Flu Shots, Work Absences and Hospitalizations: Is an Ounce of Prevention Worth a Pound of Cure?
Bibliographic record
Abstract
In this study, I evaluate the health and economic consequences of a broad-based flu vaccination program. The Ontario Influenza Immunization Campaign was introduced in 2001, and delivers free flu shots to healthy children and adults. This program is novel and controversial. Traditionally, the flu shot is recommended only for the elderly or infirm and it is assumed that benefits outside these groups are relatively small. The Ontario flu shot campaign offers a useful policy experiment to evaluate the impact of expansion to children and younger adults. Given that a simple before and after comparison for Ontario may incorrectly attribute all changes in outcomes to the flu shot campaign, and even conventional difference-in-difference comparisons with other provinces may be confounded with differential trends, I instead develop a triple difference identification strategy that exploits variation in the match of the flu shot to the flu. I find that after the campaign, when the vaccine is a good match against circulating strains of flu, Ontario has significantly greater decreases in illness and lost work-time relative to other provinces. Furthermore, based on the results for hospitalizations alone, an ounce of prevention is worth a pound of cure: the program costs approximately $33 million per year while, in a good match season, the program saves $144 million in respiratory hospitalization costs. I am also able to provide the first largescale evidence of the benefits of vaccination in terms of worker productivity. Using labor force data, I find that the program saved an additional $109 million in worker absence costs. There is also evidence of significant external effects from vaccination. While the results are strongest for children and younger adults, hospitalization rates for those older than 65 fell also even though this group saw no increase in vaccination. This suggests that increased vaccination of the young exerted a positive health externality for the elderly. I owe thanks to Dwayne Benjamin, Mark Stabile, Gustavo Bobonis and Robert McMillan for invaluable discussions and
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".