Evidence map›Paper›PMID 40370830›Full record

ArticleFrontiers in veterinary science2025

Measuring the impact of sow farm outbreaks with PRRS virus on the downstream mortality using causal inference methods.

E S Magalhães, D Zhang, C A A Moura, Annette O'Connor, C Wang, D J Holtkamp, G S Silva, D C L Linhares

Abstract read
In one paragraph

Article in Frontiers in veterinary science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–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

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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

4 citing papers in PubMed.

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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

8 authors.

E S MagalhãesDepartment of Animal Science, Iowa State University, Ames, IA, United States.
D ZhangDepartment of Statistics, College of Liberal Arts and Sciences, Iowa State University, Ames, IA, United States.
C A A MouraIowa Select Farms, Iowa Falls, IA, United States.
Annette O'ConnorDepartment of Large Animal Clinical Sciences, College of Veterinary Medicine, Michigan State University, East Lansing, MI, United States.
C WangDepartment of Statistics, College of Liberal Arts and Sciences, Iowa State University, Ames, IA, United States.
D J HoltkampDepartment of Veterinary Diagnostic and Production Animal Medicine, College of Veterinary Medicine, Iowa State University, Ames, IA, United States.
G S SilvaDepartment of Veterinary Diagnostic and Production Animal Medicine, College of Veterinary Medicine, Iowa State University, Ames, IA, United States.
D C L LinharesDepartment of Veterinary Diagnostic and Production Animal Medicine, College of Veterinary Medicine, Iowa State University, Ames, IA, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Porcine reproductive and respiratory syndrome virus (PRRSV) remains a significant challenge to the swine industry, resulting in substantial productivity and, consequently, economic losses. This study aimed to quantify the impact of PRRSV outbreaks in sow farms on nursery mortality using causal inference methods. The study design followed a retrospective observational approach, where PRRSV epidemic status in source sow farms was the exposure, and nursery mortality (percentage of dead pigs in the first 60 days post-weaning) was the outcome. Causal inference techniques were employed to estimate the effect of the exposure (PRRSV epidemic status) on the outcome (nursery mortality). Data from a Midwestern US swine production system comprising 2,592 lots of pigs, representing approximately 5 million pigs marketed between January 2021 and December 2022, were analyzed. A causal diagram was constructed to visualize the relationship between PRRSV epidemic exposure and nursery mortality, while controlling for potential confounding factors including season, average parity at farrow, and sow farm

Indexed as

breeding herdcausal inferencenursery mortalityPRRS outbreakswine

Identifiers

PMID40370830
PMCPMC12076741

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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.