How to Estimate Optimal Malaria Readiness Indicators at Health-District Level: Findings from the Burkina Faso Service Availability and Readiness Assessment (SARA) Data.

Toussaint Rouamba,Fati Kirakoya-Samadoulougou,Jean Gaudart,Sekou Samadoulougou,Halidou Tinto,Cheick Saïd Compaoré

doi:10.3390/ijerph17113923

Abstract

One of the major contributors of malaria-related deaths in Sub-Saharan African countries is the limited accessibility to quality care. In these countries, malaria control activities are implemented at the health-district level (operational entity of the national health system), while malaria readiness indicators are regionally representative. This study provides an approach for estimating health district-level malaria readiness indicators from survey data designed to provide regionally representative estimates. A binomial-hierarchical Bayesian spatial prediction method was applied to Burkina Faso Service Availability and Readiness Assessment (SARA) survey data to provide estimates of essential equipment availability and readiness for malaria care. Predicted values of each indicator were adjusted by the type of health facility, location, and population density. Then, a health district composite readiness profile was built via hierarchical ascendant classification. All surveyed health-facilities were mandated by the Ministry of Health to manage malaria cases. The spatial distribution of essential equipment and malaria readiness was heterogeneous. Around 62.9% of health districts had a high level of readiness to provide malaria care and prevention during pregnancy. Low-performance scores for managing malaria cases were found in big cities. Health districts with low coverage for both first-line antimalarial drugs and rapid diagnostic tests were Baskuy, Bogodogo, Boulmiougou, Nongr-Massoum, Sig-Nonghin, Dafra, and Do. We provide health district estimates and reveal gaps in basic equipment and malaria management resources in some districts that need to be filled. By providing local-scale estimates, this approach could be replicated for other types of indicators to inform decision makers and health program managers and to identify priority areas.

Highlights

Since the early 2000s across Sub-Saharan African (SSA) countries, the burden of malaria has considerably declined
This paper provides an alternative method based on Hierarchical Bayesian Spatial Modeling (HBSM) for optimal estimation of subnational indicators drawn from health facility-based survey data with a much smaller sample size
Given that in the Burkina Faso context the evaluation of Service Availability and Readiness Assessment (SARA) indicators is assessed every two years and given that our results found that there is a variability between health districts, it might be interesting that health authorities increase the frequency of SARA surveys, especially for health district with weak performances; for instance, every six months until a desired threshold has been reached and every one after the threshold is reached, and 2 years

Summary

Introduction

Since the early 2000s across Sub-Saharan African (SSA) countries, the burden of malaria has considerably declined. This is partly explained by a major increase in the mobilization of funding and scaling up of malaria control interventions [1,2,3]. Malaria-related deaths have significantly decreased worldwide, the 2018 World Health Organization (WHO) report stated that all WHO regions reported either only slight progress or an increase in the malaria incidence rate [3]. Seven of the SSA countries, including Burkina Faso, account for more than half of all malaria cases and deaths worldwide [3]. Reliable health services, including health services’ availability and geographical, sociocultural, and financial accessibility, are essential for effectively improving health outcomes, especially malaria incidence cases and case fatality rates [5,6,7]

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