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

Cerecero-García, Diego; Pineda-Antúnez, Carlos; Alexander, Lily; Cameron, Drew; Martínez-Silva, Gisela; Obure, Carol Dayo; Marseille, Elliot; Vu, Lung; Kahn, James G.; Vassall, Anna; Gomez, Gabriela B.; Bollinger, L; Levin, Carol; Bautista‐Arredondo, Sergio

doi:10.2989/16085906.2019.1688362

Cited by 7 publications

(9 citation statements)

References 15 publications

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“…The per capita GDP variable showed that our cost function could potentially be applied to other countries. This is in line with the study by Cerecero-García and colleagues 49 that used per capita GDP as a determinant to predict HIV treatment average costs in out-of-sample countries. The extrapolation of cost projections to other Southern African countries seems possible with our parsimonious empirical cost function; however, it would probably require additional or different variables in other settings such as in West Africa.…”

Section: Bmj Global Healthsupporting

confidence: 90%

Modelling costs of community-based HIV self-testing programmes in Southern Africa at scale: an econometric cost function analysis across five countries

et al. 2021

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BackgroundFollowing success demonstrated with the HIV Self-Testing AfRica Initiative, HIV self-testing (HIVST) is being added to national HIV testing strategies in Southern Africa. An analysis of the costs of scaling up HIVST is needed to inform national plans, but there is a dearth of evidence on methods for forecasting costs at scale from pilot projects. Econometric cost functions (ECFs) apply statistical inference to predict costs; however, we often do not have the luxury of collecting large amounts of location-specific data. We fit an ECF to identify key drivers of costs, then use a simpler model to guide cost projections at scale.MethodsWe estimated the full economic costs of community-based HIVST distribution in 92 locales across Malawi, Zambia, Zimbabwe, South Africa and Lesotho between June 2016 and June 2019. We fitted a cost function with determinants related to scale, locales organisational and environmental characteristics, target populations, and per capita Growth Domestic Product (GDP). We used models differing in data intensity to predict costs at scale. We compared predicted estimates with scale-up costs in Lesotho observed over a 2-year period.ResultsThe scale of distribution, type of community-based intervention, percentage of kits distributed to men, distance from implementer’s warehouse and per capita GDP predicted average costs per HIVST kit distributed. Our model simplification approach showed that a parsimonious model could predict costs without losing accuracy. Overall, ECF showed a good predictive capacity, that is, forecast costs were close to observed costs. However, at larger scale, variations of programme efficiency over time (number of kits distributed per agent monthly) could potentially influence cost predictions.DiscussionOur empirical cost function can inform community-based HIVST scale-up in Southern African countries. Our findings suggest that a parsimonious ECF can be used to forecast costs at scale in the context of financial planning and budgeting.

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Section: Bmj Global Healthsupporting

confidence: 90%

Modelling costs of community-based HIV self-testing programmes in Southern Africa at scale: an econometric cost function analysis across five countries

et al. 2021

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“… 15 ART service delivery unit costs in Kenya, Eswatini, Uganda, Zambia and Nigeria were also found to be inversely related to the number of patients served in a year. 13 …”

Section: Key Strategies To Improve Efficiencymentioning

confidence: 99%

“…Studies indicate that there are large variations in unit costs for HIV services, including ART service delivery, HCT and PMTCT across and within countries. [13][14][15] Most of these variations can be linked to service delivery characteristics and management practices in HIV healthcare. In a nationwide study in Nigeria, for example, a large proportion of the variations in costs for HCT and PMTCT facilities could be explained by scale-that is, facilities with the highest volume of clients saw the lowest costs.…”

Section: Introductionmentioning

confidence: 99%

“…15 ART service delivery unit costs in Kenya, Eswatini, Uganda, Zambia and Nigeria were also found to be inversely related to the number of patients served in a year. 13 Budgetary savings can also be achieved by task shiftingasking less costly, decentralised health facilities, often managed by less expensive workers, to deliver the same HIV prevention and treatment services at lower costs. 16 Studies found cost savings from task shifting HIV prevention and care for a high-risk group (men who have sex with men) from government facilities to community-based organisations.…”

Section: Introductionmentioning

confidence: 99%

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Approaches to improving the efficiency of HIV programme investments

et al. 2022

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“…One critical element to optimize funding decisions involves the cost and efficiency implications of implementing alternative program components and configurations. Contrary to widely accepted conceptions in both policy and academic arenas, average costs per service (socalled "unit costs") vary considerably across implementation settings and facilities [3][4][5][6][7]. The factors behind this variation are complex and include both justifiable and systematic causes, as well as health system inefficiencies and waste.…”

Section: Introductionmentioning

confidence: 96%

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

et al. 2021

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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.

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

Cited by 7 publications

References 15 publications

Modelling costs of community-based HIV self-testing programmes in Southern Africa at scale: an econometric cost function analysis across five countries

Modelling costs of community-based HIV self-testing programmes in Southern Africa at scale: an econometric cost function analysis across five countries

Approaches to improving the efficiency of HIV programme investments

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

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