SA TB epidemic and Strategies to reduce TB incidence

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National TB epidemic and Strategies
to reduce TB incidence in South
Africa
Research, Information, Monitoring, Evaluation & Surveillance,
National TB Control and Management Cluster,
National Department of Health, South Africa
NATIONAL TB EPIDEMIC – FIGURES
FROM 2014
Data flow from facility to national level
National
RESPONSIBLE
RIMES Director
In-charge Sign-off to next level
Data Flow (Hierarchical flow)
Province
Programme Manager
District
Coordinator
Sub-District
Coordinator & Data Capturer
Facility
Nurse & Data Clerk
ACTIVITIES
Analyze, report & use
information
Validate data & Support
Receive & merge dispatched
data
Analyze & use information
Validate data & Support
Receive & merge dispatched
data
Support & checks data for
quality & completeness
Capture data on ETR.Net
Dispatch data
Record data on paper-based
data collection tools
Validate data before forwarding
Feedback at all levels
FLOW
TB cases per province, 2013
100,000
96,413
90,000
80,000
Total TB cases
70,000
60,000
50,000
51,783
49,416
40,000
43,909
30,000
20,000
19,937
19,513
19,263
20,201
10,000
8,461
0
EC
FS
GP
KZN
LP
MP
NW
NC
WC
Distribution of population and registered TB cases in South Africa
Cases as % of
Province
Population size
Registered TB Cases
Total (RSA)
Registered Cases
TB Case rate/100,000
population
Eastern Cape
6, 620,138
51,783
15.7%
782
Free State
2,753,141
19,937
6.1%
724
Gauteng
12,728,438
49,488
15.0%
388
KwaZulu-Natal
10,456,907
96,413
29.3%
922
Limpopo
5,517,967
19,513
5.9%
354
Mpumalanga
4,127,972
19,263
5.9%
467
Northern Cape
1,162,913
8,461
2.6%
562
North West
3,597,589
20,201
6.1%
728
Western Cape
6,016,925
43,837
15.7%
730
52,981,990
328,896
100
621
South Africa
TB cases per province, showing proportion smear
positive per province
80,000
70,000
31%
No of cases
60,000
50,000
40,000
47%
38%
30,000
33%
20,000
40%
10,000
40%
43%
37%
53%
0
EC
FS
GP
KZN
No smear or smear negative
LP
MP
NW
NC
Smear positive TB
WC
TB cases per province, showing proportion EPTB
100,000
90,000
17%
80,000
No of cases
70,000
60,000
50,000
8%
18%
12%
40,000
30,000
20,000
14%
18%
10,000
10%
9%
7%
0
EC
FS
GP
KZN
LP
MP
NW
NC
WC
EPTB 3,666 2,451 7,459 14,284 3,044 1,697 1,675 525 4,648
PTB 48,117 17,486 41,957 82,129 16,469 17,566 18,526 7,936 39,261
PTB
EPTB
Total TB cases
TB cases per province indicating proportion of cases in
children <15 years in 2012 (source: ETR.Net 2013)
100,000
90,000
80,000
70,000
60,000
50,000
40,000
30,000
20,000
10,000
0
15%
10%
12%
15%
12%
12%
9%
13%
13%
EC
FS
GP
KZN
LP
MP
NW
Children<15 4757
2197
5171
12411 2064
1513
2272
Adult
47,026 17,740 44,245 84,002 17,449 17,750 17,929
Adult
Children<15
NC
WC
964
7,497
5849
38,060
DS TB Initiations 2005 - 2013
450,000
388,882
400,000
341,165
Total TB cases
350,000
300,000
406,082 401,048
389,974
353,879
349,582
328,896
302,467
250,000
200,000
150,000
100,000
50,000
0
2005
2006
2007
2008
2009
2010
2011
2012
2013
Cure rate among SS+ patients (2004 – 2011)
80.0%
70.0%
2006
2007
67.5%
74.2%
75.9%
2010
2011
2012
57.6%
60.0%
50.0%
62.9%
64.0%
71.1%
73.1%
50.8%
40.0%
30.0%
20.0%
10.0%
0.0%
