Pre-pandemic activity on a myalgic encephalomyelitis/chronic fatigue syndrome support forum is highly associated with later activity on a long COVID support forum for a variety of reasons: A mixed methods study.

Encephalomyelitis/chronic fatigue syndrome (ME/CFS) and long COVID share some clinical and social characteristics. We predicted that this would lead to an increased interaction between pre-pandemic members of an ME/CFS online support community and a long COVID community. We performed a mixed-methods retrospective observational study of the Reddit activity of 7,544 users active on Reddit's long COVID forum. From among 1600 forums, pre-pandemic activity specifically on a ME/CFS forum is the top predictor of later participation on the long COVID forum versus an acute COVID support forum. In the qualitative portion, motives for this co-participation included seeking mutual support and dual identification with both conditions. Some of this effect may be explained by pre-existing ME/CFS possibly being a risk factor for long COVID and/or SARS-CoV-2 infection being a cause of ME/CFS relapse. The high rate of ME/CFS patients seeking mutual support on a long COVID forum speaks to the long-suffering experience of these patients not feeling heard or respected, and the hope of some ME/CFS patients to gain legitimacy through the public's growing recognition of long COVID.

Use of Quantile Treatment Effects Analysis to Describe Antidepressant Response in Randomized Clinical Trials Submitted to the US Food and Drug Administration: A Secondary Analysis of Pooled Trial Data.

Importance: Major depressive disorder (MDD) is a leading cause of global distress and disability. Earlier studies have indicated that antidepressant therapy confers a modest reduction in depressive symptoms on average, but the distribution of this reduction requires more research. Objective: To estimate the distribution of antidepressant response by depression severity. Design, Setting, and Participants: In this secondary analysis of pooled trial data, quantile treatment effect (QTE) analysis was conducted from the US Food and Drug Administration (FDA) database of antidepressant monotherapy for patients with MDD, encompassing 232 positive and negative trials submitted to the FDA between 1979 and 2016. Analysis was restricted to participants with severe MDD (17-item Hamilton Rating Scale for Depression [HAMD-17] score ≥20). Data analysis was conducted from August 16, 2022, to April 16, 2023. Intervention: Antidepressant monotherapy compared with placebo. Main Outcomes and Measures: The distribution of percentage depression response was compared between the pooled treatment arm and pooled placebo arm. Percentage depression response was defined as 1 minus the ratio of final depression severity to baseline depression severity, expressed as a percentage. Depression severity was reported in HAMD-17-equivalent units. Results: A total of 57 313 participants with severe depression were included in the analysis. There was no significant imbalance in baseline depression severity between the pooled treatment arm and pooled placebo arm, with a mean HAMD-17 difference of 0.037 points (P = .11 by Wilcoxon rank sum test). An interaction term test for rank similarity did not reject the rank similarity governing percentage depression response (P > .99). The entire distribution of depression response was more favorable in the pooled treatment arm than in the pooled placebo arm. The maximum separation between treatment and placebo occurred at the 55th quantile and corresponded to an absolute improvement in depression due to active drug of 13.5% (95% CI, 12.4%-14.4%). The separation between treatment and placebo diminished near the tails of the distribution. Conclusions and Relevance: In this QTE analysis of pooled clinical trial data from the FDA, antidepressants were found to confer a small reduction in depression severity that was broadly distributed across participants with severe depression. Alternatively, if the assumptions behind the QTE analysis are not met, then the data are also compatible with antidepressants eliciting more complete response in a smaller subset of participants than is suggested by this QTE analysis.

The association between evening social media use and delayed sleep may be causal: Suggestive evidence from 120 million Reddit timestamps.

Public health officials and clinicians routinely advise social media users to avoid nighttime social media use due to the perception that this delays the onset of sleep and predisposes to the health risks of insufficient sleep. With some exceptions, the evidence behind this advice mostly derives from surveys identifying an association between self-reported social media usage and self-reported sleep patterns. In principle, these associations could alternatively be explained by users turning to social media to pass the time when they are otherwise having difficulty sleeping, or by individual differences that draw some people to frequent social media use, or by offline activities that overlap with both social media use and delayed sleep. To attempt to distinguish among these explanations, we leveraged estimated bedtimes from 44,000 Reddit users reported in a recent study and their 120 million posts to test whether the relationship between sleep and social media has properties suggestive of a causal relationship. We find that users are especially likely to be active on Reddit after their bedtime (and therefore awake) on nights that they posted to Reddit shortly before bedtime, especially if they posted multiple times or in high-engagement forums that night. Overall, this study lends additional support to the notion that there likely is some causal effect of evening social media use on delayed sleep onset.

Estimation of Bedtimes of Reddit Users: Integrated Analysis of Time Stamps and Surveys.

BACKGROUND: Individuals with later bedtimes have an increased risk of difficulties with mood and substances. To investigate the causes and consequences of late bedtimes and other sleep patterns, researchers are exploring social media as a data source. Pioneering studies inferred sleep patterns directly from social media data. While innovative, these efforts are variously unscalable, context dependent, confined to specific sleep parameters, or rest on untested assumptions, and none of the reviewed studies apply to the popular Reddit platform or release software to the research community. OBJECTIVE: This study builds on this prior work. We estimate the bedtimes of Reddit users from the times tamps of their posts, test inference validity against survey data, and release our model as an R package (The R Foundation). METHODS: We included 159 sufficiently active Reddit users with known time zones and known, nonanomalous bedtimes, together with the time stamps of their 2.1 million posts. The model's form was chosen by visualizing the aggregate distribution of the timing of users' posts relative to their reported bedtimes. The chosen model represents a user's frequency of Reddit posting by time of day, with a flat portion before bedtime and a quadratic depletion that begins near the user's bedtime, with parameters fitted to the data. This model estimates the bedtimes of individual Reddit users from the time stamps of their posts. Model performance is assessed through k-fold cross-validation. We then apply the model to estimate the bedtimes of 51,372 sufficiently active, nonbot Reddit users with known time zones from the time stamps of their 140 million posts. RESULTS: The Pearson correlation between expected and observed Reddit posting frequencies in our model was 0.997 on aggregate data. On average, posting starts declining 45 minutes before bedtime, reaches a nadir 4.75 hours after bedtime that is 87% lower than the daytime rate, and returns to baseline 10.25 hours after bedtime. The Pearson correlation between inferred and reported bedtimes for individual users was 0.61 (P<.001). In 90 of 159 cases (56.6%), our estimate was within 1 hour of the reported bedtime; 128 cases (80.5%) were within 2 hours. There was equivalent accuracy in hold-out sets versus training sets of k-fold cross-validation, arguing against overfitting. The model was more accurate than a random forest approach. CONCLUSIONS: We uncovered a simple, reproducible relationship between Reddit users' reported bedtimes and the time of day when high daytime posting rates transition to low nighttime posting rates. We captured this relationship in a model that estimates users' bedtimes from the time stamps of their posts. Limitations include applicability only to users who post frequently, the requirement for time zone data, and limits on generalizability. Nonetheless, it is a step forward for inferring the sleep parameters of social media users passively at scale. Our model and precomputed estimated bedtimes of 50,000 Reddit users are freely available.

Author Correction: Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples.

Correction to this paper has been published: https://doi.org/10.1038/s41467-020-20128-w.

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples.

The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts.