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      Health care costs of cardiovascular disease in China: a machine learning-based cross-sectional study

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          Abstract

          Background

          Cardiovascular disease (CVD) causes substantial financial burden to patients with the condition, their households, and the healthcare system in China. Health care costs for treating patients with CVD vary significantly, but little is known about the factors associated with the cost variation. This study aims to identify and rank key determinants of health care costs in patients with CVD in China and to assess their effects on health care costs.

          Methods

          Data were from a survey of patients with CVD from 14 large tertiary grade-A general hospitals in S City, China, between 2018 and 2020. The survey included information on demographic characteristics, health conditions and comorbidities, medical service utilization, and health care costs. We used re-centered influence function regression to examine health care cost concentration, decomposing and estimating the effects of relevant factors on the distribution of costs. We also applied quantile regression forests—a machine learning approach—to identify the key factors for predicting the 10th (low), 50th (median), and 90th (high) quantiles of health care costs associated with CVD treatment.

          Results

          Our sample included 28,213 patients with CVD. The 10th, 50th and 90th quantiles of health care cost for patients with CVD were 6,103 CNY, 18,105 CNY, and 98,637 CNY, respectively. Patients with high health care costs were more likely to be older, male, and have a longer length of hospital stay, more comorbidities, more complex medical procedures, and emergency admissions. Higher health care costs were also associated with specific CVD types such as cardiomyopathy, heart failure, and stroke.

          Conclusion

          Machine learning methods are useful tools to identify determinants of health care costs for patients with CVD in China. Findings may help improve policymaking to alleviate the financial burden of CVD, particularly among patients with high health care costs.

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          Most cited references38

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          Mortality, morbidity, and risk factors in China and its provinces, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017

          Summary Background Public health is a priority for the Chinese Government. Evidence-based decision making for health at the province level in China, which is home to a fifth of the global population, is of paramount importance. This analysis uses data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 to help inform decision making and monitor progress on health at the province level. Methods We used the methods in GBD 2017 to analyse health patterns in the 34 province-level administrative units in China from 1990 to 2017. We estimated all-cause and cause-specific mortality, years of life lost (YLLs), years lived with disability (YLDs), disability-adjusted life-years (DALYs), summary exposure values (SEVs), and attributable risk. We compared the observed results with expected values estimated based on the Socio-demographic Index (SDI). Findings Stroke and ischaemic heart disease were the leading causes of death and DALYs at the national level in China in 2017. Age-standardised DALYs per 100 000 population decreased by 33·1% (95% uncertainty interval [UI] 29·8 to 37·4) for stroke and increased by 4·6% (–3·3 to 10·7) for ischaemic heart disease from 1990 to 2017. Age-standardised stroke, ischaemic heart disease, lung cancer, chronic obstructive pulmonary disease, and liver cancer were the five leading causes of YLLs in 2017. Musculoskeletal disorders, mental health disorders, and sense organ diseases were the three leading causes of YLDs in 2017, and high systolic blood pressure, smoking, high-sodium diet, and ambient particulate matter pollution were among the leading four risk factors contributing to deaths and DALYs. All provinces had higher than expected DALYs per 100 000 population for liver cancer, with the observed to expected ratio ranging from 2·04 to 6·88. The all-cause age-standardised DALYs per 100 000 population were lower than expected in all provinces in 2017, and among the top 20 level 3 causes were lower than expected for ischaemic heart disease, Alzheimer's disease, headache disorder, and low back pain. The largest percentage change at the national level in age-standardised SEVs among the top ten leading risk factors was in high body-mass index (185%, 95% UI 113·1 to 247·7]), followed by ambient particulate matter pollution (88·5%, 66·4 to 116·4). Interpretation China has made substantial progress in reducing the burden of many diseases and disabilities. Strategies targeting chronic diseases, particularly in the elderly, should be prioritised in the expanding Chinese health-care system. Funding China National Key Research and Development Program and Bill & Melinda Gates Foundation.
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            Burden of Cardiovascular Diseases in China, 1990-2016

            Cardiovascular disease (CVD) remains the top cause of death in China. To our knowledge, no consistent and comparable assessments of CVD burden have been produced at subnational levels, and little is understood about the spatial patterns and temporal trends of CVD in China.
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              Unconditional Quantile Regressions

              (2009)
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                Author and article information

                Contributors
                Role: Role: Role:
                Role: Role: Role:
                Role:
                Role: Role:
                Role: Role:
                URI : https://loop.frontiersin.org/people/2175659/overviewRole:
                URI : https://loop.frontiersin.org/people/1003700/overviewRole: Role: Role:
                URI : https://loop.frontiersin.org/people/1523616/overviewRole: Role: Role:
                Journal
                Front Public Health
                Front Public Health
                Front. Public Health
                Frontiers in Public Health
                Frontiers Media S.A.
                2296-2565
                06 November 2023
                2023
                : 11
                : 1301276
                Affiliations
                [1] 1School of Public Health, Shanghai Jiao Tong University School of Medicine , Shanghai, China
                [2] 2Center for HTA, China Hospital Development Institute, Shanghai Jiao Tong University , Shanghai, China
                [3] 3Shanghai Municipal Health Commission , Shanghai, China
                [4] 4Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai , New York, NY, United States
                [5] 5Icahn School of Medicine at Mount Sinai, Institute for Healthcare Delivery Science , New York, NY, United States
                [6] 6China Hospital Development Institute, Shanghai Jiao Tong University , Shanghai, China
                Author notes

                Edited by: Thomas T. H. Wan, University of Central Florida, United States

                Reviewed by: Varadraj Prabhu Gurupur, University of Central Florida, United States; Jay J. Shen, University of Nevada, Las Vegas, United States

                *Correspondence: Yan Li, yanliacademic@ 123456gmail.com

                These authors have contributed equally to this work and share first authorship

                Article
                10.3389/fpubh.2023.1301276
                10657803
                38026337
                9e7e952d-c342-47c8-a4f8-a06114b8b857
                Copyright © 2023 Lu, Gao, Shi, Xiao, Li, Li, Li and Li.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 24 September 2023
                : 24 October 2023
                Page count
                Figures: 2, Tables: 2, Equations: 0, References: 39, Pages: 10, Words: 6590
                Funding
                Funded by: National Natural Science Foundation of China, doi 10.13039/501100001809;
                Award ID: 72074147
                Award ID: 72293585
                The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was supported by the National Natural Science Foundation of China (Nos. 72074147, 72293585).
                Categories
                Public Health
                Original Research
                Custom metadata
                Health Economics

                health care costs,cardiovascular disease,quantile regression forest,machine learning,financial burden

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