The impact of COVID-19 pandemic on the incidence and treatment of anxiety and depression among men and women : a population-based analysis
Bibliographic record
Abstract
Introduction: As a global crisis, COVID-19 pandemic heightened stress-related mental illnesses such as anxiety and depression, disproportionately affecting women. Few studies have examined sex differences in the treated incidence and follow-up care for anxiety and depression at the population level before and during the pandemic. Objectives: This study explores whether COVID-19 led to 1) changes in the trend and level of treated incidence rates for anxiety and depression between males and females, and 2) changes in the rate and type of treatment received for anxiety and depression between males and females. Methods: Health administrative datasets, including physician billing, ambulatory care, and demographic files, were linked and analyzed. Study cohort included BC residents aged 18 and older with provincial health insurance coverage. Monthly treated incident cases were extracted and stratified by sex and age groups. Controlled interrupted time series analysis examined trends of sex disparities. Individuals with incident cases of anxiety and depression were tracked for one year before and during the pandemic to assess receipt of counseling, psychiatric treatment, emergency visits, or doctor’s visits. Log binomial regression analysis assessed sex differences in follow-up treatment. Results: Prior to COVID-19, females had higher treated incidence rates of anxiety (7.96 per 10,000) and depression (8.61 per 10,000) compared to males (3.84 per 10,000 and 4.59 per 10,000). At the onset of COVID-19, incidence rates of anxiety increased significantly for females compared to males, gradually returning to pre-pandemic levels. During COVID-19, treated incidence rates of depression decreased for males and marginally increased for females. For follow-up care, females were more likely to receive counseling and doctor’s visits compared to males before the pandemic. During the pandemic, counseling, psychiatric treatment, and emergency visits decreased for both males and females, while doctor’s visits increased. This suggests comparable impacts of COVID-19 on treatment access for both sexes. Conclusion: This study highlights significant pre-pandemic sex differences in the treated incidence and treatment of anxiety and depression. Our findings indicate that COVID-19 exacerbated sex disparities in treated incidence but not follow-up care. These findings underscore the need for targeted mental health interventions to address these disparities in Canada.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.002 | 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".