Pertussis vaccination in pregnancy in Canada: a cost-utility analysis
Bibliographic record
Abstract
BACKGROUND: The Canadian National Advisory Committee on Immunization recommends universal vaccination against pertussis in pregnancy. We assessed the cost-effectiveness of vaccination with tetanus-diphtheria-acellular pertussis (Tdap) vaccine in pregnancy in Canada. METHODS: We conducted a cost-utility analysis comparing a vaccination program to no program corresponding with the 2017 Canadian guideline for economic evaluation from the Canadian Agency for Drugs and Technologies in Health. We developed 2 models - part decision tree, part Markov model - to estimate the long-term cost and quality-adjusted life-years (QALYs) for pregnant women and their infants. We obtained epidemiologic data from 2006 to 2015, and derived costs and utility values from relevant sources. Results were reported in 2019 Canadian dollars. We obtained expected values through probabilistic analysis, with methodologic and structural uncertainty assessed through scenario analyses. The analysis adopted an acquisition price of Tdap vaccine of $12.50, with scenario analysis conducted to identify the threshold price for vaccination to be cost-effective. RESULTS: In the base-case scenario, for every 1000 pregnant women vaccinated, the program would lead to a gain of 0.3 QALYs, occurring solely in infants, at an increased total cost of $12 987, or $44 301 per QALY gained. Based on a threshold of $50 000 per QALY gained, vaccination would have been cost-effective in 6 of the 10 years included in the model (range of incremental costs $20 463-$100 348 per QALY gained). The threshold cost for Tdap vaccine to be cost-effective over the 10-year horizon was $14.03. INTERPRETATION: Based on a threshold of $50 000 per QALY gained, vaccination against pertussis in pregnancy would be cost-effective if the acquisition cost per vaccine were $14.03 or less. Province- and territory-specific analyses should be done to inform local decision-making.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".