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How should HIV resources be allocated? Lessons learnt from applying Optima HIV in 23 countries.

ABSTRACT: INTRODUCTION:With limited funds available, meeting global health targets requires countries to both mobilize and prioritize their health spending. Within this context, countries have recognized the importance of allocating funds for HIV as efficiently as possible to maximize impact. Over the past six years, the governments of 23 countries in Africa, Asia, Eastern Europe and Latin America have used the Optima HIV tool to estimate the optimal allocation of HIV resources. METHODS:Each study commenced with a request by the national government for technical assistance in conducting an HIV allocative efficiency study using Optima HIV. Each study team validated the required data, calibrated the Optima HIV epidemic model to produce HIV epidemic projections, agreed on cost functions for interventions, and used the model to calculate the optimal allocation of available funds to best address national strategic plan targets. From a review and analysis of these 23 country studies, we extract common themes around the optimal allocation of HIV funding in different epidemiological contexts. RESULTS AND DISCUSSION:The optimal distribution of HIV resources depends on the amount of funding available and the characteristics of each country's epidemic, response and targets. Universally, the modelling results indicated that scaling up treatment coverage is an efficient use of resources. There is scope for efficiency gains by targeting the HIV response towards the populations and geographical regions where HIV incidence is highest. Across a range of countries, the model results indicate that a more efficient allocation of HIV resources could reduce cumulative new HIV infections by an average of 18% over the years to 2020 and 25% over the years to 2030, along with an approximately 25% reduction in deaths for both timelines. However, in most countries this would still not be sufficient to meet the targets of the national strategic plan, with modelling results indicating that budget increases of up to 185% would be required. CONCLUSIONS:Greater epidemiological impact would be possible through better targeting of existing resources, but additional resources would still be required to meet targets. Allocative efficiency models have proven valuable in improving the HIV planning and budgeting process.

SUBMITTER: Stuart RM

PROVIDER: S-EPMC5898225 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

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How should HIV resources be allocated? Lessons learnt from applying Optima HIV in 23 countries.

Stuart Robyn M RM Grobicki Laura L Haghparast-Bidgoli Hassan H Panovska-Griffiths Jasmina J Skordis Jolene J Keiser Olivia O Estill Janne J Baranczuk Zofia Z Kelly Sherrie L SL Reporter Iyanoosh I Kedziora David J DJ Shattock Andrew J AJ Petravic Janka J Hussain S Azfar SA Grantham Kelsey L KL Gray Richard T RT Yap Xiao F XF Martin-Hughes Rowan R Benedikt Clemens J CJ Fraser-Hurt Nicole N Masaki Emiko E Wilson David J DJ Gorgens Marelize M Mziray Elizabeth E Cheikh Nejma N Shubber Zara Z Kerr Cliff C CC Wilson David P DP

Journal of the International AIDS Society 20180401 4

<h4>Introduction</h4>With limited funds available, meeting global health targets requires countries to both mobilize and prioritize their health spending. Within this context, countries have recognized the importance of allocating funds for HIV as efficiently as possible to maximize impact. Over the past six years, the governments of 23 countries in Africa, Asia, Eastern Europe and Latin America have used the Optima HIV tool to estimate the optimal allocation of HIV resources.<h4>Methods</h4>Eac ...[more]

PMID: 29652100

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Project description:Infectious disease forecasting is gaining traction in the public health community; however, limited systematic comparisons of model performance exist. Here we present the results of a synthetic forecasting challenge inspired by the West African Ebola crisis in 2014-2015 and involving 16 international academic teams and US government agencies, and compare the predictive performance of 8 independent modeling approaches. Challenge participants were invited to predict 140 epidemiological targets across 5 different time points of 4 synthetic Ebola outbreaks, each involving different levels of interventions and "fog of war" in outbreak data made available for predictions. Prediction targets included 1-4 week-ahead case incidences, outbreak size, peak timing, and several natural history parameters. With respect to weekly case incidence targets, ensemble predictions based on a Bayesian average of the 8 participating models outperformed any individual model and did substantially better than a null auto-regressive model. There was no relationship between model complexity and prediction accuracy; however, the top performing models for short-term weekly incidence were reactive models with few parameters, fitted to a short and recent part of the outbreak. Individual model outputs and ensemble predictions improved with data accuracy and availability; by the second time point, just before the peak of the epidemic, estimates of final size were within 20% of the target. The 4th challenge scenario - mirroring an uncontrolled Ebola outbreak with substantial data reporting noise - was poorly predicted by all modeling teams. Overall, this synthetic forecasting challenge provided a deep understanding of model performance under controlled data and epidemiological conditions. We recommend such "peace time" forecasting challenges as key elements to improve coordination and inspire collaboration between modeling groups ahead of the next pandemic threat, and to assess model forecasting accuracy for a variety of known and hypothetical pathogens.

Dataset Information

How should HIV resources be allocated? Lessons learnt from applying Optima HIV in 23 countries.

Publications

How should HIV resources be allocated? Lessons learnt from applying Optima HIV in 23 countries.

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