Modeling heterogeneity for count data: A study of maternal mortality in health facilities in Mozambique

Authors

  • Osvaldo Loquiha,

    1. Department of Mathematics and Informatics, Universidade Eduardo Mondlane, Maputo, Mozambique
    2. Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), B-3590 Diepenbeek, Belgium
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  • Niel Hens,

    1. Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), B-3590 Diepenbeek, Belgium
    2. Centre for Health Economics Research and Modeling Infectious Diseases and Centre for the Evaluation of Vaccination, Vaccine & Infectious Disease Institute, University of Antwerp, B2610 Wilrijk, Belgium
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  • Leonardo Chavane,

    1. Jhpiego, MCHIP Maternal and Child Health Integrated Program, Maputo, Mozambique
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  • Marleen Temmerman,

    1. International Centre for Reproductive Health, Ghent University, 9000 Ghent, Belgium
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  • Marc Aerts

    Corresponding author
    • Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), B-3590 Diepenbeek, Belgium
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Corresponding author: e-mail: marc.aerts@uhasselt.be, Phone: +32-11-268247, Fax: +32-11-268299

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.

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