Race, Ethnicity, Neighborhood Characteristics, and In-Hospital Coronavirus Disease-2019 Mortality.

BACKGROUND: Despite many studies reporting disparities in coronavirus disease-2019 (COVID-19) incidence and outcomes in Black and Hispanic/Latino populations, mechanisms are not fully understood to inform mitigation strategies. OBJECTIVE: The aim was to test whether neighborhood factors beyond individual patient-level factors are associated with in-hospital mortality from COVID-19. We hypothesized that the Area Deprivation Index (ADI), a neighborhood census-block-level composite measure, was associated with COVID-19 mortality independently of race, ethnicity, and other patient factors. RESEARCH DESIGN: Multicenter retrospective cohort study examining COVID-19 in-hospital mortality. SUBJECTS: Inclusion required hospitalization with positive SARS-CoV-2 test or COVID-19 diagnosis at three large Midwestern academic centers. MEASURES: The primary study outcome was COVID-19 in-hospital mortality. Patient-level predictors included age, sex, race, insurance, body mass index, comorbidities, and ventilation. Neighborhoods were examined through the national ADI neighborhood deprivation rank comparing in-hospital mortality across ADI quintiles. Analyses used multivariable logistic regression with fixed site effects. RESULTS: Among 5999 COVID-19 patients median age was 61 (interquartile range: 44-73), 48% were male, 30% Black, and 10.8% died. Among patients who died, 32% lived in the most disadvantaged quintile while 11% lived in the least disadvantaged quintile; 52% of Black, 24% of Hispanic/Latino, and 8.5% of White patients lived in the most disadvantaged neighborhoods.Living in the most disadvantaged neighborhood quintile predicted higher mortality (adjusted odds ratio: 1.74; 95% confidence interval: 1.13-2.67) independent of race. Age, male sex, Medicare coverage, and ventilation also predicted mortality. CONCLUSIONS: Neighborhood disadvantage independently predicted in-hospital COVID-19 mortality. Findings support calls to consider neighborhood measures for vaccine distribution and policies to mitigate disparities.

The Development of the Rheumatology Informatics System for Effectiveness Learning Collaborative for Improving Patient-Reported Outcome Collection and Patient-Centered Communication in Adult Rheumatology.

OBJECTIVE: Patient-reported outcomes (PROs) are an integral part of treat-to-target approaches in managing rheumatoid arthritis (RA). In clinical practice, however, routine collection, documentation, and discussion of PROs with patients are highly variable. The RISE LC (Rheumatology Informatics System for Effectiveness Learning Collaborative) was established to develop and share best practices in PRO collection and use across adult rheumatology practices in the United States METHODS: The goals of the RISE LC were developed through site surveys and in-person meetings. Participants completed a baseline survey on PRO collection and use in their practices. RISE LC learning sessions focused on improving communication around PROs with patients and enhancing shared decision-making in treatment plans. During the coronavirus disease 2019 (COVID-19) pandemic, the RISE LC pivoted to adapt PRO tools for telehealth. RESULTS: At baseline, all responding sites (n = 15) had established workflows for collecting PROs. Most sites used paper forms alone. PRO documentation in electronic health records was variable, with only half of the sites using structured data fields. To standardize and improve the use of PROs, participants iteratively developed a Clinical Disease Activity Index-based RA Disease Activity Communication Tool to solicit treatment goals and improve shared decision-making across sites. The COVID-19 pandemic necessitated developing a tool to gauge PROs via telehealth. CONCLUSION: The RISE LC is a continuous, structured method for implementing strategies to improve PRO collection and use in rheumatological care, initially adapting from the Learning Collaborative model and extending to include features of a learning network. Future directions include measuring the impact of standardized PRO collection and discussion on shared decision-making and RA outcomes.

COVID-19 in Health Care Personnel: Significance of Health Care Role, Contact History, and Symptoms in Those Who Test Positive for SARS-CoV-2 Infection.

OBJECTIVE: To identify significant factors that help predict whether health care personnel (HCP) will test positive for severe acute respiratory coronavirus 2 (SARS-CoV-2). PATIENTS AND METHODS: We conducted a prospective cohort study among 7015 symptomatic HCP from March 25, 2020, through November 11, 2020. We analyzed the associations between health care role, contact history, symptoms, and a positive nasopharyngeal swab SARS-CoV-2 polymerase chain reaction test results, using univariate and multivariable modelling. RESULTS: Of the symptomatic HCP, 624 (8.9%) were positive over the study period. On multivariable analysis, having a health care role other than physician or advanced practice provider, contact with family or community member with known or suspected coronavirus disease 2019 (COVID-19), and seven individual symptoms (cough, anosmia, ageusia, fever, myalgia, chills, and headache) were significantly associated with higher adjusted odds ratios for testing positive for SARS-CoV-2. For each increase in symptom number, the odds of testing positive nearly doubled (odds ratio, 1.93; 95% CI, 1.82 to 2.07, P<.001). CONCLUSION: Symptomatic HCP have higher adjusted odds of testing positive for SARS-CoV-2 based on three distinct factors: (1) nonphysician/advanced practice provider role, (2) contact with a family or community member with suspected or known COVID-19, and (3) specific symptoms and symptom number. Differences among health care roles, which persisted after controlling for contacts, may reflect the influence of social determinants. Contacts with COVID-19-positive patients and/or HCP were not associated with higher odds of testing positive, supporting current infection control efforts. Targeted symptom and contact questionnaires may streamline symptomatic HCP testing for COVID-19.