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

(2013) BIOMETRICAL JOURNAL. 55(5). p.647-660
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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.
Keywords
ZERO-INFLATED POISSON, Maternal mortality, Zero-inflated model, Overdispersion, Negative binomial, Hierarchical model, CONDITIONAL AKAIKE INFORMATION, MIXED-EFFECTS MODELS, MIXTURE MODEL, HURDLE MODELS, REGRESSION

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Chicago
Loquiha, Osvaldo, Niel Hens, Leonardo António Chavane, Marleen Temmerman, and Marc Aerts. 2013. “Modeling Heterogeneity for Count Data: a Study of Maternal Mortality in Health Facilities in Mozambique.” Biometrical Journal 55 (5): 647–660.
APA
Loquiha, O., Hens, N., Chavane, L. A., Temmerman, M., & Aerts, M. (2013). Modeling heterogeneity for count data: a study of maternal mortality in health facilities in Mozambique. BIOMETRICAL JOURNAL, 55(5), 647–660.
Vancouver
1.
Loquiha O, Hens N, Chavane LA, Temmerman M, Aerts M. Modeling heterogeneity for count data: a study of maternal mortality in health facilities in Mozambique. BIOMETRICAL JOURNAL. 2013;55(5):647–60.
MLA
Loquiha, Osvaldo, Niel Hens, Leonardo António Chavane, et al. “Modeling Heterogeneity for Count Data: a Study of Maternal Mortality in Health Facilities in Mozambique.” BIOMETRICAL JOURNAL 55.5 (2013): 647–660. Print.
@article{4290105,
  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.},
  author       = {Loquiha, Osvaldo and Hens, Niel and Chavane, Leonardo António and Temmerman, Marleen and Aerts, Marc},
  issn         = {0323-3847},
  journal      = {BIOMETRICAL JOURNAL},
  keywords     = {ZERO-INFLATED POISSON,Maternal mortality,Zero-inflated model,Overdispersion,Negative binomial,Hierarchical model,CONDITIONAL AKAIKE INFORMATION,MIXED-EFFECTS MODELS,MIXTURE MODEL,HURDLE MODELS,REGRESSION},
  language     = {eng},
  number       = {5},
  pages        = {647--660},
  title        = {Modeling heterogeneity for count data: a study of maternal mortality in health facilities in Mozambique},
  url          = {http://dx.doi.org/10.1002/bimj.201200233},
  volume       = {55},
  year         = {2013},
}

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