JPH JMIR Public Health Surveill JMIR Public Health and Surveillance 2369-2960 JMIR Publications Toronto, Canada v10i1e54421 39326040 10.2196/54421 Original Paper Original Paper Identifying Potential Factors Associated With Racial Disparities in COVID-19 Outcomes: Retrospective Cohort Study Using Machine Learning on Real-World Data Mavragani Amaryllis Wang Ke El Khamlichi Sokaina Jonathan Stephanie Dasa Osama MD, MPH, PhD 1
Department of Epidemiology College of Public Health and Health Professions and College of Medicine University of Florida PO Box 100238 Gainesville, FL, 32610 United States 1 3522650379 1 3527331234 osama.dasa@gmail.com
2 https://orcid.org/0000-0003-2645-8530 Bai Chen MS 3 https://orcid.org/0000-0003-0961-1927 Sajdeya Ruba MD, PhD 1 https://orcid.org/0000-0002-1101-3603 Kimmel Stephen E MD, MSCE 1 https://orcid.org/0000-0003-3673-2361 Pepine Carl J MD 2 https://orcid.org/0000-0002-6011-681X Gurka J Matthew J PhD 4 https://orcid.org/0000-0002-3371-4582 Laubenbacher Reinhard PhD 5 https://orcid.org/0000-0002-9143-9451 Pearson Thomas A MD, MPH, PhD 1 https://orcid.org/0000-0001-8370-0595 Mardini Mamoun T MS, PhD 3 https://orcid.org/0000-0002-5345-8811 Department of Epidemiology College of Public Health and Health Professions and College of Medicine University of Florida Gainesville, FL United States Division of Cardiovascular Medicine Department of Medicine University of Florida Gainesville, FL United States Department of Health Outcomes and Biomedical Informatics University of Florida Gainesville, FL United States Department of Public Health Sciences School of Medicine University of Virginia Charlottesville, VA United States Laboratory for Systems Medicine Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine University of Florida Gainesville, FL United States Corresponding Author: Osama Dasa osama.dasa@gmail.com 2024 26 9 2024 10 e54421 12 11 2023 7 2 2024 1 4 2024 29 5 2024 ©Osama Dasa, Chen Bai, Ruba Sajdeya, Stephen E Kimmel, Carl J Pepine, Matthew J Gurka J, Reinhard Laubenbacher, Thomas A Pearson, Mamoun T Mardini. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 26.09.2024. 2024

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Public Health and Surveillance, is properly cited. The complete bibliographic information, a link to the original publication on https://publichealth.jmir.org, as well as this copyright and license information must be included.

 Background

Racial disparities in COVID-19 incidence and outcomes have been widely reported. Non-Hispanic Black patients endured worse outcomes disproportionately compared with non-Hispanic White patients, but the epidemiological basis for these observations was complex and multifaceted.

 Objective

This study aimed to elucidate the potential reasons behind the worse outcomes of COVID-19 experienced by non-Hispanic Black patients compared with non-Hispanic White patients and how these variables interact using an explainable machine learning approach.

 Methods

In this retrospective cohort study, we examined 28,943 laboratory-confirmed COVID-19 cases from the OneFlorida Research Consortium’s data trust of health care recipients in Florida through April 28, 2021. We assessed the prevalence of pre-existing comorbid conditions, geo-socioeconomic factors, and health outcomes in the structured electronic health records of COVID-19 cases. The primary outcome was a composite of hospitalization, intensive care unit admission, and mortality at index admission. We developed and validated a machine learning model using Extreme Gradient Boosting to evaluate predictors of worse outcomes of COVID-19 and rank them by importance.

 Results

Compared to non-Hispanic White patients, non-Hispanic Blacks patients were younger, more likely to be uninsured, had a higher prevalence of emergency department and inpatient visits, and were in regions with higher area deprivation index rankings and pollutant concentrations. Non-Hispanic Black patients had the highest burden of comorbidities and rates of the primary outcome. Age was a key predictor in all models, ranking highest in non-Hispanic White patients. However, for non-Hispanic Black patients, congestive heart failure was a primary predictor. Other variables, such as food environment measures and air pollution indicators, also ranked high. By consolidating comorbidities into the Elixhauser Comorbidity Index, this became the top predictor, providing a comprehensive risk measure.

 Conclusions

The study reveals that individual and geo-socioeconomic factors significantly influence the outcomes of COVID-19. It also highlights varying risk profiles among different racial groups. While these findings suggest potential disparities, further causal inference and statistical testing are needed to fully substantiate these observations. Recognizing these relationships is vital for creating effective, tailored interventions that reduce disparities and enhance health outcomes across all racial and socioeconomic groups.

 health disparities racial disparities COVID-19 outcomes social determinants of health area deprivation index health outcomes machine learning real-world data COVID-19 SARS-CoV-2 real-world data socioeconomic status Introduction

The COVID-19 pandemic has exposed significant racial disparities, with non-Hispanic Black populations experiencing higher incidence rates and worse outcomes than non-Hispanic White populations [1,2]. Previous research has suggested that these disparities might be attributed to a higher burden of comorbidities in non-Hispanic Black populations [3]. Additional hypothesized contributing factors include systemic socioeconomic disadvantages and the compounding impact of chronic exposure to social and economic stressors, as well as experiences of marginalization [4,5]. Furthermore, some studies have raised the possibility of biological and genetic determinants as contributory risk factors [6,7].

Analyzing racial health disparities solely through isolated data can lead to misleading interpretations, such as attributing higher disease rates to inherent biological factors or perpetuating racial stereotypes, particularly concerning perceived health behaviors [4]. Similarly, dissecting COVID-19 data by geography demands caution to avoid reinforcing negative stereotypes about marginalized communities and “territorial stigmatization” [8]. In the context of the COVID-19 pandemic, a comprehensive framework is necessary that considers social determinants of health (SDOH)—social, economic, and geographical—to properly address the complexities surrounding disparities in outcomes [9-11]. Gathering data at both the neighborhood and individual level is crucial. Such an approach dispels myths rooted in racial biology and highlights the significance of socioeconomic factors, discrimination, and location-based risks [4,5].

It has been recognized that the COVID-19 pandemic is a syndemic involving interactions of multiple factors and conditions [12] where advanced data-driven approaches are needed to capture complex underlying association patterns. Machine learning (ML) offers valuable tools for this complex analysis. Unlike traditional methods, ML can handle vast multidimensional datasets, enabling researchers to discern intricate patterns and relationships that would be impossible to discern using conventional statistical methods. Although many studies have used ML in evaluating the outcomes of COVID-19 [13-18], few have applied these techniques specifically to investigate racial disparities [9,19], which often result from complex interplay among multiple stressors. Most studies focused on a single stressor or a single domain of stressors or were purely ecological, with only county-level COVID-19 data [20].

The purpose of this study is to provide a more comprehensive understanding of the factors contributing to these disparities by analyzing large real-world data from the OneFlorida Research Consortium, using 9-digit zip codes to link individuals to measures of SDOH and other geospatial data related to the place of residence. This will give insight into the importance or relevance of each variable (feature) in predicting worse outcomes of COVID-19. By incorporating comorbidities and SDOH, we seek to explore potential reasons behind the worse outcomes of COVID-19 experienced by non-Hispanic Black patients compared with non-Hispanic White patients and how these variables interact in an ML model.

