Healthcare utilization and adverse outcomes stratified by sex, age and long-term care residency using the Alberta COVID-19 Analytics and Research Database (ACARD): a population-based descriptive study
Bibliographic record
Abstract
Abstract Background Understanding the epidemiology of Coronavirus Disease of 2019 (COVID-19) in a local context is valuable for both future pandemic preparedness and potential increases in COVID-19 case volume, particularly due to variant strains. Methods Our work allowed us to complete a population-based study on patients who tested positive for COVID-19 in Alberta from March 1, 2020 to December 15, 2021. We completed a multi-centre, retrospective population-based descriptive study using secondary data sources in Alberta, Canada. We identified all adult patients (≥ 18 years of age) tested and subsequently positive for COVID-19 (including only the first incident case of COVID-19) on a laboratory test. We determined positive COVID-19 tests, gender, age, comorbidities, residency in a long-term care (LTC) facility, time to hospitalization, length of stay (LOS) in hospital, and mortality. Patients were followed for 60 days from a COVID-19 positive test. Results Between March 1, 2020 and December 15, 2021, 255,037 adults were identified with COVID-19 in Alberta. Most confirmed cases occurred among those less than 60 years of age (84.3%); however, most deaths (89.3%) occurred among those older than 60 years. Overall hospitalization rate among those who tested positive was 5.9%. Being a resident of LTC was associated with substantial mortality of 24.6% within 60 days of a positive COVID-19 test. The most common comorbidity among those with COVID-19 was depression. Across all patients 17.3% of males and 18.6% of females had an unplanned ambulatory visit subsequent to their positive COVID-19 test. Conclusions COVID-19 is associated with extensive healthcare utilization. Residents of LTC were substantially impacted during the COVID-19 pandemic with high associated mortality. Further work should be done to better understand the economic burden associated with related healthcare utilization following a COVID-19 infection to inform healthcare system resource allocation, planning, and forecasting.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".