Tuberculosis in Indonesia: Epidemic Projections and Opportunities to Accelerate Control Findings from an Optima TB analysis 2020 1 Table of Contents Acknowledgments 3 Abbreviations 4 Executive Summary 5 1. Introduction 8 1.1 Country Context 8 2. Methodology 10 2.1 Optima TB model 10 2.2 Scope of Analysis 11 3. Results 12 3.1 Question 1 – What is the projected trend for Indonesia’s TB epidemic if status quo conditions were maintained? 12 3.2 Question 2 – What is the optimized allocation of the current budget? 13 3.3 Question 3 – What is the optimized allocation at varying budget levels? 15 3.4 Question 4 – How would a future TB epidemic be influenced by the implementation of specific programmatic changes? 17 3.5 Question 5 – What changes in the TB care cascade would be necessary to support reaching the End TB targets? 18 BOX 3.1: How Will the Coronavirus Outbreak Impact TB Health Outcomes in Indonesia? [[ DF Note: Usually, boxes are shaded and enclosed. ]] 21 4. Discussion 23 References 25 Appendix A: Program Details and Model Constraints 26 Appendix B: Detailed Optimization Results 30 2 Acknowledgments These results were generated through a collaboration with the following contributors. Country Team/National TB Program (NTP) Wiendra Waworuntu, Imran Pambudi, Nurjannah Sulaiman, Sulistya Widada, Eko Sulistyo, Nurul Badriyah, Rizka Nur Fadila, Shena Masyita Deviernur, and the NTP team. Burnet Institute Rowan Martin-Hughes, Sherrie Kelly, Romesh Abeysuriya, Aaron Osborne, Debra ten Brink, Anna Roberts, David P. Wilson World Bank Pandu Harimurti, Elina Pradhan, Nejma Cheikh, Reem Hafez, Nicole Fraser-Hurt, Muhammad Noor Farid, Ery Setiawan, Pandu Riono 3 Abbreviations ART Antiretroviral therapy BCG Bacillus Calmette-Guérin BPOM Badan Pengawasan Obat dan Makanan/National Food and Drug Authority DOT Directly observed treatment DR Drug-resistant DS Drug-sensitive GeneXpert GeneXpert MTB/RIF or Xpert GOI Government of Indonesia HIV Human immunodeficiency virus IPT Isoniazid preventive therapy LKPP Lembaga Kebijakan Pengadaan Barang dan Jasa/National Procurement Agency LTBI Latent tuberculosis infection MDR Multidrug-resistant MTB Mycobacterium tuberculosis NSP National Strategic Plan NTP National Tuberculosis Program PLHIV People living with HIV Puskesmas Community health centers RIF Rifampicin SDG Sustainable Development Goals SN Smear negative SP Smear positive TB Tuberculosis WHO World Health Organization XDR Extensively drug-resistant 4 Executive Summary Indonesia is the third largest contributor to the global tuberculosis (TB) burden and among the top 20 countries in the world for TB-HIV (human Immunodeficiency virus) coinfection, and multidrug-resistant TB (MDR-TB). In 2017, 842,000 people 842,000 people 116,000 23,000 people fell ill with TB, including 36,000 people living with HIV, 116,000 fell ill with TB TB deaths with drug-resistant TB people who died, and 23,000 who were affected by drug-resistant TB. In addition to the significant toll of TB to health and human capital, the economic burden of TB is substantial. Based on a business-as usual-scenario, estimates suggest an overall cost of US$6.9 billion from the loss of productivity due to illness, with premature death by far accounting for the largest share. But an appropriate public health and medical response can mitigate the economic impact. In 2017, the Ministry of Health’s National Strategic Plan (NSP) estimated an annual budget of US$185 million to respond to the TB epidemic. Approximately 30 percent was funded domestically and 25 percent by international development partners, leaving an annual funding gap of US$83 million. As Indonesia nears upper-middle income status, it will gradually lose access to international funding, potentially leaving an even larger funding gap. Controlling TB in Indonesia will require not only that the Government of Indonesia (GOI) spend more on TB but that it spends better. This means that decisions on which interventions or programs to prioritize and how best to implement them will be critical to maximizing health outcomes. The Optima TB modeling analysis was conducted to estimate (i) how existing and additional resources might be optimally allocated to maximize the impact of the national TB response, and (ii) which gaps in the TB treatment cascade offered the greatest scope for improvement. The model looks at several scenarios and compares projections on TB incidence, prevalence, and deaths with the GOI’s own targets of reducing the incidence of TB by 90 percent and TB-related deaths by 95 percent, by 2035, relative to 2016, in accordance with global End TB targets. The main scenarios explored are projections based on (i) status quo spending, (ii) an optimized allocation of status quo spending among TB interventions, and (iii) an optimized allocation of a four-fold increase in spending. Projections indicate that TB incidence will remain relatively stagnant under status quo spending and that there is limited scope for improving allocative efficiency. Under status quo spending conditions, by 2035, the number of new TB infections would decrease by 6 percent (780,000 new TB infections), the number of TB-related deaths by 16 percent (110,000 TB-related deaths), and the number of prevalent TB cases by 22 percent (1,470,000 prevalent TB cases) relative to 2016. Optimizing the allocation of current spending would add modest gains – a less than 2 percent decrease in the number of new TB cases, TB-related deaths, and prevalent TB cases compared to status quo. This implies that resources are already allocated efficiently. Given the level of current spending, the GOI prioritizes the treatment of drug-sensitive (DS) cases detected through passive case-finding, as it should; however, limited resources mean that the TB program’s ability to expand active case-finding, to ensure protocol- 5 based treatment especially for MDR-TB, and to provide preventive therapy to all contacts is not currently possible even though these interventions are necessary to further advance the GOI’s goals. Even if TB spending increased four-fold and allocations were optimized, TB incidence falls far shy of End TB 2035 targets. Total Number of Incident Cases for Different Spending and Resource Allocation Scenarios 1,000,000 900,000 800,000 Status quo 700,000 Optimized status quo 600,000 TB cases 500,000 400,000 Optimized four-fold budget increase 300,000 200,000 100,000 End TB target 0 2005 2010 2015 2020 2025 2030 2035 Nevertheless, allocative efficiencies could be gained from treating more drug-sensitive TB cases in primary health care facilities and fewer in hospitals, shortening standard MDR-TB treatment regimens, and increasing preventive therapy for children among active TB contacts. In fact, whether the TB budget increases or decreases in the future, optimization will always prioritize more spending in these three areas. At higher spending levels if more resources were available, isoniazid preventive treatment for latent TB among people living with HIV (PLHIV) and contact tracing at the household level also become cost-effective. A more efficient allocation of public TB resources would see more spending at puskesmas, shorter MDR- TB regimens, and more preventive therapy for children. Change in Spending if Current Allocations Were Optimized (in IDR Millions) Public hospital, DS treatment Private hospital, DS treatment Public hospital, DOT, MDR standard Private primary, DS treatment Preventive therapy for latent TB (children) Public hospital, DOT, MDR short Public primary, DS treatment -100,000 -50,000 0 50,000 100,000 150,000 Note: DS=drug-sensitive; DOT=directly observed therapy; MDR=multi-drug resistant. 