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Direct economic burden attributable to age-related diseases in China: An econometric modelling study

Xin Ye1,2, Ming Wang3,4, Yiqi Xia3,4, Ping He4, Xiaoying Zheng5

1 Institute for Global Public Policy, Fudan University, Shanghai, China
2 LSE-Fudan Research Center for Global Public Policy, Fudan University, Shanghai, China
3 School of Public Health, Peking University, Beijing, China
4 China Center for Health Development Studies, Peking University, Beijing, China
5 School of Population Medicine and Public Health, Chinese Academy of Medical Sciences / Peking Union Medical College, Beijing, China
† Joint senior authorship.

DOI: 10.7189/jogh.13.04042

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Abstract

Background

Aging is a strong risk factor for many chronic diseases. However, the economic burden attributable to age-related diseases remains unclear. We aimed to calculate the economic burden attributable to age-related diseases in China.

Methods

We used an econometric modelling approach from the China Health and Retirement Longitudinal Survey (CHARLS), which is based on a longitudinal observational data set from middle-aged and older adults aged 45+ in 2011, 2013, and 2015.

Results

We calculated the total direct economic burden attributable to age-related diseases for outpatient and inpatient services among adults aged 45 and above in China, which was approximately 288.368 billion US dollars (US$), US$379.901 billion, and US$616.809 billion in 2011, 2013, and 2015, respectively, taking up 19.48%, 21.11% and 32.03% of the overall health care expenses in the same year. The proportion of dyslipidemia was the largest, followed by hypertension in all the three years; hearing problems accounted for the lowest proportion.

Conclusions

The alarming upward trend in age-related economic burden in China calls for urgent interventions to prevent or slow down the accumulation of damage associated with age-related diseases.

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Not only developed countries, but many developing countries including China are facing an unprecedented and rapidly growing elderly population [1]. By 2021, China has achieved its first centenary goal of building a moderately prosperous society in all respects, with average life expectancy rising from 70.1 years in 1996 to 77.3 years in 2019 [2]. According to China’s second national census, the number of population aged 60 and above in the mid-1960s was about 38.17 million (5.5% of the total population) [3]. While in 2020, the number of people aged 60 years and above increased seven-fold to 264.02 million (18.70% of the total population), and the number of people aged 65 years and above was 190.64 million (13.50%) [4]. China was expected to become an aged society by 2022 and a super-aged society by 2033 [5].

An aging population is accompanied by an increasing burden of non-communicable diseases (NCDs). Aging is a recognized risk factor for the development of multiple NCDs, including cardiovascular diseases, stroke, cancer, osteoarthritis, and dementia, etc. [6]. Because of the combination of risk factors such as diet, tobacco, etc. in the elderly, the incidence of NCDs has been increasing rapidly [7]. NCDs have high rates of mortality and disability, and require expensive hospitalization expenses [8]. Hospitalization rates in China are even higher than that in the Organization for Economic Co-operation and Development (OECD) countries (about 18%), and the associated high medical expenses bring a huge burden to the whole society and individuals [8,9]. Non-communicable diseases will also put at risk the financial sustainability of all public health care systems [9].

Available research on the exact economic burden attributable to age-related diseases is rather limited. An Italian study using accurate clinical and drug prescription data from Health Search CSD-LPD between 2005 and 2014 found that age-related disease burden accounted for about 20% of the public health care budget every year [10]. Age-related diseases can increase economic burden through mortality, early retirement [11], and reduced productivity [12]. Also, current interventions related to NCDs, including medical treatment and prevention, require a substantial amount of resources [13]. Additionally, lower productivity and reduced labour supply, combined with higher health care expenses, will lead to a decline in total income, further increasing the economic burden [13]. The increased burden of chronic disease is particularly severe in low-income and middle-income countries, which can least afford a health-related backsliding in development [9].

