Economic analysis of pharmacist-administered influenza vaccines in Ontario, Canada
Bibliographic record
Abstract
Daria J O’Reilly,1,2 Gord Blackhouse,1,2 Sheri Burns,2 James M Bowen,1,2 Natasha Burke,1,2 Jeff Mehltretter,3 Nancy M Waite,4 Sherilyn KD Houle4 1Department of Health Research Methods, Evidence, and Impact, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada; 2Programs for Assessment of Technology in Health (PATH), The Research Institute of St. Joe’s Hamilton, Hamilton, ON, Canada; 3Neighbourhood Pharmacy Association of Canada, Toronto, ON, Canada; 4School of Pharmacy, Faculty of Science, University of Waterloo, Waterloo, ON, Canada Objective: The aim of this study was to evaluate the impact of pharmacist administration of influenza vaccine in Ontario on: 1) vaccination-associated costs related to the number of people vaccinated; 2) annual influenza-related outcomes and costs; and 3) change in productivity costs. Methods: Using available data for Ontario, the total number of vaccinations given by providers in the 2011/12 influenza season (pre) was compared to the 2013/14 influenza season (post). Vaccine costs and provider fees for administration were assigned for both periods. An economic model was created to estimate the impact of the change in influenza vaccination volume on influenza-related outcomes and on the health care costs associated with treating influenza-related outcomes. Productivity costs due to both time off work due to getting vaccinated and influenza illness were considered. One-way sensitivity analysis was used to assess parameter uncertainty. Results: The number of vaccinations received by Ontarians increased by 448,000 (3% of the population), with pharmacists vaccinating approximately 765,000 people/year. The increased cost to the Ontario Ministry of Health and Long-term Care was $6.3 million, while the money saved due to reduced influenza-related outcome costs was $763,158. Productivity losses were reduced by $4.5 million and $3.4 million for the time invested to get vaccinated and time off work due to influenza illness, respectively. Conclusion: After two influenza seasons, following the introduction of pharmacist-administered influenza vaccinations, there was a net immunization increase of almost 450,000, which potentially saved $2.3 million in direct health care costs and lost productivity in the province. Keywords: universal influenza vaccination, costs, pharmacist, productivity, immunization
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| 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) | 0.004 | 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".