6 However, even much higher levels of spending would not allow Indonesia to reach its End TB targets without significant improvements in the quality of service delivery along the entire TB care cascade. A four-fold increase in spending would still fall far shy of End TB targets. This suggests systemic changes are needed in how TB services are delivered. Currently the TB care cascade shows significant gaps in diagnosis and treatment because 28 percent of all active TB cases remain undiagnosed and only 34 percent are successfully treated without a relapse within 2 years. Improvements in the quality of service delivery along the entire TB care cascade are needed to shorten the time to diagnosis, ensure protocol-based treatment, and verify treatment success. The breakdown in service delivery happens early in Indonesia’s care cascade at the initiation of diagnosis and treatment. TB treatment outcomes along Indonesia's continuum of care 100% 72% 52% 47% 34% Active TB Diagnosed Initiated protocol- Completed treatment Treated without based treatment relapse 7 1. Introduction 1.1 Country Context Indonesia is the third largest contributor to the global tuberculosis (TB) burden. The burden of TB disease can be measured in terms of: (i) prevalence – the number of cases of TB at a given point in time; (ii) incidence – the number of new and relapse cases of TB arising in a given time period, usually 1 year; and (iii) mortality – the number of deaths caused by TB in a given time period, usually 1 year. By all measures, Indonesia is a high burden country. In 2017, it accounted for 1.6 million or 8 percent of the 10 million TB cases worldwide; had a TB incidence of 391 per 100,000 people (1) or more than 842,000 people, including 36,000 people living with HIV (PLHIV); and lost 116,000 people to TB, including 9,400 deaths among PLHIV. In addition, Indonesia also faces a rising burden of multidrug- resistant TB (MDR-TB) with an estimated total of 23,000 people falling ill with drug-resistant TB. Besides the significant toll to health and human capital, the economic burden of TB is also substantial. In a recent study on the economic burden of TB in Indonesia by Collins et al. (2), the overall annual cost of TB was estimated to be US$6.9 billion, with the loss of productivity due to illness and premature death by far accounting for the largest share. The direct burden on households due to out-of-pocket expenses was estimated at US$74 million, the costs to the healthcare system amounted to US$156 million, with the remainder attributed to the loss of productivity due to illness (US$700 million) and to premature death (US$6 billion). A targeted public health and medical response can mitigate the economic impact. Fortunately, programs to treat TB are some of the most cost-effective of all health programs. However, it is imperative to identify active TB cases and initiate their treatment early on. Directly observed therapy, short course (DOTS),1 which promotes standardized treatment with patient supervision and patient support, is the preferred response because it enables compliance throughout the treatment period. However, rising resistance to drugs threatens progress in TB treatment success. In Indonesia, late diagnosis or undetected TB and incomplete treatment lead to longer treatment periods and significantly costlier care for rifampicin resistant TB (RR-TB) and MDR-TB (Figure 1.1). While Indonesia has one of the most widespread installations of GeneXpert machines,2 considered the gold standard in TB diagnostics, the primary method used remains clinical diagnosis and X-ray because it is 5.2 times cheaper.3 1 Isoniazid and rifampicin are the main drugs used. 2 Over 900 machines nationwide. 3 Smear and culture have a unit cost of IDR 26,194 (US$2) and a turnaround time of 2-3 days for smear, several weeks for culture; X-ray has a unit cost of IDR 69,841 (US$5) and is immediate; GeneXpert has a unit cost of IDR 367,240 (US$26) and a turnaround time of 2 hours. 8 Multidrug-resistant TB is 68 times more expensive to treat than drug sensitive TB. Figure 1.1: Comparison of Treatment Costs at Different Stages of TB Note: Exchange rate: US$1=IDR 14,573. Source: National TB Program Health remains an underfunded and underprioritized sector in Indonesia, which has direct implications for the TB response. Public expenditure on health—at 1.5 percent of gross domestic product (GDP), or 8.8 percent of total government expenditure, in 2018—is about half of that in countries with a similar level of income (averaging about 2.7 percent of GDP). This amounts to just US$56 per capita, well below regional and lower middle-income averages, as well as the recommended US$110 per capita needed to deliver an essential universal health coverage (UHC) package. This suggests that current public health spending in Indonesia should double. In 2017, the Ministry of Health’s National Strategic Plan (NSP) estimated an annual budget of US$185 million to respond to the TB epidemic. Of the estimated US$185 million, US$54 million (30 percent) was funded domestically and US$45 million (25 percent) by international development partners, leaving an annual funding gap of US$84 million. As Indonesia nears upper-middle income status, it will need to find additional fiscal space to ensure the continuity of delivery of TB services. Indonesia has just recently been classified as an upper- middle-income country with a gross national income (GNI) per capita of US$4,050 (Atlas method, current US$) – although this is before COVID-19 broke out – threatening its access to development assistance because eligibility criteria is frequently tied to income thresholds. In particular, donor resources are predominantly used to fund 2nd line TB drugs for rifampicin and multidrug-resistant TB, GeneXpert machines and cartridges for TB testing, and sputum collection and transport – much needed services for the continued early detection and treatment of TB, especially MDR-TB. Even though Indonesia remains eligible to access support from the Global Fund to Fight AIDS, TB, Malaria (the main donor for TB), at least until 2022, there is a strong push to increasingly use domestic resources from the government. 9 Following a request for technical assistance from the Government of Indonesia (GOI) on how to make available TB resources go further, especially in a context of shrinking external funding, consultations were held with program managers and experts in the National TB Program, the Ministry of Health, and the Ministry of Finance. From these discussions, it emerged that a TB allocative efficiency analysis would be helpful in identifying (i) how existing and additional resources might be optimally allocated to maximize the impact of the national TB response, and (ii) which gaps in the TB treatment cascade offered the greatest scope for improvement. 2. Methodology 2.1 Optima TB model Optima TB is a mathematical optimization model that informs policy makers and program managers on how to allocate the available resources across TB programs to maximize impact. Optima TB’s scenario planning helps program managers answer “what-if” questions (for example, what would TB prevalence look like if current epidemiologic and spending remained the same or if we spent more; and what would we have to spend on TB if we wanted to reach national TB targets?). In contrast, the model’s optimization function helps answer “how-to” questions (for example, how could we allocate (current, more, or less) resources more efficiently to optimize outcomes?). By comparing an infinite number of allocations to each other using a mathematical optimization algorithm (3), the model is able to find how to optimally allocate resources among different programs to reach specific TB program objectives (for example, reducing new infections or disease-related deaths, increasing the number of patients on treatment, minimizing the costs required to achieve specific targets, or a combination thereof) within a given resource envelope. Optima TB is a dynamic, population-based model that partitions the Indonesian population by population group,4 TB health state (for example, suspect, latent TB, active TB), diagnosis and drug resistant status, and tracks people’s movement among health states. The model brings together three types of data: (i) epidemiological data (for example, disease burden, including transmission and progression patterns); (ii) service coverage data (for example, intervention coverage and related outcomes); and (iii) cost information, for example, intervention unit cost data and budget allocation (See Figure 2.1, the Optima Modeling Approach). In addition, in consultation with national TB experts, the Optima TB model was calibrated to match available epidemiologic data as listed in T2.1. To assess how incremental changes in spending – including optimized resource allocation – might affect TB outcomes, data on coverage, unit cost, and expenditures was collected on 18 current and prospective TB programs (five prevention programs, six screening and diagnosis protocols, and seven treatment regimens. Sources are listed in Table 2.1 and full program details, assumptions, and constraints are given in Appendix A: Program 4The five populations included in this modeling analysis are: children aged 0-14; males aged 15-64; females aged 15-64; adults aged 65 and over; and people living with HIV (PLHIV) 10 Figure 2.1: The Optima Modeling Approach Table 2.1: Sources of Data Used in the Optima TB Model Data type Source Epidemiologic data National TB Program (2017); National TB Prevalence Survey (2013-2014) (4); WHO Indonesia population estimates (5); UNAIDS Indonesia PLHIV population estimates (6); Additional epidemiology supplemented by (7, 8). Program coverage Treatment initiations and outcomes by smear status, strain, and program modality data supplied by National TB Program (2017). Additional Indonesian program efficacy data from (9-17). Cost data Program unit cost estimates: National TB Program (2017). Top-down budgeting: Ministry of Health (2017) with reference to OneHealth costing estimates (2018) and previous estimates (18). 