Bayesian spatial analysis of childhood diseases in Zimbabwe

BMC Public Health
Rodney Godfrey Tsiko

Abstract

Many sub-Saharan countries are confronted with persistently high levels of childhood morbidity and mortality because of the impact of a range of demographic, biological and social factors or situational events that directly precipitate ill health. In particular, under-five morbidity and mortality have increased in recent decades due to childhood diarrhoea, cough and fever. Understanding the geographic distribution of such diseases and their relationships to potential risk factors can be invaluable for cost effective intervention. Bayesian semi-parametric regression models were used to quantify the spatial risk of childhood diarrhoea, fever and cough, as well as associations between childhood diseases and a range of factors, after accounting for spatial correlation between neighbouring areas. Such semi-parametric regression models allow joint analysis of non-linear effects of continuous covariates, spatially structured variation, unstructured heterogeneity, and other fixed effects on childhood diseases. Modelling and inference made use of the fully Bayesian approach via Markov Chain Monte Carlo (MCMC) simulation techniques. The analysis was based on data derived from the 1999, 2005/6 and 2010/11 Zimbabwe Demographic and Health S...Continue Reading

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Citations

Dec 14, 2018·International Journal of Environmental Health Research·Eve MackinnonEdward Fottrell
Sep 3, 2020·Journal of Biosocial Science·Tulsi AdhikariM Vishnu Vardhana Rao
Jun 21, 2020·BMC Pediatrics·Kasahun TakeleDenis Ndanguza
Oct 11, 2017·Nature Communications·Diego HerreraTaylor H Ricketts
May 2, 2020·International Journal of Environmental Research and Public Health·Samuel MandaRobert Bergquist

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