Objectives: To quantify and characterise non-household contact and to identify the effect of shielding and isolating on contact patterns.
Design: Cross-sectional study.
Setting And Participants: Anyone living in the UK was eligible to take part in the study. We recorded 5143 responses to the online questionnaire between 28 July 2020 and 14 August 2020.
Outcome Measures: Our primary outcome was the daily non-household contact rate of participants. Secondary outcomes were propensity to leave home over a 7 day period, whether contacts had occurred indoors or outdoors locations visited, the furthest distance travelled from home, ability to socially distance and membership of support bubble.
Results: The mean rate of non-household contacts per person was 2.9 d. Participants attending a workplace (adjusted incidence rate ratio (aIRR) 3.33, 95% CI 3.02 to 3.66), self-employed (aIRR 1.63, 95% CI 1.43 to 1.87) or working in healthcare (aIRR 5.10, 95% CI 4.29 to 6.10) reported significantly higher non-household contact rates than those working from home. Participants self-isolating as a precaution or following Test and Trace instructions had a lower non-household contact rate than those not self-isolating (aIRR 0.58, 95% CI 0.43 to 0.79). We found limited evidence that those shielding had reduced non-household contacts compared with non-shielders.
Conclusion: The daily rate of non-household interactions remained lower than prepandemic levels measured by other studies, suggesting continued adherence to social distancing guidelines. Individuals attending a workplace in-person or employed as healthcare professionals were less likely to maintain social distance and had a higher non-household contact rate, possibly increasing their infection risk. Shielding and self-isolating individuals required greater support to enable them to follow the government guidelines and reduce non-household contact and therefore their risk of infection.
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http://dx.doi.org/10.1136/bmjopen-2021-059231 | DOI Listing |
BMC Public Health
October 2024
Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, United States of America.
Background: Understanding how infectious disease transmission varies from person to person, including associations with age and contact behavior, can help design effective control strategies. Within households, transmission may be highly variable because of differing transmission risks by age, household size, and individual contagiousness. Our aim was to disentangle those factors by fitting mathematical models to SARS-CoV-2 household survey and serologic data.
View Article and Find Full Text PDFR Soc Open Sci
October 2024
School of Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand.
Accounting for population age structure and age-specific contact patterns is crucial for accurate modelling of human infectious disease dynamics and impact. A common approach is to use contact matrices, which estimate the number of contacts between individuals of different ages. These contact matrices are frequently based on data collected from populations with very different demographic and socio-economic characteristics from the population of interest.
View Article and Find Full Text PDFAJPM Focus
August 2024
Division of Allergy and Infectious Diseases, Department of Medicine, University of Washington, Seattle, Washington.
J Infect
August 2024
School of Population Health, Faculty of Health Sciences, Curtin University, Perth, Australia; Geospatial and Tuberculosis Research Team, Telethon Kids Institute, Perth, Australia.
Introduction: Contact investigations with drug-susceptible tuberculosis (DS-TB) patients have demonstrated a high prevalence of tuberculosis infection (TBI). However, the prevalence of TBI among individuals in close contact with drug-resistant tuberculosis (DR-TB) patients is poorly understood. This systematic review and meta-analysis aimed to determine the prevalence of TBI among household and non-household contacts of DR-TB patients.
View Article and Find Full Text PDFFront Immunol
June 2024
Institute of Evidence-Based Medicine, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, China.
Little studies evaluated the effectiveness of booster vaccination of inactivated COVID-19 vaccines against being infected (susceptibility), infecting others (infectiousness), and spreading the disease from one to another (transmission). Therefore, we conducted a retrospective cohort study to evaluate the effectiveness of booster vaccination of inactivated COVID-19 vaccines against susceptibility, infectiousness, and transmission in Shenzhen during an Omicron BA.2 outbreak period from 1 February to 21 April 2022.
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