Abstract Details
Activity Number:
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374
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Type:
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Contributed
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Date/Time:
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Tuesday, August 6, 2013 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract - #308228 |
Title:
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Bayesian Multilevel Modeling for Calculating Small-Area Estimates of Diagnosed Diabetes, Obesity, and Physical Inactivity Prevalence in Puerto Rico
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Author(s):
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Elizabeth Ely*+ and Theodore J Thompson and Ed F Tierney and Roberta H. Hilsdon and Deborah B. Rolka
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Companies:
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Centers for Disease Control and Prevention and Centers for Disease Control and Prevention and Centers for Disease Control and Prevention and Centers for Disease Control and Prevention and Centers for Disease Control and Prevention
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Keywords:
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Diabetes ;
Obesity ;
Physical Inactivity ;
Puerto Rico ;
Bayesian Models ;
Small Area Estimates
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Abstract:
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Puerto Rico is divided into 78 municipios (county equivalents). Combining data from the 2009, 2010 and 2011 Behavioral Risk Factor Surveillance System with 2010 U.S. Census estimates, we used Bayesian multilevel models to produce municipio-level prevalence estimates of self-reported diabetes, obesity and leisure-time physical inactivity. Our model included the person-level covariates of age and sex. Municipio-level covariates included 2003 rural-urban continuum codes from the U. S. Department of Agriculture, as well as the percentage of people over 25 who completed high school and the percentage of people below the poverty level in the last 12 months from the 2007-2011 Puerto Rico Community Survey. While the prevalence of diagnosed diabetes and its risk factors is high throughout Puerto Rico (age-adjusted estimates of diabetes ranged from 9.9% to 16.2%), the municipio-level estimates exhibit geographical variation and indicate regions of the island having a higher burden of disease. These first estimates of the municipio-level prevalence of diabetes, obesity, and physical inactivity indicate a strong need for interventions and can help target resources to areas of greater need.
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Authors who are presenting talks have a * after their name.
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