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      The association between mental-physical multimorbidity and disability, work productivity, and social participation in China: a panel data analysis

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          Abstract

          Background

          The co-occurrence of mental and physical chronic conditions (mental-physical multimorbidity) is a growing and largely unaddressed challenge for health systems and wider economies in low-and middle-income countries. This study investigated the independent and combined (additive or synergistic) effects of mental and physical chronic conditions on disability, work productivity, and social participation in China.

          Methods

          Panel data study design utilised two waves of the China Health and Retirement Longitudinal Study (2011, 2015), including 5616 participants aged ≥45 years, 12 physical chronic conditions and depression. We used a panel data approach of random-effects regression models to assess the relationships between mental-physical multimorbidity and outcomes.

          Results

          After adjusting for socio-economic and demographic factors, an increased number of physical chronic conditions was independently associated with a higher likelihood of disability (Adjusted odds ratio (AOR) = 1.39; 95% CI: 1.33, 1.45), early retirement (AOR = 1.37 [1.26, 1.49]) and increased sick leave days (1.25 days [1.16, 1.35]). Depression was independently associated with disability (AOR = 3.78 [3.30, 4.34]), increased sick leave days (2.18 days [1.72, 2.77]) and a lower likelihood of social participation (AOR = 0.57 [0.47, 0.70]), but not with early retirement (AOR = 1.24 [0.97, 1.58]). There were small and statistically insignificant interactions between physical chronic conditions and mental health on disability, work productivity and social participation, suggesting an additive effect of mental-physical multimorbidity on productivity loss.

          Conclusion

          Mental-physical multimorbidity poses substantial negative health and economic effects on individuals, health systems, and societies. More research that addresses the challenges of mental-physical multimorbidity is needed to inform the development of interventions that can be applied to the workplace and the wider community in China.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12889-021-10414-7.

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          Most cited references61

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          Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980–2017: a systematic analysis for the Global Burden of Disease Study 2017

          Summary Background Global development goals increasingly rely on country-specific estimates for benchmarking a nation's progress. To meet this need, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2016 estimated global, regional, national, and, for selected locations, subnational cause-specific mortality beginning in the year 1980. Here we report an update to that study, making use of newly available data and improved methods. GBD 2017 provides a comprehensive assessment of cause-specific mortality for 282 causes in 195 countries and territories from 1980 to 2017. Methods The causes of death database is composed of vital registration (VR), verbal autopsy (VA), registry, survey, police, and surveillance data. GBD 2017 added ten VA studies, 127 country-years of VR data, 502 cancer-registry country-years, and an additional surveillance country-year. Expansions of the GBD cause of death hierarchy resulted in 18 additional causes estimated for GBD 2017. Newly available data led to subnational estimates for five additional countries—Ethiopia, Iran, New Zealand, Norway, and Russia. Deaths assigned International Classification of Diseases (ICD) codes for non-specific, implausible, or intermediate causes of death were reassigned to underlying causes by redistribution algorithms that were incorporated into uncertainty estimation. We used statistical modelling tools developed for GBD, including the Cause of Death Ensemble model (CODEm), to generate cause fractions and cause-specific death rates for each location, year, age, and sex. Instead of using UN estimates as in previous versions, GBD 2017 independently estimated population size and fertility rate for all locations. Years of life lost (YLLs) were then calculated as the sum of each death multiplied by the standard life expectancy at each age. All rates reported here are age-standardised. Findings At the broadest grouping of causes of death (Level 1), non-communicable diseases (NCDs) comprised the greatest fraction of deaths, contributing to 73·4% (95% uncertainty interval [UI] 72·5–74·1) of total deaths in 2017, while communicable, maternal, neonatal, and nutritional (CMNN) causes accounted for 18·6% (17·9–19·6), and injuries 8·0% (7·7–8·2). Total numbers of deaths from NCD causes increased from 2007 to 2017 by 22·7% (21·5–23·9), representing an additional 7·61 million (7·20–8·01) deaths estimated in 2017 versus 2007. The death rate from NCDs decreased globally by 7·9% (7·0–8·8). The number of deaths for CMNN causes decreased by 22·2% (20·0–24·0) and the death rate by 31·8% (30·1–33·3). Total deaths from injuries increased by 2·3% (0·5–4·0) between 2007 and 2017, and the death rate from injuries decreased by 13·7% (12·2–15·1) to 57·9 deaths (55·9–59·2) per 100 000 in 2017. Deaths from substance use disorders also increased, rising from 284 000 deaths (268 000–289 000) globally in 2007 to 352 000 (334 000–363 000) in 2017. Between 2007 and 2017, total deaths from conflict and terrorism increased by 118·0% (88·8–148·6). A greater reduction in total deaths and death rates was observed for some CMNN causes among children younger than 5 years than for older adults, such as a 36·4% (32·2–40·6) reduction in deaths from lower respiratory infections for children younger than 5 years compared with a 33·6% (31·2–36·1) increase in adults older than 70 years. Globally, the number of deaths was greater for men than for women at most ages in 2017, except at ages older than 85 years. Trends in global YLLs reflect an epidemiological transition, with decreases in total YLLs from enteric infections, respiratory infections and tuberculosis, and maternal and neonatal disorders between 1990 and 2017; these were generally greater in magnitude at the lowest levels of the Socio-demographic Index (SDI). At the same time, there were large increases in YLLs from neoplasms and cardiovascular diseases. YLL rates decreased across the five leading Level 2 causes in all SDI quintiles. The leading causes of YLLs in 1990—neonatal disorders, lower respiratory infections, and diarrhoeal diseases—were ranked second, fourth, and fifth, in 2017. Meanwhile, estimated YLLs increased for ischaemic heart disease (ranked first in 2017) and stroke (ranked third), even though YLL rates decreased. Population growth contributed to increased total deaths across the 20 leading Level 2 causes of mortality between 2007 and 2017. Decreases in the cause-specific mortality rate reduced the effect of population growth for all but three causes: substance use disorders, neurological disorders, and skin and subcutaneous diseases. Interpretation Improvements in global health have been unevenly distributed among populations. Deaths due to injuries, substance use disorders, armed conflict and terrorism, neoplasms, and cardiovascular disease are expanding threats to global health. For causes of death such as lower respiratory and enteric infections, more rapid progress occurred for children than for the oldest adults, and there is continuing disparity in mortality rates by sex across age groups. Reductions in the death rate of some common diseases are themselves slowing or have ceased, primarily for NCDs, and the death rate for selected causes has increased in the past decade. Funding Bill & Melinda Gates Foundation.
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            Cohort profile: the China Health and Retirement Longitudinal Study (CHARLS).

