Statistical methods for assessing the impact of the Covid-19 pandemic on health services use: Paediatric primary care and mental health access.
Bibliographic record
Abstract
ObjectivesThere were large disruptions to health care services after the onset of the COVID pandemic. We compared outpatient primary care and mental health care physician visits before and during the COVID-19 pandemic in Ontario, Canada. ApproachPopulation-based study of pediatric primary care and mental health visit rates after vs. before Covid restrictions. We used Poisson GEE regression to model 3-year pre-Covid trends and forecast expected trends after restrictions. The model included age, sex, a secular trend, and pre-Covid month indicators. Expected visit rates and 95% CIs post-restrictions were estimated by applying the linear combination of pre-Covid regression coefficients to the post-pandemic data and exponentiating. Relative changes in post Covid visit rates were expressed as an adjusted rate ratio of observed to expected rates by exponentiating the difference of observed and expected post-pandemic log rates and CIs. ResultsIn a population of 2.5 million children, primary care visit rates declined by 20% of expected (adjusted rate ratio [aRR], 0.80; 95% CI, 0.77–0.82). The largest monthly decrease occurred in April 2020. Virtual visits accounted for 53% of overall visits. Although visit rates increased slowly after April 2020, they did not return to pre-restriction levels by November 2020. Mental health visit rates declined rapidly to below expected in April 2020 (aRR, 0.81; 95% CI, 0.79-0.82) followed by an increase to 7% above expected (aRR, 1.07; 95% CI, 1.04-1.09) by July 2020 and sustained at 10-15% above expected to February 2021. Adolescent females had the greatest overall increase in mental health visit rates relative to expected (aRR, 1.26; 95% CI, 1.25-1.28). ConclusionThe pandemic contributed to rapid decreases in primary and mental health care, with some recovery and a shift to virtual care. There was a disproportionate increase in mental health care services among adolescent females. System-level planning to address increasing needs and monitor quality with such large shifts is warranted.
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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 | 0.138 | 0.326 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".