2014 change in
high-risk medication
-0.113
base
-1.372
-0.574
2014 high-risk
2013 getting needed prescription drugs baseb
0.551
medication base
0.051
2013 rating of drug plan baseb
-1.447
2013 complaints about drug plan baseb
0.155
2013-2014 change in
-0.146
base
-1.569
a Dependent variable has the same benefit year (2012) as the 2014 HRM star rating.
bDependent variable has the same measurement year (2012) as the 2014 HRM star rating.
member experience measures of interest are collected in a time
period following the benefit year. It is therefore possible that
changes in the formulary management of HRMs could affect
member experience measures of the previous benefit year’s star
ratings report for members enrolled in the same contract in
both years. Regressions were performed for each independent
variable and dependent variable pair within the same report
year and within the same year of data collection.
Regression analyses were also performed on a subgroup of
Medicare contracts that achieved a 2-star or greater increase in
the HRM measure from 2013 star ratings (2011 benefit year) to
2014 star ratings (2012 benefit year). These contracts may represent those that made significant changes in the management
of HRMs to achieve this improvement.
If achieving an excellent HRM base score (lower is better)
was associated with a negative impact on member experience
measures, positive associations would be observed for the 2
measures Getting Needed Prescription Drugs and Rating of
Drug Plan (higher is better) and negative associations would
be observed for the 2 measures Complaints About the Drug
Plan and Members Choosing to Leave the Plan (lower is better).
Bivariate linear regression models were performed in
Microsoft Excel 2010. A two-tailed P value < 0.05 was considered statistically significant.
■■  Results
Medicare contracts included in the 2013 and 2014 star ratings
reports (2011 and 2012 benefit years, respectively) with complete information for all 5 measures of interest were included
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Vol. 20, No. 11
Standard Error
0.031
0.045
0.427
0.126
0.034
0.050
0.471
0.137
0.035
0.045
0.439
0.038
0.050
0.485
R2
0.04
0.005
0.01
0.01
0.08
0.01
0.02
0.04
0.02
0.003
0.02
0.04
0.02
0.02
P Value
< 0.001
0.14
0.013
0.04
< 0.001
0.02
0.004
< 0.001
0.002
0.27
0.001
< 0.001
0.004
0.001
in regression models. A total of 443 contracts were included,
which represents 73.5% of the total Medicare contracts present
in both years and 80.7% of the total Medicare contracts with
summary star ratings for both years (Figure 1). The distribution of organization type was similar for Medicare contracts
with complete information for all 5 measures of interest in the
2013 star ratings, Medicare contracts with complete information for all 5 measures of interest in 2014 star ratings, and
Medicare contracts meeting both criteria. The most prevalent
organization type (83.1%-83.3%) was local coordinated care
(CCP) MA-PD plans.
Medicare contracts achieving a 2-star or greater improvement in the HRM star ratings between the 2013 and the 2014
star ratings reports numbered 101. Of the 101 contracts, 81
(80.2%) had complete data for all measures of interest. The
contracts in this subgroup had a greater proportion of local
CCP MA-PD plans (87.7% vs. 83.1%-83.3%) and private feefor-service plans (4.9% vs. 2.0%-2.4%) compared with all plans
with complete information for the measures of interest.
The bivariate linear regressions demonstrated associations
that accounted for 1% to 4% (R 2) of variance between the
explanatory and dependent variables between the 2014 star
ratings HRM base score and 3 of the 4 2014 star ratings member experience measures: Getting Needed Prescription Drugs
(R 2 = 0.04; P < 0.001); Complaints about Drug Plan (R 2 = 0.01;
P = 0.013); and Members Choosing to Leave the Plan (R 2 = 0.01;
P = 0.04; Table 3). As the data collection is measured after the
benefit year for 3 of the 4 member experience measures, bivariate linear regressions were performed for the 2014 star ratings
November 2014
JMCP
TABLE 4
Bivariate Linear Regression for All Plans with Complete Information for 5 Measures
in 2013 and 2014 and a ≥ 2-Star Increase in HRM Measure Stars (n = 81)
High-Risk
Medication Measure
Member Satisfaction Measure
(Independent Variable)
(Dependent Variable)
Beta Coefficient
0.267
2013-2014 change in
0.205
base
0.356
-0.078
0.177
2013-2014 change in
0.076
base
-1.829
a Dependent variable has the same benefit year (2012) as the 2014 HRM star rating.
bDependent variable has the same measurement year (2012) as the 2014 HRM star rating.
HRM base scores and the 2013 star ratings member experience
measures with data collection in the same calendar year. The
2014 HRM measure had associations with the 2013 star ratings
Getting Needed Prescription Drugs (R 2 = 0.02; P = 0.002) and
Complaints about Drug Plan (R 2 = 0.02; P = 0.001), explaining
2% (R 2) of variance. The 2014 HRM base score was not significantly associated with the 2013 or the 2014 Rating of Drug
Plan measure scores.
The bivariate regressions using the change in HRM base
score from the 2013 to the 2014 star ratings reports as the
explanatory variable demonstrated associations accounting for
1% to 8% of variance with 2014 member experience measures
of interest: Getting Needed Prescription Drugs (R 2 = 0.08;
P < 0.001); Rating of Drug Plan (R 2 = 0.01; P = 0.02); Complaints
about Drug Plan (R 2 = 0.02; P = 0.004); and Members Choosing
to Leave the Plan (R 2 = 0.01; P < 0.001). Associations between
the change in the HRM base score and the 2013 member
experience measures of interest with overlapping data collection time frames explain 2% to 4% (R 2) of variance: Getting
Needed Prescription Drugs (R 2 = 0.04; P < 0.001); Rating of
Drug Plan (R 2 = 0.02; P = 0.004); and Complaints about Drug
Plan (R 2 = 0.02; P = 0.001).
As with the 2014 HRM base score explanatory variable
regressions, the associations between the 2013-2014 change
in HRM base score and the Complaints about Drug Plan base
score dependent variables (2013 and 2014) and 2014 Members
Choosing to Leave the Plan were consistently negative. Also
consistent with the 2014 HRM base score explanatory variable regressions, the 2013-2014 change in the HRM base score
explanatory variable and Getting Needed Prescriptions Drug
measures (2013 and 2014) were consistently positively associated. In contrast to the 2014 HRM base score regressions,
which did not demonstrate an association with Rating of Drug
Plan scores, the change in HRM measure base scores from 2013
to 2014 had negative associations with both the 2013 and 2014
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November 2014
Standard Error
0.112
0.155
2.340
0.500
0.124
0.157
1.397
R2
0.07
0.02
0.0003
0.0003
0.03
0.003
0.02
P Value
0.02
0.19
0.88
0.88
0.16
0.63
0.19
Rating of Drug Plan measure base scores, which explain 1% to
2% of the variance (R 2).