 Methods Design and Population

We conducted a retrospective cohort study of patients diagnosed with COVID-19 infection who were followed prospectively for outcomes. We derived patient-level data from the OneFlorida Clinical Research Network, which includes Floridians enrolled in Medicaid, and robust patient-level electronic health record (EHR) data from public and private health care systems [21]. OneFlorida partners encompass hospitals, clinic settings, and physicians, which provide care for 17 million patients across all of Florida’s 67 counties [21,22]. The data query included adult patients (≥18 years old) with laboratory-confirmed COVID-19 diagnoses between December 1, 2019, and April 28, 2021. Then, using 9-digit zip codes, we linked patients to neighborhood and geospatial variables, as established by previous research [20]. These supplementary variables were derived from several external publicly available datasets, enabling a more comprehensive understanding of the impact of socioeconomic and geospatial factors on outcomes of COVID-19.

 Study Variables (Features) Exposure

We used self-reported race as our exposure variable. It is a surrogate for many interconnected socioeconomic and environmental factors. Race is recognized as a social construct, not a biological determinant [23-25].

 Outcome

We used a combined measure of severe outcomes of COVID-19. This measure includes intensive care unit admission, intubation, and mortality at the time of the first index encounter. Through this composite outcome, we intended to capture a broader overview of the disease’s severity and short-term impacts. We used encounter, procedures, and death common data model domains in OneFlorida to assess outcomes [26].

 Covariates Individual-Level Variables

At the index encounter, we collected sociodemographic data, including age, self-reported sex, race and ethnicity as non-Hispanic Black or non-Hispanic White, and previous insurance status. We also included 9-digit zip codes, BMI, smoking, alcohol, and substance use status.

In line with the Centers for Disease Control and Prevention’s compilation of medical conditions associated with an increased risk for severe outcomes of COVID-19 [27], we examined comorbidities during the encounters from January 1, 2012, leading up to the COVID-19 index encounter. Conditions with a less than 1% prevalence in our study population were excluded. We derived COVID-19–related comorbid conditions by mapping available International Classification of Diseases, Ninth Revision and International Statistical Classification of Diseases, Tenth Revision codes to the Healthcare Cost and Utilization Project (HCUP) Clinical Classification Software definitions [28]. We also used the well-established Elixhauser Combined Comorbidity Index (ECI) to quantify the aggregate burden of comorbid conditions. This index includes 31 comorbidities and reliably predicts outcomes like in-hospital mortality, length of stay, adverse events, and hospital discharges [29-32].

 Group-Level Variables Area Deprivation Index

The area deprivation index (ADI) evaluates community deprivation, impacts health outcomes, and guides policy and health care use patterns [33]. It has demonstrated that residing in a disadvantaged area can be as detrimental to health as certain chronic diseases [33]. The Centers for Medicare and Medicaid Services (CMS) leverage the ADI in their strategies [34]. During the COVID-19 pandemic, the ADI informed equitable resource distribution, emphasizing the role of socioeconomic factors in disease outcomes [35,36].

Using the 2018 ADI, we evaluated community-level disparities. Calculated at the United States census block group level, it reflects a “neighborhood” of approximately 600 to 3000 residents [33]. This granularity offers a localized view of health-related social and geospatial determinants. We geocoded patient addresses, assigning an ADI rank based on their Florida residential census block group, sourced from their 9-digit zip codes. Higher ADI ranks signify greater social disadvantage.

 Geospatial Variables

We used an extensive exposome-wide association study to identify additional external exposome elements that may correlate with COVID-19 mortality. This study included 337 external exposome factors encompassing 9 distinct categories [20]. It identified 4 external exposome factors at the county level significantly associated with worse outcomes of COVID-19. These included variables characterizing the natural (criteria air pollutants and air toxicants), built (food environment), and social environment (vacant land) [20]. The results of this exposome study reaffirm the importance of environmental and geospatial variables in understanding and predicting outcomes of COVID-19. Our analysis incorporated data relating to these 4 variables by linking them to patients’ places of residence in Florida (Table 1).

Data sources, time periods, and spatial scales of environmental measures.

Spatial variable Data source Time period Spatial scale Temporal scale
Particulate matter 2.5 Atmospheric composition analysis group, WUSTLa [37] 2006-2018 0.01 degree in lon and lat 1 year
Nitrogen dioxide (air pollution) The center for air, climate, and energy solutions [38] 2006-2015 Census block group 1 year
Percent students eligible for reduced-price lunch, 2015 (food environment) Food environment atlas [39] 2007-2018 County Cross-sectional
Percent addresses in the previous quarter with no-stat currently in service (vacant land)b Aggregated USPSc administrative data on address vacancies, HUDd [40] 2006-2019 Census tract 3 months

aWUSTL: Washington University in St Louis.

bTotal no-stat addresses are the addresses that can be classified as “No-Stat” for many reasons, including, (1) rural route addresses that are vacant for 90 days or longer and (2) addresses for businesses or homes under construction and not yet occupied addresses.

cUSPS: United States Postal Service.

dHUD: US Department of Housing and Urban Development.

This table outlines the data sources, time periods, and spatial scales of various environmental measures used in our retrospective cohort study of COVID-19 patients from the OneFlorida Clinical Research Network. These environmental variables, sourced from diverse national databases, were analyzed at different spatial and temporal scales to assess their impact on COVID-19 outcomes among Floridians. The study encompasses adult patients (≥18 years old) with confirmed COVID-19 diagnoses between December 1, 2019, and April 28, 2021.

 Statistical Analysis

Categorical variables were reported as frequencies and percentages, while continuous variables were reported as means with SDs or medians with IQR. We used the Chi-square test for categorical variables. For continuous variables, we used the t test for normally distributed data or the Wilcoxon-Mann-Whitney U test when data were not normally distributed. Appropriate transformations were applied to certain continuous variables with skewness to achieve a normal distribution. Following this, z score standardization was used to establish a standard scale for interfactor comparison. We also evaluated mixed correlation coefficients between all variables included in our models.

K-nearest neighbors imputation was used when missing data was present. This technique estimates the missing values based on attributes of the most similar observations, where similarity is calculated using a distance function [41]. K-nearest neighbors imputation is frequently regarded as a more robust and sensitive method for missing value estimation than conventional techniques [42].

 Machine Learning Model Development

We used Extreme Gradient Boosting (XGBoost), a robust ML framework known for its efficiency, flexibility, and portability [43]. It is an ensemble learning algorithm based on the gradient boosting framework, in which models are built sequentially to boost (increase) the performance of the previous models by using the gradient descent algorithm to minimize errors [43]. Our selection of XGBoost is based on simplified interpretability and the inclusion of feature selection as part of the model-building process. XGBoost exhibits various advantages that render it a compelling alternative to conventional statistical techniques and other ML algorithms.

We developed and validated 3 consecutive ML models to improve the understanding of the models’ operational dynamics and the incremental contribution of different sets of variables to the outcome. Model 1 consisted exclusively of individual-level variables, which included sociodemographic data and individual comorbidities. Following this, the model was extended to encompass variables at the group level; specifically, we incorporated ADI and environmental measures (particulate matter 2.5 [PM2.5], nitric oxide [NO2], vacant land measure, and food environment measure), resulting in the development of model 2. In our final model, model 3, instead of individual comorbidities, we incorporated the ECI while preserving the group-level variables from model 2. Implementing a unidimensional singular numerical summary of comorbidities facilitates the modeling and integration with other covariates instead of requiring the modeling and interactions between covariates and each constituent of the comorbidity score, thereby enhancing computational efficiency. All models incorporated baseline variables (age, sex, and BMI; Figure 1).

