Exploring the Intersection of Poverty, Environment and Canine Health

Background
Patterns of poverty and environmental risk across places can point to early or hidden threats to
both animal and human health. This project investigates how socioeconomic and environmental
conditions influence the health of canines by linking Golden Retriever Lifetime Study data with
detailed geospatial information. Researchers will use machine-learning methods to examine how
neighborhood-level factors relate to infectious diseases, cancer, and unhealthy body weight.
Project Description
Using data from the Golden Retriever Lifetime Study (GRLS), we will link each dog’s
geolocation to national environmental and socioeconomic datasets. These connections will allow
us to explore whether dogs living in areas with higher pollution levels, lower household income,
or reduced access to care experience more frequent or severe diseases. Perhaps the most high-
profile investigation of poverty and animal health (2018) is the Access to Veterinary Care
Coalition white paper, which found that over a quarter of pet owners reported being unable to
access preventive care for a pet within the previous two years, with finances being identified as
the most frequent and most substantial barrier to access. Historically more common geospatial
data sources and analytical techniques have also been shown to underperform in certain
conditions, further increasing misclassification (2025).

Abstract
Socioeconomic and environmental conditions are well-established determinants of human health,
but their effects on companion animal health remain comparatively understudied. This project
uses data from the Golden Retriever Lifetime Study (GRLS), a longitudinal cohort of 3,044
dogs, to investigate how neighborhood poverty, environmental exposures, housing conditions,
and other place-based factors are associated with canine health outcomes. GRLS health and
household data are integrated with detailed geospatial measures of socioeconomic and
environmental conditions to examine geographic patterns of disease and identify potential risk
factors. Statistical and machine-learning methods are used to evaluate relationships between
these exposures and outcomes including cancer, infectious disease, endocrine conditions,
dermatological conditions, body condition, and other health outcomes. By examining dogs as
potential sentinels of shared environmental and community health risks, this research applies a
One Health framework to better understand how socioeconomic and environmental disparities
may affect both animal and human well-being.

Partner Organizations/Programs
Morris Animal Foundation

Additional Information

Target Population:
The study population consists of the 3,044 dogs enrolled in the Golden Retriever Lifetime Study(GRLS). The project uses longitudinal veterinary, owner-reported, household, environmental, and geospatial information to investigate relationships between place-based exposures and canine health. The proposal specifically identifies all 3,044 GRLS participants with valid geolocation data as the study sample.

Research Strategy:
GRLS participant data are linked with socioeconomic and environmental information to
characterize the conditions surrounding each dog's residence. Exposure measures include
socioeconomic indicators, housing and household characteristics, environmental hazards, water
source and quality, pesticide and chemical exposure, smoke exposure, heating sources, and
geographic indicators. External datasets may include measures such as the CDC Social
Vulnerability Index, U.S. Census poverty data, EPA air-quality information, and USDA pesticide
data.
The project combines traditional epidemiological and statistical analyses with machine-learning
approaches to determine which socioeconomic, environmental, demographic, and household
characteristics are most predictive of canine health outcomes. Analyses incorporate dog-level
covariates such as age, sex and spay/neuter status while examining both individual household
characteristics and broader place-based exposures.

The project also takes advantage of the longitudinal structure of the GRLS to investigate whether
social and environmental exposures are associated not only with disease occurrence but also with
the timing and progression of health outcomes over the dogs' lifetimes
One Health Relevance:
Because companion dogs share many of the same household and neighborhood environments as
humans while aging more rapidly, changes in canine health may provide early indications of
environmental and socioeconomic risks affecting the wider community. The project therefore
uses canine health as a potential sentinel for identifying geographic disparities and overlapping
risks to animal, environmental, and human health