title: Modeling heterogeneity for count data: A study of maternal mortality in health facilities in Mozambique.
abstract: Count data are very common in health services research, and very commonly the basic Poisson regression model has to be extended in several ways to accommodate several sources of heterogeneity: (i) an excess number of zeros relative to a Poisson distribution, (ii) hierarchical structures, and correlated data, (iii) remaining "unexplained" sources of overdispersion. In this paper, we propose hierarchical zero-inflated and overdispersed models with independent, correlated, and shared random effects for both components of the mixture model. We show that all different extensions of the Poisson model can be based on the concept of mixture models, and that they can be combined to account for all different sources of heterogeneity. Expressions for the first two moments are derived and discussed. The models are applied to data on maternal deaths and related risk factors within health facilities in Mozambique. The final model shows that the maternal mortality rate mainly depends on the geographical location of the health facility, the percentage of women admitted with HIV and the percentage of referrals from the health facility.
authors.affiliation: Department of Mathematics and Informatics, Universidade Eduardo Mondlane, Av. Julius Nyerere, Campus, 3453, P.O. Box 257, Maputo, Mozambique.
authors.forename: Osvaldo
authors.initials: O
authors.lastname: Loquiha
authors.forename: Niel
authors.initials: N
authors.lastname: Hens
authors.forename: Leonardo
authors.initials: L
authors.lastname: Chavane
authors.forename: Marleen
authors.initials: M
authors.lastname: Temmerman
authors.forename: Marc
authors.initials: M
authors.lastname: Aerts
mesh_terms: D001699:Biometry / Q000379:methods*; D003625:Data Collection; D005260:Female; D006268:Health Facilities; D006801:Humans; D008428:Maternal Mortality; D015233:Models, Statistical; D009073:Mozambique; D016012:Poisson Distribution
keywords: Hierarchical model; Maternal mortality; Negative binomial; Overdispersion; Zero-inflated model