Bivariate regressions were performed using the change in
HRM base scores for a subgroup of Medicare contracts that
achieved a 2-star or greater improvement in the HRM measure
(Table 4). The only significant association between the explanatory variable and member experience measures was for the
2014 Getting Needed Prescription Drugs measure (P = 0.02),
which explains 7% (R 2) of the variance in the model.
The results of this analysis indicate that the HRM score and
the HRM score change are marginally able to predict member
experience star ratings measure scores. The HRM score and the
HRM score change had the greatest influence on the Getting
Needed Prescription Drugs measure (R 2 = 0.04 and R 2 = 0.08,
respectively). However, the magnitude of this association is
small. The corresponding correlation coefficients (R) of 0.21
and 0.29 suggest that there are likely limited linear associations
between the variables.11 Scatter plots of the data points (plots
not shown) for each dependent and independent variable pair
show single clusters with 1 to 4 outliers. Bivariate regressions
were repeated after excluding outliers. The size of the variance
and association did not change appreciably after removing the
outliers (data not shown). The only change in the significance
testing occurred for the result of the 2014 HRM and the 2014
Complaints about Drug Plan regression, which became nonsignificant after outlier removal.
The HRM score was generally more predictive of the concurrent member experience star ratings measures than previous
year’s member experience star ratings measures. Since the
member experience data are often captured after the benefit
year, the effect of HRM formulary restrictions may be diluted
over multiple years. The 2013 to 2014 star ratings report
change in the HRM score explained more variance in the
Vol. 20, No. 11
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models than the 2014 the HRM score. It is intuitive that an
increase in HRM score, the more significant of which is likely
the result of increased formulary restrictiveness, would be
more likely to be associated with a marked impact to member
experience measures. The models’ beta-coefficients suggest
that HRM score improvement negatively impacts all member
satisfaction measures.
However, the results indicate an overall minimal contribution to the member experience measures. This is likely due to
many factors influencing the overall member experience and
the intentionally broad nature of the CAHPS survey questions.
That is, there may be several reasons influencing member
response to the CAHPS survey questions that may or may
not be related to HRM usage. Members may give greater consideration to perceived cost burdens, consisting of pharmacy
benefit premiums and prescription copayments, rather than to
prescription medication access when rating their prescription
drug plans or choosing to leave their plans. It is also possible
that the timing of survey administration chosen by CMS results
in significant recall bias. Additionally, only a small proportion
of members surveyed are likely to have utilized or tried to fill
an HRM. The mean prevalence of members meeting HRM criteria for the 2014 star ratings report across all contracts is 6%.10
The degree of restrictiveness and the extent to which drug
utilization is managed through prior authorization across the
entire formulary may have considerable negative repercussions
on the Getting Needed Prescription Drug star rating, compared
with strict management of the HRM category alone. Plans with
greater HRM dispensing rates may observe a more significant
impact on patient satisfaction measures by restricting access to
HRMs than plans with smaller HRM prevalence.
The subanalysis of Medicare contracts achieving a 2-star
or greater increase in the HRM measure yielded 1 significant
regression between the change in the HRM score and the
2014 Getting Needed Prescription Drugs that explained 7% of
variance. Contracts with a 2-star or greater increase represent
those who likely implemented formulary changes between the
2011 and 2012 benefit years to restrict the use of HRMs. It is
likely that these improvements were accomplished through
formulary restrictions, since educational outreach is generally
associated with more gradual improvements. However, the
specific utilization management techniques employed by these
plans are unknown, and some interventions may have greater
negative impact on the Getting Needed Prescription Drugs
score than others. The subanalysis may also be limited by the
small sample size (n = 81) and unmeasured differences from the
entire cohort of contracts.
Limitations
The applicability of these results may be limited by the changes
in the HRM list. Beginning with the 2015 star ratings (2013
www.amcp.org
Vol. 20, No. 11
benefit year), the HRM star ratings measure has been updated
based on the 2012 AGS Beers Criteria. There are many significant differences between the HRMs listed prior to the 2013
benefit compared with the present version. Many medications
were deleted (e.g., cimetidine, diazepam, and phentermine),
and several new medications have been added (e.g., glyburide,
indomethacin, and guanfacine). Another significant change is
the addition of medications with daily dosage or cumulative
days’ supply limits in addition to or in place of the 2 fills criterion. Nonbenzodiazepine hypnotics (i.e., esczopiclone, zolpidem, and zaleplon) are included as HRMs when the cumulative
days’ supply is greater than 90 days. Future analysis should be
conducted to determine the relationship between HRM management strategies, HRM score, and member experience scores
in the 2015 star ratings (2013 benefit year) and beyond.
Other limitations of this analysis include not being able to
control for socioeconomic differences in patient populations
across Medicare contracts. This study used publicly available
Medicare contract performance results, which do not include
demographic data. Also, we did not have access to reasons for
disenrollment or to complaint type logged in the CTM, which
may explicate the impact of formulary restrictions on these
measures.
The results of regression analyses suggest that the HRM star
rating measure score and its improvement have minimal influence on the member experience star ratings measure scores.
Plan sponsors may be more aggressive in the management of
HRM utilization to increase patient safety and clinical quality without compromising member experience performance
measures. Safety-related formulary restriction implementations
intended to improve the HRM star rating score are unlikely to
cause decreases in member satisfaction that would negatively
impact summary star ratings. Furthermore, increasing the
HRM star rating score is likely to improve summary star ratings. Further research is needed to determine the impact of
different methods of HRM star rating improvement on member
satisfaction using the current AGS Beers Criteria.
Authors
SARA C. ERICKSON, PharmD, is Health Outcomes Researcher;
R. SCOTT LESLIE, MPH, is Health Outcomes Researcher; and
BIMAL V. PATEL, PharmD, MS, is Director, Health Outcomes
Research, MedImpact Healthcare Systems, Inc., San Diego, California.
AUTHOR CORRESPONDENCE: Sara C. Erickson, PharmD,
MedImpact Healthcare Systems, Inc., 10181 Scripps Gateway Ct.,
San Diego, CA 92131. Tel.: 858.790.7494;
November 2014
JMCP
DISCLOSURES
This research was conducted by MedImpact Healthcare Systems, Inc., San
Diego, California, without external funding. All authors are employed by
MedImpact Healthcare Systems, Inc.
Study concept and design were contributed by Erickson, Patel, and Leslie.
Erickson collected the data, which were interpreted by Erickson, Leslie, and
Patel. Erickson wrote the manuscript, which was revised by Leslie and Patel.
REFERENCES
1. Beers MH, Ouslander JG, Rollingher I, Reuben DB, Brooks J, Beck JC.
Explicit criteria for determining inappropriate medication use in nursing
home residents. Arch Intern Med. 1991;151(9):1825-32.
