Occupational and non-occupational risk factors for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection have been reported in healthcare workers (HCWs), but studies evaluating risk factors for infection among physician trainees are lacking. We aimed to identify sociodemographic, occupational, and community risk factors among physician trainees during the first wave of coronavirus disease 2019 (COVID-19) in New York City. In this retrospective study of 328 trainees at the Mount Sinai Health System in New York City, we administered a survey to assess risk factors for SARS-CoV-2 infection between 1 February and 30 June 2020. SARS-CoV-2 infection was determined by self-reported and laboratory-confirmed IgG antibody and reverse transcriptase-polymerase chain reaction test results. We used Bayesian generalized linear mixed effect regression to examine associations between hypothesized risk factors and infection odds. The cumulative incidence of infection was 20.1%. Assignment to medical-surgical units (OR, 2.51; 95% CI, 1.18–5.34), and training in emergency medicine, critical care, and anesthesiology (OR, 2.93; 95% CI, 1.24–6.92) were independently associated with infection. Caring for unfamiliar patient populations was protective (OR, 0.16; 95% CI, 0.03–0.73). Community factors were not statistically significantly associated with infection after adjustment for occupational factors. Our findings may inform tailored infection prevention strategies for physician trainees responding to the COVID-19 pandemic.
Background: Occupational and non-occupational risk factors for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection have been reported in healthcare workers (HCWs), but studies evaluating risk factors for infection among physician trainees are lacking. We aimed to identify sociodemographic, occupational, and community risk factors among physician trainees during the first wave of coronavirus disease 2019 in New York City. Methods:In this retrospective study of 328 trainees at the Mount Sinai Health System (MSHS) in New York City, we administered a survey to assess risk factors for SARS-CoV-2 infection between February 1 and June 30, 2020. SARS-CoV-2 infection was determined by self-reported and laboratory-confirmed IgG antibody and reverse transcriptase-polymerase chain reaction test results. We used Bayesian generalized linear mixed effect regression to examine associations between hypothesized risk factors and infection odds.
Background: As a harm reduction-focused primary care clinic for people who use drugs, the Respectful and Equitable Access to Comprehensive Healthcare (REACH) Program faced multiple barriers due to the COVID-19 pandemic. We describe and evaluate how the telemedicine-driven adaptations REACH made allowed the program to engage its patients. Methods: REACH expanded its telemedicine capabilities by transitioning its in-person clinic and methods of connecting with referrals to telemedicine. The program provided patients with phones to increase access to needed technology. Results: Throughout 2020, patient visits continuously shifted from being entirely in-person, to entirely telemedicine, to a hybrid model. Clinic show rates averaged 71% with this hybrid model, compared with 57% pre-COVID-19. Phones were distributed to 88 patients, 77% of which engaged in at least one telemedicine visit. Conclusions: Telemedicine allowed REACH to provide uninterrupted care during the pandemic. The program is now refining its hybrid model of telemedicine and in-person care to more equitably serve all patients.
Risk factors for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are not well-defined in resident physicians and fellows (trainees). We aimed to identify sociodemographic, occupational and community factors associated with SARS-CoV-2 infection among trainees during the first wave of the coronavirus disease 2019 (COVID-19) pandemic in New York City (NYC). In this retrospective cohort study, we administered an electronic survey between June 26 and August 31, 2020 to trainees at the Mount Sinai Health System in NYC to assess risk factors for SARS-CoV-2 infection between February 1 and June 30, 2020. We used Bayesian generalized linear mixed effect regression and structural equation models to examine associations. SAR-CoV-2 infection was determined by self-reported IgG antibody and reverse transcriptase-polymerase chain reaction results and confirmed with laboratory results. Among 2354 trainees invited to participate, 328 (14%) completed the survey and reported test results. The cumulative incidence of SARS-CoV-2 infection was 20.1%. Assignment to medical-surgical units (odds ratio [OR], 2.51; 95% CI, 1.18-5.34), and training in emergency medicine, critical care and anesthesiology (OR, 2.93; 95% CI, 1.24-6.92) were independently associated with infection. Deployment to care for unfamiliar patient populations was protective against infection (OR, 0.16; 95% CI, 0.03-0.73). Community factors were not significantly associated with infection after adjustment for occupational factors. Our findings may inform tailored infection prevention strategies for trainees responding to the COVID-19 pandemic.
Accurate prediction of SARS-CoV-2 infection based on symptoms can be a cost-efficient tool for remote screening in healthcare settings with limited SARS-CoV-2 testing capacity. We used a machine learning approach to determine self-reported symptoms that best predict a positive SARS-CoV-2 test result in physician trainees from a large healthcare system in New York. We used survey data on symptoms history and SARS-CoV-2 testing results collected retrospectively from 328 physician trainees in the Mount Sinai Health System, over the period 1 February 2020 to 31 July 2020. Prospective data on symptoms reported prior to SARS-CoV-2 test results were available from the employee health service COVID-19 registry for 186 trainees and analyzed to confirm absence of recall bias. We estimated the associations between symptoms and IgG antibody and/or reverse transcriptase polymerase chain reaction test results using Bayesian generalized linear mixed effect regression models adjusted for confounders. We identified symptoms predicting a positive SARS-CoV-2 test result using extreme gradient boosting (XGBoost). Cough, chills, fever, fatigue, myalgia, headache, shortness of breath, diarrhea, nausea/vomiting, loss of smell, loss of taste, malaise and runny nose were associated with a positive SARS-CoV-2 test result. Loss of taste, myalgia, loss of smell, cough and fever were identified as key predictors for a positive SARS-CoV-2 test result in the XGBoost model. Inclusion of sociodemographic and occupational risk factors in the model improved prediction only slightly (from AUC = 0.822 to AUC = 0.838). Loss of taste, myalgia, loss of smell, cough and fever are key predictors for symptom-based screening of SARS-CoV-2 infection in healthcare settings with remote screening and/or limited testing capacity.
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