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      The impact of multimorbidity on adult physical and mental health in low- and middle-income countries: what does the study on global ageing and adult health (SAGE) reveal?

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          Abstract

          Background

          Chronic diseases contribute a large share of disease burden in low- and middle-income countries (LMICs). Chronic diseases have a tendency to occur simultaneously and where there are two or more such conditions, this is termed as ‘multimorbidity’. Multimorbidity is associated with adverse health outcomes, but limited research has been undertaken in LMICs. Therefore, this study examines the prevalence and correlates of multimorbidity as well as the associations between multimorbidity and self-rated health, activities of daily living (ADLs), quality of life, and depression across six LMICs.

          Methods

          Data was obtained from the WHO’s Study on global AGEing and adult health (SAGE) Wave-1 (2007/10). This was a cross-sectional population based survey performed in LMICs, namely China, Ghana, India, Mexico, Russia, and South Africa, including 42,236 adults aged 18 years and older. Multimorbidity was measured as the simultaneous presence of two or more of eight chronic conditions including angina pectoris, arthritis, asthma, chronic lung disease, diabetes mellitus, hypertension, stroke, and vision impairment. Associations with four health outcomes were examined, namely ADL limitation, self-rated health, depression, and a quality of life index. Random-intercept multilevel regression models were used on pooled data from the six countries.

          Results

          The prevalence of morbidity and multimorbidity was 54.2 % and 21.9 %, respectively, in the pooled sample of six countries. Russia had the highest prevalence of multimorbidity (34.7 %) whereas China had the lowest (20.3 %). The likelihood of multimorbidity was higher in older age groups and was lower in those with higher socioeconomic status. In the pooled sample, the prevalence of 1+ ADL limitation was 14 %, depression 5.7 %, self-rated poor health 11.6 %, and mean quality of life score was 54.4. Substantial cross-country variations were seen in the four health outcome measures. The prevalence of 1+ ADL limitation, poor self-rated health, and depression increased whereas quality of life declined markedly with an increase in number of diseases.

          Conclusions

          Findings highlight the challenge of multimorbidity in LMICs, particularly among the lower socioeconomic groups, and the pressing need for reorientation of health care resources considering the distribution of multimorbidity and its adverse effect on health outcomes.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s12916-015-0402-8) contains supplementary material, which is available to authorized users.

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          Most cited references73

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          Multimorbidity in older adults.

          M Salive (2013)
          Multimorbidity, the coexistence of 2 or more chronic conditions, has become prevalent among older adults as mortality rates have declined and the population has aged. We examined population-based administrative claims data indicating specific health service delivery to nearly 31 million Medicare fee-for-service beneficiaries for 15 prevalent chronic conditions. A total of 67% had multimorbidity, which increased with age, from 50% for persons under age 65 years to 62% for those aged 65-74 years and 81.5% for those aged ≥85 years. A systematic review identified 16 other prevalence studies conducted in community samples that included older adults, with median prevalence of 63% and a mode of 67%. Prevalence differences between studies are probably due to methodological biases; no studies were comparable. Key methodological issues arise from elements of the case definition, including type and number of chronic conditions included, ascertainment methods, and source population. Standardized methods for measuring multimorbidity are needed to enable public health surveillance and prevention. Multimorbidity is associated with elevated risk of death, disability, poor functional status, poor quality of life, and adverse drug events. Additional research is needed to develop an understanding of causal pathways and to further develop and test potential clinical and population interventions targeting multimorbidity. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health 2013.
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            Socioeconomic status and obesity: a review of the literature.

            A review of 144 published studies of the relationship between socioeconomic status (SES) and obesity reveals a strong inverse relationship among women in developed societies. The relationship is inconsistent for men and children in developed societies. In developing societies, however, a strong direct relationship exists between SES and obesity among men, women, and children. A review of social attitudes toward obesity and thinness reveals values congruent with the distribution of obesity by SES in different societies. Several variables may mediate the influence of attitudes toward obesity and thinness among women in developed societies that result in the inverse relationship between SES and obesity. They include dietary restraint, physical activity, social mobility, and inheritance.
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              Causes and consequences of comorbidity: a review.

              A literature search was carried out to identify and summarize the existing information on causes and consequences of comorbidity of chronic somatic diseases. A selection of 82 articles met our inclusion criteria. Very little work has been done on the causes of comorbidity. On the other hand, much work has been done on consequences of comorbidity, although comorbidity is seldom the main subject of study. We found comorbidity in general to be associated with mortality, quality of life, and health care. The consequences of specific disease combinations, however, depended on many factors. We recommend more etiological studies on shared risk factors, especially for those comorbidities that occur at a higher rate than expected. New insights in this field can lead to better prevention strategies. Health care workers need to take comorbid diseases into account in monitoring and treating patients. Future studies on consequences of comorbidity should investigate specific disease combinations.
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                Author and article information

                Contributors
                arokiasamy_peria@yahoo.com
                uttamacharya.iips@gmail.com
                kshipraaa@googlemail.com
                biritwum@africaonline.com.gh
                aeyawson@yahoo.com
                wufan@scdc.sh.cn
                guoyanfei@scdc.sh.cn
                tmaximova@mail.ru
                bmanrique@insp.mx
                asalinas@insp.mx
                sa2706@soton.ac.uk
                sanghamitra.pati@iiphb.org
                iceg@ohio.edu
                s.banerjee@bsms.ac.uk
                liebert@uoregon.edu
                jjosh@uoregon.edu
                naidoon@who.int
                chatterjis@who.int
                kowalp@who.int
                Journal
                BMC Med
                BMC Med
                BMC Medicine
                BioMed Central (London )
                1741-7015
                3 August 2015
                3 August 2015
                2015
                : 13
                : 178
                Affiliations
                [ ]International Institute for Population Sciences, Mumbai, India
                [ ]Department of Community Health, University of Ghana, Accra, Ghana
                [ ]Shanghai Municipal Center for Disease Control and Prevention (CDC), Shanghai, China
                [ ]Russian Academy of Medical Sciences (RAMS), Moscow, Russian Federation
                [ ]National Institute of Public Health (INSP), Centre for Evaluation Research and Surveys, Cuernavaca, Morelos Mexico
                [ ]Academic Unit of Primary Care and Population Sciences, Faculty of Medicine, University of Southampton, University Road, Southampton, SO17 1BJ UK
                [ ]Indian Institute of Public Health, Bhubaneswar, Public Health Foundation of India, Bhubaneswar, Odisha India
                [ ]Ohio University, Department of Social Medicine and Director of Global Health, Athens, OH USA
                [ ]Centre for Dementia Studies, Brighton and Sussex Medical School, University of Sussex, Brighton, UK
                [ ]University of Oregon, Department of Anthropology, Eugene, OR USA
                [ ]World Health Organization, Statistics Measurement and Analysis Unit, Geneva, Switzerland
                [ ]World Health Organization Study on global AGEing and adult health (SAGE), Geneva, Switzerland
                [ ]University of Newcastle Priority Research Centre for Gender, Health and Ageing, Newcastle, NSW Australia
                Article
                402
                10.1186/s12916-015-0402-8
                4524360
                26239481
                3bd126c8-9138-4b4d-9ac0-ce8c9854f70d
                © Arokiasamy et al. 2015

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. 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.

                History
                : 16 April 2015
                : 17 June 2015
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2015

                Medicine
                activities of daily living,low- and middle-income countries,mental health,multimorbidity,non-communicable diseases,quality of life

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