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Background: Social media platforms are frequently used by the general public to access health information, including information relating to complementary and alternative medicine (CAM). The aim of this study was to measure how often naturopathic influencers make evidence-informed recommendations on Instagram, and to examine associations between the level of evidence available or presented, and user engagement. Methods: A retrospective observational study using quantitative content analysis on health-related claims made by naturopathic influencers with 30000 or more followers on Instagram was conducted. Linear regression was used to measure the association between health-related posts and the number of Likes, and Comments. Results: A total of 494 health claims were extracted from eight Instagram accounts, of which 242 (49.0%) were supported by evidence and 34 (6.9%) included a link to evidence supporting the claim. Three naturopathic influencers did not provide any evidence to support the health claims they made on Instagram. Posts with links to evidence had fewer Likes (B=-1343.9, 95% CI=-2424.4 to -263.4, X=-0.1, P=0.02) and fewer Comments (B=-82.0, 95% CI=-145.9 to -18.2, X=-0.2, P=0.01), compared to posts without links to evidence. The most common areas of health were claims relating to ‘women’s health’ (n=94; 19.0%), and ‘hair, nail and skin’ (n=74; 15.0%). Conclusion: This study is one of the first to look at the evidence available to support health-related claims by naturopathic influencers on Instagram. Our findings indicate that around half of Instagram posts from popular naturopathic influencers with health claims are supported by high-quality evidence.

Objectives Online health information (OHI) has been shown to influence patients’ health decisions and behaviours. OHI about statins has created confusion among healthcare professionals and the public. This study explored the views and experiences of patients with high cardiovascular risk on OHI-seeking about statins and how OHI influenced their decision. Design This was a qualitative study using semi-structured in-depth interviews. An interpretive description approach with thematic analysis was used for data analysis. Setting An urban primary care clinic in Kuala Lumpur, Malaysia. Participants Patients aged 18 years and above who had high cardiovascular risk and sought OHI on statins were recruited. Results A total of 20 participants were interviewed. The age of the participants ranged from 38 to 74 years. Twelve (60%) participants took statins for primary cardiovascular disease prevention. The duration of statin use ranged from 2 weeks to 30 years. Six themes emerged from the data analysis: (i) seeking OHI throughout the disease trajectory, (ii) active and passive approaches to seeking OHI, (iii) types of OHI, (iv) views about statin-related OHI, (v) influence of OHI on patients’ health decisions, and (vi) patient–doctor communication about OHI. Conclusion This study highlights the changing information needs throughout patient journeys, suggesting the opportunity to provide needs-oriented OHI to patients. Unintentional passive exposure to OHI appears to have an influence on patients’ adherence to statins. The quality of patient–doctor communication in relation to OHI-seeking behaviour remains a critical factor in patient decision-making.

Background: During a public health emergency, accurate and useful information can be drowned out by questions, concerns, information voids, conflicting information and misinformation. Very few studies connect information exposure and trust to health behaviours, which limits available evidence to inform when and where to act to mitigate the burden of infodemics, especially in low resource settings. In this paper, we describe the need for a toolkit that can link information exposure to health behaviours at the individual level. Methods: Most data-driven infodemiology research is designed to characterise content rather than measure associations between information exposure and health behaviours. Studies also tend to be limited to specific social media platforms, are unable to capture the breadth of individual information exposure that occur online and offline, and cannot measure differences in trust by information source or content. Studies are also designed very differently, limiting synthesis of results. To meet the needs of the research community, we propose a research toolkit that can be used in studies measuring topic-specific information exposure and health behaviours, and implements a standardised protocol so results can be combined in a meta-analysis. Other desirable features of the toolkit would include the ability to tailor studies to local contexts, ease of use for participants, and frictionless sharing of de-identified data for aggregating individual participant data. Results: We demonstrate one solution: a web-based study platform that participants use to record topic-specific information exposure in a diary application and a browser plugin for tracking access to relevant webpages, supports questionnaires, and supports incentives for participation such as visual analytics to compare trust levels with other participants. Conclusions: Our proposed solution will be able to capture detailed data about information exposure and health behaviour data, standardise study design while simultaneously supporting localisation, and make it easy to synthesise individual participant data across studies. Future research will need to evaluate the toolkit in realistic scenarios to understand the usability of the toolkit for both participants and investigators.

