ArticlePreventing chronic disease2016
Estimating the Prevalence of Childhood Obesity in Alaska Using Partial, Nonrandom Measurement Data.
Article in Preventing chronic disease, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- First Nations Approaches to Childhood Obesity: Healthy Lifestyles in Canada Compared with Alternatives for Alaska Native Communities.Children (Basel, Switzerland) · 2017Article
- Transcriptome Profile Reveals that Pu-Erh Tea Represses the Expression of Vitellogenin Family to Reduce Fat Accumulation in Caenorhabditis elegans.Molecules (Basel, Switzerland) · 2016Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Although monitoring childhood obesity prevalence is critical for state public health programs to assess trends and the effectiveness of interventions, few states have comprehensive body mass index measurement systems in place. In some states, however, assorted school districts collect measurements on student height and weight as part of annual health screenings. To estimate childhood obesity prevalence in Alaska, we created a logistic regression model using such annual measurements along with public data on demographics and socioeconomic status. Our mixed-effects model-generated prevalence estimates validated well against weighted estimates, with 95% confidence intervals overlapping between methodologies among 7 of 8 participating school districts. Our methodology accounts for variation in school-level and student-level demographic factors across the state, and the approach we describe can be applied by other states that have existing nonrandom student measurement data to estimate childhood obesity prevalence.
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