Community-Based COVID-19 Vaccine Clinics in Medically Underserved Neighborhoods to Improve Access and Equity, Philadelphia, 2021-2022.

Vaccination remains key to reducing the risk of COVID-19-related severe illness and death. Because of historic medical exclusion and barriers to access, Black communities have had lower rates of COVID-19 vaccination than White communities. We describe the efforts of an academic medical institution to implement community-based COVID-19 vaccine clinics in medically underserved neighborhoods in Philadelphia, Pennsylvania. Over a 13-month period (April 2021-April 2022), the initiative delivered 9038 vaccine doses to community members, a majority of whom (57%) identified as Black. (Am J Public Health. 2022;112(12):1721-1725. https://doi.org/10.2105/AJPH.2022.307030).

Accessibility of HIV Services in Philadelphia: Location-Allocation Analysis.

INTRODUCTION: As the first step in the HIV care continuum, timely diagnosis is central to reducing transmission of the virus and ending the HIV epidemic. Studies have shown that distance from a testing site is essential for ease of access to services and educational material. This study shows how location-allocation analysis can be used to improve allocation of HIV testing services utilizing existing publicly available data from 2015 to 2019 on HIV prevalence, testing site location, and factors related to HIV in Philadelphia, Pennsylvania. METHODS: The ArcGIS Location-Allocation analytic tool was used to calculate locations for HIV testing sites using a method that minimizes the distance between demand-point locations and service facilities. ZIP code level demand was initially specified on the basis of the percentage of late HIV diagnoses and in a sensitivity analysis on the basis of a composite of multiple factors. Travel time and distance from demand to facilities determined the facility location allocation. This analysis was conducted from 2021 to 2022. RESULTS: Compared with the 37 facilities located in 20 (43%) Philadelphia ZIP codes, the model proposed reallocating testing facilities to 37 (79%) ZIP codes using percent late diagnoses to define demand. On average, this would reduce distance to the facilities by 65% and travel time to the facilities by 56%. Results using the sensitivity analysis were similar. CONCLUSIONS: A wider distribution of HIV testing services across the city of Philadelphia may reduce distance and travel time to facilities, improve accessibility of testing, and in turn increase the percentage of people with knowledge of their status.

Excess Mortality From Non-COVID-19 Causes During the COVID-19 Pandemic in Philadelphia, Pennsylvania, 2020-2021.

Objectives. To estimate excess mortality from non-COVID-19 causes during the COVID-19 pandemic in Philadelphia, Pennsylvania, and understand disparities by race/ethnicity, age, and sex. Methods. We used Poisson regression models of weekly deaths using data from Pennsylvania's vital registration system (2018-2021). Results. There was significant excess mortality as a result of heart disease, homicide, diabetes, drug overdoses, traffic crashes, and falls in 2020-2021; the burden of this excess non-COVID-19 mortality fell on non-Hispanic Black Philadelphians. Among younger non-Hispanic Black men, homicide and drug overdoses were responsible for 54% and 18% of excess deaths-more than COVID-19 (17%). For younger non-Hispanic Black women, drug overdoses accounted for 51% of excess deaths, whereas COVID-19 accounted for 40%. Conclusions. Excess mortality was not solely caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2; the causative agent of COVID-19), particularly at younger ages. Indirect pandemic mortality exacerbated prepandemic disparities by race/ethnicity. Public Health Implications. Excess mortality as a result of non-COVID-19 causes may reflect indirect pandemic mortality. National cause-of-death data lag behind local cause-of-death data; local data should be examined as an early indication of trends and disparities. Public health practitioners must center health equity in pandemic response and planning. (Am J Public Health. 2022;112(12):1800-1803. https://doi.org/10.2105/AJPH.2022.307096).

Association between race, shooting hot spots, and the surge in gun violence during the COVID-19 pandemic in Philadelphia, New York and Los Angeles.

Gun violence rates increased in U.S. cities in 2020 and into 2021. Gun violence rates in U.S. cities is typically concentrated in racially segregated neighborhoods with higher poverty levels. However, poverty levels and demographics alone do not explain the high concentration of violence or its relative change over time. In this paper, we examine the extent to which the increase in shooting victimization in Philadelphia, New York, and Los Angeles during the 2020-2021 pandemic was concentrated in gun violence hot spots, and how the increase impacted race and ethnic disparities in shooting victimization rates. We find that 36% (Philadelphia), 47% (New York), and 55% (Los Angeles) of the increase in shootings observed during the period 2020-2021 occurred in the top decile of census block groups, by aggregate number of shootings, and that the race/ethnicity of victims in these gun violence hot spots were disproportionately Black and Hispanic. We discuss the implications of these findings as they relate to racial disparities in victimization and place-based efforts to reduce gun violence.

