TY - JOUR
T1 - Bayesian conditional autoregressive models to assess spatial patterns of diarrhoea risk among children under the age of 5 years in Mbour, Senegal
AU - Thiam, Sokhna
AU - Cisse, Gueladio
AU - Stensgaard, Anna-Sofie
AU - Niang-Diene, Aminata
AU - Utzinger, Juerg
AU - Vounatsou, Penelope
PY - 2019/11/12
Y1 - 2019/11/12
N2 - 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 exist- ing 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 hetero-geneities 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 area-specific interventions for reducing diarrhoea.
AB - 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 exist- ing 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 hetero-geneities 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 area-specific interventions for reducing diarrhoea.
KW - Bayesian conditional autoregressive models
KW - Diarrhoea
KW - Risk profiling
KW - Senegal
KW - Spatial pattern
U2 - 10.4081/gh.2019.823
DO - 10.4081/gh.2019.823
M3 - Journal article
C2 - 31724382
SN - 1827-1987
VL - 14
SP - 321
EP - 328
JO - Geospatial Health
JF - Geospatial Health
IS - 2
ER -