Study flowchart and machine learning analytical framework. ADI: area deprivation index; AUC: area under the receiver operating characteristic curve; EHR: electronic health record; KNN: k-nearest neighbors; SHAP: Shapley Additive Explanations.

The figure illustrates the study flowchart and the machine learning analytical framework used in the retrospective cohort study of COVID-19 patients from the OneFlorida Clinical Research Network. It provides a visual summary of the study design, data sources, patient selection criteria, and the ML models used to analyze the data, capturing adult patients (≥18 years old) diagnosed with COVID-19 between December 1, 2019, and April 28, 2021.

In addition, to ensure a comprehensive understanding of our models’ performance, we initiated our analysis by constructing a global model that includes the entire population. Subsequently, we created individualized models for non-Hispanic White and non-Hispanic Black populations across all 3 models. This allowed us to compare the relative importance of features within and between each demographic group.

 Model Performance and Evaluation

We used nested cross-validation (CV) to evaluate our ML models with 5 outer and inner folds (55 nested CV). A nested CV can help ensure rigor and enhance confidence in model generalizability and scalability [44] (Methods S1 in Multimedia Appendix 1 [44-47]). We calculated and reported the following measures of the models’ performance: the average value and SD of the area under the receiver operating characteristic curve (AUC), balanced accuracy, sensitivity, and specificity from the 5 outer folds (Figure 1). AUC measures the overall discriminative ability, balanced accuracy gauges fair assessment across classes, and sensitivity and specificity offer insight into the model’s prediction accuracy for each class.

 Model Interpretation and Feature Ranking

Interpreting complex models like XGBoost can be challenging due to their reliance on numerous decision trees, making it difficult to intuit the relationships between features and outcomes. To address this, we used Shapley Additive Explanations (SHAP) values, a method rooted in coalitional game theory [48], which decomposes individual predictions to quantify the influence of each feature (Methods S2 in Multimedia Appendix 1 [44-47]) [45].

Finally, to assess the robustness of our findings, we conducted a sensitivity analysis using a complete-cases-only approach, excluding cases with missing data and without resorting to data imputation (model 4). We also assessed the potential for divergent feature importance rankings with alternative models using light gradient boosting machine (LightGBM) algorithm [49]. It is another high-performance, gradient-boosting framework that uses tree-based learning algorithms (Figure 1) [49]. Data analyses were performed using (R version 3.6.1; R Core Team) and Python (Version 3.7; Python Software Foundation). The data preprocessing, imputation, and grid search steps were implemented using the Python Sklearn package. The XGBoost algorithm was implemented using the XGBoost package. We followed Enhancing the Quality and Transparency of Health Research Network guidelines for reporting ML analyses in observational studies [50,51].

 Ethical Considerations

This study used a deidentified electronic health record dataset provided by the OneFlorida Clinical Research Consortium. The data were deidentified before analysis to ensure participant privacy and confidentiality, with no identifying information accessible to the researchers. The study was reviewed and approved by the Institutional Review Board at the University of Florida (Institutional Review Board 202001531), which determined that informed consent was not required as this research involved secondary analysis of existing, deidentified data. No compensation was provided to participants as the study did not involve direct interaction with individuals. In addition, no images or supplementary materials in this manuscript could potentially identify individual participants.

 Results

Our initial data collection included 49,461 patients, of which 35% (n=17,311) were non-Hispanic White and 22.8% (n=11,277) were non-Hispanic Black. After limiting our study to only non-Hispanic White and non-Hispanic Black populations, the final sample size for this analysis was 28,943 patients. Compared with non-Hispanic White patients, non-Hispanic Black patients tended to be younger; had higher BMI; had fewer outpatient visits; and had higher rates of no insurance, visits to the emergency department, and hospital admissions (Table 2). In the non-Hispanic Black group, the burden of comorbidities and the total ECI scores were significantly higher, especially hypertension, coronary artery disease, congestive heart failure (CHF), and chronic kidney disease. Geospatial and neighborhood variables revealed that non-Hispanic Black populations resided in regions with higher (hence less favorable) ADI rankings, NO2, and PM2.5 concentrations, as well as lower vacant land and food environment measures. Finally, the non-Hispanic Black group experienced the highest rates of the composite primary outcome (Table 2). Correlation analysis revealed moderately high correlations when insurance status was compared with PM2.5 and NO2 concentrations. Unsurprisingly, there were also high correlations between ECI scores and most comorbidities (Figure S1 in Multimedia Appendix 1 [44-47]).

Baseline characteristics and outcomes of the study population stratified by racea. The table presents the demographic characteristics of adult patients (≥18 years old) diagnosed with COVID-19 within the OneFlorida Clinical Research Network between December 1, 2019, and April 28, 2021.

Non-Hispanic White (n=17,651) Non-Hispanic Black (n=11,293) Total (N=28,944)
Age, (years), mean (SD) 52.82 (20.24) 48.16 (18.22) 51 (19.61)
Sex (female), n (%) 9490 (53.8) 6791 (60.1) 16,281 (56.3)
BMI, kg/m2, mean (SD) 29.96 (7.66) 33.19 (9.22) 31.31 (8.50)
Insurance status, n (%)
Medicare or private 15,494 (87.8) 8591 (76.1) 24,085 (83.2)
Medicaid or no insurance 1566 (8.9) 2191 (19.4) 3757 (13.0)
Unknown 589 (3.3) 511 (4.5) 1100 (3.8)
Encounter type, n (%)
Outpatient 7476 (42.4) 2712 (24) 10,188 (35.2)
EDb 4551 (25.8) 4525 (40.1) 9076 (31.4)
Inpatient 4884 (27.7) 3344 (29.6) 8228 (28.4)
ICUc 740 (4.2) 712 (6.3) 1452 (5.0)
Comorbidities
ECId, median (IQR) 2 (0-5) 3 (1-6) 2 (0-6)
Hypertension, n (%) 7645 (43.3) 6123 (54.2) 13,768 (47.6)
Hyperlipidemia, n (%) 5989 (33.9) 3924 (34.7) 9913 (34.2)
Diabetes mellitus, n (%) 5273 (29.9) 4615 (40.9) 9888 (34.2)
Coronary artery disease, n (%) 3089 (17.5) 1902 (16.8) 4991 (17.2)
Congestive heart failure, n (%) 1970 (11.2) 1656 (14.7) 3626 (12.5)
Stroke or TIAe, n (%) 1655 (9.4) 1328 (11.8) 2983 (10.3)
Dementia, n (%) 1437 (8.6) 845 (7.7) 2282 (8.2)
Chronic liver disease, n (%) 2424 (13.7) 1878 (16.6) 4302 (14.9)
CKD-Vf/ESRDg, n (%) 272 (1.5) 565 (5.0) 837 (2.9)
Respiratory disorders, n (%) 9862 (59.1) 7436 (67.7) 17,298 (62.5)
Smoking history, n (%) 3115 (17.6) 1833 (16.2) 4948 (17.1)
Substance use, n (%) 4103 (23.2) 3133 (27.7) 7236 (25.0)
Alcohol use, n (%) 902 (5.1) 747 (6.6) 1649 (5.7)
Mental health disorders, n (%) 5331 (32.0) 3227 (29.4) 8558 (30.9)
Common solid cancer, n (%) 1074 (6.1) 586 (5.2) 1660 (5.7)
Hematologic malignancies, n (%) 291 (1.7) 147 (1.3) 438 (1.6)
Geospatial variables, median (IQR)
Florida ADIh 5 (3-7) 8 (6-9) 6.0 (4-8)
Mean PM2.5i, μg/m3 7.35(6.88-7.77) 7.45(7.05-7.96) 7.41 (6.94-7.86)
Mean NO2j, ppbk 2.73 (1.61-4.63) 3.17(1.88-4.62) 2.87 (1.69-4.63)
Food environmentl 4.37 (1.85-4.56) 3.68 (1.85-4.25) 4.10 (1.85-4.25)
Vacant landm 0.22 (0.11-0.48) 0.16 (0.07-0.31) 0.19 (0.10-0.40)
Unadjusted outcomes, n (%)
Composite outcome 1423 (8.1) 1233 (10.9) 2656 (9.2)
Death at index encounter 251 (1.4) 238 (2.1) 489 (1.7)

