Evidence map›Paper›PMID 28880209›Full record

ArticleInternational journal of environmental research and public health2017

Variations in Obesity Rates between US Counties: Impacts of Activity Access, Food Environments, and Settlement Patterns.

Peter Congdon

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

19 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Area-Level Determinants in Colorectal Cancer Spatial Clustering Studies: A Systematic Review.International journal of environmental research and public health · 2021
    Pooled it
  2. Article
  3. Article
  4. Article
  5. Observational
  6. Article
  7. Spatial Clustering of County-Level COVID-19 Rates in the U.S.International journal of environmental research and public health · 2021
    Article
  8. Article
  9. Article
  10. Article
  11. Can Age-Friendly Planning Promote Equity in Community Health Across the Rural-Urban Divide in the US?International journal of environmental research and public health · 2020
    Article
  12. Article
  13. Article
  14. Data Collection Instruments for Obesogenic Environments in Adults: A Scoping Review.International journal of environmental research and public health · 2019
    Article
  15. Article
  16. Article
  17. Obesity and Urban Environments.International journal of environmental research and public health · 2019
    Review
  18. Article
  19. A Community-Driven Approach to Generate Urban Policy Recommendations for Obesity Prevention.International journal of environmental research and public health · 2018
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Peter CongdonSchool of Geography, Queen Mary University of London, London E1 4NS, UK. p.congdon@qmul.ac.uk.ORCID 0000-0003-1934-9205

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

There is much ongoing research about the effect of the urban environment as compared with individual behaviour on growing obesity levels, including food environment, settlement patterns (e.g., sprawl, walkability, commuting patterns), and activity access. This paper considers obesity variations between US counties, and delineates the main dimensions of geographic variation in obesity between counties: by urban-rural status, by region, by area poverty status, and by majority ethnic group. Available measures of activity access, food environment, and settlement patterns are then assessed in terms of how far they can account for geographic variation. A county level regression analysis uses a Bayesian methodology that controls for spatial correlation in unmeasured area risk factors. It is found that environmental measures do play a significant role in explaining geographic contrasts in obesity.

Indexed as

Bayes TheoremExerciseFoodHumansObesityPoverty AreasRegression AnalysisRisk FactorsRural PopulationTransportationUnited StatesUrban PopulationBayesian regressioncommutingfood environmentinactivityobesitypoverty effectspatial clusteringurban-rural

Identifiers

PMID28880209
PMCPMC5615560

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.