Evidence map›Paper›PMID 37580543›Full record

ArticleJournal of urban health : bulletin of the New York Academy of Medicine2023

Neighborhood-Level Risk Factors for Severe Hyperglycemia among Emergency Department Patients without a Prior Diabetes Diagnosis.

Christian A Koziatek, Isaac Bohart, Reed Caldwell, Jordan Swartz, Perry Rosen, Sagar Desai, Katarzyna Krol, Daniel B Neill, David C Lee

Abstract read
In one paragraph

Article in Journal of urban health : bulletin of the New York Academy of Medicine, 2023. 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

9 authors.

Christian A KoziatekRonald O. Perelman Department of Emergency Medicine, New York University School of Medicine, 462 First Avenue, Room A345, New York, NY, 10016, USA.
Isaac BohartRonald O. Perelman Department of Emergency Medicine, New York University School of Medicine, 462 First Avenue, Room A345, New York, NY, 10016, USA.
Reed CaldwellRonald O. Perelman Department of Emergency Medicine, New York University School of Medicine, 462 First Avenue, Room A345, New York, NY, 10016, USA.
Jordan SwartzRonald O. Perelman Department of Emergency Medicine, New York University School of Medicine, 462 First Avenue, Room A345, New York, NY, 10016, USA.
Perry RosenRonald O. Perelman Department of Emergency Medicine, New York University School of Medicine, 462 First Avenue, Room A345, New York, NY, 10016, USA.
Sagar DesaiRonald O. Perelman Department of Emergency Medicine, New York University School of Medicine, 462 First Avenue, Room A345, New York, NY, 10016, USA.
Katarzyna KrolRonald O. Perelman Department of Emergency Medicine, New York University School of Medicine, 462 First Avenue, Room A345, New York, NY, 10016, USA.
Daniel B NeillCourant Institute of Mathematical Sciences, Department of Computer Science, New York University, New York, NY, USA.
David C LeeRonald O. Perelman Department of Emergency Medicine, New York University School of Medicine, 462 First Avenue, Room A345, New York, NY, 10016, USA. david.lee@nyumc.org.ORCID http://orcid.org/0000-0002-7202-1893

Funding

New York Regional Center for Diabetes Translation Research - Translational Intervention Methodology CoreP30DK111022 · NIDDK · ALBERT EINSTEIN COLLEGE OF MEDICINE, INC · PI JEFFREY GONZALEZ · 2016 to 2026
$7.8M
Identifying Risk Factors for Poor Glycemic Control among Emergency Department Patients and Improving Linkage to Outpatient CareR01DK134668 · NIDDK · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI David C Lee · 2023 to 2026
$2.8M
NIDDK NIH HHS P30 DK111022NIDDK NIH HHS R01 DK134668
6 · The paper itself

Abstract

A person's place of residence is a strong risk factor for important diagnosed chronic diseases such as diabetes. It is unclear whether neighborhood-level risk factors also predict the probability of undiagnosed disease. The objective of this study was to identify neighborhood-level variables associated with severe hyperglycemia among emergency department (ED) patients without a history of diabetes. We analyzed patients without previously diagnosed diabetes for whom a random serum glucose value was obtained in the ED. We defined random glucose values ≥ 200 mg/dL as severe hyperglycemia, indicating probable undiagnosed diabetes. Patient addresses were geocoded and matched with neighborhood-level socioeconomic measures from the American Community Survey and claims-based surveillance estimates of diabetes prevalence. Neighborhood-level exposure variables were standardized based on z-scores, and a series of logistic regression models were used to assess the association of selected exposures and hyperglycemia adjusting for biological and social individual-level risk factors for diabetes. Of 77,882 ED patients without a history of diabetes presenting in 2021, 1,715 (2.2%) had severe hyperglycemia. Many geospatial exposures were associated with uncontrolled hyperglycemia, even after controlling for individual-level risk factors. The most strongly associated neighborhood-level variables included lower markers of educational attainment, higher percentage of households where limited English is spoken, lower rates of white-collar employment, and higher rates of Medicaid insurance. Including these geospatial factors in risk assessment models may help identify important subgroups of patients with undiagnosed disease.

Indexed as

Diabetes MellitusHyperglycemiaUndiagnosed DiseasesEmergency Service, HospitalGlucoseHumansResidence CharacteristicsRisk FactorsGlucoseDiabetes screeningEmergency departmentGeographic information systemsPublic health surveillance

Identifiers

PMID37580543
PMCPMC10447789

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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.