aAll P values were <.001 (except CAD and history of smoking (P=.148 and .002, respectively). Independent sample t test, Wilcoxon-Mann-Whitney U test, or Pearson chi-square test were used wherever appropriate. Values are presented as means (SD), n (%), or median (IQR).

bED: emergency department.

cICU: intensive care unit.

dECI: Elixhauser Comorbidity Score.

eTIA: transient ischemic attack.

fCKD-V: chronic kidney disease stage 5.

gESRD: end-stage renal disease.

hADI: area deprivation index.

iPM2.5: particulate matter 2.5.

jNO2: nitrogen dioxide.

kppb: parts per billion.

lPercent students are eligible for reduced-price lunch, 2015.

mPercent addresses in the previous quarter with “no-stat” currently in service. “Total No-Stat Addresses” are addresses that can be classified as “No-Stat” for many reasons, including, (1) rural route addresses that are vacant for 90 days or longer and (2) addresses for businesses or homes under construction and not yet occupied addresses.

When examining the outcomes from the XGBoost modeling, the first model, which solely included individual-level variables, had an AUC value of 0.80, indicating reasonably good model performance (Table 3). The balanced accuracy, sensitivity, and specificity were all around the 0.72-0.74 range, suggesting a well-balanced model capable of predicting both positive and negative outcomes with similar accuracy. The outcomes of model 1 subgroups constructed for non-Hispanic White and non-Hispanic Black populations separately exhibited a comparable performance to the overall model 1, suggesting the racial factor has not significantly influenced these particular model outcomes. In model 2, the performance slightly improved when group-level variables were added. The AUC for the general and race-specific models increased to 0.81-0.83. Likewise, the balanced accuracy also increased. There was a marginal reduction in sensitivity for the non-Hispanic White demographic, implying this model predicted fewer true positives for this subgroup. Finally, model 3, which included the ECI (instead of individual comorbidities) and group-level variables from model 2, demonstrated the most optimal performance across all subgroups, with an AUC of 0.82-0.83. Balanced accuracy was also highest for these models, particularly in sensitivity, which indicates these models are better at predicting true-positive outcomes (Table 3).

Comparative performance metrics of predictive models by racea.

Models AUCb Balanced accuracy Sensitivity Specificity
Model 1, mean (SD)
All 0.80 (0.01) 0.73 (0.01) 0.75 (0.02) 0.71 (0.01)
Whites 0.80 (0.01) 0.72 (0.01) 0.74 (0.03) 0.71 (0.01)
Blacks 0.80 (0.01) 0.73 (0.01) 0.74 (0.01) 0.72 (0.01)
Model 2, mean (SD)
All 0.83 (0.01) 0.74 (0.01) 0.74 (0.02) 0.75 (0.01)
Whites 0.82 (0.01) 0.73 (0.01) 0.72 (0.03) 0.75 (0.01)
Blacks 0.82 (0.01) 0.74 (0.01) 0.74 (0.01) 0.74 (0.01)
Model 3, mean (SD)
All 0.84 (0.01) 0.77 (0.01) 0.81 (0.01) 0.72 (0)
Whites 0.84 (0.01) 0.76 (0.01) 0.81 (0.02) 0.72 (0.01)
Blacks 0.84 (0.01) 0.76 (0.01) 0.81 (0.01) 0.71 (0.01)

aThe table presents comparative performance metrics of predictive models by race, derived from a retrospective cohort study of COVID-19 patients within the OneFlorida Clinical Research Network. The data encompasses adult patients (≥18 years old) diagnosed with COVID-19 between December 1, 2019, and April 28, 2021.

bAUC: the area under the receiver operating characteristic curve.

SHAP value analysis in model 1 provided insight into the impact of individual-level variables on predicting COVID-19 outcomes (Figures S2 and S3 in Multimedia Appendix 1 [44-47]). Overall, comorbid conditions such as diabetes mellitus type 2, hypertension, CHF, respiratory disorders, and chronic liver disease consistently ranked the highest across the models and contributed positively to predicting the outcome. Sociodemographic factors such as age and sex ranked consistently high in all models; old age and being female contributed positively to predicting the outcome. BMI and smoking history were important predictors but ranked lower than age and sex. Notably, comorbid conditions such as CHF, rheumatological disorders, and chronic kidney disease stage 5/end-stage renal disease were more significant predictors in the non-Hispanic Black population model, with CHF being the leading predictor.

The incorporation of group-level variables in model 2 yielded additional nuanced findings. Discernible patterns were noted within specific racial subgroups. Comorbid conditions such as diabetes mellitus type 2, hypertension, CHF, and respiratory disorders remained highly ranked and contributed positively to predicting the outcome. Sociodemographic factors such as age and sex remained important predictors, but BMI and smoking history became less important. Group-level variables such as the food environment measure and air pollution measure, PM2.5, were among the top predictors. Notably, CHF remained the top predictor for the non-Hispanic Black population model. In addition, Florida ADI ranked relatively higher in the overall and the non-Hispanic White population models compared with the non-Hispanic Black population model. However, the food environment measures ranked higher in the non-Hispanic Black population model than in other models (Figures S4 and S5 in Multimedia Appendix 1 [44-47]).

Model 3 incorporated comorbidities previously distributed across individual conditions in models 1 and 2, consolidating them into the ECI. This provided a more comprehensive risk measure, where the ECI emerged as the topmost predictor, followed by age, sex, air pollution, and food environment measures. Finally, race generally ranked relatively lower than other predictors in all models (Figures 2 and 3).

SHAP feature importance model results for outcomes of the COVID-19 pandemic (model 3). ADI: area deprivation index; NO2: nitrogen dioxide; PM2.5: particulate matter 2.5; SHAP: Shapley Additive Explanations.

Heat map ranking of Shapley Additive Explanations features importance for outcomes of the COVID-19 pandemic in model 3. ADI: area deprivation index; NO2: nitric oxide; PM2.5: particulate matter 2.5.

The diagrams represent feature importance, arranged from top to bottom. Higher priority features are displayed at the top due to their greater influence on model prediction (having higher predictive power). Color coding indicates the value of the feature: red signifies a higher feature value, while blue represents a lower one. Points to the right of the 0 midline suggest a higher likelihood of the predicted outcome, while those to the left indicate a lower probability of the outcome. Figure 2A displays results for the whole study population. Figure 2B displays results for the non-Hispanic White population. Figure 2C displays results for the non-Hispanic Black population.

