INDEPENDENT AND JOINT ASSOCIATIONS OF SOCIOECONOMIC STATUS AND LIFESTYLE BEHAVIORS WITH COGNITIVE IMPAIRMENT AMONG ELDERLY CHINESE POPULATION

 

Y. Feng¹, S. Jia¹, W. Zhao¹, X. Wu¹, Y. Zuo¹, S. Wang¹, L. Zhao¹, M. Ma¹, X. Guo¹, C.S. Tarimo^1,2, Y. Miao¹, J. Wu^1,3

 

1. Department of Health management, College of Public Health, Zhengzhou University, Zhengzhou, Henan, China; 2. Department of Science and Laboratory Technology, Dar es salaam Institute of Technology, P.O. Box 2958, Dar es Salaam, Tanzania; 3. Institute for Hospital Management of Henan Province, Zhengzhou, Henan, China

Corresponding Author: Jian Wu, MD, PHD, Department of Health management, College of Public Health, Zhengzhou University, 100 Kexue Road, Gaoxin district, Zhengzhou 450001, China, Telephone: +8613393729001, E-mail: wujian@zzu.edu.cn

J Prev Alz Dis 2024;
Published online July 2, 2024, http://dx.doi.org/10.14283/jpad.2024.127

 

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Abstract

BACKGROUND: Numerous studies have shown that there are socioeconomic disparities in people’s health. Health behavior is considered to be an effective strategy to alleviate socio-economic differences. However, the independent or joint relationship between socioeconomic status (SES) and lifestyle behaviors (LBs) on the cognition of Chinese elderly are not clear. Therefore, this study aimed to reveal the impact of SES and LBs on cognitive impairment in elder Chinese.
METHODS: The data from the 2017-2018 wave of Chinese Longitudinal Healthy Longevity Survey was used. SES was created using latent class analysis based on annual per-capita household income, education level, and occupation. Six LBs were considered in calculating LB scores. Restricted cubic splines were used to model the association of LB scores and cognitive impairment to investigate the dose-response relationship. LB scores were divided into three groups: unhealthy, intermediate, and healthy lifestyle. Multivariate Logistic regression models were applied to explore both the independent and joint effects of SES and LB scores on cognitive impairment.
RESULTS: Among 10,116 participants, 1,872 (18.51%) were recorded as having cognitive impariment. After adjusting for multivariable confounding factors, compared with participants of high SES, those of low SES had higher risks of cognitive impairment [Odds ratio (OR): 1.385; 95% confidence interval (CI): 1.137-1.689]. In contrast to those with unhealthy lifestyle, participants adhering to a healthy lifestyle were found to be associated with a reduced risk of cognitive impairment (OR: 0.198; 95%CI: 0.150-0.263). A non-linear relationship was observed between LB scores and cognitive impairment (Pnonlinearity =0.001), indicating a protective effect on cognitive impairment when having more than two LBs. Participants with high SES and engaged in healthy lifestyle had the lowest risk of cognitive impairment compared to those with low SES and unhealthy lifestyle (OR: 0.123; 95% CI 0.073-0.207).
CONCLUSION: Cognitive impairment has socioeconomic disparities among the elderly Chinese population. A healthy lifestyle may attenuate the impact of socioeconomic inequality on cognitive impairment, emphasizing the important role of LBs modification in reducing the disease burden of cognitive impairment, especially in the elderly population with low SES.

Key words: Healthy behavior, socioeconomic status, cognitive impairment, elderly population.

