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      Spatial and temporal distribution of the prevalence of unemployment and early retirement in people with multiple sclerosis: A systematic review with meta-analysis

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          Abstract

          Background

          We aimed to summarise the prevalence of unemployment and early retirement among people with MS and analyze data according to a spatio-temporal perspective.

          Methods

          We undertook a systematic search of PubMed/MEDLINE, Scopus, SciVerse ScienceDirect, and Web of Science. We included any peer-reviewed original article reporting the prevalence of unemployment and early retirement in the working-age population with MS. We excluded articles off-topic, with other study designs, whose study sample were unlikely to be representative of the MS population and in case of unavailability of the full text or essential information. A random-effects meta-analysis was used to measure overall prevalence estimates of unemployment and early retirement. We used meta-regression and subgroup analysis to evaluate potential moderators of prevalence estimates and the leave-one-out method for sensitivity analyses.

          Results

          Our research identified 153 studies across 29 countries encompassing 188436 subjects with MS. The pooled overall effect size for unemployment and early retirement was 35.6% (95% CI 32.8–38.4; I 2 = 99.31) and 17.2% (95% CI 14.6–20.2; I 2 = 99.13), respectively. The prevalence of unemployment varied according to the year of publication (p < 0.001) and there was a statistically significant decrease in the prevalence of unemployment over time (p = 0.042). Regarding early retirement, only seven (31.8%) estimates obtained from studies that were published before 2010 were below the overall effect size in comparison to 27 (60.0%) estimates extracted from data published between 2010 and 2021 (p = 0.039). There was a significant difference in prevalence according to countries (p < 0.001). Psychiatric illness was an important clinical feature responsible for patients leaving the workforce in regions with a high MS prevalence.

          Conclusions

          Unemployment and early retirement due to MS remain highly prevalent, despite a slight decline in the last decade. The prevalence of unemployment and early retirement varies globally.

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          Most cited references182

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          The PRISMA 2020 statement: an updated guideline for reporting systematic reviews

          The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement, published in 2009, was designed to help systematic reviewers transparently report why the review was done, what the authors did, and what they found. Over the past decade, advances in systematic review methodology and terminology have necessitated an update to the guideline. The PRISMA 2020 statement replaces the 2009 statement and includes new reporting guidance that reflects advances in methods to identify, select, appraise, and synthesise studies. The structure and presentation of the items have been modified to facilitate implementation. In this article, we present the PRISMA 2020 27-item checklist, an expanded checklist that details reporting recommendations for each item, the PRISMA 2020 abstract checklist, and the revised flow diagrams for original and updated reviews.
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            Bias in meta-analysis detected by a simple, graphical test

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              Quantifying heterogeneity in a meta-analysis.

              The extent of heterogeneity in a meta-analysis partly determines the difficulty in drawing overall conclusions. This extent may be measured by estimating a between-study variance, but interpretation is then specific to a particular treatment effect metric. A test for the existence of heterogeneity exists, but depends on the number of studies in the meta-analysis. We develop measures of the impact of heterogeneity on a meta-analysis, from mathematical criteria, that are independent of the number of studies and the treatment effect metric. We derive and propose three suitable statistics: H is the square root of the chi2 heterogeneity statistic divided by its degrees of freedom; R is the ratio of the standard error of the underlying mean from a random effects meta-analysis to the standard error of a fixed effect meta-analytic estimate, and I2 is a transformation of (H) that describes the proportion of total variation in study estimates that is due to heterogeneity. We discuss interpretation, interval estimates and other properties of these measures and examine them in five example data sets showing different amounts of heterogeneity. We conclude that H and I2, which can usually be calculated for published meta-analyses, are particularly useful summaries of the impact of heterogeneity. One or both should be presented in published meta-analyses in preference to the test for heterogeneity. Copyright 2002 John Wiley & Sons, Ltd.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Writing – review & editing
                Role: ConceptualizationRole: InvestigationRole: MethodologyRole: Writing – review & editing
                Role: Data curationRole: InvestigationRole: Methodology
                Role: Resources
                Role: Resources
                Role: Resources
                Role: Resources
                Role: Resources
                Role: Resources
                Role: Writing – review & editing
                Role: Funding acquisitionRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: Project administrationRole: SupervisionRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                28 July 2022
                2022
                : 17
                : 7
                : e0272156
                Affiliations
                [1 ] Department of Health Sciences, University of Genoa, Genoa, Italy
                [2 ] IRCCS Ospedale Policlinico San Martino, Occupational Medicine Unit, Genoa, Italy
                [3 ] Italian Multiple Sclerosis Association (AISM), Genoa, Italy
                [4 ] Scientific Research Area, Italian Multiple Sclerosis Foundation (FISM), Genoa, Italy
                [5 ] Department of Life Science, University of Siena, Siena, Italy
                [6 ] Italian Workers’ Compensation Authority (INAIL), Genoa, Italy
                [7 ] Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DiNOGMI) and Center of Excellence for Biomedical Research (CEBR), University of Genoa, Genoa, Italy
                [8 ] IRCCS Ospedale Policlinico San Martino, Genoa, Italy
                Universita degli Studi di Napoli Federico II, ITALY
                Author notes

