title: Estimating prevalence of overweight or obese children and adolescents in small geographic areas using publicly available data.
abstract: BACKGROUND
  Interventions for pediatric obesity can be geographically targeted if high-risk populations can be identified. We developed an approach to estimate the percentage of overweight or obese children aged 2 to 17 years in small geographic areas using publicly available data. We piloted our approach for Georgia.
  
  
  METHODS
  We created a logistic regression model to estimate the individual probability of high body mass index (BMI), given data on the characteristics of the survey participants. We combined the regression model with a simulation to sample subpopulations and obtain prevalence estimates. The models used information from the 2001-2010 National Health and Nutrition Examination Survey, the 2010 Census, and the 2010 American Community Survey. We validated our results by comparing 1) estimates for adults in Georgia produced by using our approach with estimates from the Centers for Disease Control and Prevention (CDC) and 2) estimates for children in Arkansas produced by using our approach with school examination data. We generated prevalence estimates for census tracts in Georgia and prioritized areas for interventions.
  
  
  RESULTS
  In DeKalb County, the mean prevalence among census tracts varied from 27% to 40%. For adults, the median difference between our estimates and CDC estimates was 1.3 percentage points; for Arkansas children, the median difference between our estimates and examination-based estimates data was 1.7 percentage points.
  
  
  CONCLUSIONS
  Prevalence estimates for census tracts can be different from estimates for the county, so small-area estimates are crucial for designing effective interventions. Our approach validates well against external data, and it can be a relevant aid for planning local interventions for children.
authors.affiliation: Georgia Institute of Technology, Atlanta, Georgia.
authors.forename: Carlo
authors.initials: C
authors.lastname: Davila-Payan
authors.affiliation: Columbia University Medical Center, New York, New York.
authors.forename: Michael
authors.initials: M
authors.lastname: DeGuzman
authors.affiliation: Georgia Institute of Technology, Atlanta, Georgia.
authors.forename: Kevin
authors.initials: K
authors.lastname: Johnson
authors.affiliation: Georgia Institute of Technology, Atlanta, Georgia.
authors.forename: Nicoleta
authors.initials: N
authors.lastname: Serban
authors.affiliation: Department of Industrial and Systems Engineering, Georgia Institute of Technology, 755 Ferst Dr NW, Atlanta, GA 30333. Email: jswann@isye.gatech.edu.
authors.forename: Julie
authors.initials: J
authors.lastname: Swann
mesh_terms: D000293:Adolescent; D001131:Arkansas / Q000453:epidemiology; D015992:Body Mass Index; D019352:Censuses; D002487:Centers for Disease Control and Prevention, U.S.; D002648:Child; D002675:Child, Preschool; D005006:Ethnicity / Q000706:statistics & numerical data; D005845:Georgia / Q000453:epidemiology; D006801:Humans; D016015:Logistic Models; D009749:Nutrition Surveys; D050177:Overweight / Q000453:epidemiology*; D063766:Pediatric Obesity / Q000453:epidemiology*; D010865:Pilot Projects; D011159:Population Surveillance / Q000379:methods*; D015995:Prevalence; D012307:Risk Factors; D017062:Small-Area Analysis; D012923:Social Class; D062206:Spatial Analysis; D014481:United States