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Int. J. Environ. Res. Public Health 2010, 7(2), 333-352; doi:10.3390/ijerph7020333

A Multilevel Model for Comorbid Outcomes: Obesity and Diabetes in the US

Department of Geography and Centre for Statistics, Queen Mary University of London, Mile End Rd, London E1 4NS, UK
Received: 16 November 2009 / Accepted: 21 January 2010 / Published: 27 January 2010
(This article belongs to the Special Issue Advances in Epidemiology)
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Multilevel models are overwhelmingly applied to single health outcomes, but when two or more health conditions are closely related, it is important that contextual variation in their joint prevalence (e.g., variations over different geographic settings) is considered. A multinomial multilevel logit regression approach for analysing joint prevalence is proposed here that includes subject level risk factors (e.g., age, race, education) while also taking account of geographic context. Data from a US population health survey (the 2007 Behavioral Risk Factor Surveillance System or BRFSS) are used to illustrate the method, with a six category multinomial outcome defined by diabetic status and weight category (obese, overweight, normal). The influence of geographic context is partly represented by known geographic variables (e.g., county poverty), and partly by a model for latent area influences. In particular, a shared latent variable (common factor) approach is proposed to measure the impact of unobserved area influences on joint weight and diabetes status, with the latent variable being spatially structured to reflect geographic clustering in risk. View Full-Text
Keywords: diabetes; obesity; multilevel; multinomial; latent variable; spatial; poverty diabetes; obesity; multilevel; multinomial; latent variable; spatial; poverty

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Congdon, P. A Multilevel Model for Comorbid Outcomes: Obesity and Diabetes in the US. Int. J. Environ. Res. Public Health 2010, 7, 333-352.

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