Despite the high prevalence of age-related diseases, little is known about the exact economic burden attributable to age-related diseases in China. An important improvement over existing literature is proposed in this study, which uses nationally representative survey from 2011 to 2015 to calculate the direct economic burden attributable to age-related diseases in China using an econometric modelling. To our knowledge, this is one of the largest longitudinal population samples ever used in this diseases burden calculation, and it can provide evidence of potential economic savings or cost-effective interventions for age-related diseases. There is a need for further investment in interventions aimed at alleviating age-related diseases, as it will contribute to expressively reducing expenditures on the public health care system and improving the quality of life and health of the Chinese population.

METHODS

Data and sample

The main data used is from the China Health and Retirement Longitudinal Survey (CHARLS), which aims to collect high-quality data set representing households and individuals aged 45 and older in China and to address the challenges associated with population aging in China [14]. This is a long-term follow-up project, observing the life trajectory and changes of the interviewees. CHARLS combines detailed socioeconomic data with high-quality data on individuals’ physical and mental health. The sample was stratified by urban / rural areas and by gross domestic product (GDP) using probability proportional to size (PPS) to ensure that the sample is representative of the Chinese population, and the data are collected using standardized protocols to ensure consistency across respondents. In addition, survey weights were further taken into account in our estimates to correct for nonresponse and sampling-frame errors and to ensure the representativeness of the sample.

The baseline survey was conducted in 2011 and follow-up surveys were conducted in 2013 and 2015. CHARLS 2011 covered 28 provincial-level units, 150 county-level units, 450 village-level units, 10 257 households and 17 708 samples, representing the middle-aged and older population in China as a whole. In 2013, the project completed the first follow-up interview, and the follow-up rate reached 88%. New individuals were also being added to the CHARLS cohort at each survey wave. In the second follow-up survey in 2015, a total of 11 797 households and 20 284 people were interviewed, with a follow-up rate of 87%. The number of population by sex and age in 2011, 2013 and 2015 are from the China Statistical Yearbooks and China Demographic and Employment Statistical Yearbooks for the corresponding year. The results for China did not include Hong Kong and Macao special administrative regions.

Variables

Age-related diseases

The selection of age-related diseases was based on both the existing literature and the types of age-related diseases covered in the CHARLS questionnaire. Age-related diseases include hearing problems, vision problems, hypertension, dyslipidemia, heart diseases, stroke, lung diseases, asthma, digestive diseases, liver diseases, arthritis, kidney diseases, cancer, diabetes, etc. [15]. In CHARLS 2011, 2013 and 2015, age-related diseases were identified through self-reporting. Participants were asked questions of whether they had these age-related diseases by trained investigators through face-to-face interviews.

Healthcare probability and direct economic burden

Healthcare probability and related economic burden were used to calculate direct economic burden attributable to age-related diseases. Healthcare probability included participants’ probability of using inpatient services last month or using outpatient services last year. Direct economic burden attributable to age-related diseases referred to the health care expenditure incurred from having age-related diseases by the patient in the health and non-health sectors during the period of illness. In the survey, respondents were also asked to separately report health care expenditure for each category: monthly outpatient expenses, annual inpatient expenses (only include the fees paid to the hospital), as well as outpatient transportation expenses, inpatient transportation and accommodation expenses incurred by accompanying patients. These expenses included both out-of-pocket health care expenses paid by survey participants and expenses paid by private and public health insurers.

Control variables

Control variables were presented as the following: (1) sociodemographic characteristics, including age (continuous), sex (female, male), education (illiterate, primary school, junior middle school, senior middle school and above), registered residents (rural, urban), marital status (single, partnered); (2) health status, including activities of daily living ((ADLs) impaired or unimpaired), instrumental activities of daily living ((IADLs) impaired or unimpaired), depressive symptoms measured by the 10-item Center for Epidemiological Studies Depression Scale (CES-D-10) scores (yes, no); (3) health behaviours, including current smoking (yes, no), current drinking (yes, no).