The heat map ranks features in model 3 (all population, White population, and Black population) by importance, with number 1 being the most important feature. The food environment measure is the percentage of students eligible for reduced-price lunch, 2015; vacant land measure is the percentage of addresses in the previous quarter with “no-stat” currently in service.

Results from the sensitivity analysis (complete cases only without imputation) showed minor differences compared with the primary analysis. ECI, age, and sex remained the top predictors. In the White population, ADI and vacant land measure ranked higher than PM2.5, while in the Black population, no change was noted in feature ranking (Figures S6 and S7 in Multimedia Appendix 1 [44-47]). Similarly, when retraining the model using LightGBM, minimal variations were noted compared with the XGBoost model (Figures S8 and S9 and Table S1 in Multimedia Appendix 1 [44-47]).

 Discussion Principal Findings

The COVID-19 pandemic exposed substantial racial disparities. Our study explored the intricate relationship between comorbidities, SDOH, and poor outcomes of COVID-19 in non-Hispanic Black patients. We analyzed real-world data using explainable ML methods to better understand these disparities while acknowledging the need for further causal inference and statistical testing to fully substantiate our findings.

Comorbid conditions and ECI were the most important predictors of outcomes of COVID-19, demonstrating the critical role of baseline comorbidities in predicting poor outcomes of COVID-19, irrespective of race. Mainly, CHF was the most important predictor in non-Hispanic Blacks; the reasons for that are multifaceted and complex [52,53]. Patients with CHF who are diagnosed with COVID-19 infection are often older, predominately Black or Hispanic, have a higher prevalence of diabetes and kidney disease, and use more health care resources. This may serve as a proxy for broader health disparities; Black patients, for example, are generally diagnosed with CHF at later stages and experience worse outcomes despite similar symptoms to their White counterparts [54]. Such disparities can be traced back to limited access to preventive care, higher prevalence of CHF risk factors like hypertension and diabetes, and socioeconomic barriers that hinder effective CHF management among Black communities [54].

Similarly, the food environment measure (county-level percent of students eligible for reduced-price lunch) [39] emerged as one of the top 3 predictors of adverse outcomes among Black individuals. This finding aligns with earlier research [20] and may be partly attributed to historically discriminatory US policies that have resulted in lasting economic and ethnic segregation, manifesting as present-day health disparities [55]. Such inequities are evident in the prevalence of “food deserts,” areas with limited access to healthy food, and the increased risk of repeated hospitalizations, including those due to CHF [56]. Despite adjusting for traditional cardiovascular risk factors, residents of these deprived, racially segregated neighborhoods still faced heightened risk for CVD and CHF [57,58]. Contributory factors include limited recreational facilities [59]; poor walkability [60,61]; and scarce availability of fresh, nutritious foods, particularly in low income and predominantly Black neighborhoods [62].

The feature importance analysis indicated that “race” has the lowest ranking, which may seem counterintuitive at first glance. Nonetheless, this implies that the racial disparities in outcomes of COVID-19 are predominantly linked to inequalities in health conditions, socioeconomic status, and geospatial factors rather than solely to racial identity [2]. This finding should not be construed as minimizing the importance of race; instead, it suggests that the disparities observed across races may be primarily attributable to the unequal distribution of these conditions across racial groups [63]. Race is accepted as a social construct rather than a biological one [64,65]. Health disparities, such as with COVID-19 infection, often arise from enduring inequalities affecting racial and ethnic minorities, notably non-Hispanic Black individuals [63]. Societal and structural dynamics, more than biological distinctions, reinforce these disparities [63]. These discrepancies remain even after considering biological factors and personal health behaviors [2]. The primary drivers are likely the socioeconomic and environmental conditions experienced by diverse racial and ethnic groups. Our model underscores the dominant role of socioeconomic and environmental factors in health outcomes and disparities.

Building on the understanding of race as a key determinant, PM2.5 exposure emerged as a notable predictor of health outcomes, ranking fourth for the White population and third for the Black population, ahead of food environment metrics. Previous research links maintained exposure to air pollutants, like PM2.5 and NO2, with increased COVID-19 mortality [66-68]. Originating mainly from fossil fuel burning, PM2.5 could signify heightened pollution exposure in certain demographics, exacerbating outcomes of COVID-19. Environmental findings report greater short- [69] and long-term [70] PM2.5 exposure in racial minorities, especially Black individuals, than in White individuals. Following closely is the food environment measure, emphasizing the combined effects of socioeconomic challenges and prolonged PM2.5 exposure on health. It highlights the multifaceted roots of health disparities, emphasizing the urgency to tackle both socioeconomic and environmental factors for enhanced public health [71]. Such a holistic perspective is pivotal in understanding spatial variations in outcomes of COVID-19 due to interconnected biological, clinical, socioeconomic, and environmental factors [9,72].

Interestingly, the ADI ranked last in the Black-only model, whereas it came sixth in the White-only model (model 3). This discrepancy does not inherently imply that ADI is inconsequential for the black population but rather that it may interact with other determinants in complex ways. It suggests that although socioeconomic factors are crucial in determining health outcomes, their significance may vary by race. These differences emphasize the importance of considering race-specific factors when analyzing outcomes of COVID-19 and the need for individualized interventions. The higher ranking of the ADI for the White population reflects the well-established relationship between socioeconomic status and health outcomes [34,35]. As measured by the ADI, lower socioeconomic status may contribute to poor health outcomes in this group due to limited access to health care, poorer education, and increased exposure to environmental pollutants and stressors [34,35]. However, the lower ranking of the ADI in the model for the Black population suggests that other factors may be more predictive of outcomes of COVID-19 in this racial group. Another possible explanation is that there is less variation in ADI among Black populations and a greater proportion of Black populations with higher ADIs. The varying ADI values across different socioeconomic levels enable ADI to serve as a strong predictor of outcomes in White populations. In contrast, the higher concentration of individuals with higher ADIs limits the discriminatory power of ADI in this model for Black populations.

Furthermore, in the model focusing on Black individuals, the food environment measure, indicative of food security and nutrition, ranked higher than ADI, a typical socioeconomic marker. This suggests that factors like food insecurity and neighborhood conditions may have a heightened influence on health outcomes in predominantly Black communities [56]. Research also points to racial differences in stress responses, further influencing health outcomes [73]. Importantly, the persisting impact of systemic racism could contribute to poorer health in Black communities [74], irrespective of socioeconomic factors, which may account for the lower ranking of ADI in the Black-specific model.

 Limitations

The study has several limitations. The findings in this study should be considered as part of a broader discussion on racial disparities in health. We acknowledge the need for further causal inference and statistical testing to fully substantiate our observations. The data might not necessarily apply to other states with different demographics or health disparities than Florida. Using EHR data for research is subject to informatic challenges and disadvantages [75]. Race, as used in our study, was self-reported and extracted from EHR. While this method of racial identification is standard in epidemiological research, it comes with inherent challenges that may affect the accuracy and interpretability of the findings. The study could not assess the outcomes of patients who did not require hospitalization or experienced mortality outside the clinical setting. Furthermore, although the XGBoost algorithm has a low risk of overfitting, the lack of an external validation cohort undermines the generalizability of our model.

