ArticlePreventing chronic disease2015
Estimating prevalence of overweight or obese children and adolescents in small geographic areas using publicly available data.
Article in Preventing chronic disease, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed.
- Hotspots of childhood obesity in a large metropolitan area: does neighbourhood social and built environment play a part?International journal of epidemiology · 2020Article
- Geospatial mapping and resource utilization tool in support of a national smoke-free public housing rule.BMC research notes · 2019Article
- Dataset for estimation of obesity levels based on eating habits and physical condition in individuals from Colombia, Peru and Mexico.Data in brief · 2019Article
- Using Geospatial Analysis and Emergency Claims Data to Improve Minority Health Surveillance.Journal of racial and ethnic health disparities · 2018Article
- Estimating the Prevalence of Childhood Obesity in Alaska Using Partial, Nonrandom Measurement Data.Preventing chronic disease · 2016Article
- Estimating Demand for and Supply of Pediatric Preventive Dental Care for Children and Identifying Dental Care Shortage Areas, Georgia, 2015.Public health reports (Washington, D.C. : 1974)Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionInterventions 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.
methodsWe 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.
resultsIn 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.
conclusionPrevalence 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.
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