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Introduction

Cognitive impairment refers to varying degrees of damage to cognitive function caused by various factors, which can impinge an individual’s social functioning and quality of life. Depending on the severity, it is categorized into mild cognitive impairment and dementia (1–3). In population-based epidemiological studies, the prevalence of cognitive impairment in adults over 65 years is 3% to 19%, and more than half of people with mild cognitive impairment can develop into dementia within 5 years (4–6). The results of the Global Burden of Disease study indicated that 57.4 million people were affected by dementia in 2019 worldwide, and by 2050, this number is expected to increase to 152.8 million (7). It is also estimated that the number of dementia patients in Chinese elderly population aged 60 and over is 15.07 million (8). With the heavily growth of Chinese aging, a large number of elderly people with cognitive impairment will bring burdens not only to individuals, but also to their families and society. Owing to the absence of curative strategies, primary prevention aimed at reducing the incidence of cognitive impairment and delaying the onset of dementia is particularly important (9). Hence, figuring out what variables are related to cognitive impairment has become incredibly pressing.
Socioeconomic status (SES) is a complex concept reflecting a person’s overall status in society, which encompasses income, education, and occupation (10). Recently, some studies have investigated the socioeconomic differences in health, that is, people with high SES can provide more resources to help individuals avoid disease than people with poor social status (11, 12). Healthy behaviors are widely acknowleged as effective strategies to mitigate the adverse health effects associated with socioeconomic disparities. They are deemed cost-effective approaches to enhance overall health and well being (13–15). Previous researches had suggested that low SES may increase the risk of cognitive impairment (16, 17). Therefore, it is imperative to seek effective measures to mitigate socioeconomic disparity in health. Healthy lifestyle is usually regarded as a protective factor for cognitive impairment. A number of studies have demonstrated the protective effect of one or more lifestyle behaviors (LBs) on cognitive impairment (18–20). However, there is limited comprehensive research on the independent and joint relationship between SES and LBs with cognitive impairment, and it is not clear whether the results of the study are consistent among different demographic characteristics.
We used data from the 2017-2018 wave of Chinese Longitudinal Healthy Longevity Survey (CLHLS) to assess the complex relationship of SES and LBs with cognitive impairment, and to provide interventions to improve the cognitive function of the elderly Chinese population.

 

Methods

Study design and study population

The CLHLS is a nationwide, community-based, ongoing longitudinal cohort study collecting data among older adults aged 65 years and over since 1998 with follow-up surveys every 2 to 3 years. The CLHLS recruited participants from 631 randomly selected counties and cities in 22 out of 31 provinces. More details of the study design and data collection have been previously described (21). The data used in this study were derived from the 2017-2018 wave of CLHLS. Accordingly, the current analysis included 15,874 participants who responded in the 2017-2018 wave. Those with missing information on SES factors (n=3,678), LBs (n=1,226), and cognitive function (n=854) were excluded from the analysis. Overall, 10,116 participants from CLHLS were included (Supplementary Figure 1). The current study received approval from the research ethics committees of Peking University (IRB00001052-13074). Prior to their participation, all individuals involved provided informed consent by signing a consent form.

Assessment of SES

Self-reported annual per-capita household income, education level, and occupation were used to measure SES according to previous studies (22, 23). The annual per-capita household income was divided into four levels by quartile. Education was categorized into three categories: low (no schooling, 0 years), intermediate (primary school, 1-6 years) and high (middle school or more, 7 years or more) (16). Occupation was categorized into farmer and non-farmer. An overall SES variable was created using latent class analysis. Two mutually exclusive latent classes were identified and represented low and high SES, based on the following criteria: (a) fit indices including the Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), and adjusted BIC, and lower values of fit indices suggest better fit; (b) entropy (entropy values can range from 0 to 1.0, with classification accuracy suggested by values greater than 0.70) (24); (c) Lo-Mendell-Rubin likelihood ratio test (LMR-LRT) and Bootstrapped likelihood ratio test (BLRT); for both tests, results that are not statistically significant suggest that fewer profiles are appropriate; and (d) profile size, where each profile should include at least 1% of the sample so that replication and generalization are possible (25). Details are reported in the Table 1 and Supplementary Table 1.

Abbreviations: AIC, Akaike’s information criterion; BIC, Bayesian information criterion; LMR-LRT, Lo-Mendell-Rubin likelihood ratio test; BLRT, Bootstrapped likelihood ratio test.