Similarly, interpreting features’ importance from ML models, such as XGBoost, presents inherent challenges compared with traditional statistical approaches. These outputs do not offer conventional statistical significance measures and can be highly sensitive to model specification and training data characteristics. Also, the models’ evaluation metrics have limitations. The AUC might not fully capture the model’s performance when there is a significant class imbalance or different types of misclassifications vary. Balanced accuracy might not always reflect the practical significance of prediction errors. Sensitivity and specificity can provide a misleading picture of model performance if not considered together, especially in datasets with imbalanced class distributions.

Finally, associations obtained from part of the data at the county level may not reflect individual associations.

In conclusion, our study demonstrates the critical role of model constructs and assumptions in estimating health-related associations, advocating for frameworks that better account for data behaviors. Using a comprehensive ML approach that integrates individual- and group-level exposomic health associations, we used sequential modeling and universal Shapley effect plots for objective comparisons. Our findings emphasize the complexities of health inequalities, particularly persistent racial disparities, and stress the need for multidimensional strategies to address them. Interpretable ML serves as a valuable adjunct to traditional statistical methods, revealing nuanced patterns that can inform resource allocation and policy development for outcomes of COVID-19. Further research is required to clarify the influence of these variables and their contributions to racial health disparities.

Supplementary material.

 Abbreviations ADI

area deprivation index

 AUC

area under the receiver operating characteristic curve

 CHF

congestive heart failure

 CV

cross-validation

 ECI

Elixhauser combined comorbidity index

 HCUP

Healthcare Cost and Utilization Project

 ML

machine learning

 NO2

nitrogen dioxide

 PM2.5

particulate matter 2.5

 SDOH

Social Determinants of Health

 SHAP

Shapley Additive Explanations

 XGBoost

Xtreme Gradient Boosting

 Data Availability

The datasets analyzed during this study are not publicly available due to institutional review board protections and privacy considerations. However, the data may be accessed upon reasonable request through the OneFlorida Research Consortium. Information on the data request process is available on the OneFlorida Clinical Research Network’s website.

None declared.