Statistical analysis

This analysis followed previous literature to focus on all-cause health care spending [16,17], because age-related diseases could result in deterioration of overall physical and mental health outcomes. An econometric modelling was used to compare the annual per capita direct economic burden of individuals with and without age-related diseases, thus obtaining the direct economic burden of age-related diseases.

Age-related Diseases-attributable Fraction through the econometric modelling (ADAFEC)

The regression applied multiple mixed models based on the panel data of CHARLS 2011, 2013 and 2015. As age-related diseases can gradually progress over time, we used mixed models to account for the dynamic longitudinal association between age-related diseases and health care utilization [18,19]. Mixed models took into account within-subject correlation of time-varying age-related diseases and health care utilization over four years of follow-up (two waves).

The first step was the selection model, and the mixed logistic regression model was used to estimate the probability of using health care services. The specific regression model is as follows:

DExpitx = γ1ARDitk + γ2Xitk + γ3Yitk + γ4Zitk + αik + υk + ωt (1)

The second step was to estimate the direct economic burden by mixed linear regression model on log (positive) expenditure:

ln Expitk = β1ARDitk + β2Xitk + β3Yitk + β4Zitk + α’ix + υ’k + ω’t (2)

Where DExpitk is a dummy variable, indicating whether individual i in the community k uses health care services (including inpatient and outpatient treatment) in the year ; ln Expitk represents the natural log of self-reported overall health care expenditures of individual i in the community k in the year t (not specific to age-related diseases); ARDitk is the status of age-related diseases; Xitk are sociodemographic characteristics; Yitk are the health variables of ADLs, IADLs, and depressive symptoms; Zitk are health behaviours of smoking and drinking. υk、υ’k are community-level variance; ωt、ω’t are time variance.

After obtaining the parameters estimated by the above two equations, we applied them to the following formula to calculate the Age-related Diseases-attributable Fraction (ADAFEC) based on the econometric modelling by inpatient and outpatient services, marked as ADAFυEC and ADAFhEC, respectively. The estimates were to predict the share of the annual health care spending that would be reduced if people had no age-related diseases. The attributable fraction was calculated by dividing the total age-related diseases-attributable health care spending by the total predicted spending for the entire population. The former was projected by subtracting the predicted health care spending for those with age-related diseases from their predicted spending had they had no age-related diseases.

(3)

Where Var are individual-level variables; B is the regression parameter of the “actual population”; B’ is the regression parameter of the “hypothetical population”. The “factual population” refers to the actual survey objects, and the “hypothetical population” refers to the population under the assumption that all people have no age-related diseases.

Direct Economic Burden (DEBEC)

To obtain the average Direct Economic Burden (DEBEC) over three years, we first multiplied ADAFEC and the direct economic burden of each category of medical services, then summed the values of all groups. Then we calculated the economic burden per person per year from the CHARLS survey, and multiplied by the number of middle-aged and older adults in China, so as to extrapolate cost estimates from the CHARLS survey to all individuals aged 45 or over in China. The formula is as follows:

DEBEC = (PVt + PVNt) × QVt × POPt × ADAFvEC + (PHt + PHNt) × QHt × POPt × ADAFhEC  (4)

Where PV is the medical cost per outpatient visit; PVN is the transportation cost per outpatient visit; QV is the average number of outpatient visits per person per year; PH is the medical cost per hospital stay; PHN is the average cost of transportation, nutrition, and nursing expenses per hospital stay; QH is the average number of hospitalizations per person per year; POP is the number of population aged 45 years and above in the year; t is the year 2011, 2013 and 2015. All monetary amounts were adjusted to 2015 dollars using the health care component of the Consumer Price Index (CPI) provided by the National Bureau of Statistics [20].

