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Bayesian spatial analysis of childhood diseases in Zimbabwe
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...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2015
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4556011/ https://www.ncbi.nlm.nih.gov/pubmed/26329616 http://dx.doi.org/10.1186/s12889-015-2182-7 |
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author | Tsiko, Rodney Godfrey |
author_facet | Tsiko, Rodney Godfrey |
author_sort | Tsiko, Rodney Godfrey |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-4556011 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-45560112015-09-02 Bayesian spatial analysis of childhood diseases in Zimbabwe Tsiko, Rodney Godfrey BMC Public Health Research Article 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. BioMed Central 2015-09-02 /pmc/articles/PMC4556011/ /pubmed/26329616 http://dx.doi.org/10.1186/s12889-015-2182-7 Text en © Tsiko. 2015 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Tsiko, Rodney Godfrey Bayesian spatial analysis of childhood diseases in Zimbabwe |
title | Bayesian spatial analysis of childhood diseases in Zimbabwe |
title_full | Bayesian spatial analysis of childhood diseases in Zimbabwe |
title_fullStr | Bayesian spatial analysis of childhood diseases in Zimbabwe |
title_full_unstemmed | Bayesian spatial analysis of childhood diseases in Zimbabwe |
title_short | Bayesian spatial analysis of childhood diseases in Zimbabwe |
title_sort | bayesian spatial analysis of childhood diseases in zimbabwe |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4556011/ https://www.ncbi.nlm.nih.gov/pubmed/26329616 http://dx.doi.org/10.1186/s12889-015-2182-7 |
work_keys_str_mv | AT tsikorodneygodfrey bayesianspatialanalysisofchildhooddiseasesinzimbabwe |