2.2 Scope of Analysis The analysis examined the following policy questions: Question 1. What is the projected trend for Indonesia’s TB epidemic if status quo conditions were maintained? Question 2. What is the optimized allocation of resources across TB programs at current budget levels in order to minimize the number of TB-related deaths? Question 3. What is the optimized allocation of resources at varying budget levels ranging from 60 percent of the most recently reported spending up to 800 percent of the most recently reported spending? Question 4. How would a future TB epidemic be influenced by the implementation of specific programmatic changes? - How would prioritizing GeneXpert testing (that is, increasing GeneXpert coverage from 20 to 75 percent) ahead of other programs impact TB incidence and TB-related deaths? - How would scaled up antiretroviral therapy (ART) coverage (that is, increased ART coverage from 11 to 75 percent among TB-HIV co-infected) impact TB incidence and TB-related deaths in people living with HIV? 11 Question 5. What changes in the TB care cascade would be necessary to support reaching the End TB targets? The scope of the analysis was further expanded to examine the impact of the ongoing COVID-19 epidemic on TB outcomes. These findings are included as a separate box following question 5. 3. Results 3.1 Question 1 – What is the projected trend for Indonesia’s TB epidemic if status quo conditions were maintained? According to the modeling results, Indonesia is unlikely to meet key program targets as defined by its National Strategic Plan for Tuberculosis 2016-2020 as well as the End TB 2035 targets. The government of Indonesia’s (GOI) “Temukan Obati Sampai Sembuh” (TOSS) or “Find And Treat Until Cured” strategy has committed to decreasing TB incidence by 50 percent and reducing the number of TB deaths by 70 percent by 2025 from a baseline of 2016. The GOI has also signed on to the global End TB Strategy initiative 2016-2035, which has even more ambitious targets of slashing TB incidence by 90 percent and reducing TB deaths by 95 percent. However, under status quo conditions – that is assuming no changes to current spending, transmission dynamics, or service delivery – TB incidence is projected to remain relatively stagnant, decreasing by just 6 percent from 835 to 783 thousand from 2016 to 2035 (Figure 3.1). TB deaths are projected to decline by 16 percent from 128 thousand to 108 thousand per year during the same period. TB prevalence is projected to decline by 22 percent from 1,871,887 to 1,468,625. Incident cases are predicted to decline most rapidly among children aged 0 to 14 years with a projected 36 percent decline, with smaller decreases in incident cases and prevalence among adults aged 15 to 64. These gains are projected to be partially offset by a 25 percent increase in incident cases among PLHIV and a 30 percent increase in people aged 65 and older (Figure 3.1). Projections indicate that under status quo spending Indonesia is far off track from reaching the End TB 2035 targets. Figure 3.1: Total Number of TB Deaths, Prevalent Cases, and Incident Cases 2,000,000 Prevalence 1,500,000 1,468,625 1,000,000 Incidence 782,619 500,000 Deaths 108,194 - End TB targets 2005 2010 2015 2020 2025 2030 2035 12 3.2 Question 2 – What is the optimized allocation of the current budget? The bulk of current TB spending is allocated towards treatment instead of active case finding or preventive therapy. According to most recently available estimates, the GOI allocated IDR 1,486 billion (US$102 million5) to programmatic TB spending, excluding management, monitoring and evaluation, and antiretroviral treatment for PLHIV that is not covered under the NTP. The largest expenditure item is for the treatment of drug-sensitive TB (DS-TB), accounting for over IDR 1 trillion or 67 percent of total TB spending. Passive case findings at health facilities account for IDR 270 billion (18 percent) while bacillus calmette-guerin (BCG) vaccination for infants and treatment of drug- resistant TB account for approximately IDR 100 billion each (7 percent). More than half (56 percent) of DS-TB treatment expenses occur at community health centers (puskesmas), 25 percent at public hospitals, and the remainder through private care. There is limited spending on active case finding or preventive therapy. Diagnosis using GeneXpert accounts for over 60 percent of diagnosis budget in Indonesia, even though it covers only 20 percent of screened TB patients. See Appendix A: Program for full details of the most recently reported spending and constraints on reallocation of resources as determined by the National Tuberculosis Program (NTP). An optimized allocation of TB resources would result in more DS-TB cases being treated in lower level facilities, increased preventive therapy for children of TB contacts, and shorter treatment regimens for MDR-TB in hospitals. Treatment of DS-TB cases and passive case finding using GeneXpert continue to dominate program spending even under optimized allocation. While highly desirable, increased spending on preventive measures and more active case detection is not possible given the current budget constraints. However, it is more cost-effective to shift DS-TB treatment downward to primary health care facilities. On average, treatment for DS-TB patients is more effective at puskesmas because follow-up and continuity of care throughout the 6 to 9 month regimen is better if they are provided closer to a patient’s home.6 The cost per person treated is also almost double at hospitals or in private care settings compared with treatment in puskesmas.7 The potential savings are best reinvested in preventive therapy for child contacts of active TB cases leading to a projected 35 percent reduction in TB incidence and TB-related deaths in children.8 Finally, switching to shorter MDR-TB treatment regimens would also lead to better outcomes because patients would be less likely to be lost to follow-up. In keeping with World Health Organization (WHO) guidelines on drug-resistant tuberculosis treatment (19, 20) and other emerging evidence (21) that the shorter duration MDR-TB treatment regimen of 6 months of bedaquiline is noninferior to the standard duration MDR-TB treatment regimen of 18 months or more, the switch to a shorter and cheaper treatment regimen9 would also allow more people to be treated. While the optimization model does suggest an increase in spending on passive case finding program at private facilities, this should not be considered a recommendation, but rather a reflection of the need to prioritize treatment of diagnosed drug- sensitive cases because of the limited available resources (Figure 3.2). Nevertheless, optimizing current spending yields limited improvement in TB outcomes compared to status quo conditions suggesting that what limited TB resources are available are already being allocated efficiently (Figure 3.3). Error! Reference source not found.This shows that allocating 5 Exchange rate US$ 1=IDR 14,573. 6 Puskesmas report a 94 percent treatment completion rate compared to 85 percent at higher level facilities. Conversely, puskesmas only report a 3 percent loss to follow-up compared with 11-12 percent at other facilities. 7 The cost per person treated is IDR 1.9 million per course at puskesmas, IDR 2.8 million per course at hospitals, and IDR 2.9 million per course in private settings. 8 Preventive therapy for latent TB has a lower cost-effectiveness in other populations and hence lower priority in view of current budget constraints. 9 The unit cost for the shorter regimen is IDR 18 million versus IDR 30 million for the standard regimen (see Appendix B). 