RESULTS

Table 1 reports the characteristics of respondents from the CHARLS 2011-2015. Among the respondents, 20.25% reported having age-related diseases, and they might be more likely to be older (P < 0.001), female (P < 0.001), less-educated (P < 0.001), single (P < 0.001), smoking (P = 0.046), drinking (P < 0.001), having impaired ADLs (P < 0.001), impaired IADLs (P < 0.001), depressive symptoms (P < 0.001), and having outpatient (P < 0.001) or inpatient treatment (P < 0.001) last year. It was worth noting that, P-values in Table 1 are probably caused by the large sample sizes. Table S1 in the Online Supplementary Document presents summary statistics of detailed age-related diseases.

Table 1.  Summary statistics of study sample, 2011-2015 (n = 32 418)

WordPress Data Table

SD – standard deviation, ADLs – activities of daily living, IADLs – instrumental activities of daily living

Age-related Diseases-attributable Fraction

Next, ADAFEC was estimated by age, sex and residency according to the regression results, as shown in Table 2. People with dyslipidaemia and hypertension had higher age-related diseases-attributable fraction score, suggesting their close relationship with aging. There was no age-related diseases-attributable fraction to inpatient and outpatient service use for urban male adults aged above 60 years old with hearing problems.

Table 2.  Age-related Diseases-Attributable Fraction (ADAFEC) for outpatient and inpatient services among people aged 45 and above in China 2011-2015

WordPress Data Table

Direct Economic Burden attributable to age-related diseases for subgroups

Table 3 reports DEB (including transportation and accommodation expenses) per capita for outpatient / inpatient services by age, sex, and residency. Per capita direct economic burden was the lowest in 2011, and increased in 2013 and 2015. Direct Economic Burden per capita of older adults aged 60 and above were mostly higher than those of middle-aged adults aged below 60.

Table 3.  Direct economic burden per capita for outpatient and inpatient services among people aged 45 and above in China 2011-2015 in US dollars (US$)

WordPress Data Table

Table 4 reports DEB per capita attributable to age-related diseases for outpatient / inpatient services by age, sex, and residency. In general, the DEB per capita attributable to age-related diseases increased year by year in 2011, 2013 and 2015, and economic burden was generally higher for people from urban areas and aged 60 and above.

Table 4.  Direct economic burden per capita attributable to age-related diseases for outpatient and inpatient services among people aged 45 and above in China 2011-2015 in US dollars (US$)

WordPress Data Table

Total direct economic burden attributable to age-related diseases

Table 5 shows the total DEB attributable to age-related diseases for outpatient and inpatient services among adults aged 45 and above in China from 2011 to 2015. The total direct cost attributable to age-related diseases was about US$288.368 billion in 2011, US$379.901 billion in 2013, and US$616.809 billion in 2015. According to the China Health Statistical Yearbook, the overall health care expenses were US$1480.106 billion in 2011, US$1799.665 billion in 2013, and US$1925.900 billion in 2015. The total direct cost attributable to age-related diseases accounted for 19.48%, 21.11% and 32.03% of the overall health care expenses respectively in the same year.

Table 5.  Total direct economic burden attributable to age-related diseases among adults aged 45 and above in China 2011-2015 in million US dollars (US$)

WordPress Data Table

Detailed proportions of the economic burden attributable to age-related diseases in 2011, 2013, and 2015 are shown in Figures S1-S3 in the Online Supplementary Document. Dyslipidaemia accounted for the largest proportion, followed by hypertension; Hearing problems accounted for the least proportion in all the three years.

DISCUSSION

Our findings demonstrated that the direct economic burden attributable to age-related diseases was heavy and increased over time among middle-aged and older adults in China. The total direct economic burden attributable to age-related diseases among adults aged 45 and above in China was about US$288.368 billion in 2011, US$379.901 billion in 2013, and US$616.809 billion in 2015, accounting for 19.48%, 21.11% and 32.03% of the overall health care expenses in the same year. Dyslipidaemia accounted for the largest proportion, followed by hypertension; hearing problems accounted for the least proportion in all the three years. Conceivably, the direct economic burden could be expected to increase without timely prevention or treatment of age-related diseases.

