SARS-CoV-2 Seroprevalence in Those Utilizing Public Transportation or Working in the Transportation Industry: A Rapid Review
Abstract
:1. Introduction
2. Materials and Methods
3. Results
4. Review
4.1. Transportation Industry
4.1.1. Very High HDI Countries
4.1.2. High HDI Countries
4.1.3. Medium HDI Countries
4.1.4. Low HDI Countries
4.1.5. Transportation Industry Trends
4.2. Healthcare Workers
4.2.1. Very High HDI Countries
4.2.2. High HDI Countries
4.2.3. Medium HDI Countries
4.2.4. Healthcare Worker Trends
4.3. Population-Based Studies
4.3.1. Very High HDI Countries
4.3.2. High HDI Countries
4.3.3. Medium HDI Countries
4.3.4. Population-Based Study Trends
4.4. Race and Ethnicity
4.5. Factors Associated with Seropositivity
5. Study Limitations
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
References
- WHO Coronavirus (COVID-19) Dashboard. Available online: https://covid19.who.int/ (accessed on 7 July 2022).
- Turcotte, M.; Savage, K. Commuting to Work during COVID-19. Available online: https://www150.statcan.gc.ca/n1/pub/45-28-0001/2020001/article/00069-eng.htm (accessed on 9 February 2021).
- Sy, K.T.L.; Martinez, M.E.; Rader, B.; White, L.F. Socioeconomic Disparities in Subway Use and COVID-19 Outcomes in New York City. Am. J. Epidemiol. 2021, 190, 1234–1242. [Google Scholar] [CrossRef] [PubMed]
- Brough, R.; Freedman, M.; Phillips, D.C. Understanding Socioeconomic Disparities in Travel Behavior during the COVID-19 Pandemic. J. Reg. Sci. 2021, 61, 753–774. [Google Scholar] [CrossRef] [PubMed]
- Rostami, A.; Sepidarkish, M.; Leeflang, M.M.; Riahi, S.M.; Shiadeh, M.N.; Esfandyari, S.; Mokdad, A.H.; Hotez, P.J.; Gasser, R.B. SARS-CoV-2 Seroprevalence Worldwide: A Systematic Review and Meta-Analysis. Clin. Microbiol. Infect. 2021, 27, 331–340. [Google Scholar] [CrossRef] [PubMed]
- Galanis, P.; Vraka, I.; Fragkou, D.; Bilali, A.; Kaitelidou, D. Seroprevalence of SARS-CoV-2 Antibodies and Associated Factors in Healthcare Workers: A Systematic Review and Meta-Analysis. J. Hosp. Infect. 2021, 108, 120–134. [Google Scholar] [CrossRef] [PubMed]
- Chen, X.; Chen, Z.; Azman, A.S.; Deng, X.; Sun, R.; Zhao, Z.; Zheng, N.; Chen, X.; Lu, W.; Zhuang, T. Serological Evidence of Human Infection with SARS-CoV-2: A Systematic Review and Meta-Analysis. Lancet Glob. Health 2021, 9, e598–e609. [Google Scholar] [CrossRef]
- Rostami, A.; Sepidarkish, M.; Fazlzadeh, A.; Mokdad, A.H.; Sattarnezhad, A.; Esfandyari, S.; Riahi, S.M.; Mollalo, A.; Dooki, M.E.; Bayani, M.; et al. Update on SARS-CoV-2 Seroprevalence: Regional and Worldwide. Clin. Microbiol. Infect. Off. Publ. Eur. Soc. Clin. Microbiol. Infect. Dis. 2021, 27, 1762–1771. [Google Scholar] [CrossRef]
- Palmateer, N.E.; Dickson, E.; Furrie, E.; Godber, I.; Goldberg, D.J.; Gousias, P.; Jarvis, L.; Mathie, L.; Mavin, S.; McMenamin, J. National Population Prevalence of Antibodies to SARS-CoV-2 in Scotland during the First and Second Waves of the COVID-19 Pandemic. Public Health 2021, 198, 102–105. [Google Scholar] [CrossRef]
- Paduano, S.; Galante, P.; Berselli, N.; Ugolotti, L.; Modenese, A.; Poggi, A.; Malavolti, M.; Turchi, S.; Marchesi, I.; Vivoli, R. Seroprevalence Survey of Anti-SARS-CoV-2 Antibodies in a Population of Emilia-Romagna Region, Northern Italy. Int. J. Environ. Res. Public. Health 2022, 19, 7882. [Google Scholar] [CrossRef]
- Kislaya, I.; Gonçalves, P.; Gómez, V.; Gaio, V.; Roquette, R.; Barreto, M.; Sousa-Uva, M.; Torres, A.R.; Santos, J.; Matos, R. SARS-CoV-2 Seroprevalence in Portugal Following the Third Epidemic Wave: Results of the Second National Serological Survey (ISN2COVID-19). Infect. Dis. 2022, 54, 418–424. [Google Scholar] [CrossRef]
- Lai, C.-C.; Wang, J.-H.; Hsueh, P.-R. Population-Based Seroprevalence Surveys of Anti-SARS-CoV-2 Antibody: An up-to-Date Review. Int. J. Infect. Dis. 2020, 101, 314–322. [Google Scholar] [CrossRef]
- Human Development Reports. Human Development Index (HDI). Available online: https://hdr.undp.org/data-center/human-development-index?utm_source=EN&utm_medium=GSR&utm_content=US_UNDP_PaidSearch_Brand_English&utm_campaign=CENTRAL&c_src=CENTRAL&c_src2=GSR&gclid=CjwKCAjw6MKXBhA5EiwANWLODGdWKBmqNIDKXTIr8ZN82ThQa5vaG_eYYrE5-UIEmgsSO9r1Go8yjhoCWrkQAvD_BwE#/indicies/HDI (accessed on 2 August 2022).