 

Lifestyle behavior

LBs including smoking, alcohol consumption, sufficient sleep, physical activity, diet, and social participantion. All data were collected through face-to-face interviews by trained interviewers who are local staff members from the county-level network system of the National Bureau of Statistics of China. Current not smoking and current no alcohol consumption were regarded as the healthy LBs. Physical activity was defined by one question “Do you do exercises regularly at present?” If the participants answered “Yes”, the physical activity was defined as a healthy LB. The dietary data were collected based on a food frequency questionnaire including nine major food groups (fruits, vegetables, meat, fish, eggs, beans, milk products, nuts, and tea). The responses each food group were recorded as “almost everyday”, “not every day, but at least once per week”, “not every week, but at least once per month”, “not every month, but occasionally” and “rarely or never”. Individuals with responses of “almost everyday” received a score of 1; the others received a score of 0. The total dietary diversity score was calculated by summing the scores across all items, for a range score of 0-9, with a higher score indicating better dietary diversity. Dietary diversity was defined as a healthy LB, if the dietary diversity score was at or above the mean value(Mean value=3) (26,27). Sleeping time ≥7 h and ≤ 9 h per day was defined as sufficient sleep according to the question of how many hours do you sleep every day(28). Social participation was assessed using the survey questions “Do you now perform the following activities (Tai Ji, Square dance, Visit and interact with friends, Play cards or mah-jong, and Social activities (organized)) regularly?” The response options for questions were “almost everyday,” “not every day, but at least once in a week,” “not every week, but once a month,” “not every month, but sometimes,” and “never”. We defined the active social participation with responses of “almost everyday” or “not every day, but at least once in a week”. Any of these five types of activities indicates active social participation, which is defined as a healthy LB.
For each LB, we assigned 1 point for a healthy LB and 0 points for otherwise (Supplementary Table 2), based on previous research(29–31). Thus, a combined LB score was the sum of the points and ranged between 0 and 6, with higher scores indicating healthier lifestyles. The scores were classified into three lifestyle groups: unhealthy lifestyle (0-2 scores), intermediate lifestyle (3-4 scores) and healthy lifestyle (5-6 scores).

Assessment of cognitive function

Cognitive functioning was measured by the Chinese version of Mini-Mental State Examination (MMSE). For the Chinese version of MMSE, several items were adapted to suit the cultural contexts of China, which was based on the international MMSE(32). Prior studies have demonstrated that the validity and reliability of Chinese MMSE measurement is good (KMO = 0.818 and Cronbach’s α =0.940)(33,34). Total of 24 items were included, covering seven subscales including orientation, naming foods, registration of three words, attention and calculation, copy a figure, recall, and language(35,36). The MMSE score ranged from 0 to 30 and a higher score represents a better cognitive function. Education level is an important factor affecting cognitive function. According to the level of education, different MMSE scores were used to define cognitive impairment (<18 for those without formal education, <21 for those with 1 to 6 years of education, and <25 for those with more than 6 years of education), which is widely accepted and used. Given that nearly half of our participants lacked formal education, we defined cognitive impairment as an MMSE scores below 18 (37).

Other covariates

Other covariates were obtained through a structured questionnaire, including age, sex (men and women), marital status (married and others), ethnicity (Han and others), residence (urban and rural), and self-reported the history of hypertension, diabetes, cardiovascular disease (CVD), or cancer diagnosed by a doctor. Disability was evaluated by six Activities of Daily Living (ADL) including bathing, dressing, toilet, indoor transfer, continence, and eating and classified into “not disabled” and “disabled”. Participants with at least one ADL disability were coded as “disabled”(38–40). Body weight and height were measured by trained medical staff using a standardized protocol, with body mass index (BMI) calculated as weight (kilograms) divided by height (meters) squared.

