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Race Versus Social Determinants of Health in COVID-19 Hospitalization Prediction

      Introduction

      Including race as a biological construct in risk prediction models may guide clinical decisions in ways that cause harm and widen racial disparities. This study reports on using race versus social determinants of health (SDoH) in predicting the associations between cardiometabolic disease severity (assessed using cardiometabolic disease staging) and COVID-19 hospitalization.

      Methods

      Electronic medical record data on patients with a positive COVID-19 polymerase chain reaction test in 2020 and a previous encounter in the electronic medical record where cardiometabolic disease staging clinical data (BMI, blood glucose, blood pressure, high-density lipoprotein cholesterol, and triglycerides) were available from 2017 to 2020, were analyzed in 2021. Associations between cardiometabolic disease staging and COVID-19 hospitalization adding race and SDoH (individual and neighborhood level [e.g., Social Vulnerability Index]) in different models were examined. Area under the curve was used to assess predictive performance.

      Results

      A total of 2,745 patients were included (mean age of 58 years, 59% female, 47% Black). In the cardiometabolic disease staging model, area under the curve was 0.767 vs 0.777 when race was included. Adding SDoH to the cardiometabolic model improved the area under the curve to 0.809 (p<0.001), whereas the addition of SDoH and race increased the area under the curve to 0.811. In race-stratified models, the area under the curve for non-Hispanic Blacks was 0.781, whereas the model for non-Hispanic Whites performed better with an area under the curve of 0.821.

      Conclusions

      Cardiometabolic disease staging was predictive of hospitalization after a positive COVID-19 test. Adding race did not markedly increase the predictive ability; however, adding SDoH to the model improved the area under the curve to ≥0.80. Future research should include SDoH with biological variables in prediction modeling to capture social experience of race.

      INTRODUCTION

      Including self-identified race in risk prediction may guide clinical decisions in ways that cause harm and widen racial disparities.
      • Vyas DA
      • Eisenstein LG
      • Jones DS
      Hidden in plain sight - reconsidering the use of race correction in clinical algorithms.
      Including race as a predictor has become standard, with many prediction models developed without explanation of causal biological mechanisms of racial differences
      • Vyas DA
      • Eisenstein LG
      • Jones DS
      Hidden in plain sight - reconsidering the use of race correction in clinical algorithms.
      • Kowalsky RH
      • Rondini AC
      • Platt SL
      The case for removing race from the American Academy of Pediatrics Cnical Practice Guideline for urinary tract infection in infants and young children with fever.
      and others with reasons that are outdated or fraught with racial bias.
      • Kowalsky RH
      • Rondini AC
      • Platt SL
      The case for removing race from the American Academy of Pediatrics Cnical Practice Guideline for urinary tract infection in infants and young children with fever.
      • Eneanya ND
      • Yang W
      • Reese PP
      Reconsidering the consequences of using race to estimate kidney function.
      • Vyas DA
      • Jones DS
      • Meadows AR
      • Diouf K
      • Nour NM
      • Schantz-Dunn J
      Challenging the use of race in the vaginal birth after cesarean section calculator.
      Furthermore, electronic medical record (EMR) data, often the entry point for recording race data in health care,
      • Cooper RS
      • Nadkarni GN
      • Ogedegbe G
      Race, ancestry, and reporting in medical journals.
      are notoriously characterized by high levels of missingness or unknown race
      • Lee SJ
      • Grobe JE
      • Tiro JA
      Assessing race and ethnicity data quality across cancer registries and EMRs in two hospitals.
      and inaccuracies.
      • Polubriaginof FCG
      • Ryan P
      • Salmasian H
      • et al.
      Challenges with quality of race and ethnicity data in observational databases.
      ,
      • Magaña López M
      • Bevans M
      • Wehrlen L
      • Yang L
      • Wallen GR
      Discrepancies in race and ethnicity documentation: a potential barrier in identifying racial and ethnic disparities.
      A growing body of literature points to race/ethnicity as a social construct,
      • Kowalsky RH
      • Rondini AC
      • Platt SL
      The case for removing race from the American Academy of Pediatrics Cnical Practice Guideline for urinary tract infection in infants and young children with fever.
      conceptualized as a measure that captures a life-long social experience,
      • Cooper RS
      • Nadkarni GN
      • Ogedegbe G
      Race, ancestry, and reporting in medical journals.
      wherein the causal effect of race on outcomes is assumed through racism and social factors as opposed to biological differences.
      • Julian BA
      • Gaston RS
      • Brown WM
      • et al.
      Effect of replacing race with apolipoprotein L1 genotype in calculation of kidney donor risk index.
      For these reasons, examining the inclusion of race and social determinants of health (SDoH) in models has become increasingly important, further highlighted by the coronavirus disease 2019 (COVID-19) pandemic.
      • Patel AGM
      • Hsiao CJ
      • Fasanya HO
      • Stoffel M
      • Lyon ME
      Accuracy in measuring racial disparities during the COVID-19 pandemic needs improvement.
      This holds particularly true when examining obesity-related risk factors and outcomes where racial and low-income disparities have been well described.
      • Petersen R
      • Pan L
      • Blanck HM
      Racial and ethnic disparities in adult obesity in the United States: CDC’s tracking to inform state and local action.
      ,
      • Thornton PL
      • Kumanyika SK
      • Gregg EW
      • et al.
      New research directions on disparities in obesity and type 2 diabetes.
      Obesity can exacerbate insulin resistance, progressing to metabolic syndrome and prediabetes, culminating in manifestations of Type 2 diabetes and cardiovascular disease. This one disease process, cardiometabolic disease (CMD), could explain the associations between poor COVID-19 outcomes and obesity, hypertension, diabetes, and cardiovascular disease.
      • Nadolsky KZ
      • Hurley DL
      • Garvey WT
      Covid-19 & obesity: beyond Bmi.
      This study reports the lessons learned using race versus SDoH measures to account for disparity when predicting the associations between CMD severity, using cardiometabolic disease staging (CMDS),
      • Guo F
      • Garvey WT
      Development of a weighted cardiometabolic disease staging (CMDS) system for the prediction of future diabetes.
      • Guo F
      • Moellering DR
      • Garvey WT
      The progression of cardiometabolic disease: validation of a new cardiometabolic disease staging system applicable to obesity.
      • Wilkinson L
      • Yi N
      • Mehta T
      • Judd S
      • Garvey WT
      Development and validation of a model for predicting incident type 2 diabetes using quantitative clinical data and a Bayesian logistic model: a nationwide cohort and modeling study.
      and hospitalization after a positive COVID-19 polymerase chain reaction (PCR) test among patients captured in the EMR at an academic medical center in Alabama.

