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Developing a Statewide Childhood Body Mass Index Surveillance Program
ABSTRACT 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...
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Published in: | The Journal of school health 2014-10, Vol.84 (10), p.661-667 |
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Main Authors: | , , , , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | ABSTRACT
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. |
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ISSN: | 0022-4391 1746-1561 |
DOI: | 10.1111/josh.12194 |