Statistical analysis

The baseline characteristics of the study population are presented as numbers (percentages) for categorical data, mean (standard deviation) for continuous data if normally distributed and medians (interquartile range) if non-normally distributed. Chi-square and ANOVA tests were applied to test the significance levels of the differences, and the Kruskal-Wallis test was used if data were non-normally distributed. The independent and joint associations between SES, LB scores, and cognitive impariment were assessed using multivariable logistic regression model. We developed three models: model 1, unadjusted; model 2, adjusted for age, sex, married status, ethnicity, marital status, residence, and ADL; and model 3, adjusted for model 2 variables as well as SES or LB scores. We used restricted cubic splines with four knots at the 5th, 25th, 75th, and 95th centiles to flexibly model the association between LB scores and cognitive impariment, with the knot at the 5th percentile of the distribution as the reference.
In subgroup analyses, we examined whether the independent and joint associations of cognitive impairment with SES and LB scores differed by sex (men and women), residence (urban and rural), hypertension (with and without), diabetes (with and without), CVD (with and without), and cancer (with and without), after adjusting for age, sex, married status, ethnicity, marital status, residence, ADL, and LB scores/SES.
To test the robustness of our results, we conducted several sensitivity analyses. First, we repeated all independent analyses with each socioeconomic factor and each LB, and these factors were mutually adjusted in the models. Second, we used different MMSE cutoff scores to define cognitive impairment. There were more than 30% of the participants with 1-6 years of education, so we further defined cognitive impairment as an MMSE score lower than 21. Third, we added BMI into the combined LB scores, and BMI with a range of 18.5-24.9 was defined as the healthy weight. Because whether BMI was a behavior is still controversial, it is not included in the LB scores in the main result analysis (15). Fourth, we excluded individuals with prevalent hypertension, diabetes, CVD, or cancer, because both LB and SES could be influenced by major chronic diseases.
Latent class analysis was conducted using Mplus Version 7 and restricted cubic splines were implemented using R 4.3.1 (R Foundation). Other analyses were performed using SAS V9.4 for Windows (SAS Inst., Cary, NC). All statistical analyses were considered statistically significant, with two-sided P < 0.05.

 

Results

Baseline characteristics of participants

Table 2 shows characteristics of participants. Among 10,116 participants with a median age (interquartile range) of 84 (75-95) years, 1,872 (18.51%) had cognitive impairment. Participants who with cognitive impairment were more likely to be older, women, not married, in rural area, impaired ADL (P<0.05). However, participants without cognitive impairment were frequently non-farmer, smoking, alcohol consumption, active physical activity, sufficient sleep, active social participantion, and diverse dietary, and had high per capita annual household income, educationl level, SES, LB scores, and cognitive function score, when compared with those with cognitive impairment (P<0.05). However, race did not differ with and without cognitive impairment (P>0.05).

LB, lifestyle behavior; SES, socioeconomic status; 0-2 scores, unhealthy lifestyle; 3-4 scores, intermediate lifestyle; 5-6 scores, healthy lifestyle.

 

Independent analysis of SES and LBs with cognitive impairment

The independent associations of SES and LBs with cognitive impairment are represented in Figure 1. In the multivariable logistic regression, after adjusting age, sex, marital status, ethnicity, residence, ADL, and LB score, the odds ratio (OR) was 1.385 (95% CI: 1.137-1.689) for the risk of cognitive impairment when participants of low SES were compared with participants of high SES, respectively (Figure 1A; Model 3). Among participants with intermediate and healthy lifestyle, the ORs for cognitive impairment were 0.602 (95%CI: 0.524-0.692) and 0.198 (95%CI: 0.150-0.263), compared with those with unhealthy lifestyle, after adjusting for age, sex, marital status, ethnicity, residence, ADL, and SES, respectively (Figure 1B; Model 3). Furthermore, restricted cubic splines indicated a significant nonlinear dose–response association between LB scores and cognitive impairment risk (Pnonlinearity = 0.001, Supplementary Figure 2), after adjusting age, sex, marital status, ethnicity, residence, ADL, and SES, respectively.

CI, confidence interval; LB, lifestyle behavior; OR, Odds ratio; SES, socioeconomic status; 0-2 scores, unhealthy lifestyle; 3-4 scores, intermediate lifestyle; 5-6 scores, healthy lifestyle.

 

Joint analysis of SES and LBs with cognitive impairment

Figure 2 shows the joint associations of SES and LBs for the risk of cognitive impairment. Compared with participants with low SES and unhealthy lifestyle, ORs for those of low SES and intermediate and healthy lifestyle were 0.567 (0.487-0.659) and 0.201 (0.145-0.278); and ORs for those of high SES and unhealthy, intermediate and healthy lifestyle were 0.537 (0.377-0.765), 0.453 (0.356-0.577), and 0.123 (0.073-0.207), after adjusting age, sex, marital status, ethnicity, residence, ADL, respectively.