      METHODS

      This retrospective study included patients in the EMR with a positive PCR test (collected in 2020), as well as a previous encounter where clinical data were available (2017‒2020), and were aged ≥35 years (Appendix 1, available online). Data were extracted as part of a larger study (reviewed and approved as exempt by the institution's IRB) examining the associations between retrospective cardiometabolic markers, SDoH, and poor COVID-19 outcomes (hospitalization, intensive care unit stay, mortality). For brevity, only results for the hospitalization outcome defined as new hospital admission and a positive COVID-19 PCR within 14 days of admission are reported. Cardiometabolic severity was defined using risk factors from the CMDS,
      • Guo F
      • Garvey WT
      Development of a weighted cardiometabolic disease staging (CMDS) system for the prediction of future diabetes.
      • Wilkinson L
      • Yi N
      • Mehta T
      • Judd S
      • Garvey WT
      Development and validation of a model for predicting incident type 2 diabetes using quantitative clinical data and a Bayesian logistic model: a nationwide cohort and modeling study.
      which included BMI, glucose, blood pressure, high-density lipoprotein cholesterol, and triglycerides. Owing to small group numbers of Hispanics and Asian Americans in the sample, the analysis was limited to non-Hispanic Black and non-Hispanic White. Guided by the National Institute on Minority Health and Health Disparities SDoH Framework,
      • Alvidrez J
      • Castille D
      • Laude-Sharp M
      • Rosario A
      • Tabor D
      The National Institute on Minority Health and Health Disparities research framework.
      marital and insurance statuses were included as individual SDoH; neighborhood-level SDoH included census tract level Social Vulnerability Index,
      • Flanagan BE
      • Hallisey EJ
      • Adams E
      • Lavery A
      Measuring community vulnerability to natural and anthropogenic hazards: the Centers for Disease Control and Prevention’s Social Vulnerability Index.
      rurality,

      Rural-urban commuting area codes. U.S. Department of Agriculture Economic Research Service. https://www.ers.usda.gov/data-products/rural-urban-commuting-area-codes.aspx. Accessed June 5, 2018.

      and Health Professional Shortage Area

      HPSA data downloads. Health Resource and Service Administration. https://data.hrsa.gov/data/download. Accessed April 30, 2019.

