Evidence map›Paper›PMID 24040768›Full record

ArticleEpidemiology and infection2014

What is the price of neglecting parasite groups when assessing the cost of co-infection?

E Serrano, J Millán

Open access · bronzeAbstract read
In one paragraph

Article in Epidemiology and infection, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 1 pooled it
2.6field-weighted citation impact, top 11% 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

22 citing papers in PubMed, 1 synthesis or guideline pooled it, 38 citations in OpenAlex.

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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

2 authors at 1 institution in 1 country.

E SerranoServei d´Ecopatologia de Fauna Salvatge (SEFaS), Departament de Medicina i Cirurgia Animals,Universitat Autònoma de Barcelona,Bellaterra,Spain.
J MillánServei d´Ecopatologia de Fauna Salvatge (SEFaS), Departament de Medicina i Cirurgia Animals,Universitat Autònoma de Barcelona,Bellaterra,Spain.
Universitat Autònoma de Barcelona · ES

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Although co-infection by multiple groups of pathogens is the norm rather than the exception in nature, most research on the effects of pathogens on their hosts has been largely based on a single or few pathogen species. Nevertheless, the health impact of co-occurring infections is evident, and it is important that scientists should consider pathogen communities rather than single relevant pathogen species when assessing the impact of multiple infections. In this work we illustrate the consequences of neglecting different pathogen taxa (viruses, protozoa, helminths, arthropods) in the explanatory power of a set of Partial Least Squares Regression (PLS-R) models used for exploring the impact of co-infections on the body condition of 57 adult feral cats; 71·5% cats were co-infected by ≥3 groups of pathogens. The best two PLS-R models provided a first component based on the combination of helminths, protozoa and viruses, explaining 29·15% of body-condition variability. Statistical models, partially considering the pathogen community, lost between 24% and 94% of their explanatory power for explaining the cost of multiple infections. We believe that in the future, researchers assessing the impact of diseases on host life-history traits should take into account a broad representation of the pathogen community, especially during early assessment of the impact of diseases on host health.

Indexed as

Models, BiologicalModels, StatisticalAnimalsAnimals, WildCat DiseasesCatsCoinfectionLeast-Squares AnalysisParasitic Diseases, Animal

Identifiers

PMID24040768
PMCPMC9151198
OpenAlexW2099213503

What OpenQuestion holds

Textmetadata
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.