Evidence map›Paper›PMID 40895276›Full record

ArticleHealth services & outcomes research methodology2024

Diabetes Belt has lower efficiency in providing diabetes preventive care than surrounding counties.

Hyojung Kang, Min-Woong Sohn, Soyoun Kim, Siyao Zhang, Rajesh Balkrishnan, Roger Anderson, Anthony McCall, Timothy McMurry, Jennifer Mason Lobo

Abstract read
In one paragraph

Article in Health services & outcomes research methodology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Hyojung KangDepartment of Kinesiology and Community Health, College of Applied Health Sciences, University of Illinois at Urbana-Champaign, Champaign, IL, USA.
Min-Woong SohnDepartment of Health Management and Policy, College of Public Health, University of Kentucky, Lexington, KY, USA.
Soyoun KimDepartment of Public Health Sciences, School of Medicine, University of Virginia, Hospital West, 3rd Floor, Room 3003, PO Box 800717, Charlottesville, VA 22908-0717, USA.
Siyao ZhangDepartment of Kinesiology and Community Health, College of Applied Health Sciences, University of Illinois at Urbana-Champaign, Champaign, IL, USA.
Rajesh BalkrishnanDepartment of Public Health Sciences, School of Medicine, University of Virginia, Hospital West, 3rd Floor, Room 3003, PO Box 800717, Charlottesville, VA 22908-0717, USA.
Roger AndersonDepartment of Public Health Sciences, School of Medicine, University of Virginia, Hospital West, 3rd Floor, Room 3003, PO Box 800717, Charlottesville, VA 22908-0717, USA.
Anthony McCallDivision of Endocrinology and Metabolism, Department of Medicine, School of Medicine, University of Virginia, Charlottesville, VA, USA.
Timothy McMurryDepartment of Public Health Sciences, School of Medicine, University of Virginia, Hospital West, 3rd Floor, Room 3003, PO Box 800717, Charlottesville, VA 22908-0717, USA.
Jennifer Mason LoboDepartment of Public Health Sciences, School of Medicine, University of Virginia, Hospital West, 3rd Floor, Room 3003, PO Box 800717, Charlottesville, VA 22908-0717, USA.ORCID 0000-0001-8375-2475

Funding

Detecting, Understanding, and Reducing Diabetes Belt Preventive Care DisparitiesR01DK113295 · NIDDK · UNIVERSITY OF VIRGINIA · PI LOBO, JENNIFER MASON, SOHN, MIN-WOONG · 2018 to 2021
$2.2M
NIDDK NIH HHS R01 DK113295
6 · The paper itself

Abstract

Annual preventive care is essential for diabetes patients to reduce the risk of complications including hypoglycemic events and blindness. Our aim was to examine the relative efficiency of Diabetes Belt (DB) and non-Diabetes Belt (NDB) counties in providing recommended preventive care for Medicare beneficiaries with diabetes using available health professional resources and to understand county-level socioeconomic factors associated with inefficient provision of preventive care. A data envelopment analysis (DEA) model was developed to assess relative efficiency of counties in providing diabetes preventive care. Logistic regression was performed to identify socioeconomic characteristics associated with inefficiencies. We used Medicare claims data to extract individual-level information of diabetes preventive service use and obtained county-level estimates of health resources information from the Area Health Resources File. More than 80% of counties had more than 10% inefficiencies on average. Compared to counties in the NDB, the odds of being inefficient were 2.44 times more likely in the DB (OR 2.44, CI 1.67-3.58). Counties with lower median income, with a smaller proportion of non-Hispanic Black population, and in a rural area had higher odds of being inefficient in providing preventive care. Our DEA results showed that counties in the DB and NDB were mostly inefficient. The availability of care providers may be less of a problem than how efficiently the resources are used in providing preventive care. Identifying sources of inefficiency within each community with low resource utilization and developing targeted strategies is needed to improve uptake of preventive care cost-effectively.

Indexed as

Data envelopment analysisDiabetesEfficiencyPreventive care

Identifiers

PMID40895276
PMCPMC12392157

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.