We found that hypertension, dyslipidaemia, and diabetes, accounted for the largest proportion of the direct economic burden. It may be due to their widespread prevalence, and higher age-related diseases-attributable fraction, as revealed in our study. Hypertension, dyslipidaemia, and diabetes are major risk factors for cardiovascular diseases [21], which require ongoing medical care, medication, and other resources, and is the leading cause of death in China [22]. There may also be cultural factors that contribute to the high economic burden in China. For example, the traditional Chinese diet is high in salt and fat, which can increase the risk of hypertension and dyslipidaemia [23]. In addition, hypertension, dyslipidaemia, and diabetes are also major contributors to the economic burden of disease in other parts of the world such as the United States [24]. It highlighted that the focus of reducing the burden of age-related diseases should be on the prevention and treatment of cardiovascular diseases. Hearing problems accounted for the least proportion, which may be due to the concealment of hearing loss and the neglect of hearing problems by the elderly [25,26].

The proportion of age-related disease burden in 2011, 2013, and 2015 was in some extent consistent with the calculation in Italy, which suggested that age-related disease burden accounted for approximately 20% of the public health budget every year [10]. At the same time, the total direct economic burden attributable to age-related diseases has been rising rapidly in all three years, as the prevalence of multiple chronic diseases and health care utilization increased among older adults [10]. The growing number of older adults with multiple comorbidities, who are at greater risk of disability, consume multiple drugs, spend longer time in hospital, and make health care more complex and expensive by increasing the need for organized, multidisciplinary care both within and outside hospitals [27]. In countries where resources for treatment are already stretched to the limit, chronic disease prevention – with a focus on reducing known, modifiable risk factors – will therefore be central to reducing economic burden associated with age-related diseases [9].

To our knowledge, this study is the first to estimate the direct economic burden attributable to age-related diseases by an econometric modelling suitable for China’s health system, and to obtain comprehensive cost estimates. However, CHARLS does not provide objective measurements of diseases, and does not cover all age-related diseases, which can lead to bias. Second, the study only included people aged 45 and older, so it cannot get a full picture of the economic burden of age-related diseases. Third, in the statistical modelling, we did not assume some kind of modelling an error and add an error component, so that our model may not be able to accurately capture the variability and uncertainty present in the data. Given the trend of increasing health care utilization and expenses in recent years, the economic burden attributable to age-related diseases will continue to increase, placing a heavy burden on society. It is expected that the results of our study can be applied in policy decision-making, and provide scientific basis for the effective allocation of limited health resources in China.

CONCLUSIONS

In conclusion, the results showed that the direct economic burden attributable to age-related diseases in China was about US$288.368 billion in 2011, US$379.901 billion in 2013, and US$616.809 billion in 2015, respectively. Dyslipidaemia accounted for the largest proportion, followed by hypertension in all the three years. These estimates indicate the substantial and increasing economic burden attributable to age-related diseases, which provides information for understanding the effect of age-related diseases and designing strategies to prevent or treat age-related diseases for middle-aged and older adults in China.

Additional material

Online Supplementary Document

Acknowledgements

Disclaimer: The findings and conclusions in this paper are those of the authors and do not necessarily represent the official position of Peking University.

Ethics statement: The ethics application for collecting data on human subjects was approved and updated annually by Peking University’s Institutional Review Board (No. IRB00001052-11015). All participants were given a subject adequate information concerning the study and they provided written informed consent.

Data availability: The data used are publicly available at the CHARLS database (http://charls.pku.edu.cn/).

[1] Funding: This work was funded by the Major Project of the National Social Science Fund of China (No. 21&ZD187), the Major Project of the National Social Science Foundation of China (No. 21ZD107), and the Project of the Beijing Municipal Science & Technology Commission (21I10124).

