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

被引:0
|
作者
Howell, Carrie R. [1 ]
Zhang, Li [2 ]
Clay, Olivio J. [3 ,4 ,5 ]
Dutton, Gareth [1 ]
Horton, Trudi [1 ]
Mugavero, Michael J. [6 ]
Cherrington, Andrea L. [1 ]
机构
[1] Univ Alabama Birmingham, Dept Med, Div Prevent Med, Med Towers Suite 638,1717 11th Ave South, Birmingham, AL 35294 USA
[2] Univ Alabama Birmingham, Sch Publ Hlth, Birmingham, AL USA
[3] Univ Alabama Birmingham, Alzheimers Dis Res Ctr, Birmingham, AL USA
[4] Univ Alabama Birmingham, Deep South Resource Ctr Minor Aging Res, Birmingham, AL USA
[5] Univ Alabama Birmingham, Dept Psychol, Birmingham, AL USA
[6] Univ Alabama Birmingham, Dept Med, Div Infect Dis, Birmingham, AL USA
来源
关键词
social determinants of health; electronic medical record; phenotypes; diabetes; cardiovascular disease; obese; social determinants; social determinant; cardiometabolic; risk factors; latent class analysis; cardiometabolic disease; EMR; EHR; electronic health record; PHYSICAL-ACTIVITY; BREAST-CANCER; SOCIOECONOMIC-STATUS; SUBPHENOTYPES; CALIFORNIA; IMPACT;
D O I
10.2196/53371
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
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-leveldata from the institutional electronic medical record and evaluate the prevalence of diabetes, obesity, and other cardiometabolicdiseases 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 statuswas coded in the electronic medical record using International Classification of Diseases codes; obesity was defined usingmeasured BMI >= 30 kg/m2. Latent class analysis was used to identify clusters of SDoH (eg, phenotypes); we then examineddifferences 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 pheno-types: (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 lowindividual- and neighborhood-level risks. Patients with an adverse neighborhood SDoH phenotype had higher prevalence ofdiagnosed 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 cardiometabolicconditions compared to phenotypes determined by individual-level characteristics, suggesting that neighborhood environmentplays a role, even if individual measures of socioeconomic status are not suboptimal.
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页数:17
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