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Record W4388549569 · doi:10.1016/j.lana.2023.100629

Cost-effectiveness analysis of nirsevimab and maternal RSVpreF vaccine strategies for prevention of Respiratory Syncytial Virus disease among infants in Canada: a simulation study

2023· article· en· W4388549569 on OpenAlexafffundabout
Affan Shoukat, Elaheh Abdollahi, Alison P. Galvani, Scott A. Halperin, Joanne M. Langley, Seyed M. Moghadas

Bibliographic record

VenueThe Lancet Regional Health - Americas · 2023
Typearticle
Languageen
FieldMedicine
TopicRespiratory viral infections research
Canadian institutionsNova Scotia Health AuthorityDalhousie UniversityYork University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Immunization Research NetworkCanada Foundation for InnovationCanadian Institutes of Health ResearchNotsew Orm Sands Foundation
KeywordsMedicineCohortVaccinationCost effectivenessPediatricsHealth careCohort studyEnvironmental healthDemographyImmunologyInternal medicine

Abstract

fetched live from OpenAlex

Background: The cost-effectiveness of immunisation strategies with a long-acting monoclonal antibody (nirsevimab) and/or a protein-based maternal vaccine (RSVpreF) for protecting infants from Respiratory Syncytial Virus (RSV)-associated illness has not been previously determined for Canada. We estimated the health benefits and cost-effectiveness of nirsevimab for immunising the entire birth cohort, regardless of gestational age or other risk factors. Additionally, we evaluated the health benefits and cost-effectiveness of a combined strategy of year-round vaccination of pregnant women with RSVpreF and immunisation of infants at high risk, including those born preterm or with chronic conditions, with nirsevimab during the RSV season. Methods: We developed a discrete-event simulation model, parameterized with the data on medically-attended RSV infections among infants under one year of age from 2010 to 2019, including outpatient care, hospitalisations, and deaths. Intervention scenarios targeting twelve monthly birth cohorts and pregnant women, reflecting the 2021 census data for Ontario, Canada were evaluated over a follow-up time horizon of one year from birth. Taking into account the costs (in 2023 Canadian dollars) associated with RSV-related outcomes, we calculated the net monetary benefit using the quality-adjusted life-year (QALY) gained. Further, we determined the range of price-per-dose (PPD) for nirsevimab and RSVpreF within which the program was cost-effective. Cost-effectiveness analyses were conducted from both healthcare and societal perspectives. Findings: Using a willingness-to-pay of CAD$50,000 per QALY gained, we found that immunising the entire birth cohort with nirsevimab would be cost-effective from a societal perspective for a PPD of up to $290, with an annual budget impact of $83,978 for 1113 infants per 100,000 population. An alternative, combined strategy of vaccinating pregnant women and immunising only infants at high risk of severe disease would lead to a lower budget impact of $49,473 per 100,000 population with a PPD of $290 and $195 for nirsevimab and RSVpreF vaccine, respectively. This combined strategy would reduce infant mortality by 76%-85%, comparable to a 78% reduction achieved through a nirsevimab-only program of the entire birth cohort. The PPD for cost-effective programs with nirsevimab was sensitive to the target population among infants. Interpretation: Passive immunisation of infants under 6 months of age with nirsevimab and vaccination of pregnant women with RSVpreF could be a cost-effective strategy for protecting infants during their first RSV season. Funding: This study was supported by the Canadian Immunisation Research Network (CIRN) and the Canadian Institutes of Health Research (CIHR). Seyed M. Moghadas acknowledges support from the Natural Sciences and Engineering Research Council of Canada (MfPH and Discovery grants). Alison P. Galvani acknowledges support from the The Notsew Orm Sands Foundation.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.027
Threshold uncertainty score0.686

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
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 head0.227
GPT teacher head0.496
Teacher spread0.269 · 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

Citations70
Published2023
Admission routes3
Has abstractyes

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