Short-term and Long-Term Healthcare Costs Attributable to diagnosed COVID-19 in Ontario; Canada: A Population-Based Matched Cohort Study
Bibliographic record
Abstract
Abstract Objectives Estimates of health system costs due to COVID-19, especially for long-term disability (post COVID-19 condition [PCC]) are key to health system planning, but attributable cost data remain scarce. We characterized COVID-19-attributable costs from the health system perspective. Methods Population-based matched cohort study in Ontario, Canada, using health administrative data. To assign attribution to COVID-19, individuals, defined as exposed (positive SARS-CoV-2 PCR test, 01/2020-12/2020) were matched 1:1 to an unexposed individuals (01/2016-12/2018). Historical matching was used to reduce biases due to overall reductions in healthcare during the pandemic and contamination bias. The index date was defined as the first occurrence of positive SARS-CoV-2 PCR test. We used phase-of-care costing to calculate mean attributable per-person costs (2023 CAD), standardized to 10 days, during four phases of illness: pre-index date, acute care, post-acute care (suggestive of PCC), and terminal phase (stratified by early and late deaths). Finally, we estimated total costs at 360 days by combining costs with survival estimates. Results Of 165,838 exposed individuals, 159,817 were matched (mean age 40±20 years, 51% female). Mean (95%CI) attributable 10-day costs per person were $1 ($-4, $6) pre-index, $240 ($231, $249) during acute care, and $18 ($14, $21) during post-acute phases. During the terminal phase, mean attributable costs were $3,928 ($3,471, $4,384) for early deaths and $1,781 ($1,182, $2,380) for late deaths. Hospitalizations accounted for 42% to 100% of total costs. Compared to males, costs among females were lower during the acute care phase, but higher during the post-acute care phase. Mean cumulative per-person cost at 360 days was $2,553 ($2,348, $2,756); females had lower costs ($2,194 [$1,945, $2,446]) than males ($2,921 [$2,602, $3,241]). Conclusions SARS-CoV-2 infection is associated with substantial long-term healthcare costs, consistent with our understanding of the PCC. Understanding phase-specific costs can inform health sector budget planning, future economic evaluations, and pandemic planning.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".