      designation (Appendix 2, available online). Bayesian logistic regression with Cauchy priors
      • Gelman A
      • Jakulin A
      • Pittau MG
      • Su YS
      A weakly informative default prior distribution for logistic and other regression models.
      was used to model COVID-19 hospitalization (reported as OR and 95% CI) using CMDS, age, and sex adding race and SDoH in different models. Model 1 included CMDS only, with Model 2 adding race; Model 3 included CMDS and SDoH with Model 4 adding both SDoH and race to CMDS. To evaluate the predictive performance of fitted models, tenfold cross-validation with several measures was used, including area under the curve (AUC) (a measure that reflects the accuracy of the model to correctly classify those with and without the outcome; higher values indicated better performance, whereas a value of 1 means a perfect prediction), mean squared error, and misclassification. AUCs were compared using DeLong's test. Statistical analysis was performed in 2021 using R software (version 4.0.3).

      RESULTS

      A total of 2,745 patients had complete data for analysis. Overall, patients had a mean age of 58 years (SD=13.2) and were mostly female (59%), and 47% were Black. Table 1 shows the study population characteristics by race category. Black patients had higher proportions of suboptimal SDoH, had higher BMI and clinical parameter values, and were more likely to be hospitalized. Table 2 shows the predictive power and validation for models. In the CMDS only model, AUC was 0.767 vs AUC of 0.777 when race was included (p=0.002). Adding SDoH to the CMDS model improved AUC to 0.809 (p<0.0001), whereas the addition of race to the CMDS and SDoH model increased AUC to 0.811 (p<0.0001). Black race was significant (footnote in Table 2) when added to the CMDS (OR=2.10, 95% CI=1.72, 2.57) but was attenuated and marginally nonsignificant when SDoH were added (OR=1.28, 95% CI=0.99, 1.65). Figure 1 shows the ORs, AUC, mean squared error, and misclassification statistics for models stratified by race. Male sex, higher glucose levels, insurance status, and higher Social Vulnerability Index were associated with increased odds of hospitalization for both Blacks and Whites at mostly comparable magnitudes. AUC for Blacks was 0.781, whereas the model for Whites performed better with an AUC of 0.821 (p=0.02). Although misclassification was higher in the model for Black patients, the false-negative rate (e.g., underestimation) decreased when SDoH were added (Appendix 3, available online).
      Table 1Comparison of the Characteristics of the Study Population Between Black and White Patients
      CharacteristicsNon-Hispanic Black, n=1,285Non-Hispanic White, n=1,460p-value
      Comparison of characteristics using chi-square tests for categorical variables and t tests for continuous variables.
      Age, mean (SD)56.8 (12.8)59.7 (13.4)<0.0001
      Sex, n (%)<0.0001
       Male434 (33.8)687 (47.0)
       Female851 (66.2)773 (53.0)
      Cardiometabolic parameters
       BMI, mean (SD), kg/m234.0 (7.7)30.9 (6.7)<0.0001
       Plasma glucose, mean (SD), mg/dl128.5 (48.0)116.7 (36.2)<0.0001
       Systolic blood pressure, mean (SD), mmHg134.5 (12.1)129.9 (11.7)<0.0001
       Diastolic blood pressure, mean (SD), mmHg81.4 (6.8)78.7 (6.5)<0.0001
       HDL cholesterol, mean (SD), mg/dl49.0 (12.6)48.9 (13.2)0.86
       Triglycerides, mean (SD), mg/dl128.0 (69.4)149.8 (80.6)<0.0001
      Individual SDoH
       Marital status, n (%)<0.0001
        Married510 (39.7)1,012 (69.3)
        Single485 (37.7)174 (11.9)
        Divorced/widowed290 (22.6)274 (18.8)
       Insurance status, n (%)<0.0001
        Private664 (51.7)903 (61.9)
        Public558 (43.4)531 (36.4)
        None36 (2.8)17 (1.2)
        Other27 (2.1)9 (0.6)
