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BackgroundThe success of a governments COVID-19 control strategy relies on public trust and broad acceptance of response measures. We investigated public perceptions of the UK governments COVID-19 response, focusing on the relationship between trust and transparency, during the first wave (April 2020) of the COVID-19 pandemic in the United Kingdom. MethodsAnonymous survey data were collected (2020-04-06 to 2020-04-22) from 9,322 respondents, aged 20+ using an online questionnaire. We took a mixed methods approach to data analysis, combining statistical analyses, structural topic modelling (STM) and qualitative thematic coding of a sub-set of responses. Missing data were imputed via multiple imputation. ResultsMost respondents (95.1%) supported government enforcement of behaviour change. While 52.1% of respondents thought the government was making good decisions, differences were apparent across demographic groups, for example respondents from Scotland had lower odds of responding positively than respondents in London. Higher educational levels saw decreasing odds of having a positive opinion of the government response and decreasing household income associated with decreasing positive opinion. Of respondents who thought the government was not making good decisions 60% believed the economy was being prioritised over people and their health. Positive views on government decision-making were associated with positive views on government transparency about the COVID-19 response. Qualitative analysis about government transparency highlighted five key themes: (1) the justification of opacity due to the condition of crisis, (2) generalised mistrust of politics, (3) concerns about the role of scientific evidence, (4) quality of government communication and (5) questions about political decision-making processes. ConclusionWe recommend targeted community engagement tailored to different groups experiences and a focus on accountability and openness around how decisions are made in the response to the UK COVID-19 pandemic.
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ImportanceThere are growing concerns that the UK COVID-19 lockdown has reduced opportunities to maintain health through physical activity, placing individuals at higher risk of chronic disease and leaving them more vulnerable to severe sequelae of COVID-19. ObjectiveTo examine whether the UKs lockdown measures have had disproportionate impacts on intensity of physical activity in groups who are, or who perceive themselves to be, at heightened risk from COVID-19. Designs, Setting, ParticipantsUK-wide survey of adults aged over 20, data collected between 2020-04-06 and 2020-04-22. ExposuresSelf-reported doctor-diagnosed obesity, hypertension, type I/II diabetes, lung disease, cancer, stroke, heart disease. Self-reported disabilities and depression. Sex, gender, educational qualifications, household income, caring for school-age children. Narrative data on coping strategies. Main Outcomes and MeasuresChange in physical activity intensity after implementation of UK COVID-19 lockdown (self-reported). ResultsMost (60%) participants achieved the same level of intensity of physical activity during the lockdown as before the epidemic. Doing less intensive physical activity during the lockdown was associated with obesity (OR 1.21, 95% CI 1.02-1.41), hypertension (OR 1.52, 1.33-1.71), lung disease (OR 1.31,1.13-1.49), depression (OR 2.02, 1.82-2.22) and disability (OR 2.34, 1.99-2.69). Participants who reduced their physical activity intensity also had higher odds of being female, living alone or having no garden, and more commonly expressed sentiments about personal or household risks in narratives on coping. Conclusions and relevanceGroups who reduced physical activity intensity included disproportionate numbers of people with either heightened objective clinical risks or greater tendency to express subjective perceptions of risk. Policy on exercise for health during lockdowns should include strategies to facilitate health promoting levels of physical activity in vulnerable groups, including those with both objective and subjective risks.
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BackgroundThe COVID-19 pandemic has placed an unprecedented strain on health systems, with rapidly increasing demand for healthcare in hospitals and intensive care units (ICUs) worldwide. As the pandemic escalates, determining the resulting needs for healthcare resources (beds, staff, equipment) has become a key priority for many countries. Projecting future demand requires estimates of how long patients with COVID-19 need different levels of hospital care. MethodsWe performed a systematic review to gather data on length of stay (LoS) of patients with COVID-19 in hospital and in ICU. We subsequently developed a method to generate LoS distributions which combines summary statistics reported in multiple studies, accounting for differences in sample sizes. Applying this approach we provide distributions for general hospital and ICU LoS from studies in China and elsewhere, for use by the community. ResultsWe identified 52 studies, the majority from China (46/52). Median hospital LoS ranged from 4 to 53 days within China, and 4 to 21 days outside of China, across 45 studies. ICU LoS was reported by eight studies - four each within and outside China - with median values ranging from 6 to 12 and 4 to 19 days, respectively. Our summary distributions have a median hospital LoS of 14 (IQR: 10-19) days for China, compared with 5 (IQR: 3-9) days outside of China. For ICU, the summary distributions are more similar (median (IQR) of 8 (5-13) days for China and 7 (4-11) days outside of China). There was a visible difference by discharge status, with patients who were discharged alive having longer LoS than those who died during their admission, but no trend associated with study date. ConclusionPatients with COVID-19 in China appeared to remain in hospital for longer than elsewhere. This may be explained by differences in criteria for admission and discharge between countries, and different timing within the pandemic. In the absence of local data, the combined summary LoS distributions provided here can be used to model bed demands for contingency planning and then updated, with the novel method presented here, as more studies with aggregated statistics emerge outside China.