Open AccessA Bayesian Two Part Model Applied to Analyze Risk Factors of Adult Mortality with Application to Data from Namibia
Author(s) -
Lawrence Kazembe
Publication year - 2013
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0073500
Subject(s) - negative binomial distribution , life expectancy , random effects model , count data , poisson regression , demography , logistic regression , generalized linear mixed model , poverty , bayesian probability , bayesian inference , statistics , poisson distribution , bayes' theorem , psychological intervention , covariate , econometrics , inference , medicine , mathematics , environmental health , computer science , population , economics , meta analysis , psychiatry , sociology , economic growth , artificial intelligence
Despite remarkable gains in life expectancy and declining mortality in the 21st century, in many places mostly in developing countries, adult mortality has increased in part due to HIV/AIDS or continued abject poverty levels. Moreover many factors including behavioural, socio-economic and demographic variables work simultaneously to impact on risk of mortality. Understanding risk factors of adult mortality is crucial towards designing appropriate public health interventions. In this paper we proposed a structured additive two-part random effects regression model for adult mortality data. Our proposal assumed two processes: (i) whether death occurred in the household (prevalence part), and (ii) number of reported deaths, if death did occur (severity part). The proposed model specification therefore consisted of two generalized linear mixed models (GLMM) with correlated random effects that permitted structured and unstructured spatial components at regional level. Specifically, the first part assumed a GLMM with a logistic link and the second part explored a count model following either a Poisson or negative binomial distribution. The model was used to analyse adult mortality data of 25,793 individuals from the 2006/2007 Namibian DHS data. Inference is based on the Bayesian framework with appropriate priors discussed.