Impact of the COVID-19 pandemic on health services utilisation and mortality in Ontario, Canada: an interrupted time series analysis
Bibliographic record
Abstract
Background: This study explores changing patterns of healthcare utilisation for chronic diseases during the COVID-19 pandemic in Ontario, Canada. It compares prepandemic and pandemic morbidity and mortality, focusing on physician and emergency department visits, hospitalisations for anxiety, depression and chronic diseases, as well as all-cause mortality rates. Methods: We constructed a cohort of 2 950 384 adults (18+ years), using administrative health databases, who were living in Ontario, Canada, between the period of January 2017 and March 2023 and recorded the number of visits each individual had in the follow-up period related to chronic conditions. The data were then analysed using an interrupted time-series design to observe changes from before compared with during the pandemic in (1) monthly physician or emergency visits and hospitalisations and (2) monthly all-cause deaths. The exposure in this study was the onset of the COVID-19 pandemic in Ontario, Canada. Results: In the prepandemic period, mean monthly PCR-tested visits in Ontario were 364 880, with a steady increase of 1210 visits per month. During the initial phase of the COVID-19 pandemic, there was a decline in physician visits and hospitalisations for chronic diseases. This trend changed, leading to a significant rise in visits that peaked in March 2021, increasing by 1690 visits monthly. From 2022 onwards, visits saw a notable decline, decreasing by 6830 per month (p<0.05), reflecting reduced healthcare utilisation in the later pandemic phases. Conclusions: The COVID-19 pandemic caused significant fluctuations in healthcare utilisation in Ontario. These changes suggest increased risks of missed diagnoses and delayed care, impacting morbidity and mortality. The results emphasise the importance of adaptable healthcare systems and strong pandemic preparedness to maintain care continuity, especially for chronic disease management, during resource-limited periods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".