Neighborhood Characteristics and Racial Disparities in Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Seropositivity in Pregnancy.

OBJECTIVE: To quantify the extent to which neighborhood characteristics contribute to racial and ethnic disparities in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seropositivity in pregnancy. METHODS: This cohort study included pregnant patients who presented for childbirth at two hospitals in Philadelphia, Pennsylvania from April 13 to December 31, 2020. Seropositivity for SARS-CoV-2 was determined by measuring immunoglobulin G and immunoglobulin M antibodies by enzyme-linked immunosorbent assay in discarded maternal serum samples obtained for clinical purposes. Race and ethnicity were self-reported and abstracted from medical records. Patients' residential addresses were geocoded to obtain three Census tract variables: community deprivation, racial segregation (Index of Concentration at the Extremes), and crowding. Multivariable mixed effects logistic regression models and causal mediation analyses were used to quantify the extent to which neighborhood variables may explain racial and ethnic disparities in seropositivity. RESULTS: Among 5,991 pregnant patients, 562 (9.4%) were seropositive for SARS-CoV-2. Higher seropositivity rates were observed among Hispanic (19.3%, 104/538) and Black (14.0%, 373/2,658) patients, compared with Asian (3.2%, 13/406) patients, White (2.7%, 57/2,133) patients, and patients of another race or ethnicity (5.9%, 15/256) (P<.001). In adjusted models, per SD increase, deprivation (adjusted odds ratio [aOR] 1.16, 95% CI 1.02-1.32) and crowding (aOR 1.15, 95% CI 1.05-1.26) were associated with seropositivity, but segregation was not (aOR 0.90, 95% CI 0.78-1.04). Mediation analyses revealed that crowded housing may explain 6.7% (95% CI 2.0-14.7%) of the Hispanic-White disparity and that neighborhood deprivation may explain 10.2% (95% CI 0.5-21.1%) of the Black-White disparity. CONCLUSION: Neighborhood deprivation and crowding were associated with SARS-CoV-2 seropositivity in pregnancy in the prevaccination era and may partially explain high rates of SARS-CoV-2 seropositivity among Black and Hispanic patients. Investing in structural neighborhood improvements may reduce inequities in viral transmission.

How education and racial segregation intersect in neighborhoods with persistently low COVID-19 vaccination rates in Philadelphia.

BACKGROUND: COVID-19 infection has disproportionately affected socially disadvantaged neighborhoods. Despite this disproportionate burden of infection, these neighborhoods have also lagged in COVID-19 vaccinations. To date, we have little understanding of the ways that various types of social conditions intersect to explain the complex causes of lower COVID-19 vaccination rates in neighborhoods. METHODS: We used configurational comparative methods (CCMs) to study COVID-19 vaccination rates in Philadelphia by neighborhood (proxied by zip code tabulation areas). Specifically, we identified neighborhoods where COVID-19 vaccination rates (per 10,000) were persistently low from March 2021 - May 2021. We then assessed how different combinations of social conditions (pathways) uniquely distinguished neighborhoods with persistently low vaccination rates from the other neighborhoods in the city. Social conditions included measures of economic inequities, racial segregation, education, overcrowding, service employment, public transit use, health insurance and limited English proficiency. RESULTS: Two factors consistently distinguished neighborhoods with persistently low COVID-19 vaccination rates from the others: college education and concentrated racial privilege. Two factor values together - low college education AND low/medium concentrated racial privilege - identified persistently low COVID-19 vaccination rates in neighborhoods, with high consistency (0.92) and high coverage (0.86). Different values for education and concentrated racial privilege - medium/high college education OR high concentrated racial privilege - were each sufficient by themselves to explain neighborhoods where COVID-19 vaccination rates were not persistently low, likewise with high consistency (0.93) and high coverage (0.97). CONCLUSIONS: Pairing CCMs with geospatial mapping can help identify complex relationships between social conditions linked to low COVID-19 vaccination rates. Understanding how neighborhood conditions combine to create inequities in communities could inform the design of interventions tailored to address COVID-19 vaccination disparities.

