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Developing a Statewide Childhood Body Mass Index Surveillance Program
Author(s) -
Paul David R.,
Scruggs Philip W.,
Goc Karp Grace,
Ransdell Lynda B.,
Robinson Clay,
Lester Michael J.,
Gao Yong,
Petranek Laura J.,
Brown Helen,
Shimon Jane M.
Publication year - 2014
Publication title -
journal of school health
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.851
H-Index - 86
eISSN - 1746-1561
pISSN - 0022-4391
DOI - 10.1111/josh.12194
Subject(s) - overweight , obesity , body mass index , ethnic group , socioeconomic status , legislation , logistic regression , medicine , demography , childhood obesity , gerontology , environmental health , population , political science , sociology , pathology , law
BACKGROUND Several states have implemented childhood obesity surveillance programs supported by legislation. Representatives from Idaho wished to develop a model for childhood obesity surveillance without the support of state legislation, and subsequently report predictors of overweight and obesity in the state. METHODS A coalition comprised of the Idaho State Department of Education and 4 universities identified a randomized cluster sample of schools. After obtaining school administrator consent, measurement teams traveled to each school to measure height and weight of students. Sex and race/ethnicity data were also collected. RESULTS The collaboration between the universities resulted in a sample of 6735 students from 48 schools and 36 communities. Overall, 29.2% of the youth in the sample were classified as overweight or obese, ranging from 24.0% for grade 1 to 33.8% for grade 5. The prevalence of overweight and obesity across schools was highly variable (31.2 ± 7.58%). Hierarchical logistic regression indicated that sex, age, race/ethnicity, socioeconomic status, and region were all significant predictors of overweight and obesity, whereas school was not. CONCLUSIONS This coalition enabled the state of Idaho to successfully estimate the prevalence of overweight and obesity on a representative sample of children from all regions of the state, and subsequently identify populations at greatest risk.