Depression and Anxiety Symptoms Among Cisgender Gay and Bisexual Men During the Onset of the COVID-19 Pandemic: Time Series Analysis of a US National Cohort Study
Bibliographic record
Abstract
BACKGROUND: The onset of the COVID-19 pandemic in the United States in March 2020 caused a dramatic change in the way many people lived. Few aspects of daily life were left undisrupted by the pandemic's onset as well as the accompanying policies to control the spread of the disease. Previous research has found that the pandemic may have significantly impacted the mental health of lesbian, gay, bisexual, transgender, and queer (LGBTQ) individuals-potentially more so than other individuals. However, the pandemic did not affect all areas of the United States at the same time, and there may be regional variation in the impact of the onset of the pandemic on depressive symptoms among LGBTQ individuals. OBJECTIVE: To assess regional variation of the impact of the pandemic, we conducted a time series analysis stratified by US geographic region to examine symptoms of depression and anxiety among a sample of primarily cisgender gay and bisexual men before and after the onset of the COVID-19 pandemic in the United States. METHODS: In total, 5007 participants completed assessments as part of the Together 5000 study, an ongoing prospective cohort study. Depressive and anxiety symptoms were measured using the Patient Health Questionnaire-4. Patient Health Questionnaire-4 scores were graphed as a function of days from March 15, 2020. Locally estimated scatterplot smoothing trend lines were applied. A sieve-bootstrap Mann-Kendall test for monotonic trend was conducted to assess the presence and direction of trends in the scatterplots. We then compared the observed trends to those observed for 1 year prior (2018-2019) to the pandemic onset using data collected from the same sample. RESULTS: Significant positive trends were detected for the Northeast (P=.03) and Midwest (P=.01) regions of the United States in the 2020 assessment, indicating that symptoms of anxiety and depression were increasing in the sample in these regions immediately prior to and during the onset of the pandemic. In contrast, these trends were not present in data from the 2018 to 2019 assessment window. CONCLUSIONS: Symptoms of anxiety and depression increased among the study population in the Northeast and Midwest during the beginning months of the COVID-19 pandemic, but similar increase was not observed in the South and West regions. These trends were also not found for any region in the 2018 to 2019 assessment window. This may indicate region-specific trends in anxiety and depression, potentially driven by the burden of the pandemic and policies that varied from region to region. Future studies should consider geographic variation in COVID-19 spread and policies as well as explore potential mechanisms by which this could influence the mental health of LGBTQ individuals.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".