Two-year retrospective review of costs associated with COVID-19 case management in Regina, Saskatchewan
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic, declared in March 2020, caused significant morbidity and mortality globally. This study aims to estimate the costs associated with managing COVID-19 infected patients in Regina. METHOD: The study focuses on the direct and indirect healthcare costs of managing a COVID-19 case. Costing elements included are diagnostic, public health, inpatient and outpatient management costs. The costing analysis estimates the total cost of COVID-19 case management in Regina, the average cost per case based on disease severity, and the costs for diagnostics, public health management, and clinical areas. RESULTS: Severe cases, representing 1.3% of cases, accounted for a quarter of the total cost of illness, while moderate cases (1.8%) contributed to less than 5% of the overall cost. Mild cases (96.9%) were responsible for three-quarters of the associated illness costs. Over two years, approximately $85 million was spent on the care of 28,733 cases, primarily due to hospitalization costs. Annual per-patient expenses increased from $45 in 2020 to $183 in 2021, reflecting a higher case burden and greater health care utilization. Furthermore, the Omicron variant accounted for 44% of the disease burden and 36% of the illness costs. Patients older than 80 accounted for 10% of illness costs, while children aged less than 18 accounted for about 17%. CONCLUSION: The primary costs were human resources and hospitalizations for older individuals, significantly impacting the Saskatchewan Health Authority's budget due to the pandemic. This analysis does not fully capture the effects in Regina.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".