An epidemiologic systems analysis of diarrhea in children in Pakistan is presented. Application of additive Bayesian network modeling to 2005-2006 data from the Pakistan Social and Living Standards Measurement 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 analyses is that the latter attempt to not only identify statistically associated variables but also, additionally and empirically, separate these into those directly and indirectly dependent upon 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 analyses across 41 variables from the Pakistan Social and Living Standards Measurement Survey identified 182 direct dependencies but with only 3 variables: 1) access to a dry pit latrine (protective; odds ratio 0.67); 2) access to an atypical water source (protective; odds ratio 0.49); and 3) no formal garbage collection (unprotective; odds ratio 1.32), supported as directly dependent with the presence of diarrhea. All but 2 of the remaining variables were also, in turn, directly or indirectly dependent upon these 3 key variables. These results are contrasted with the use of a standard approach (multivariable regression).
Author affiliations: Section of Epidemiology, Vetsuisse Faculty, University of Zürich, Zürich, Switzerland (Fraser I. Lewis); and Fogarty International Center, National Institutes of Health, Bethesda, Maryland (Benjamin J. J. McCormick).
This research was partly funded (B. J. J. M.) by the Bill and Melinda Gates Foundation and the Fogarty International Center of the National Institutes of Health as part of the MAL-ED (pronounced “mal a dee”) project network (i.e., the Interactions of Malnutrition and Enteric Infections: Consequences for Child Health and Development).
The authors thank Safdar Parvez, Asian Development Bank, based in Manila, Philippines, for assisting in gaining access to the PSLSM data.
Conflict of interest: none declared
- Bayesian network
- epidemiologic determinants
- graphical model
- socioeconomic factors