2. Beers MH. Explicit criteria for determining potentially inappropriate medication use by the elderly: an update. Arch Intern Med. 1997;157(14):1531-36.
3. Fick DM, Cooper JW, Wade WE, Waller JL, Maclean JR, Beers MH.
Updating the Beers criteria for potentially inappropriate medication use in
older adults: results of a US consensus panel of experts. Arch Intern Med.
2003;163:271(22):2716-24.
4. The American Geriatrics Society 2012 Beers Criteria Update Expert
Panel. American Geriatrics Society Updated Beers Criteria for Potentially
Inappropriate Medication Use in Older Adults. J Am Geriatr Soc.
2012;60(4):616-631. Available at: http://www.ncbi.nlm.nih.gov/pmc/articles/
PMC3571677/. Accessed September 25, 2014.
JMCP
November 2014
5. Graham R, Mancer M, Wolman DM, Greenfield S, Steinberg E, eds.
Clinical Practice Guidelines We Can Trust. Institute of Medicine. Washington,
DC: National Academies Press; 2011. Available at: http://www.iom.edu/
Reports/2011/Clinical-Practice-Guidelines-We-Can-Trust.aspx. Accessed
September 25, 2014.
6. National Committee for Quality Assurance. HEDIS 2006 Technical
Specifications. Item 10284-100-06. Washington, DC: National Committee for
Quality Assurance; 2005.
7. Kaiser Family Foundation. Quality ratings of Medicare Advantage Plans:
key changes in the health reform law and 2010 enrollment data. Issue Brief.
September 2010. Publication #8097. Available at: http://kaiserfamilyfoundation.files.wordpress.com/2013/01/8097.pdf. Accessed September 25, 2014.
8. Tudor, CG. 2012 application cycles past performance review methodology. Memo dated December 12, 2010. Center for Medicare.
Available at: http://www.cms.gov/Medicare/Prescription-Drug-Coverage/
PrescriptionDrugCovContra/Downloads/PastPerformanceMethodology_121
010Final.pdf. Accessed September 25, 2014.
9. Reid RO, Deb P, Howell BL, Shrank WH. Association between Medicare
Advantage plan star ratings and enrollment. JAMA. 2013;309(3):267-74.
10. Centers for Medicare & Medicaid Services. Parts C & D performance
data. 2014 Part C & D Medicare star ratings data. Available at: http://www.
cms.gov/Medicare/Prescription-Drug-Coverage/PrescriptionDrugCovGenIn/
PerformanceData.html. Accessed September 25, 2014.
11. Elston RD, Johnson WD. Essentials of Biostatistics. Philadelphia, PA: FA
Davis, Co.; 1987.
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RESEARCH
Perceptions and Attitudes of Community
Pharmacists Towards Generic Medicines
Suzanne S. Dunne, BSc (Hons), MSc; Bill Shannon, MD, FRCGP, MICGP;
Walter Cullen, MD, MICGP, MRCGP; and Colum P. Dunne, BSc (Hons), MBA, PhD
ABSTRACT
BACKGROUND: Following the enactment of legislation in June 2013, generic
substitution and reference pricing of medicines has been introduced, for
the first time, in Ireland. This novel study is the first assessment of the perceptions of community pharmacists in Ireland towards generic medicines
completed in the period immediately prior to the introduction of generic
substitution and reference pricing.
•Pharmacist perceptions of, and attitudes towards, generic medicines are a relatively unexplored area internationally, with only
10 publications found in PubMed on this topic since 2003.
•No studies of the views of community pharmacists in Ireland
towards generic medicines have ever been published.
OBJECTIVE: To determine the perceptions towards generic medicines
among community pharmacists.
METHODS: One-to-one semistructured interviews were performed with a
convenience sample of 44 community pharmacists (from approximately
4,500 pharmacists in Ireland) recruited from Ireland’s Midwest, South, and
Southwest regions. Interviews were transcribed and analysed using NVivo
(version 9).
RESULTS: 98% of pharmacists believed that generics were of a similar
quality to the originator, and 96% stated that they were as effective as
the originator. However, a small number demonstrated some reticence
regarding generics: 9% believed that generics were not manufactured to
the same quality as the originator; 7% stated they would prefer to take
an originator medicine themselves; and 7% reported having experienced
quality issues with generic medicines. 89% of pharmacists reported receiving patient complaints regarding use of generic medicine, although 64%
suggested that this was due to a nocebo effect (i.e., a result of patients’
preconceived notions that generics were inferior). Only a minority (21%)
reported that they had attempted to educate patients as to the equivalency
of generics. Although 80% were in favor of Ireland’s new legislation promoting the use generic medicines, 46% expressed concerns regarding its
practical implementation.
CONCLUSIONS: This key stakeholder group had positive attitudes towards
generics and the legislation that promotes their use. Concerns regarding
patient perception and experience, clinical effectiveness, and manufacturing quality were identified. We propose that interventions supporting implementation of the new legislation should address these concerns.
JMCP
November 2014
•This is the first study of pharmacist perceptions of generic
medicines in Ireland and 1 of only 6 other studies on pharmacist
perceptions of generics in Europe.
•This is only the second qualitative investigation of pharmacist
views in Europe—the other being from Sweden and published in
2012.
•This study adds to the body of knowledge on pharmacist attitudes
towards generics, providing in-depth, qualitative data that can be
used as a basis for policy implementation and decision making.
I
n June 2013, new legislation came into effect in Ireland1—
the Health (Pricing and Supply of Medical Goods) Act
2013)2—that introduced generic substitution and reference
pricing for the first time in this country. As a result of this
new legislation, Irish patients will now have a greater opportunity to receive a generic medicine instead of a brand-name
prescription medication. In an effort to ensure that this legislation is successful, pharmacists’ opinions of, and attitudes
towards, generic medicines are critical to the changes being
implemented—that is, to increase the use of generic medicines
in Ireland.
Attitudes of Irish pharmacists towards generics have not
been published in the past. While assessments of pharmacist
perceptions of generic medicines have been carried out in a
limited number of other countries, a PubMed search covering
the period from January 2003 to January 2014 did not return
any peer-reviewed publications on the topic of pharmacist
perceptions of generic medicines in Ireland. In fact, only 10
publications since 2003 were found in PubMed on the topic
of pharmacist perceptions of, and attitudes towards, generic
Vol. 20, No. 11
www.amcp.org
medicines, indicating that this is a relatively underexplored
area internationally.3-12
With Ireland on the cusp of a major modification in health
care practices, there are many potential hurdles to overcome
during the introduction of such changes.13 The attitudes and
behaviors of health care professionals towards generic medicines are pivotal to the successful implementation of the new
legislation. The objective of this novel study was to assess these
perceptions among community pharmacists in Ireland in the
time leading up to the enactment of the new legislation and
to determine what challenges might arise as a result of these
stakeholder opinions.