13 resources more efficiently across various TB programs, holding all else constant, would decrease TB incidence by just 2.5 percent by 2035 compared to the status quo spending allocations. Similarly, the change in TB prevalence and TB deaths is less than 1 percent. This highlights the severely constrained spending environment under which the TB program operates where the treatment of diagnosed drug- sensitive cases through passive case finding are prioritized. Optimizing current resource allocations means spending more… Figure 3.2: Current and Optimized Spending per Program in IDR Thousands Public primary (Puskesmas), DS treatment Public hospital, DS treatment ... at puskesmas, ... Private hospital, DS treatment Passive case finding with Xpert based algorithm Passive case finding where Xpert based algorithm is not… Public hospital, DOT, MDR standard Public hospital, DOT, MDR short ... on shorter MDR- TB regimens, and ... Passive case finding (private non-protocol) Private primary (clinic, GPs), DS treatment Public hospital, DOT, XDR current IPT for Latent TB (HIV) Preventive therapy for Latent TB (child contacts) ... preventive therapy for Active case finding (prisoners) children. Preventive therapy for Latent TB (adult contacts) Status quo spending Contact tracing (household) Optimized status quo spending Contact tracing (community) 0 100 200 300 400 500 600 700 Spending per program in IDR thousands Notes: DS=drug sensitive; DOT=directily observed therapy; MDR/XDR=multi/extensively drug resistant; GP=general practitioner; IPT=isoniazid preventive therapy Projections indicate that optimizing current spending yields limited improvement in TB outcomes. Figure 3.3: Total Projected TB Incidence 1,200,000 1,000,000 800,000 Status quo Optimized status quo 600,000 400,000 200,000 End TB target - 2005 2010 2015 2020 2025 2030 2035 14 3.3 Question 3 – What is the optimized allocation at varying budget levels? Any reduction in funding will jeopardize Indonesia’s TB response at large and lead to an upsurge in TB burden. A 40 percent reduction in spending (the current estimated contribution from the Global Fund) would result in TB prevalence increasing by 52 percent (an additional 760 thousand cases), the number of incident cases increasing by 23 percent (an additional 180 thousand new cases), and the annual number of TB-related deaths rising by 56 percent (an additional 60 thousand deaths) compared to the status quo (Figure 3.4). However, even if the budget were to decrease, DS-TB treatment at primary health care facilities, preventive therapy for children aged 0-14 of known contacts and shorter MDR-TB treatment regimens would always be prioritized. At higher spending levels, additional programs become cost-effective, as discussed below. Also, while a four-fold increase of the budget would result in significant gains to TB outcomes, achievements still fall shy of End TB targets, and any further budget increase would only yield limited improvement. Optimizing and increasing resources four-fold would see the number of incident cases drop by 46 percent to 419 thousand, the number of prevalent cases decline by 62 percent to 552 thousand, and the number of TB deaths fall by 61 percent to 42 thousand compared with the status quo (Figure 3.4). Even if the TB spending increased four-fold, TB incidence still falls far shy of End TB 2035 targets. Budget increases beyond four-fold were explored in this analysis but are not recommended without introducing new program modalities, because the modeled programs begin to reach “saturation” coverage as they reach the limit at which additional spending is able to increase coverage in a cost-efficient way. This leaves an achievement gap of 40 and 28 percent relative to End TB targets for TB incidence and deaths, respectively (Figure 3.5). The suggested expansion of TB programs as the budget gradually increases10 is summarized in Figure 3.5, with the full allocation of spending and projected impact at each budget level detailed in Appendix B. Recommendations include: 1. At current spending levels, scale-up preventive therapy for children aged 0-14. This would come from savings from treating more patients at lower level public facilities versus hospital settings. At current spending levels, expanding GeneXpert testing is not possible without compromising care for those already diagnosed through passive case-finding at health facilities. 2. With a 20% (1.2-fold) increase of the budget, scale-up preventive therapy for PLHIV who are receiving ART as a high-risk key population in addition to children aged 0-14. 3. With a 40% (1.4-fold) increase of the budget, increase coverage of GeneXpert testing. Only at higher spending levels does expanding GeneXpert coverage and associated treatment of both DS-TB and MDR-TB become recommended (see GeneXpert expansion scenarios below). 4. With a 100% (two-fold) increase in budget, increase contact tracing in households of people with active TB to identify cases earlier and prevent the spread of TB, especially MDR-TB. 5. With a 300% (four-fold) increase in budget, expand preventive therapy to adult contacts of active TB cases. Only if significant additional resources are available does expanding preventive therapy to adults become attractive as adults are much less likely to develop TB than child contacts. Nevertheless, even with this level of increased spending, a 40 percent gap to the End TB 2035 targets is projected. 6. Beyond a 300% (four-fold) increase in budget, the program modalities begin to reach saturation – that is expanding coverage further is unlikely given current service delivery arrangements. Community contact tracing is also not deemed cost-effective unless they could be targeted to higher-risk communities such as boarding schools or urban poor enclaves. For this reason, implanting novel service delivery arrangements and/or improving the 10Budget optimizations were conducted at each 20 percent budget increment from 60 percent to 200 percent of the most recently reported spending (2017), and additionally at 400 percent and 800 percent. 15 implementation efficiency of current service delivery modalities is recommended rather than budget increases beyond this point (see section below on the TB care cascade). Even if the TB spending increased four-fold, TB incidence still falls far shy of End TB 2035 targets. Figure 3.4: Total Number of TB Incident Cases for Different Budgets and Resources 1,200,000 1,000,000 40% decrease 800,000 Status quo Optimized status quo 600,000 400,000 Optimized four-fold increase 200,000 End TB target - 2005 2010 2015 2020 2025 2030 2035 Suggested expansion of TB programs with increased budget will still leave gaps of 28 percent and 40 percent in TB-related deaths and incidence respectively, compared to End TB 2035 targets. Figure 3.5: Percentage Reduction in TB Incidence (Outer Ring) and Deaths (Inner Ring) as Budgets and At current spending At optimized current spending : expand 95% reduction in preventive therapy for child contacts TB-related deaths 6 90% reduction 3 With a 1.2 fold increase in in TB incidence 6 spending : expand preventive 16 therapy for PLHIV receiving ART 4 28 With a 1.4 fold increase in 11 spending : increase 12 GeneXpert coverage 5 40 14 With a 2 fold increase in 21 spending : expand Residual gap to End TB 19 household contract tracing targets that cannot be met under current service deilvery arrangements With a 4 fold increase in spending : roll- out preventive therapy for adult contacts 16 3.4 Question 4 – How would a future TB epidemic be influenced by the implementation of specific programmatic changes? 3.4.1 Increased GeneXpert Coverage Increases in GeneXpert testing only become cost-effective if the total budget increases 1.4-fold, although the benefits would be relatively modest. In recent years, there has been a push for expanding the availability of rapid diagnostic machines such as GeneXpert in Indonesia because they provide accurate results within 45 minutes. As mentioned earlier, Indonesia already has one of the most widespread installations of GeneXpert machines. However, despite the availability of these machines, only 20 percent of TB testing is done using GeneXpert – mostly for drug resistant patients – leading to far larger diagnosis gaps (2,400 to 31,000 days) for drug-resistant patients. Even at this limited level of coverage, GeneXpert consumable costs account for over 90 percent of the TB testing and prevention budget and is reliant on donor funding (see footnote 3 on the unit costs of various TB diagnostic methods). If protocol-based GeneXpert coverage were increased to 75 percent in 2020, in line with the 2016-2020 National Strategic Plan target, the prevalence of drug-resistant TB and TB- related deaths among people with MDR-TB would decrease by nearly 20 percent by 2035, relative to status quo coverage (Figure 3.6). However, without additional spending on treatment or preventive therapy, it would only lead to an overall reduction in TB incidence of less than 5 percent (11). Expanded GeneXpert coverage leads to modest improvements in TB incidence without additional spending on treatment and preventive therapy. Figure 3.6: Total Number of TB Incident Cases if GeneXpert Is Increased 800,000 Status quo Increased GeneXpert 700,000 600,000 500,000 400,000 300,000 200,000 100,000 - 2005 2015 2025 2035 3.4.2 Increased Antiretroviral Therapy Coverage Increased ART coverage would dramatically reduce new TB infections among PLHIV. Approximately 5 percent of incident TB cases are among PLHIV, despite this group representing only 0.4 percent of the total Indonesian population. While the TB program is not responsible for funding HIV treatment,11 an additional scenario was estimated that increased ART coverage from 11 percent to 75 percent among TB-HIV co-infected patients, in line with 2020 National Strategic Plan targets. Under expanded ART coverage, estimates show that TB prevalence among people living with HIV would decrease by 70 percent from over 9,000 in 2016 to 2,700 in 2035, and TB incidence among PLHIV and new HIV infections would reduce by nearly 50 percent by 2035 relative to 2016 (Figure 3.7). Without increases 11 As such, this cost was not included in the budget optimization. 