The changing epidemiology of interpersonal firearm violence during the COVID-19 pandemic in Philadelphia, PA.

Recent increases in firearm violence in U.S. cities are well-documented, however dynamic changes in the people, places and intensity of this public health threat during the COVID-19 pandemic are relatively unexplored. This descriptive epidemiologic study spanning from January 1, 2015 - March 31, 2021 utilizes the Philadelphia Police Department's registry of shooting victims, a database which includes all individuals shot and/or killed due to interpersonal firearm violence in the city of Philadelphia. We compared victim and event characteristics prior to the pandemic with those following implementation of pandemic containment measures. In this study, containment began on March 16, 2020, when non-essential businesses were ordered to close in Philadelphia. There were 331 (SE = 13.9) individuals shot/quarter pre-containment vs. 545 (SE = 66.4) individuals shot/quarter post-containment (p = 0.031). Post-containment, the proportion of women shot increased by 39% (95% CI: 1.21, 1.59), and the proportion of children shot increased by 17% (95% CI: 1.00, 1.35). Black women and children were more likely to be shot post-containment (RR 1.11, 95% CI: 1.02, 1.20 and RR 1.08, 95% CI: 1.03, 1.14, respectively). The proportion of mass shootings (≥4 individuals shot within 100 m within 1 h) increased by 53% post-containment (95% CI: 1.25, 1.88). Geographic analysis revealed relative increases in all shootings and mass shootings in specific city locations post-containment. The observed changes in firearm injury epidemiology following COVID-19 containment in Philadelphia demonstrate an intensification in firearm violence, which is increasingly impacting people who are likely made more vulnerable by existing social and structural disadvantage. These findings support existing knowledge about structural causes of interpersonal firearm violence and suggest structural solutions are required to address this public health threat.

Association of race/ethnicity and socioeconomic status with COVID-19 30-day mortality at a Philadelphia medical center using a retrospective cohort study.

COVID-19 has disproportionately affected low-income communities and people of color. Previous studies demonstrated that race/ethnicity and socioeconomic status (SES) are not independently correlated with COVID-19 mortality. The purpose of our study is to determine the effect of race/ethnicity and SES on COVID-19 30-day mortality in a diverse, Philadelphian population. This is a retrospective cohort study in a single-center tertiary care hospital in Philadelphia, PA. The study includes adult patients hospitalized with polymerase-chain-reaction-confirmed COVID-19 between March 1, 2020 and June 6, 2020. The primary outcome was a composite of COVID-19 death or hospice discharge within 30 days of discharge. The secondary outcome was intensive care unit (ICU) admission. The study included 426 patients: 16.7% died, 3.3% were discharged to hospice, and 20.0% were admitted to the ICU. Using multivariable analysis, race/ethnicity was not associated with the primary nor secondary outcome. In Model 4, age greater than 75 (odds ratio [OR]: 11.01; 95% confidence interval [CI]: 1.96-61.97) and renal disease (OR: 2.78; 95% CI: 1.31-5.90) were associated with higher odds of the composite primary outcome. Living in a "very-low-income area" (OR: 0.29; 95% CI: 0.12-0.71) and body mass index (BMI) 30-35 (OR: 0.24; 95% CI: 0.08-0.69) were associated with lower odds of the primary outcome. When controlling for demographics, SES, and comorbidities, race/ethnicity was not independently associated with the composite primary outcome. Very-low SES, as extrapolated from census-tract-level income data, was associated with lower odds of the composite primary outcome.

Disparities of COVID-19 and HIV Occurrence Based on Neighborhood Infection Incidence in Philadelphia, Pennsylvania.

