Impact of COVID-19 Pandemic on Mental Health Visits: An interrupted time series across 9 INTRePID countries through Dec 2021
Bibliographic record
Abstract
Context The COVID-19 pandemic impacted mental health globally, yet there is still limited evidence regarding the use of mental health services in primary care. Objective: To assess the impact of the COVID-19 pandemic on mental health visit rates in primary care. Study design and analysis: Interrupted time series analysis to examine primary care mental health visit rate changes. Sub-group analysis considered service type and mental health categories. A generalized linear mixed model with penalized quasi-likelihood estimation was used to model trends. A country survey data on care delivery, referral, and insurance were also considered. Dataset and population: Data were from the International Consortium of Primary Care Big Data Researchers (INTRePID) including Argentina, Australia, Canada, China, Norway, Peru, Singapore, Sweden, and the USA. The consortium accesses primary care data in each country, facilitating comparative studies. Data coverage varied, including national, subsystem, and regional data from 2018 to 2021. Outcome The primary measure was rates of monthly mental health visits (per 100 visits), by service type (inperson, virtual) and mental health categories: Anxiety and Mood Disorders, Bipolar, Schizophrenia, Sleep Disorders, Dementia, ADHD and Eating Disorders and Substance-Related Disorders Results: Mental health visit rates increased immediately after the onset of the pandemic in all countries. In Argentina, Canada, Australia, China, Peru, Singapore, and Sweden, this rise was statistically significant, with increases ranging from 1.07 (95%CI: 1.03 - 1.12), p = 0.002 to 2.12 (95%CI: 2.06 to 2.17), p < 0.001. Trend increases in the following months varied across countries. The primary reasons for visits were anxiety and mood disorders (50 to 90%), followed by sleep disorders, substance-related and addictive disorders. Virtual mental health visits emerged as a notable modality for service delivery in Australia, Canada, Norway, Peru, Sweden, and the USA, comprising between 26%to 75% of the total mental health visits. Conclusions We found an overall increase in mental health visits during the pandemic, though not uniform across countries, possibly due to differences in healthcare priorities and responses. Anxiety and mood disorders were the main drivers of increased visits. Primary care plays a crucial role in addressing mental health issues, especially during times of crisis.
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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 | 0.002 | 0.006 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".