Effects of COVID-19 pandemic on anxiety and depression in primary care: A retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Population-based surveys indicate that many people experienced increased psychological distress during the COVID-19 pandemic. We aimed to determine if there was a corresponding increase in patients receiving services for anxiety and depression from their family physicians. METHODS: Electronic medical records from the University of Toronto Practice Based-Research Network (UTOPIAN; N = 322,920 patients) were used to calculate incidence rates for anxiety/depression related visits and antidepressant prescriptions before the COVID-19 pandemic (January 2018-February 2020) and during the COVID-19 pandemic (March-December 2020). Data from the pre-pandemic period were used to predict expected rates during the pandemic period which was compared to the observed rate. RESULTS: The number of patients presenting with anxiety/depression symptoms in primary care varied across age groups, sex, and time since pandemic onset. Among the youngest patients (ages 10-18 years), there were fewer patients than pre-pandemic visiting for new episodes of anxiety/depression and being prescribed antidepressants in April 2020, but by the end of 2020 this trend had reversed such that incidence rates for anxiety/depression related visits were higher than pre-pandemic levels. Among older adults, incidence rates of anxiety/depression related visits increased in April 2020 with the onset of the pandemic, and remained higher than expected throughout 2020. LIMITATIONS: A convenience sample of 362 family physicians in Ontario was used. CONCLUSION: Demand for mental health services from family physicians varied by patient age and sex and changed with the onset of the COVID-19 pandemic. By the end of 2020, more patients were seeking treatment for anxiety/depression related concerns.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".