[2] Authorship contributions: XY, PH, and XZ conceived and designed the study. XY conducted data analysis, drafted and revised the manuscript; MW and YX helped conduct data analysis, and verified the underlying data. PH, and XZ critically revised the manuscript and supervised this work. All authors critical revised the article and approved the final manuscript.

[3] Disclosure of interest: The authors completed the ICMJE Disclosure of Interest Form (available upon request from the corresponding author) and disclose no relevant interests.

references

[1] Y Jing, R Torenvlied, M van Gerven, and J Cao. Nonprofit contracting and partnership in elderly care: a comparison between china and the Netherlands. Global Public Policy and Governance. 2021;1:136-58. DOI: 10.1007/s43508-021-00009-8

[2] H Wang and H Chen. Aging in China: Challenges and Opportunities. China CDC Wkly. 2022;4:601-2. [PMID:35919296]

[3] National Bureau of Statistics of the People’s Republic of China. The second national census bulletin. 2001. Available: http://www.stats.gov.cn/tjsj/tjgb/rkpcgb/qgrkpcgb/200204/t20020404_30317.html. Accessed: 2 August 2022.

[4] National Bureau of Statistics of the People’s Republic of China. The seventh national census bulletin (No. 5). 2021. Available: http://www.stats.gov.cn/tjsj/tjgb/rkpcgb/qgrkpcgb/202106/t20210628_1818824.html. Accessed: 2 August 2022.

[5] Y Luo, B Su, and X Zheng. Trends and challenges for population and health during population aging—China, 2015–2050. China CDC Wkly. 2021;3:593 DOI: 10.46234/ccdcw2021.158. [PMID:34594944]

[6] BK Kennedy, SL Berger, A Brunet, J Campisi, AM Cuervo, and ES Epel. Aging: a common driver of chronic diseases and a target for novel interventions. Cell. 2014;159:709 DOI: 10.1016/j.cell.2014.10.039. [PMID:25417146]

[7] X Xu and L Chen. Influencing factors of disability among the elderly in China, 2003–2016: Application of Bayesian quantile regression. J Med Econ. 2019;22:605-11. DOI: 10.1080/13696998.2019.1600525. [PMID:30913934]

[8] Xu X, Huang X, Zhang X, Chen L, editors. Family economic burden of elderly chronic diseases: evidence from China. Healthcare; 2019: MDPI.

[9] DO Abegunde, CD Mathers, T Adam, M Ortegon, and K Strong. The burden and costs of chronic diseases in low-income and middle-income countries. Lancet. 2007;370:1929-38. DOI: 10.1016/S0140-6736(07)61696-1. [PMID:18063029]

[10] V Atella, A Piano Mortari, J Kopinska, F Belotti, F Lapi, and C Cricelli. Trends in age-related disease burden and healthcare utilization. Aging Cell. 2019;18:e12861. DOI: 10.1111/acel.12861. [PMID:30488641]

[11] AM Jones, N Rice, and J Roberts. Sick of work or too sick to work? Evidence on self-reported health shocks and early retirement from the BHPS. Econ Model. 2010;27:866-80. DOI: 10.1016/j.econmod.2009.10.001

[12] R Jäckle and O Himmler. Health and Wages Panel data estimates considering selection and endogeneity. J Hum Resour. 2010;45:364-406. DOI: 10.3368/jhr.45.2.364

[13] DE Bloom, S Chen, M Kuhn, ME McGovern, L Oxley, and K Prettner. The economic burden of chronic diseases: estimates and projections for China, Japan, and South Korea. J Econ Ageing. 2020;17:100163. DOI: 10.1016/j.jeoa.2018.09.002

[14] China Health and Retirement Longitudinal Study. About CHARLS. 2019. Available: http://charls.pku.edu.cn/pages/about/111/zh-cn.html. Accessed: 5 August 2022.

