Modelling the Ecological Comorbidity of Acute Respiratory Infection,Diarrhoea and Stunting among Children Under the Age of 5 Years in Somalia

The aim of this study was to assess spatial co‐occurrence of acute respiratory infections (ARI), diarrhoea and stunting among children of the age between 6 and 59 months in Somalia. Data were obtained from routine biannual nutrition surveys conducted by the Food and Agriculture Organization 2007–2010. A Bayesian hierarchical geostatistical shared component model was fitted to the residual spatial components of the three health conditions. Risk maps of the common spatial effects at 1×1 km resolution were derived. The empirical correlations of the enumeration area proportion were 0.37, 0.63 and 0.66 for ARI and stunting, diarrhoea and stunting and ARI and diarrhoea, respectively. Spatially, the posterior residual effects ranged 0.03–20.98, 0.16–6.37 and 0.08–9.66 for shared component between ARI and stunting, diarrhoea and stunting and ARI and diarrhoea, respectively. The analysis showed clearly that the spatial shared component between ARI, diarrhoea and stunting was higher in the southern part of the country. Interventions aimed at controlling and mitigating the adverse effects of these three childhood health conditions should focus on their common putative risk factors, particularly in the South in Somalia.     

A Geo-Additive Bayesian Discrete-Time Survival Model and its Application to Spatial Analysis of Childhood Mortality in Malawi

We describe a flexible geo-additive Bayesian survival model that controls, simultaneously, for spatial dependence and possible nonlinear or time-varying effects of other variables. Inference is fully Bayesian and is based on recently developed Markov Chain Monte Carlo techniques. In illustrating the model we introduce a spatial dimension in modelling under-five mortality among Malawian children using data from Malawi Demographic and Health Survey of 2000. The results show that district-level socioeconomic characteristics are important determinants of childhood mortality. More importantly, a separate spatial process produces district clustering of childhood mortality indicating the importance of spatial effects. The visual nature of the maps presented in this paper highlights relationships that would, otherwise, be overlooked in standard methods.