IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v21y2024i3p356-d1358716.html
   My bibliography  Save this article

Prevalence and Correlates of Food and/or Housing Instability among Men and Women Post-9/11 US Veterans

Author

Listed:
  • Yasmin S. Cypel

    (Health Outcomes Military Exposures, Epidemiology Program, Office of Patient Care Services, US Department of Veterans Affairs, Washington, DC 20420, USA)

  • Shira Maguen

    (San Francisco VA Health Care System, San Francisco, CA 94121, USA
    Department of Psychiatry and Behavioral Sciences, School of Medicine, University of California—San Francisco, San Francisco, CA 94143, USA)

  • Paul A. Bernhard

    (Health Outcomes Military Exposures, Epidemiology Program, Office of Patient Care Services, US Department of Veterans Affairs, Washington, DC 20420, USA)

  • William J. Culpepper

    (Health Outcomes Military Exposures, Epidemiology Program, Office of Patient Care Services, US Department of Veterans Affairs, Washington, DC 20420, USA)

  • Aaron I. Schneiderman

    (Health Outcomes Military Exposures, Epidemiology Program, Office of Patient Care Services, US Department of Veterans Affairs, Washington, DC 20420, USA)

Abstract

Food and/or housing instability (FHI) has been minimally examined in post-9/11 US veterans. A randomly selected nationally representative sample of men and women veterans (n = 38,633) from the post-9/11 US veteran population were mailed invitation letters to complete a survey on health and well-being. Principal component analysis and multivariable logistic regression were used to identify FHI’s key constructs and correlates for 15,166 men and women respondents (9524 men, 5642 women). One-third of veterans reported FHI; it was significantly more likely among women than men (crude odds ratio = 1.31, 95% CI:1.21–1.41) and most prevalent post-service (64.2%). “Mental Health/Stress/Trauma”, “Physical Health”, and “Substance Use” were FHI’s major constructs. In both sexes, significant adjusted associations ( p < 0.01) were found between FHI and homelessness, depression, adverse childhood experiences, low social support, being enlisted, being non-deployed, living with seriously ill/disabled person(s), and living in dangerous neighborhoods. In men only, posttraumatic stress disorder (adjusted odds ratio (AOR) = 1.37, 95% CI:1.14–1.64), cholesterol level (elevated versus normal, AOR = 0.79, 95% CI:0.67–0.92), hypertension (AOR = 1.25, 95% CI:1.07–1.47), and illegal/street drug use (AOR = 1.28, 95% CI:1.10–1.49) were significant ( p < 0.01). In women only, morbid obesity (AOR = 1.90, 95%CI:1.05–3.42) and diabetes (AOR = 1.53, 95% CI:1.06–2.20) were significant ( p < 0.05). Interventions are needed that jointly target adverse food and housing, especially for post-9/11 veteran women and enlisted personnel.

