Bayesian spatial analysis of childhood diseases in Zimbabwe
نویسنده
چکیده
BACKGROUND 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. METHODS 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 Surveys (ZDHS). RESULTS AND CONCLUSIONS The results suggest that until recently, sex of child had little or no significant association with childhood diseases. However, a higher proportion of male than female children within a given province had a significant association with childhood cough, fever and diarrhoea. Compared to their counterparts in rural areas, children raised in an urban setting had less exposure to cough, fever and diarrhoea across all the survey years with the exception of diarrhoea in 2010. In addition, the link between sanitation, parental education, antenatal care, vaccination and childhood diseases was found to be both intuitive and counterintuitive. Results also showed marked geographical differences in the prevalence of childhood diarrhoea, fever and cough. Across all the survey years Manicaland province reported the highest cases of childhood diseases. There is also clear evidence of significant high prevalence of childhood diseases in Mashonaland than in Matabeleland provinces.
منابع مشابه
Bayesian Analysis of Spatial Probit Models in Wheat Waste Management Adoption
The purpose of this study was to identify factors influencing the adoption of wheat waste management by wheat farmers. The method used in this study using the spatial Probit models and Bayesian model was used to estimate the model. MATLAB software was used in this study. The data of 220 wheat farmers in Khouzestan Province based on random sampling were collected in winter 2016. To calculate Bay...
متن کاملBayesian Analysis of Survival Data with Spatial Correlation
Often in practice the data on the mortality of a living unit correlation is due to the location of the observations in the study. One of the most important issues in the analysis of survival data with spatial dependence, is estimation of the parameters and prediction of the unknown values in known sites based on observations vector. In this paper to analyze this type of survival, Cox...
متن کاملBayesian Analysis of Censored Spatial Data Based on a Non-Gaussian Model
Abstract: In this paper, we suggest using a skew Gaussian-log Gaussian model for the analysis of spatial censored data from a Bayesian point of view. This approach furnishes an extension of the skew log Gaussian model to accommodate to both skewness and heavy tails and also censored data. All of the characteristics mentioned are three pervasive features of spatial data. We utilize data augme...
متن کاملSpatial count models on the number of unhealthy days in Tehran
Spatial count data is usually found in most sciences such as environmental science, meteorology, geology and medicine. Spatial generalized linear models based on poisson (poisson-lognormal spatial model) and binomial (binomial-logitnormal spatial model) distributions are often used to analyze discrete count data in which spatial correlation is observed. The likelihood function of these models i...
متن کامل