ArticlePediatric obesity2020
Identifying geographic differences in children's sugar-sweetened beverage and 100% fruit juice intake using health system data.
Article in Pediatric obesity, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Who Reports Greater Chronic Disease Risk Behaviours? A Closer Look at Sociodemographic Differences Among Australian Adolescents: A Cross-Sectional Analysis.Health promotion journal of Australia : official journal of Australian Association of Health Promotion Professionals · 2025Article
- Family Social Support and Weight-Related Behaviors of School-Age Children: An Exploratory Analysis.International journal of environmental research and public health · 2022Article
- eHealth Interventions Targeting Poor Diet, Alcohol Use, Tobacco Smoking, and Vaping Among Disadvantaged Youth: Protocol for a Systematic Review.JMIR research protocols · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
backgroundIdentifying areas with higher sugar-sweetened beverage intake could help tailor policy and public health efforts intended to reduce sugary beverage consumption.
objectiveDemonstrate the feasibility of using health system data to examine the geographic distribution of sugar-sweetened beverage intake and evaluate neighbourhood characteristics associated with intake.
methodsWe extracted electronic health record data from a sugar-sweetened beverage and 100% fruit juice screener used for children ages 1 to 17 years in eight pediatric practices in North Carolina (March 2017-2018) and dichotomized intake to high (≥3 sugar-sweetened beverages/day) vs not. We geocoded address and mapped the proportion of consumers in each census tract. We combined electronic health record data with US census data and evaluated associations of census tract income and race/ethnicity with intake. We used multivariable models to evaluate the association between geographic concentrations of income and race/ethnicity and sugar-sweetened beverage intake, controlling for demographics extracted from the electronic health record and clustering by tract.
resultsOf 19 451 patients, 4579 (23.5%) reported consuming ≥3 sugar-sweetened beverages/day. In multivariable models, children living in tracts with high concentrations of low-income (OR: 1.45, 95% CI: 1.26, 1.68) and non-white residents (OR: 1.44, 95% CI: 1.21, 1.71) were more likely to consume ≥3 sugar-sweetened beverages/day than children in tracts with a high concentration of high-income and white residents.
conclusionWe demonstrate how health system data could be used to characterize geographic variation in sugar-sweetened beverage and 100% fruit juice consumption. This approach could help target public health efforts and monitor the effects of community-level interventions.
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Registered trials
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