The Impact of the COVID-19 Pandemic and Associated Control Measures on the Mental Health of the General Population : A Systematic Review and Dose-Response Meta-analysis.

BACKGROUND: To what extent the COVID-19 pandemic and its containment measures influenced mental health in the general population is still unclear. PURPOSE: To assess the trajectory of mental health symptoms during the first year of the pandemic and examine dose-response relations with characteristics of the pandemic and its containment. DATA SOURCES: Relevant articles were identified from the living evidence database of the COVID-19 Open Access Project, which indexes COVID-19-related publications from MEDLINE via PubMed, Embase via Ovid, and PsycInfo. Preprint publications were not considered. STUDY SELECTION: Longitudinal studies that reported data on the general population's mental health using validated scales and that were published before 31 March 2021 were eligible. DATA EXTRACTION: An international crowd of 109 trained reviewers screened references and extracted study characteristics, participant characteristics, and symptom scores at each timepoint. Data were also included for the following country-specific variables: days since the first case of SARS-CoV-2 infection, the stringency of governmental containment measures, and the cumulative numbers of cases and deaths. DATA SYNTHESIS: In a total of 43 studies (331 628 participants), changes in symptoms of psychological distress, sleep disturbances, and mental well-being varied substantially across studies. On average, depression and anxiety symptoms worsened in the first 2 months of the pandemic (standardized mean difference at 60 days, -0.39 [95% credible interval, -0.76 to -0.03]); thereafter, the trajectories were heterogeneous. There was a linear association of worsening depression and anxiety with increasing numbers of reported cases of SARS-CoV-2 infection and increasing stringency in governmental measures. Gender, age, country, deprivation, inequalities, risk of bias, and study design did not modify these associations. LIMITATIONS: The certainty of the evidence was low because of the high risk of bias in included studies and the large amount of heterogeneity. Stringency measures and surges in cases were strongly correlated and changed over time. The observed associations should not be interpreted as causal relationships. CONCLUSION: Although an initial increase in average symptoms of depression and anxiety and an association between higher numbers of reported cases and more stringent measures were found, changes in mental health symptoms varied substantially across studies after the first 2 months of the pandemic. This suggests that different populations responded differently to the psychological stress generated by the pandemic and its containment measures. PRIMARY FUNDING SOURCE: Swiss National Science Foundation. (PROSPERO: CRD42020180049).

An evaluation of the COVID-19 recommendation map identified diverging clinical and public health guidance.

OBJECTIVES: To describe divergence between actionable statements issued by coronavirus disease 2019 (COVID-19) guideline developers cataloged on the "COVID-19 Recommendations and Gateway to Contextualization" platform. STUDY DESIGN AND SETTING: We defined divergence as at least two comparable actionable statements with different explicit judgments of strength, direction, or subgroup consideration of the population or intervention. We applied a content analysis to compare guideline development methods for a sample of diverging statements and to evaluate factors associated with divergence. RESULTS: Of the 138 guidelines evaluated, 85 (62%) contained at least one statement that diverged from another guideline. We identified 223 diverging statements in these 85 guidelines. We grouped statements into 66 clusters. Each cluster addressed the same population, intervention, and comparator group or just similar interventions. Clinical practice statements were more likely to diverge in an explicit judgment of strength or direction compared to public health statements. Statements were more likely to diverge in strength than direction. The date of publication, used evidence, interpretation of evidence, and contextualization considerations were associated with divergence. CONCLUSION: More than half of the assessed guidelines issued at least one diverging statement. This study helps in understanding the types of differences between guidelines issuing comparable statements and factors associated with their divergence.

The Plebeian Algorithm: A Democratic Approach to Censorship and Moderation.

BACKGROUND: The infodemic created by the COVID-19 pandemic has created several societal issues, including a rise in distrust between the public and health experts, and even a refusal of some to accept vaccination; some sources suggest that 1 in 4 Americans will refuse the vaccine. This social concern can be traced to the level of digitization today, particularly in the form of social media. OBJECTIVE: The goal of the research is to determine an optimal social media algorithm, one which is able to reduce the number of cases of misinformation and which also ensures that certain individual freedoms (eg, the freedom of expression) are maintained. After performing the analysis described herein, an algorithm was abstracted. The discovery of a set of abstract aspects of an optimal social media algorithm was the purpose of the study. METHODS: As social media was the most significant contributing factor to the spread of misinformation, the team decided to examine infodemiology across various text-based platforms (Twitter, 4chan, Reddit, Parler, Facebook, and YouTube). This was done by using sentiment analysis to compare general posts with key terms flagged as misinformation (all of which concern COVID-19) to determine their verity. In gathering the data sets, both application programming interfaces (installed using Python's pip) and pre-existing data compiled by standard scientific third parties were used. RESULTS: The sentiment can be described using bimodal distributions for each platform, with a positive and negative peak, as well as a skewness. It was found that in some cases, misinforming posts can have up to 92.5% more negative sentiment skew compared to accurate posts. CONCLUSIONS: From this, the novel Plebeian Algorithm is proposed, which uses sentiment analysis and post popularity as metrics to flag a post as misinformation. This algorithm diverges from that of the status quo, as the Plebeian Algorithm uses a democratic process to detect and remove misinformation. A method was constructed in which content deemed as misinformation to be removed from the platform is determined by a randomly selected jury of anonymous users. This not only prevents these types of infodemics but also guarantees a more democratic way of using social media that is beneficial for repairing social trust and encouraging the public's evidence-informed decision-making.