      Neighborhood SDoH
       Urbanicity, n (%)<0.0001
        Metropolitan1,236 (96.2)1,337 (91.6)
        Micropolitan32 (2.5)78 (5.3)
        Rural6 (0.5)20 (1.4)
        Small town11 (0.8)25 (1.7)
       Social Vulnerability Index, n (%)<0.0001
        Low220 (17.1)841 (57.6)
        Moderate309 (24.1)406 (27.8)
        High756 (58.8)213 (14.6)
       Healthcare access, n (%)<0.0001
        Not designated HPSA546 (42.5)1,348 (92.3)
        Designated HPSA739 (57.5)112 (7.7)
      COVID-19 hospitalization, n (%)<0.0001
       Yes494 (38.4)406 (27.8)
       No791 (61.6)1,054 (72.2)
      Note: Boldface indicates statistical significance (p<0.01).
      HDL, high-density lipoprotein; HPSA, health professional shortage area; SDoH, social determinants of health.
      a Comparison of characteristics using chi-square tests for categorical variables and t tests for continuous variables.
      Table 2Predictive Power and Validation for Models With and Without Race Variable Added
      ModelAUCMSEMisclassification rate
      CMDS only0.767
      Groups with the different superscript letters are significantly different from each other, DeLong's test for 2 correlated ROC curves.
      0.1790.260
      CMDS+race
      Black race was significant in the model (OR=2.10; 95% CI=1.72, 2.57).
      0.777
      Groups with the different superscript letters are significantly different from each other, DeLong's test for 2 correlated ROC curves.
      0.1750.258
      CMDS+SDoH0.809
      Groups with the different superscript letters are significantly different from each other, DeLong's test for 2 correlated ROC curves.
      0.1620.245
      CMDS+SDoH+race
      Race became nonsignificant in the model once SDoHs were added (OR=1.28; 95% CI=0.99, 1.65).
      0.811
      Groups with the different superscript letters are significantly different from each other, DeLong's test for 2 correlated ROC curves.
      0.1620.243
      Note: CMDS includes BMI, glucose, blood pressure, HDL and triglycerides, age, and sex; SDoH individual level includes marital and insurance status; and SDoH neighborhood level includes census tract Social Vulnerability Index, urbanicity, and Health Professional Shortage Area designation.
      AUC, area under the curve; CMDS, cardiometabolic disease staging; HDL, high-density lipoprotein; MSE, mean squared error; ROC, receiver operating characteristic; SDoH, social determinant of health.
      a Groups with the different superscript letters are significantly different from each other, DeLong's test for 2 correlated ROC curves.
      b Black race was significant in the model (OR=2.10; 95% CI=1.72, 2.57).
      c Groups with the different superscript letters are significantly different from each other, DeLong's test for 2 correlated ROC curves.
      d Groups with the different superscript letters are significantly different from each other, DeLong's test for 2 correlated ROC curves.
      e Race became nonsignificant in the model once SDoHs were added (OR=1.28; 95% CI=0.99, 1.65).
      Figure 1
      Figure 1OR plots predicting hospitalization outcome, stratified by Black and White patients.
      Note: The points and lines present the estimated values and 95% CIs, respectively. The points and lines present the estimated values and 95% CIs.
      aMarital status reference category is married.
      bInsurance status reference category is private insurance.
      cUrbanicity reference category is metropolitan.
      dSVI reference category is low SVI.
      eHPSA reference category is no HPSA designation.
      AUC, area under the curve; DBP, diastolic blood pressure; HDL, high-density lipoprotein; HPSA, Health Professional Shortage Area; MSE, mean squared error; SBP, systolic blood pressure; SVI, Social Vulnerability Index; TG, triglyceride.

