Evidence map›Paper›PMID 40018151›Full record

ArticleBMJ public health2024

Community credit scores and community socioeconomic deprivation in association with type 2 diabetes across an urban to rural spectrum in Pennsylvania: a case-control study.

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

Abstract read
In one paragraph

Article in BMJ public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
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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

9 authors.

Melissa N PoulsenDepartment of Population Health Sciences, Geisinger, Danville, Pennsylvania, USA.ORCID 0000-0002-5915-371X
Annemarie G HirschDepartment of Population Health Sciences, Geisinger, Danville, Pennsylvania, USA.
Lorraine DeanDepartment of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA.
Jonathan PollakDepartment of Environmental Health and Engineering, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA.
Joseph DeWalleDepartment of Population Health Sciences, Geisinger, Danville, Pennsylvania, USA.
Katherine MoonDepartment of Environmental Health and Engineering, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA.ORCID 0000-0002-7604-075X
Meghann ReederDepartment of Population Health Sciences, Geisinger, Danville, Pennsylvania, USA.
Karen Bandeen-RocheDepartment of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA.
Brian S SchwartzDepartment of Environmental Health and Engineering, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA.ORCID 0000-0002-0739-9865

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Area-level credit scores (the mean of credit scores for persons in a community) may be a unique indicator of community-level socioeconomic conditions associated with health outcomes. We analysed community credit scores (CCS) in association with new onset type 2 diabetes (T2D) across a geographically heterogeneous region of Pennsylvania and evaluated whether associations were independent of community socioeconomic deprivation (CSD), which is known to be related to T2D risk. Methods: In a nested case-control study, we used medical records to identify 15 888 T2D cases from diabetes diagnoses, medication orders and laboratory test results and 79 435 diabetes-free controls frequency matched on age, sex and encounter year. CCS was derived from Equifax VantageScore V.1.0 data and categorised as 'good', 'high fair', 'low fair' and 'poor'. Individuals were geocoded and assigned the CCS of their residential community. Logistic regression models adjusted for confounding variables and stratified by community type (townships (rural/suburban), boroughs (small towns) and city census tracts). Independent associations of CSD were assessed through models stratified by high/low CSD and high/low CCS. Results: Compared with individuals in communities with 'high fair' CCS, those with 'good' CCS had lower T2D odds (42%, 24% and 12% lower odds in cities, boroughs and townships, respectively). Stratified models assessing independent effects of CCS and CSD showed mainly consistent associations, indicating each community-level measure was independently associated with T2D. Conclusion: CCS may capture novel, health-salient aspects of community socioeconomic conditions, though questions remain regarding the mechanisms by which it influences T2D and how these differ from CSD.

Indexed as

Community HealthEpidemiologyPublic Health

Identifiers

PMID40018151
PMCPMC11812851

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