- Pathela, P.; Crawley, A.; Weiss, D.; Maldin, B.; Cornell, J.; Purdin, J.; Schumacher, P.K.; Marovich, S.; Li, J.; Daskalakis, D. Seroprevalence of SARS-CoV-2 Following the Largest Initial Epidemic Wave in the United States: Findings from New York City, 13 May–21 July 2020. J. Infect. Dis. 2021, jiab200. [Google Scholar]
- Feehan, A.K.; Velasco, C.; Fort, D.; Burton, J.H.; Price-Haywood, E.G.; Katzmarzyk, P.T.; Garcia-Diaz, J.; Seoane, L. Racial and Workplace Disparities in Seroprevalence of SARS-CoV-2, Baton Rouge, Louisiana, USA. Emerg. Infect. Dis. 2021, 27, 314. [Google Scholar] [CrossRef] [PubMed]
- Pollán, M.; Pérez-Gómez, B.; Pastor-Barriuso, R.; Oteo, J.; Hernán, M.A.; Pérez-Olmeda, M.; Sanmartín, J.L.; Fernández-García, A.; Cruz, I.; de Larrea, N.F. Prevalence of SARS-CoV-2 in Spain (ENE-COVID): A Nationwide, Population-Based Seroepidemiological Study. Lancet 2020, 396, 535–544. [Google Scholar] [CrossRef]
- Airoldi, C.; Calcagno, A.; Di Perri, G.; Valinotto, R.; Gallo, L.; Locana, E.; Trunfio, M.; Patrucco, F.; Vineis, P.; Faggiano, F. Seroprevalence of SARS-CoV-2 among Workers in Northern Italy. Ann. Work Expo. Health 2022, 66, 224–232. [Google Scholar] [CrossRef] [PubMed]
- Berselli, N.; Filippini, T.; Paduano, S.; Malavolti, M.; Modenese, A.; Gobba, F.; Borella, P.; Marchesi, I.; Vivoli, R.; Perlini, P. Seroprevalence of Anti-SARS-CoV-2 Antibodies in the Northern Italy Population before the COVID-19 Second Wave. Int. J. Occup. Med. Environ. Health 2022, 35, 63–74. [Google Scholar] [CrossRef] [PubMed]
- Alsuwaidi, A.R.; Al Hosani, F.I.; Al Memari, S.; Narchi, H.; Abdel Wareth, L.; Kamal, H.; Al Ketbi, M.; Al Baloushi, D.; Elfateh, A.; Khudair, A. Seroprevalence of COVID-19 Infection in the Emirate of Abu Dhabi, United Arab Emirates: A Population-Based Cross-Sectional Study. Int. J. Epidemiol. 2021, 50, 1077–1090. [Google Scholar] [CrossRef]
- Poustchi, H.; Darvishian, M.; Mohammadi, Z.; Shayanrad, A.; Delavari, A.; Bahadorimonfared, A.; Eslami, S.; Javanmard, S.H.; Shakiba, E.; Somi, M.H. SARS-CoV-2 Antibody Seroprevalence in the General Population and High-Risk Occupational Groups across 18 Cities in Iran: A Population-Based Cross-Sectional Study. Lancet Infect. Dis. 2021, 21, 473–481. [Google Scholar] [CrossRef]
- Colmenares-Mejía, C.C.; Serrano-Díaz, N.; Quintero-Lesmes, D.C.; Meneses, L.; Salazar Acosta, I.; Idrovo, Á.J.; Sanabria-Echeverry, D.Y.; Cordero-Rebolledo, H.; Castillo, V. Seroprevalence of SARS-CoV-2 Infection among Occupational Groups from the Bucaramanga Metropolitan Area, Colombia. Int. J. Environ. Res. Public. Health 2021, 18, 4172. [Google Scholar] [CrossRef]
- Babu, G.R.; Sundaresan, R.; Athreya, S.; Akhtar, J.; Pandey, P.K.; Maroor, P.S.; Padma, M.R.; Lalitha, R.; Shariff, M.; Krishnappa, L. The Burden of Active Infection and Anti-SARS-CoV-2 IgG Antibodies in the General Population: Results from a Statewide Sentinel-Based Population Survey in Karnataka, India. Int. J. Infect. Dis. 2021, 108, 27–36. [Google Scholar] [CrossRef]
- Halatoko, W.A.; Konu, Y.R.; Gbeasor-Komlanvi, F.A.; Sadio, A.J.; Tchankoni, M.K.; Komlanvi, K.S.; Salou, M.; Dorkenoo, A.M.; Maman, I.; Agbobli, A. Prevalence of SARS-CoV-2 among High-Risk Populations in Lomé (Togo) in 2020. PLoS ONE 2020, 15, e0242124. [Google Scholar] [CrossRef]
- Meylan, S.; Dafni, U.; Lamoth, F.; Tsourti, Z.; Lobritz, M.A.; Regina, J.; Bressin, P.; Senn, L.; Grandbastien, B.; Andre, C. SARS-CoV-2 Seroprevalence in Healthcare Workers of a Swiss Tertiary Care Centre at the End of the First Wave: A Cross-Sectional Study. BMJ Open 2021, 11, e049232. [Google Scholar] [CrossRef] [PubMed]
- Soffin, E.M.; Reisener, M.-J.; Padgett, D.E.; Kelly, B.T.; Sama, A.A.; Zhu, J.; Salzmann, S.N.; Chiapparelli, E.; Okano, I.; Oezel, L. Coronavirus Disease 2019 Exposure in Surgeons and Anesthesiologists at a New York City Specialty Hospital: A Cross-Sectional Study of Symptoms and SARS-CoV-2 Antibody Status. J. Occup. Environ. Med. 2021, 63, 521. [Google Scholar] [CrossRef] [PubMed]
- Venugopal, U.; Jilani, N.; Rabah, S.; Shariff, M.A.; Jawed, M.; Batres, A.M.; Abubacker, M.; Menon, S.; Pillai, A.; Shabarek, N. SARS-CoV-2 Seroprevalence among Health Care Workers in a New York City Hospital: A Cross-Sectional Analysis during the COVID-19 Pandemic. Int. J. Infect. Dis. 2021, 102, 63–69. [Google Scholar] [CrossRef] [PubMed]