 Disparities in COVID-19-associated hospitalizations Centers for Disease Control and Prevention 2022 2022-06-14 https://www.cdc.gov/coronavirus/2019-ncov/community/health-equity/racial-ethnic-disparities/disparities-hospitalization.html Magesh S John D Li W Li Y Mattingly-App A Jain S Chang EY Ongkeko WM Disparities in COVID-19 outcomes by race, ethnicity, and socioeconomic status: a systematic-review and meta-analysis JAMA Netw Open 2021 4 11 e2134147 10.1001/jamanetworkopen.2021.34147 34762110 2785980 PMC8586903 Clark A Jit M Warren-Gash C Guthrie B Wang HHX Mercer SW Sanderson C McKee M Troeger C Ong KL Checchi F Perel P Joseph S Gibbs HP Banerjee A Eggo RM Centre for the Mathematical Modelling of Infectious Diseases COVID-19 working group Global, regional, and national estimates of the population at increased risk of severe COVID-19 due to underlying health conditions in 2020: a modelling study Lancet Glob Health 2020 8 8 e1003 e1017 10.1016/S2214-109X(20)30264-3 32553130 S2214-109X(20)30264-3 PMC7295519 Chowkwanyun M Reed AL Racial health disparities and COVID-19 - caution and context N Engl J Med 2020 383 3 201 203 10.1056/NEJMp2012910 32374952 Geronimus AT Hicken M Keene D Bound J "Weathering" and age patterns of allostatic load scores among blacks and whites in the United States Am J Public Health 2006 96 5 826 833 10.2105/AJPH.2004.060749 16380565 AJPH.2004.060749 PMC1470581 Vinciguerra M Greco E Sars-CoV-2 and black population: ACE2 as shield or blade? Infect Genet Evol 2020 84 104361 10.1016/j.meegid.2020.104361 32405281 S1567-1348(20)30192-1 PMC7219352 McCoy J Wambier CG Vano-Galvan S Shapiro J Sinclair R Ramos PM Washenik K Andrade M Herrera S Goren A Racial variations in COVID-19 deaths may be due to androgen receptor genetic variants associated with prostate cancer and androgenetic alopecia. are anti-androgens a potential treatment for COVID-19? J Cosmet Dermatol 2020 19 7 1542 1543 10.1111/jocd.13455 32333494 PMC7267367 Wacquant L Territorial stigmatization in the age of advanced marginality Thesis Elev 2016 91 1 66 77 10.1177/0725513607082003 Ren X Mi Z Georgopoulos PG Socioexposomics of COVID-19 across New Jersey: a comparison of geostatistical and machine learning approaches J Expo Sci Environ Epidemiol 2024 34 2 197 207 10.1038/s41370-023-00518-0 36725924 10.1038/s41370-023-00518-0 PMC9889956 Andrianou XD Pronk A Galea KS Stierum R Loh M Riccardo F Pezzotti P Makris KC Exposome-based public health interventions for infectious diseases in urban settings Environ Int 2021 146 106246 10.1016/j.envint.2020.106246 33181410 S0160-4120(20)32201-7 PMC7834142 Martin-Sanchez F Atienza-Maderuelo M Lopez-Campos G Collado P Use of informatics to characterise the exposome of COVID-19 BMJ Health Care Inform 2021 28 1 e100371 10.1136/bmjhci-2021-100371 34413119 bmjhci-2021-100371 PMC8380474 Horton R Offline: COVID-19 is not a pandemic Lancet Lond Engl 2020 396 10255 874 10.1016/S0140-6736(20)32000-6 32979964 S0140-6736(20)32000-6 PMC7515561 Mei X Lee HC Diao K Huang M Lin B Liu C Xie Z Ma Y Robson PM Chung M Bernheim A Mani V Calcagno C Li K Li S Shan H Lv J Zhao T Xia J Long Q Steinberger S Jacobi A Deyer T Luksza M Liu F Little BP Fayad ZA Yang Y Artificial intelligence-enabled rapid diagnosis of COVID-19 patients Nat Med 2020 26 1224 1228 10.1101/2020.04.12.20062661 32511559 2020.04.12.20062661 PMC7274240 Zhang K Liu X Shen J Li Z Sang Y Wu X Zha Y Liang W Wang C Wang K Ye L Gao M Zhou Z Li L Wang J Yang Z Cai H Xu J Yang L Cai W Xu W Wu S Zhang W Jiang S Zheng L Zhang X Wang L Lu L Li J Yin H Wang W Li O Zhang C Liang L Wu T Deng R Wei K Zhou Y Chen T Yiu-Nam Lau J Fok M He J Lin T Li W Wang G Clinically applicable AI system for accurate diagnosis, quantitative measurements, and prognosis of COVID-19 pneumonia using computed tomography Cell 2020 182 5 1360 10.1016/j.cell.2020.08.029 32888496 S0092-8674(20)31071-0 PMC7470724 Ucar F Korkmaz D COVIDiagnosis-Net: deep bayes-squeezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images Med Hypotheses 2020 140 109761 10.1016/j.mehy.2020.109761 32344309 109761 PMC7179515 Shi F Xia L Shan F Song B Wu D Wei Y Yuan H Jiang H He Y Gao Y Sui H Shen D Large-scale screening to distinguish between COVID-19 and community-acquired pneumonia using infection size-aware classification Phys Med Biol 2021 66 6 065031 10.1088/1361-6560/abe838 33729998 Wang P Zheng X Li J Zhu B Prediction of epidemic trends in COVID-19 with logistic model and machine learning technics Chaos Solitons Fractals 2020 139 110058 10.1016/j.chaos.2020.110058 32834611 S0960-0779(20)30455-0 PMC7328553 Cheng FY Joshi H Tandon P Freeman R Reich DL Mazumdar M Kohli-Seth R Levin M Timsina P Kia A Using machine learning to predict ICU transfer in hospitalized COVID-19 patients J Clin Med 2020 9 6 1668 10.3390/jcm9061668 32492874 jcm9061668 PMC7356638 Mathur P Sethi T Mathur A Maheshwari K Cywinski JB Khanna AK Dua S Papay F Explainable machine learning models to understand determinants of COVID-19 mortality in the United States medRxiv 2020 10.1101/2020.05.23.20110189 Hu H Zheng Y Wen X Smith SS Nizomov J Fishe J Hogan WR Shenkman EA Bian J An external exposome-wide association study of COVID-19 mortality in the United States Sci Total Environ 2021 768 144832 10.1016/j.scitotenv.2020.144832 33450687 S0048-9697(20)38365-0 PMC7788319 Clinical and translational science institute university of florida OneFlorida+ Clinical Research Network 2022-11-17 https://www.ctsi.ufl.edu/ctsa-consortium-projects/oneflorida-clinical-research-network/ Fleurence RL Curtis LH Califf RM Platt R Selby JV Brown JS Launching PCORnet, a national patient-centered clinical research network J Am Med Inform Assoc 2014 21 4 578 582 10.1136/amiajnl-2014-002747 24821743 amiajnl-2014-002747 PMC4078292 Brett AS Goodman CW First impressions - should we include race or ethnicity at the beginning of clinical case presentations? N Engl J Med 2021 385 27 2497 2499 10.1056/NEJMp2112312 34951753 VanderWeele TJ Robinson WR On the causal interpretation of race in regressions adjusting for confounding and mediating variables Epidemiology 2014 25 4 473 484 10.1097/EDE.0000000000000105 24887159 00001648-201407000-00001 PMC4125322 Corbie-Smith G Henderson G Blumenthal C Dorrance J Estroff S Conceptualizing race in research J Natl Med Assoc 2008 100 10 1235 1243 10.1016/s0027-9684(15)31470-x 18942287 S0027-9684(15)31470-X PMC3057432 Hogan WR Shenkman EA Robinson T Carasquillo O Robinson PS Essner RZ Bian J Lipori G Harle C Magoc T Manini L Mendoza T White S Loiacono A Hall J Nelson D The OneFlorida data trust: a centralized, translational research data infrastructure of statewide scope J Am Med Inform Assoc 2022 29 4 686 693 10.1093/jamia/ocab221 34664656 6401981 PMC8922180 Certain Medical Conditions and Risk for Severe COVID-19 Illness | CDC 2020-08-01 https://www.cdc.gov/coronavirus/2019-ncov/need-extra-precautions/people-with-medical-conditions.html Clinical Classifications Software Refined 2023-02-01 https://www.hcup-us.ahrq.gov/toolssoftware/ccsr/ccs_refined.jsp Elixhauser A Steiner C Harris DR Coffey RM Comorbidity measures for use with administrative data Med Care 1998 36 1 8 27 10.1097/00005650-199801000-00004 9431328 Chang HJ Chen PC Yang CC Su YC Lee CC Comparison of elixhauser and charlson methods for predicting oral cancer survival Medicine (Baltimore) 2016 95 7 e2861 10.1097/MD.0000000000002861 26886653 00005792-201602150-00060 PMC4998653 Menendez ME Neuhaus V van Dijk CN Ring D The elixhauser comorbidity method outperforms the charlson index in predicting inpatient death after orthopaedic surgery Clin Orthop Relat Res 2014 472 9 2878 2886 10.1007/s11999-014-3686-7 24867450 PMC4117875 Qeadan F VanSant-Webb E Tingey B Rogers TN Brooks E Mensah NA Winkfield KM Saeed AI English K Rogers CR Racial disparities in COVID-19 outcomes exist despite comparable elixhauser comorbidity indices between blacks, hispanics, native Americans, and whites Sci Rep 2021 11 1 8738 10.1038/s41598-021-88308-2 33888833 10.1038/s41598-021-88308-2 PMC8062526 Kind AJH Jencks S Brock J Yu M Bartels C Ehlenbach W Greenberg C Smith M Neighborhood socioeconomic disadvantage and 30-day rehospitalization: a retrospective cohort study Ann Intern Med 2014 161 11 765 774 10.7326/M13-2946 25437404 1983380 PMC4251560 Maroko AR Doan TM Arno PS Hubel M Yi S Viola D Integrating social determinants of health with treatment and prevention: a new tool to assess local area deprivation Prev Chronic Dis 2016 13 E128 10.5888/pcd13.160221 27634778 E128 PMC5027849 Adjei-Fremah S Lara N Anwar A Garcia DC Hemaktiathar S Ifebirinachi CB Anwar M Lin FC Samuel R The effects of race/ethnicity, age, and area deprivation index (ADI) on COVID-19 disease early dynamics: Washington, DC case study J Racial Ethn Health Disparities 2023 10 2 491 500 