17 in ART coverage (that is, under status quo ART coverage rates), TB incidence and related deaths among PLHIV are both projected to increase by 28 percent. Increased ART coverage leads to a dramatic drop in TB prevalence among PLHIV. Figure 3.7: Total Number of TB Prevalent Cases per 100,000 PLHIV if ART Expanded 10,000 8,000 Status quo 6,000 4,000 Increased ART 2,000 - 2005 2015 2025 2035 3.5 Question 5 – What changes in the TB care cascade would be necessary to support reaching the End TB targets? Indonesia’s current TB care cascade shows significant gaps in diagnosis and treatment. Based on 2019 calibrated model parameters, the TB care cascade in Figure 3.8 shows the outcome probabilities for each stage in the continuum of care. Twenty eight percent of all active TB cases remain undiagnosed and only 34 percent are successfully treated without a relapse within 2 years. Reliance on passive case finding from patients who go with symptoms to health care facilities leads to an average time to diagnosis of over a year and to more advanced cases. While the treatment success rate is high, a little over half of TB active patients ever even initiate protocol-based treatment. This gap includes those who delay treatment initiation and those who receive a non-protocol-based treatment regimen. Among active TB, smear positive (SP) DS cases are more likely to be diagnosed (85 percent) compared with smear negative (SN) DS cases (60 percent) but they are also more likely to die (not shown). This is due to a combination of care-seeking behavior, more pronounced symptoms, and better testing accuracy for high bacilli counts found in advanced infections.12 Multidrug resistant mortality is especially high (42 percent) given the even lower diagnosis of MDR-TB – only 20 percent of MDR-TB are diagnosed (Figure 3.9). 12This is consistent with findings from the national TB prevalence survey 2013-14 (4), and the number of notified smear positive cases relative to smear negative cases in Indonesia (NTP program data). 18 The breakdown in the TB care cascade happens early on with delayed or undiagnosed TB and initiation of protocol-based treatment. Figure 3.8: TB treatment outcomes along Indonesia's continuum of care 100% 72% 52% 47% 34% Active TB Diagnosed Initiated protocol- Completed treatment Treated without based treatment relapse MDR-TB cases are diagnosed too late or not at all leading to high mortality rates. Figure 3.9: Number of MDR-TB Cases that are Treated Successfully, Go Undiagnosed or Lost to Follow-up, or Die 16,000 TB-related deaths 12,000 8,000 Undiagnosed or lost to follow-up 4,000 - Projected active TB Diagnosed Treated Success Significant improvements in the quality of care along the entire TB care cascade are needed to shorten the time to diagnosis, ensure protocol-based treatment, and verify treatment success. Table 3.1 shows the dramatic parameter changes that are needed to reach End TB 2035 targets, without defining which new interventions or implementation efficiencies would be necessary. In particular, the average number of days until treatment initiation would have to be slashed from 560 to 5 days for DS-TB diagnosed patients and from 390 to 10 days for DR-TB diagnosed patients. The average number of days for diagnosis itself would have to drop from 200 days for smear positive and 900 days for smear negative patients down to 30 days. Treatment success rates, particularly for DR-TB would also need to improve to 85 percent. 19 Table 3.1: Necessary Parameter Changes Needed to Achieve End TB 2035 Targets Status quo conditions End TB 2035 targets Parameter in 2019 Implemented in 2020 Achieved by 2030 560 days (reflects gap 30 days 5 days Average days until in protocol-based (reflecting further improved linkages to care, all diagnosis being treatment initiation for treatment rather than conducted through rapid protocol-based screening and testing diagnosed DS-TB delay for diagnosed and elimination of loose drugs to ensure all treatment is cases) protocol-based) 109,000 people (all 145,000 people (all Number of LTBI populations, reflecting a 2,300 children (0-14 populations, reflecting a rapid treatment initiations decline after the initial rapid years only) scale up of contact tracing in through contact tracing scale up as new infections fall both children and adults) in the future) Number of LTBI treatment initiations 0 0 0 through mass screening Average days until 10 days 10 days 90 days (MDR) treatment initiation for (reflecting improved availability of treatment for DR-TB and 390 days (XDR) diagnosed DR-TB improved linkages to care) From 190 (Females 15- 90 days 30 days Average days until 64) to 1,700 days (reflecting effective combinations of contact tracing and active diagnosis for SP-DS (People living with HIV) case finding) 90 days 30 days From 610 (0-14 years, (reflecting effective combinations of contact tracing and active Average days until Females 15-64) to case finding, combined with the use of GeneXpert-based diagnosis for SN-DS 2,600 days (People diagnostic routines to make SN cases as likely to be diagnosed as living with HIV) SP cases) 180 days 60 days Very low rates of Average days until (reflecting a substantial increase in the use of GeneXpert-based diagnosis from 2,400 to diagnosis for DR-TB diagnostic routines to make DR cases as likely to be diagnosed 31,000 days as DS cases) Treatment success rate 95% (reflecting Approximately 90% 90% for DS-TB implementation efficiencies) 75% (reflecting Treatment success rate implementation efficiencies 85% (reflecting Approximately 50% for DR-TB such as improved treatment implementation efficiencies) regimens) Relapse rate after 20% 10% 5% successful treatment Reduced rate of progression Reduced rate of progression Progression to active Varies by population, from status quo by 50% from status quo by 75% TB for people infected reflecting (reflecting improvements in environmental factors such as with latent TB more environmental factors general public health improvements and a reduction in non- than 5 years previously modeled comorbidities) Notes: DS=drug-sensitive; LTBI=latent TB infection; DR = drug-resistant; SP=smear positive; SN=smear negative 20 BOX 3.1: How Will the Coronavirus Outbreak Impact TB Health Outcomes in Indonesia? Background: Indonesia accounts for the largest coronavirus (COVID-19) outbreak in the East Asia and Pacific region, with the number of cases (and fatalities) set to rise sharply in the coming months. Although the government was reluctant to impose a strict lockdown, it did implement social distancing measures on April 1, 2020, which were gradually lifted during June. In addition to the lockdown, over half of the districts (52 percent) reported 50-75 percent of their total TB budget shifted towards COVID-19 response and up to 60 percent of TB personnel were mobilized to work on the pandemic. GeneXpert machines are also being repurposed to ramp up COVID-19 testing capacity. This has led to increases in TB treatment failure and loss to follow-up. Using the same Optima TB approach, additional scenarios were carried out to help estimate the potential impact of COVID-19 on TB outcomes. COVID-model scenarios: Projections were estimated based on assumptions concerning i) the interruption of services such as suspended outreach activities, supply chain disruptions, movement restrictions, and unwillingness to visit health care facilities; ii) reduced resources including personnel, equipment, GeneXpert machines, and direct funding; and iii) catch-up activities such as immunization drives to catch up on BCG vaccinations and expanded active case finding. The impact of service disruptions during 2020 and 2021 was examined over 5 years from April 1, 2020 to March 31, 2024. Neither the direct impact of COVID-19/TB comorbidities nor the likely impact of interrupted treatments may have on increases in TB drug resistance were modeled