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

Abstract

Objective: This study aimed to determine the ability of retrospective cardiometabolic disease staging (CMDS) and social determinants of health (SDoH) to predict COVID-19 outcomes.

Methods: Individual and neighborhood SDoH and CMDS clinical parameters (BMI, glucose, blood pressure, high-density lipoprotein, triglycerides), collected up to 3 years prior to a positive COVID-19 test, were extracted from the electronic medical record. Bayesian logistic regression was used to model CMDS and SDoH to predict subsequent hospitalization, intensive care unit (ICU) admission, and mortality, and whether adding SDoH to the CMDS model improved prediction was investigated. Models were cross validated, and areas under the curve (AUC) were compared.

Results: A total of 2,873 patients were identified (mean age: 58 years [SD 13.2], 59% were female, 45% were Black). CMDS, insurance status, male sex, and higher glucose values were associated with increased odds of all outcomes; area-level social vulnerability was associated with increased odds of hospitalization (odds ratio: 1.84, 95% CI: 1.38-2.45) and ICU admission (odds ratio 1.98, 95% CI: 1.45-2.85). The AUCs improved when SDoH were added to CMDS (p < 0.001): hospitalization (AUC 0.78 vs. 0.82), ICU admission (AUC 0.77 vs. 0.81), and mortality (AUC 0.77 vs. 0.83).

Conclusions: Retrospective clinical markers of cardiometabolic disease and SDoH were independently predictive of COVID-19 outcomes in the population.

FIGURE 1

Flow of participants extracted from the electronic medical record and selected for analysis. CMDS, cardiometabolic disease staging; SDoH, social determinants of health

FIGURE 2

Odds ratio plots of calculated CMDS score, individual‐level SDoH (marital status, insurance status), and neighborhood‐level SDoH (rurality, SVI, HPSA status) in n = 2,745 White and Black participants. The points and lines present the estimated values and 95% CIs, respectively, and the values at the right side are p values. CMDS calculated using Pr (diabetes) = logit^‐1 (−8.464 − 0.014*Age + 0.053*BMI + 0.006*SBP + 0.003*DBP + 0.062*Blood Glucose – 0.018*HDL + 0.001*Triglycerides – 0.084*Sex – 0.446*Race), in which Pr (diabetes) is the probability of 10‐year incident diabetes for any individual; the function, logit^‐1 (x), equals exp(x) / [1 + exp(x)]; Sex equals 1 for male and 0 for female, and Race equals 1 for White and 0 for Black. CMDS, cardiometabolic disease staging; HPSA, health professional shortage area; ICU, intensive care unit; SDoH, social determinants of health; SVI, social vulnerability index

FIGURE 3

Odds ratio plots of models using cardiometabolic disease staging components, individual‐level and neighborhood‐level SDoH for each outcome, n = 2,873. The points and lines present the estimated values and 95% CIs, respectively, and the values at the right side are p values. DBP, diastolic blood pressure; HDL, high‐density lipoprotein cholesterol; HPSA, health professional shortage area; ICU, intensive care unit; SBP, systolic blood pressure; SDoH, social determinants of health; SVI, social vulnerability index; TG, triglycerides