Modeling the impact of universal tuberculosis molecular testing and timing of tuberculosis preventive treatment during antiretroviral therapy initiation in South Africa.

OBJECTIVES: Targeted universal tuberculosis (TB) testing can improve TB detection among people with HIV. This approach is being scaled up in South Africa through Xpert MTB/RIF Ultra testing for individuals starting antiretroviral therapy and annually thereafter. Clarity is needed on how Universal Xpert testing may affect TB preventive treatment (TPT) provision, and on whether TPT should be delayed until TB is ruled out. DESIGN: State-transition microsimulation. METHODS: We simulated a cohort of South African patients being screened for TB while entering HIV care. We compared clinical and cost outcomes between four TB screening algorithms: symptom-based, C-reactive protein-based, and Universal Xpert testing with either simultaneous or delayed TPT initiation. RESULTS: Prompt TB treatment initiation among simulated patients with TB increased from 26% (24-28%) under symptom screening to 53% (50-56%) with Universal Xpert testing. Universal Xpert testing led to increased TPT uptake when TPT initiation was simultaneous, but to approximately 50% lower TPT uptake if TPT was delayed. Universal Xpert with simultaneous TPT prevented incident TB compared to either symptom screening (median 17 cases averted per 5000 patients) or Universal Xpert with delayed TPT (median 23 averted). Universal Xpert with Simultaneous TPT cost approximately $39 per incremental TPT course compared to Universal Xpert with delayed TPT. CONCLUSIONS: Universal Xpert testing can promote timely treatment for newly diagnosed people with HIV who have active TB. Pairing universal testing with immediate TPT will improve the promptness, uptake, and preventive effects of TPT. Simultaneous improvements to TB care cascades are needed to maximize impact.

Exploring equity in health and poverty impacts of control measures for SARS-CoV-2 in six countries.

BACKGROUND: Policy makers need to be rapidly informed about the potential equity consequences of different COVID-19 strategies, alongside their broader health and economic impacts. While there are complex models to inform both potential health and macro-economic impact, there are few tools available to rapidly assess potential equity impacts of interventions. METHODS: We created an economic model to simulate the impact of lockdown measures in Pakistan, Georgia, Chile, UK, the Philippines and South Africa. We consider impact of lockdown in terms of ability to socially distance, and income loss during lockdown, and tested the impact of assumptions on social protection coverage in a scenario analysis. RESULTS: In all examined countries, socioeconomic status (SES) quintiles 1-3 were disproportionately more likely to experience income loss (70% of people) and inability to socially distance (68% of people) than higher SES quintiles. Improving social protection increased the percentage of the workforce able to socially distance from 48% (33%-60%) to 66% (44%-71%). We estimate the cost of this social protection would be equivalent to an average of 0.6% gross domestic product (0.1% Pakistan-1.1% Chile). CONCLUSIONS: We illustrate the potential for using publicly available data to rapidly assess the equity implications of social protection and non-pharmaceutical intervention policy. Social protection is likely to mitigate inequitable health and economic impacts of lockdown. Although social protection is usually targeted to the poorest, middle quintiles will likely also need support as they are most likely to suffer income losses and are disproportionately more exposed.

Estimating Cost Functions for Resource Allocation Using Transmission Models: A Case Study of Tuberculosis Case Finding in South Africa.

OBJECTIVE: Cost functions linked to transmission dynamic models are commonly used to estimate the resources required for infectious disease policies. We present a conceptual and empirical approach for estimating these functions, allowing for nonconstant marginal costs. We aim to expand on the current approach which commonly assumes linearity of cost over scale. METHODS: We propose a theoretical framework adapted from the field of transport economics. We specify joint functions of production of services within a disease-specific program. We expand these functions to include qualitative insights of program expansion patterns. We present the difference in incremental total costs between an approach assuming constant unit costs and alternative approaches that assume economies of scale, scope and homogeneous or heterogeneous facility recruitment into the programme during scale-up. We illustrate the framework's application in tuberculosis, using secondary data from the literature and routine reporting systems in South Africa. RESULTS: Economies of capacity and scope substantially change cost estimates over time. Cost data requirements for the proposed approach included standardized and disaggregated unit costs (for a limited number of outputs) and information on the facilities network available to the program. CONCLUSIONS: The defined functional form will determine the magnitude and shape of costs when outputs and coverage are increasing. This in turn will impact resource allocation decisions. Infectious diseases modelers and economists should use transparent and empirically based cost models for analyses that inform resource allocation decisions. This framework describes a general approach for developing these models.

Informing Balanced Investment in Services and Health Systems: A Case Study of Priority Setting for Tuberculosis Interventions in South Africa.

