Trends in Health Care Services Use and Mortality in Adults With Cardiometabolic Diseases During the First Year of the Pandemic in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic disrupted health services, particularly affecting individuals with cardiometabolic diseases. This study compared health care use and mortality trends in adults with cardiometabolic diseases during the first pandemic year. It also examined associations between changes in outpatient visits and diagnostic tests with acute health care utilization and mortality. METHODS: Using health administrative databases, we conducted a retrospective population-based study using an open-cohort sampling on adult (≥18 years of age) Ontario residents with a prior diagnosis of angina, congestive heart failure, hypertension, or diabetes between January 2016 and March 2021. During the pandemic’s first year (March 2020–March 2021), observed (per 100 000 at-risk) versus projected event rates were compared for all-cause outpatient visits, diagnostic testing, emergency department visits, hospitalizations and mortality across the 4 time periods. Auto-regressive integrated moving-average models were used to calculate projected rates from observed monthly rates from similar periods pre-COVID (January 2016–December 2019). Quasi-Poisson models examined interactions between care access and acute outcomes. RESULTS: In the first pandemic quarter, rates of outpatient visits, diagnostic testing, emergency department visits, and hospitalizations for adults with cardiometabolic diseases decreased. By year-end, outpatient visits exceeded projections for angina, congestive heart failure, and diabetes, while most diagnostic test rates remained below projections. Mortality was as projected, except in adults with hypertension during the first quarter (observed 54 964 versus projected, 50 134 [95% CI, 46 686–53 840]). In adults with diabetes and hypertension, fewer cardiac investigations and echocardiograms were associated with greater mortality than prepandemic (interaction P <0.01). Unlike other populations, diabetes and hypertension patients showed reduced emergency department visits, hospitalizations, and mortality during months with the highest virtual care use ( P <0.02). CONCLUSIONS: The pandemic impacted health care utilization and mortality for adults with cardiometabolic diseases. Access to diagnostic testing is critical, particularly for those with diabetes and hypertension. Virtual care may benefit frequent health care users.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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".