and therefore results may be underestimates. The main scenarios include: – Baseline: no interruption – Best case: 3-month interruption with low severity, followed by additional investment of resources over 12 months to catch-up on TB programs such as BCG and preventive therapy – Most likely: 3-month interruption with low severity suggested by initial data, followed by resources continuing to be diverted from TB to respond to COVID-19 for an additional 12 months – Worse case: 6-month interruption representing extended or additional periods of lockdown with a more severe impact on TB service delivery, followed by resources continuing to be diverted from TB for 12 months. Results: As detection and diagnosis is delayed even under normal circumstances, COVID-19 interruptions will likely show a relatively minor short-term impact on TB incidence. The number of new incident cases is projected to increase by 2 percent from 2020 to 2024 relative to what would have happened in the absence of COVID-19 – an additional 90,000 (50,000 to 120,000 depending on the scenario) cumulative new cases (Figure 3.10). TB prevalence is projected to increase by 6 percent or an additional 100,000 active cases by 2024. TB-related deaths due to COVID-19 are projected to increase by 10 percent by 2024 corresponding to an additional 60,000 (25,000 to 80,000 depending on the scenario) cumulative TB-related deaths (Figure 3.11). If TB resources continue to be diverted due to an ongoing burden of COVID-19, it is projected that increased deaths will continue until 2024. To return incidence and TB-related deaths to status quo levels by 2024, additional investments over the next 12 months are needed to catch-up missed BCG vaccinations, expand diagnosis by 20 percent and double preventive therapy program from status quo. 21 COVID-19 will have a modest impact on TB incidence due to delayed detection even under normal circumstances … Figure 3.10: Total Number of TB Cases under Various COVID-19 Scenarios 900,000 Worse case Most likely case 800,000 Best case Status quo 700,000 600,000 500,000 400,000 End TB target 300,000 200,000 100,000 - 2018 2019 2020 2021 2022 2023 2024 … but will have a larger impact on TB-related deaths. Figure 3.11: Total Number of TB-Related Deaths under Various COVID-19 Scenarios 150,000 130,000 Worse case Most likely case Best case 110,000 Status quo 90,000 70,000 50,000 30,000 End TB target 10,000 (10,000) 2018 2019 2020 2021 2022 2023 2024 22 4. Discussion Indonesia is far off-track to meeting 2035 End TB targets. Given status quo spending, TB incidence will remain relatively stagnant, although prevalence per capita is estimated to decrease partly because of population growth. However, even if the NTP were to increase spending four-fold, inefficiencies in program implementation would still leave achievement gaps of 40 and 28 percent for TB incidence and TB-related deaths respectively relative to targets. This highlights the need for systemic changes in TB service delivery. This is a problem that more money cannot solve on its own. The limited scope to improve the allocative efficiency of NTP’s existing resources puts the focus squarely on improving implementation efficiency. The bulk of NTP’s available resources are spent on treatment – a mostly optimal allocation for their constrained budget. However, the Optima model did recognize some efficiency savings from shifting TB treatment from hospitals towards primary health care facilities and from adopting shorter MDR-TB regimens. With these savings, preventive treatment for child contacts among active TB cases should be increased. In order to encourage the behavior changes needed to improve implementation efficiency, both financial and nonfinancial incentives should be considered. Fundamental to improving implementation efficiency is having better information and management systems. As Indonesia operationalizes its National One Data Plan, it should consider a whole-of- government approach that brings together stakeholders from the Ministry of Health, the Food and Drug Authority (BPOM), the National Procurement Agency (LKPP), province and district health offices, and the health insurance agency (BPJS-K). The more integrated the data systems, the easier it will be to facilitate performance monitoring, disease surveillance, and logistics and inventory management. It would also help strengthen the reference lab network, enhance specimen transport, and improve communication between laboratories, health facilities, and drug warehouses. In particular, better information systems allow for facility benchmarking and/or financial incentives to be used to improve treatment outcomes. For example, given that the bulk NTP’s resources are focused on treatment, actively benchmarking facilities’ performance on notification rates, time-to-treatment initiation, loss- to-follow, and relapse rates and/or introducing performance-based payments tied to these measures would ensure that diagnosed patients can be monitored and cured more effectively. Combined with TB diagnostic and treatment protocols, the TB notification and management system can trigger prompts for follow-up visits, services, and pending tasks based on the information in each patient’s profile. Improved case management could also be part of ongoing national data collection initiatives that use community health workers and mobile applications to facilitate daily workflow. The hospital tariff and payment structure for TB services could also be revised to better incentivize desired behaviors. At secondary and tertiary care facilities, providers are paid a bundled amount (case-based rate) per visit which includes a visit fee, diagnostics, and treatment. Currently there is no inpatient payment rate for TB at hospitals; instead TB admissions are coded as respiratory infections, costing the system 8.5 to 14.8 times more than what it actually costs to treat a noncomplicated TB case as an inpatient. This presents a strong financial incentive to admit TB patients unnecessarily. Instead, introducing an inpatient payment code and tariff for TB that better reflects actual costs encourages outpatient treatment. Similarly, the payment method for primary care providers could better encourage TB detection and notification and push care down from hospitals. While notification is mandatory for all providers, enforcement is weak. Importantly, only confirmed cases are notified. At the primary care level, the diagnostic testing fee is included under the National Health Insurance (JKN) capitation rate that is paid to all JKN contracted providers – both public and private. However, the budget for testing equipment, medical supplies, and technicians comes out of facilities’ fixed operational budgets; this encourages an incentive therefore to refer to hospitals for diagnosis or forgo formal testing. In 2016, the most 23 common reason given for not doing a diagnostic test among puskesmas was lack of reagent and supplies (49 percent) while the unavailability of a medical lab analyst was most common for private clinics (41 percent). Private providers also resist referring patients for testing altogether because they do not want to lose business that comes with prescription treatments. It is estimated that 74 percent of initial care-seeking for TB occurs at private providers, but only 27 percent offer diagnostic tests. Given that TB is a priority program, introducing a fee-for-service type arrangement to encourage testing at the primary care level among both public and private providers may be another option to consider increasing earlier detection. Without improvements in implementation efficiency, only at significantly higher spending levels does additional preventive therapy and active case finding become cost-effective. The suggested expansion pathway would be to first provide isoniazid preventive therapy (IPT) for latent TB among PLHIV; then expand GeneXpert coverage to ensure that health workers prescribe the correct treatment regimen for DS-TB and MDR-TB; and finally to carry out household contact tracing, including preventive therapy among adult contacts. As active case-finding has been limited to date, the data on yield (percentage of positive tests among those tested for TB) for different modalities of active case- finding has also been limited. Conception and trialing of active case-finding programs among additional high-risk populations including those with comorbidities beyond HIV and those in higher- risk settings (for example, boarding schools, urban poor) should be explored because they may have higher yields compared with testing in the general population and could potentially be introduced earlier in this sequencing. 24 References 1. World Health Organization. 