Assessment of Racial/Ethnic Disparities in Hospitalization and Mortality in Patients With COVID-19 in New York City.

Importance: Black and Hispanic populations have higher rates of coronavirus disease 2019 (COVID-19) hospitalization and mortality than White populations but lower in-hospital case-fatality rates. The extent to which neighborhood characteristics and comorbidity explain these disparities is unclear. Outcomes in Asian American populations have not been explored. Objective: To compare COVID-19 outcomes based on race and ethnicity and assess the association of any disparities with comorbidity and neighborhood characteristics. Design, Setting, and Participants: This retrospective cohort study was conducted within the New York University Langone Health system, which includes over 260 outpatient practices and 4 acute care hospitals. All patients within the system's integrated health record who were tested for severe acute respiratory syndrome coronavirus 2 between March 1, 2020, and April 8, 2020, were identified and followed up through May 13, 2020. Data were analyzed in June 2020. Among 11 547 patients tested, outcomes were compared by race and ethnicity and examined against differences by age, sex, body mass index, comorbidity, insurance type, and neighborhood socioeconomic status. Exposures: Race and ethnicity categorized using self-reported electronic health record data (ie, non-Hispanic White, non-Hispanic Black, Hispanic, Asian, and multiracial/other patients). Main Outcomes and Measures: The likelihood of receiving a positive test, hospitalization, and critical illness (defined as a composite of care in the intensive care unit, use of mechanical ventilation, discharge to hospice, or death). Results: Among 9722 patients (mean [SD] age, 50.7 [17.5] years; 58.8% women), 4843 (49.8%) were positive for COVID-19; 2623 (54.2%) of those were admitted for hospitalization (1047 [39.9%] White, 375 [14.3%] Black, 715 [27.3%] Hispanic, 180 [6.9%] Asian, 207 [7.9%] multiracial/other). In fully adjusted models, Black patients (odds ratio [OR], 1.3; 95% CI, 1.2-1.6) and Hispanic patients (OR, 1.5; 95% CI, 1.3-1.7) were more likely than White patients to test positive. Among those who tested positive, odds of hospitalization were similar among White, Hispanic, and Black patients, but higher among Asian (OR, 1.6, 95% CI, 1.1-2.3) and multiracial patients (OR, 1.4; 95% CI, 1.0-1.9) compared with White patients. Among those hospitalized, Black patients were less likely than White patients to have severe illness (OR, 0.6; 95% CI, 0.4-0.8) and to die or be discharged to hospice (hazard ratio, 0.7; 95% CI, 0.6-0.9). Conclusions and Relevance: In this cohort study of patients in a large health system in New York City, Black and Hispanic patients were more likely, and Asian patients less likely, than White patients to test positive; once hospitalized, Black patients were less likely than White patients to have critical illness or die after adjustment for comorbidity and neighborhood characteristics. This supports the assertion that existing structural determinants pervasive in Black and Hispanic communities may explain the disproportionately higher out-of-hospital deaths due to COVID-19 infections in these populations.

FACETS: using open data to measure community social determinants of health.

Objective: To develop a dataset based on open data sources reflective of community-level social determinants of health (SDH). Materials and Methods: We created FACETS (Factors Affecting Communities and Enabling Targeted Services), an architecture that incorporates open data related to SDH into a single dataset mapped at the census-tract level for New York City. Results: FACETS (https://github.com/mcantor2/FACETS) can be easily used to map individual addresses to their census-tract-level SDH. This dataset facilitates analysis across different determinants that are often not easily accessible. Discussion: Wider access to open data from government agencies at the local, state, and national level would facilitate the aggregation and analysis of community-level determinants. Timeliness of updates to federal non-census data sources may limit their usefulness. Conclusion: FACETS is an important first step in standardizing and compiling SDH-related data in an open architecture that can give context to a patient's condition and enable better decision-making when developing a plan of care.

Preliminary evaluation of a comprehensive provider feedback report.

Provider feedback reports (PFRs) offer one way for providers to use their electronic health record (EHR) data to understand aspects of their clinical performance and improve quality of care. The Primary Care Information Project (PCIP) serves as a bureau of the New York City Department of Health and Mental Hygiene and as a Regional Extension Center that helps area healthcare providers adopt and achieve Meaningful Use of EHR systems. This study analyzes improvement on multiple quality measures pre- and post-receipt of a comprehensive, EHR-based PFR that PCIP created for its member providers. We analyzed improvement among low- versus high-performing providers pre- and post-receipt of the PFR. Pre-PFR receipt, improvement between high and low performers varied per measure. Post-PFR receipt, low performers improved more than high performers on all measures, and more than themselves in the pre-PFR period. Findings suggest PFRs derived directly from provider EHRs may particularly assist lower-performing providers to improve performance.