Early perceptions and behavioural responses during the COVID-19 pandemic: a cross-sectional survey of UK adults

OBJECTIVE: To examine risk perceptions and behavioural responses of the UK adult population during the early phase of the COVID-19 epidemic in the UK. DESIGN: A cross-sectional survey. SETTING: Conducted with a nationally representative sample of UK adults within 48 hours of the UK Government advising the public to stop non-essential contact with others and all unnecessary travel. PARTICIPANTS: 2108 adults living in the UK aged 18 years and over. Response rate was 84.3% (2108/2500). Data collected between 17 March and 18 March 2020. MAIN OUTCOME MEASURES: Descriptive statistics for all survey questions, including number of respondents and weighted percentages. Robust Poisson regression used to identify sociodemographic variation in: (1) adoption of social distancing measures, (2) ability to work from home, and (3) ability and (4) willingness to self-isolate. RESULTS: Overall, 1992 (94.2%) respondents reported at least one preventive measure: 85.8% washed their hands with soap more frequently; 56.5% avoided crowded areas and 54.5% avoided social events. Adoption of social distancing measures was higher in those aged over 70 years compared with younger adults aged 18–34 years (adjusted relative risk/aRR: 1.2; 95% CI: 1.1 to 1.5). Those with lowest household income were three times less likely to be able to work from home (aRR: 0.33; 95% CI: 0.24 to 0.45) and less likely to be able to self-isolate (aRR: 0.92; 95% CI: 0.88 to 0.96). Ability to self-isolate was also lower in black and minority ethnic groups (aRR: 0.89; 95% CI: 0.79 to 1.0). Willingness to self-isolate was high across all respondents. CONCLUSIONS: Ability to adopt and comply with certain non-pharmaceutical interventions (NPIs) is lower in the most economically disadvantaged in society. Governments must implement appropriate social and economic policies to mitigate this. By incorporating these differences in NPIs among socioeconomic subpopulations into mathematical models of COVID-19 transmission dynamics, our modelling of epidemic outcomes and response to COVID-19 can be improved.