ArticleObesity science & practice2018
A new method to visualize obesity prevalence in Seattle-King County at the census block level.
Article in Obesity science & practice, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Precision risk assessment for pediatric hospitalization using address-level data in Cincinnati, Ohio.Journal of clinical and translational science · 2026Article
- From the clinic to the community: Can health system data accurately estimate population obesity prevalence?Obesity (Silver Spring, Md.) · 2021Article
- Inedible Food Waste Linked to Diet Quality and Food Spending in the Seattle Obesity Study SOS III.Nutrients · 2021Article
- Community Socioeconomic Deprivation and Nonalcoholic Fatty Liver Disease Severity.Journal of pediatric gastroenterology and nutrition · 2020Article
- Article
- Soda, salad, and socioeconomic status: Findings from the Seattle Obesity Study (SOS).SSM - population health · 2019Article
- A new method to visualize obesity prevalence in Seattle-King County at the census block level.Obesity science & practice · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Objective: The aim of this study is to map obesity prevalence in Seattle King County at the census block level. Methods: Data for 1,632 adult men and women came from the Seattle Obesity Study I. Demographic, socioeconomic and anthropometric data were collected via telephone survey. Home addresses were geocoded, and tax parcel residential property values were obtained from the King County tax assessor. Multiple logistic regression tested associations between house prices and obesity rates. House prices aggregated to census blocks and split into deciles were used to generate obesity heat maps. Results: Deciles of property values for Seattle Obesity Study participants corresponded to county-wide deciles. Low residential property values were associated with high obesity rates (odds ratio, OR: 0.36; 95% confidence interval, CI [0.25, 0.51] in tertile 3 vs. tertile 1), adjusting for age, gender, race, home ownership, education, and incomes. Heat maps of obesity by census block captured differences by geographic area. Conclusion: Residential property values, an objective measure of individual and area socioeconomic status, are a useful tool for visualizing socioeconomic disparities in diet quality and health.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.