Objectives Respiratory co‐infections have the potential to affect the diagnosis and treatment of COVID‐19 patients. This meta‐analysis was performed to analyze the prevalence of respiratory pathogens (viruses and atypical bacteria) in COVID‐19 patients. Methods This review was consistent with Preferred Reporting Items for Systematic reviews and Meta‐Analyses (PRISMA). Searched databases included: PubMed, EMBASE, Web of Science, Google Scholar, and grey literature. Studies with a series of SARS‐CoV‐2‐positive patients with additional respiratory pathogen testing were included. Independently, 2 authors extracted data and assessed quality of evidence across all studies using Cochrane's Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology and within each study using the Newcastle Ottawa scale. Data extraction and quality assessment disagreements were settled by a third author. Pooled prevalence of co‐infections was calculated using a random‐effects model with univariate meta‐regression performed to assess the effect of study subsets on heterogeneity. Publication bias was evaluated using funnel plot inspection, Begg's correlation, and Egger's test. Results Eighteen retrospective cohorts and 1 prospective study were included. Pooling of data (1880 subjects) showed an 11.6% (95% confidence interval [CI] = 6.9–17.4, I2 = 0.92) pooled prevalence of respiratory co‐pathogens. Studies with 100% co‐pathogen testing (1210 subjects) found a pooled prevalence of 16.8% (95% CI = 8.1–27.9, I2 = 0.95) and studies using serum antibody tests (488 subjects) found a pooled prevalence of 26.8% (95%, CI = 7.9–51.9, I2 = 0.97). Meta‐regression found no moderators affecting heterogeneity. Conclusion Co‐infection with respiratory pathogens is a common and potentially important occurrence in patients with COVID‐19. Knowledge of the prevalence and type of co‐infections may have diagnostic and management implications.

This study was performed to analyze the accuracy of health‐related information on Twitter during the coronavirus disease 2019 (COVID‐19) pandemic. Authors queried Twitter on three dates for information regarding COVID‐19 and five terms (cure, emergency or emergency room, prevent or prevention, treat or treatments, vitamins or supplements) assessing the first 25 results with health‐related information. Tweets were authoritative if written by governments, hospitals, or physicians. Two physicians assessed each tweet for accuracy. Metrics were compared between accurate and inaccurate tweets using χ 2 analysis and Mann–Whitney U . A total of 25.4% of tweets were inaccurate. Accurate tweets were more likely written by Twitter authenticated authors (49.8% vs. 20.9%, 28.9% difference, 95% confidence interval [CI]: 17.7–38.2) with accurate tweet authors having more followers (19,491 vs. 7346; 3446 difference, 95% CI: 234–14,054) versus inaccurate tweet authors. Likes, retweets, tweet length, botometer scores, writing grade level, and rank order did not differ between accurate and inaccurate tweets. We found 1/4 of health‐related COVID‐19 tweets inaccurate indicating that the public should not rely on COVID‐19 health information written on Twitter. Ideally, improved government regulatory authority, public/private industry oversight, independent fact‐checking, and artificial intelligence algorithms are needed to ensure inaccurate information on Twitter is removed.

Background: E-cigarette (ECIG) use has increased substantially in the past decade. Co-use of alcohol and ECIGS may have serious consequences in adolescents. Objectives: To evaluate the association of e-cigarettes (ECIGs) with alcohol use in adolescents. Methods: Searched databases included PubMed, EMBASE, CINAHL, PsycINFO, Web of Science, and gray literature. Studies were included if they contained a non-E-cigarette group (NON-ECIG), an ECIG group, detailed rates of alcohol use, binge drinking, or drunkenness and included adolescents. Two independent reviewers extracted data using MOOSE guidelines. Evidence quality across studies was assessed using Cochrane GRADE methodology. Summary effects of ECIG vs. NON-ECIG use on any alcohol use and a composite of binge drinking/drunkenness were calculated using a random-effects model. Results: 28 of 3768 initially identified studies were included: 25 cross-sectional, 3 cohort studies, N = 458,357 total subjects (49.6% females). Pooling of data showed that ECIG users had a higher risk for any alcohol use compared to NON-ECIG users (Odds Ratio/OR 6.62, 95% confidence interval/CI 5.67-7.72) and a higher rate of binge drinking/drunkenness compared to NON-ECIG users (OR 6.73, 95% CI 4.5 − 10.07). The subset of high school ECIG users had higher rates of alcohol use (OR 8.17,) and binge drinking/drunkenness (OR 7.98, 95% CI 5.98-10.63) compared to NON-ECIG users. Conclusion: ECIG users had a higher risk of alcohol use and binge drinking/drunkenness compared to NON-ECIG users. Our findings indicate that interventions to reduce ECIG use should be coupled with measures to reduce alcohol use in adolescents.