Suggested Citation

  • Yasmin S. Cypel & Shira Maguen & Paul A. Bernhard & William J. Culpepper & Aaron I. Schneiderman, 2024. "Prevalence and Correlates of Food and/or Housing Instability among Men and Women Post-9/11 US Veterans," IJERPH, MDPI, vol. 21(3), pages 1-16, March.
    • Handle: RePEc:gam:jijerp:v:21:y:2024:i:3:p:356-:d:1358716
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/21/3/356/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/21/3/356/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Brady T West & Joseph W Sakshaug & Guy Alain S Aurelien, 2016. "How Big of a Problem is Analytic Error in Secondary Analyses of Survey Data?," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-29, June.
    2. Metraux, S. & Clegg, L.X. & Daigh, J.D. & Culhane, D.P. & Kane, V., 2013. "Risk factors for becoming homeless among a cohort of veterans who served in the era of the Iraq and Afghanistan conflicts," American Journal of Public Health, American Public Health Association, vol. 103(S2), pages 255-261.
    3. Rabbitt, Matthew P. & Smith, Michael D., 2021. "Food Insecurity Among Working-Age Veterans," USDA Miscellaneous 311332, United States Department of Agriculture.
    4. Bossarte, R.M. & Blosnich, J.R. & Piegari, R.I. & Hill, L.L. & Kane, V., 2013. "Housing instability and mental distress among US veterans," American Journal of Public Health, American Public Health Association, vol. 103(S2), pages 213-216.
    5. Stanislav Kolenikov, 2010. "Resampling variance estimation for complex survey data," Stata Journal, StataCorp LP, vol. 10(2), pages 165-199, June.
    6. Rabbitt, Matthew P. & Smith, Michael D., 2021. "Food Insecurity Among Working-Age Veterans," USDA Miscellaneous 311332, United States Department of Agriculture.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Alkahtani Mohammed Ali, 2021. "E-learning for Students With Disabilities During COVID-19: Faculty Attitude and Perception," SAGE Open, , vol. 11(4), pages 21582440211, October.
    2. Aslim, Erkmen Giray & Fu, Wei & Tekin, Erdal & You, Shijun, 2023. "From Syringes to Dishes: Improving Food Security through Vaccination," IZA Discussion Papers 16009, Institute of Labor Economics (IZA).
    3. West Brady T. & Sakshaug Joseph W. & Aurelien Guy Alain S., 2018. "Accounting for Complex Sampling in Survey Estimation: A Review of Current Software Tools," Journal of Official Statistics, Sciendo, vol. 34(3), pages 721-752, September.
    4. Jones, Jordan W & Toossi, Saied & Hodges, Leslie, 2022. "The Food and Nutrition Assistance Landscape: Fiscal Year 2021 Annual Report," Economic Information Bulletin 327356, United States Department of Agriculture, Economic Research Service.
    5. Rodrigo M. Leifert & Claudio R. Lucinda, 2015. "Linear Symmetric "Fat Taxes": Evidence from Brazil," Applied Economic Perspectives and Policy, Agricultural and Applied Economics Association, vol. 37(4), pages 634-666.
    6. Matthew Robson & Miqdad Asaria & Richard Cookson & Aki Tsuchiya & Shehzad Ali, 2017. "Eliciting the Level of Health Inequality Aversion in England," Health Economics, John Wiley & Sons, Ltd., vol. 26(10), pages 1328-1334, October.
    7. Robert E. Hall & Sam Schulhofer-Wohl, 2018. "Measuring Job-Finding Rates and Matching Efficiency with Heterogeneous Job-Seekers," American Economic Journal: Macroeconomics, American Economic Association, vol. 10(1), pages 1-32, January.
    8. Armin Falk & Fabian Kosse & Pia Pinger & Hannah Schildberg-Hörisch & Thomas Deckers, 2021. "Socioeconomic Status and Inequalities in Children’s IQ and Economic Preferences," Journal of Political Economy, University of Chicago Press, vol. 129(9), pages 2504-2545.
    9. James G. MacKinnon, 2019. "How cluster‐robust inference is changing applied econometrics," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 52(3), pages 851-881, August.
    10. Kenneth Owusu Ansah & Nutifafa Eugene Yaw Dey & Abigail Esinam Adade & Pascal Agbadi, 2022. "Determinants of life satisfaction among Ghanaians aged 15 to 49 years: A further analysis of the 2017/2018 Multiple Cluster Indicator Survey," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-18, January.
    11. Alexander Robitzsch, 2023. "Linking Error in the 2PL Model," J, MDPI, vol. 6(1), pages 1-27, January.
    12. Ackerman, Adam & Porter, Ben & Sullivan, Ryan, 2020. "The effect of combat exposure on veteran homelessness," Journal of Housing Economics, Elsevier, vol. 49(C).
    13. Rat für Sozial- und Wirtschaftsdaten RatSWD (ed.), 2023. "Erhebung und Nutzung unstrukturierter Daten in den Sozial-, Verhaltens- und Wirtschaftswissenschaften," RatSWD Output Series, German Data Forum (RatSWD), volume 7, number 7-2de.
    14. Paul Eckerstorfer & Johannes Halak & Jakob Kapeller & Bernhard Schütz & Florian Springholz & Rafael Wildauer, 2016. "Correcting for the Missing Rich: An Application to Wealth Survey Data," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 62(4), pages 605-627, December.
    15. Tim Goedemé, 2013. "How much Confidence can we have in EU-SILC? Complex Sample Designs and the Standard Error of the Europe 2020 Poverty Indicators," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 110(1), pages 89-110, January.
    16. Marie T. Mora & Alberto D?vila, 2014. "Gender and Business Outcomes of Black and Hispanic New Entrepreneurs in the United States," American Economic Review, American Economic Association, vol. 104(5), pages 245-249, May.
    17. Owen Gallupe & Martin Bouchard, 2015. "The influence of positional and experienced social benefits on the relationship between peers and alcohol use," Rationality and Society, , vol. 27(1), pages 40-69, February.
    18. Heng Chen & Q. Rallye Shen, 2019. "Variance Estimation for Survey-Weighted Data Using Bootstrap Resampling Methods: 2013 Methods-of-Payment Survey Questionnaire," Advances in Econometrics, in: The Econometrics of Complex Survey Data, volume 39, pages 87-106, Emerald Group Publishing Limited.
    19. Viktoria Hnatkovska & Amartya Lahiri & Sourabh Paul, 2012. "Castes and Labor Mobility," American Economic Journal: Applied Economics, American Economic Association, vol. 4(2), pages 274-307, April.
    20. Philippe Van Kerm, 2013. "Repeated half-sample bootstrap resampling," United Kingdom Stata Users' Group Meetings 2013 10, Stata Users Group.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:21:y:2024:i:3:p:356-:d:1358716. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.