      DISCUSSION

      Using CMDS, CMD severity was predictive of hospitalization after a positive COVID-19 test in Black and White patients in the sample. Although adding race to the CMDS model did slightly improve predictive ability, race became nonsignificant when SDoH were accounted for. In addition, adding SDoH to the model improved AUC to >0.80, indicating excellent predictive ability. In models stratified by Black and White race, similar SDoH risk factors and magnitudes of risk for hospitalization were found. Although stratified models revealed that cardiometabolic markers and SDoH had better predictive ability in White patients than in Black patients, these results indicate the need to interrogate additional social determinants specific to health inequalities among Black patients. Importantly, the false-negative rate (e.g., underestimation) decreased for Black patients, indicating that SDoH aided in identifying those truly at risk.
      These findings are similar to those of a recent study
      • Quan D
      • Luna Wong L
      • Shallal A
      • et al.
      Impact of race and socioeconomic status on outcomes in patients hospitalized with COVID-19.
      where census tract income but not race was associated with COVID-19 intensive care unit stay after controlling for BMI, age, sex, and comorbidities. Conversely, another study
      • Muñoz-Price LS
      • Nattinger AB
      • Rivera F
      • et al.
      Racial disparities in incidence and outcomes among patients with COVID-19.
      found that both Black race and insurance status were associated with hospitalization early in the pandemic (e.g., March 2020). However, the authors found that the associations did not persist when examining ventilation or mortality outcomes (e.g., worse survival). Instead, they found a greater incidence of COVID-19 among Blacks, likely attributable to social vulnerabilities, which may explain the higher likelihood of hospitalization.
      • Muñoz-Price LS
      • Nattinger AB
      • Rivera F
      • et al.
      Racial disparities in incidence and outcomes among patients with COVID-19.
      Both studies, along with these findings and others,
      • Kabarriti R
      • Brodin NP
      • Maron MI
      • et al.
      Association of race and ethnicity with comorbidities and survival among patients with COVID-19 at an urban medical center in New York.
      find that social factors rather than race are stronger risks for poor COVID-19 outcomes.
      The impact of Black race on hospitalization was attenuated when SDoH were accounted for. This suggests that racial differences in outcome were driven more by social vulnerabilities and social experiences rooted in structural racism as opposed to biological race differences.
      • Julian BA
      • Gaston RS
      • Brown WM
      • et al.
      Effect of replacing race with apolipoprotein L1 genotype in calculation of kidney donor risk index.
      Of interest, the model limited to Black patients did not perform as well, even when accounting for SDoH. This could be due to unmeasured social factors, specific to Black patients, not captured in this study's models such as perceived discrimination and racism.
      • Khazanchi R
      • Evans CT
      • Marcelin JR
      Racism, not race, drives inequity across the COVID-19 continuum.
      Future research should focus on including, examining, and reporting on such factors to inform risk prediction modeling.
      In exploring the use of race versus SDoHs in COVID-19 outcome prediction models, several key lessons were learned (limitations of the study noted in Appendix 4, available online). First, it is imperative that social factors such as socioeconomic disparities and social experience be included in health prediction modeling, instead of solely conceptualizing race through a biological framework.
      • Khazanchi R
      • Evans CT
      • Marcelin JR
      Racism, not race, drives inequity across the COVID-19 continuum.
      Second, there is a pressing need to explore the risk factors specific to minority and low-income populations. For the EMR, this can be accomplished by assimilating data warehouses that contain rich data on social and behavioral factors in these populations that can be harnessed for prediction models. Finally, researchers and clinicians should question and appreciate the social context that may lead to a patient's poor health outcome as opposed to myopically focusing on biological racial differences.

      CONCLUSIONS

      Considering SDoH in the context of race in prediction modeling should become standard practice in health research, particularly in the age of precision public health,
      • Khoury MJ
      • Iademarco MF
      • Riley WT
      Precision public health for the era of precision medicine.
      where healthcare models are moving toward tailoring interventions to a population's specific risk and circumstances.

      ACKNOWLEDGMENTS

      The authors would like to thank the COVID-19 Collaborative Outcomes Research Enterprise, a collaboration involving the institution's COVID-19 Enterprise Research Initiative, Scientific Community of Outcomes Researchers, and the Center for Clinical and Translational Science, for facilitating the utilization of institutional data to examine population health outcomes.
      The National Institute on Minority Health and Health Disparities had no role in study design; collection, analysis, and interpretation of data; writing of the report; or the decision to submit the report for publication.
      This work was supported by funding from the National Institute on Minority Health and Health Disparities (U54MD000502).
      No financial disclosures were reported by the authors of this paper.

      CRediT AUTHOR STATEMENT

      Carrie R. Howell: Conceptualization, Funding acquisition, Methodology, Supervision, Writing - original draft. Li Zhang: Data curation, Formal analysis, Visualization. Nengjun Yi: Formal analysis, Supervision, Writing - review & editing. Tapan Mehta: Formal analysis, Supervision, Writing - review & editing. W. Timothy Garvey: Conceptualization, Supervision, Writing - review & editing. Andrea L. Cherrington: Conceptualization, Methodology, Supervision, Writing - review & editing.

      Appendix. SUPPLEMENTAL MATERIAL

      SUPPLEMENT NOTE

      This article is part of a supplement entitled Obesity-Related Health Disparities: Addressing the Complex Contributors, which is sponsored by the National Institute on Minority Health and Health Disparities (NIMHD), National Institutes of Health (NIH), U.S. Department of Health and Human Services (HHS). The findings and conclusions in this article are those of the authors and do not necessarily represent the official position of NIMHD, NIH, or HHS.

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