Evidence map›Paper›PMID 39107030›Full record

ArticleBMJ open2024

Demographic and geographic distribution of diabetes and pre-diabetes risk in rural settings: results from a cross-sectional, countywide rural health survey in Sullivan County, New York.

David C Lee, Leah Ross, Carolina Quintero Arias, Melissa Rony, Rahi Patel, Erica Jensen, Robert Petcu, Daniel Imas, Brian Elbel, Lorna E Thorpe and 1 more

Abstract read
In one paragraph

Article in BMJ open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

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

  1. Pooled it
  2. Article
  3. Observational
  4. Article
  5. Voice-based prediction of prediabetes using classical machine learning models.Frontiers in clinical diabetes and healthcare · 2025
    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

11 authors.

David C LeeEmergency Medicine, New York University School of Medicine, New York, New York, USA David.Lee@nyulangone.org.ORCID 0000-0002-7202-1893
Leah RossGraduate School of Arts and Sciences, NYU, New York, New York, USA.
Carolina Quintero AriasEmergency Medicine, New York University School of Medicine, New York, New York, USA.
Melissa RonyEmergency Medicine, New York University School of Medicine, New York, New York, USA.
Rahi PatelNortheast Ohio Medical University, Rootstown, Ohio, USA.
Erica JensenTouro College of Osteopathic Medicine Harlem Campus, New York, New York, USA.
Robert PetcuMedical College of Georgia, Augusta, Georgia, USA.
Daniel ImasEmergency Medicine, New York University School of Medicine, New York, New York, USA.
Brian ElbelPopulation Health, New York University School of Medicine, New York, New York, USA.
Lorna E ThorpePopulation Health, New York University School of Medicine, New York, New York, USA.
Rebecca AnthopolosPopulation Health, New York University School of Medicine, New York, New York, USA.

Funding

The Impact of the Food Environment and Other Environmental Exposures on the Risk of Diabetes in Rural SettingsR01DK124400 · NIDDK · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI LEE, DAVID C · 2020 to 2024
$3.5M
NIDDK NIH HHS R01 DK124400
6 · The paper itself

Abstract

objectiveTo perform a detailed characterisation of diabetes burden and pre-diabetes risk in a rural county with previously documented poor health outcomes in order to understand the local within-county distribution of diabetes in rural areas of America. DESIGN, SETTING, AND

participantsIn 2021, we prospectively mailed health surveys to all households in Sullivan County, a rural county with the second-worst health outcomes of all counties in New York State. Our survey included questions on demographics, medical history and the American Diabetes Association's Pre-diabetes Risk Test. PRIMARY OUTCOME AND

methodsOur primary outcome was an assessment of diabetes burden within this rural county. To help mitigate non-response bias in our survey, raking adjustments were performed across strata of age, sex, race/ethnicity and health insurance. We analysed diabetes prevalence by demographic characteristics and used geospatial analysis to assess for clustering of diagnosed diabetes cases.

resultsAfter applying raking procedures for the 4725 survey responses, our adjusted diagnosed diabetes prevalence for Sullivan County was 12.9% compared with the 2019 Behavioural Risk Factor Surveillance System (BRFSS) estimate of 8.6%. In this rural area, diagnosed diabetes prevalence was notably higher among non-Hispanic Black (21%) and Hispanic (15%) residents compared with non-Hispanic White (12%) residents. 53% of respondents without a known history of pre-diabetes or diabetes scored as high risk for pre-diabetes. Nearest neighbour analyses revealed that hotspots of diagnosed diabetes were primarily located in the more densely populated areas of this rural county.

conclusionsOur mailed health survey to all residents in Sullivan County demonstrated higher diabetes prevalence compared with modelled BRFSS estimates that were based on small telephone samples. Our results suggest the need for better diabetes surveillance in rural communities, which may benefit from interventions specifically tailored for improving glycaemic control among rural residents.

Indexed as

Diabetes MellitusHealth SurveysPrediabetic StateRural PopulationAdolescentAdultAgedCross-Sectional StudiesFemaleHumansMaleMiddle AgedNew YorkPrevalenceRisk FactorsYoung Adultepidemiologic studiesgeneral diabetespublic health

Identifiers

PMID39107030
PMCPMC11308904

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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.