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Social Determinants of Health Phenotypes and Cardiometabolic Condition Prevalence Among Patients in a Large Academic Health System: Latent Class Analysis.

作者信息

Howell Carrie R, Zhang Li, Clay Olivio J, Dutton Gareth, Horton Trudi, Mugavero Michael J, Cherrington Andrea L

机构信息

Division of Preventive Medicine, Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, United States.

School of Public Health, University of Alabama at Birmingham, Birmingham, AL, United States.

出版信息

JMIR Public Health Surveill. 2024 Aug 7;10:e53371. doi: 10.2196/53371.


DOI:10.2196/53371
PMID:39113389
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11322797/
Abstract

BACKGROUND: Adverse social determinants of health (SDoH) have been associated with cardiometabolic disease; however, disparities in cardiometabolic outcomes are rarely the result of a single risk factor. OBJECTIVE: This study aimed to identify and characterize SDoH phenotypes based on patient-reported and neighborhood-level data from the institutional electronic medical record and evaluate the prevalence of diabetes, obesity, and other cardiometabolic diseases by phenotype status. METHODS: Patient-reported SDoH were collected (January to December 2020) and neighborhood-level social vulnerability, neighborhood socioeconomic status, and rurality were linked via census tract to geocoded patient addresses. Diabetes status was coded in the electronic medical record using International Classification of Diseases codes; obesity was defined using measured BMI ≥30 kg/m2. Latent class analysis was used to identify clusters of SDoH (eg, phenotypes); we then examined differences in the prevalence of cardiometabolic conditions based on phenotype status using prevalence ratios (PRs). RESULTS: Complete data were available for analysis for 2380 patients (mean age 53, SD 16 years; n=1405, 59% female; n=1198, 50% non-White). Roughly 8% (n=179) reported housing insecurity, 30% (n=710) reported resource needs (food, health care, or utilities), and 49% (n=1158) lived in a high-vulnerability census tract. We identified 3 patient SDoH phenotypes: (1) high social risk, defined largely by self-reported SDoH (n=217, 9%); (2) adverse neighborhood SDoH (n=1353, 56%), defined largely by adverse neighborhood-level measures; and (3) low social risk (n=810, 34%), defined as low individual- and neighborhood-level risks. Patients with an adverse neighborhood SDoH phenotype had higher prevalence of diagnosed type 2 diabetes (PR 1.19, 95% CI 1.06-1.33), hypertension (PR 1.14, 95% CI 1.02-1.27), peripheral vascular disease (PR 1.46, 95% CI 1.09-1.97), and heart failure (PR 1.46, 95% CI 1.20-1.79). CONCLUSIONS: Patients with the adverse neighborhood SDoH phenotype had higher prevalence of poor cardiometabolic conditions compared to phenotypes determined by individual-level characteristics, suggesting that neighborhood environment plays a role, even if individual measures of socioeconomic status are not suboptimal.

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e14b/11322797/5f62894bbbee/publichealth-v10-e53371-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e14b/11322797/205735d14eab/publichealth-v10-e53371-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e14b/11322797/79f1eb07da64/publichealth-v10-e53371-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e14b/11322797/e70d6a379079/publichealth-v10-e53371-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e14b/11322797/5f62894bbbee/publichealth-v10-e53371-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e14b/11322797/205735d14eab/publichealth-v10-e53371-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e14b/11322797/79f1eb07da64/publichealth-v10-e53371-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e14b/11322797/e70d6a379079/publichealth-v10-e53371-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e14b/11322797/5f62894bbbee/publichealth-v10-e53371-g004.jpg

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本文引用的文献

[1]
Real-world integration of the protocol for responding to and assessing patients' assets, risks, and experiences tool to assess social determinants of health in the electronic medical record at an academic medical center.

Digit Health. 2023-5-22

[2]
Perspective: Acknowledging a Hierarchy of Social Needs in Diabetes Clinical Care and Prevention.

Diabetes Metab Syndr Obes. 2023-1-19

[3]
Evaluating Social Determinants of Health Domains and Their Predictive Validity Within Black/African American and White Older Adults From the ACTIVE Trial.

J Aging Health. 2023-10

[4]
Associations between cardiometabolic disease severity, social determinants of health (SDoH), and poor COVID-19 outcomes.

Obesity (Silver Spring). 2022-7

[5]
Social Determinants of Health and Cardiovascular Disease: Current State and Future Directions Towards Healthcare Equity.

Curr Atheroscler Rep. 2021-7-26

[6]
Latent class analysis-derived subphenotypes are generalisable to observational cohorts of acute respiratory distress syndrome: a prospective study.

Thorax. 2022-1

[7]
Heart Disease and Stroke Statistics-2021 Update: A Report From the American Heart Association.

Circulation. 2021-2-23

[8]
Collecting Social Determinants of Health Data in the Clinical Setting: Findings from National PRAPARE Implementation.

J Health Care Poor Underserved. 2020

[9]
Number of Social Determinants of Health and Fatal and Nonfatal Incident Coronary Heart Disease in the REGARDS Study.

Circulation. 2021-1-19

[10]
The Lancet Commission on diabetes: using data to transform diabetes care and patient lives.

Lancet. 2021-12-19

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