Objectives. To evaluate the occurrence of HIV and COVID-19 infections in Philadelphia, Pennsylvania, through July 2020 and identify ecological correlates driving racial disparities in infection incidence. Methods. For each zip code tabulation area, we created citywide comparison Z-score measures of COVID-19 cases, new cases of HIV, and the difference between the scores. Choropleth maps were used to identify areas that were similar or dissimilar in terms of disease patterning, and weighted linear regression models helped identify independent ecological predictors of these patterns. Results. Relative to COVID-19, HIV represented a greater burden in Center City Philadelphia, whereas COVID-19 was more apparent in Northeast Philadelphia. Areas with a greater proportion of Black or African American residents were overrepresented in terms of both diseases. Conclusions. Although race is a shared nominal upstream factor that conveys increased risk for both infections, an understanding of separate structural, demographic, and economic risk factors that drive the overrepresentation of COVID-19 cases in racial/ethnic communities across Philadelphia is critical. Public Health Implications. Difference-based measures are useful in identifying areas that are underrepresented or overrepresented with respect to disease occurrence and may be able to elucidate effective or ineffective mitigation strategies. (Am J Public Health. 2022;112(3):408-416. https://doi.org/10.2105/AJPH.2021.306538).

Community correlates of change: A mixed-effects assessment of shooting dynamics during COVID-19.

This study examines changes in gun violence at the census tract level in Philadelphia, PA before and after the onset of the COVID-19 pandemic. Piecewise generalized linear mixed effects models are used to test the relative impacts of social-structural and demographic factors, police activity, the presence of and proximity to drug markets, and physical incivilities on shooting changes between 2017 and June, 2021. Model results revealed that neighborhood structural characteristics like concentrated disadvantage and racial makeup, as well as proximity to drug markets and police activity were associated with higher shooting rates. Neighborhood drug market activity and police activity significantly predicted changes in shooting rates over time after the onset of COVID-19. This work demonstrates the importance of understanding whether there are unique factors that impact the susceptibility to exogenous shocks like the COVID-19 pandemic. The increasing risk of being in a neighborhood with an active drug market during the pandemic suggests efforts related to disrupting drug organizations, or otherwise curbing violence stemming from drug markets, may go a long way towards quelling citywide increases in gun violence.

COVID-19 and Antibiotic Prescribing in Pediatric Primary Care.

BACKGROUND AND OBJECTIVES: With the onset of the coronavirus disease 2019 (COVID-19) pandemic, pediatric ambulatory encounter volume and antibiotic prescribing both decreased; however, the durability of these reductions in pediatric primary care in the United States has not been assessed. METHODS: We conducted a retrospective observational study to assess the impact of the COVID-19 pandemic and associated public health measures on antibiotic prescribing in 27 pediatric primary care practices. Encounters from January 1, 2018, through June 30, 2021, were included. The primary outcome was monthly antibiotic prescriptions per 1000 patients. Interrupted time series analysis was performed. RESULTS: There were 69 327 total antibiotic prescriptions from April through December in 2019 and 18 935 antibiotic prescriptions during the same months in 2020, a 72.7% reduction. The reduction in prescriptions at visits for respiratory tract infection (RTI) accounted for 87.3% of this decrease. Using interrupted time series analysis, overall antibiotic prescriptions decreased from 31.6 to 6.4 prescriptions per 1000 patients in April 2020 (difference of -25.2 prescriptions per 1000 patients; 95% CI: -32.9 to -17.5). This was followed by a nonsignificant monthly increase in antibiotic prescriptions, with prescribing beginning to rebound from April to June 2021. Encounter volume also immediately decreased, and while overall encounter volume quickly started to recover, RTI encounter volume returned more slowly. CONCLUSIONS: Reductions in antibiotic prescribing in pediatric primary care during the COVID-19 pandemic were sustained, only beginning to rise in 2021, primarily driven by reductions in RTI encounters. Reductions in viral RTI transmission likely played a substantial role in reduced RTI visits and antibiotic prescriptions.

Impact of telemedicine adoption on accessibility and time to treatment in patients with thoracic malignancies during the COVID-19 pandemic.

