Understanding patterns of adherence to COVID-19 mitigation measures: A qualitative interview study.

Sarah Denford, Kate S Morton, Helene Lambert, Juan Zhang, Louise Smith, James Rubin, Shenghan Cai, Tingting Zhang, Charlotte Robin, Gemma Lasseter, Matthew Hickman, Isabel Oliver, Lucy Yardley

Research output: Contribution to journalArticlepeer-review

41 Citations (Scopus)

Abstract

BACKGROUND: Evidence highlights the disproportionate impact of measures that have been introduced to reduce the spread of coronavirus on individuals from Black, Asian and minority ethnic (BAME) communities, and among those on a low income. An understanding of barriers to adherence in these populations is needed. In this qualitative study, we examined the patterns of adherence to mitigation measures and reasons underpinning these behaviors. METHODS: Semi-structured interviews were conducted with 20 participants from BAME and low-income White backgrounds. The topic guide was designed to explore how individuals are adhering to social distancing and self-isolation during the pandemic and to explore the reasons underpinning this behavior. RESULTS: We identified three categories of adherence to lockdown measures: (i) caution-motivated super-adherence (ii) risk-adapted partial-adherence and (iii) necessity-driven partial-adherence. Decisions about adherence considered potential for exposure to the virus, ability to reduce risk through use of protective measures and perceived importance of/need for the behavior. CONCLUSIONS: This research highlights a need for a more nuanced understanding of adherence to lockdown measures. Provision of practical and financial support could reduce the number of people who have to engage in necessity-driven partial-adherence. More evidence is required on population level risks of people adopting risk-adapted partial-adherence.

Original languageEnglish
Pages (from-to)508-516
Number of pages9
JournalJournal of Public Health (United Kingdom)
Volume43
Issue number3
DOIs
Publication statusPublished - 22 Sept 2021

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