Evaluation of In-Hospital and Community-Based Healthcare Utilization and Costs During the Coronavirus 2019 (COVID-19) Pandemic in Alberta, Canada: A Population-Based Descriptive Study
Bibliographic record
Abstract
Background: Assessing the financial burden of COVID-19 is important for planning health services and resource allocation to inform future pandemic response. Objectives: This study examines the changing dynamics in healthcare utilization patterns and costs from a public healthcare perspective during the COVID-19 pandemic in Alberta, Canada. Design: Population-based descriptive study. Methods: All adult patients over the age of 18 years who had a laboratory-confirmed COVID-19 diagnosis in Alberta, Canada from March 1, 2020 to December 15, 2021. We described demographic information and community- and hospital-based healthcare utilization and costs. We compared changes in each outcome throughout the first four waves of the pandemic. Results: Among 255,037 patients, hospitalization incurred significantly higher costs ( N = 20,603; aRR = 755.51; marginal cost: $21,738.17 CAD; P < .01). Wave 2 recorded the highest cost for Emergency Department (ED) visits (aRR = 1.10; marginal cost: $79.19 CAD; P < .01). Compared to Wave 1, Waves 2-4 all recorded significantly lower costs for out-patient visits. Wave 2’s in-patient cost for patients that required ICU admission was significantly lower than Wave 1 (aRR = 0.75; marginal cost: −$24,142.47 CAD; P = .02). Conclusion: COVID-19 exerted a heavy toll on healthcare services, and the dynamics of this continue to evolve. Utilization of ED and in-patient services were particularly high. Severe infections requiring hospitalization and ICU admission are more expensive than non-hospitalized and non-ICU hospital admits. Future studies should clarify specific factors, such as sociodemographic determinants, that contribute to evolving patterns of health services consumption and changing trends in cost to holistically inform responses to future pandemics.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".