[15] E Jaul and J Barron. Age-related diseases and clinical and public health implications for the 85 years old and over population. Front Public Health. 2017;5:335 DOI: 10.3389/fpubh.2017.00335. [PMID:29312916]

[16] EA Finkelstein, IC Fiebelkorn, and G Wang. National medical expenditures attributable to overweight and obesity: How much, and who’s paying? Health Aff. 2003;22:8

[17] X Xu, EE Bishop, SM Kennedy, SA Simpson, and TF Pechacek. Annual healthcare spending attributable to cigarette smoking: an update. Am J Prev Med. 2015;48:326-33. DOI: 10.1016/j.amepre.2014.10.012. [PMID:25498551]

[18] T Xie, D Liu, J Guo, and B Zhang. The longitudinal effect of sensory loss on depression among Chinese older adults. J Affect Disord. 2021;283:216-22. DOI: 10.1016/j.jad.2021.01.081. [PMID:33561802]

[19] Y Zhou, Y Hu, J Luo, Y Li, H Liu, and X Sun. Association Between Sensory Loss and Falls Among Middle-Aged and Older Chinese Population: Cross-Sectional and Longitudinal Analyses. Frontiers in Medicine. 2022;8:810159. DOI: 10.3389/fmed.2021.810159. [PMID:35096898]

[20] S Huang, H Wei, T Yao, Z Mao, Q Sun, and L Yang. The impact of smoking on annual healthcare cost: an econometric model analysis in China, 2015. BMC Health Serv Res. 2021;21:187 DOI: 10.1186/s12913-021-06199-5. [PMID:33639939]

[21] SC Smith. Multiple risk factors for cardiovascular disease and diabetes mellitus. Am J Med. 2007;120:S3-11. DOI: 10.1016/j.amjmed.2007.01.002. [PMID:17320520]

[22] D Zhao, J Liu, M Wang, X Zhang, and M Zhou. Epidemiology of cardiovascular disease in China: current features and implications. Nat Rev Cardiol. 2019;16:203-12. DOI: 10.1038/s41569-018-0119-4. [PMID:30467329]

[23] L Zhang, L-Q Qin, A-P Liu, and P-Y Wang. Prevalence of risk factors for cardiovascular disease and their associations with diet and physical activity in suburban Beijing, China. J Epidemiol. 2010;20:237-43. DOI: 10.2188/jea.JE20090119. [PMID:20431234]

[24] M McClellan, N Brown, RM Califf, and JJ Warner. Call to action: urgent challenges in cardiovascular disease: a presidential advisory from the American Heart Association. Circulation. 2019;139:e44-54. DOI: 10.1161/CIR.0000000000000652. [PMID:30674212]

[25] HS Chew and S Yeak. Quality of life in patients with untreated age-related hearing loss. J Laryngol Otol. 2010;124:835 DOI: 10.1017/S0022215110000757. [PMID:20403224]

[26] X Ye, D Zhu, S Chen, X Shi, R Gong, and J Wang. Effects of providing free hearing aids on multiple health outcomes among middle-aged and older adults with hearing loss in rural China: a randomized controlled trial. BMC Med. 2022;20:124 DOI: 10.1186/s12916-022-02323-2. [PMID:35436911]

[27] C Vogeli, AE Shields, TA Lee, TB Gibson, WD Marder, and KB Weiss. Multiple chronic conditions: prevalence, health consequences, and implications for quality, care management, and costs. J Gen Intern Med. 2007;22:391-5. DOI: 10.1007/s11606-007-0322-1. [PMID:18026807]

Correspondence to:
Ping He
China Center for Health Development Studies, Peking University
38 Xueyuan Road, Haidian District, Beijing
China
[email protected]

Xiaoying Zheng
School of Population Medicine and Public Health, Chinese Academy of Medical Sciences / Peking Union Medical College
No. 31, Beijige-3, Dongcheng District, Beijing
China
[email protected]