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Approximately 85% of tuberculosis (TB) related deaths occur in low- and middle-income countries where health resources are scarce. Effective priority setting is required to maximise the impact of limited budgets. The Optima TB tool has been developed to support analytical capacity and inform evidence-based priority setting processes for TB health benefits package design. This paper outlines the Optima TB framework and how it was applied in Belarus, an upper-middle income country in Eastern Europe with a relatively high burden of TB. Optima TB is a population-based disease transmission model, with programmatic cost functions and an optimisation algorithm. Modelled populations include age-differentiated general populations and higher-risk populations such as people living with HIV. Populations and prospective interventions are defined in consultation with local stakeholders. In partnership with the latter, demographic, epidemiological, programmatic, as well as cost and spending data for these populations and interventions are then collated. An optimisation analysis of TB spending was conducted in Belarus, using program objectives and constraints defined in collaboration with local stakeholders, which included experts, decision makers, funders and organisations involved in service delivery, support and technical assistance. These analyses show that it is possible to improve health impact by redistributing current TB spending in Belarus. Specifically, shifting funding from inpatient- to outpatient-focused care models, and from mass screening to active case finding strategies, could reduce TB prevalence and mortality by up to 45% and 50%, respectively, by 2035. In addition, an optimised allocation of TB spending could lead to a reduction in drug-resistant TB infections by 40% over this period. This would support progress towards national TB targets without additional financial resources. The case study in Belarus demonstrates how reallocations of spending across existing and new interventions could have a substantial impact on TB outcomes. This highlights the potential for Optima TB and similar modelling tools to support evidence-based priority setting. Author summary: Tuberculosis (TB) remains a leading global cause of death and morbidity, and 85% of deaths occur in countries where resources for TB care and control are limited. Many countries cannot finance all TB interventions or technologies, which means difficult decisions on what to prioritise and publically finance. Modelling tools can help decision-makers set priorities based on evidence, in a systematic and transparent way. This study presents Optima TB, a tool that estimates which allocations of spending across interventions will most likely maximise specified objectives—such as minimising TB deaths, prevalence and incidence. In partnership with local decision-makers and stakeholders, Optima TB was applied in Belarus. Recommendations from the model findings include focussing investment on outpatient rather than inpatient care and actively finding people with TB (e.g. through contact tracing) rather than mass testing of the population. The recommended reallocations of spending could reduce TB prevalence and deaths by up to 45% and 50%, respectively, by 2035 for the same amount of spending. Key stakeholders were engaged throughout the analysis and findings and uncertainty around the results were clearly communicated with decision-makers. The timeliness of the results helped inform national dialogue on TB care reform, among other key policy discussions. [ABSTRACT FROM AUTHOR]

Introduction: In 2014, the South African government adopted a differentiated service delivery (DSD) model in its "National Adherence Guidelines for Chronic Diseases (HIV, TB and NCDs)" (AGL) to strengthen the HIV care cascade. We describe the barriers and facilitators of the AGL implementation as experienced by various stakeholders in eight intervention and control sites across four districts. Methods: Embedded within a cluster‐randomized evaluation of the AGL, we conducted 48 in‐depth interviews (IDIs) with healthcare providers, 16 IDIs with Department of Health and implementing partners and 24 focus group discussions (FGDs) with three HIV patient groups: new, stable and those not stable on treatment or not adhering to care. IDIs were conducted from August 2016 to August 2017; FGDs were conducted in January to February 2017. Content analysis was guided by the Consolidated Framework for Implementation Research. Findings were triangulated among respondent types to elicit barriers and facilitators to implementation. Results: New HIV patients found counselling helpful but intervention respondents reported sub‐optimal counselling and privacy concerns as barriers to initiation. Providers felt insufficiently trained for this intervention and were confused by the simultaneous rollout of the Universal Test and Treat strategy. For stable patients, repeat prescription collection strategies (RPCS) were generally well received. Patients and providers concurred that RPCS reduced congestion and waiting times at clinics. There was confusion though, among providers and implementers, around implementation of RPCS interventions. For patients not stable on treatment, enhanced counselling and tracing patients lost‐to‐follow‐up were perceived as beneficial to adherence behaviours but faced logistical challenges. All providers faced difficulties accessing data and identifying patients in need of tracing. Congestion at clinics and staff attitude were perceived as barriers preventing patients returning to care. Conclusions: Implementation of DSD models at scale is complex but this evaluation identified several positive aspects of AGL implementation. The positive perception of RPCS interventions and challenges managing patients not stable on treatment aligned with results from the larger evaluation. While some implementation challenges may resolve with experience, ensuring providers and implementers have the necessary training, tools and resources to operationalize AGL effectively is critical to the overall success of South Africa's HIV control strategy. [ABSTRACT FROM AUTHOR]

