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IntroductionSeveral non-pharmaceutical interventions such as physical distancing, hand washing, self-isolation, and schools and business closures, were implemented in British Columbia (BC) following the first laboratory-confirmed case of coronavirus disease 2019 (COVID-19) on January 26, 2020, to minimize in-person contacts that could spread infections. The BC COVID-19 Population Mixing Patterns survey (BC-Mix) was established as a surveillance system to measure behaviour and contact patterns in BC over time to inform the timing of the easing/re-imposition of control measures. In this paper, we describe the BC-Mix survey design and the demographic characteristics of respondents. MethodsThe ongoing repeated online survey was launched in September 2020. Participants are mainly recruited through social media platforms (including Instagram, Facebook, YouTube, WhatsApp). A follow up survey is sent to participants two to four weeks after completing the baseline survey. Survey responses are weighted to BCs population by age, sex, geography, and ethnicity to obtain generalizable estimates. Additional indices such as the material and social deprivation index, residential instability, economic dependency, and others are generated using census and location data. ResultsAs of July 26, 2021, over 61,000 baseline survey responses were received of which 41,375 were eligible for analysis. Of the eligible participants, about 60% consented to follow up and about 27% provided their personal health numbers for linkage with healthcare databases. Approximately 50% of respondents were female, 39% were 55 years or older, 65% identified as white and 50% had at least a university degree. ConclusionThe pandemic response is best informed by surveillance systems capable of timely assessment of behaviour patterns. BC-Mix survey respondents represent a large cohort of British Columbians providing near real-time information on behavioural and contact patterns in BC. Data from the BC-Mix survey would inform provincial COVID-19-related control measures.
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BackgroundPrevious research has shown that articles may be cited more frequently on the basis of title or abstract positivity. Whether a similar selective sharing practice exists on Twitter is not well understood. The objective of this study was to assess if COVID-19 articles with positive titles or abstracts were tweeted more frequently than those with non-positive titles or abstracts. MethodsCOVID-19 related articles published between January 1st and April 14th, 2020 were extracted from the LitCovid database and all articles were screened for eligibility. Titles and abstracts were classified using a list of positive and negative words from a previous study. A negative binomial regression analysis controlling for confounding variables (2018 impact factor, open access status, continent of the corresponding author, and topic) was performed to obtain regression coefficients, with the p values obtained by likelihood ratio testing. ResultsA total of 3752 COVID-19 articles were included. Of the included studies, 44 titles and 112 abstracts were positive; 1 title and 7 abstracts were negative; and 3707 titles and 627 abstracts were neutral. Articles with positive titles had a lower tweet rate relative to articles with non-positive titles, with a regression coefficient of -1.10 (P < .001), while the positivity of the abstract did not impact tweet rate (P = .2218). ConclusionCOVID-19 articles with non-positive titles are preferentially tweeted, while abstract positivity does not influence tweet rate.
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BackgroundThe COVID-19 pandemic has resulted in over 1,000,000 cases across 181 countries worldwide. The global impact of COVID-19 has resulted in a surge of related research. Researchers have turned to social media platforms, namely Twitter, to disseminate their articles. The online database Altmetric is a tool which tracks the social media metrics of articles and is complementary to traditional, citation-based metrics. Citation-based metrics may fail to portray dissemination accurately, due to the lengthy publication process. Altmetrics are not subject to this time-lag, suggesting that they may be an effective marker of research dissemination during the COVID-19 pandemic. ObjectivesTo assess the dissemination of COVID-19 articles as measured by Twitter dissemination, compared to traditional citation-based metrics, and determine article characteristics associated with tweet rates. MethodsCOVID-19 articles obtained from LitCovid published between January 1st to March 18th, 2020 were screened for inclusion. The following article characteristics were extracted independently, in single: Topic (General Info, Mechanism, Diagnosis, Transmission, Treatment, Prevention, Case Report, and Epidemic Forecasting), open access status (open access and subscription-based), continent of corresponding author (Asia, Australia, Africa, North America, South America, and Europe), tweets, and citations. A sign test was used to compare the tweet rate and citation rate per day. A negative binomial regression analysis was conducted to evaluate the association between tweet rate and article characteristics of interest. Results1328 articles were included in the analysis. Tweet rates were found to be significantly higher than citation rates for COVID-19 articles, with a median tweet rate of 1.09 (IQR 6.83) tweets per day and median citation rate of 0.00 (IQR 0.00) citations per day, resulting in a median of differences of 1.09 (95% CI 0.86-1.33, P < .001). 2018 journal impact factors were positively correlated with tweet rate (P < .001). The topics Diagnosis (P = .01), Transmission (P < .001), Treatment (P = .01), and Epidemic Forecasting (P < .001) were positively correlated with tweet rate, relative to Case Report. The following continents of the corresponding author were negatively correlated with tweet rate, Africa (P < .001), Australia (P = .03), and South America (P < .001), relative to Asia. Open access journals were negatively correlated with tweet rate, relative to subscription-based journals (P < .001). ConclusionsCOVID-19 articles had significantly higher tweets rates compared to citation rates. This study further identified article characteristics that are correlated with the dissemination of articles on Twitter, such as 2018 journal impact factor, continent of the corresponding author, topic, and open access status. This highlights the importance of altmetrics in periods of rapidly expanding research, such as the COVID-19 pandemic to localize highly disseminated articles.
