Evidence map›Paper›PMID 35968865›Full record

ArticleThe Journal of infectious diseases2022

Inequalities in Health Impact of Alternative Reimbursement Pathways for Nirsevimab in the United States.

Gabriela B Gomez, Christopher B Nelson, Christopher Rizzo, Donald S Shepard, Sandra S Chaves

Open access · hybridAbstract read
In one paragraph

Article in The Journal of infectious diseases, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
0.9field-weighted citation impact, top 26% of its field
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

6 citing papers in PubMed, 1 synthesis or guideline pooled it, 6 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Review
  4. Review
  5. Article
  6. Article
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

5 authors at 3 institutions in 3 countries.

Gabriela B GomezSanofi, Lyon, France.ORCID 0000-0002-7409-798X
Christopher B NelsonSanofi, Swiftwater, Pennsylvania, USA.ORCID 0000-0003-1046-7635
Christopher RizzoSanofi, Swiftwater, Pennsylvania, USA.ORCID 0000-0002-9828-6769
Donald S ShepardHeller School for Social Policy and Management, Brandeis University, Waltham, Massachusetts, USA.ORCID 0000-0003-2187-0593
Sandra S ChavesSanofi, Lyon, France.ORCID 0000-0002-9453-7057
Sanofi (France) · FRSanofi (United States) · USBrandeis University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The target populations and financing mechanisms for a new health technology may affect health inequalities in access and impact. We projected the distributional consequences of introducing nirsevimab for prevention of respiratory syncytial virus in a US birth cohort of infants through alternative reimbursement pathway scenarios. Using the RSV immunization impact model, we estimated that a vaccine-like reimbursement pathway would cover 32% more infants than a pharmaceutical pathway. The vaccine pathway would avert 30% more hospitalizations and 39% more emergency room visits overall, and 44% and 44%, respectively, in publicly insured infants. The vaccine pathway would benefit infants from poorer households.

Indexed as

Respiratory Syncytial Virus, HumanRespiratory Syncytial Virus InfectionsRespiratory Syncytial Virus VaccinesAntibodies, Monoclonal, HumanizedHumansInfantSocioeconomic FactorsUnited StatesAntibodies, Monoclonal, HumanizednirsevimabRespiratory Syncytial Virus Vaccinesequityhealth care utilizationinfantsinsurancelower respiratory tract infectionsmodelrespiratory syncytial virus infectionsUnited States

Identifiers

PMID35968865
PMCPMC9377036
OpenAlexW4285302070

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.