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Lin Dai

Medical University of South Carolina



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Mulugeta Gebregziabher

Medical University of South Carolina



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130 – Statistical Methods for Spatial Epidemiology

Novel Application of a Weighted Zero-inflated Negative Binomial Model in Modeling Count Data from a Complex Survey

Sponsor: Section on Statistics in Epidemiology
Keywords: HIV, multi-country survey data, negative binomial, regional variation, zero-inflation

Lin Dai

Medical University of South Carolina

Mulugeta Gebregziabher

Medical University of South Carolina

We demonstrate a novel application of a weighted zero-inflated negative binomial model to quantify regional variation in HIV-AIDS prevalence in sub-Saharan African countries. We use data from latest round of the Demographic and Health survey (DHS) conducted in three countries (Ethiopia-2011, Kenya-2009 and Rwanda-2010). The outcome is an aggregate count of HIV cases in each census enumeration area (CEA) from the DHS of the three sub-Saharan African countries. Data are characterized by excess zeros and heterogeneity due to clustering. We compare several scale-weighting approaches to account for the complex survey design and clustering in a zero inflated negative binomial (ZINB) model. Finally, we provide marginalized rate ratio (RR) estimates from the best ZINB model.

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