Economic Appraisal of Ontario’s Universal Influenza Immunization Program: A Cost-Utility Analysis
Bibliographic record
Abstract
Background: In July 2000, the province of Ontario, Canada, initiated a universal influenza immunization program (UIIP) to provide free seasonal influenza vaccines for the entire population. This is the first large-scale program of its kind worldwide. The objective of this study was to conduct an economic appraisal of Ontario’s UIIP compared to a targeted influenza immunization program (TIIP). Methods and Findings: A cost-utility analysis using Ontario health administrative data was performed. The study was informed by a companion ecological study comparing physician visits, emergency department visits, hospitalizations, and deaths between 1997 and 2004 in Ontario and nine other Canadian provinces offering targeted immunization programs. The relative change estimates from pre-2000 to post-2000 as observed in other provinces were applied to pre-UIIP Ontario event rates to calculate the expected number of events had Ontario continued to offer targeted immunization. Main outcome measures were quality-adjusted life years (QALYs), costs in 2006 Canadian dollars, and incremental cost-utility ratios (incremental cost per QALY gained). Program and other costs were drawn from Ontario sources. Utility weights were obtained from the literature. The incremental cost of the program per QALY gained was calculated from the health care
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".