A Multilevel Model for Comorbid Outcomes
Obesity and Diabetes in the US
Bibliographic Data
| ID | 15465331 |
|---|---|
| Authors | Peter Congdon (0000-0003-1934-9205, Queen Mary University of London, corresponding author) |
| Year | 2010 |
| Volume | 7 |
| Issue | 2 |
| Pages | 333-352 |
| Publication date | 2010-01-27 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Environmental Research and Public Health (JOURNAL) |
| Journal identifiers | ISSN: 1661-7827 • E-ISSN: 1660-4601 |
| Publisher | Multidisciplinary Digital Publishing Institute (PUBLISHER • CH) |
| DOI | 10.3390/ijerph7020333 |
| PMID | 20616977 |
| PMCID | PMC2872282 |
| OpenAlex | W2027808326 |
| Language | PT |
| Citations received | 2 |
| References cited | 47 |
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
Behavioral Risk Factor Surveillance System · Context (archaeology · Econometrics · Environmental health · Geography · Latent class model · Latent variable · Latent variable model · Logistic regression · Multilevel model · Multinomial logistic regression · Obesity · Overweight · Population · Statistics · Demography · Health disparities and outcomes · Mathematics · Medicine · Spatial and Panel Data Analysis · Urban Transport and Accessibility · Gerontology
Bayesian image restoration, with two applications in spatial statistics
Relationship between Urban Sprawl and Physical Activity, Obesity, and Morbidity
The Built Environment and Obesity
General Methods for Monitoring Convergence of Iterative Simulations
Neighborhood Risk Factors for Obesity
The Obesity Epidemic in the United States Gender, Age, Socioeconomic, Racial/Ethnic, and Geographic Characteristics
A mixed‐effects multinomial logistic regression model
Sampling-Based Approaches to Calculating Marginal Densities
Bayesian Measures of Model Complexity and Fit
Rural Residence and Hispanic Ethnicity
High Self‐Reported Prevalence of Diabetes Mellitus, Heart Disease, and Stroke in 11 Counties of Rural Appalachian Ohio
Changes in racial/ethnic disparities in the prevalence of Type 2 diabetes by obesity level among US adults
Time and place
Wider income gaps, wider waistbands? An ecological study of obesity and income inequality
Personal, neighbourhood and urban factors associated with obesity in the United States
Contribution of neighbourhood socioeconomic status and physical activity resources to physical activity among women
Individual Health Status and Racial Minority Concentration in US States and Counties
Racial Disparities in Context
Effect of Individual or Neighborhood Disadvantage on the Association Between Neighborhood Walkability and Body Mass Index
Race/Ethnicity, Gender, and Monitoring Socioeconomic Gradients in Health
Prevalence of self-rated visual impairment among adults with diabetes
US state- and county-level social capital in relation to obesity and physical inactivity
Does place explain racial health disparities? Quantifying the contribution of residential context to the Black/white health gap in the United States
Context, composition and heterogeneity
Disparities in obesity rates
| Unique citing works | 2 |
|---|---|
| Citations per year | 0,15 |
| Citation span | 2013 - 2015 (3) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 2 |