MétaCan
Menu
Back to cohort
Record W4408543868 · doi:10.3201/eid3104.241463

Population-Based Matched Cohort Study of COVID-19 Healthcare Costs, Ontario, Canada

2025· article· en· W4408543868 on OpenAlexaboutno aff
Beate Sander, Sharmistha Mishra, Sarah Swayze, Yeva Sahakyan, Raquel Duchen, Kieran L. Quinn, Naveed Z. Janjua, Hind Sbihi, Jeffrey Kwong

Bibliographic record

VenueEmerging infectious diseases · 2025
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsnot available
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Health careCohortMedicineSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakCohort studyPopulationPandemicGeographyDemographyEnvironmental healthVirologyOutbreakEconomic growthDiseaseInternal medicineInfectious disease (medical specialty)Economics

Abstract

fetched live from OpenAlex

Estimates of COVID-19-related healthcare costs are key to health system planning, but attributable cost data remain limited. We characterized healthcare costs attributable to COVID-19 through a population-based matched cohort study in Ontario, Canada, by using health administrative data. We matched SARS-CoV-2-positive persons from 2020 to unexposed historical control persons from 2016-2018. We estimated phase-based and survival-adjusted COVID-19-attributable healthcare costs from the health system perspective. We matched 159,817 persons. Mean (95% CI) attributable 10-day costs per person were $1 ($-4 to $6) preindex, $240 ($231-$249) during acute care, $18 ($14-$21) in postacute phases, $3,928 ($3,471-$4,384) in the terminal phase for early deaths, and $1,781 ($1,182-$2,380) for late deaths. Mean cumulative survival-adjusted cost at 360 days was $2,553 ($2,348-$2,756) per person. SARS-CoV-2 infection is associated with substantial long-term healthcare costs, consistent with understanding of post-COVID condition. Determining phase-specific costs can inform budget and pandemic planning.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.353
Teacher spread0.334 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueEmerging infectious diseasesSame topicCOVID-19 and healthcare impactsFrench-language works237,207