- Public Health Guidance for Community-Related Exposure. Available online: https://www.cdc.gov/coronavirus/2019ncov/php/public-health-recommendations (accessed on 22 December 2020).
- Yamamoto, S.; Tanaka, A.; Oshiro, Y.; Ishii, M.; Ishiwari, H.; Konishi, M.; Matsuda, K.; Ozeki, M.; Miyo, K.; Maeda, K. Seroprevalence of SARS-CoV-2 Antibodies in a National Hospital and Affiliated Facility after the Second Epidemic Wave of Japan. J. Infect. 2021, 83, 237–279. [Google Scholar] [CrossRef] [PubMed]
- Nishida, T.; Iwahashi, H.; Yamauchi, K.; Kinoshita, N.; Okauchi, Y.; Suzuki, N.; Inada, M.; Abe, K. Seroprevalence of SARS-CoV-2 Antibodies among 925 Staff Members in an Urban Hospital Accepting COVID-19 Patients in Osaka Prefecture, Japan: A Cross-Sectional Study. Medicine 2021, 100, e26433. [Google Scholar] [CrossRef] [PubMed]
- Cruz-Arenas, E.; Cabrera-Ruiz, E.; Laguna-Barcenas, S.; Colin-Castro, C.A.; Chavez, T.; Franco-Cendejas, R.; Ibarra, C.; Perez-Orive, J. Serological Prevalence of SARS-CoV-2 Infection and Associated Factors in Healthcare Workers in a “Non-COVID” Hospital in Mexico City. PLoS ONE 2021, 16, e0255916. [Google Scholar] [CrossRef]
- de Oliveira, M.S.; Lobo, R.D.; Detta, F.P.; Vieira-Junior, J.M.; de Souza Castro, T.L.; Zambelli, D.B.; Cardoso, L.F.; Borges, I.C.; Tozetto-Mendoza, T.R.; Costa, S.F. SARS-CoV-2 Seroprevalence and Risk Factors among Health Care Workers: Estimating the Risk of COVID-19 Dedicated Units. Am. J. Infect. Control 2021, 49, 1197–1199. [Google Scholar] [CrossRef]
- Gupta, R.; Dwivedi, T.; Gajendra, S.; Sahoo, B.; Gupta, S.K.; Vikas, H.; Singh, A.R.; Mohan, A.; Bhatnagar, S.; Singh, S. Seroprevalence of Antibodies to SARS-CoV-2 in Healthcare Workers & Implications of Infection Control Practice in India. Indian J. Med. Res. 2021, 153, 207. [Google Scholar]
- Chan, P.A.; King, E.; Xu, Y.; Goedel, W.; Lasher, L.; Vargas, M.; Brindamour, K.; Huard, R.; Clyne, A.; McDonald, J. Seroprevalence of SARS-CoV-2 Antibodies in Rhode Island from a Statewide Random Sample. Am. J. Public Health 2021, 111, 700–703. [Google Scholar] [CrossRef]
- Mahajan, S.; Srinivasan, R.; Redlich, C.A.; Huston, S.K.; Anastasio, K.M.; Cashman, L.; Massey, D.S.; Dugan, A.; Witters, D.; Marlar, J.; et al. Seroprevalence of SARS-CoV-2-Specific IgG Antibodies Among Adults Living in Connecticut: Post-Infection Prevalence (PIP) Study. Am. J. Med. 2021, 134, 526–534.e11. [Google Scholar] [CrossRef]
- Acurio-Páez, D.; Vega, B.; Orellana, D.; Charry, R.; Gómez, A.; Obimpeh, M.; Verhoeven, V.; Colebunders, R. Seroprevalence of SARS-CoV-2 Infection and Adherence to Preventive Measures in Cuenca, Ecuador, October 2020, a Cross-Sectional Study. Int. J. Environ. Res. Public. Health 2021, 18, 4657. [Google Scholar] [CrossRef] [PubMed]
- Naushin, S.; Sardana, V.; Ujjainiya, R.; Bhatheja, N.; Kutum, R.; Bhaskar, A.K.; Pradhan, S.; Prakash, S.; Khan, R.; Rawat, B.S.; et al. Insights from a Pan India Sero-Epidemiological Survey (Phenome-India Cohort) for SARS-CoV-2. eLife 2021, 10, e66537. [Google Scholar] [CrossRef] [PubMed]
- Millett, G.A.; Jones, A.T.; Benkeser, D.; Baral, S.; Mercer, L.; Beyrer, C.; Honermann, B.; Lankiewicz, E.; Mena, L.; Crowley, J.S.; et al. Assessing Differential Impacts of COVID-19 on Black Communities. Ann. Epidemiol. 2020, 47, 37–44. [Google Scholar] [CrossRef]
- Wilbur, M.; Ayman, A.; Ouyang, A.; Poon, V.; Kabir, R.; Vadali, A.; Pugliese, P.; Freudberg, D.; Laszka, A.; Dubey, A. Impact of COVID-19 on Public Transit Accessibility and Ridership. arXiv 2020, arXiv:200802413. [Google Scholar]
- Bobrovitz, N.; Arora, R.K.; Cao, C.; Boucher, E.; Liu, M.; Donnici, C.; Cheng, M.P. Global seroprevalence of SARS-CoV-2 antibodies: A systematic review and meta-analysis. PLoS ONE 2021, 16, e0252617. [Google Scholar] [CrossRef] [PubMed]
- Narula, R. Policy Opportunities and Challenges from the COVID-19 Pandemic for Economies with Large Informal Sectors. J. Int. Bus. Policy 2020, 3, 302–310. [Google Scholar] [CrossRef]
- Assefa, Y.; Gilks, C.F.; Reid, S.; van de Pas, R.; Gete, D.G.; Van Damme, W. Analysis of the COVID-19 pandemic: Lessons towards a more effective response to public health emergencies. Glob. Health 2022, 18, 10. [Google Scholar] [CrossRef]