BACKGROUND: To ensure safe delivery of oncologic care during the COVID-19 pandemic, telemedicine has been rapidly adopted. However, little data exist on the impact of telemedicine on quality and accessibility of oncologic care. This study assessed whether conducting an office visit for thoracic oncology patients via telemedicine affected time to treatment initiation and accessibility. METHODS: This was a retrospective cohort study of patients with thoracic malignancies seen by a multidisciplinary team during the first surge of COVID-19 cases in Philadelphia (March 1 to June 30, 2020). Patients with an index visit for a new phase of care, defined as a new diagnosis, local recurrence, or newly discovered metastatic disease, were included. RESULTS: 240 distinct patients with thoracic malignancies were seen: 132 patients (55.0%) were seen initially in-person vs 108 (45.0%) via telemedicine. The majority of visits were for a diagnosis of a new thoracic cancer (87.5%). Among newly diagnosed patients referred to the thoracic oncology team, the median time from referral to initial visit was significantly shorter amongst the patients seen via telemedicine vs. in-person (median 5.0 vs. 6.5 days, p < 0.001). Patients received surgery (32.5%), radiation (24.2%), or systemic therapy (30.4%). Time from initial visit to treatment initiation by modality did not differ by telemedicine vs in-person: surgery (22 vs 16 days, p = 0.47), radiation (27.5 vs 27.5 days, p = 0.86, systemic therapy (15 vs 13 days, p = 0.45). CONCLUSIONS: Rapid adoption of telemedicine allowed timely delivery of oncologic care during the initial surge of the COVID19 pandemic by a thoracic oncology multi-disciplinary clinic.

Risk And Resilience Factors Influencing Postpartum Depression And Mother-Infant Bonding During COVID-19.

Acute stress during pregnancy can have adverse effects on maternal health and increase the risk for postpartum depression and impaired mother-infant bonding. The COVID-19 pandemic represents an acute environmental stressor during which it is possible to explore risk and resilience factors that contribute to postpartum outcomes. To investigate prenatal risk and resilience factors as predictors of postpartum depression and impaired mother-infant bonding, this study recruited a diverse cohort of 833 pregnant women from an urban medical center in Philadelphia, Pennsylvania, and assessed them once during pregnancy in the early phase of the COVID-19 pandemic (April-July 2020) and again at approximately twelve weeks postpartum. Adverse childhood experiences, prenatal depression and anxiety, and COVID-19-related distress predicted a greater likelihood of postpartum depression. Prenatal depression was the only unique predictor of impaired maternal-infant bonding after postpartum depression was controlled for. Women reporting greater emotion regulation, self-reliance, and nonhostile relationships had healthier postpartum outcomes. Policies to increase the number of nonspecialty providers providing perinatal mental health services as well as reimbursement for integrated care and access to mental health screening and care are needed to improve lifelong outcomes for women and their children.

Experiences of coping with the first wave of COVID-19 epidemic in Philadelphia, PA: Mixed methods analysis of a cross-sectional survey of worries and symptoms of mood disorders.

Our objective was to describe how residents of Philadelphia, Pennsylvania, coped psychologically with the first wave of COVID-19 pandemic. In a cross-sectional design, we aimed to estimate the rates and correlates of anxiety and depression, examine how specific worries correlated with general anxiety and depression, and synthesize themes of "the most difficult experiences" shared by the respondents. We collected data through an on-line survey in a convenience sample of 1,293 adult residents of Philadelphia, PA between April 17 and July 3, 2020, inquiring about symptoms of anxiety and depression (via the Hospital Anxiety and Depression Scale), specific worries, open-ended narratives of "the most difficult experiences" (coded into themes), demographics, perceived sources of support, and general health. Anxiety was evident among 30 to 40% of participants and depression-about 10%. Factor analysis revealed two distinct, yet inter-related clusters of specific worries related to mood disorders: concern about "hardships" and "fear of infection". Regression analyses revealed that anxiety, depression, and fear of infection, but not concern about hardships, worsened over the course of the epidemic. "The most difficult experiences" characterized by loss of income, poor health of self or others, uncertainty, death of a relative or a friend, and struggle accessing food were each associated with some of the measures of worries and mood disorders. Respondents who believed they could rely on support of close personal network fared better psychologically than those who reported relying primarily on government and social services organizations. Thematic analysis revealed complex perceptions of the pandemic by the participants, giving clues to both positive and negative experiences that may have affected how they coped. Despite concerns about external validity, our observations are concordant with emerging evidence of psychological toll of the COVID-19 pandemic and measures employed to mitigate risk of infection.

Coronavirus disease 2019 (COVID-19) vaccine hesitancy among physicians, physician assistants, nurse practitioners, and nurses in two academic hospitals in Philadelphia.