            The China Health and Retirement Longitudinal Study (CHARLS) is a nationally representative longitudinal survey of persons in China 45 years of age or older and their spouses, including assessments of social, economic, and health circumstances of community-residents. CHARLS examines health and economic adjustments to rapid ageing of the population in China. The national baseline survey for the study was conducted between June 2011 and March 2012 and involved 17 708 respondents. CHARLS respondents are followed every 2 years, using a face-to-face computer-assisted personal interview (CAPI). Physical measurements are made at every 2-year follow-up, and blood sample collection is done once in every two follow-up periods. A pilot survey for CHARLS was conducted in two provinces of China in 2008, on 2685 individuals, who were resurveyed in 2012. To ensure the adoption of best practices and international comparability of results, CHARLS was harmonized with leading international research studies in the Health and Retirement Study (HRS) model. Requests for collaborations should be directed to Dr Yaohui Zhao (yhzhao@nsd.edu.cn). All data in CHARLS are maintained at the National School of Development of Peking University and will be accessible to researchers around the world at the study website. The 2008 pilot data for CHARLS are available at: http://charls.ccer.edu.cn/charls/. National baseline data for the study are expected to be released in January 2013.
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              2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes—2019

              (2018)
              The American Diabetes Association (ADA) "Standards of Medical Care in Diabetes" includes ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations, please refer to the Standards of Care Introduction Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.
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                Author and article information

                Contributors
                tianxin.pan1@unimelb.edu.au
                Journal
                BMC Public Health
                BMC Public Health
                BMC Public Health
                BioMed Central (London )
                1471-2458
                18 February 2021
                18 February 2021
                2021
                : 21
                : 376
                Affiliations
                [1 ]GRID grid.1008.9, ISNI 0000 0001 2179 088X, Melbourne School of Population and Global Health, , The University of Melbourne, ; 207 Bouverie Street, Melbourne, Victoria 3010 Australia
                [2 ]GRID grid.4305.2, ISNI 0000 0004 1936 7988, Usher Institute, College of Medicine and Veterinary Medicine, University of Edinburgh, ; Edinburgh, UK
                [3 ]GRID grid.452860.d, The George Institute for Global Health at Peking University Health Science Center, ; Beijing, China
                [4 ]WHO Collaborating Centre on Implementation Research for Prevention & Control of NCDs, Melbourne, Victoria Australia
                [5 ]GRID grid.38142.3c, ISNI 000000041936754X, Harvard T.H. Chan School of Public Health, , Harvard University, ; Cambridge, USA
                [6 ]GRID grid.7445.2, ISNI 0000 0001 2113 8111, Department of Primary Care and Public Health, School of Public Health, , Imperial College London, ; London, UK
                Author information
                http://orcid.org/0000-0002-2243-8818
                Article
                10414
                10.1186/s12889-021-10414-7
                7890601
                33602174
                71afb9da-fcef-4325-ab80-83abe3a6a5ab
                © The Author(s) 2021

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 15 July 2020
                : 9 February 2021
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2021

                Public health
                multimorbidity,physical conditions,mental health conditions,productivity,disability,economic impact,china

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