- Verity, R.; Okell, L.C.; Dorigatti, I.; Winskill, P.; Whittaker, C.; Imai, N.; Cuomo-Dannenburg, G.; Thompson, H.; Walker, P.G.; Fu, H.; et al. Estimates of the severity of coronavirus disease 2019: A model-based analysis. Lancet Infect. Dis. 2020, 20, 669–677. [Google Scholar] [CrossRef]
- den Hartog, G.; Schepp, R.M.; Kuijer, M.; GeurtsvanKessel, C.; van Beek, J.; Rots, N.; Koopmans, M.P.; van der Klis, F.R.; van Binnendijk, R.S. SARS-CoV-2–Specific Antibody Detection for Seroepidemiology: A Multiplex Analysis Approach Accounting for Accurate Seroprevalence. J. Infect. Dis. 2020, 222, 1452–1461. [Google Scholar] [CrossRef]
- Scozzari, G.; Costa, C.; Migliore, E.; Coggiola, M.; Ciccone, G.; Savio, L.; Scarmozzino, A.; Pira, E.; Cassoni, P.; Galassi, C. Prevalence, Persistence, and Factors Associated with SARS-CoV-2 IgG Seropositivity in a Large Cohort of Healthcare Workers in a Tertiary Care University Hospital in Northern Italy. Viruses 2021, 13, 1064. [Google Scholar] [CrossRef]
- Khan, S.; Nakajima, R.; Jain, A.; De Assis, R.R.; Jasinskas, A.; Obiero, J.M.; Adenaiye, O.; Tai, S.; Hong, F.; Milton, D.K. Analysis of Serologic Cross-Reactivity between Common Human Coronaviruses and SARS-CoV-2 Using Coronavirus Antigen Microarray. BioRxiv 2020. [Google Scholar] [CrossRef]
- Li, Q.; Deng, H.-J.; Yuan, J.; Hu, J.-L.; Xu, W.; Zhang, Y.; Lv, F.-J. Clinical and Immunological Assessment of Asymptomatic SARS-CoV-2 Infections. Nat. Med. 2020, 26, 1200–1204. [Google Scholar]
- Galipeau, Y.; Siragam, V.; Laroche, G.; Marion, E.; Greig, M.; McGuinty, M.; Booth, R.A.; Durocher, Y.; Cuperlovic-Culf, M.; Bennett, S.A. Relative Ratios of Human Seasonal Coronavirus Antibodies Predict the Efficiency of Cross-Neutralization of SARS-CoV-2 Spike Binding to ACE2. EBioMedicine 2021, 74, 103700. [Google Scholar] [CrossRef] [PubMed]
Author | Location | HDI | Date | Type of Study | Study Population | # Participants | Serology | |||
---|---|---|---|---|---|---|---|---|---|---|
Assay | Target | Sensitivity (%) * | Specificity (%) * | |||||||
Meylan | Lausanne, Switzerland | 0.955 | 18 May–12 June 2020 | Cross-sectional | Centre Hospitalier Universitaire Vaudois and Centre for Primary Care and Public Health staff | 1874 | Luminex-based assay (IgG) | S-protein | 97 | 98 |
Pathela | New York City, USA | 0.926 | 13 May–21 July 2020 | Cross-sectional | NYC adult resident; occupation subgroups | 45,367 | Liaison SARS-CoV-2 S1/S2 | S1/S2 subunits of S protein | 97.6 | 99.3 |
Soffin | New York City, USA | 0.926 | 6 May–5 June 2020 | Cross-sectional | Surgeons and Anaesthesiologists at Hospital for Special Surgery | 143 | Abbott Architect SARS-CoV-2 IgG | Nucleocapsid | 94–100 a | 99.4–100 a |
Venugopal | New York City, USA | 0.926 | May 2020 | Cross sectional | Frontline HCWs of NYC hospitals | 500 | Abbott Architect IgG Assay | Nucleocapsid | 100 (95% CI 95.8–100%) | 99.6 (95% CI: 99–99.99%) |
Feehan | Baton Rouge, USA | 0.926 | 15–31 July 2020 | Cross sectional | Representative sample of residents | 2138 | Abbott Architect i2000SR IgG Assay | Not specified | Not specified | Not specified |
Chan | Rhode Island, USA | 0.926 | 5–22 May 2020 | Cross-sectional | Households, oversampled African Americans/Blacks and Hispanics/Latinos | 1043 | Not specified | Not specified | Not specified | Not specified |
Mahajan | Connecticut, USA | 0.926 | 4 June–29 July 2020 | Adults living in non- congregate settings (exclude those living in LTC homes, nursing homes, prisons); also oversampled non-Hispanic black and Hispanic individuals | 567 | Ortho-Clinical Diagnostics Vitros anti-SARS-CoV-2 IgG (some negative samples retested with Abbott Architect IgG—targeting nucleocapsid protein) | S-protein | 90 | 100 | |
Yamamoto | Toyama and Kohnoda, Japan | 0.919 | October–December 2020 | Repeated cross-sectional | National Center for Global Health and Medicine employees | 2563 | Abbott Architect (IgG); Roche Elecsys (total antibodies), confirmatory analysis of positive results using EUROIMMUN anti-S IgG immunoassay | Nucleocapsid protein | Not specified | Not specified |