Objective Delays in seeking health care for dengue are associated with poor health outcomes. Despite this, the factors influencing such delays remain unclear, rendering interventions to improve health care-seeking for dengue ineffective. This systematic review aimed to synthesise the factors influencing health care-seeking of patients with dengue and form a comprehensive framework. Methods This review included both qualitative and quantitative studies. Studies were obtained by searching five databases, contacting field experts, and performing backward reference searches. The best-fit meta-synthesis approach was used during data synthesis, where extracted data were fitted into the social-ecological model. Sub-analyses were conducted to identify the commonly reported factors and their level of statistical significance. Results Twenty studies were selected for meta-synthesis. Eighteen factors influencing health care-seeking in dengue were identified and categorised under four domains: individual (11 factors), interpersonal (1 factor), organisational (4 factors), and community (2 factors). The most reported factors were knowledge of dengue, access to health care, quality of health service and resource availability. Overall, more barriers to dengue health seeking than facilitators were found. History of dengue infection and having knowledge of dengue were found to be ambiguous as they both facilitated and hindered dengue health care-seeking. Contrary to common belief, women were less likely to seek help for dengue than men. Conclusions The factors affecting dengue health care-seeking behaviour are diverse, can be ambiguous and are found across multiple social-ecological levels. Understanding these complexities is essential for the development of effective interventions to improve dengue health care-seeking behaviour.

BACKGROUND An infodemic is excess information, including false or misleading information, that spreads in digital and physical environments during a public health emergency. The COVID-19 pandemic has been accompanied by an unprecedented global infodemic that has led to confusion about the benefits of medical and public health interventions, with substantial impact on risk-taking and health-seeking behaviors, eroding trust in health authorities and compromising the effectiveness of public health responses and policies. Standardized measures are needed to quantify the harmful impacts of the infodemic in a systematic and methodologically robust manner, as well as harmonizing highly divergent approaches currently explored for this purpose. This can serve as a foundation for a systematic, evidence-based approach to monitoring, identifying, and mitigating future infodemic harms in emergency preparedness and prevention. OBJECTIVE In this paper, we summarize the Fifth World Health Organization (WHO) Infodemic Management Conference structure, proceedings, outcomes, and proposed actions seeking to identify the interdisciplinary approaches and frameworks needed to enable the measurement of the burden of infodemics. METHODS An iterative human-centered design (HCD) approach and concept mapping were used to facilitate focused discussions and allow for the generation of actionable outcomes and recommendations. The discussions included 86 participants representing diverse scientific disciplines and health authorities from 28 countries across all WHO regions, along with observers from civil society and global public health–implementing partners. A thematic map capturing the concepts matching the key contributing factors to the public health burden of infodemics was used throughout the conference to frame and contextualize discussions. Five key areas for immediate action were identified. RESULTS The 5 key areas for the development of metrics to assess the burden of infodemics and associated interventions included (1) developing standardized definitions and ensuring the adoption thereof; (2) improving the map of concepts influencing the burden of infodemics; (3) conducting a review of evidence, tools, and data sources; (4) setting up a technical working group; and (5) addressing immediate priorities for postpandemic recovery and resilience building. The summary report consolidated group input toward a common vocabulary with standardized terms, concepts, study designs, measures, and tools to estimate the burden of infodemics and the effectiveness of infodemic management interventions. CONCLUSIONS Standardizing measurement is the basis for documenting the burden of infodemics on health systems and population health during emergencies. Investment is needed into the development of practical, affordable, evidence-based, and systematic methods that are legally and ethically balanced for monitoring infodemics; generating diagnostics, infodemic insights, and recommendations; and developing interventions, action-oriented guidance, policies, support options, mechanisms, and tools for infodemic managers and emergency program managers.