2017. Global tuberculosis report 2017. Geneva, Switzerland: WHO. 2. Collins, D, F. Hafidz, and D. Mustikawati. 2017. "The economic burden of tuberculosis in Indonesia." The International Journal of Tuberculosis and Lung Disease 21(9):1041-8. 3. Kerr C. C., S. Dura-Bernal, T. G. Smolinski, G. L. Chadderdon, and D. P. Wilson. 2018. "Optimization by adaptive stochastic descent." PloS one 13(3). 4. Ministry of Health RoI. 2015. Indonesia Tuberculosis Prevalence Survey 2013–2014. 5. United Nations Department of Economic and Social Affairs/Population Division. 2019. World Population Prospects 2019: Online edition. 6. Joint United Nations Programme on HIV/AIDS (UNAIDS). 2018. AIDSinfo online (database). 7. Houben, R. M., and P. J. Dodd. 2016. "The global burden of latent tuberculosis infection: a re-estimation using mathematical modelling." PLoS medicine 13(10):e1002152. 8. Kemenkes, R. 2014. Estimasi dan proyeksi HIV/AIDS di Indonesia tahun 2011-2016. Jakarta. 9. Triasih, R, R. Padmawati, T. Duke, C. Robertson, S. Sawyer, and S. Graham. 2016. "A mixed-methods evaluation of adherence to preventive treatment among child tuberculosis contacts in Indonesia." The International Journal of Tuberculosis and Lung Disease 20(8):1078-83. 10. Marks, S. M., J. Flood, B. Seaworth, Y. Hirsch-Moverman, L. Armstrong, S. Mase, et al. 2014. "Treatment practices, outcomes, and costs of multidrug-resistant and extensively drug-resistant tuberculosis, United States, 2005–2007." Emerging infectious diseases 20(5):812. 11. Triasih, R, M. Rutherford, T. Lestari, A. Utarini, C. F. Robertson, and S. M. Graham. 2012. "Contact investigation of children exposed to tuberculosis in South East Asia: a systematic review." Journal of tropical medicine 2012. 12. Triasih, R, C. F. Robertson, T. Duke, and S. M. Graham. 2015. "A prospective evaluation of the symptom- based screening approach to the management of children who are contacts of tuberculosis cases." Clinical Infectious Diseases 60(1):12-8. 13. Triasih, R., C. Robertson, J. De Campo, T. Duke, L. Choridah, and S. M. Graham. 2015. "An evaluation of chest X-ray in the context of community-based screening of child tuberculosis contacts." The International Journal of Tuberculosis and Lung Disease 19(12):1428-34. [[ Note: added "M" to Graham ]] 14. Yani, F. F., N. I. Lipoeto, B. Supriyatno, E. Darwin, and D. Basir. 2017. "Vitamin D status in under-five children with a history of close tuberculosis contact in Padang, West Sumatra." Asia Pacific journal of clinical nutrition 26 (Supplement):S68. 15. Goldberg, J, M. Macis, and P. K. Chintagunta. 2018. "Incentivized Peer Referrals for Tuberculosis Screening: Evidence from India." NBER Working Paper w25279. 16. Satiavan, I, Y. Hartantri, B. Werry, Y. Nababan, R. Wisaksana, and B. Alisjahban, eds. 2018. "Effect of isoniazid preventive therapy on tuberculosis incidence in people living with HIV-AIDS at Hasan Sadikin hospital." IOP Conference Series: Earth and Environmental Science. IOP Publishing. 17. Mboi, N, I. M. Surbakti, I. Trihandini, I. Elyazar, K. H. Smith, P. B. Ali, et al. 2018. "On the road to universal health care in Indonesia, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016." The Lancet 392(10147):581-91. 18. Jarrah, Z, D. Collins, and F. Hafidz. 2013. The cost of scaling up TB services in Indonesia. TB CARE 1. Cambridge: Management Sciences for Health. 19. World Health Organization. 2016. The shorter MDR-TB regimen. Available from: https://www.who.int/tb/Short_MDR_regimen_factsheet.pdf. 20. World Health Organization. 2016. WHO consolidated guidelines on drug-resistant tuberculosis treatment. World Health Organization. 21. Silva, D. R., F. C. dQ. Mello, and G. B. Migliori. 2020. "Shortened tuberculosis treatment regimens: what is new?" Jornal Brasileiro de Pneumologia 46(2). 22. Ministry of Health RoI. 2019. Roadmap Towards Eliminating Tuberculosis in Indonesia 2020-2030. Jakarta. 23. Anselmo, M. 2012. The Management of TB Case Contacts at a Lung Clinic in Indonesia. University of Otago. 25 Appendix A: Program Details and Model Constraints Most recent reported Unit cost (IDR) Assumptions and constraints spending (2017, IDR) TB prevention programs BCG vaccination 113 billion 25,000 per vaccinated child in puskesmas with Spending cannot decrease from 113 billion government subsidy (NTP priority program). Preventive therapy for latent 500 million 81,728 per child receiving preventive therapy, estimated Most recently reported (2017) approximately TB (child contacts) at 213,000 per child with latent TB based on an 7 percent of child contacts of active TB cases assumption that 30 percent of child contacts have (7,681 children) received preventive therapy. recently acquired latent TB based on findings from active It is estimated that maximum possible case finding from the Roadmap Towards Eliminating coverage with increased spending is 35 Tuberculosis in Indonesia 2020-2030 (22). percent of child contacts (NTP estimate). Preventive therapy for latent 0 81,728 per adult receiving preventive therapy, estimated Most recently reported 0 percent of adult TB (adult contacts) at 1,816,000 per adult with recently acquired latent TB, contacts of active TB cases received based on an assumption that 4.5 percent of adult preventive therapy. It is estimated that contacts have recently acquired latent TB based on maximum possible coverage with increased active cases found in Jakarta (23). There is high spending is 35 percent of adult contacts (NTP uncertainty concerning this value, but adults have lower estimate). susceptibility than infants, as well as having had previous exposure to TB in many cases, so the effective unit cost for preventive therapy in adults will be higher than children. Isoniazid preventive therapy 1.8 billion 292,552 per year based on continuous coverage ART coverage among PLHIV capped at 34 (IPT) for latent TB among (national TB program). percent, so maximum possible coverage of PLHIV IPT among PLHIV is also 34 percent (NTP estimates). Antiretroviral therapy (ART) N/A (estimated 460 N/A (based on regional ART cost of US$337 per person, Assumed that ART coverage will continue at for PLHIV billion) estimated to be 4.5 million IDR per person per year) most recently reported levels, because funding is not through the TB program and cannot be reallocated, but the impact of higher coverage is considered in additional scenarios. Screening and diagnosis programs 26 Passive case finding (private, 15 billion 43,613 per person tested (national TB program). 20 percent of TB suspect cases are estimated nonprotocol) Estimated at 281 per capita per year in the population to be screened in private sector without for this testing modality to be available for those who following the national TB testing protocols are symptomatic, given the current testing rate (Error! Reference source not found.) (based o n NTP estimates). This may include diagnosis by clinical symptoms, x-rays, and/or smear tests. Passive case finding where 92 billion 91,714 per person tested (national TB program). 60 percent of the population (based on NTP GeneXpert-based algorithm is Estimated at 590 per capita per year in the population estimates). not available for this testing modality to be available for those who are symptomatic, given the current testing rate This includes diagnosis by protocol-based tests such as cultures, smears and x-ray as defined in Error! Reference source not f ound. where GeneXpert machines are not available. Passive case finding with 164 billion 491,858 per person tested (national TB program). 