OBJECTIVES: Health systems face nonfinancial constraints that can influence the opportunity cost of interventions. Empirical methods to explore their impact, however, are underdeveloped. We develop a conceptual framework for defining health system constraints and empirical estimation methods that rely on routine data. We then present an empirical approach for incorporating nonfinancial constraints in cost-effectiveness models of health benefit packages for the health sector. METHODS: We illustrate the application of this approach through a case study of defining a package of services for tuberculosis case-finding in South Africa. An economic model combining transmission model outputs with unit costs was developed to examine the cost-effectiveness of alternative screening and diagnostic algorithms. Constraints were operationalized as restrictions on achievable coverage based on: (1) financial resources; (2) human resources; and (3) policy constraints around diagnostics purchasing. Cost-effectiveness of the interventions was assessed under one "unconstrained" and several "constrained" scenarios. For the unconstrained scenario, incremental cost-effectiveness ratios were estimated with and without the costs of "relaxing" constraints. RESULTS: We find substantial differences in incremental cost-effectiveness ratios across scenarios, leading to variations in the decision rules for prioritizing interventions. In constrained scenarios, the limiting factor for most interventions was not financial, but rather the availability of human resources. CONCLUSIONS: We find that optimal prioritization among different tuberculosis control strategies in South Africa is influenced by whether and how constraints are taken into consideration. We thus demonstrate both the importance and feasibility of considering nonfinancial constraints in health sector resource allocation models.

Examining Approaches to Estimate the Prevalence of Catastrophic Costs Due to Tuberculosis from Small-Scale Studies in South Africa.

BACKGROUND AND OBJECTIVE: In context of the End TB goal of zero tuberculosis (TB)-affected households encountering catastrophic costs due to TB by 2020, the estimation of national prevalence of catastrophic costs due to TB is a priority to inform programme design. We explore approaches to estimate the national prevalence of catastrophic costs due to TB from existing datasets as an alternative to nationally representative surveys. METHODS: We obtained, standardized and merged three patient-level datasets from existing studies on patient-incurred costs due to TB in South Africa. A deterministic cohort model was developed with the aim of estimating the national prevalence of catastrophic costs, using national data on the prevalence of TB and likelihood of loss to follow-up by income quintile and HIV status. Two approaches were tested to parameterize the model with existing cost data. First, a meta-analysis summarized study-level data by HIV status and income quintile. Second, a regression analysis of patient-level data also included employment status, education level and urbanicity. We summarized findings by type of cost and examined uncertainty around resulting estimates. RESULTS: Overall, the median prevalence of catastrophic costs for the meta-analysis and regression approaches were 11% (interquartile range [IQR] 9-13%) and 6% (IQR 5-8%), respectively. Both approaches indicated that the main burden of catastrophic costs falls on the poorest households. An individual-level regression analysis produced lower uncertainty around estimates than a study-level meta-analysis. CONCLUSIONS: This paper presents a novel application of existing data to estimate the national prevalence of catastrophic costs due to TB. This type of model could be useful for researchers and policy makers looking to inform certain policy decisions; however, some uncertainties remain due to limitations in data availability. There is an urgent need for standardized reporting of cost data and improved guidance on methods to collect income data to improve these estimates going forward.

Estimating the Impact of Tuberculosis Case Detection in Constrained Health Systems: An Example of Case-Finding in South Africa.

Mathematical models are increasingly being used to compare strategies for tuberculosis (TB) control and inform policy decisions. Models often do not consider financial and other constraints on implementation and may overestimate the impact that can be achieved. We developed a pragmatic approach for incorporating resource constraints into mathematical models of TB. Using a TB transmission model calibrated for South Africa, we estimated the epidemiologic impact and resource requirements (financial, human resource (HR), and diagnostic) of 9 case-finding interventions. We compared the model-estimated resources with scenarios of future resource availability and estimated the impact of interventions under these constraints. Without constraints, symptom screening in public health clinics and among persons receiving care for human immunodeficiency virus infection was predicted to lead to larger reductions in TB incidence (9.5% (2.5th-97.5th percentile range (PR), 8.6-12.2) and 14.5% (2.5th-97.5th PR, 12.2-16.3), respectively) than improved adherence to diagnostic guidelines (2.7%; 2.5th-97.5th PR, 1.6-4.1). However, symptom screening required large increases in resources, exceeding future HR capacity. Even under our most optimistic HR scenario, the reduction in TB incidence from clinic symptom screening was 0.2%-0.9%-less than that of improved adherence to diagnostic guidelines. Ignoring resource constraints may result in incorrect conclusions about an intervention's impact and may lead to suboptimal policy decisions. Models used for decision-making should consider resource constraints.

Application of provincial data in mathematical modelling to inform sub-national tuberculosis program decision-making in South Africa.