2004
2005
2008
2009
Treatment success rates among SS+ patients
(2004 – 2011)
90.0%
80.0%
70.0%
70.8%
73.8%
73.9%
2006
2007
76.4%
77.1%
78.9%
79.8%
80.8%
2008
2009
2010
2011
2012
65.5%
60.0%
50.0%
40.0%
30.0%
20.0%
10.0%
0.0%
2004
2005
Defaulter rate among SS+ patients (2004 – 2012)
12.0%
10.3%
10.4%
10.0%
9.1%
8.5%
8.0%
7.5%
7.1%
6.8%
6.0%
6.1%
6.2%
2011
2012
4.0%
2.0%
0.0%
2004
2005
2006
2007
2008
2009
2010
Death rate among SS+ patients (2005 – 2012)
9.0%
8.0%
7.8%
7.8%
7.8%
7.3%
7.2%
7.0%
6.5%
6.2%
6.0%
5.8%
5.0%
4.0%
3.0%
2.0%
1.0%
0.0%
2005
2006
2007
2008
2009
2010
2011
2012
DR TB Initiations 2010 - 2013
12,000
10,719
Total cases
10,000
8,000
6,494
6,000
5,706
4,000
2,000
701
661
610
0
2011
2012
MDR
XDR
2013
OTHER SUPPORTING EVIDENCE
TB Laboratory data
900
TB/100,000
850
800
750
700
650
600
TB cases over ART coverage
450,000
2500000
400,000
2000000
350,000
1500000
250,000
200,000
1000000
150,000
100,000
500000
50,000
0
0
2005 2006 2007 2008 2009 2010 2011 2012 2013
ART coverage
TB cases
300,000
Deaths associated with TB - STATSSA
Year
Total Number
of Deaths
TB-associated deaths
as % of Total
Deaths
2006
607,184
77,009
12.7%
2007
604,360
66,500
11.0%
2008
595,624
75,281
12.6%
2009
579,978
69,816
12.0%
2010
547,724
63,289
11,6%
2011
505,803
54,112
10,7%
TB MAC Targets SOUTH AFRICA
Modelling - Update
Background
• In May 2014 World Health
Assembly approved post2015 Global TB Strategy and
targets
• Set ‘ambitious but feasible’
targets for 2020/30/35
• 2025: ↓ 50% TB disease
incidence & 75% TB
mortality
• But how achievable are
they at country level?
Aims, objectives and methods
•
Aim
•
Focus efforts of modellers, economists and other experts to assess the post-2015 GTB global TB
targets in South Africa, India and China
•
Objectives
•
What health impact (TB incidence, mortality, DALYs) if a list of existing/near-existing interventions
is scaled up to ‘ambitious but feasible’ levels?
•
What are the costs and cost effectiveness of the alternative strategies, and the optimal strategies
under different budget/resource constraints?
•
Methods
•
Multi-modelling comparison (8 models for South Africa)
– Harvard (HAR), Johns Hopkins (HOP), Institute for Disease Modelling (IDM), Institut de
Recherche pour le Développement (IRD), University of Melbourne (MEL), Stanford (SIPTM),
University of Georgia (UGA), LSHTM/Futures/TB Mac (TIME)
•
2 independently elicited scenarios for intervention scale-up
– Country experts (NDOH and academics)
– Advocates (Treatment Action Group (TAG) and StopTB)
Data and calibration
• Data sources
– WHO/GTB report, IHME Global Burden of Disease, UN Pop
Division
– Discussions with country experts and NTP
• Model Calibration (point value + range)
– Incidence, mortality, population size (2012 values)
– Annual decline in TB incidence
– HIV prevalence
– Baseline performance of TB care and control activities
• E.g. Linkage to care, treatment success, ART scale up
Baseline Predictions – Incidence and Mortality
Incidence
Vertical bars indicate the range
used for model calibration
Mortality
Vertical bars indicate the range
used for model calibration
Intervention scenarios
• Definition of ‘Intervention scenarios’
