SA TB epidemic and Strategies to reduce TB incidence
Transcription
SA TB epidemic and Strategies to reduce TB incidence
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 In-charge Sign-off to next level District Coordinator In-charge Sign-off to next level Sub-District Coordinator & Data Capturer In-charge Sign-off to next level 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 Analyze & use information Support & checks data for quality & completeness Capture data on ETR.Net Dispatch data Analyze & use information Record data on paper-based data collection tools Validate data before forwarding Analyze & use information 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