LB, lifestyle behavior; SES, socioeconomic status; 0-2 scores, unhealthy lifestyle; 3-4 scores, intermediate lifestyle; 5-6 scores, healthy lifestyle.

 

Stratified analysis, subgroup analyses and sensitivity analyses

The associations between LB and risk for cognitive impairment were evaluated by different SES. As compared with unhealthy lifestyle, the ORs for healthy lifestyle for cognitive impairment were lower both in low and high SES groups, and the OR in the group with low SES status was lower than that in the group with high SES (OR:0.208 and 0.237; Figure 3).

LB, lifestyle behavior; SES, socioeconomic status

 

Supplementary Tables 3-8 show the results of subgroup analysis by sex, residence, and with or without hypertension, diabetes, CVD and cancer. Similar to the main results, LBs remained positively associated with cognitive impairment risk in all subgroups. However, low SES did not contribute to the risk of cognitive impairment in hypertensive, diabetic, and cancer individuals.
Sensitivity analyses were shown in Table 3. The significant associations were found between educational level, physical activity, sleep, social participantion, diet and cognitive impairment. The associations between SES and LBs and cognitive impairment were consistent with our main analysis after defining cognitive impairment with the MMSE cutoff scores of 21, including BMI into LB scores, and exceluding participants with hypertension, diabetes, CVD, or cancer.

 

Discussion

The currrent study examined the associations of SES and LBs with cognitive impairment among older Chinese people based on a nationally representative cross-sectional survey. Our results suggested that low SES was associated with higher risks of cognitive impairment, and these participants with healthy lifestyle had lower risk of cognitive impairment after adjusting potential confounding factors. Furthermore, compared with the participants of low SES and less healthy LBs, the lowest risks of cognitive impairment were seen in those of high SES and with the multiple healthy LBs.
Socioeconomic inequity in cognitive function has been widely discussed. A longitudinal cohort study of 1,789 community-dwelling older Mexican Americans found that participants with a continuously high SES in life-course had lower risk for dementia or cognitive impairment, compared with those with a continuously low SES (hazard ratio: 0.49, 95% CI: 0.24-0.98) (41). Moreover, the relationship between single socioeconomic factors and cognitive impairment has also been extensively explored in various countries globally. Studies have consistently reported a positive correlation between higher income and a deceleration in cognitive aging, while elevated education levels have been identified as a protective factors against cognitive decline (42–45). Another study examining data from NHANES found that older adults with lower educational attainment were more strongly associated with lower cognitive function (46). Our analysis confirmed the socioeconomic disparity in cognitive impairment and similar trends were also observed in educational level. Although there was no statistical significance in the separate analysis of the relationships of income and occupation with cognitive impairment, it can be seen that ORs increased as income decreased, and the OR was higher in farmers than in non-farmers. We observed in the subgroup analysis that socioeconomic inequality was associated with cognitive impairment in different genders and regions, and also observed that the risk of cognitive impairment caused by low SES was higher in men than in women, and higher in rural than in urban areas. Our finding was consistent with a published meta-analysis, which found that males are more likely to develop cognitive impairment than females(10). Previous study had shown that the discrepancy of SES in urban populations is smaller than that of rural populations, which may be the reason why SES has a lower effect on cognitive impairment in urban area (47). No associations between low SES and cognitive impairment in the subgroup of participants with diabetes or cancer were found, which can be explained by the limited sample size of these subgroups.
Our study confirmed the effects of combined LBs on cognitive function and further extended the results to the elderly population. The present analysis supported that adhering to healthy LBs could reduce the risk of cognitive impairment, which is consistent with prior researches (48–51). Additionally, our study found the benefits of each additional LB increament in alleviating the risk of cognitive impairment in the dose-response relationship analysis. Besides, we also observed that the protective effect of LB on cognitive impairment was not obvious in individuals with less than two LBs, suggesting that the elderly population should be encouraged to adhere to multiple LBs instead of a single one. The association of LBs and cognitive impairment was significant in all subgroup analyses, which further clarified that adherence to multiple LBs is beneficial to prevent cognitive impairment in elderly people with different characteristics. Of the six LBs we examined, only 2 (smoking and drinking consumption) were found to be not significant in multivariable models, which is similar to the results of previous studies (52), this did not imply that individuals can overlook the importance of quitting smoking and drinking alcohol. When adopting different cut-off values to determine cognitive impairment and adding BMI into the LB scores, the protective effect of LB scores on cognitive impairment did not change.
Previous studies had investigated the role of some LBs, in mitigating the health damages caused by socioeconomic inequality (15, 53). A study involving two large cohorts provided evidence that maintaining healthy LBs appeared to counteract the adverse effects of SES on cardiovascular desease and mortality (15). A study conducted in China revealed that adopting a high healthy lifestyle score may attenuate the detrimental effects of inequality of SES on physical multimorbidity (53). In addition, studies have shown that healthy lifestyles can also reduce the impact of environmental pollution on cognitive function. However, there was scarce evidence on whether the association between SES and cognitive function differed by healthy LBs. Our study added the evidence in this field by analyzing the combined effects of LBs and SES, and found that the protective association between healthy lifestyles and health outcomes is stronger among people with low SES, which highlights the necessity of lifestyle modification, especially among those with low SES who were more susceptible to unhealthy lifestyles.
The mechanism of how SES affects cognitive impairment involves many aspects. Some studies suggest that individuals with high SES are usually exposed to more cognitive stimulation and less social discrimination (54, 55), which may play a key role in brain development or activation, thereby reducing the risk of cognitive decline. It is worth noting that the relationship between social status and cognition can also be explained by health economics (56). In Grossman’s model, health is seen as a form of capital that can be invested (57). People with higher SES are more conscious and have assets to invest in their health, have a stronger demand for health care services, have a better ability to access good health care and disease prevention, thereby further reduce the risk of cognitive impairment (58). Healthy LBs, through healthy diet, physical exercise, sufficient sleeping, social interaction, etc., regulate neurotransmitters, such as dopamine, serotonin, acetylcholine, etc., affect attention, memory and other cognitive processes (59). In addition, healthy lifestyle behavior can inhibit the deposition of amyloid protein and prevent the degenerative changes of neurons (60).

