Multilevel and geo-statistical modeling of malaria risk in children of Burkina Faso

Abstract

BackgroundPrevious research on determinants of malaria in Burkina Faso has largely focused on individual risk factors. Malaria risk, however, is also shaped by community, health system, and climatic/environmental characteristics. The aims of this study were: i) to identify such individual, household, community, and climatic/environmental risk factors for malaria in children under five years of age, and ii) to produce a parasitaemia risk map of Burkina Faso.MethodsThe 2010 Demographic and Health Survey (DHS) was the first in Burkina Faso that tested children for malaria parasitaemia. Multilevel and geo-statistical models were used to explore determinants of malaria using this nationally representative database.ResultsParasitaemia was collected from 6,102 children, of which 66.0% (95% confidence interval (CI): 64.0-68.0%) were positive for Plasmodium spp. Older children (>23 months) were more likely to be parasitaemic than younger ones, while children from wealthier households and whose mother had higher education were at a lower risk. At the community level, living in a district with a rate of attendance to health facilities lower than 2 visits per year was significantly associated with greater odds of being infected. Malaria prevalence was also associated with higher normalized difference vegetation index, lower average monthly rainfall, and lower population densities. Predicted malaria parasitaemia was spatially variable with locations falling within an 11%-92% prevalence range. The number of parasitaemic children was nonetheless concentrated in areas of high population density, albeit malaria risk was notably higher in the sparsely populated rural areas.ConclusionMalaria prevalence in Burkina Faso is considerably higher than in neighbouring countries. Our spatially-explicit population-based estimates of malaria risk and infected number of children could be used by local decision-makers to identify priority areas where control efforts should be enhanced.Electronic supplementary materialThe online version of this article (doi:10.1186/1756-3305-7-350) contains supplementary material, which is available to authorized users.

Distance sampling for epidemiology: an interactive tool for estimating under-reporting of cases from clinic data

“…The distance sampling model described above was applied to a case study of malaria incidence quantification in rural Burkina Faso. Burkina Faso has one of the high rates of malaria in Africa [50,51], with the bulk of transmission occurring in rural areas during or shortly after the rainy season between July to December. The primary level of the national health system is constituted by a network of health centres (centre de santé et de promotion sociale, CSPS).…”

“…Here, we begin the process of adapting distance sampling methods for epidemiological prediction by presenting the fundamental concepts for estimation of a detection function for clinic data, implementing them within a Bayesian framework of statistical inference and illustrating their use through a case study of malaria reporting in rural south-western Burkina Faso. This area of Africa experiences a particularly high burden of malaria [50,51], creating an urgent need for accurate prediction of incidence. Finally, to illustrate how our approach could be used for public health planning, we present an interactive mapping tool (R Shiny app), built upon the model results from the malaria case-study.…”

Effect of Free Healthcare Policy for Children under Five Years Old on the Incidence of Reported Malaria Cases in Burkina Faso by Bayesian Modelling: “Not only the Ears but also the Head of the Hippopotamus”

“…The relative humidity data from the period January 2013 to December 2017 for each district were obtained from NASA (MERRA-2) [36]. These covariates have been widely used in mapping malaria in sub-Saharan Africa [3,17,18,21,26,37,38].…”

“…Indeed, these analyses do not take into account subnational heterogeneity and disparities and fail to assess the effects of interventions on the spatial and temporal evolution of the malaria burden. Bayesian hierarchical spatio-temporal models have recently been developed, validated, and applied to malaria data to produce robust and reliable estimators [17][18][19][20][21]. These models have proven useful in overcoming the problems of data complexity [21][22][23][24].…”

Malaria Case Fatality Rate among Children under Five in Burkina Faso: An Assessment of the Spatiotemporal Trends Following the Implementation of Control Programs

“…Indeed, classic statistical methods do not allow reliable adjustment for intra-national heterogeneity of malaria case fatality rate. Recently developed and validated hierarchical Bayesian spatiotemporal models were implemented on malaria surveillance data and produced robust estimates [20][21][22][23][24][25][26][27][28]. These models proved useful in addressing complex issues in the data structure.…”