South Africa has the highest tuberculosis (TB) disease incidence rate in the world, and TB is the leading infectious cause of death. Decisions on, and funding for, TB prevention and care policies are decentralised to the provincial governments and therefore, tools to inform policy need to operate at this level. We describe the use of a mathematical model planning tool at provincial level in a high HIV and TB burden country, to estimate the impact on TB burden of achieving the 90-(90)-90 targets of the Stop TB Partnership Global Plan to End TB. "TIME Impact" is a freely available, user-friendly TB modelling tool. In collaboration with provincial TB programme staff, and the South African National TB Programme, models for three (of nine) provinces were calibrated to TB notifications, incidence, and screening data. Reported levels of TB programme activities were used as baseline inputs into the models, which were used to estimate the impact of scale-up of interventions focusing on screening, linkage to care and treatment success. All baseline models predicted a trend of decreasing TB incidence and mortality, consistent with recent data from South Africa. The projected impacts of the interventions differed by province and were greatly influenced by assumed current coverage levels. The absence of provincial TB burden estimates and uncertainty in current activity coverage levels were key data gaps. A user-friendly modelling tool allows TB burden and intervention impact projection at the sub-national level. Key sub-national data gaps should be addressed to improve the quality of sub-national model predictions.

Empirical estimation of resource constraints for use in model-based economic evaluation: an example of TB services in South Africa.

BACKGROUND: Evidence on the relative costs and effects of interventions that do not consider 'real-world' constraints on implementation may be misleading. However, in many low- and middle-income countries, time and data scarcity mean that incorporating health system constraints in priority setting can be challenging. METHODS: We developed a 'proof of concept' method to empirically estimate health system constraints for inclusion in model-based economic evaluations, using intensified case-finding strategies (ICF) for tuberculosis (TB) in South Africa as an example. As part of a strategic planning process, we quantified the resources (fiscal and human) needed to scale up different ICF strategies (cough triage and WHO symptom screening). We identified and characterised three constraints through discussions with local stakeholders: (1) financial constraint: potential maximum increase in public TB financing available for new TB interventions; (2) human resource constraint: maximum current and future capacity among public sector nurses that could be dedicated to TB services; and (3) diagnostic supplies constraint: maximum ratio of Xpert MTB/RIF tests to TB notifications. We assessed the impact of these constraints on the costs of different ICF strategies. RESULTS: It would not be possible to reach the target coverage of ICF (as defined by policy makers) without addressing financial, human resource and diagnostic supplies constraints. The costs of addressing human resource constraints is substantial, increasing total TB programme costs during the period 2016-2035 by between 7% and 37% compared to assuming the expansion of ICF is unconstrained, depending on the ICF strategy chosen. CONCLUSIONS: Failure to include the costs of relaxing constraints may provide misleading estimates of costs, and therefore cost-effectiveness. In turn, these could impact the local relevance and credibility of analyses, thereby increasing the risk of sub-optimal investments.

The patient costs of care for those with TB and HIV: a cross-sectional study from South Africa.

BACKGROUND: This study describes the post-diagnosis care-seeking costs incurred by people living with TB and/or HIV and their households, in order to identify the potential benefits of integrated care. METHODS: We conducted a cross-sectional study with 454 participants with TB or HIV or both in public primary health care clinics in Ekurhuleni North Sub-District, South Africa. We collected information on visits to health facilities, direct and indirect costs for participants and for their guardians and caregivers. We define 'integration' as receipt of both TB and HIV services at the same facility, on the same day. Costs were presented and compared across participants with TB/HIV, TB-only and HIV-only. Costs exceeding 10% of participant income were considered catastrophic. RESULTS: Participants with both TB and HIV faced a greater economic burden (US$74/month) than those with TB-only (US$68/month) or HIV-only (US$40/month). On average, people with TB/HIV made 18.4 visits to health facilities, more than TB-only participants or HIV-only participants who made 16 and 5.1 visits, respectively. However, people with TB/HIV had fewer standalone TB (10.9) and HIV (2.2) visits than those with TB-only (14.5) or HIV-only (4.4). Although people with TB/HIV had access to 'integrated' services, their time loss was substantially higher than for other participants. Overall, 55% of participants encountered catastrophic costs. Access to official social protection schemes was minimal. CONCLUSIONS: People with TB/HIV in South Africa are at high risk of catastrophic costs. To some extent, integration of services reduces the number of standalone TB and HIV of visits to the health facility. It is however unlikely that catastrophic costs can be averted by service integration alone. Our results point to the need for timely social protection, particularly for HIV-positive people starting TB treatment.

Methodological Issues to Consider When Collecting Data to Estimate Poverty Impact in Economic Evaluations in Low-income and Middle-income Countries.

Out-of-pocket spending is increasingly recognized as an important barrier to accessing health care, particularly in low-income and middle-income countries (LMICs) where a large portion of health expenditure comes from out-of-pocket payments. Emerging universal healthcare policies prioritize reduction of poverty impact such as catastrophic and impoverishing healthcare expenditure. Poverty impact is therefore increasingly evaluated alongside and within economic evaluations to estimate the impact of specific health interventions on poverty. However, data collection for these metrics can be challenging in intervention-based contexts in LMICs because of study design and practical limitations. Using a set of case studies, this letter identifies methodological challenges in collecting patient cost data in LMIC contexts. These components are presented in a framework to encourage researchers to consider the implications of differing approaches in data collection and to report their approach in a standardized and transparent way.