Evidence map›Paper›PMID 26401319›Full record

ReviewCanine genetics and epidemiology2014

Approaches to canine health surveillance.

Dan G O'Neill, David B Church, Paul D McGreevy, Peter C Thomson, Dave C Brodbelt

Abstract readReview
In one paragraph

Review in Canine genetics and epidemiology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 86 papers.

0numbers the graph read from it
0cells of the map it votes in
86citing 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

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

86 citing papers in PubMed.

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  20. Utilising a livestock model for wildlife health planning.Frontiers in veterinary science · 2024
    Article

26 more citing papers are in PubMed but not listed here.

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.

Dan G O'NeillFaculty of Veterinary Science, The University of Sydney, R.M.C. Gunn Building (B19), Sydney, NSW 2006 Australia.
David B ChurchFaculty of Veterinary Science, The University of Sydney, R.M.C. Gunn Building (B19), Sydney, NSW 2006 Australia.
Paul D McGreevyFaculty of Veterinary Science, The University of Sydney, R.M.C. Gunn Building (B19), Sydney, NSW 2006 Australia.
Peter C ThomsonFaculty of Veterinary Science, The University of Sydney, R.M.C. Gunn Building (B19), Sydney, NSW 2006 Australia.
Dave C BrodbeltFaculty of Veterinary Science, The University of Sydney, R.M.C. Gunn Building (B19), Sydney, NSW 2006 Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Effective canine health surveillance systems can be used to monitor disease in the general population, prioritise disorders for strategic control and focus clinical research, and to evaluate the success of these measures. The key attributes for optimal data collection systems that support canine disease surveillance are representativeness of the general population, validity of disorder data and sustainability. Limitations in these areas present as selection bias, misclassification bias and discontinuation of the system respectively. Canine health data sources are reviewed to identify their strengths and weaknesses for supporting effective canine health surveillance. Insurance data benefit from large and well-defined denominator populations but are limited by selection bias relating to the clinical events claimed and animals covered. Veterinary referral clinical data offer good reliability for diagnoses but are limited by referral bias for the disorders and animals included. Primary-care practice data have the advantage of excellent representation of the general dog population and recording at the point of care by veterinary professionals but may encounter misclassification problems and technical difficulties related to management and analysis of large datasets. Questionnaire surveys offer speed and low cost but may suffer from low response rates, poor data validation, recall bias and ill-defined denominator population information. Canine health scheme data benefit from well-characterised disorder and animal data but reflect selection bias during the voluntary submissions process. Formal UK passive surveillance systems are limited by chronic under-reporting and selection bias. It is concluded that active collection systems using secondary health data provide the optimal resource for canine health surveillance.

Indexed as

Cancer registryCanineData sourceDisorderEpidemiologyHealth schemeInsurancePrimary-care practiceQuestionnaireReferral practiceSurveillance

Identifiers

PMID26401319
PMCPMC4574389

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.