Nishida | Osaka Prefecture, Japan | 0.919 | 12–19 June 2020 | Cross-sectional | Toyonaka Municipal Hospital employees | 925 | Abbott Architect SARS-CoV-2 IgG Assay | Nucleocapsid | 100 | 99.6 |
Pollan | Spain | 0.904 | 27 April–11 May 2020 | Population-based cohort study | Spanish population | 66,805 | Orient Gene Biotech COVID-19 IgG/IgM Rapid Test Cassette (Point-of-Care Test); | RBD of S protein | IgG: 97.2; IgM: 87.9 | 100 |
Abbott Architect IgG assay | Nucleoprotein | 100 a | 99.6 | |||||||
Airoldi | Piedmont region, Northwest Italy | 0.892 | 28 April–7 August 2020 | Cross-sectional | Company workers through screening program | 23,568 | ZEUS ELISA SARS-CoV-2 IgG Test system | Not specified | 93.3 (95% CI: 78.7–98.2) | 100 (95% CI: 94.8–100) |
Berselli | Emilia Romagna region, Northern Italy | 0.892 | 1 June–25 September 2020 | Cross-sectional | Company workers, self-referred individuals | 7561 | EUROIMMUNE ELISA anti-SARS-CoV-2 test for IgA and IgG | Not specified | 100 c | 92.5 |
Roche Elecsys | Not specified | 100 a | 99.8 | |||||||
KHB SARS-CoV-2 IgM/IgG antibody Colloidal Gold | Not specified | 98.81 | 98.02 | |||||||
Alsuwaldi | Abu, Dhabi, United Arab Emirates | 0.890 | July 19–August 14 2020 | Cross-sectional | Households in region; labour camps | 8831 (households); 4855 (labour camp worker) | Roche Elecsys Anti-SARS-CoV-2 | Nucleocapsid | 100 (95% CI: 88.1–100) a | 99.8 (95% CI: 99.6–99.1) |
LIAISON SARS-CoV-2 S1/S2 IgG Assay | S1 and S2 subunits of S protein | 97.4 (95% CI: 86.6–99.5) a | 98·5 (95% CI: 97·6–99·1) | |||||||
Poustchi | 18 Iranian Cities | 0.783 | 17 April –2 June 2020 | Cross sectional | General population; high-risk occupations | 8902 | Pishtaz Teb SARS-CoV-2 ELISA IgG and IGM | Not specified | IgG: 94.1; IgM: 79.4 | IgG: 98.3; IgM: 97.3 |
Cruz-Arenas | Mexico City, Mexico | 0.779 | 10 August–9 September 2020 | Cross-sectional | Instituto Nacional de Rehabilitación employees | 300 | LFA: IgG/IgM Rapid Test Cassette; | Not specified | 79.5 | 100 |
ELISA: Euroimmun Anti-SARS-CoV-2 NCP IgG Assay | Nucleocapsid protein | Not specified | Not specified | |||||||
Colmenares-Mejía | Bucaramanga, Colombia | 0.767 | 28 September–24 December 2020 | Cross-sectional | Workers from health, construction, public transportation, public force (army, police, transit officers), bike delivery messengers, independent or informal commercial (shopkeepers) | 7045 | Abbot ARC COV2 (IgG and IgM) | Not specified | 85.2 | 97.3 |
De Oliveira | São Paulo, Brazil | 0.765 | March–July 2020 | Cross-sectional | Sírio-Libanês Hospital employees | 1996 | ELISA (IgG), unspecified | Nucleocapsid | 86–95 a | 100 a |
Acurio-Paez | Cuenca, Ecuador | 0.759 | 11 August–1 November 2020 | Cross sectional | Randomly selected inhabitants of Cuenca, Ecuador | 2457 | SD BIOSNSOR Standard Q COVID-19 IgG/IgM Plus | Not specified | 94.3 b | 87.9 b |
Babu | Karnataka, India | 0.645 | 3–16 September 2020 | Cross-sectional | Statewide population; risk subgroups | 16,416 | COVID Kavach Anti SARS-CoV-2 IgG antibody detection ELISA | Not specified | 92.1 | 97.7 |
Gupta | New Delhi, India | 0.645 | 22 June–24 July 2020 | Cross-sectional | HCW—All India Institute of Medical Sciences Staff | 3739 | ADVIA Centaur COV2T chemiluminescence IgG and IgM immunoassay | S-protein RBD | 100 a | 99.8 a |
Naushin | India | 0.645 | August–September 2020 | Longitudinal, Cohort | Phenome-India Cohort | 10,427 | Roche Elecsys Anti-SARS-CoV-2; positive samples tested using GENScript cPass SARS-CoV-2 Neutralization Antibody Detection Kit | Nucleocapsid; S-protein | Undefined | Undefined |
Halatoko | Lome, Togo | 0.515 | 23 April 2020–8 May 2020 | Cross sectional | Occupational sectors: health care, air transport, police, road transport, informal (market sellers, craftsmen) | 955 | Lungene Rapid Test (IgG and IgM) | Not specified | 72.9 | 85.0 |
Author | HDI Category | Outcome | Overall Seroprevalence (%) | Transit Outcomes | Variables Associated with Seropositivity | Conclusion | |
---|---|---|---|---|---|---|---|
Seroprevalence (%) | Regression Analysis (i.e., OR, RR) | ||||||
