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

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.

Spatial-temporal pattern of malaria in Burkina Faso from 2013 to 2020

“…3 . Although Rouamba et al in 2020 have proposed a Bayesian data imputation method in this context, we have excluded the four months where malaria data were not reported as we believe that this would not have changed the overall monthly trend in cases at the municipality level ( Rouamba et al, 2020 ).…”

Surveillance des effets indésirables lors des campagnes de la chimioprévention du paludisme saisonnier chez les enfants de 3-59 mois au Burkina Faso

“…Aussi, la pharmacovigilance en était à ses débuts et, pour l'ensemble de tous les médicaments utilisés au Burkina Faso, seulement 76 rapports ont été enregistrés dans VigiBase® entre 2010 et 2015 (15). La diminution du taux de notification en 2019 s'expliquerait par la non-transmission des données du fait des grèves des professionnels de santé d'une durée d'environ six mois (32). Quant à l'année 2020, l'absence de notifications pourrait se justifier par la perturbation du système de pharmacovigilance due à la pandémie de Covid-19 (33).…”

A joint Bayesian spatiotemporal risk prediction model of COVID-19 incidence, IC admission, and death with application to Sweden

“… 3 The pure model is also known as the random intercept or null model (Rouamba et al 2020 ; Waller and Carlin 2010 ). …”

“…The pure model is also known as the random intercept or null model(Rouamba et al 2020; Waller and Carlin 2010).4 Note that pure models can be used to generate hypotheses about the causes of the disease, notably identification possible risk factors(Huque et al 2016;Jaya and Folmer 2021a;Wakefield 2007). 5 The latent correlation between the random effects and the covariates is known as spatiotemporal confounding(Azevedo et al 2020;Adin et al 2022;Clayton et al 1993; Jaya and Folmer 2022a, under review;Johnston et al 2018).…”