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

                Author information
                https://orcid.org/0000-0002-2821-9042
                Article
                PONE-D-22-09900
                10.1371/journal.pone.0272156
                9333213
                35901070
                b41071e2-808b-4d2f-8566-cffdbb79247e
                © 2022 Vitturi 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
                : 4 April 2022
                : 14 July 2022
                Page count
                Figures: 5, Tables: 2, Pages: 25
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100007378, Associazione Italiana Sclerosi Multipla;
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100007707, Istituto Nazionale per l'Assicurazione Contro Gli Infortuni sul Lavoro;
                Award Recipient :
                This work was supported by the Italian Multiple Sclerosis Association (AISM) and Italian Workers’ Compensation Authority (INAIL), in the framework of BRIC 2019: “PRISMA” project (Bando BRIC 2019_ID 24). This work was developed within the frameworks of the Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DiNOGMI) of the University of Genoa - Department of Excellence of MIUR 2018-2022 (legge 232 del 2016), of the Department of Health Sciences (DISSAL) of the University of Genoa, and of the Occupational Medicine Unit of the IRCCS Ospedale Policlinico San Martino of Genoa, Italy. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Medicine and Health Sciences
                Clinical Medicine
                Clinical Immunology
                Autoimmune Diseases
                Multiple Sclerosis
                Biology and Life Sciences
                Immunology
                Clinical Immunology
                Autoimmune Diseases
                Multiple Sclerosis
                Medicine and Health Sciences
                Immunology
                Clinical Immunology
                Autoimmune Diseases
                Multiple Sclerosis
                Medicine and Health Sciences
                Medical Conditions
                Demyelinating Disorders
                Multiple Sclerosis
                Medicine and Health Sciences
                Neurology
                Demyelinating Disorders
                Multiple Sclerosis
                Medicine and Health Sciences
                Medical Conditions
                Neurodegenerative Diseases
                Multiple Sclerosis
                Medicine and Health Sciences
                Neurology
                Neurodegenerative Diseases
                Multiple Sclerosis
                Research and Analysis Methods
                Research Assessment
                Systematic Reviews
                Biology and Life Sciences
                Neuroscience
                Cognitive Science
                Cognitive Neuroscience
                Cognitive Neurology
                Cognitive Impairment
                Biology and Life Sciences
                Neuroscience
                Cognitive Neuroscience
                Cognitive Neurology
                Cognitive Impairment
                Medicine and Health Sciences
                Neurology
                Cognitive Neurology
                Cognitive Impairment
                Research and Analysis Methods
                Mathematical and Statistical Techniques
                Statistical Methods
                Metaanalysis
                Physical Sciences
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                Statistical Methods
                Metaanalysis
                Social Sciences
                Economics
                Labor Economics
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                Medicine and Health Sciences
                Public and Occupational Health
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