Pathela | Very high | Seroprevalence (%), Poisson regression (RR; 95% CI) | 23.6% (95% CI: 23.2–24) | Air transport (n = 137): 25%; Public transit, taxis and private drivers (n = 479): 35%; Other transportation and warehousing (n = 440) 27% | Essential worker (food services, construction, retail trade, transportation) compared to other industries RR: 1.63 (95% CI: 1.5–1.7); Adjusted for sex at birth, age, borough, poverty level, working outside the home RR: 1.33 (95% CI: 1.3–1.4) | Male sex, age 44–64, non-White race/ethnicity, living in a borough other than Manhattan or Staten Island, living in neighborhoods with high or very high poverty levels, employment in health care or essential worker category, not being unemployed at the time of serosurvey, working outside the home, having contact with someone with COVID-19, COVID-19 symptoms, being overweight or obese, increasing number of household members | Those working in the transportation industry more likely to have SARS-CoV-2 antibodies |
Feehan | Very high | Seroprevalence (%), census weighted bivariate analysis (OR) | 3.6% (95% CI: 2.8–4.4) | N/A | Working in the transportation industry (n = 11) compared to an office OR: 6 (95% CI: 0.1–100) | Single marital status, public-facing job compared to office, healthcare career, black non-Hispanic race/ethnicity, younger than 29 years old | Work in the transportation industry comparable to risk associated with work in an office |
Pollan | Very high | Seroprevalence (%) using two assays | POC test: 5% (95% CI: 4.7–5.4); Immunoassay: 4.6% (95% CI: 4.3–5.0) | POC test: (n = 800); 5.9% (95% CI: 3.9–8.7); Immunoassay (n = 731): 5.8% (3.6–9.2) | N/A | Province, working in healthcare, confirmed COVID-19 case in household or among non-cohabitating family members and friends or among caregivers and cleaning staff or clients, COVID-19 symptoms | Seroprevalence of those working in the transport industry comparable to overall seroprevalence; comparable between tests |
Airoldi | Very high | Seroprevalence (%) | 4.97% (95% CI: 4.69–5.25) | 4.36% (95% CI: 1.95–6.78) | N/A | Geographical location, those working in logistics or weaving factories | Seroprevalence in transportation industry workers comparable to general population |
Berselli | Very high | Seroprevalence (%) | 4.7% (95% CI: 4.2–5.2) | 1% | N/A | Seroprevalence higher in women, older age groups, HCW, dealers and vehicle repair workers, sport sector employees | No evidence of increased seroprevalence |
Alsuwaldi -Household population * | Very high | Seroprevalence (%), bivariate model, multiple logistic regression model (OR) | 10.4% (95% CI: 9.5–11.4) | 20.8% (95% CI: 15.9–26.7) | OR: 1.5 (95% CI: 0.7–3.2) adjusted for age, sex, region, education, nationality, ethnicity, occupation, contact with someone diagnosed with COVID-19 | Households: Region, education level, Asian ethnicity, not from UAE, contact with someone with COVID-19, COVID-19 symptoms | No association with transit use in multivariable analysis |
Alsuwaldi -Labour camp population * | Very high | Seroprevalence (%), bivariate model, multiple logistic regression model (OR) | 68.6% (95% CI: 61.7–74.7) | 72.1% (95% CI: 60.4–81.5) | OR: 2.7 (1.8–4.0) adjusted for age, sex, region, education, nationality, ethnicity, occupation, contact with someone diagnosed with COVID-19 | Education, non-Arabic ethnicity, occupation, contact with someone with COVID-19, COVID-19 symptoms | Transit use and high-risk occupations associate with seropositivity |
Poustchi | High | Seroprevalence (%) adjusted for population weighting and test performance | General population: 17.1% (95% CI: 14.6–19.5); High-risk population: 20% (95% CI: 18.5–21.7) | Taxi drivers (n = 718): 18.8% (95% CI: 14.7–23.2) | N/A | 60 years or older, those in contact with someone with COVID-19, region, COVID-19 symptoms | Seroprevalence similar between high-risk occupations |
Colmenares-Mejía | High | Seroprevalence (%) corrected for test performance and study design | 19.5% (95% CI: 18.6–20.4) | Commute to work: bike: 25.7% (95% CI: 16.6–34.8); public transportation: 23.9% (95% CI: 21.8–26); taxi 15.5% (95% CI: 12.3–18.7) Those working in the public transport industry: 16% (95% CI: 11.7–20.3) | N/A | Occupational groups with multiple contacts with others during work hours, delivery drivers, grocery store tenants, informal commerce workers, those that used a bike, motorcycle, public transit than own car, COVID-19 symptoms | Similar seroprevalence in those working in the transportation industry and other high-risk occupations. Higher seroprevalence in those that use public transit to commute to work compared to those that use their own vehicle |
