Evidence map›Paper›PMID 39170821›Full record

ArticleEnvironmental epidemiology (Philadelphia, Pa.)2024

Community profiles in northeastern and central Pennsylvania characterized by distinct social, natural, food, and physical activity environments and their relation to type 2 diabetes.

Katherine A Moon, Melissa N Poulsen, Karen Bandeen-Roche, Annemarie G Hirsch, Joseph DeWalle, Jonathan Pollak, Brian S Schwartz

Abstract read
In one paragraph

Article in Environmental epidemiology (Philadelphia, Pa.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. 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

7 authors.

Katherine A MoonDepartment of Environmental Health and Engineering, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD.ORCID https://orcid.org/0000-0002-7604-075X
Melissa N PoulsenDepartment of Population Health Sciences, Geisinger, Danville, PA.
Karen Bandeen-RocheDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.ORCID https://orcid.org/0000-0003-3895-8313
Annemarie G HirschDepartment of Population Health Sciences, Geisinger, Danville, PA.ORCID https://orcid.org/0000-0001-7699-2171
Joseph DeWalleDepartment of Population Health Sciences, Geisinger, Danville, PA.ORCID https://orcid.org/0000-0002-2053-737X
Jonathan PollakDepartment of Environmental Health and Engineering, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD.
Brian S SchwartzDepartment of Environmental Health and Engineering, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD.ORCID https://orcid.org/0000-0002-0739-9865

Funding

Component B: Coordinating Center for Community Characteristics Associated with Geographic Disparities in DiabetesU01DP006293 · DP · DREXEL UNIVERSITY · PI MCCLURE, LESLIE AIN · 2017 to 2021
$3.1M
NCCDPHP CDC HHS U01 DP006293
6 · The paper itself

Abstract

Background: Understanding geographic disparities in type 2 diabetes (T2D) requires approaches that account for communities' multidimensional nature. Methods: In an electronic health record nested case-control study, we identified 15,884 cases of new-onset T2D from 2008 to 2016, defined using encounter diagnoses, medication orders, and laboratory test results, and frequency-matched controls without T2D (79,400; 65,069 unique persons). We used finite mixture models to construct community profiles from social, natural, physical activity, and food environment measures. We estimated T2D odds ratios (OR) with 95% confidence intervals (CI) using logistic generalized estimating equation models, adjusted for sociodemographic variables. We examined associations with the profiles alone and combined them with either community type based on administrative boundaries or Census-based urban/rural status. Results: We identified four profiles in 1069 communities in central and northeastern Pennsylvania along a rural-urban gradient: "sparse rural," "developed rural," "inner suburb," and "deprived urban core." Urban areas were densely populated with high physical activity resources and food outlets; however, they also had high socioeconomic deprivation and low greenness. Compared with "developed rural," T2D onset odds were higher in "deprived urban core" (1.24, CI = 1.16-1.33) and "inner suburb" (1.10, CI = 1.04-1.17). These associations with model-based community profiles were weaker than when combined with administrative boundaries or urban/rural status. Conclusions: Our findings suggest that in urban areas, diabetogenic features overwhelm T2D-protective features. The community profiles support the construct validity of administrative-community type and urban/rural status, previously reported, to evaluate geographic disparities in T2D onset in this geography.

Indexed as

Built environmentEnvironmental factorsFood environmentNatural environmentSocial environmentType 2 diabetes

Identifiers

PMID39170821
PMCPMC11338261

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.