A model of disparities: risk factors associated with COVID-19 infection.

BACKGROUND: By mid-May 2020, there were over 1.5 million cases of (SARS-CoV-2) or COVID-19 across the U.S. with new confirmed cases continuing to rise following the re-opening of most states. Prior studies have focused mainly on clinical risk factors associated with serious illness and mortality of COVID-19. Less analysis has been conducted on the clinical, sociodemographic, and environmental variables associated with initial infection of COVID-19. METHODS: A multivariable statistical model was used to characterize risk factors in 34,503cases of laboratory-confirmed positive or negative COVID-19 infection in the Providence Health System (U.S.) between February 28 and April 27, 2020. Publicly available data were utilized as approximations for social determinants of health, and patient-level clinical and sociodemographic factors were extracted from the electronic medical record. RESULTS: Higher risk of COVID-19 infection was associated with older age (OR 1.69; 95% CI 1.41-2.02, p < 0.0001), male gender (OR 1.32; 95% CI 1.21-1.44, p < 0.0001), Asian race (OR 1.43; 95% CI 1.18-1.72, p = 0.0002), Black/African American race (OR 1.51; 95% CI 1.25-1.83, p < 0.0001), Latino ethnicity (OR 2.07; 95% CI 1.77-2.41, p < 0.0001), non-English language (OR 2.09; 95% CI 1.7-2.57, p < 0.0001), residing in a neighborhood with financial insecurity (OR 1.10; 95% CI 1.01-1.25, p = 0.04), low air quality (OR 1.01; 95% CI 1.0-1.04, p = 0.05), housing insecurity (OR 1.32; 95% CI 1.16-1.5, p < 0.0001) or transportation insecurity (OR 1.11; 95% CI 1.02-1.23, p = 0.03), and living in senior living communities (OR 1.69; 95% CI 1.23-2.32, p = 0.001). CONCLUSION: sisk of COVID-19 infection is higher among groups already affected by health disparities across age, race, ethnicity, language, income, and living conditions. Health promotion and disease prevention strategies should prioritize groups most vulnerable to infection and address structural inequities that contribute to risk through social and economic policy.

Who was at risk for COVID-19 late in the US pandemic? Insights from a population health machine learning model.

Notable discrepancies in vulnerability to COVID-19 infection have been identified between specific population groups and regions in the USA. The purpose of this study was to estimate the likelihood of COVID-19 infection using a machine-learning algorithm that can be updated continuously based on health care data. Patient records were extracted for all COVID-19 nasal swab PCR tests performed within the Providence St. Joseph Health system from February to October of 2020. A total of 316,599 participants were included in this study, and approximately 7.7% (n = 24,358) tested positive for COVID-19. A gradient boosting model, LightGBM (LGBM), predicted risk of initial infection with an area under the receiver operating characteristic curve of 0.819. Factors that predicted infection were cough, fever, being a member of the Hispanic or Latino community, being Spanish speaking, having a history of diabetes or dementia, and living in a neighborhood with housing insecurity. A model trained on sociodemographic, environmental, and medical history data performed well in predicting risk of a positive COVID-19 test. This model could be used to tailor education, public health policy, and resources for communities that are at the greatest risk of infection.