■■  Methods
Preparation of Study Instrument
The study instrument was developed based on a recently
published review of the usage of generic medicines and how
policy changes to promote the use of generic medicines may
affect health care provision14 and the personal experience of
the primary author and study designer (who has over 15 years
of quality management and regulatory affairs within the pharmaceutical and biopharmaceutical industry).
Questions for the semistructured interview were prepared
and validated by cognitive testing, the purpose of which was
to ensure that the test questions were understood as intended.
The purpose of the interviews was to elucidate perceptions
relating to general opinion and understanding of generic medicines; behaviors towards generic medicines (e.g., dispensing
behaviors in the case of community pharmacists); opinions
as to the historical poor usage of generics in Ireland; beliefs
held as to the quality and efficacy of generics and how these
compare with proprietary (that is, brand-name) medicines; and
knowledge and opinion of the impending legislative change.
Cognitive testing was performed with 3 individuals who
were first asked the questions to be included in the survey,
allowed to provide responses, and after responding were asked
what their understanding of the questions were. Amendments
were made to questions based on responses from all 3 test participants. The responses of these participants to the interview
questions were not included in those finally analysed for this
study. The interviews used in the study began after cognitive
testing had been completed, and the interview questions had
been amended.
Sampling, Recruitment, and Interviews
A convenience sample of community pharmacists was
recruited, and interviews completed and analysed. Pharmacists
were approached in person, while in the pharmacy, and invited
to participate in the study. A verbal explanation of the study
was provided, and an invitation letter was offered. One-to-
www.amcp.org
Vol. 20, No. 11
TABLE 1
Study Instrument: Questions That
Formed the Basis for Semistructured
Interviews
What is your understanding of what a generic medicine is?
What is your understanding of how a generic medicine differs from an originator medicine?
What is your understanding of bioequivalence?
To the best of your knowledge, what percentage of difference is allowed in
terms of bioequivalence between an originator medicine and an equivalent
generic product?
What is your understanding of why generic medicines are cheaper than
originator medicines?
What do you believe about how generic medicines compare with brandname medicines?
What is your opinion as to why use of generic drugs in Ireland has historically been much lower than other European countries?
Have you ever had a patient report that a generic medicine, which you dispensed for them, did not work as effectively as an originator medicine?
If yes, what type of medicine(s) have you seen this with?
Can you please give some brief details of what the patient reported having
experienced?
What action did you take in this case?
Did you then dispense the originator medicine?
If yes, was there any reported lack of efficacy from the substituted originator medicine?
Have you ever had a patient report that an originator medicine, which you
dispensed for them, did not work as effectively as a generic medicine?
If yes, what type of medicine(s) have you seen this with?
Can you please give some brief details of what the patient reported having
experienced?
What action did you take in this case?
Are you aware of the government’s plans to introduce reference pricing and
generic substitution in Ireland?
What is your opinion of this proposed change in Irish legislation?
one interviews were carried out with consenting pharmacists
between June and October 2012: 34 face to face and 10 via telephone. Interview lengths were as follows: minimum 10 minutes 44 seconds; maximum 36 minutes and 15 seconds; mean
19 minutes 29 seconds. Interviews that were recorded (with
the interviewee’s consent) were semistructured and based on
the described study instrument (see Table 1). Additional supporting assessment of opinions was completed using a series
of structured questions to which participants could select from
predefined answers (Table 2). In this instance, a 5-point Likert
scale was used with a single response allowed for each question.15 Participants were free to volunteer additional commentary on each question. Furthermore, participants were offered
the opportunity to express freely any additional opinions or
views at the end of the interview session. Participating pharmacists were located in counties Limerick, Tipperary, Kilkenny,
Cork, and Waterford.
November 2014
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TABLE 2
Study Instrument: Supporting
Structured Questions and
Pharmacist Responses
Do you strongly agree, agree,
neither agree nor disagree, disagree,
strongly disagree with the following
statements:
Pharmacists, N = 44
SA/A a
SD/Db
Nc
n
%
n
%
n
%
Generic medicines are generally of the
same quality as originator medicines.
43
97.7
1
2.3
0
0.0
Generic medicines are generally poorer
quality than originator medicines.
1
2.3 42
95.5
1
2.3
Generic medicines are generally better
quality than originator medicines.
1
2.3 27
61.4
16
36.4
Generic medicines work as effectively
as originator medicines.
42
0
0.0
2
4.5
95.5
Generic medicines work better than
originator medicines.
0
0.0 35
79.5
9
20.5
Generic medicines don’t work as well
as originator medicines.
1
2.3 43
97.7
0
0.0
Generic medicines may be dangerous
compared with originator medicines.
2
4.5 39
88.6
3
6.8
Generic medicines are as safe as
originator medicines.
44 100.0
0
0.0
0
0.0
Generic medicines are manufactured
to the same quality as originator
medicines.
35
4
9.1
5
11.4
to a poorer quality than originator
medicines.
4
9.1 39
88.6
1
2.3
to a higher quality than originator
medicines.
0
0.0 39
88.6
5
11.4
Generic medicines are cheaper to buy
than originator medicines.
41
2
4.5
1
2.3
2.3 43
97.7
0
0.0
2
4.5
1
2.3
6.8 39
88.6
2
4.5
Generic medicines are cheaper because
they are of inferior quality to originator
medicines.
1
If I were ill, I would be happy to
take a generic medicine if my doctor
prescribed it for me.
41
If I were ill, I would prefer to take
an originator medicine rather than a
generic medicine, even if it is more
expensive.
3
79.5
93.2
93.2
a Strongly
agree/agree.
disagree/disagree.
c Neutral/no opinion.
bStrongly
Approval of the design and the implementation of this study
was granted by the Research Ethics Committee of the Irish
College of General Practitioners.
Analysis of Data
Using a grounded theory approach,16 interviews were transcribed verbatim and imported into NVivo, version 9 (QSR
JMCP
November 2014
International, Melbourne, Australia) for analysis. Using an
inductive process, transcripts were open coded for themes
relating to interviewee opinions, perceptions, and behaviors, including any other emerging themes, and the results
were analysed using Nvivo. To facilitate visualization and
understanding of the numbers of participants holding the
perceptions/behaviors that were coded into specific themes,
responses were expressed as a percentage of the total number
of participants. Interviews were conducted until saturation of
data was observed. Analysis was completed by the primary
researcher (SD) and reviewed to ensure reliability and rigor of
the analysis by a senior investigator (CD).
The coding framework included (but was not limited to)
such themes as opinions regarding safety and efficacy; previous
experience with use of generics; personal preferences; beliefs
regarding historical usage of generics in Ireland; experiences
with patient reports regarding generics; prescribing rationales;
personal knowledge of, and attitudes towards, generic medicines; and opinions regarding the proposed legislative changes.
