Background During the Covid-19 pandemic fake news has been circulating impacting on the general population’s opinion about a vaccine against the SARS-CoV-2. Health literacy measures the capacity of navigating health information. Methods We used data from a prospective national online cohort of 1647 participants. Descriptive statistics, Chi2 and ANOVA independence tests and two multivariable multinomial regression models were performed. Interactions between each variable were tested. Results Detection of fake news and health literacy scores were associated with intention to get vaccinated against SARS-CoV-2 (p < 0.01). The risk of being “anti-vaccination” or “hesitant”, rather than “pro-vaccination”, was higher among individuals reporting bad detection of fake news, respectively OR = 1.93 (95%CI = [1.30;2.87]) and OR = 1.80 (95%CI = [1.29;2.52]). The risk of being in “hesitant”, rather than “pro-vaccination” was higher among individuals having a bad health literacy score (OR = 1.44; 95%CI = [1.04;2.00]). No interaction was found between detection of fake news and health literacy. Conclusions To promote acceptance of a vaccine against SARS-CoV-2, it is recommended to increase individuals’ ability to detect fake news and health literacy through education and communication programs.
BackgroundThe underreporting of adverse drug reactions (ADRs) through traditional reporting channels is a limitation in the efficiency of the current pharmacovigilance system. Patients’ experiences with drugs that they report on social media represent a new source of data that may have some value in postmarketing safety surveillance.ObjectiveA scoping review was undertaken to explore the breadth of evidence about the use of social media as a new source of knowledge for pharmacovigilance.MethodsDaubt et al’s recommendations for scoping reviews were followed. The research questions were as follows: How can social media be used as a data source for postmarketing drug surveillance? What are the available methods for extracting data? What are the different ways to use these data? We queried PubMed, Embase, and Google Scholar to extract relevant articles that were published before June 2014 and with no lower date limit. Two pairs of reviewers independently screened the selected studies and proposed two themes of review: manual ADR identification (theme 1) and automated ADR extraction from social media (theme 2). Descriptive characteristics were collected from the publications to create a database for themes 1 and 2.ResultsOf the 1032 citations from PubMed and Embase, 11 were relevant to the research question. An additional 13 citations were added after further research on the Internet and in reference lists. Themes 1 and 2 explored 11 and 13 articles, respectively. Ways of approaching the use of social media as a pharmacovigilance data source were identified.ConclusionsThis scoping review noted multiple methods for identifying target data, extracting them, and evaluating the quality of medical information from social media. It also showed some remaining gaps in the field. Studies related to the identification theme usually failed to accurately assess the completeness, quality, and reliability of the data that were analyzed from social media. Regarding extraction, no study proposed a generic approach to easily adding a new site or data source. Additional studies are required to precisely determine the role of social media in the pharmacovigilance system.
Background: The Food and Drug Administration (FDA) in the United States and the European Medicines Agency (EMA) have recognized social media as a new data source to strengthen their activities regarding drug safety.Objective: Our objective in the ADR-PRISM project was to provide text mining and visualization tools to explore a corpus of posts extracted from social media. We evaluated this approach on a corpus of 21 million posts from five patient forums, and conducted a qualitative analysis of the data available on methylphenidate in this corpus.Methods: We applied text mining methods based on named entity recognition and relation extraction in the corpus, followed by signal detection using proportional reporting ratio (PRR). We also used topic modeling based on the Correlated Topic Model to obtain the list of the matics in the corpus and classify the messages based on their topics.Results: We automatically identified 3443 posts about methylphenidate published between 2007 and 2016, among which 61 adverse drug reactions (ADR) were automatically detected. Two pharmacovigilance experts evaluated manually the quality of automatic identification, and a f-measure of 0.57 was reached. Patient's reports were mainly neuro-psychiatric effects. Applying PRR, 67% of the ADRs were signals, including most of the neuro-psychiatric symptoms but also palpitations. Topic modeling showed that the most represented topics were related to Childhood and Treatment initiation, but also Side effects. Cases of misuse were also identified in this corpus, including recreational use and abuse.Conclusion: Named entity recognition combined with signal detection and topic modeling have demonstrated their complementarity in mining social media data. An in-depth analysis focused on methylphenidate showed that this approach was able to detect potential signals and to provide better understanding of patients' behaviors regarding drugs, including misuse.
The prevalence of asthma in the Maghreb countries is moderate, but its impact is high.
MIS-A is a promising, easy-to-use self-report tool that can capture accurately different adherence properties over a long time period.
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