10.1007/s40615-022-01238-1 35169993 10.1007/s40615-022-01238-1 PMC8853370 Srivastava T Schmidt H Sadecki E Kornides ML Disadvantage indices deployed to promote equitable allocation of COVID-19 vaccines in the US: a scoping review of differences and similarities in design JAMA Health Forum 2022 3 1 e214501 10.1001/jamahealthforum.2021.4501 35977227 ald210028 PMC8903102 Atmospheric Composition Analysis Group Washington University in St. Louis 2022-11-16 https://sites.wustl.edu/acag/ CACES 2022-11-16 https://www.caces.us USDA ERS - Food Environment Atlas 2022-11-16 https://www.ers.usda.gov/data-products/food-environment-atlas/ HUD aggregated USPS administrative data on address vacancies HUD USER 2022-11-16 https://www.huduser.gov/portal/datasets/usps.html Batista GEAPA Monard MC An analysis of four missing data treatment methods for supervised learning Appl Artif Intell 2003 17 5-6 519 533 10.1080/713827181 Hron K Templ M Filzmoser P Imputation of missing values for compositional data using classical and robust methods Comput Stat Data Anal 2010 54 12 3095 3107 10.1016/j.csda.2009.11.023 Chen T Guestrin C GBoost: A Scalable Tree Boosting System 2016 GBoost: A Scalable Tree Boosting System 13 August 2016 New York, NY, USA Association for Computing Machinery 785 794 10.1145/2939672.2939785 Cawley CG Talbot NLC On over-fitting in model selection and subsequent selection bias in performance evaluation J Mach Learn Res 2010 11 70 2079 2107 Lundberg S Interpretable Machine Learning with XGBoost Medium 2020 2023-05-25 https://towardsdatascience.com/interpretable-machine-learning-with-xgboost-9ec80d148d27 Jiménez ÁB Lázaro JL Dorronsoro JR Corchado E Corchado JM Abraham A Finding optimal model parameters by discrete grid search Innovations in Hybrid Intelligent Systems. Advances in Soft Computing 2007 Berlin, Heidelberg, Germany Springer-Verlag 120 127 SHAP values Kaggle 2024-08-19 https://kaggle.com/code/dansbecker/shap-values Arrow KJ Barankin EW Blackwell D Bott R Dalkey N Dresher M Contributions to the Theory of Games (AM-28), Volume II 1953 USA Princeton University Press Ke G Meng Q Finley T Wang T Chen W Ma W LightGBM: a highly efficient gradient boosting decision tree 2017 Proc. 31st Int. Conf. Neural Inf. Process. Syst., Red Hook 04 December 2017 NY, USA Curran Associates Inc 3149 3157 Recommendations for Reporting Machine Learning Analyses in Clinical Research | EQUATOR Network 2023-05-25 https://www.equator-network.org/reporting-guidelines/recommendations-for-reporting-machine-learning-analyses-in-clinical-research/ Stevens LM Mortazavi BJ Deo RC Curtis L Kao DP Recommendations for reporting machine learning analyses in clinical research Circ Cardiovasc Qual Outcomes 2020 10 13 10 e006556 10.1161/circoutcomes.120.006556 Italia L Tomasoni D Bisegna S Pancaldi E Stretti L Adamo M Metra M COVID-19 and heart failure: from epidemiology during the pandemic to myocardial injury, myocarditis, and heart failure sequelae Front Cardiovasc Med 2021 8 713560 10.3389/fcvm.2021.713560 34447795 PMC8382715 John KJ Mishra AK Ramasamy C George AA Selvaraj V Lal A Heart failure in COVID-19 patients: critical care experience World J Virol 2022 11 1 1 19 10.5501/wjv.v11.i1.1 35117968 PMC8788216 Nayak A Hicks AJ Morris AA Understanding the complexity of heart failure risk and treatment in black patients Circ Heart Fail 2020 13 8 e007264 10.1161/CIRCHEARTFAILURE.120.007264 32787445 PMC7644144 Mitchell B Franco J HOLC “redlining” maps: The persistent structure of segregation and economic inequality Home Owners' loan corporation “redlining” maps: The persistent structure of segregation and economic inequality 2018 10.3102/1439273 Morris AA McAllister P Grant A Geng S Kelli HM Kalogeropoulos A Quyyumi A Butler J Relation of living in a "Food Desert" to recurrent hospitalizations in patients with heart failure Am J Cardiol 2019 123 2 291 296 10.1016/j.amjcard.2018.10.004 30442360 S0002-9149(18)31956-8 PMC6497163 Akwo EA Kabagambe EK Harrell FE Blot WJ Bachmann JM Wang TJ Gupta DK Lipworth L Neighborhood deprivation predicts heart failure risk in a low-income population of blacks and whites in the southeastern United States Circ Cardiovasc Qual Outcomes 2018 11 1 e004052 10.1161/CIRCOUTCOMES.117.004052 29317456 CIRCOUTCOMES.117.004052 PMC5769153 Diez Roux AV Merkin SS Arnett D Chambless L Massing M Nieto FJ Sorlie P Szklo M Tyroler HA Watson RL Neighborhood of residence and incidence of coronary heart disease N Engl J Med 2001 345 2 99 106 10.1056/NEJM200107123450205 11450679 Lu Y Chen L Liu X Yang Y Sullivan WC Xu W Webster C Jiang B Green spaces mitigate racial disparity of health: a higher ratio of green spaces indicates a lower racial disparity in SARS-CoV-2 infection rates in the USA Environ Int 2021 152 106465 10.1016/j.envint.2021.106465 33684736 S0160-4120(21)00090-8 PMC9754786 Howell Nicholas A Tu Jack V Moineddin Rahim Chu Anna Booth Gillian L Association between neighborhood walkability and predicted 10-year cardiovascular disease risk: the CANHEART (Cardiovascular Health in Ambulatory Care Research Team) cohort J Am Heart Assoc 2019 11 05 8 21 e013146 10.1161/JAHA.119.013146 31665997 PMC6898849 Powell LM Slater S Chaloupka FJ Harper D Availability of physical activity-related facilities and neighborhood demographic and socioeconomic characteristics: a national study Am J Public Health 2006 96 9 1676 1680 10.2105/AJPH.2005.065573 16873753 AJPH.2005.065573 PMC1551946 Morland K Wing S Diez Roux A Poole C Neighborhood characteristics associated with the location of food stores and food service places Am J Prev Med 2002 22 1 23 29 10.1016/s0749-3797(01)00403-2 11777675 S0749379701004032 National Academies of Sciences, Engineering, and Medicine Health and Medicine Division Board on Population Health and Public Health Practice Committee on Community-Based Solutions to Promote Health Equity in the United States The state of health disparities in the United States Communities in Action: Pathways to Health Equity 2017 Washington, DC National Academies Press New AMA policies recognize race as a social, not biological, construct American Medical Association 2020 11 16 2023-05-27 https://www.ama-assn.org/press-center/press-releases/new-ama-policies-recognize-race-social-not-biological-construct Braveman P Parker Dominguez T Abandon "Race." focus on racism Front Public Health 2021 9 689462 10.3389/fpubh.2021.689462 34557466 PMC8452910 Zhu Y Xie J Huang F Cao L Association between short-term exposure to air pollution and COVID-19 infection: evidence from China Sci Total Environ 2020 727 138704 10.1016/j.scitotenv.2020.138704 32315904 S0048-9697(20)32221-X PMC7159846 Konstantinoudis G Padellini T Bennett J Davies B Ezzati M Blangiardo M Long-term exposure to air-pollution and COVID-19 mortality in England: a hierarchical spatial analysis Environ Int 2021 146 106316 10.1016/j.envint.2020.106316 33395952 S0160-4120(20)32271-6 PMC7786642 Wu X Nethery RC Sabath MB Braun D Dominici F Air pollution and COVID-19 mortality in the United States: strengths and limitations of an ecological regression analysis Sci Adv 2020 6 45 eabd4049 10.1126/sciadv.abd4049 33148655 6/45/eabd4049 PMC7673673 Collins TW Grineski SE Racial/Ethnic disparities in short-term air pollution exposures in the United States Environ Health Perspect 2022 130 8 87701 10.1289/EHP11479 35983969 PMC9389641 Jbaily A Zhou X Liu J Lee TH Kamareddine L Verguet S Dominici F Air pollution exposure disparities across US population and income groups Nature 2022 601 7892 228 233 10.1038/s41586-021-04190-y 35022594 10.1038/s41586-021-04190-y PMC10516300 Lebow-Skelley E Young L Noibi Y Blaginin K Hooker M Williamson D Tomlinson MS Kegler MC Pearson MA Defining the exposome using popular education and concept mapping with communities in Atlanta, Georgia Front Public Health 2022 10 842539 10.3389/fpubh.2022.842539 35493396 PMC9039048 Koch S Yoon L Gils B From the exposome to the socioexposome in COVID-19 research-a call for more multidisciplinary research JAMA Netw Open 2020 3 12 e2032287 10.1001/jamanetworkopen.2020.32287 33372971 2774485 Brown LL Abrams LR Mitchell UA Ailshire JA Measuring more than exposure: does stress appraisal matter for black-white differences in anxiety and depressive symptoms among older adults? Innov Aging 2020 4 5 igaa040 10.1093/geroni/igaa040 33123630 igaa040 PMC7580160 Williams DR Mohammed SA Leavell J Collins C Race, socioeconomic status, and health: complexities, ongoing challenges, and research opportunities Ann N Y Acad Sci 2010 1186 69 101 10.1111/j.1749-6632.2009.05339.x 20201869 NYAS5339 PMC3442603 Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research Journal of the American Medical Informatics Association | Oxford Academic 2021-09-11 https://academic.oup.com/jamia/article/20/1/144/2909176?login=true