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      How much of the effect of disability acquisition on mental health is mediated through employment and income? A causal mediation analysis quantifying interventional indirect effects using data from four waves of an Australian cohort study

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          Abstract

          Objectives

          There is evidence that disability acquisition causes a decline in mental health, but few studies have examined the causal mechanisms through which the effect operates. This study used a novel approach to mediation analysis to quantify interventional indirect effects (IIEs) through employment and income.

          Design and setting

          We used four waves of longitudinal data (2011–2014) from the Household, Income and Labour Dynamics in Australia Survey, a nationally representative survey of Australian households.

          Participants

          Working aged individuals who acquired a disability (n=233) were compared with those who remained disability-free in all four waves (n=5419).

          Primary outcome measure

          Self-reported mental health was measured using the Mental Health Inventory subscale of the Short Form 36 general health questionnaire, which measures symptoms of depression, anxiety and psychological well-being.

          Statistical analysis

          We conducted a causal mediation analysis quantifying IIEs of disability acquisition on mental health operating through two distinct mediators: employment status and income. We used multiple imputation with 50 imputed datasets to account for missing data.

          Results

          The total causal effect of disability acquisition on mental health was estimated to be a 4.8-point decline in mental health score (estimated mean difference: −4.8, 95% CI −7.0 to –2.7). The IIE through employment was estimated to be a 0.5-point difference (−0.5, 95% CI −1.0 to 0.0), accounting for 10.6% of the total effect, whereas there was no evidence that income explained any of the effects.

          Conclusions

          This study estimated that disability-related mental health inequalities could be reduced by 10.6% if employment rates were the same for people with disability as those without disability. The results suggest that employment is implicated in the relationship between disability acquisition and mental health and that more research is needed to understand the influence of other aspects of employment and other socioeconomic characteristics.

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          Most cited references40

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          The MOS 36-Item Short-Form Health Survey (SF-36): II. Psychometric and clinical tests of validity in measuring physical and mental health constructs.

          Cross-sectional data from the Medical Outcomes Study (MOS) were analyzed to test the validity of the MOS 36-Item Short-Form Health Survey (SF-36) scales as measures of physical and mental health constructs. Results from traditional psychometric and clinical tests of validity were compared. Principal components analysis was used to test for hypothesized physical and mental health dimensions. For purposes of clinical tests of validity, clinical criteria defined mutually exclusive adult patient groups differing in severity of medical and psychiatric conditions. Scales shown in the components analysis to primarily measure physical health (physical functioning and role limitations-physical) best distinguished groups differing in severity of chronic medical condition and had the most pure physical health interpretation. Scales shown to primarily measure mental health (mental health and role limitations-emotional) best distinguished groups differing in the presence and severity of psychiatric disorders and had the most pure mental health interpretation. The social functioning, vitality, and general health perceptions scales measured both physical and mental health components and, thus, had the most complex interpretation. These results are useful in establishing guidelines for the interpretation of each scale and in documenting the size of differences between clinical groups that should be considered very large.
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            Measuring the mental health status of the Norwegian population: a comparison of the instruments SCL-25, SCL-10, SCL-5 and MHI-5 (SF-36).

            A great number of questionnaires and instruments have been developed in order to measure psychological distress/mental health problems in populations. The Survey of Level of Living in 1998 conducted by Statistics Norway used both Hopkins Symptom Checklist (SCL-25) and the Short Form 36 (SF-36), including the five-item mental health index (MHI-5). Five-item and 10-item versions of the SCL-25 have also been used in Norwegian surveys. The purpose of this study was to investigate the correlation between the various instruments, and to assess and to compare psychometric characteristics. A random sample of 9735 subjects over 15 years of age drawn from the Norwegian population received a questionnaire about their health containing SCL-25 and SF-36. Response rate was 71.9%. Reliability of the SCLs and MHI-5 were assessed by Cronbach alpha. The scores from full and abbreviated instruments were compared regarding possible instrument-specific effects of gender, age and level of education. The correlations between the instruments were calculated. The capacity of the various instruments to identify cases was assessed in terms of sensitivity, specificity, predictive values, receiver operating characteristics (ROC) and area under the curve (AUC). The reliabilities were high (Cronbach alpha>0.8). All instruments showed a significant difference in the mean scores for men and women. The correlation between the various versions of SCL ranged from 0.91 to 0.97. The correlation between the MHI-5 and the SCLs ranged from -0.76 to -0.78. The prevalence rate was 11.1% for SCL-25 scores above 1.75 and 9.7% for scores below 56 in MHI-5. AUC values indicated good screening accordance between the measures (AUC>0.92). The results suggest that the shorter versions of SCL perform almost as well as the full version. The corresponding cut-off points to the conventional 1.75 for SCL-25 are 1.85 for SCL-10 and 2.0 for SCL-5. MHI-5 correlates highly with the SCL and the AUC indicate that the instruments might replace each other in population surveys, at least when considering depression. An operational advantage of the MHI-5 over the SCL instruments is that it has been widely used not only in surveys of mental health, but also in surveys of general health.
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              Persons with disabilities as an unrecognized health disparity population.

              Disability is an emerging field within public health; people with significant disabilities account for more than 12% of the US population. Disparity status for this group would allow federal and state governments to actively work to reduce inequities. We summarize the evidence and recommend that observed differences are sufficient to meet the criteria for health disparities: population-level differences in health outcomes that are related to a history of wide-ranging disadvantages, which are avoidable and not primarily caused by the underlying disability. We recommend future research and policy directions to address health inequities for individuals with disabilities; these include improved access to health care and human services, increased data to support decision-making, strengthened health and human services workforce capacity, explicit inclusion of disability in public health programs, and increased emergency preparedness.
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                Author and article information

                Journal
                BMJ Open
                BMJ Open
                bmjopen
                bmjopen
                BMJ Open
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                2044-6055
                2021
                22 November 2021
                : 11
                : 11
                : e055176
                Affiliations
                [1 ]departmentDisability and Health Unit, Centre for Health Equity, Melbourne School of Population and Global Health , The University of Melbourne , Parkville, Victoria, Australia
                [2 ]departmentBiostatistics Unit, Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health , The University of Melbourne , Parkville, Victoria, Australia
                [3 ]departmentHealthy Housing Unit, Centre for Health Policy, Melbourne School of Population and Global Health , The University of Melbourne , Parkville, Victoria, Australia
                Author notes
                [Correspondence to ] Dr Zoe Aitken; zoe.aitken@ 123456unimelb.edu.au
                Author information
                http://orcid.org/0000-0002-5413-2450
                http://orcid.org/0000-0003-3334-7353
                http://orcid.org/0000-0002-1573-3464
                Article
                bmjopen-2021-055176
                10.1136/bmjopen-2021-055176
                8609928
                34810192
                1c5ec103-db9e-48b4-b5ce-a07ac10fd736
                © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

                This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:  http://creativecommons.org/licenses/by-nc/4.0/.

                History
                : 05 July 2021
                : 02 November 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100000923, Australian Research Council;
                Award ID: FT15010013
                Funded by: FundRef http://dx.doi.org/10.13039/501100000925, National Health and Medical Research Council;
                Award ID: 1093740
                Award ID: 1116385
                Award ID: 1196068
                Funded by: Australian Government Research Training Program Scholarship;
                Categories
                Epidemiology
                1506
                1692
                Original research
                Custom metadata
                unlocked

                Medicine
                mental health,epidemiology,public health,social medicine,statistics & research methods
                Medicine
                mental health, epidemiology, public health, social medicine, statistics & research methods

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