Literature DB >> 21209297

Employment status of patients in the VA health system: implications for mental health services.

Kara Zivin1, Amy S B Bohnert, Briana Mezuk, Mark A Ilgen, Deborah Welsh, Scott Ratliff, Erin M Miller, Marcia Valenstein, Amy M Kilbourne.   

Abstract

OBJECTIVE: Most veterans who use Department of Veterans Affairs (VA) health care are not employed. This study evaluated the association between mental disorders and labor force status among VA health care users.
METHODS: Multinomial logistic regression analyses modeled the relationship between mental disorders and employment among patients aged 18 to 64 who completed the 2005 Survey of Healthcare Experiences of Patients.
RESULTS: Of the 98,867 patients who met eligibility criteria, 36% were disabled, 35% were employed, 20% were retired, and 7% were unemployed. Those with bipolar disorder, depression, posttraumatic stress disorder, schizophrenia, or a substance use disorder were more likely to be unemployed, disabled, or retired than employed.
CONCLUSIONS: This study confirmed a negative relationship between having a mental disorder and being employed. Future studies of barriers associated with veterans' employment could help policy makers target mental health treatments and supportive employment services to the unique needs of veterans.

Entities:  

Mesh:

Year:  2011        PMID: 21209297     DOI: 10.1176/ps.62.1.pss6201_0035

Source DB:  PubMed          Journal:  Psychiatr Serv        ISSN: 1075-2730            Impact factor:   3.084


  9 in total

Review 1.  Prevalence of, risk factors for, and consequences of posttraumatic stress disorder and other mental health problems in military populations deployed to Iraq and Afghanistan.

Authors:  Rajeev Ramchand; Rena Rudavsky; Sean Grant; Terri Tanielian; Lisa Jaycox
Journal:  Curr Psychiatry Rep       Date:  2015-05       Impact factor: 5.285

2.  Financial well-being and postdeployment adjustment among Iraq and Afghanistan war veterans.

Authors:  Eric B Elbogen; Sally C Johnson; H Ryan Wagner; Virginia M Newton; Jean C Beckham
Journal:  Mil Med       Date:  2012-06       Impact factor: 1.437

3.  Socioeconomic Disparities and Metabolic Risk in Veterans with Serious Mental Illness.

Authors:  Stanley N Caroff; Shirley H Leong; Daisy Ng-Mak; E Cabrina Campbell; Rosalind M Berkowitz; Krithika Rajagopalan; Chien-Chia Chuang; Antony Loebel
Journal:  Community Ment Health J       Date:  2017-12-28

4.  Job Offers to Individuals With Severe Mental Illness After Participation in Virtual Reality Job Interview Training.

Authors:  Matthew J Smith; Michael F Fleming; Michael A Wright; Neil Jordan; Laura Boteler Humm; Dale Olsen; Morris D Bell
Journal:  Psychiatr Serv       Date:  2015-07-01       Impact factor: 3.084

5.  The association between early-onset schizophrenia with employment, income, education, and cohabitation status: nationwide study with 35 years of follow-up.

Authors:  Christian Hakulinen; John J McGrath; Allan Timmerman; Niels Skipper; Preben Bo Mortensen; Carsten Bøcker Pedersen; Esben Agerbo
Journal:  Soc Psychiatry Psychiatr Epidemiol       Date:  2019-08-27       Impact factor: 4.328

6.  Cerebral bioenergetic differences measured by phosphorus-31 magnetic resonance spectroscopy between bipolar disorder and healthy subjects living in two different regions suggesting possible effects of altitude.

Authors:  Jaeuk Hwang; Lynn E DeLisi; Dost Öngür; Colin Riley; Chun Zuo; Xianfeng Shi; Young-Hoon Sung; Douglas Kondo; Tae-Suk Kim; Rosemond Villafuerte; Diane Smedberg; Deborah Yurgelun-Todd; Perry F Renshaw
Journal:  Psychiatry Clin Neurosci       Date:  2019-07-03       Impact factor: 5.188

7.  A health economics study of long-acting injectable once-monthly paliperidone palmitate in schizophrenia: a one-year mirror-image study in China.

Authors:  Jie Liu; Qian Wang; Lei Su; Limin Yang; Lianyong Zou; Ludong Bai
Journal:  BMC Psychiatry       Date:  2022-02-08       Impact factor: 3.630

8.  Identifying individuals with undiagnosed post-traumatic stress disorder in a large United States civilian population - a machine learning approach.

Authors:  Patrick Gagnon-Sanschagrin; Jeff Schein; Annette Urganus; Elizabeth Serra; Yawen Liang; Primrose Musingarimi; Martin Cloutier; Annie Guérin; Lori L Davis
Journal:  BMC Psychiatry       Date:  2022-09-29       Impact factor: 4.144

9.  Social adaptability and substance abuse: predictors of depression among hemodialysis patients?

Authors:  Paulo Roberto Santos; Francisco Plácido Nogueira Arcanjo
Journal:  BMC Nephrol       Date:  2013-01-15       Impact factor: 2.388

  9 in total

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