ArticleObesity (Silver Spring, Md.)2021
From the clinic to the community: Can health system data accurately estimate population obesity prevalence?
Article in Obesity (Silver Spring, Md.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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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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
5 citing papers in PubMed, 5 citations in OpenAlex.
- Where, when, and how to close dialysis access gaps: incorporation of geographic information system into data-driven planning : A commentary.Israel journal of health policy research · 2025Article
- Regional variation in overweight and associations with regional profiles using a Japanese national open-source database.PloS one · 2025Article
- Impact of employment status and work setting on body weight and health behaviors among people with obesity.Work (Reading, Mass.) · 2024Article
- Geospatial divide in real-world EHR data: Analytical workflow to assess regional biases and potential impact on health equity.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2023Article
- Changes in body weight, health behaviors, and mental health in adults with obesity during the COVID-19 pandemic.Obesity (Silver Spring, Md.) · 2022Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 1 country.
Funding
Abstract
objectiveHealth system data were assessed for how well they can estimate obesity prevalence in census tracts.
methodsClinical visit data were available from two large health systems (Kaiser Permanente Washington and University of Washington Medicine) in King County, Washington, as were census tract-level obesity prevalence estimates from the Behavioral Risk Factor Surveillance System (BRFSS). The health system data were geocoded to identify each patient's tract of residence, and the cross-sectional concordance between census tract-level obesity prevalence estimates computed from the two health systems in 2005 to 2006 and the concordance between University of Washington Medicine and BRFSS from 2012 to 2016 were assessed.
resultsThe spatial distribution of obesity was similar between the health systems (Spearman r = 0.63). The University of Washington Medicine estimates of rank order correlated well with BRFSS estimates (Spearman r = 0.85), though prevalence estimates from BRFSS were lower (mean obesity prevalence = 26% for University of Washington Medicine versus 20% for BRFSS, Wilcoxon rank sum test p < 0.001). Across all data sources, obesity was more prevalent in tracts with less educational attainment.
conclusionsHealth system clinical weight data can reliably replicate census tract-level spatial patterns in the ranking of obesity prevalence. Health system data may be an efficient resource for geographic obesity surveillance.
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