Bayesian conditional autoregressive models to assess spatial patterns of diarrhoea risk among children under the age of 5 years in Mbour, Senegal

• Main Authors: Sokhna Thiam, Guéladio Cissé, Anna-Sofie Stensgaard, Aminata Niang-Diène, Jürg Utzinger, Penelope Vounatsou
• Format: Article
• Language: English
• Published: PAGEPress Publications 2019-11-01
• Series: Geospatial Health
• Subjects: Bayesian conditional autoregressive models; Diarrhoea; Risk profiling; Senegal; Spatial pattern
• Online Access: https://www.geospatialhealth.net/index.php/gh/article/view/823

Diarrhoeal diseases remain a major public health problem, causing more than half a million child deaths every year, particularly in low- and middle-income countries (LMICs). Despite existing knowledge on the aetiologies and causes of diarrhoeal diseases, relatively little is known about its spatial patterns in LMICs, including Senegal. In the present study, data from a cross-sectional survey carried out in 2016 were analysed to describe the spatial pattern of diarrhoeal prevalence in children under the age of 5 years in the secondary city of Mbour in the south-western part of Senegal. Bayesian conditional autoregressive (CAR) models with spatially varying coefficients were employed to determine the effect of sociodemographic, economic and climate parameters on diarrhoeal prevalence. We observed substantial spatial heterogeneities in diarrhoea prevalence. Risk maps, stratified by age group, showed that diarrhoeal prevalence was higher in children aged 25-59 months compared to their younger counterparts with the highest risk observed in the north and south peripheral neighbourhoods, especially in Grand Mbour, Médine, Liberté and Zone Sonatel. The posterior relative risk estimate obtained from the Bayesian CAR model indicated that a unit increase in the proportion of people with untreated stored drinking water was associated with a 29% higher risk of diarrhoea. A unit increase in rainfall was also associated with an increase in diarrhoea risk. Our findings suggest that public health officials should integrate disease mapping and cluster analyses and consider the varying effects of sociodemographic factors in developing and implementing areaspecific interventions for reducing diarrhoea.