TY  - JOUR
AU  - Howell, Carrie R
AU  - Zhang, Li
AU  - Clay, Olivio J
AU  - Dutton, Gareth
AU  - Horton, Trudi
AU  - Mugavero, Michael J
AU  - Cherrington, Andrea L
PY  - 2024
DA  - 2024/8/7
TI  - Social Determinants of Health Phenotypes and Cardiometabolic Condition Prevalence Among Patients in a Large Academic Health System: Latent Class Analysis
JO  - JMIR Public Health Surveill
SP  - e53371
VL  - 10
KW  - social determinants of health
KW  - electronic medical record
KW  - phenotypes
KW  - diabetes
KW  - obesity
KW  - cardiovascular disease
KW  - obese
KW  - social determinants
KW  - social determinant
KW  - cardiometabolic
KW  - risk factors
KW  - risk factor
KW  - latent class analysis
KW  - cardiometabolic disease
KW  - EMR
KW  - EHR
KW  - electronic health record
AB  - 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. 
SN  - 2369-2960
UR  - https://publichealth.jmir.org/2024/1/e53371
UR  - https://doi.org/10.2196/53371
DO  - 10.2196/53371
ID  - info:doi/10.2196/53371
ER  -