Moving away from the "unit cost". Predicting country-specific average cost curves of VMMC services accounting for variations in service delivery platforms in sub-Saharan Africa.

BACKGROUND: One critical element to optimize funding decisions involves the cost and efficiency implications of implementing alternative program components and configurations. Program planners, policy makers and funders alike are in need of relevant, strategic data and analyses to help them plan and implement effective and efficient programs. Contrary to widely accepted conceptions in both policy and academic arenas, average costs per service (so-called "unit costs") vary considerably across implementation settings and facilities. The objective of this work is twofold: 1) to estimate the variation of VMMC unit costs across service delivery platforms (SDP) in Sub-Saharan countries, and 2) to develop and validate a strategy to extrapolate unit costs to settings for which no data exists. METHODS: We identified high-quality VMMC cost studies through a literature review. Authors were contacted to request the facility-level datasets (primary data) underlying their results. We standardized the disparate datasets into an aggregated database which included 228 facilities in eight countries. We estimated multivariate models to assess the correlation between VMMC unit costs and scale, while simultaneously accounting for the influence of the SDP (which we defined as all possible combinations of type of facility, ownership, urbanicity, and country), on the unit cost variation. We defined SDP as any combination of such four characteristics. Finally, we extrapolated VMMC unit costs for all SDPs in 13 countries, including those not contained in our dataset. RESULTS: The average unit cost was 73 USD (IQR: 28.3, 100.7). South Africa showed the highest within-country cost variation, as well as the highest mean unit cost (135 USD). Uganda and Namibia had minimal within-country cost variation, and Uganda had the lowest mean VMMC unit cost (22 USD). Our results showed evidence consistent with economies of scale. Private ownership and Hospitals were significant determinants of higher unit costs. By identifying key cost drivers, including country- and facility-level characteristics, as well as the effects of scale we developed econometric models to estimate unit cost curves for VMMC services in a variety of clinical and geographical settings. CONCLUSION: While our study did not produce new empirical data, our results did increase by a tenfold the availability of unit costs estimates for 128 SDPs in 14 priority countries for VMMC. It is to our knowledge, the most comprehensive analysis of VMMC unit costs to date. Furthermore, we provide a proof of concept of the ability to generate predictive cost estimates for settings where empirical data does not exist

A meta-analysis approach for estimating average unit costs for ART using pooled facility-level primary data from African countries

Objective: To estimate facility-level average cost for ART services and explore unit cost variations using pooled facility-level cost estimates from four HIV empirical cost studies conducted in five African countries. Methods: Through a literature search we identified studies reporting facility-level costs for ART programmes. We requested the underlying data and standardised the disparate data sources to make them comparable. Subsequently, we estimated the annual cost per patient served and assessed the cost variation among facilities and other service delivery characteristics using descriptive statistics and meta-analysis. All costs were converted to 2017 US dollars ($). Results: We obtained and standardised data from four studies across five African countries and 139 facilities. The weighted average cost per patient on ART was$251 (95% CI: 193–308). On average, 46% of the mean unit cost correspond to antiretroviral (ARVs) costs, 31% to personnel costs, 20% other recurrent costs, and 2% to capital costs. We observed a lot of variation in unit cost and scale levels between countries. We also observed a negative relationship between ART unit cost and the number of patients served in a year. Conclusion: Our approach allowed us to explore unit cost variation across contexts by pooling ART costs from multiple sources. Our research provides an example of how to estimate costs based on heterogeneous sources reconciling methodological differences across studies and contributes by giving an example on how to estimate costs based on heterogeneous sources of data. Also, our study provides additional information on costs for funders, policy-makers, and decision-makers in the process of designing or scaling-up HIV interventions

Genomic insights into the early peopling of the Caribbean

The Caribbean was one of the last regions of the Americas to be settled by humans, but where they came from and how and when they reached the islands remain unclear. We generated genome-wide data for 93 ancient Caribbean islanders dating between 3200 and 400 calibrated years before the present and found evidence of at least three separate dispersals into the region, including two early dispersals into the Western Caribbean, one of which seems connected to radiation events in North America. This was followed by a later expansion from South America. We also detected genetic differences between the early settlers and the newcomers from South America, with almost no evidence of admixture. Our results add to our understanding of the initial peopling of the Caribbean and the movements of Archaic Age peoples in the Americas.The research was funded by the Max Planck Society and the European Research Council under the 7th Framework Program (grant agreement no. 319209, ERC Synergy Project NEXUS1492). H.S. was supported by the HERA (Humanities in the European Research Area) Joint Research Program “Uses of the Past” (CitiGen) and the European Union’s Horizon 2020 research and innovation program under grant agreement no. 649307. W.J.P. and M.A.N.-C. were supported by the National Science Foundation (BCS-0612727 and BCS1622479). C.L.-F. was supported by a grant from the Ministry of Science, Innovation and Universities (PGC2018-0955931-B-100, AEI/FEDER, UE). M.R. was supported by the Social Sciences and Humanities Research Council of Canada (435-2016-0529). M.R., Y.C.d.A., U.M.G.H., and S.T.H.G. were supported by the Social Sciences and Humanities Research Council of Canada (standard research grant SSHRC ‐ 410‐2011‐1179 and SSHRC postdoctoral fellowship ‐ 756‐2016‐0180) and several University of Winnipeg internal grants (Major grant 2017, 2018; Partnership Development grant 2017, 2018; and Discretionary grant 2017, 2018)