International health data trends in anxiety-related and depression-related visits to primary care before, during, and after the COVID-19 pandemic: a cross-sectional study by INTRePID
Bibliographic record
Abstract
Background The COVID-19 pandemic forced primary care to adapt, balancing routine care with rising mental health demands. We examined international trends in anxiety-related and depression-related visits to primary care to better understand population needs during and after the pandemic era. Methods This retrospective cross-sectional study by the International Consortium of Primary Care Big Data Researchers (INTRePID) examined anxiety-related and depression-related visits to primary care among adults aged 20 years and older in Australia, Brazil, Canada, China, Indonesia, Mexico, Norway, Peru, Singapore, Sweden, and the USA, from Jan 1, 2018 to Dec 31, 2023. No visits were excluded based on patients' sociodemographic characteristics or comorbidities, and all available visits were analysed. Aggregated data sourced from electronic medical records and administrative databases were stratified by sex and psychosocial life stages (20–29 years, 30–49 years, 50–69 years, and 70 years and older). We calculated the proportion of all primary care visits that were attributed to anxiety and depression. Generalised linear models estimated changes in visit rates across three periods: pre-pandemic (Jan 1, 2018–Mar 31, 2020), pandemic (April 1, 2020–Dec 31, 2021), and recovery (Jan 1, 2022–Dec 31, 2023). In China, periods were defined as: pre-pandemic (Jan 1, 2018–Dec 31, 2019), pandemic (Jan 1, 2020–Sept 30, 2021), and recovery (Oct 1, 2021–Dec 31, 2023). Findings We examined 1 059 101 897 primary care visits from 11 countries, of which 49 022 341 (4·6%) visits were attributed to anxiety and depression among adults aged 20 years and older. The highest proportions of anxiety-related and depression-related primary care visits were observed in Canada, with 18 240 011 (8·1%) of 224 512 695 visits, and in the USA, with 451 067 (11·3%) of 3 998 109 visits. Visit rates increased during the pandemic compared with the pre-pandemic period, with the largest rise observed in Peru (RR 2·17 [95% CI 1·96–2·40]) and the smallest, non-significant increase in Sweden (RR 1·03 [0·96–1·10]). During the recovery period, visit rates in Canada, China, Peru, Sweden, and the USA remained significantly elevated compared with pre-pandemic levels (ranging from RR 1·91 [95% CI 1·65–2·22] in Peru to RR 1·18 [1·07–1·30] in Sweden). The highest visit rates were observed in females, ranging from a significant increase in Mexico (rate ratio [RR] 1·88 [95% CI 1·69–2·10]) to a non-significant change in Indonesia (RR 1·05 [0·90–1·22]); and emerging adults (aged 20–29 years), except in Brazil, where adults aged 30–49 years had higher rates than emerging adults (RR 1·29 [1·22–1·36]). Middle and late adults (aged 50 years and older) had a higher relative increase in visit rates during the pandemic compared with the pre-pandemic period in several countries, with significant changes ranging from RR 2·84 (95% CI 2·62–3·07) in females aged 50–69 years in Peru to RR 1·08 (95% CI 1·01–1·16) in males aged 50–69 years in the USA. Interpretation Our findings underscore the central role of primary care in anxiety and depression management and highlight the need for sustained funding, workforce support, and age-sensitive and sex-sensitive interventions to address both ongoing and crisis-related mental health demands. Funding Rathlyn Foundation Primary Care Big Data Research and Discovery Fund.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".