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Revealing the complexity of health determinants in resource poor settings


Lewis, F I; McCormick, B J J (2012). Revealing the complexity of health determinants in resource poor settings. American Journal of Epidemiology, 176(11):1051-1059.

Abstract

An epidemiological systems analysis of diarrhea in children in Pakistan is presented. Applying additive Bayesian network (ABN) modeling to data from the Pakistan Social and Living Standards Measurement (PSLSM) survey reveals the
complexity of child diarrhea as a disease system. The key distinction between standard analytical approaches, such as multivariable regression, and Bayesian network analyzes is that the latter attempts not only to identify statistically associated variables, but to additionally, and empirically, separate these into those directly and indirectly dependent with the outcome variable. Such discrimination is vastly more ambitious but has the potential to reveal far more about key features of complex disease systems. Additive Bayesian network analyzes across 41 variables from the PSLSM identified 182 direct dependencies, but with only
three variables: Access to a dry pit latrine (protective: OR=0.67); Access to an atypical water source (protective: OR=0.49); and No formal garbage collection (unprotective: OR=1.32), supported as directly dependent with the presence of diarrhea. All but two of the remaining variables were also in turn directly or indirectly dependent with these three key variables. These results are contrasted with the use of a standard approach (multivariable regression).

Abstract

An epidemiological systems analysis of diarrhea in children in Pakistan is presented. Applying additive Bayesian network (ABN) modeling to data from the Pakistan Social and Living Standards Measurement (PSLSM) survey reveals the
complexity of child diarrhea as a disease system. The key distinction between standard analytical approaches, such as multivariable regression, and Bayesian network analyzes is that the latter attempts not only to identify statistically associated variables, but to additionally, and empirically, separate these into those directly and indirectly dependent with the outcome variable. Such discrimination is vastly more ambitious but has the potential to reveal far more about key features of complex disease systems. Additive Bayesian network analyzes across 41 variables from the PSLSM identified 182 direct dependencies, but with only
three variables: Access to a dry pit latrine (protective: OR=0.67); Access to an atypical water source (protective: OR=0.49); and No formal garbage collection (unprotective: OR=1.32), supported as directly dependent with the presence of diarrhea. All but two of the remaining variables were also in turn directly or indirectly dependent with these three key variables. These results are contrasted with the use of a standard approach (multivariable regression).

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:05 Vetsuisse Faculty > Chair in Veterinary Epidemiology
Dewey Decimal Classification:570 Life sciences; biology
610 Medicine & health
Language:English
Date:2012
Deposited On:04 Feb 2013 12:41
Last Modified:04 Aug 2017 02:58
Publisher:Oxford University Press
ISSN:0002-9262
Additional Information:This is a pre-copy-editing, author-produced PDF of an article accepted for publication in American Journal of Epidemiology following peer review. The definitive publisher-authenticated version Lewis, F I; McCormick, B J J (2012). Revealing the complexity of health determinants in resource poor settings. American Journal of Epidemiology, 176(11):1051-1059. is available online at: http://aje.oxfordjournals.org/content/176/11/1051.long
Free access at:PubMed ID. An embargo period may apply.
Publisher DOI:https://doi.org/10.1093/aje/KWS183
PubMed ID:23139247

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