Costable activity <-> Change in NTP performance indicator <-> Model effect
• Existing or near-existing tools
– No game-changing vaccine, diagnostic or drug regimen
sufficiently far in pipeline
• ‘Ambitious but feasible’ --> Two scenarios
1. Country Experts (National TB Programme, NDOH, etc..)
2. Advocacy community
Intervention scenario
Prevention
Active
Disease
ACF
Diagnostic
tool
Access to
care
Linkage to
care
Diagnosed
Access to high quality care
Improved
Rx success
Started
Rx
Complete
d Rx
Interventions – Base-case
0. No change from current programme
Includes scale up of
ART (UNAIDS proj)
Interventions – Access to care
1. Increase access to high quality TB services
Reduce % never accessing care + increase % accessing high quality care
Non-access
Base = 5%
Target: 0% (CE) 0% (A)
High Quality care
(Screen PHC visitors for TB)
Base = 20%
Target = 100% (CE), 100% (A)
Modelled Impact
-Reduce proportion not
accessing any TB care
-Of those with care
access, increase
proportion accessing
high quality care
Claassens IJTLD 2013,
Activities
-Improve geographical access
through outreach clinics
-TB Symptom screening for
all health clinic attendees to
ensure all in need receive TB
diagnosis
Interventions – Improve care
2. Improve post-diagnostic TB care (in high quality TB services)
Reduce pre-treatment LTFU + Improve treatment success
Pre-Treatment LTFU
Base (FL/MDR) = 17%/50%
Target: 5/5% (CE) 0% (A)
Treatment Success
Base (FL/MDR) = 76/60%
Target = 85/67% (CE)
= 86/75% (A)
Modelled Impact
-Reduce pre-treatment
loss to follow-up – first
line
-Reduce pre-treatment
loss to follow-up – MDR
-Increase first line
treatment success
-Increase MDR
treatment success
Activities
-Expand monitoring and
evaluation capacity,
implement mhealth and
outreach teams to trace
patients in communities
-As above
-Provide patient with
adherence counseling and
psychosocial support, as well
as improved monitoring and
evaluation
-All of above, as well as
decentralization of electronic
register
Interventions – ACF
3. Active Case Finding in general population
ACF
Base = 0%
Target: 0% (CE)
50% - bi-annually (A)
Modelled Impact
-Periodically screen a
proportion of the
general population for
TB disease
Activities
-As general description
Interventions – ACF + PT
4. ACF in general population + Preventive Therapy
Screen for active disease and LTBI, provide LTBI treatment
ACF
Base = 0%
Target: 0% (CE)
50% - bi-annually (A)
PT
Target: 100% of ACF (A)
Modelled Impact
-Provide LTBI screening
and preventive therapy
when positive to
proportion of ACF
population where active
TB was excluded
Smeja et al. Cochrane Database Sys Rev 2005 , Jereb et al. MMWR 2011, Pai et al. CMR 2014
Interventions – IPT for HIV pos
5. Continuous IPT for HIV pos on ART
Continuous IPT + repeated screening for TB disease
IPT for HIV pos on ART
Base = 5%
Target: 80% (CE)
100% (A)
Modelled Impact
-Provide continuous IPT
as part of ART in
PLWHIV.
Activities
-Includes pre-initiation
screening, and rescreening
of defaulters
Rangaka et al. Lancet 2014, Steingart et al. Cochrane Database Sys Rev 2013
Interventions – Combinations
6. Combination
All interventions scaled-up simultaneously
Not modelled
• Active Case Finding in high risk groups
– Requires specific model structure and data
– Neither are available for key populations e.g. mines,
prisons
• Social determinants and interventions
--> Critical enabler for several intervention scenarios
– Social security, UHC, cash/food transfer programmes
Impact of interventions - incidence
Country Experts
Combination
HIVpos IPT
ACF+PT
ACF
Care
Access
Baseline
Advocates
Can we reach the targets?
Incidence
Mortality
74% (65-82%)
0.3 million deaths averted
55% (31-62%)
1.2 million cases averted
Country Experts
Advocates
Country Experts
Advocates
Key results
•
No single intervention will get there but combination prevention and treatment
can meet the targets
•
Strengthening the system (improved use of symptom screening, linkage to and
completion of care ) can have a strong effect on TB incidence
•
Results suggest continuous IPT for HIV+ individuals can also help reduce TB
incidence.
•
Advocates scenarios predicts a big impact of LTBI treatment but this is unlikely
to be ‘feasible’. Highlights the need for new tools (tests or drugs) to address
LTBI
•
While there is uncertainty about the size of the effect of each intervention,
there is relative consistency in the ranking of intervention impact across models
Key message
• A combination of interventions has the
potential to achieve the WHO 2025 targets
without any new tools
Acknowledgements
www.tb-mac.org

SA TB epidemic and Strategies to reduce TB incidence

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