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10.3390/ijerph18105274

http://scihub22266oqcxt.onion/10.3390/ijerph18105274
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34063533!8156350!34063533
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suck abstract from ncbi


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pmid34063533      Int+J+Environ+Res+Public+Health 2021 ; 18 (10): ä
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  • Factors Associated with SARS-CoV-2 Infection in Physician Trainees in New York City during the First COVID-19 Wave #MMPMID34063533
  • Pawloski KR; Kolod B; Khan RF; Midya V; Chen T; Oduwole A; Camins B; Colicino E; Leitman IM; Nabeel I; Oliver K; Valvi D
  • Int J Environ Res Public Health 2021[May]; 18 (10): ä PMID34063533show ga
  • 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.
  • |*COVID-19[MESH]
  • |*Physicians[MESH]
  • |Bayes Theorem[MESH]
  • |Health Personnel[MESH]
  • |Humans[MESH]
  • |New York City/epidemiology[MESH]
  • |Pandemics[MESH]
  • |Retrospective Studies[MESH]


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