Evidence map›Paper›PMID 26464932›Full record

ArticleJournal of veterinary medicine2014

Disparities in Spatial Prevalence of Feline Retroviruses due to Data Aggregation: A Case of the Modifiable Areal Unit Problem.

Bimal K Chhetri, Olaf Berke, David L Pearl, Dorothee Bienzle

Abstract read
In one paragraph

Article in Journal of veterinary medicine, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

4 authors.

Bimal K ChhetriDepartment of Population Medicine, Ontario Veterinary College, University of Guelph, Guelph, ON, Canada N1G 2W1.
Olaf BerkeDepartment of Population Medicine, Ontario Veterinary College, University of Guelph, Guelph, ON, Canada N1G 2W1 ; Department of Mathematics and Statistics, University of Guelph, Guelph, ON, Canada N1G 2W1 ; Institute of Biometry, Epidemiology and Information Processing, University of Veterinary Medicine Hannover (Foundation), 30173 Hannover, Germany.
David L PearlDepartment of Population Medicine, Ontario Veterinary College, University of Guelph, Guelph, ON, Canada N1G 2W1.
Dorothee BienzleDepartment of Pathobiology, Ontario Veterinary College, University of Guelph, Guelph, ON, Canada N1G 2W1.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The knowledge of the spatial distribution feline immunodeficiency virus and feline leukemia virus infections, which are untreatable, can inform on their risk factors and high-risk areas to enhance control. However, when spatial analysis involves aggregated spatial data, results may be influenced by the spatial scale of aggregation, an effect known as the modifiable areal unit problem (MAUP). In this study, area level risk factors for both infections in 28,914 cats tested with ELISA were investigated by multivariable spatial Poisson regression models along with MAUP effect on spatial clustering and cluster detection (for postal codes, counties, and states) by Moran's I test and spatial scan test, respectively. The study results indicate that the significance and magnitude of the association of risk factors with both infections varied with aggregation scale. Further more, Moran's I test only identified spatial clustering at postal code and county levels of aggregation. Similarly, the spatial scan test indicated that the number, size, and location of clusters varied over aggregation scales. In conclusion, the association between infection and area was influenced by the choice of spatial scale and indicates the importance of study design and data analysis with respect to specific research questions.

Identifiers

PMID26464932
PMCPMC4590838

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Registered trials

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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.