20% of the population (based on NTP GeneXpert-based algorithm Estimated at 3,165 per capita per year in the population estimates). for this testing modality to be available for those who are symptomatic, given the current testing rate This includes protocol-based testing using GeneXpert machines as defined in. Contact tracing (household) 0 91,714 per person tested (typically through protocol- based testing without GeneXpert) Most recently reported coverage of household contact tracing is 0 percent. Estimated at 2 million IDR per person diagnosed given an assumption of 4.5 percent yield based on a 2011 review The maximum coverage achievable through of case contacts at a lung clinic in Jakarta. Because this this program was capped at 20 percent study was carried out almost a decade ago and is (based on NTP estimates). geographically limited, it is important to collate evidence for any new implementations of active case finding studies. Contact tracing (community) 0 91,714 per person tested (typically through protocol- based testing without GeneXpert). Most recently reported coverage of household contact tracing is 0 percent. Estimated at 4 million IDR per person diagnosed given an assumption of 2.5 percent yield, as the midpoint 27 between household contact tracing (4.5 percent) and The maximum coverage achievable through the estimated prevalence of undiagnosed active TB this program was capped at 10 percent among adults (0.4 percent). This assumption should also (based on NTP estimates). be revised should any new evidence become available. Active case finding (prisoners) 250 million Based on reported diagnoses through this program, Coverage capped at current levels (NTP estimated cost per person diagnosed 2.2 million IDR. As estimated program could not be expanded). many diagnoses are reported through puskesmas, actual cost per diagnosis may be lower. Treatment programs Public primary (puskesmas), DS 565 billion 1,919,751 per person treated including services and Currently 56 percent of DS-treatment costs, treatment drugs (national TB program) constrained to the range of 28% to 78% of DS-treatment costs. Public hospital, DS treatment 250 billion 2,806,357 per person treated including services and Currently 25 percent of DS-treatment costs, drugs (national TB program) constrained to the range of 12.5% to 63% of DS-treatment costs. Private primary (clinic, GPs), DS 13 billion 2,946,675 per person treated including services and Currently just over 1 percent of DS-treatment treatment drugs, estimated at 5 percent higher than public hospital costs, constrained to the range of 0.5 percent costs (national TB program) to 50 percent of DS-treatment costs. Private hospital, DS treatment 173 billion 2,946,675 per person treated including services and Currently 18 percent of DS-treatment costs, drugs, estimated at 5 percent higher than public hospital constrained to the range of 9 percent to 60 costs (national TB program) percent of DS-treatment costs. Public hospital, directly 67 billion 29,774,693 per person treated including services and Drug-resistant (DR-) TB was constrained to the observed treatment (DOT), drugs (national TB program) latest reported budget level for two reasons: MDR standard (i) Ethical constraints on equity of access Public hospital, DOT, MDR 27 billion 18,206,034 per person treated including services and suggest that we should not deny treatment to short drugs (national TB program) those with DR-TB despite the greater cost, and Public hospital, DOT, XDR 4.3 billion 59,549,386 per person treated including services and (ii) The NTP reports that funding sources for current drugs (national TB program) DR-TB are separated from DS-TB (funded by donors rather than government), so it would be logistically challenging to reallocate spending away from DR-TB. Spending may be reallocated between DR- treatment programs, but a minimum of 20 percent of MDR cases (29 percent of MDR spending) must continue to be treated through standard duration MDR courses. 28 Total targeted TB spending 1,486 billion This total excludes all other nontargeted costs that cannot be directly attributed to program implementation, as well as ART. For all programs, the minimum spending in the optimized allocation is 50 percent of the most recently reported allocation, based on discussion with the country team, to represent realistic constraints on rapid change between programs and personal preferences, for example, private treatment. Six additional prospective and cross-cutting programs were considered as part of this analysis but were not included in the optimization due to insufficient data or estimates on the direct impact of these programs on TB diagnosis, treatment, or transmission. Prospective and cross-cutting programs (not modeled) Active case finding (among high-risk groups including those from boarding schools and the urban poor) Strengthened management and more streamlined health information systems Performance-based provider payments Patient incentives Communication and advocacy Management and coordination 29 Appendix B: Detailed Optimization Results Table B.1 and Table B.2 give the projected absolute number of new active TB infections and TB-related deaths in 2035 if the most recently reported spending (2017) or optimized allocations of that spending as defined in Table B.3 were projected from 2019 until 2035. The percentages given are the relative change from 2016 values under each scenario. Table B.1: Differences Estimated for New Active TB Infections with Varying Resource Availability Most New active recently TB Optimized Optimized reported Optimized Optimized Optimized Optimized Optimized Optimized Optimized infections 60% 2019 80% 2019 spending 100% 2019 120% 2019 140% 2019 160% 2019 180% 2019 200% 2019 400% 2019 Children 63,000 47,000 50,000 37,000 30,000 28,000 22,000 21,000 20,000 15,000 0-14 years (-20%) (-40%) (-36%) (-52%) (-62%) (-65%) (-72%) (-74%) (-74%) (-81%) Females 278,000 243,000 222,000 220,000 204,000 196,000 173,000 167,000 164,000 114,000 15-64 (12%) (-2%) (-11%) (-11%) (-18%) (-21%) (-30%) (-32%) (-34%) (-47%) Males 342,000 306,000 284,000 282,000 265,000 256,000 231,000 224,000 220,000 161,000 15-64 (13%) (1%) (-7%) (-7%) (-13%) (-16%) (-24%) (-26%) (-27%) (-54%) Older 105,000 95,000 89,000 88,000 84,000 81,000 74,000 72,000 71,000 52,000 adults 65+ (56%) (41%) (31%) (30%) (24%) (20%) (9%) (6%) (5%) (-23%) People 52,000 44,000 40,000 40,000 36,000 35,000 31,000 30,000 29,000 24,000 living with (63%) (39%) (25%) (24%) (13%) (8%) (-3%) (-7%) (-9%) (-26%) HIV Total (sum) 841,000 735,000 684,000 667,000 618,000 595,000 531,000 514,000 505,000 366,000 (15%) (1%) (-6%) (-9%) (-15%) (-19%) (-27%) (-30%) (-31%) (-50%) Table B.2: Differences Estimated for TB-related Deaths with Varying Resource Availability Most TB- recently Optimized Optimized Optimized Optimized Optimized Optimized Optimized related Optimized Optimized reported 100% 120% 140% 160% 180% 200% 400% deaths 60% 2019 80% 2019 spending 2019 2019 2019 2019 2019 2019 2019 Children <500 4,000 3,000 2,000 2,000 1,000 1,000 1,000 1,000 1,000 0-14 (-91%) (-21%) (-47%) (-53%) (-62%) (-73%) (-76%) (-84%) (-86%) (-87%) years Females 55,000 42,000 34,000 34,000 29,000 27,000 21,000 20,000 19,000 13,000 15-64 (46%) (10%) (-9%) (-10%) (-22%) (-28%) (-43%) (-47%) (-49%) (-67%) Males 66,000 49,000 41,000 41,000 35,000 33,000 26,000 24,000 23,000 16,000 15-64 (13%) (-15%) (-30%) (-30%) (-39%) (-43%) (-56%) (-59%) (-61%) (-73%) Older 6,000 21,000 17,000 14,000 15,000 13,000 12,000 10,000 9,000 9,000 adults (-56%) (49%) (19%) (-1%) (2%) (-11%) (-16%) (-33%) (-37%) (-39%) 65+ People 7,000 23,000 19,000 17,000 17,000 15,000 14,000 9,000 9,000 9,000 living (-49%) (74%) (46%) (25%) (28%) (13%) (7%) (-29%) (-33%) (-35%) with HIV Total 169,000 130,000 108,000 108,000 94,000 87,000 67,000 62,000 60,000 42,000 (sum) (32%) (2%) (-16%) (-16%) (-27%) (-32%) (-48%) (-51%) (-53%) (-67%) 30 Table B.3: Optimized Spending Allocation with Varying Resource Availability. All Values in Millions of IDR (Indonesian Rupiah) Most recently Optimized 60% Optimized 80% reported Optimized 100% Optimized 120% Optimized 140% Optimized 160% Optimized 180% Optimized 200% Optimized 400% TB program 2019 2019 spending 2019 2019 2019 2019 2019 2019 2019 Active case finding 180 240 250 310 250 290 220 250 250 250 (prisoners) Contact tracing 0 0 0 0 0 0 0 0 0 41,000 (community) Contact tracing 0 0 0 0 0 0 160,000 180,000 190,000 145,000 (household) Passive case finding where Xpert based 21,000 28,000 92,000 34,000 97,000 110,000 81,000 91,000 99,000 0 algorithm is not available Passive case finding 20,000 26,000 15,000 33,000 20,000 23,000 11,000 12,000 5,500 0 (private non- protocol) Passive case finding with 99,000 130,000 160,000 160,000 170,000 200,000 190,000 210,000 230,000 822,000 Xpert based algorithm IPT for Latent 1,100 1,400 1,800 1,800 28,000 32,000 25,000 28,000 28,000 29,000 TB (HIV) Preventive therapy for Latent TB 0 0 0 0 0 0 0 0 0 1,676,000 (adult contacts) Preventive therapy for 23,000 31,000 500 39,000 100,000 120,000 110,000 120,000 130,000 156,000 Latent TB 31 (child contacts) Public hospital, DS 100,000 140,000 250,000 170,000 220,000 260,000 320,000 360,000 420,000 510,000 treatment Public primary (Puskesmas), 440,000 580,000 570,000 730,000 730,000 850,000 910,000 1,000,000 1,100,000 1,345,000 DS treatment Private primary (clinic, 4,400 5,900 13,000 7,400 11,000 13,000 16,000 18,000 21,000 26,000 GPs), DS treatment Private hospital, DS 60,000 79,000 170,000 99,000 140,000 170,000 220,000 240,000 280,000 345,000 treatment Public hospital, DOT, 16,000 22,000 67,000 27,000 34,000 39,000 46,000 51,000 57,000 114,000 MDR standard Public hospital, DOT, 40,000 54,000 27,000 67,000 82,000 96,000 110,000 130,000 140,000 280,000 MDR short Public hospital, DOT, 2,500 3,400 4,300 4,200 4,300 4,300 4,300 4,300 4,300 4,300 XDR current 32