RESUMO
ObjectivesIn the Netherlands, during the first phase of the COVID-19 epidemic, the hotspot of COVID-19 overlapped with the countrys main livestock area, while in subsequent phases this distinct spatial pattern disappeared. Previous studies show that living near livestock farms influence human respiratory health and immunological responses. This study aimed to explore whether proximity to livestock was associated with SARS-CoV-2 infection. MethodsThe associations between residential (6-digit postal-code) distance to the nearest livestock farm and individuals SARS-CoV-2 status was studied in multilevel logistic regression models, comparing individuals notified with a positive SARS-CoV-2 test to the general population in the Netherlands. Data included all reported Dutch laboratory-confirmed patients with disease onset before 1 January 2022. Individuals living in strongly urbanised areas and border areas were excluded. Models were adjusted for individuals age categories, the social status of the postal code area, particulate matter (PM10)-and nitrogen dioxide (NO2)-concentrations. We analysed data for the entire period and population as well as separately for eight time periods (Jan-Mar, Apr-Jun, Jul-Sep and Oct-Dec in 2020 and 2021), four geographic areas of the Netherlands (north, east, west and south), and for five age categories (0-14, 15-24, 25-44, 45-64 and > 65 years). ResultsOver the period 2020-2021, individuals SARS-CoV-2 status was associated with living closer to livestock farms. This association increased from an Odds Ratio (OR) of 1.01 (95% Confidence Interval [CI] 1.01-1.02) for patients living at a distance of 751-1000 m to a farm to an OR of 1.04 (95% CI 1.04-1.04), 1.07 (95% CI 1.06-1.07) and 1.11 (95% CI 1.10-1.12) for patients living in the more proximate 501-750 m, 251-500m and 0-250 m zones around farms, all relative to patients living further than 1000 m around farms. This association was observed in three out of four quarters of the year in both 2020 and 2021, and in all studied geographic areas and age groups. ConclusionsIn this exploratory study with individual SARS-CoV-2 notification data and high-resolution spatial data associations were found between living near livestock farms and individuals SARS-CoV-2 status in the Netherlands. Verification of the results in other countries is warranted, as well as investigations into possible underlying mechanisms.
RESUMO
BackgroundIndoor environments are considered a main setting for transmission of SARS-CoV-2. Households in particular present a close-contact environment with high probability of transmission between persons of different ages and with different roles in society. MethodsComplete households with a laboratory-confirmed SARS-CoV-2 positive case in the Netherlands (March-May 2020) were included. At least three home visits were performed during 4-6 week of follow-up, collecting naso- and oropharyngeal swabs, oral fluid, faeces and blood samples for molecular and serological analyses of all household members. Symptoms were recorded from two weeks before the first visit up to the last visit. Secondary attack rates (SAR) were estimated with logistic regression. A transmission model was used to assess transmission routes in the household. ResultsA total of 55 households with 187 household contacts were included. In 17 households no transmission took place, and in 11 households all persons were infected. Estimated SARs were high, ranging from 35% (95%CI: 24%-46%) in children to 51% (95%CI: 39%-63%) in adults. Estimated transmission rates in the household were high, with reduced susceptibility of children compared to adolescents and adults (0.67; 95%CI: 0.40-1.1). ConclusionEstimated SARs were higher than reported in earlier household studies, presumably owing to a dense sampling protocol. Children were shown to be less susceptible than adults, but the estimated SAR in children was still high. Our results reinforce the role of households as main multiplier of SARS-CoV-2 infection in the population. Key pointsWe analyze data from a SARS-CoV-2 household study and find higher secondary attack rates than reported earlier. We argue that this is due to a dense sampling strategy that includes sampling at multiple time points and of multiple anatomical sites.
