Behaviours and attitudes in response to the COVID-19 pandemic: insights from a cross-national Facebook survey
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Abstract
Background
In the absence of medical treatment and vaccination, individual behaviours are key to curbing the spread of COVID-19. Here we describe efforts to collect attitudinal and behavioural data and disseminate insights to increase situational awareness and inform interventions.
Methods
We developed a rapid data collection and monitoring system based on a cross-national online survey, the “COVID-19 Health Behavior Survey”. Respondent recruitment occurred via targeted Facebook advertisements in Belgium, France, Germany, Italy, the Netherlands, Spain, the United Kingdom, and the United States. We investigated how the threat perceptions of COVID-19, the confidence in the preparedness of organisations to deal with the pandemic, and the adoption of preventive and social distancing behaviours are associated with respondents’ demographic characteristics.
Results
We analysed 71,612 questionnaires collected between March 13-April 19, 2020. We found substantial spatio-temporal heterogeneity across countries at different stages of the pandemic and with different control strategies in place. Respondents rapidly adopted the use of face masks when they were not yet mandatory. We observed a clear pattern in threat perceptions, sharply increasing from a personal level to national and global levels. Although personal threat perceptions were comparatively low, all respondents significantly increased hand hygiene. We found gender-specific patterns: women showed higher threat perceptions, lower confidence in the healthcare system, and were more likely to adopt preventive behaviours. Finally, we also found that older people perceived higher threat to themselves, while all respondents were strongly concerned about their family.
Conclusions
Rapid population surveys conducted via Facebook allow us to monitor behavioural changes, adoption of protective measures, and compliance with recommended practices. As the pandemic progresses and new waves of infections are a threatening reality, timely insights from behavioural and attitudinal data are crucial to guide the decision-making process.
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SciScore for 10.1101/2020.05.09.20096388: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Data analysis was performed with the programming language Python (version 3.7). Pythonsuggested: (IPython, RRID:SCR_001658)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:These advantages notwithstanding, our approach also has some limitations. First, online surveys potentially suffer from bias due to self-selection and non-representativeness of the sample. In the case …
SciScore for 10.1101/2020.05.09.20096388: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Data analysis was performed with the programming language Python (version 3.7). Pythonsuggested: (IPython, RRID:SCR_001658)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:These advantages notwithstanding, our approach also has some limitations. First, online surveys potentially suffer from bias due to self-selection and non-representativeness of the sample. In the case of Facebook, there is increasing evidence that samples obtained from this social media network are not significantly different in central demographic and psychometric characteristics from samples obtained by more traditional recruitment and sampling techniques [12]. Furthermore, by applying post-stratification weighting, which is a standard procedure in survey research, we can correct for non-representativeness in observable characteristics (but not necessarily for self-selection based on unobservable characteristics), at least at the level of the entire sample. Ideally, in our cross-temporal comparisons, we would apply this approach at the level of the week, to warrant complete comparability of observations over time, but issues of data sparsity complicate this approach. We do not expect this to strongly affect our results, but it should be kept in mind that our weekly results might suffer from somewhat larger bias than our aggregate results. Second, our data collection started at different time points across countries, and also pertains to different points in the trajectory of the pandemic across countries. This also encompasses differences in the implementation of non-pharmaceutical interventions ordered by local and national governments, and needs to be kept in mind when com...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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