Evidence map›Paper›PMID 37294570›Full record

ArticleJAMA network open2023

Risk of Rabies and Implications for Postexposure Prophylaxis Administration in the US.

Kelly Charniga, Yoshinori Nakazawa, Jen Brown, Seonghye Jeon, Ryan M Wallace

Open access · goldAbstract read
In one paragraph

Article in JAMA network open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.9field-weighted citation impact, top 29% 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

5 citing papers in PubMed, 6 citations in OpenAlex.

  1. Human Rabies Deaths - Minnesota and California, 2024.MMWR. Morbidity and mortality weekly report · 2026
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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

5 authors at 3 institutions in 1 country.

Kelly CharnigaDivision of High-Consequence Pathogens and Pathology, National Center for Emerging and Zoonotic Infectious Diseases, US Centers for Disease Control and Prevention, Atlanta, Georgia.
Yoshinori NakazawaDivision of High-Consequence Pathogens and Pathology, National Center for Emerging and Zoonotic Infectious Diseases, US Centers for Disease Control and Prevention, Atlanta, Georgia.
Jen BrownIndiana Department of Health, Indianapolis.
Seonghye JeonDivision of Preparedness and Emerging Infections, National Center for Emerging and Zoonotic Infectious Diseases, US Centers for Disease Control and Prevention, Atlanta, Georgia.
Ryan M WallaceDivision of High-Consequence Pathogens and Pathology, National Center for Emerging and Zoonotic Infectious Diseases, US Centers for Disease Control and Prevention, Atlanta, Georgia.
Centers for Disease Control and Prevention · USNational Center for Emerging and Zoonotic Infectious Diseases · USIndiana State Department of Health · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Importance: In the US, rabies postexposure prophylaxis (PEP) is often administered without a comprehensive and regionally appropriate rabies risk assessment. For low-risk exposures, this can result in patients incurring out-of-pocket expenses or experiencing adverse effects of PEP unnecessarily. Objective: To use a model to estimate (1) the probability that an animal would test positive for rabies virus (RABV) given that a person was exposed, and (2) the probability that a person would die from rabies given that they were exposed to a suspect rabid animal and did not receive PEP, and to propose a risk threshold for recommending PEP according to model estimates and a survey. Design, Setting, and Participants: In this decision analytical modeling study, positivity rates were calculated using more than 900 000 animal samples tested for RABV between 2011 and 2020. Other parameters were estimated from a subset of the surveillance data and the literature. Probabilities were estimated using Bayes' rule. A survey was administered among a convenience sample of state public health officials in all US states (excluding Hawaii) plus Washington, DC and Puerto Rico to determine a risk threshold for PEP recommendation. Respondents were asked whether they would recommend PEP given 24 standardized exposure scenarios while accounting for local rabies epidemiology. Main Outcomes and Measures: A quantitative and regionally appropriate approach for helping health care practitioners and public health professionals determine whether to recommend and/or administer rabies PEP. Results: A total of 1728 unique observations were obtained from the model for the probability that an animal would test positive for RABV given that a person was exposed, and 41 472 for ) the probability that a person would die from rabies given that they were exposed to a suspect rabid animal and did not receive PEP. The median probability that an animal would test positive for RABV given that a person was exposed ranged from 3 × 10-7 to 0.97, while the probability that a person would die from rabies given that they were exposed to a suspect rabid animal and did not receive PEP ranged from 1 × 10-10 to 0.55. Fifty public health officials out of a target sample size of 102 responded to the survey. Using logistic regression, a risk threshold was estimated for PEP recommendation of 0.0004; PEP may not be recommended for exposures with probabilities below this threshold. Conclusions and Relevance: In this modeling study of rabies in the US, the risk of death|exposure was quantified and a risk threshold was estimated. These results could be used to inform the decision-making process as to the appropriateness of recommending rabies PEP.

Indexed as

Bites and StingsRabiesAnimalsBayes TheoremHealth PersonnelHumansPublic Health

Identifiers

PMID37294570
PMCPMC10257100
OpenAlexW4379967404

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.