Access and use of general and mental health services before and during the COVID-19 pandemic: a systematic review and meta-analysis
Bibliographic record
Abstract
Objectives To quantify access to health services during the COVID-19 pandemic and measure the change in use between the prepandemic and the pandemic periods in a population with assessment of psychological distress or diagnosis of mental disorders. Data sources We developed and piloted a search syntax and adapted it to enter the following databases from 1 January 2020 to 31 March 2023: PubMed/MEDLINE, PsycINFO, Web of Science, Epistemonikos and the WHO International Clinical Trials Registry Platform. We reran the searches from the end of the original search to 3 December 2024. Design We systematically screened titles, abstracts and full texts of retrieved records. Eligibility criteria We included observational studies on any populations and regions, covering health services such as doctor visits, hospital admissions, diagnostic examinations, pharmaceutical therapies and mental health (MH) services. Only studies using validated scales to assess psychological distress or mental disorders as defined in the Diagnostic and Statistical Manual of Mental Disorders were included. Data extraction and synthesis We extracted data using a purposefully designed form and evaluated the studies’ quality with the Newcastle-Ottawa Scale. We measured the incidence rate (IR) of access to health services and the IR ratio (IRR) between the prepandemic and the pandemic periods. We calculated contacts days and catchment areas in the different periods. We used the random effects DerSimonian-Laird inverse-variance model and calculated heterogeneity with statistics I² and τ². We computed pooled IR and pooled IRR and tested the hypothesis of no variation (IRR=1). Results We retrieved 10 014 records and examined the full text of 580 articles. We included 136 primary studies of which 44 were meta-analysed. The IR of access to services during the pandemic was 2.59 contact months per 10 000 inhabitants (IR=2.592; 95% CI: 1.301 to 5.164). We observed a reduction of 28.5% in the use of services with negligible differences by age group and type of services (IRR=0.715; 95% CI: 0.651 to 0.785). We observed significant differences in effect sizes across studies (τ2=5.44; p<0.001 and τ2=0.090; p<0.001). Conclusion By considering MH, our study provides consolidated evidence and quantifies the reduction in the use of health services during the COVID-19 pandemic. PROSPERO registration number CRD42023403778.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | Meta-epidemiology (broad) Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | high |
| opus | Meta-epidemiology (broad) Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | medium |
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 | 0.019 | 0.048 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.023 | 0.038 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".