BACKGROUND Throughout the COVID-19 pandemic, US Centers for Disease Control and Prevention policies on face mask use fluctuated. Understanding how public health communications evolve around key policy decisions may inform future decisions on preventative measures by aiding the design of communication strategies (eg, wording, timing, and channel) that ensure rapid dissemination and maximize both widespread adoption and sustained adherence. OBJECTIVE We aimed to assess how sentiment on masks evolved surrounding 2 changes to mask guidelines: (1) the recommendation for mask use on April 3, 2020, and (2) the relaxation of mask use on May 13, 2021. METHODS We applied an interrupted time series method to US Twitter data surrounding each guideline change. Outcomes were changes in the (1) proportion of positive, negative, and neutral tweets and (2) number of words within a tweet tagged with a given emotion (eg, trust). Results were compared to COVID-19 Twitter data without mask keywords for the same period. RESULTS There were fewer neutral mask-related tweets in 2020 (β=–3.94 percentage points, 95% CI –4.68 to –3.21; PPPPPP=.008). CONCLUSIONS The US Twitter population responded negatively and with less trust following guideline shifts related to masking, regardless of whether the guidelines recommended or relaxed mask usage. Federal agencies should ensure that changes in public health recommendations are communicated concisely and rapidly.

Background Few studies have investigated the relationship between industry funding/conflicts of interest and authors’ positions in opinion pieces on drug safety. Harmful effects of varenicline, a treatment for smoking cessation, have been highly contested. Objective To examine the association between pharmaceutical industry funding/authors’ financial conflicts of interest and position on varenicline in opinion articles, especially in relation to the minimization of harms; to assess whether opinion pieces on drug safety issues written by authors with conflicts of interest are more frequently cited in the news or social media. Design Cross-sectional analysis. Participants English language opinion pieces and narrative reviews about varenicline published between May 2006 and February 2019. Main Measures Odds ratios and 95% confidence intervals; the Mann-Whitney two-sample statistic was used to test for differences in Altmetric scores, a measure of media attention. Key Results Of the 221 included articles, 30.3% (67) disclosed the funding source and 62.9% (139) disclosed authors’ conflicts of interest. Authors of opinion pieces on varenicline who reported financial ties to the pharmaceutical industry (as a conflict of interest or funding source) were more likely to minimise the cardiovascular and psychiatric risk of varenicline compared to those without conflicts of interest or industry funding (OR: 4.00; 95% CI: 1.32 to 12.16 for cardiovascular risk; OR: 8.51; 95% CI: 3.79 to 19.11 for psychiatric risk). These associations persisted in sensitivity analyses. No statistically significant difference in Altmetric score was found between articles with (mean 15.83, median 3) and without (mean 11.90, median 1) conflicts of interest, indicating similar media attention (p-value=0.11). Conclusions We found that authors with financial ties to drug companies were more likely to publish opinion pieces that minimised harms of varenicline. These results raise questions about journals’ editorial policies to accept reviews of treatments from authors with financial relationships with manufacturers.

