Logistics Workers Are a Key Factor for SARS-CoV-2 Spread in Brazilian Small Towns: Case-Control Study

Abstract

Background Data on how SARS-CoV-2 enters and spreads in a population are essential for guiding public policies. Objective This study seeks to understand the transmission dynamics of SARS-CoV-2 in small Brazilian towns during the early phase of the epidemic and to identify core groups that can serve as the initial source of infection as well as factors associated with a higher risk of COVID-19. Methods Two population-based seroprevalence studies, one household survey, and a case-control study were conducted in two small towns in southeastern Brazil between May and June 2020. In the population-based studies, 400 people were evaluated in each town; there were 40 homes in the household survey, and 95 cases and 393 controls in the case-control study. SARS-CoV-2 serology testing was performed on participants, and a questionnaire was applied. Prevalence, household secondary infection rate, and factors associated with infection were assessed. Odds ratios (ORs) were calculated by logistic regression. Logistics worker was defined as an individual with an occupation focused on the transportation of people or goods and whose job involves traveling outside the town of residence at least once a week. Results Higher seroprevalence of SARS-CoV-2 was observed in the town with a greater proportion of logistics workers. The secondary household infection rate was 49.1% (55/112), and it was observed that in most households (28/40, 70%) the index case was a logistics worker. The case-control study revealed that being a logistics worker (OR 18.0, 95% CI 8.4-38.7) or living with one (OR 6.9, 95% CI 3.3-14.5) increases the risk of infection. In addition, having close contact with a confirmed case (OR 13.4, 95% CI 6.6-27.3) and living with more than four people (OR 2.7, 95% CI 1.1-7.1) were also risk factors. Conclusions Our study shows a strong association between logistics workers and the risk of SARS-CoV-2 infection and highlights the key role of these workers in the viral spread in small towns. These findings indicate the need to focus on this population to determine COVID-19 prevention and control strategies, including vaccination and sentinel genomic surveillance.

The effect of variation of individual infectiousness on SARS-CoV-2 transmission in households

“…We conducted a systematic review to gather information on the number of secondary cases with the number of household contacts for each household, in the form of number of households with X cases among households of size Y . In total, we identified 17 studies, comprising 13,098 index cases and 31,359 household contacts ( Appendix 1—figure 1 , Appendix 1—table 1 ; Bernardes-Souza et al, 2021 ; Carazo et al, 2022 ; Dattner et al, 2021 ; Han et al, 2022 ; Hart et al, 2022 ; Hsu et al, 2021 ; Hubiche et al, 2021 ; Koureas et al, 2021 ; Laxminarayan et al, 2020 ; Layan et al, 2022 ; Lyngse et al, 2022 ; Méndez-Echevarría et al, 2021 ; Posfay-Barbe et al, 2020 ; Reukers et al, 2021 ; Wilkinson et al, 2021 ). Most studies covered a period from January to November 2020, which was dominated by ancestral strains, except for Layan et al (from December 2020 to April 2021) and Hsu et al (from January 2020 to February 2021), which covered both ancestral strains and the alpha variant.…”

Notificação de COVID-19 relacionada ao trabalho: estudo descritivo sobre o perfil sociodemográfico e ocupacional, Brasil, 2020 e 2021

“…Já outros trabalhadores, que também se destacaram, como recepcionistas e motoristas, têm atividades que envolvem contato diário com muitas pessoas diferentes. Ademais, os trabalhadores envolvidos com o transporte, seja de cargas ou de pessoas, além de estarem associados com maior risco de contaminação pelo SARS-CoV-2, mostraram também ser fontes importantes de disseminação do vírus pela comunidade 37 .…”

The effect of variation of individual infectiousness on SARS-CoV-2 transmission in households

“…In total, we identified 17 studies, comprising 13098 index cases and 31359 household contacts (Fig. S1, Table S1) (14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30). Most studies covered the cases from January to November 2020, which was dominated by ancestral strains, except for Layan et We then developed a statistical model to quantify the degree and the impact of variation of infectiousness of cases on transmission dynamics.…”