Evidence map›Paper›PMID 38497518›Full record

ArticleTranslational vision science & technology2024

Social Risk Groups in Patients With Diabetes With Differing Eye Care Utilization and Vision Outcomes.

Cindy X Cai, Dingfen Han, Diep Tran, Jose Amezcua Moreno, Scott L Zeger, Deidra C Crews

Abstract read
In one paragraph

Article in Translational vision science & technology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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3citing papers in PubMed
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1 · What the graph read from it

What it found

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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

Who cites it

3 citing papers in PubMed.

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4 · The record

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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

6 authors.

Cindy X CaiWilmer Eye Institute, Johns Hopkins School of Medicine, Baltimore, MD, USA.
Dingfen HanDepartment of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA.
Diep TranWilmer Eye Institute, Johns Hopkins School of Medicine, Baltimore, MD, USA.
Jose Amezcua MorenoWilmer Eye Institute, Johns Hopkins School of Medicine, Baltimore, MD, USA.
Scott L ZegerDepartment of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
Deidra C CrewsDepartment of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA.

Funding

Impact of Social Determinants of Health in Diabetic RetinopathyK23EY033440 · NEI · JOHNS HOPKINS UNIVERSITY · PI Cindy Xinji Cai · 2022 to 2026
$1.1M
Patient-Oriented Research Addressing Disparities in Hypertension and Kidney DiseaseK24HL148181 · NHLBI · JOHNS HOPKINS UNIVERSITY · PI CREWS, DEIDRA CANDICE · 2019 to 2023
$541k
NEI NIH HHS K23 EY033440NHLBI NIH HHS K24 HL148181
6 · The paper itself

Abstract

Purpose: To evaluate whether latent class analysis on social determinants of health (SDoH) data can identify social risk groups that differ by adverse SDoH and vision outcomes in patients with diabetes. Methods: This was a prospective cohort study of adults ≥18 years with diabetes who completed a SDoH survey. Latent class analysis was used to cluster patients into social risk groups. The association of social risk group and severity of diabetic retinopathy, history of lapses in diabetic retinopathy care, and visual acuity was evaluated. Results: A total of 1006 participants were included. The three social risk groups differed by sociodemographic characteristics. The average age was 65, 60, and 54 in Groups 1, 2, and 3 respectively. Most (51%) patients in group 1 were non-Hispanic White, 66% in group 2 were non-Hispanic Black, and 80% in group 3 were Hispanic. Group 1 had the lowest burden of adverse SDoH per person (average 3.6), group 2 had 8.2, and group 3 had 10.5. In general, group 1 lacked diabetic retinopathy knowledge, group 2 had financial insecurity and difficulties with transportation, and group 3 had financial insecurity and did not have health insurance. Social risk group was associated with a history of lapses in diabetic retinopathy care, and presenting with worse vision. Conclusions and Translational Relevance: We identified distinct social risk groups among patients seeking care for diabetic retinopathy that differed by social needs, eye care utilization, and vision. Identifying these groups and their specific needs can help guide interventions to effectively address adverse SDoH and improve eye care utilization and vision outcomes among patients with diabetes.

Indexed as

Diabetes MellitusDiabetic RetinopathyAdultHumansProspective StudiesVision, OcularVisual Acuity

Identifiers

PMID38497518
PMCPMC10950035

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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.