Background Adverse events identified during clinical trials can be important early indicators of drug safety, but complete and timely data on safety results have historically been difficult to access. The aim was to compare the availability, completeness, and concordance of safety results reported in ClinicalTrials.gov and peer-reviewed publications. Methods We analyzed clinical trials used in the Food and Drug Administration safety assessment of new drugs approved between 1 July 2018 and 30 June 2019. The key safety outcomes examined were all-cause mortality, serious adverse events, adverse events, and withdrawals due to adverse events. Availability of safety results was measured by the presence and timing of a record of trial-level results in ClinicalTrials.gov and a corresponding peer-reviewed publication. For the subset of trials with available results, completeness was defined as the reporting of safety results for all participants and compared between ClinicalTrials.gov and publications. To assess concordance, we compared the numeric results for safety outcomes reported in ClinicalTrials.gov and publications to results in Food and Drug Administration trial reports. Results Among 156 trials studying 52 drugs, 91 (58.3%) trials reported safety results in ClinicalTrials.gov and 106 (67.9%) in peer-reviewed publications (risk difference = −9.6%, 95% confidence interval = −20.3 to 1.0). All-cause mortality was reported sooner in published articles compared with ClinicalTrials.gov (log-rank test, p = 0.01). There was no difference in time to reporting for serious adverse events (p = 0.05), adverse events (p = 0.09), or withdrawals due to adverse events (p = 0.20). Complete reporting of all-cause mortality was similar in ClinicalTrials.gov and publications (74.7% vs 78.3%, respectively; risk difference = −3.6%, 95% confidence interval = −15.5 to 8.3) and higher in ClinicalTrials.gov for serious adverse events (100% vs 79.2%; risk difference = 20.8%, 95% confidence interval = 13.0 to 28.5) and adverse events (100% vs 86.8%; risk difference = 13.2%, 95% confidence interval = 6.8 to 19.7). Withdrawals due to adverse events were less often completely reported in ClinicalTrials.gov (62.6% vs 92.5%; risk difference = −29.8%, 95% confidence interval = −40.1 to −18.7). No difference was found in concordance of results between ClinicalTrials.gov and publications for all-cause mortality, serious adverse events, or withdrawals due to adverse events. Conclusion Safety results were available in ClinicalTrials.gov at a similar rate as in peer-reviewed publications, with more complete reporting of certain safety outcomes in ClinicalTrials.gov. Future efforts should consider adverse event reporting in ClinicalTrials.gov as an accessible data source for post-marketing surveillance and other evidence synthesis tasks.

Availability of data and materials: Pre-trained weights of BioALBERT models together with the datasets analysed in this paper are available at https:// github.com/usmaann/BioALBERT. The PubMed data are available at https://www.ncbi.nlm.nih.gov/pubmed/. The PMC data are available at https://www.ncbi.nlm.nih.gov/pmc/. The MIMIC data are available at https://mimic.mit.edu/. Copyright © 2022 The Author(s). Background: The abundance of biomedical text data coupled with advances in natural language processing (NLP) is resulting in novel biomedical NLP (BioNLP) applications. These NLP applications, or tasks, are reliant on the availability of domain-specific language models (LMs) that are trained on a massive amount of data. Most of the existing domain-specific LMs adopted bidirectional encoder representations from transformers (BERT) architecture which has limitations, and their generalizability is unproven as there is an absence of baseline results among common BioNLP tasks. Results: We present 8 variants of BioALBERT, a domain-specific adaptation of a lite bidirectional encoder representations from transformers (ALBERT), trained on biomedical (PubMed and PubMed Central) and clinical (MIMIC-III) corpora and fine-tuned for 6 different tasks across 20 benchmark datasets. Experiments show that a large variant of BioALBERT trained on PubMed outperforms the state-of-the-art on named-entity recognition (+ 11.09% BLURB score improvement), relation extraction (+ 0.80% BLURB score), sentence similarity (+ 1.05% BLURB score), document classification (+ 0.62% F1-score), and question answering (+ 2.83% BLURB score). It represents a new state-of-the-art in 5 out of 6 benchmark BioNLP tasks. Conclusions: The large variant of BioALBERT trained on PubMed achieved a higher BLURB score than previous state-of-the-art models on 5 of the 6 benchmark BioNLP tasks. Depending on the task, 5 different variants of BioALBERT outperformed previous state-of-the-art models on 17 of the 20 benchmark datasets, showing that our model is robust and generalizable in the common BioNLP tasks. We have made BioALBERT freely available which will help the BioNLP community avoid computational cost of training and establish a new set of baselines for future efforts across a broad range of BioNLP tasks.

Efforts aimed at increasing the pace of evidence synthesis have been primarily focused on the use of published articles, but these are a relatively delayed, incomplete, and at times biased source of study results data. Compared to those in bibliographic databases, structured results data available in trial registries may be more timely, complete, and accessible, but these data remain underutilized. Key advantages of using structured results data include the potential to automatically monitor the accumulation of relevant evidence and use it to signal when a systematic review requires updating, as well as to prospectively assign trials to already published reviews. Shifting focus to emerging sources of structured trial data may provide the impetus to build a more proactive and efficient system of continuous evidence surveillance.

