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      Physical multimorbidity, health service use, and catastrophic health expenditure by socioeconomic groups in China: an analysis of population-based panel data

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          Summary

          Background

          Multimorbidity, the presence of two or more mental or physical chronic non-communicable diseases, is a major challenge for the health system in China, which faces unprecedented ageing of its population. Here we examined the distribution of physical multimorbidity in relation to socioeconomic status; the association between physical multimorbidity, health-care service use, and catastrophic health expenditures; and whether these associations varied by socioeconomic group and social health insurance schemes.

          Methods

          In this population-based, panel data analysis, we used data from three waves of the nationally representative China Health and Retirement Longitudinal Study (CHARLS) for 2011, 2013, and 2015. We included participants aged 50 years and older in 2015, who had complete follow-up for the three waves. We used 11 physical non-communicable diseases to measure physical multimorbidity and annual per-capita household consumption spending as a proxy for socioeconomic status.

          Findings

          Of 17 708 participants in CHARLS, 11 817 were eligible for inclusion in our analysis. The median age of participants was 62 years (IQR 56–69) in 2015, and 5766 (48·8%) participants were male. 7320 (61·9%) eligible participants had physical multimorbidity in China in 2015. The prevalence of physical multimorbidity was increased with older age (odds ratio 2·93, 95% CI 2·71–3·15), among women (2·70, 2·04–3·57), within a higher socioeconomic group (for quartile 4 [highest group] 1·50, 1·24–1·82), and higher educational level (5·17, 3·02–8·83); however, physical multimorbidity was more common in poorer regions than in the more affluent regions. An additional chronic non-communicable disease was associated with an increase in the number of outpatient visits (incidence rate ratio 1·29, 95% CI 1·27–1·31), and number of days spent in hospital as an inpatient (1·38, 1·35–1·41). We saw similar effects in health service use of an additional chronic non-communicable disease in different socioeconomic groups and among those covered by different social health insurance programmes. Overall, physical multimorbidity was associated with a significantly increased likelihood of catastrophic health expenditure (for the overall population: odds ratio 1·29, 95% CI 1·26–1·32, adjusted for sociodemographic variables). The effect of physical multimorbidity on catastrophic health expenditures persisted even among the higher socioeconomic groups and across all health insurance programmes.

          Interpretation

          Concerted efforts are needed to reduce health inequalities that are due to physical multimorbidity, and its adverse economic effect in population groups in China. Social health insurance reforms must place emphasis on reducing out-of-pocket spending for patients with multimorbidity to provide greater financial risk protection.

          Funding

          None.

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          Most cited references16

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          Management of multimorbidity using a patient-centred care model: a pragmatic cluster-randomised trial of the 3D approach

          Summary Background The management of people with multiple chronic conditions challenges health-care systems designed around single conditions. There is international consensus that care for multimorbidity should be patient-centred, focus on quality of life, and promote self-management towards agreed goals. However, there is little evidence about the effectiveness of this approach. Our hypothesis was that the patient-centred, so-called 3D approach (based on dimensions of health, depression, and drugs) for patients with multimorbidity would improve their health-related quality of life, which is the ultimate aim of the 3D intervention. Methods We did this pragmatic cluster-randomised trial in general practices in England and Scotland. Practices were randomly allocated to continue usual care (17 practices) or to provide 6-monthly comprehensive 3D reviews, incorporating patient-centred strategies that reflected international consensus on best care (16 practices). Randomisation was computer-generated, stratified by area, and minimised by practice deprivation and list size. Adults with three or more chronic conditions were recruited. The primary outcome was quality of life (assessed with EQ-5D-5L) after 15 months' follow-up. Participants were not masked to group assignment, but analysis of outcomes was blinded. We analysed the primary outcome in the intention-to-treat population, with missing data being multiply imputed. This trial is registered as an International Standard Randomised Controlled Trial, number ISRCTN06180958. Findings Between May 20, 2015, and Dec 31, 2015, we recruited 1546 patients from 33 practices and randomly assigned them to receive the intervention (n=797) or usual care (n=749). In our intention-to-treat analysis, there was no difference between trial groups in the primary outcome of quality of life (adjusted difference in mean EQ-5D-5L 0·00, 95% CI −0·02 to 0·02; p=0·93). 78 patients died, and the deaths were not considered as related to the intervention. Interpretation To our knowledge, this trial is the largest investigation of the international consensus about optimal management of multimorbidity. The 3D intervention did not improve patients' quality of life. Funding National Institute for Health Research.
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            Epidemiology of multimorbidity in China and implications for the healthcare system: cross-sectional survey among 162,464 community household residents in southern China