Background: We describe CD4 count recovery among HIV positive individuals who initiated antiretroviral therapy (ART) with and without severe immune suppression using complete laboratory data from South Africa’s national HIV treatment programme between 2010 and 2014 and discuss implications for CD4 count monitoring. Methods: Retrospective analysis of routinely collected laboratory data from South Africa’s National Health Laboratory Service (NHLS). A probabilistic record linkage algorithm was used to create a cohort of HIV positive individuals who initiated ART between 2010 and 2014 based on timing of CD4 count and viral load measurements. A CD4 count < 50 copies/μl at ART initiation was considered severe immunosuppression. A multivariable piecewise mixed-effects linear regression model adjusting for age, gender, year of starting ART, viral suppression in follow up and province was used to predict CD4 counts during follow up. Results: 1,070,900 individuals had evidence of starting ART during 2010–2014 and met the criteria for inclusion in the cohort -46.6% starting ART with CD4 < 200 cells/μl and 10.1% with CD4 < 50 cells/ μl. For individuals with CD4 counts < 200 cells/μl, predicted CD4 counts > 200 cells/μl, >350 cells/μl and >500 cells/μl corresponded with mean follow up durations of 1.5 years (standard deviation [s.d] 1.1), 1.9years (s.d 1.2) and 2.1 years (s.d 1.3 years). For those with CD4 counts < 50 cells/μl, predicted CD4 count above these threshold corresponded with mean follow up durations of 2.5 years (s.d 0.9 years), 4.4 years (s.d 0.4 years) and 5.0 years (s.d 0.1years) for recovery to the same thresholds. CD4 count recovery varied mostly with duration on ART, CD4 count at the start of ART and gender. Conclusion: For individuals starting with ART with severe immunosuppression, CD4 recovery to 200cells/μl did not occur or took longer than 12 month for significant proportions. CD4 monitoring and interventions recommended for advanced HIV disease should continue until full recovery. [ABSTRACT FROM AUTHOR]

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Introduction: In 2014, city leaders from around the world endorsed the Paris Declaration on Fast-Track Cities, pledging to achieve the 2020 and 2030 HIV targets championed by UNAIDS. The City of Johannesburg - one of South Africa's metropolitan municipalities and also a health district - has over 600,000 people living with HIV (PLHIV), more than any other city worldwide. We estimate what it would take in terms of programmatic targets and costs for the City of Johannesburg to meet the Fast-Track targets, and demonstrate the impact that this would have. Methods: We applied the Optima HIV epidemic and resource allocation model to demographic, epidemiological and behavioural data on 26 sub-populations in Johannesburg. We used data on programme costs and coverage to produce baseline projections. We calculated how many people must be diagnosed, put onto treatment and maintained with viral suppression to achieve the 2020 and 2030 targets. We also estimated how treatment needs - and therefore fiscal commitments - could be reduced if the treatment targets are combined with primary HIV prevention interventions (voluntary medical male circumcision (VMMC), an expanded condom programme, and comprehensive packages for female sex workers (FSW) and young females). Results: If current programmatic coverage were maintained, Johannesburg could expect 303,000 new infections and 96,000 AIDS-related deaths between 2017 and 2030 and 769,000 PLHIV by 2030. Achieving the Fast-Track targets would require an additional 135,000 diagnoses and 232,000 people on treatment by 2020 (an increase in around 80% over 2016 treatment numbers), but would avert 176,000 infections and 56,500 deaths by 2030. Assuming stable ART unit costs, this would require ZAR 29 billion (USD 2.15 billion) in cumulative treatment investments over the 14 years to 2030. Plausible scale-ups of other proven interventions (VMMC, condom distribution and FSW strategies) could yield additional reductions in new infections (between 4 and 15%), and in overall treatment investment needs. Scaling up VMMC in line with national targets is found to be cost-effective in the medium term. Conclusions: The scale-up in testing and treatment programmes over this decade has been rapid, but these efforts must be doubled to reach 2020 targets. Strategic investments in proven interventions will help Johannesburg achieve the treatment targets and be on track to end AIDS by 2030. [ABSTRACT FROM AUTHOR]

Background: Population HIV prevalence across West Africa varies substantially. We assess the national epidemiological and behavioural factors associated with this. Methods: National, urban and rural data on HIV prevalence, the percentage of younger (15–24) and older (25–49) women and men reporting multiple (2+) partners in the past year, HIV prevalence among female sex workers (FSWs), men who have bought sex in the past year (clients), and ART coverage, were compiled for 13 countries. An Ecological analysis using linear regression assessed which factors are associated with national variations in population female and male HIV prevalence, and with each other. Findings: National population HIV prevalence varies between 0 4–2 9% for men and 0 4–5.6% for women. ART coverage ranges from 6–23%. National variations in HIV prevalence are not shown to be associated with variations in HIV prevalence among FSWs or clients. Instead they are associated with variations in the percentage of younger and older males and females reporting multiple partners. HIV prevalence is weakly negatively associated with ART coverage, implying it is not increased survival that is the cause of variations in HIV prevalence. FSWs and younger female HIV prevalence are associated with client population sizes, especially older men. Younger female HIV prevalence is strongly associated with older male and female HIV prevalence. Interpretation: In West Africa, population HIV prevalence is not significantly higher in countries with high FSW HIV prevalence. Our analysis suggests, higher prevalence occurs where more men buy sex, and where a higher percentage of younger women, and older men and women have multiple partnerships. If a sexual network between clients and young females exists, clients may potentially bridge infection to younger females. HIV prevention should focus both on commercial sex and transmission between clients and younger females with multiple partners. [ABSTRACT FROM AUTHOR]