Ongoing analysis of themes emerging from the interviews
was carried out as interviews were completed. When 4 to 5
consecutive interviews did not lead to the emergence of any
new themes, it was determined that data saturation had been
achieved and interviewing was concluded.
■■  Results
Supporting quotations from pharmacists are included in Table
3, as referenced in the text.
Analyzing Pharmacist Interviews
Forty-four community pharmacists were interviewed. Demographics of the group are available in Table 4. Participating
pharmacists were located in counties Limerick, Tipperary,
Cork, and Waterford.
Opinions Regarding Quality, Efficacy, and Safety of Generics
Table 2 shows the analysis of opinions regarding quality, efficacy, and safety of generics. The majority of pharmacists (98%)
were of the belief that generic medicines are of the same quality as the originator, with 96% holding the view that they are
as efficacious as brand-name products. All of the pharmacists
interviewed believed that generics are as safe as the originator. A small number (9%), however, were of the opinion that
generics are not manufactured to the same quality as originator
medicines and were of the view that generic manufacturing is
of a poorer standard. The majority of pharmacists (93%) stated
that they would take a generic medicine themselves, with a
small number (7%) stating that they would prefer to take the
originator rather than an equivalent generic, if offered a choice
(reference quotations 1-3, Table 3).
Vol. 20, No. 11
www.amcp.org
TABLE 3
Supporting Quotations from Pharmacists
Quotation
Number
Quotation
1
To be honest . . . for any decisions that I make, or anything I say to customers, if it was me or any of my family and I was given the option of a generic medicine I would go for it, I would absolutely take it. Female, aged 30-39 years
2
I believe that they are equivalent in therapeutic value, and I would have no hesitation in recommending a generic product over a branded one to a customer.
Male, aged 19-29 years
3
I think generic medications are brilliant. To be honest with you, the only downside is people’s perceptions—they think that the brands are better when in
reality generics are just as good. Male, aged 18-29 years
4
On paper they should be the same and should act in the same way, but we have had cases where people have come in and said that they didn’t find a generic as
effective as the original and they prefer the original . . . and from customers’ queries, some of them don’t find that they’re the same. Female, aged 30-39 years
5
[Generic medicines] work the same therapeutically, but I suppose people just have this notion that if it’s cheaper it can’t be as good—that’s the patients perception of it I think. Female, aged 30-39 years
6
I think, to be honest, any time [a patient has] had a problem with a generic instead of a brand is because they feel that they’re being cheated; they basically
feel that they’re getting second best because it’s cheaper. Female, aged 30-39 years
7
I think if [the patient] started on a generic, and it’s what they know, they prefer that; so I think that it’s maybe the change—it’s a change management issue
more than anything else. Male, aged 40-49 years
8
One woman I can’t convince that the generic coated aspirin would not have caused her to bleed, it’s totally in her mind; you can’t win those battles.
9
[If] you know what a patient is satisfied with, you generally won’t rock their boat. Female, aged over 65 years
10
The first thing I would do is I’d try and explain the situation but . . . at the end of the day, I think quite often with people who are coming in, they’ve made up
their mind and there’s really very little you can do at that stage. Male, aged 30-39 years
11
You try to explain to them that it is exactly the same medication and explain to them that it’s the same amount of drug, just called something else, that 500
milligrams of the generic drug is exactly the same, that it’s made in the same way, but then if they’re still going “no, no, no,” we’d give them the original.
Female, aged 30-39 years
12
There have been an increasing number of incidences where people have come back and said that the quality of the solid dosage form is significantly poorer,
and there is one company who are particularly culpable in this regard, whereby their tablets crumble on punching from a blister pack. Their capsules are
virtually impossible to get out of the blister pack. Now that’s not to say that there’s anything wrong with the actual raw ingredient, with the medication within
the solid dosage form, but there are significant shortcomings in the way those solid dosage forms are compounded. And I think that if it’s not rectified, it is
going to compromise patients’ attitudes towards generic medicines. Male, aged 40-49 years
13
I’ve had a couple of issues with a few [generic] tables—they have disintegrated, over time, and that problem didn’t arise with the original drug . . . but 99%
of the time there’s no issue with the quality of [generics]. Female, aged 30-39 years
14
Packaging-wise, you definitely notice a difference with some of the generics, that you wouldn’t have half as much detail on the packaging, the boxes are quite
plain. I know some customers will only take [the originator] tablets that actually have the label Monday, Tuesday, Wednesday at the back of them, and a lot
of generics won’t have any of that detail on the [foil]. Female, aged 30-39 years
15
I’d like the generic companies to package their stuff better; if it’s packaged shabbily it gives a bad impression. Now I know it’s nothing to do with the effectivity
of the substance, but some of them are very poorly packaged. Male, aged over 65 years
16
My grandmother in law . . . was on a generic simvastatin which was changed to another simvastatin which happened to be the same color and shape, with no
markings, as her blood pressure tablet, and she ended up taking double blood pressure tablets for about 2 weeks. Male, aged 30-39 years
17
If the demographic of patients you deal with are elderly people, and you know they just don’t like change, they want to stay the same, so you’re kind of on the
back foot immediately if you’re trying a new drug. Male, aged 30-39 years
18
Quotation from a non-Irish pharmacist: My experience with the Irish psyche is that they’re very brand oriented. I don’t know why, but they tend to be
very brand oriented. And, I think that could be impacting on why they don’t like generics; they like the original brand . . . but as soon as you tell them it’s a
copy, it’s a generic, they will think it’s a second-class drug. Male, aged 40-49 years
19
I just think people are very used to getting brands; they think all brands are better. It can be to do with the prescribers; some doctors prescribe a brand
because that’s what they’ve always known. Male, aged 18-29 years
20
I think that private patients, paying themselves, don’t mind, but that the people that don’t have to pay are the ones that want to stick to the original brand….
