Addressing challenges in routine health data reporting in Burkina Faso through Bayesian spatiotemporal prediction of weekly clinical malaria incidence

Toussaint Rouamba,Fati Kirakoya-Samadoulougou,Sekou Samadoulougou

doi:10.1038/s41598-020-73601-3

Abstract

Sub-Saharan African (SSA) countries’ health systems are often vulnerable to unplanned situations that can hinder their effectiveness in terms of data completeness and disease control. For instance, in Burkina Faso following a workers' strike, comprehensive data on several diseases were unavailable for a long period in 2019. Weather, seasonal-malaria-chemoprevention (SMC), free healthcare, and other contextual data, which are purported to influence malarial disease, provide opportunities to fit models to describe the clinical malaria data and predict the disease spread. Bayesian spatiotemporal modeling was applied to weekly malaria surveillance data from Burkina Faso (2011–2018) while considering the effects of weather, health programs and contextual factors. Then, a prediction was used to deal with weekly missing data for the entire year of 2019, and SMC and free healthcare effects were quantified. Our proposed model accurately predicted weekly clinical malaria incidence (correlation coefficient, r = 0.90). The distribution of clinical malaria incidence was heterogeneous across the country. Overall, national predicted clinical malaria incidence in 2019 (605 per 1000 [95% CrI: 360–990]) increased by 24.7% compared with the year 2015. SMC and the interaction between free healthcare and health facility attendance were associated with a reduction in clinical malaria incidence. Our modeling approach could be a useful tool for strengthening health systems’ resilience by addressing data completeness and could support SSA countries in developing appropriate targets and indicators to facilitate the subnational control effort.

Highlights

Since the 2000s, with the support of technical and financial partners, Burkina Faso has undertaken numerous initiatives and reforms to improve population health
The findings showed that a health district that had one additional year of the implementation of the SMC program reduced the clinical malaria incidence by 7.2% (95% CrI 6.6–7.9%)
Using historical weekly clinical malaria data to address the challenges in routine health data reporting through Bayesian spatiotemporal modeling, we found an increase in clinical malaria incidence nationwide in 2019 compared with previous years

Summary

Introduction

Since the 2000s, with the support of technical and financial partners, Burkina Faso has undertaken numerous initiatives and reforms to improve population health. In Burkina Faso in 2019, the reporting of routine health data was deliberately interrupted for half the year due to an unusually long strike of healthcare workers This was especially detrimental to the malaria control program since the strike spanned the transmission period. Statistical models with spatial and temporal correlation structures implemented in a Bayesian framework have been shown to better fit these types of data This approach can take into account missing data, weighting estimates according to observed or unobserved covariates that are close in space and time, and allows the inclusion of suitable covariates while accounting for uncertainty[17,18,19,20]. Bayesian spatiotemporal methods can generate reliable and accurate estimates of the malaria burden and measure progress in malaria control, their implementation and interpretation require a certain level of statistical skills, and these skills are not always present in malaria control p rograms[21,22]

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