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Sjörs Dahlman A, Anund A. Seroprevalence of SARS-CoV-2 antibodies among public transport workers in Sweden. JOURNAL OF TRANSPORT & HEALTH 2022; 27:101508. [PMID: 36188635 PMCID: PMC9515328 DOI: 10.1016/j.jth.2022.101508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 09/19/2022] [Accepted: 09/20/2022] [Indexed: 06/16/2023]
Abstract
INTRODUCTION Public transportation is an essential societal function in crisis situations like the coronavirus disease 2019 (COVID-19) pandemic. Bus drivers and other public transport workers are essential workers that need to keep working despite the risk of contagion. The SARS-CoV-2 virus may pose an occupational health risk to public transport workers and especially to bus drivers as they interact with passengers in a confined area. By analyzing antibodies towards SARS-CoV-2 proteins in blood samples it is possible to measure if an individual has been infected by COVID-19. Here, we report the prevalence of antibodies among bus drivers and other public transport employees in Stockholm, Sweden and relate it to socio-demographic factors. METHODS Seroprevalence of IgG antibodies towards SARS-CoV-2 proteins was investigated in a sample of 262 non-vaccinated public transport workers (182 men and 40 women) recruited between April 26 and May 7, 2021. Most of the participants were bus drivers (n = 222). The relationship between socio-demographic factors and seroprevalence was investigated with logistic regression. RESULTS The seroprevalence was 50% in the total sample of public transport workers. Among bus drivers, 51% were seropositive compared to 44% seropositive among the other public transport workers. The difference was not significant. The seroprevalence was higher than the national seroprevalence in Sweden during the same period (18.3% in non-vaccinated people aged 20-64 years). The logistic regression model using Wald forward selection showed that men had a higher risk of being seropositive (OR 2.7, 95% CI 1.3 - 5.8) and there was a higher risk with increasing number of people in the household (OR 1.3, 95% CI 1.1 - 1.6). CONCLUSIONS These findings could imply an occupational risk for COVID-19 infection among public transport workers. Infection control measures are warranted during virus epidemics to assure bus drives' safety and reduce transmission in public transport.
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Affiliation(s)
- Anna Sjörs Dahlman
- The Swedish National Road and Transport Research Institute (VTI), Linköping, Sweden
- Department of Electrical Engineering and SAFER Vehicle and Traffic Safety Centre at Chalmers University of Technology, Gothenburg, Sweden
| | - Anna Anund
- The Swedish National Road and Transport Research Institute (VTI), Linköping, Sweden
- Rehabilitation Medicine, Linköping University, Linköping, Sweden and Stockholm University, Stockholm Stress Centre, Stockholm, Sweden
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Wang J, Ma T, Ding S, Xu K, Zhang M, Zhang Z, Dai Q, Tao S, Wang H, Cheng X, He M, Du X, Feng Z, Yang H, Wang R, Xie C, Xu Y, Liu L, Chen X, Li C, Wu W, Ye S, Yang S, Fan H, Zhou N, Ding J. Dynamic characteristics of a COVID-19 outbreak in Nanjing, Jiangsu province, China. Front Public Health 2022; 10:933075. [PMID: 36483256 PMCID: PMC9723226 DOI: 10.3389/fpubh.2022.933075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Accepted: 09/21/2022] [Indexed: 12/13/2022] Open
Abstract
Objectives Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) lineage B.1.617.2 (also named the Delta variant) was declared as a variant of concern by the World Health Organization (WHO). This study aimed to describe the outbreak that occurred in Nanjing city triggered by the Delta variant through the epidemiological parameters and to understand the evolving epidemiology of the Delta variant. Methods We collected the data of all COVID-19 cases during the outbreak from 20 July 2021 to 24 August 2021 and estimated the distribution of serial interval, basic and time-dependent reproduction numbers (R0 and Rt), and household secondary attack rate (SAR). We also analyzed the cycle threshold (Ct) values of infections. Results A total of 235 cases have been confirmed. The mean value of serial interval was estimated to be 4.79 days with the Weibull distribution. The R0 was 3.73 [95% confidence interval (CI), 2.66-5.15] as estimated by the exponential growth (EG) method. The Rt decreased from 4.36 on 20 July 2021 to below 1 on 1 August 2021 as estimated by the Bayesian approach. We estimated the household SAR as 27.35% (95% CI, 22.04-33.39%), and the median Ct value of open reading frame 1ab (ORF1ab) genes and nucleocapsid protein (N) genes as 25.25 [interquartile range (IQR), 20.53-29.50] and 23.85 (IQR, 18.70-28.70), respectively. Conclusions The Delta variant is more aggressive and transmissible than the original virus types, so continuous non-pharmaceutical interventions are still needed.