OBJECTIVE: To evaluate coronavirus disease 2019 (COVID-19) vaccine hesitancy among healthcare personnel (HCP) with significant clinical exposure to COVID-19 at 2 large, academic hospitals in Philadelphia, Pennsylvania. DESIGN, SETTING, AND PARTICIPANTS: HCP were surveyed in November-December 2020 about their intention to receive the COVID-19 vaccine. METHODS: The survey measured the intent among HCP to receive a COVID-19 vaccine, timing of vaccination, and reasons for or against vaccination. Among patient-facing HCP, multivariate regression evaluated the associations between healthcare positions (medical doctor, nurse practitioner or physician assistant, and registered nurse) and vaccine hesitancy (intending to decline, delay, or were unsure about vaccination), adjusting for demographic characteristics, reasons why or why not to receive the vaccine, and prior receipt of routine vaccines. RESULTS: Among 5,929 HCP (2,253 medical doctors [MDs] and doctors of osteopathy [DOs], 582 nurse practitioners [NPs], 158 physician assistants [PAs], and 2,936 nurses), a higher proportion of nurses (47.3%) were COVID-vaccine hesitant compared with 30.0% of PAs and NPs and 13.1% of MDs and DOs. The most common reasons for vaccine hesitancy included concerns about side effects, the newness of the vaccines, and lack of vaccine knowledge. Regardless of position, Black HCP were more hesitant than White HCP (odds ratio [OR], ∼5) and females were more hesitant than males (OR, ∼2). CONCLUSIONS: Although most clinical HCP intended to receive a COVID-19 vaccine, intention varied by healthcare position. Consistent with other studies, hesitancy was also significantly associated with race or ethnicity across all positions. These results highlight the importance of understanding and effectively addressing reasons for hesitancy, especially among frontline HCP who are at increased risk of COVID exposure and play a critical role in recommending vaccines to patients.

Symptoms of Anxiety and Depression in Relation to Work Patterns During the First Wave of the COVID-19 Epidemic in Philadelphia PA: A Cross-Sectional Survey.

OBJECTIVE: We investigated whether patterns of work during COVID-19 pandemic altered by effort to contain the outbreak affected anxiety and depression. METHODS: We conducted a cross-sectional online survey of 911 residents of Philadelphia, inquiring about their working lives during early months of the epidemic, symptoms of anxiety and depression, plus demographics, perceived sources of support, and general health. RESULTS: Occupational contact with suspected COVID-19 cases was associated with anxiety. Concerns about return to work, childcare, lack of sick leave, and loss/reduction in work correlated with anxiety and depression, even when there was no evidence of occupational contact with infected persons; patterns differed by sex. CONCLUSIONS: Heightened anxiety and depression during COVID-19 pandemic can be due to widespread disruption of working lives, especially in "non-essential" low-income industries, on par with experience in healthcare.

Development and Validation of a Web-Based Severe COVID-19 Risk Prediction Model.

BACKGROUND: Coronavirus disease 2019 (COVID-19) carries high morbidity and mortality globally. Identification of patients at risk for clinical deterioration upon presentation would aid in triaging, prognostication, and allocation of resources and experimental treatments. RESEARCH QUESTION: Can we develop and validate a web-based risk prediction model for identification of patients who may develop severe COVID-19, defined as intensive care unit (ICU) admission, mechanical ventilation, and/or death? METHODS: This retrospective cohort study reviewed 415 patients admitted to a large urban academic medical center and community hospitals. Covariates included demographic, clinical, and laboratory data. The independent association of predictors with severe COVID-19 was determined using multivariable logistic regression. A derivation cohort (n=311, 75%) was used to develop the prediction models. The models were tested by a validation cohort (n=104, 25%). RESULTS: The median age was 66 years (Interquartile range [IQR] 54-77) and the majority were male (55%) and non-White (65.8%). The 14-day severe COVID-19 rate was 39.3%; 31.7% required ICU, 24.6% mechanical ventilation, and 21.2% died. Machine learning algorithms and clinical judgment were used to improve model performance and clinical utility, resulting in the selection of eight predictors: age, sex, dyspnea, diabetes mellitus, troponin, C-reactive protein, D-dimer, and aspartate aminotransferase. The discriminative ability was excellent for both the severe COVID-19 (training area under the curve [AUC]=0.82, validation AUC=0.82) and mortality (training AUC= 0.85, validation AUC=0.81) models. These models were incorporated into a mobile-friendly website. CONCLUSIONS: This web-based risk prediction model can be used at the bedside for prediction of severe COVID-19 using data mostly available at the time of presentation.