4.4 Article

Getting resources to those who need them: the evidence we need to budget for underserved populations in sub-Saharan Africa

Journal

Publisher

JOHN WILEY & SONS LTD
DOI: 10.1002/jia2.25707

Keywords

vulnerable populations; HIV infections; cost; budgets; resource allocation; Africa

Funding

  1. National Institute of Mental Health of the National Institutes of Health [K01MH119923]

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Evidence-based budgeting is a useful tool for efficient resource allocation, but in sub-Saharan Africa, there may be a lack of evidence for underserved and vulnerable populations. It is important to consider the specific needs and challenges of these populations rather than excluding them from the budgeting process. Additional resources and research are needed to address the limitations in data and evidence for these populations.
Introduction In recent years, many countries have adopted evidence-based budgeting (EBB) to encourage the best use of limited and decreasing HIV resources. The lack of data and evidence for hard to reach, marginalized and vulnerable populations could cause EBB to further disadvantage those who are already underserved and who carry a disproportionate HIV burden (USDB). We outline the critical data required to use EBB to support USDB people in the context of the generalized epidemics of sub-Saharan Africa (SSA). Discussion To be considered in an EBB cycle, an intervention needs at a minimum to have an estimate of a) the average cost, typically per recipient of the intervention; b) the effectiveness of the intervention and c) the size of the intervention target population. The methods commonly used for general populations are not sufficient for generating valid estimates for USDB populations. USDB populations may require additional resources to learn about, access, and/or successfully participate in an intervention, increasing the cost per recipient. USDB populations may experience different health outcomes and/or other benefits than in general populations, influencing the effectiveness of the interventions. Finally, USDB population size estimation is critical for accurate programming but is difficult to obtain with almost no national estimates for countries in SSA. We explain these limitations and make recommendations for addressing them. Conclusions EBB is a strong tool to achieve efficient allocation of resources, but in SSA the evidence necessary for USDB populations may be lacking. Rather than excluding USDB populations from the budgeting process, more should be invested in understanding the needs of these populations.

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