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Affiliation(s)
- Junjun Wang
- Nanjing Municipal Center for Disease Control and Prevention, Nanjing, China,Chinese Field Epidemiology Training Program, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Tao Ma
- Department of Acute Infectious Diseases Control and Prevention, Nanjing Municipal Center for Disease Control and Prevention, Nanjing, China
| | - Songning Ding
- Department of Acute Infectious Diseases Control and Prevention, Nanjing Municipal Center for Disease Control and Prevention, Nanjing, China
| | - Ke Xu
- Department of Acute Infectious Diseases Control and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China
| | - Min Zhang
- Nanjing Municipal Center for Disease Control and Prevention, Nanjing, China
| | - Zhong Zhang
- Nanjing Municipal Center for Disease Control and Prevention, Nanjing, China
| | - Qigang Dai
- Department of Acute Infectious Diseases Control and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China
| | - Shilong Tao
- Jiangning District Center for Disease Control and Prevention, Nanjing, China
| | - Hengxue Wang
- Nanjing Municipal Center for Disease Control and Prevention, Nanjing, China
| | - Xiaoqing Cheng
- Chinese Field Epidemiology Training Program, Chinese Center for Disease Control and Prevention, Beijing, China,Department of Acute Infectious Diseases Control and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China
| | - Min He
- Nanjing Municipal Center for Disease Control and Prevention, Nanjing, China
| | - Xuefei Du
- Nanjing Municipal Center for Disease Control and Prevention, Nanjing, China
| | - Zhi Feng
- Jiangning District Center for Disease Control and Prevention, Nanjing, China
| | - Huafeng Yang
- Nanjing Municipal Center for Disease Control and Prevention, Nanjing, China
| | - Rong Wang
- Nanjing Municipal Center for Disease Control and Prevention, Nanjing, China
| | - Chaoyong Xie
- Nanjing Municipal Center for Disease Control and Prevention, Nanjing, China
| | - Yuanyuan Xu
- Nanjing Municipal Center for Disease Control and Prevention, Nanjing, China
| | - Li Liu
- Nanjing Municipal Center for Disease Control and Prevention, Nanjing, China
| | - Xupeng Chen
- Nanjing Municipal Center for Disease Control and Prevention, Nanjing, China
| | - Chen Li
- Nanjing Municipal Center for Disease Control and Prevention, Nanjing, China
| | - Wen Wu
- Nanjing Municipal Center for Disease Control and Prevention, Nanjing, China
| | - Sheng Ye
- Nanjing Municipal Center for Disease Control and Prevention, Nanjing, China
| | - Sheng Yang
- Nanjing Municipal Center for Disease Control and Prevention, Nanjing, China
| | - Huafeng Fan
- Nanjing Municipal Center for Disease Control and Prevention, Nanjing, China
| | - Nan Zhou
- Nanjing Municipal Center for Disease Control and Prevention, Nanjing, China,*Correspondence: Jie Ding
| | - Jie Ding
- Nanjing Municipal Center for Disease Control and Prevention, Nanjing, China,Nan Zhou
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