It’s GMS [General Medical Services] patients that have a problem with it, not the private patients. Female, aged 30-39 years
21
I think an awful lot of people have it in their head that the generic isn’t as good. As well, medical card patients have commented a few times, ‘It’s because I’m
on a medical card that you’re giving me the cheaper tablet.’ That’s the kind of presumption I think that’s out there—people think that because it’s cheaper,
they don’t see generic as equivalent, but as a lesser tablet. Female, aged 30-39 years
22
Get rid of branded generics . . . either it’s a generic or its not; there’s no need for the middle ground of a branded generic. Female, aged 30-39 years
www.amcp.org
Vol. 20, No. 11
November 2014
JMCP
TABLE 3
Supporting Quotations from Pharmacists (continued)
Quotation
Number
Quotation
23
Bring it on—pharmacists have been waiting for it for years; we have absolutely no problem, we’re here ready to go, just give us the guidelines and let us just
work on it. I’m all about value, we have to be; as professionals and as people that are actually concerned about people’s health and their finances, we want to
give the best value. We’re not in the business of trying to rip people off, so give us that law so we can do what we’re supposed to do—which is look after our
customers in every way possible and make them feel better. Female, aged 30-39 years
24
I can envisage that people will have issues because we’ve had issues before with people, and when it does come to it, I’d say there is a certain percentage of
the population that won’t be happy with taking the generic one or whatever one is the best price at the time because they’re just comfortable taking their one
particular brand and that’s it. Female, aged 30-39 years
25
I’ve had some cases [of patient complaints] where it’s actually, say, the Pfizer atorvastatin generic, comes off the same line as Lipitor; the only thing you can
blame is patient perception when it’s exactly the same thing. Even after I told [the patient] they’re the same thing, and you know what they said: “They left
some of the good stuff out though.” Male, aged 30-39 years
26
I think maybe a lot of the time “generic” and “cheaper” are put in the same sentence, so people think because it’s cheaper it can’t be as good as the original.
27
I did have one particular incident where [a patient] reported [a problem with a generic] and [the medication] was actually exactly the same thing; it was a
parallel import as opposed to a generic. They saw the pack was different, and they said [the medicine] didn’t work the same, they didn’t want that, but it was,
in every sense, exactly the same medication. Female, aged 30-39 years
TABLE 4
Group
Pharmacists
N = 44
Demographics
Gender
Age
M
F
18-29
30-39
40-49
50-64
65+
23
21
9
17
10
5
3
Pharmacist Experiences with Patient Complaints Regarding
Generic Medicines
Of the 44 pharmacists, 39 (89%) reported receiving patient
complaints associated with use of a generic medicine. Of the
5 pharmacists who did not experienced these complaints, 1
pharmacist did not dispense generics. Pharmacists reported
that when patients had issues with generics, the main experiences described were that the generics were not as effective or
that the patients experienced altered or increased side effects.
Twenty-eight pharmacists (64%) expressed an opinion that
at least some of the negative experiences reported by patients
were not actual, but rather were caused by a nocebo effect
(i.e., patients’ preconceived ideas as to a perceived substandard
nature of generics led to them having negative experiences
with generics) rather than an actual issue with the medication
(reference quotations 4-6, Table 3).
Medication types most reported as being problematic
included protein pump inhibitors (27%, 12/44), statins (18%,
8/44), inhalers (7%, 3/44), antihypertensives (7%, 3/44), antibiotics (7%, 3/44), antidepressants (5%, 2/44), and analgesics
(2%, 1/44).
Conversely, 11 pharmacists (25%) stated that a patient had
reported an issue with an originator medicine compared with
JMCP
November 2014
a generic. In most cases, since the patient had received the
generic before the originator medication, pharmacists indicated that, in their opinion, the patient’s preference is often for
the medicine first encountered and that such issues are more
likely to be due to a change having occurred, rather than an
actual issue with the medicine (reference quotation 7, Table 3).
In the situation where pharmacists received complaints
from patients related to use of generic medicine and the
patients requested the originator instead, 34 pharmacists
(77%) stated that they would accede to the patients’ preferences
(reference quotations 8-9, Table 3). Only 9 pharmacists (21%)
stated that they would attempt to educate the patient (reference
quotations 10-11, Table 3).
When asked about the differences between an originator
and an equivalent generic, 2 pharmacists (5%) felt that there
was no difference. Given that the only requirement for similarity (in terms of ingredients) between an originator product
and a generic equivalent is that the same active ingredient be
used (excipients may vary) and that generic products are often
aesthetically different from the originator, patients can be confused if the differences in appearance and excipient content are
not adequately explained to them.
Opinions Regarding Low Historic Usage of Generics
When asked why usage of generics in Ireland has been low in
the past, the main reasons given by pharmacists were as follows:
• Lack of generic prescribing (31%, 27/44). The primary
reasons given for this opinion were familiarity with trade
names on the part of prescribers and their lack of knowledge of the generic names of medicines.
Vol. 20, No. 11
www.amcp.org
• Lack of government incentive or pressure for generics usage
(50%, 22/44).
• The influence of the pharmaceutical industry (i.e, proprietary manufacturers) in Ireland (41%, 18/44).
• Poor understanding of generics by consumers (41%, 18/44).
• Brand consciousness or loyalty on the part of the consumer,
including being used to a particular brand and having poor
cost consciousness (39%, 17/44).
• The nonallowance of generic substitution (32%, 14/44).
Pharmacist Perceptions of Quality and
Patient Issues with Generic Medicines
Three pharmacists (7%) reported having experienced quality issues with generic medicines. Issues reported included
crumbling tablets and having difficulty getting tablets out of
blister packs. The pharmacists reported that, in their opinion,
these issues affect consumer confidence in generic products
(reference quotations 12-13, Table 3). Poorer packaging was
also mentioned by 4 pharmacists (9%) as being perceived as
a negative, and 1 pharmacist (2.3%) stated, anecdotally, that
differences between originator and generic packaging can
even cause issues for patients (e.g., where an originator brand
tablet had the days of the week printed on the foil, serving as
a reminder to the patient as to whether that day’s medication
had been taken or not, but similar printing was not available
with the generics). This led to patient preference for the originator medicine (reference quotations 14-15, Table 3). Nineteen
pharmacists (43.2%) also reported the opinion that patients are
sometimes resistant to change and that the different aesthetic
presentation of generics can cause confusion and medication
errors for some patients, particularly the elderly (reference
Consequently, patient education was seen as a necessary
step for wider acceptance of generics, and 15 pharmacists
(34%) stated that, in their opinion, patients see generics as
being a substandard, or lesser, alternative because they are
cheaper, which is described as “own-brand syndrome.” Indeed,
16 pharmacists (36%) expressed the opinion that Irish patients
hold a significant preference for branded medications (reference quotations 18-19, Table 3).
Five pharmacists (11.4%) reported having patients who
asked for cheaper generics. This was a minority of cases and
tended to be limited to private patients, who, according to
the pharmacists, have a better understanding and education
regarding generics. Pharmacists additionally made reference
to General Medical Services (GMS) patients getting more
branded medication than private (i.e., self-paying) patients
(reference quotations 20-21, Table 3). In Ireland, the GMS, or
medical card, scheme is a means-tested scheme available to
www.amcp.org
Vol. 20, No. 11
persons who are unable, without undue financial hardship,
to arrange general practitioner, medical, or surgical services.
