# Exposure variables in veterinary epidemiology: are they telling us what we think they are?

**Authors:** Audrey Ruple, Jan M. Sargeant, Annette M. O’Connor, David G. Renter

PMC · DOI: 10.3389/fvets.2024.1442308 · Frontiers in Veterinary Science · 2024-07-30

## TL;DR

This paper discusses how using incorrect proxy measures in veterinary epidemiology can lead to flawed conclusions about health exposures and outcomes.

## Contribution

The paper emphasizes the importance of rigorous methodologies for selecting and validating exposure variables to reduce biases in veterinary epidemiology.

## Key findings

- Inappropriate proxy measures can lead to biased estimates of exposure-outcome associations.
- Flawed exposure variable selection can result in erroneous health decisions and policies.
- Validation studies are needed to minimize measurement errors in exposure assessments.

## Abstract

This manuscript summarizes a presentation delivered by the first author at the 2024 symposium for the Calvin Schwabe Award for Lifetime Achievement in Veterinary Epidemiology and Preventive Medicine, which was awarded to Dr. Jan Sargeant. Epidemiologic research plays a crucial role in understanding the complex relationships between exposures and health outcomes. However, the accuracy of the conclusions drawn from these investigations relies upon the meticulous selection and measurement of exposure variables. Appropriate exposure variable selection is crucial for understanding disease etiologies, but it is often the case that we are not able to directly measure the exposure variable of interest and use proxy measures to assess exposures instead. Inappropriate use of proxy measures can lead to erroneous conclusions being made about the true exposure of interest. These errors may lead to biased estimates of associations between exposures and outcomes. The consequences of such biases extend beyond research concerns as health decisions can be made based on flawed evidence. Recognizing and mitigating these biases are essential for producing reliable evidence that informs health policies and interventions, ultimately contributing to improved population health outcomes. To address these challenges, researchers must adopt rigorous methodologies for exposure variable selection and validation studies to minimize measurement errors.

## Full-text entities

- **Diseases:** Cholera (MESH:D002771), wheezing (MESH:D012135), craniofacial malformations (MESH:D019465), asthma (MESH:D001249), teratogen (MESH:C535542), breast cancer (MESH:D001943), brachycephaly (MESH:D003398), synophthalmia (MESH:C562573), cyclopia (MESH:D016142), cancer (MESH:D009369), infectious diseases (MESH:D003141)
- **Chemicals:** steroidal alkaloid (-), silver (MESH:D012834), cyclopamine (MESH:C000541)
- **Species:** Ovis aries (domestic sheep, species) [taxon 9940], Homo sapiens (human, species) [taxon 9606], Canis lupus familiaris (dog, subspecies) [taxon 9615], Veratrum californicum (California corn lily, species) [taxon 50242]

## Full text

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## References

35 references — full list in the complete paper: https://tomesphere.com/paper/PMC11323118/full.md

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Source: https://tomesphere.com/paper/PMC11323118