Evidence map›Paper›PMID 28170416›Full record

ArticlePloS one2017

Cost-effectiveness of maternal influenza immunization in Bamako, Mali: A decision analysis.

Evan W Orenstein, Lauren A V Orenstein, Kounandji Diarra, Mahamane Djiteye, Diakaridia Sidibé, Fadima C Haidara, Moussa F Doumbia, Fatoumata Diallo, Flanon Coulibaly, Adama M Keita and 6 more

Abstract read
In one paragraph

Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 4 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Pooled it
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  5. Article
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  8. Lessons learned from the A (H1N1) influenza pandemic.Best practice & research. Clinical obstetrics & gynaecology · 2021
    Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Evan W OrensteinEmory University School of Medicine, Atlanta, Georgia, United States of America.ORCID http://orcid.org/0000-0003-3756-8575
Lauren A V OrensteinEmory University School of Medicine, Atlanta, Georgia, United States of America.
Kounandji DiarraLe Centre pour le Développement des Vaccins du Mali (CVD-Mali), Bamako, Mali.
Mahamane DjiteyeLe Centre pour le Développement des Vaccins du Mali (CVD-Mali), Bamako, Mali.
Diakaridia SidibéLe Centre pour le Développement des Vaccins du Mali (CVD-Mali), Bamako, Mali.
Fadima C HaidaraLe Centre pour le Développement des Vaccins du Mali (CVD-Mali), Bamako, Mali.
Moussa F DoumbiaLe Centre pour le Développement des Vaccins du Mali (CVD-Mali), Bamako, Mali.
Fatoumata DialloLe Centre pour le Développement des Vaccins du Mali (CVD-Mali), Bamako, Mali.
Flanon CoulibalyLe Centre pour le Développement des Vaccins du Mali (CVD-Mali), Bamako, Mali.
Adama M KeitaLe Centre pour le Développement des Vaccins du Mali (CVD-Mali), Bamako, Mali.
Uma OnwuchekwaLe Centre pour le Développement des Vaccins du Mali (CVD-Mali), Bamako, Mali.
Ibrahima TegueteDepartment of Obstetrics and Gynecology, Gabriel Touré Teaching Hospital, Bamako, Mali.
Milagritos D TapiaCenter for Vaccine Development, University of Maryland School of Medicine, Baltimore, Maryland, United States of America.
Samba O SowLe Centre pour le Développement des Vaccins du Mali (CVD-Mali), Bamako, Mali.
Myron M LevineCenter for Vaccine Development, University of Maryland School of Medicine, Baltimore, Maryland, United States of America.
Richard RheingansDepartment of Environmental & Global Health, University of Florida, Gainesville, Florida, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMaternal influenza immunization has gained traction as a strategy to diminish maternal and neonatal mortality. However, efforts to vaccinate pregnant women against influenza in developing countries will require substantial investment. We present cost-effectiveness estimates of maternal influenza immunization based on clinical trial data from Bamako, Mali.

methodsWe parameterized a decision-tree model using prospectively collected trial data on influenza incidence, vaccine efficacy, and direct and indirect influenza-related healthcare expenditures. Since clinical trial participants likely had better access to care than the general Malian population, we also simulated scenarios with poor access to care, including decreased healthcare resource utilization and worse influenza-related outcomes.

resultsUnder base-case assumptions, a maternal influenza immunization program in Mali would cost $857 (95% UI: $188-$2358) per disability-adjusted life year (DALY) saved. Adjusting for poor access to care yielded a cost-effectiveness ratio of $486 (95% UI: $105-$1425) per DALY saved. Cost-effectiveness ratios were most sensitive to changes in the cost of a maternal vaccination program and to the proportion of laboratory-confirmed influenza among infants warranting hospitalization. Mean cost-effectiveness estimates fell below Mali's GDP per capita when the cost per pregnant woman vaccinated was $1.00 or less with no adjustment for access to care or $1.67 for those with poor access to care. Healthcare expenditures for lab-confirmed influenza were not significantly different than the cost of influenza-like illness.

conclusionsMaternal influenza immunization in Mali would be cost-effective in most settings if vaccine can be obtained, managed, and administered for ≤$1.00 per pregnant woman.

Indexed as

Cost-Benefit AnalysisMaternal ExposureDecision Support TechniquesFemaleHumansImmunization ProgramsInfluenza, HumanInfluenza VaccinesMaliPregnancyVaccinationInfluenza Vaccines

Identifiers

PMID28170416
PMCPMC5295679

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.