Having a medical card entitles holders and their dependents to
a number of free services, including prescription medicines (a
dispensing charge applies to prescription medicines). In quarter 4 of 2013 approximately 40% of the Irish population were
holders of medical cards.17 Furthermore, some pharmacists felt
that branding of generics should be disallowed because it is
contrary to the intent of having generic medication and made
it necessary for them to stock multiple “brands” of the same
generic medication (reference quotation 22, Table 3).
Opinions Regarding New Legislation
All of the pharmacists interviewed were aware of the Irish government’s plan to introduce reference pricing and generic substitution in Ireland. When asked about their opinions about the
new legislation, 35 pharmacists (80%) indicated that they felt
positive about the legislation or were accepting of it. Twentyfour pharmacists (55%) were of the opinion that it made
financial sense and was necessary for the country, although 20
pharmacists (46%) expressed concerns and reported that they
anticipated issues with its practical implementation (reference
According to a PubMed search in January 2014, Irish pharmacists’ perceptions of generic medicines have not been studied
in the past. Internationally, a limited number of assessments
have taken place for such countries as New Zealand,9 Portugal,4
South Africa,5 Malaysia,8 France,10 and Sweden6 that included
studies on views held regarding specific medication types, such
as antiepileptic drugs7 and inhalers.3 Given the major changes
currently underway in the Irish health care system (i.e., the
introduction for the first time of reference pricing and generic
substitution), the opinions and behaviors of this critical stakeholder group have the potential to be pivotal to the success or
failure of the changes being implemented.
In contrast to other reports of reticent pharmacist views,3,8,9
this study has shown that Irish pharmacists were generally
positive towards, and accepting of, generic medicines, with
many holding the view that they are as effective as the originator, with the exception of nonsubstitutable situations—such as
with Narrow Therapeutic Index drugs— and that differences
in presentation can be a source of problems for some patients.
Very few pharmacists expressed reticent opinions, but 1 of the
primary concerns, as has been reported elsewhere,6 was that
confusion caused by differing aesthetic presentations of generic
medicines has the potential to be problematic for patients.
November 2014
JMCP
While a majority of pharmacists were in favor of the new
legislation (with references made to the United Kingdom situation: that no clinical issues linked to a much greater use of
generic medicines are seen, thus, the same situation could reasonably be expected in Ireland without risk to patients) about
half of the pharmacists interviewed (46%, 20/44) expressed
concerns as to the practical implementation of associated
changes. Concerns included the impact on the running of the
pharmacy as well as on patients. Pharmacists felt that they
could meet considerable resistance from patients and that they,
being at the “coal face,” may need to spend substantial periods
of time explaining the new system to patients, if adequate
educative interventions are not put in place by either the government or other interested bodies (e.g., the Pharmaceutical
Society of Ireland). Indeed, the requirement for education of
the general public to improve opinions and, therefore, increase
patient acceptance of generics was a recurring theme in this
study, as it has been in other studies.4-6,10 Increased public
awareness and education were considered to be fundamental
to improved acceptance of generics by consumers. In fact, an
anecdote told by a pharmacist, regarding how she convinced a
patient who was reticent to take a generic version of an inhaler,
is indicative of how such an intervention might work. The
pharmacist told how she brought out both the generic and the
proprietary inhalers and showed both to the patient, pointing out the ingredients of both and showing the patient that
they were the same. This practical demonstration of equivalence convinced the patient to try the generic inhaler, and the
pharmacist indicated that the patient did not return with any
subsequent issues. Such examples should be made use of when
designing educational interventions for patients.
Patient preference was seen to have a considerable influence on dispensing practices, with many pharmacists (77%,
34/44) acceding to patients’ wishes for brand-name medications, despite the fact that pharmacists believed the majority
of issues/complaints from patients regarding generics are not
actual, but rather due to the nocebo effect, that is, patients’
prejudices regarding generic medicines (reference quotation
25, Table 3). Pharmacists were of the opinion that this negative patient perception may be based on the fact that generics
are less expensive so, therefore, cannot be as good (reference
quotation 26, Table 3). Also, pharmacists believed that many
negative patient experiences were due to changes in medication and that the first medication that the patient is exposed
to will tend to be the preferred option. Therefore, when this
is changed, the patient is more likely to experience a problem
(reference quotation 27, Table 3).
JMCP
November 2014
Additionally, generics manufacturers/licence holders may
play a role in improving the opinions of consumers regarding
their products. One aspect could be to ensure that packaging
is of a standard at least equivalent to that of the originator and,
where relevant, to ensure that it provides the same facilities for
prompting/reminding of patients to take the medication (e.g.,
the anecdote where a pharmacist stated that continued use of 1
proprietary brand was due to patient preference for the packaging, as the days of the week were printed on the blister pack
foil). An argument can be made for regulators approving generic
medicines to require that if patient aids are part of the originator packaging, any generic equivalents must provide similar
aids in order to obtain a marketing authorization. Moreover, a
theme emerged on the topic of branded generics: while generic
substitution makes the issue of pharmacists needing stocks of
multiple branded generics moot, (that is, unless a “do not substitute” prescription has been written), pharmacists expressed
views that branding of generics should not be permitted as,
practical aspects aside, branding of generic medications is not
in keeping with the intention of provision of generic medicines.
Indeed, a recent report from the Irish Economic and Social
Research Institute on the costing of generics in Ireland has
shown them to be similar to the original branded medication,
thereby not resulting in substantial benefit to either the Irish
exchequer or consumer.18
Since improved consumer confidence in generics was
considered to be one of the major hurdles to be overcome in
improving use of generics in Ireland (similarly noted in other
studies4-6), the question was posed by pharmacists: How can
this information/education be provided in a manner that is
easy for patients to access and understand? While 1 pharmacist showed a patient both the originator and generic products
side by side to prove their equivalency (in the case of asthma
inhalers), the practicality of doing this on a day-to-day, patientto-patient basis is obviously something that busy pharmacists
cannot undertake. Provision of educational supports could be
facilitated, for example, by use of a novel tool, recently published by our group, based on optimized quality of information and reading ability, for development of websites providing
health care information.19 The resulting availability of easy-toread handouts/pamphlets, websites, or similar sources of information may not only provide consumers with the information
to dispel myths about generics and, hence, improve their confidence but may also have the dual effect of making the role of
the pharmacist easier during a time of upheaval and change.
Vol. 20, No. 11
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Limitations
A possible limitation of this study could be in the selection of
participants. All of the pharmacists interviewed were community pharmacists, whose opinions may differ from pharmacists
working in hospitals or other settings. Furthermore, differing
interview settings (some participants were interviewed face-toface, and others were interviewed over the telephone) might
have influenced the data gathered in this study.20 However,
review and comparison of the themes emerging from participants interviewed in different settings did not show any substantial difference in the opinions, perceptions, and behaviors
expressed between participants. Moreover, while the authors
acknowledge that quantification of qualitative data is sometimes contentious, we chose to adopt this approach in order to
best provide easy visualization of results and offer a more comprehensive insight into the patient perspective. The strengths
of such an approach have been discussed by Schonfelder in
2011.21
A strength of this study is the number of subjects used for
qualitative interview; the number of participants in this study
compared favorably with the only other semistructured interview-based studies that could be found in PubMed (i.e., 16 participants were interviewed for an analogous study in Sweden6
and 6 pharmacists (from a total of 15 health care professionals)
were interviewed for a similar study in South Africa5).
