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      Impact of mental disorders on clinical outcomes of physical diseases: an umbrella review assessing population attributable fraction and generalized impact fraction

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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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            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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              Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

              Summary Background In an era of shifting global agendas and expanded emphasis on non-communicable diseases and injuries along with communicable diseases, sound evidence on trends by cause at the national level is essential. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) provides a systematic scientific assessment of published, publicly available, and contributed data on incidence, prevalence, and mortality for a mutually exclusive and collectively exhaustive list of diseases and injuries. Methods GBD estimates incidence, prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) due to 369 diseases and injuries, for two sexes, and for 204 countries and territories. Input data were extracted from censuses, household surveys, civil registration and vital statistics, disease registries, health service use, air pollution monitors, satellite imaging, disease notifications, and other sources. Cause-specific death rates and cause fractions were calculated using the Cause of Death Ensemble model and spatiotemporal Gaussian process regression. Cause-specific deaths were adjusted to match the total all-cause deaths calculated as part of the GBD population, fertility, and mortality estimates. Deaths were multiplied by standard life expectancy at each age to calculate YLLs. A Bayesian meta-regression modelling tool, DisMod-MR 2.1, was used to ensure consistency between incidence, prevalence, remission, excess mortality, and cause-specific mortality for most causes. Prevalence estimates were multiplied by disability weights for mutually exclusive sequelae of diseases and injuries to calculate YLDs. We considered results in the context of the Socio-demographic Index (SDI), a composite indicator of income per capita, years of schooling, and fertility rate in females younger than 25 years. Uncertainty intervals (UIs) were generated for every metric using the 25th and 975th ordered 1000 draw values of the posterior distribution. Findings Global health has steadily improved over the past 30 years as measured by age-standardised DALY rates. After taking into account population growth and ageing, the absolute number of DALYs has remained stable. Since 2010, the pace of decline in global age-standardised DALY rates has accelerated in age groups younger than 50 years compared with the 1990–2010 time period, with the greatest annualised rate of decline occurring in the 0–9-year age group. Six infectious diseases were among the top ten causes of DALYs in children younger than 10 years in 2019: lower respiratory infections (ranked second), diarrhoeal diseases (third), malaria (fifth), meningitis (sixth), whooping cough (ninth), and sexually transmitted infections (which, in this age group, is fully accounted for by congenital syphilis; ranked tenth). In adolescents aged 10–24 years, three injury causes were among the top causes of DALYs: road injuries (ranked first), self-harm (third), and interpersonal violence (fifth). Five of the causes that were in the top ten for ages 10–24 years were also in the top ten in the 25–49-year age group: road injuries (ranked first), HIV/AIDS (second), low back pain (fourth), headache disorders (fifth), and depressive disorders (sixth). In 2019, ischaemic heart disease and stroke were the top-ranked causes of DALYs in both the 50–74-year and 75-years-and-older age groups. Since 1990, there has been a marked shift towards a greater proportion of burden due to YLDs from non-communicable diseases and injuries. In 2019, there were 11 countries where non-communicable disease and injury YLDs constituted more than half of all disease burden. Decreases in age-standardised DALY rates have accelerated over the past decade in countries at the lower end of the SDI range, while improvements have started to stagnate or even reverse in countries with higher SDI. Interpretation As disability becomes an increasingly large component of disease burden and a larger component of health expenditure, greater research and development investment is needed to identify new, more effective intervention strategies. With a rapidly ageing global population, the demands on health services to deal with disabling outcomes, which increase with age, will require policy makers to anticipate these changes. The mix of universal and more geographically specific influences on health reinforces the need for regular reporting on population health in detail and by underlying cause to help decision makers to identify success stories of disease control to emulate, as well as opportunities to improve. Funding Bill & Melinda Gates Foundation.
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                Author and article information

                Journal
                World Psychiatry
                World Psychiatry
                Wiley
                1723-8617
                2051-5545
                February 2023
                January 14 2023
                February 2023
                : 22
                : 1
                : 86-104
                Affiliations
                [1 ]Pain and Rehabilitation Centre and Department of Health, Medicine and Caring Sciences Linköping University Linköping Sweden
                [2 ]Research Laboratory Psychology of Patients, Families and Health Professionals, School of Health Sciences University of Ioannina Ioannina Greece
                [3 ]Early Psychosis: Interventions and Clinical‐detection (EPIC) Lab, Department of Psychosis Studies Institute of Psychiatry, Psychology & Neuroscience, King's College London London UK
                [4 ]Imaging of Mood‐ and Anxiety‐Related Disorders Group Institut d'Investigacions Biomèdiques August Pi i Sunyer, CIBERSAM University of Barcelona Barcelona Spain
                [5 ]Department of Clinical Neuroscience, Centre for Psychiatric Research and Education Karolinska Institutet Stockholm Sweden
                [6 ]Department of Psychiatry University of Ottawa Ottawa ON Canada
                [7 ]Department of Mental Health, Ottawa Hospital Ottawa ON Canada
                [8 ]Centre for Innovation in Mental Health, School of Psychology, Faculty of Environmental and Life Sciences University of Southampton Southampton UK
                [9 ]Department of Child and Adolescent Psychiatry Charité Universitätsmedizin Berlin Germany
                [10 ]DysCo Lab, Paris Nanterre University Nanterre France
                [11 ]Laboratoire de Psychopathologie et Processus de Santé Université Paris Cité Boulogne‐Billancourt France
                [12 ]Department of Psychiatry University of Oxford Oxford UK
                [13 ]Department of Brain and Behavioral Sciences University of Pavia Pavia Italy
                [14 ]Clinical and Experimental Sciences (CNS and Psychiatry), Faculty of Medicine University of Southampton, and Solent NHS Trust Southampton UK
                [15 ]Division of Psychiatry and Applied Psychology, School of Medicine University of Nottingham Nottingham UK
                [16 ]Hassenfeld Children's Hospital at NYU Langone New York NY USA
                [17 ]Mind‐Brain Group, Institute for Culture and Society University of Navarra Pamplona Spain
                [18 ]Section of Psychiatry, Department of Neuroscience, Reproductive Science and Odontostomatology Federico II University of Naples Naples Italy
                [19 ]Department of Emergency Medicine Hippokration Hospital Athens Greece
                [20 ]Institute for Mental and Physical Health and Clinical Translation (IMPACT), School of Medicine and Barwon Health Deakin University Geelong VIC Australia
                [21 ]Department of Pediatrics Yonsei University College of Medicine Seoul South Korea
                [22 ]Department of Pediatrics Severance Children's Hospital Seoul South Korea
                [23 ]Division of Primary Care Geneva University Hospitals Geneva Switzerland
                [24 ]University Centre for General Medicine and Public Health, University of Lausanne Lausanne Switzerland
                [25 ]Department of Health and Community Medicine University of Geneva Geneva Switzerland
                [26 ]Department of Psychiatry Zucker Hillside Hospital, Northwell Health Glen Oaks NY USA
                [27 ]Department of Psychiatry and Molecular Medicine Zucker School of Medicine at Hofstra/Northwell Hempstead NY USA
                [28 ]Center for Psychiatric Neuroscience Feinstein Institute for Medical Research Manhasset NY USA
                [29 ]OASIS Service, South London and Maudsley NHS Foundation Trust London UK
                Article
                10.1002/wps.21068
                36640414
                93c83612-2600-4e06-941f-eba95fb38f95
                © 2023

                http://onlinelibrary.wiley.com/termsAndConditions#vor

                http://doi.wiley.com/10.1002/tdm_license_1.1

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