COVID‐19 mortality among Amalgamated Transit Union (ATU) and Transport Workers Union (TWU) workers—March–July 2020, New York City metro area

Abstract

Background Transit workers have jobs requiring close public contact for extended periods of time, placing them at increased risk for severe acute respiratory syndrome coronavirus 2 infection and more likely to have risk factors for coronavirus disease 2019 (COVID‐19)‐related complications. Collecting timely occupational data can help inform public health guidance for transit workers; however, it is difficult to collect during a public health emergency. We used nontraditional epidemiological surveillance methods to report demographics and job characteristics of transit workers reported to have died from COVID‐19. Methods We abstracted demographic and job characteristics from media scans on COVID‐19 related deaths and reviewed COVID‐19 memorial pages for the Amalgamated Transit Union (ATU) and Transport Workers Union (TWU). ATU and TWU provided a list of union members who died from COVID‐19 between March 1–July 7, 2020 and a total count of NYC metro area union members. Peer‐reviewed publications identified through a scientific literature search were used to compile comparison demographic statistics of NYC metro area transit workers. We analyzed and reported characteristics of ATU and TWU NYC metro area decedents. Results We identified 118 ATU and TWU NYC metro area transit worker COVID‐19 decedents with an incidence proportion of 0.3%. Most decedents were male (83%); median age was 58 years (range: 39–71). Median professional tenure was 20 years (range: 2–41 years). Operator (46%) was the most reported job classification. More than half of the decedents (57%) worked in positions associated with close public contact. Conclusion Data gathered through nontraditional epidemiological surveillance methods provided insight into risk factors among this workforce, demonstrating the need for mitigation plans for this workforce and informing transit worker COVID‐19 guidance as the pandemic progressed.

Workplace violence during the COVID-19 pandemic: March–October, 2020, United States

“…This study demonstrated that media scraping may be useful for identifying occupational safety and health outcomes outside of fatality counts ( Kelly-Reif et al, 2021 , Tomasi et al, 2021 ). Using a combination of multiple search engines, case identification was maximized and represented a strength of the study.…”

“…Nearly a quarter of the WVEs involved a customer voluntarily coughing or spitting on a worker while threatening infection from SARS-CoV-2. Media reports have previously been used to surveil for COVID-19 fatalities among various occupations, but the current study suggests these methods may also be useful for other occupational safety and health outcomes, such as workplace violence ( Kelly-Reif et al, 2021 , Tomasi et al, 2021 ). In addition, the Kelly-Reif et al study found the sensitivity of media scans to detect first responder COVID-19 fatalities was high at 88% ( Kelly-Reif et al, 2021 ).…”

“…Media reports can be used to augment traditional surveillance methods to provide more timely information ( Chretien et al, 2009 , Brownstein et al, 2009 ). During the COVID-19 pandemic, digital disease detection methods have been used to increase our knowledge of COVID-19’s impact on several industries and occupations ( Kelly-Reif et al, 2021 , Tomasi et al, 2021 ). The current study used similar methods to collect timely data from diverse web-based sources on COVID-19-related WVEs occurring in the early phases of the pandemic in the United States.…”

Proportionate mortality and national rate of death from COVID-19 among US law enforcement officers: 2020

“…, 2021). Also, data gathered through nontraditional epidemiological surveillance such as NLEOMF database may help to provide insight into risk factors and potential mitigation strategies among occupations (Tomasi et al. , 2021).…”

“…Routine collection of data on occupation would help to improve understanding of COVID-19 deaths in first responders (Kelly-Rief et al, 2021). Also, data gathered through nontraditional epidemiological surveillance such as NLEOMF database may help to provide insight into risk factors and potential mitigation strategies among occupations (Tomasi et al, 2021).…”