            Background China, like other countries, is facing a growing burden of chronic disease but the prevalence of multimorbidity and implications for the healthcare system have been little researched. We examined the epidemiology of multimorbidity in southern China in a large representative sample. The effects of multimorbidity and other factors on usual source of healthcare were also examined. Methods We conducted a large cross-sectional survey among approximately 5% (N = 162,464) of the resident population in three prefectures in Guangdong province, southern China in 2011. A multistage, stratified random sampling was adopted. The study population had many similar characteristics to the national census population. Interviewer-administered questionnaires were used to collect self-report data on demographics, socio-economics, lifestyles, healthcare use, and health characteristics from paper-based medical reports. Results More than one in ten of the total study population (11.1%, 95% confidence interval (CI) 10.6 to 11.6) had two or more chronic conditions from a selection of 40 morbidities. The prevalence of multimorbidity increased with age (adjusted odds ratio (aOR) = 1.36, 95% CI 1.35 to 1.38 per five years). Female gender (aOR = 1.70, 95% CI 1.64 to 1.76), low education (aOR = 1.26, 95% CI 1.23 to 1.29), lack of medical insurance (aOR = 1.79, 95% CI 1.71 to 1.89), and unhealthy lifestyle behaviours were independent predictors of multimorbidity. Multimorbidity was associated with the regular use of secondary outpatient care in preference to primary care. Conclusions Multimorbidity is now common in China. The reported preferential use of secondary care over primary care by patients with multimorbidity has many major implications. There is an urgent need to further develop a strong and equitable primary care system. Electronic supplementary material The online version of this article (doi:10.1186/s12916-014-0188-0) contains supplementary material, which is available to authorized users.
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              Impact of Noncommunicable Disease Multimorbidity on Healthcare Utilisation and Out-Of-Pocket Expenditures in Middle-Income Countries: Cross Sectional Analysis

              Background The burden of non-communicable disease (NCDs) has grown rapidly in low- and middle-income countries (LMICs), where populations are ageing, with rising prevalence of multimorbidity (more than two co-existing chronic conditions) that will significantly increase pressure on already stretched health systems. We assess the impact of NCD multimorbidity on healthcare utilisation and out-of-pocket expenditures in six middle-income countries: China, Ghana, India, Mexico, Russia and South Africa. Methods Secondary analyses of cross-sectional data from adult participants (>18 years) in the WHO Study on Global Ageing and Adult Health (SAGE) 2007–2010. We used multiple logistic regression to determine socio-demographic correlates of multimorbidity. Association between the number of NCDs and healthcare utilisation as well as out-of-pocket spending was assessed using logistic, negative binominal and log-linear models. Results The prevalence of multimorbidity in the adult population varied from 3∙9% in Ghana to 33∙6% in Russia. Number of visits to doctors in primary and secondary care rose substantially for persons with increasing numbers of co-existing NCDs. Multimorbidity was associated with more outpatient visits in China (coefficient for number of NCD = 0∙56, 95% CI = 0∙46, 0∙66), a higher likelihood of being hospitalised in India (AOR = 1∙59, 95% CI = 1∙45, 1∙75), higher out-of-pocket expenditures for outpatient visits in India and China, and higher expenditures for hospital visits in Russia. Medicines constituted the largest proportion of out-of-pocket expenditures in persons with multimorbidity (88∙3% for outpatient, 55∙9% for inpatient visit in China) in most countries. Conclusion Multimorbidity is associated with higher levels of healthcare utilisation and greater financial burden for individuals in middle-income countries. Our study supports the WHO call for universal health insurance and health service coverage in LMICs, particularly for vulnerable groups such as the elderly with multimorbidity.
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                Author and article information

                Contributors
                Journal
                Lancet Glob Health
                Lancet Glob Health
                The Lancet. Global Health
                The Author(s). Published by Elsevier Ltd.
                2214-109X
                21 May 2020
                June 2020
                21 May 2020
                : 8
                : 6
                : e840-e849
                Affiliations
                [a ]The Nossal Institute for Global Health, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
                [b ]WHO Collaborating Centre on Implementation Research for Prevention and Control of Noncommunicable Diseases, Melbourne, VIC, Australia
                [c ]The George Institute for Global Health at Peking University Health Science Center, Beijing, China
                [d ]Department of Global Health and Population, Harvard T H Chan School of Public Health and Department of Global Health and Social Medicine, Harvard Medical School, Harvard University, Boston, MA, USA
                [e ]Duke Global Health Institute, Duke University, Durham, NC, USA
                [f ]Usher Institute, College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, UK
                [g ]Department of Health Services Research and Policy, London School of Hygiene & Tropical Medicine, London, UK
                [h ]Saw Swee Hock School of Public Health, National University of Singapore, Singapore
                [i ]Public Health Policy Evaluation Unit, Department of Primary Care and Public Health, School of Public Health, Imperial College London, London, UK
                Author notes
                [* ]Correspondence to: Dr Yang Zhao, The Nossal Institute for Global Health, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC 3010, Australia zhaoyang001@ 123456hsc.pku.edu.cn
                Article
                S2214-109X(20)30127-3
                10.1016/S2214-109X(20)30127-3
                7241981
                32446349
                4f73a7de-502f-4376-9214-9e7ea4ce3eb0
                © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license.

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