Synthesizing evidence is an essential part of scientific progress, but it is often done in a slow and uncoordinated manner, sometimes producing misleading conclusions. Here, we propose the idea of an ‘open synthesis community’ to resolve this pressing issue.

Context E-cigarettes were introduced to support smoking cessation, but their popularity has increased among nonsmokers, challenging current perspectives on their safety and effectiveness as a public health intervention. The objective of this systematic review was to identify and synthesize current evidence on the influence of social factors on e-cigarette intentions and use. Evidence acquisition MEDLINE, CINAHL, PsycINFO, and Embase were searched for studies of the effects of social factors on e-cigarette intention or use in February 2019. Studies were included if they used experimental, longitudinal, qualitative, or mixed methods designs. Advertising, social interactions, and social norms were considered as social factors; social media was considered a conduit for other social factors. Two reviewers screened all studies; bias risk was evaluated for all RCTs using the Cochrane risk of bias tool. Evidence synthesis This review included 43 studies: 9 experimental, 11 longitudinal, 22 qualitative, and 1 mixed method. All experimental studies examined advertising and consistently showed that exposure increased intentions to use e-cigarettes. Evidence of the influence of social interactions and social norms came from longitudinal and qualitative studies, suggesting that these factors could increase e-cigarette use. Most participants were nonsmokers (81%; 22,233 of 27,303). Studies rarely considered differences in the effects of social factors on smokers and nonsmokers. Conclusions Given the increased popularity among nonsmokers and the potential for advertising to increase e-cigarette use, closer public health monitoring of e-cigarette uptake by nonsmokers is warranted. Future primary research should be designed to measure how social factors affect smokers and nonsmokers differently.

A substantial proportion of trial registrations are not linked to corresponding published articles, limiting analyses and new tools. Our aim was to develop a method for finding articles reporting the results of trials that are registered on ClinicalTrials.gov when they do not include metadata links. We used a set of 27,280 trial registration and article pairs to train and evaluate methods for identifying missing links in both directions-from articles to registrations and from registrations to articles. We trained a classifier with six distance metrics as feature representations to rank the correct article or registration, using recall@K to evaluate performance and compare to baseline methods. When identifying links from registrations to published articles, the classifier ranked the correct article first (recall@1) among 378,048 articles in 80.8% of evaluation cases and 34.9% in the baseline method. Recall@10 was 85.1% compared to 60.7% in the baseline. When predicting links from articles to registrations, recall@1 was 83.4% for the classifier and 39.8% in the baseline. Recall@10 was 89.5% compared to 65.8% in the baseline. The proposed method improves on our baseline document similarity method to be feasible for identifying missing links in practice. Given a ClinicalTrials.gov registration, a user checking 10 ranked articles can expect to identify the matching article in at least 85% of cases, if the trial has been published. The proposed method can be used to improve the coupling of ClinicalTrials.gov and PubMed, with applications related to automating systematic review and evidence synthesis processes.

OBJECTIVES Evidence on repeating vaccination misinformation or "myths" in debunking text is inconclusive; repeating myths may unintentionally increase agreement with myths or help discredit myths. In this study we aimed to compare the effect of repeating vaccination myths and other text-based debunking strategies on parents’ agreement with myths and their intention to vaccinate their children. METHODS For this online experiment we recruited 788 parents of children aged 0 to 5 years; 454 (58%) completed the study. We compared 3 text-based debunking strategies (repeating myths, posing questions, or making factual statements) and a control. We measured changes in agreement with myths and intention to vaccinate immediately after the intervention and at least 1 week later. The primary analysis compared the change in agreement with vaccination myths from baseline, between groups, at each time point after the intervention. RESULTS There was no evidence that repeating myths increased agreement with myths compared with the other debunking strategies or the control. Posing questions significantly decreased agreement with myths immediately after the intervention compared with the control (difference: −0.30 points, 99.17% confidence interval: −0.58 to −0.02, P = .004, d = 0.39). There was no evidence of a difference between other debunking strategies or the control at either time point, or on intention to vaccinate. CONCLUSIONS Debunking strategies that repeat vaccination myths do not appear to be inferior to strategies that do not repeat myths.