ArticleInternational journal of environmental research and public health2010
A multilevel model for comorbid outcomes: obesity and diabetes in the US.
Article in International journal of environmental research and public health, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Quantifying spatial disparities in neonatal mortality using a structured additive regression model.PloS one · 2010Article
- Human papillomavirus vaccine coverage among females aged 11 to 17 in Texas counties: an application of multilevel, small area estimation.Women's health issues : official publication of the Jacobs Institute of Women's HealthArticle
- Multimorbidity in large Canadian urban centres: A multilevel analysis of pooled 2015-2018 cross-sectional cycles of the Canadian Community Health Survey.Journal of multimorbidity and comorbidityArticle
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Abstract
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.
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