Babu | Medium | Seroprevalence (%), generalized linear model-based multinomial regression (OR) | 16.8% (95% CI: 15.5–18.1) | Bus conductors/auto drivers (n = 1008): 16.1% (95% CI: 11.7–20.6); | Bus conductors/auto drivers compared to low-risk occupations: OR: 2.12 (95% CI: 1.3–3.5) | Diarrhoea, chest-pain, rhinorrhea, fatigue, fever, professions who had more contact with the public, residence in containment zones, urbanisation level of the district | Those working in the transportation industry twice as likely to have SARS-CoV-2 antibodies |
Halatoko | Low | Seroprevalence (%) | IgM or IgG: 0.9% (95% CI: 0.4–1.8) | Air transport (n = 212); IgM positive: 0.5% (95% CI: 0.01–2.6); IgG positive: 0.9% 95% CI: 0.1–3.4) Road Transport (n = 122) IgM positive: 0% (95% CI: 0–2.9); IgG 0.8% 95% CI: 0–4.5) | N/A | N/A | Low seroprevalence in general, similar among high-risk populations |
Author | HDI Category | Outcome | Overall Seroprevalence (%) | Transit Outcomes | Variables Associated with Seropositivity | Conclusions | |
---|---|---|---|---|---|---|---|
Seroprevalence (%) | Regression Analysis (i.e., OR, RR) | ||||||
Meylan | Very high | Seropositivity (%), multivariable logistic regression (OR) | 10% (95% CI: 8.7–11.5) | Frequency of transit use (# per week) 1 (n = 104): 7.7% (95% CI: 3.4–14.6); 2 (n = 135): 9.6% (95% CI: 5.2–15.9); 3 (n = 148): 10.1% (95% CI: 5.8–16.2); 4 (n = 199): 12.1 (95% CI: 7.9–17.4); 5 (n = 275): 14.2% (95% CI: 10.3–18.9); >5 (n = 220): 5.9% (95% CI: 3.2–9.9); Use of face mask on public transport (n = 151): 5.3% (95% CI: 2.3–10.2) Does not use face mask on public transport (n = 930): 11.2% (95% CI: 9.2–13.4) | Use of mask at public transport compared to those that do not: OR = 0.42 (95% CI: 0.198–0.896) adjusted for daily contact w patients, work in ICU, COVID-19 case at home, and COVID-19 symptoms | Household contact with confirmed COVID-19, use of mask while using public transport, COVID-19 symptoms | Seropositivity increased with transit usage; face mask while using public transit reduced odds of seropositivity |
Soffin | Very high | Seroprevalence (%), bivariate logistic regression (OR) | 9.8% | N/A | OR: 1.48 (95% CI: 0.2–6.3) | Fatigue, myalgia, fever, headache, spouse diagnosed with COVID-19 | No association with mode of commute (public transport, walking/cycling, private) |
Venugopal | Very high | Seroprevalence (%), bivariable, multivariable linear regression (OR) | 27% | 29% | Public transit compared to private OR: 1.3 (95% CI: 0.9–2.0) Adjusted for ethnicity, symptoms, duration of symptoms: OR: 0.84 (95% CI: 0.47–1.52) | Ethnicity other than Caucasian, living in an apartment/condo, walking to work, symptoms of COVID-19, community exposure | Type of transport to hospital not associated with seropositivity |
Yamamoto | Very high | Seropositivity (%), Poisson regression (PR) | 0.7% (95% CI:0.4–1.1) | N/A | Compared to those that used transit <1 time/week, those that used it 1 or more times/week prevalence ratio was 0.57 (95% CI: 0.2–1.4) | Close contact with patients with COVID-19 at home and in the community | No association with transit |
Nishida | Very high | Seropositivity (%) | IgG: 0.43% (95% CI:0.2–1.1) | 0.76% (n = 396) (95% CI: 0.3–2.2) | N/A | No significant factors | No association with transit |
Cruz-Arenas | High | Seropositivity (%), multiple logistic regression (OR) | LFA: 11% ELISA: (IgG only) 13% | N/A | Use of public transport for work commute OR: 1.62 95% CI: 0.82–3.21) | Olfactory alterations, security or janitorial occupations, education below a university degree increasing number of people in household | Type of transport to hospital not associated with seropositivity |
De Oliveira | High | Prevalence (%), bivariate analysis, multivariate logistic regression (OR) | 5.5% | N/A | Public transport (bus, metro): OR 1.17 (95% CI: 0.79–1.75) Adjusted for gender, cleaning, working at COVID-19 units, type of transport OR: 1.103 (95% CI: 0.731–1.665) | Professional category of cleaning and male gender | Type of transport to hospital not associated with seropositivity |
