Résumé
Background: The quality of evidence about the effectiveness of non-pharmaceutical health interventions is often low, but little is known about the effects of communicating indications of evidence quality to the public. Methods: In two blinded, randomised, controlled, online experiments, US participants (total n=2140) were shown one of several versions of an infographic illustrating the effectiveness of eye protection in reducing COVID-19 transmission. Their trust in the information, understanding, feelings of effectiveness of eye protection, and the likelihood of them adopting it were measured. Findings: Compared to those given no quality cues, participants who were told the quality of the evidence on eye protection was "low", rated the evidence less trustworthy (p=.001), and rated it as subjectively less effective (p=.020). The same effects emerged compared to those who were told the quality of the evidence was "high", and in one of the two studies, those shown "low" quality of evidence said they were less likely to use eye protection (p=.005). Participants who were told the quality of the evidence was "high" showed no significant differences on these measures compared to those given no information about evidence quality. Interpretation: Without quality of evidence cues, participants responded to the evidence about the public health intervention as if it was high quality and this affected their subjective perceptions of its efficacy and trust in the provided information. This raises the ethical dilemma of weighing the importance of transparently stating when the evidence base is actually low quality against evidence that providing such information can decrease trust, perception of intervention efficacy, and likelihood of adopting it.
Sujets)
COVID-19Résumé
BackgroundMortality from COVID-19 shows a strong relationship with age and pre-existing medical conditions, as does mortality from other causes. However it is unclear how specific factors are differentially associated with COVID-19 mortality as compared to mortality from other causes. MethodsWorking on behalf of NHS England, we carried out a cohort study within the OpenSAFELY platform. Primary care data from England were linked to national death registrations. We included all adults (aged [≥]18 years) in the database on 1st February 2020 and with >1 year of continuous prior registration, the cut-off date for deaths was 9th November 2020. Associations between individual-level characteristics and COVID-19 and non-COVID deaths were estimated by fitting age- and sex-adjusted logistic models for these two outcomes. Results17,456,515 individuals were included. 17,063 died from COVID-19 and 134,316 from other causes. Most factors associated with COVID-19 death were similarly associated with non-COVID death, but the magnitudes of association differed. Older age was more strongly associated with COVID-19 death than non-COVID death (e.g. ORs 40.7 [95% CI 37.7-43.8] and 29.6 [28.9-30.3] respectively for [≥]80 vs 50-59 years), as was male sex, deprivation, obesity, and some comorbidities. Smoking, history of cancer and chronic liver disease had stronger associations with non-COVID than COVID-19 death. All non-white ethnic groups had higher odds than white of COVID-19 death (OR for Black: 2.20 [1.96-2.47], South Asian: 2.33 [2.16-2.52]), but lower odds than white of non-COVID death (Black: 0.88 [0.83-0.94], South Asian: 0.78 [0.75-0.81]). InterpretationSimilar associations of most individual-level factors with COVID-19 and non-COVID death suggest that COVID-19 largely multiplies existing risks faced by patients, with some notable exceptions. Identifying the unique factors contributing to the excess COVID-19 mortality risk among non-white groups is a priority to inform efforts to reduce deaths from COVID-19. FundingWellcome, Royal Society, National Institute for Health Research, National Institute for Health Research Oxford Biomedical Research Centre, UK Medical Research Council, Health Data Research UK.
Sujets)
COVID-19Résumé
Introduction: Novel coronavirus 2019 (COVID-19) has propagated a global pandemic with significant health, economic and social costs. Emerging emergence has suggested that several factors may be associated with increased risk from severe outcomes or death from COVID-19. Clinical risk prediction tools have significant potential to generate individualised assessment of risk and may be useful for population stratification and other use cases. Methods and analysis: We will use a prospective open cohort study of routinely collected data from 1205 general practices in England in the QResearch database. The primary outcome is COVID-19 mortality (in or out-of-hospital) defined as confirmed or suspected COVID-19 mentioned on the death certificate, or death occurring in a person with SARS-CoV-2 infection between 24th January and 30th April 2020. Our primary outcome in adults is COVID-19 mortality (including out of hospital and in hospital deaths). We will also examine COVID-19 hospitalisation in children. Time-to-event models will be developed in the training data to derive separate risk equations in adults (19-100 years) for males and females for evaluation of risk of each outcome within the 3-month follow-up period (24th January to 30th April 2020), accounting for competing risks. Predictors considered will include age, sex, ethnicity, deprivation, smoking status, alcohol intake, body mass index, pre-existing medical co-morbidities, and concurrent medication. Measures of performance (prediction errors, calibration and discrimination) will be determined in the test data for men and women separately and by ten-year age group. For children, descriptive statistics will be undertaken if there are currently too few serious events to allow development of a risk model. The final model will be externally evaluated in (a) geographically separate practices and (b) other relevant datasets as they become available. Ethics and dissemination: The project has ethical approval and the results will be submitted for publication in a peer-reviewed journal.