Community pharmacists in Ireland hold positive opinions
about usage of generic medicines, yet they have concerns about
the practical implementation of reference pricing and generic
substitution. Concerns were also raised about the impact on
patient acceptance due to the varying appearance of generic
medicines and regarding the lack of confidence that they
observed in the general public in relation to usage of generic
medicines.
DISCLOSURES
The authors do not have any financial interest, have not received any funding for this study, and do not have any conflicts of interest. This work was
supported in part by a scholarship from the Faculty of Education and Health
Sciences, University of Limerick, Ireland.
S. Dunne was responsible for study design, data collection, and data interpretation and was primarily responsible for the writing of the manuscript,
with assistance from C. Dunne. All authors contributed equally to manuscript
revision.
ACKNOWLEDGMENTS
The authors wish to express their sincere thanks to all of the pharmacists who
took part in these interviews.
REFERENCES
1. An Roinn Sláinte (Department of Health). Commencement of the Health
(Pricing and Supply of Medical Goods) Act 2013. June 24, 2013. Available at:
http://health.gov.ie/blog/press-release/commencement-of-the-health-pricingand-supply-of-medical-goods-act-2013/. Accessed September 19, 2014.
2. Irish Statute Book. Health (Pricing and Supply of Medical Goods) Act
2013. Available at: http://www.irishstatutebook.ie/2013/en/act/pub/0014/
index.html. Accessed September 15, 2014.
3. Williams AE, Chrystyn H. Survey of pharmacists’ attitudes towards interchangeable use of dry powder inhalers. Pharm World Sci. 2007;29(3):221-27.
4. Quintal C, Mendes P. Underuse of generic medicines in Portugal: an
empirical study on the perceptions and attitudes of patients and pharmacists. Health Policy. 2012;104(1):61-68.
5. Patel A, Gauld R, Norris P, Rades T. Quality of generic medicines in South
Africa: perceptions versus reality—a qualitative study. BMC Health Serv Res.
2012;12:297.
6. Olsson E, Kälvemark Sporrong S. Pharmacists’ experiences and attitudes
regarding generic drugs and generic substitution: two sides of the coin. Int J
Pharm Pract. 2012;20(6):377-83.
7. McAuley JW, Chen AY, Elliott JO, Shneker BF. An assessment of patient
and pharmacist knowledge of and attitudes toward reporting adverse drug
events due to formulation switching in patients with epilepsy. Epilepsy Behav.
2009;14(1):113-17.
8. Chong CP, Hassali MA, Bahari MB, Shafie AA. Exploring community
pharmacists’ views on generic medicines: a nationwide study from Malaysia.
Int J Clin Pharm. 2011;33(1):124-31.
9. Babar ZU, Grover P, Stewart J, et al. Evaluating pharmacists’ views,
knowledge, and perception regarding generic medicines in New Zealand.
Res Social Adm Pharm. 2011;7(3):294-305.
10. Allenet B, Barry H. Opinion and behaviour of pharmacists towards the
substitution of branded drugs by generic drugs: survey of 1,000 French
community pharmacists. Pharm World Sci. 2003;25(5):197-202.
Authors
SUZANNE S. DUNNE, BSc (Hons), MSc, is PhD Candidate;
BILL SHANNON, MD, FRCGP, MICGP, is Director of International
Liaison; WALTER CULLEN, MD, MICGP, MRCGP, is Professor
of General Practice; and COLUM P. DUNNE, BSc (Hons), MBA,
PhD, is Chair of Research, Centre for Interventions in Infection,
Inflammation and Immunity (4i), Graduate Entry Medical School,
University of Limerick, Limerick, Ireland.
AUTHOR CORRESPONDENCE: Suzanne S. Dunne, BSc, MSc,
Centre for Interventions in Infection, Inflammation and Immunity
(4i), Graduate Entry Medical School, University of Limerick,
Limerick, Ireland. E-mail: [email protected].
www.amcp.org
Vol. 20, No. 11
11. Auta A, Bala ET, Shalkur D. Generic medicine substitution: a crosssectional survey of the perception of pharmacists in North-Central, Nigeria.
Med Princ Pract. 2014;23(1):53-58.
12. Maly J, Dosedel M, Kubena A, Vlcek J. Analysis of pharmacists’ opinions, attitudes and experiences with generic drugs and generic substitution
in the Czech Republic. Acta Pol Pharm. 2013;70(5):923-31.
13. Moran M. Proposed model for reference pricing and generic substitution.
May 2010. Available at: http://www.webcitation.org/6FD1WRh1e. Accessed
September 15, 2014.
14. Dunne S, Shannon B, Dunne C, Cullen W. A review of the differences
and similarities between generic drugs and their originator counterparts,
including economic benefits associated with usage of generic medicines,
using Ireland as a case study. BMC Pharmacol Toxicol. 2013;14:1.
November 2014
JMCP
15. Likert R. A technique for the measurement of attitudes. Archives of
Psychology. 1932;22(140):5-55.
16. Engward H. Understanding grounded theory. Nurs Stand. 2013;28(7):37-41.
17. Health Service Executive. Health Service managment data report.
October 2013. Available at: http://www.hse.ie/eng/services/publications/
corporate/performanceassurancereports/Oct13mdreport.pdf. Accessed
September 19, 2014.
18. Brick A, Gorecki PK, Nolan A. Ireland: pharmaceutical prices, prescribing practices and usage of generics in a comparative context. Economic
and Social Research Institute Research Series No. 2. June 2013. Available
at: http://www.esri.ie/UserFiles/publications/RS32_Pharma.pdf. Accessed
September 15, 2014.
JMCP
November 2014
19. Dunne S, Cummins N, Hannigan A, Shannon B, Dunne C, Cullen W.
A method for the design and development of medical or health care information websites to optimize search engine results page rankings on Google.
J Med Internet Res. 2013;15(8):e183.
20. Elwood SA, Martin DG. “Placing” interviews: location and scales of
power in qualitative research. Prof Geogr. 2000;52(4):649-57.
21. Schönfelder W. CAQDAS and qualitative syllogism logic—NVivo 8 and
MAXQDA 10 compared. Forum Qual Soc Res. 2011;12(1):Article 21.
Vol. 20, No. 11
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November 2014 - Academy of Managed Care Pharmacy

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