Gupta | Medium | Seroprevalence (%) | 13% | Public transit (n = 235): 20%; Hospital transport (n = 676): 16.9%; Own vehicle (n = 1986): 12.4%; on foot (n = 544): 11.2%; did not declare (n = 298): 6% | N/A | Contact with COVID positive individuals, COVID-19 symptoms, region of residence | Seroprevalence significantly higher in HCW that used public, or hospital transit compared to those that used other modes of commute (p < 0.05) |
Author | HDI Category | Outcome | Overall Seroprevalence (%) | Transit Outcomes | Variables Associated with Seropositivity | Conclusions | |
---|---|---|---|---|---|---|---|
Seroprevalence (%) | Regression Analysis (i.e., OR, RR) | ||||||
Chan | Very high | Seroprevalence (%), age weighted | 2.9% (95% CI: 1–6.2) | Public transportation/carpool (n = 52): 6% (95% CI: 0.1–20.5); own vehicle (n = 920): 1.9% (95% CI: 0.4–4.1); walking/biking (n = 34): 2.8% (95% CI: 0–16.7) | N/A | Those living in a condo or apartment, those that rely on public transportation or carpool, race/ethnicity other than Caucasian, primary mode of transportation | Higher seroprevalence in transit users |
Mahajan | Very high | Seroprevalence (%), weighted for non-response and population characteristics of Connecticut | General population: 4% (90% CI: 2–6); non-Hispanic black subpopulation: 6.4% (90% CI: 0.9–11.9); Hispanic subpopulation: 19.9% (90% CI: 13.2–26.6) | General population: 0% or too small to calculate Non-Hispanic Black subpopulation Airplane: 4% (±4.8); public transportation: 23.7% (±7.5) Hispanic subpopulation Airplane: 4.8% (±3.3); public transportation: 13.1% (±5.5) * | N/A | Race and ethnicity | No association with transit use in general population, seroprevalence significantly higher in transit users of ethnic minorities |
Acurio-Paez | High | Seroprevalence (%), bivariate regression, multivariate regression (OR) | Maximum: 13.2% (95% CI: 12–14.6) (IgG or IgM); Minimum: 4% (95% CI: 3.2–4.8) (IgG and IgM positive) | Foot (n = 529): 12.5% (9.6–15.5); Bicycle/moto/trolley car (n = 106): 11.3% (6.2–19.3); Private car (n = 912): 11% (9.0–13.1); Public (bus/taxi) (n = 742): 18.2%(15.5–21.2) | Public (bus/taxi) compared to private (own car, foot, bicycle) OR: 1.73 (95% CI: 1.4–2.2) Adjusted for age, resources, COVID-19 in household, contact with flu-like symptoms, number of people in household, physical contact with someone outside the household: 1.65 (95% CI: 1.28–2.14) | Age 35–49 years old, COVID-19 positive person in the home, using public transit, at least 6 people in a household, physical contact with a person outside the household, contact with someone with flu-like symptoms, not having enough resources for living | Those using public transit at increased risk of seropositivity |
Nausin | Medium | Seropositivity (%), bivariate logistic regression (OR) | 10.14% (95% CI: 9.6–10.7) | N/A | OR: 1.79 (95% CI: 1.4–2.2) OR males: 1.91 (95% CI: 1.44–2.55); OR females: 1.83 (95% CI: 1.26–2.69) | Higher population density, high exposure work, those using public transit, non-smokers | Those using public transit at increased risk of seropositivity |
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Heiskanen, A.; Galipeau, Y.; Langlois, M.-A.; Little, J.; Cooper, C.L. SARS-CoV-2 Seroprevalence in Those Utilizing Public Transportation or Working in the Transportation Industry: A Rapid Review. Int. J. Environ. Res. Public Health 2022, 19, 11629. https://doi.org/10.3390/ijerph191811629
Heiskanen A, Galipeau Y, Langlois M-A, Little J, Cooper CL. SARS-CoV-2 Seroprevalence in Those Utilizing Public Transportation or Working in the Transportation Industry: A Rapid Review. International Journal of Environmental Research and Public Health. 2022; 19(18):11629. https://doi.org/10.3390/ijerph191811629
Chicago/Turabian StyleHeiskanen, Aliisa, Yannick Galipeau, Marc-André Langlois, Julian Little, and Curtis L. Cooper. 2022. "SARS-CoV-2 Seroprevalence in Those Utilizing Public Transportation or Working in the Transportation Industry: A Rapid Review" International Journal of Environmental Research and Public Health 19, no. 18: 11629. https://doi.org/10.3390/ijerph191811629