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      Association of mental health related quality of life and other factors with treatment seeking for substance use disorders: A comparison of SUDs rooted in legal, partially legal, and illegal substances

      research-article
      1 , * , , 1 , 2 , 3 , 1 , 3
      PLOS ONE
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          Abstract

          The association of subjective mental health-related quality of life (MHRQOL) and treatment use among people experiencing common substance use disorders (SUDs) is not known. Furthermore, the association of a given substance’s legal status with treatment use has not been studied. This work aims determine the association of MHRQOL with SUD treatment use, and how substance legal status modulates this relationship. Our analysis used nationally-representative data from the NESARC-III database of those experiencing past-year SUDs (n = 5,808) to compare rates of treatment use and its correlates among three groups: those with illicit substance use disorders (ISUDs); those with partially legal substance use disorders, i.e., cannabis use disorder (CUD); and those with fully legal substance use disorders, i.e., alcohol use disorder (AUD). Survey-weighted multiple regression analysis was used to assess the association of MHRQOL with likelihood of treatment use among these three groups, both unadjusted and adjusted for sociodemographic, behavioral, and diagnostic factors. Adults with past-year ISUDs were significantly more likely to use treatment than those with CUD and AUD. Among those with ISUDs, MHRQOL had no significant association with likelihood of treatment use. Those with past-year CUD saw significant negative association of MHRQOL with treatment use in unadjusted analysis, but not after controlling for diagnostic and other behavioral health factors. Those with past-year AUD had significant negative association of MHRQOL with treatment use in both unadjusted and adjusted analysis. If legalization and decriminalization continue, there may be a greater need for effective public education and harm reduction services to address this changing SUD landscape.

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          A 12-Item Short-Form Health Survey: construction of scales and preliminary tests of reliability and validity.

          Regression methods were used to select and score 12 items from the Medical Outcomes Study 36-Item Short-Form Health Survey (SF-36) to reproduce the Physical Component Summary and Mental Component Summary scales in the general US population (n=2,333). The resulting 12-item short-form (SF-12) achieved multiple R squares of 0.911 and 0.918 in predictions of the SF-36 Physical Component Summary and SF-36 Mental Component Summary scores, respectively. Scoring algorithms from the general population used to score 12-item versions of the two components (Physical Components Summary and Mental Component Summary) achieved R squares of 0.905 with the SF-36 Physical Component Summary and 0.938 with SF-36 Mental Component Summary when cross-validated in the Medical Outcomes Study. Test-retest (2-week)correlations of 0.89 and 0.76 were observed for the 12-item Physical Component Summary and the 12-item Mental Component Summary, respectively, in the general US population (n=232). Twenty cross-sectional and longitudinal tests of empirical validity previously published for the 36-item short-form scales and summary measures were replicated for the 12-item Physical Component Summary and the 12-item Mental Component Summary, including comparisons between patient groups known to differ or to change in terms of the presence and seriousness of physical and mental conditions, acute symptoms, age and aging, self-reported 1-year changes in health, and recovery for depression. In 14 validity tests involving physical criteria, relative validity estimates for the 12-item Physical Component Summary ranged from 0.43 to 0.93 (median=0.67) in comparison with the best 36-item short-form scale. Relative validity estimates for the 12-item Mental Component Summary in 6 tests involving mental criteria ranged from 0.60 to 107 (median=0.97) in relation to the best 36-item short-form scale. Average scores for the 2 summary measures, and those for most scales in the 8-scale profile based on the 12-item short-form, closely mirrored those for the 36-item short-form, although standard errors were nearly always larger for the 12-item short-form.
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            The estimation of a preference-based measure of health from the SF-12.

            The SF-12 is a multidimensional generic measure of health-related quality of life. It has become widely used in clinical trials and routine outcome assessment because of its brevity and psychometric performance, but it cannot be used in economic evaluation in its current form. We sought to derive a preference-based measure of health from the SF-12 for use in economic evaluation and to compare it with the original SF-36 preference-based index. The SF-12 was revised into a 6-dimensional health state classification (SF-6D [SF-12]) based on an item selection process designed to ensure the minimum loss of descriptive information. A sample of 241 states defined by the SF-6D (of 7500) have been valued by a representative sample of 611 members of the UK general population using the standard gamble (SG) technique. Models are estimated of the relationship between the SF-6D (SF-12) and SG values and evaluated in terms of their coefficients, overall fit, and the ability to predict SG values for all health states. The models have produced significant coefficients for levels of the SF-6D (SF-12), which are robust across model specification. The coefficients are similar to those of the SF-36 version and achieve similar levels of fit. There are concerns with some inconsistent estimates and these have been merged to produce the final recommended model. As for the SF-36 model, there is evidence of over prediction of the value of the poorest health states. The SF-12 index provides a useful tool for researchers and policy makers wishing to assess the cost-effectiveness of interventions.
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              Epidemiology of DSM-5 Alcohol Use Disorder: Results From the National Epidemiologic Survey on Alcohol and Related Conditions III.

              National epidemiologic information from recently collected data on the new DSM-5 classification of alcohol use disorder (AUD) using a reliable, valid, and uniform data source is needed.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: SoftwareRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: Supervision
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                PLOS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                29 April 2024
                2024
                : 19
                : 4
                : e0302544
                Affiliations
                [1 ] Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, United States of America
                [2 ] Department of Public Health Sciences, University of Connecticut School of Medicine, Farmington, Connecticut, United States of America
                [3 ] Department of Veterans Affairs Connecticut Healthcare System, West Haven, Connecticut, United States of America
                University of Connecticut Health Center: UConn Health, UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0002-0471-7995
                https://orcid.org/0000-0003-4961-3361
                Article
                PONE-D-23-18088
                10.1371/journal.pone.0302544
                11057773
                38683850
                73b5467e-6d1e-436d-ae5d-de244a884bc9
                © 2024 Havlik et al

                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
                : 30 June 2023
                : 9 April 2024
                Page count
                Figures: 0, Tables: 4, Pages: 18
                Funding
                The author(s) received no specific funding for this work.
                Categories
                Research Article
                Medicine and Health Sciences
                Mental Health and Psychiatry
                Mental Health Therapies
                Biology and Life Sciences
                Psychology
                Behavior
                Social Sciences
                Psychology
                Behavior
                Medicine and Health Sciences
                Mental Health and Psychiatry
                Medicine and Health Sciences
                Pharmacology
                Behavioral Pharmacology
                Recreational Drug Use
                Cannabis
                Medicine and Health Sciences
                Diagnostic Medicine
                Social Sciences
                Law and Legal Sciences
                Criminal Justice System
                Law Enforcement
                Police
                People and Places
                Population Groupings
                Professions
                Police
                Medicine and Health Sciences
                Health Care
                Quality of Life
                Social Sciences
                Sociology
                Education
                Schools
                Custom metadata
                The data that support findings of this study are available from the National Institute on Alcohol Abuse and Alcoholism (NIAAA), specifically their NESARC-III dataset. Restrictions apply to the availability of these data, which were used by us under a data use agreement for this study; we are not legally authorized to replicate these data in a public repository. Data are available from NIAAA [ https://www.niaaa.nih.gov/research/nesarc-iii] with the permission of the NESARC-III Data Access Committee.

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