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Accepted for/Published in: JMIR Public Health and Surveillance

Date Submitted: Oct 4, 2023
Date Accepted: Jun 6, 2024

The final, peer-reviewed published version of this preprint can be found here:

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

Howell CR, Zhang L, Clay OJ, Dutton G, Horton T, Mugavero MJ, Cherrington AL

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

JMIR Public Health Surveill 2024;10:e53371

DOI: 10.2196/53371

PMID: 39113389

PMCID: 11322797

Social determinants of health phenotypes and cardiometabolic condition prevalence among patients in a large academic health system: A Latent Class Analysis

  • Carrie R. Howell; 
  • Li Zhang; 
  • Olivio J Clay; 
  • Gareth Dutton; 
  • Trdui Horton; 
  • Michael J Mugavero; 
  • Andrea L Cherrington

ABSTRACT

Background:

Adverse social determinants of health (SDoH) have been associated with cardiometabolic disease; however, disparities in poor cardiometabolic outcomes are rarely a result of a single risk factor.

Objective:

To identify and characterize social determinant of health (SDoH) phenotypes based on patient reported and neighborhood level data from the institutional electronic medical record (EMR) and evaluate associations with the presence of diabetes, obesity and other cardiometabolic diseases.

Methods:

Patient reported SDoH were collected (January to December 2020); neighborhood level social vulnerability, neighborhood SES and rurality were linked via census tract to geocoded patient address. Diabetes status was coded in the EMR using ICD-10 codes; obesity was defined using measured BMI >= 30 kg/m2. We used latent class analysis to identify clusters of SDoH (e.g. phenotypes) and examined associations between SDoH phenotype and cardiometabolic conditions.

Results:

N=2,380 patients had complete data for analysis [mean age 53 years, female (59%), 50% Non-White]. Roughly 8% reported housing insecurity; 30% reported resource needs (food, healthcare, utilities); 49% lived in a high vulnerability census tract. We identified 3 patient SDoH phenotypes: (1) High Social Risk, defined largely by self-reported SDoH (9%); (2) Adverse Neighborhood SDoH (56%), defined largely by adverse neighborhood level measures; and (3) Low Social Risk (34%), defined as low individual and neighborhood level risks. Patients with an Adverse Neighborhood SDoH phenotype were more likely to have diagnosed T2D (OR 1.32, 95% CI 1.09-1.58), hypertension (OR 1.24, 95% CI 1.04-1.49), peripheral vascular disease (OR 1.51, 95% CI 1.09-2.10), and heart failure (OR 1.59, 95% CI 1.25-2.02).

Conclusions:

Patients with an adverse neighborhood SDoH phenotype – largely based upon neighborhood level SDoH – were more likely to have poor cardiometabolic outcomes compared to phenotypes largely determined by individual level characteristics suggesting that neighborhood environment plays a role even if individual measures of SES are not suboptimal. Clinical Trial: NA


 Citation

Please cite as:

Howell CR, Zhang L, Clay OJ, Dutton G, Horton T, Mugavero MJ, Cherrington AL

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

JMIR Public Health Surveill 2024;10:e53371

DOI: 10.2196/53371

PMID: 39113389

PMCID: 11322797

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