Exploring the differences between pet and non-pet owners: Implications for human-animal interaction research and policy

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Abstract

There is conflicting evidence about whether living with pets results in better mental and physical health outcomes, with the majority of the empirical research evidence being inconclusive due to methodological limitations. We briefly review the research evidence, including the hypothesized mechanisms through which pet ownership may influence health outcomes. This study examines how pet and non-pet owners differ across a variety of socio-demographic and health measures, which has implications for the proper interpretation of a large number of correlational studies that attempt to draw causal attributions. We use a large, population-based survey from California administered in 2003 (n = 42,044) and find that pet owners and non-pet owners differ across many traits, including gender, age, race/ethnicity, living arrangements, and income. We include a discussion about how the factors associated with the selection into the pet ownership group are related to a range of mental and physical health outcomes. Finally, we provide guidance on how to properly model the effects of pet ownership on health to accurately estimate this relationship in the general population.

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Most cited references 35

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Propensity score estimation with boosted regression for evaluating causal effects in observational studies.

Daniel McCaffrey, Greg Ridgeway, Andrew R Morral (2004)

Causal effect modeling with naturalistic rather than experimental data is challenging. In observational studies participants in different treatment conditions may also differ on pretreatment characteristics that influence outcomes. Propensity score methods can theoretically eliminate these confounds for all observed covariates, but accurate estimation of propensity scores is impeded by large numbers of covariates, uncertain functional forms for their associations with treatment selection, and other problems. This article demonstrates that boosting, a modern statistical technique, can overcome many of these obstacles. The authors illustrate this approach with a study of adolescent probationers in substance abuse treatment programs. Propensity score weights estimated using boosting eliminate most pretreatment group differences and substantially alter the apparent relative effects of adolescent substance abuse treatment. ((c) 2004 APA, all rights reserved).

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U.S. disparities in health: descriptions, causes, and mechanisms.

Nancy E Adler, David Rehkopf (2008)

Eliminating health disparities is a fundamental, though not always explicit, goal of public health research and practice. There is a burgeoning literature in this area, but a number of unresolved issues remain. These include the definition of what constitutes a disparity, the relationship of different bases of disadvantage, the ability to attribute cause from association, and the establishment of the mechanisms by which social disadvantage affects biological processes that get into the body, resulting in disease. We examine current definitions and empirical research on health disparities, particularly disparities associated with race/ethnicity and socioeconomic status, and discuss data structures and analytic strategies that allow causal inference about the health impacts of these and associated factors. We show that although health is consistently worse for individuals with few resources and for blacks as compared with whites, the extent of health disparities varies by outcome, time, and geographic location within the United States. Empirical work also demonstrates the importance of a joint consideration of race/ethnicity and social class. Finally, we discuss potential pathways, including exposure to chronic stress and resulting psychosocial and physiological responses to stress, that serve as mechanisms by which social disadvantage results in health disparities.

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The increasing disparity in mortality between socioeconomic groups in the United States, 1960 and 1986.

G. Pappas, S Queen, W. Hadden … (1993)

There is an inverse relation between socioeconomic status and mortality. Over the past several decades death rates in the United States have declined, but it is unclear whether all socioeconomic groups have benefited equally. Using records from the 1986 National Mortality Followback Survey (n = 13,491) and the 1986 National Health Interview Survey (n = 30,725), we replicated the analysis by Kitagawa and Hauser of differential mortality in 1960. We calculated direct standardized mortality rates and indirect standardized mortality ratios for persons 25 to 64 years of age according to race, sex, income, and family status. The inverse relation between mortality and socioeconomic status persisted in 1986 and was stronger than in 1960. The disparity in mortality rates according to income and education increased for men and women, whites and blacks, and family members and unrelated persons. Over the 26-year period, the inequalities according to educational level increased for whites and blacks by over 20 percent in women and by over 100 percent in men. In whites, absolute death rates declined in persons of all educational levels, but the reduction was greater for men and women with more education than for those with less. Despite an overall decline in death rates in the United States since 1960, poor and poorly educated people still die at higher rates than those with higher incomes or better educations, and this disparity increased between 1960 and 1986.

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Author and article information

Contributors

Meghan Byrne: Role: Editor

Journal

Journal ID (nlm-ta): PLoS One

Journal ID (iso-abbrev): PLoS ONE

Journal ID (publisher-id): plos

Journal ID (pmc): plosone

Title: PLoS ONE

Publisher: Public Library of Science (San Francisco, CA USA )

ISSN (Electronic): 1932-6203

Publication date (Electronic): 23 June 2017

Publication date Collection: 2017

Volume: 12

Issue: 6

Electronic Location Identifier: e0179494

Affiliations

[1 ]RAND Corporation, Santa Monica, California, United States of America

[2 ]UCLA Center for Health Policy Research, Los Angeles, California, United States of America

Public Library of Science, UNITED STATES

Author notes

Competing Interests: None of the authors have relevant financial disclosures to make. Working for the RAND Corporation, a non-profit public policy research institution, does not alter our adherence to PLOS ONE policies on sharing data and material.

Conceived and designed the experiments: JM JS LP SB.
Analyzed the data: LP.
Contributed reagents/materials/analysis tools: SB.
Wrote the paper: JS LP SB JM.

* E-mail: parast@ 123456rand.org

Article

Publisher ID: PONE-D-15-47713

DOI: 10.1371/journal.pone.0179494

PMC ID: 5482437

PubMed ID: 28644848

SO-VID: 3cac874c-179b-41dd-8b4e-1e693b34c6e5

License:

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

History

Date received : 4 November 2015

Date accepted : 31 May 2017

Page count

Figures: 0, Tables: 4, Pages: 15

Funding

Funded by: funder-id http://dx.doi.org/10.13039/100000071, National Institute of Child Health and Human Development;

Award ID: R01HD066591

Award Recipient : Jeremy V. Miles

This work was funded by a grant from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD066591) to Jeremy Miles ( https://www.nichd.nih.gov/about/org/der/branches/cdbb/programs/psad/HAI/Pages/overview.aspx). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Data Availability Data are available from UCLA Center for Health Policy Research: http://healthpolicy.ucla.edu/chis/data/Pages/overview.aspx.

Exploring the differences between pet and non-pet owners: Implications for human-animal interaction research and policy

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Most cited references 35

Propensity score estimation with boosted regression for evaluating causal effects in observational studies.

U.S. disparities in health: descriptions, causes, and mechanisms.

The increasing disparity in mortality between socioeconomic groups in the United States, 1960 and 1986.

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