Strengths and limitations

Our study has several strengths. Firstly, we constructed an overall SES variable and LB scores to comprehensively evaluate the complex relations of SES and LBs with cognitive impairment, based on a nationally representative dataset. Secondly, unlike earlier studies, our study focused on LBs can be easily integrated into daily life and be promoted in the context of an accessible and scalable behavioral intervention, including sufficient sleeping, physical activity, healthy diet, social activity, and so on. Thirdly, we regarded the LB scores as a continuous variable and explored the dose-response relationship between the LB scores and cognitive impairment. Finally, we also conducted a series of subgroup analyses and sensitivity analyses to show the robustness of the findings and assessed single SES factors and LBs.
There are some limitations to consider when interpreting our results. First, our study utilized a cross-sectional design, which does not enable us to establish temporality between the independent variables and the outcome, thereby limiting our ability to infer causation. Therefore, further longitudinal studies are warranted to evaluate the relationship between LBs, SES, and cognitive impairment. Additionally, we cannot capture the long-term SES trajectory and lifestyle changes, and future research is best to have repeated measurements. Second, the socio-economic level and lifestyle information were primarily self-reported, which may have introduced measurement errors and recall bias. Third, cognitive impairment was assessed using the Chinese version of MMSE. While this tool does not differentiate between dementia and mild cognitive impairment, nor is it recognized as a clinical diagnostic criterion, it remains an effective instrument for evaluating cognitive function in large-scale and population-based epidemiological surveys. Fourth, LB scores derived from the sum of the number of healthy LBs assume that all LBs have the same impact on health outcomes, which may not accurately assess the complex relationship between LBs and cognitive impairment. However, this method is widely used in related fields. Fifth, due to the nature of post hoc subgroup analysis, the sample size of each subgroup was not calculated before data collection. In particular, the number of participants and events may be insufficient in the subgroups of diabetic and cancer patients, and the results should be interpreted with caution.

 

Conclusion

In conclusion, our study revealed a significant association between low SES and elevated risks of cognitive impairment. Additionally, a healthy lifestyle was associated with a reduced risk of cognitive impairment, and the risk of cognitive impairment gradually decreased with the number of LBs increament. Promoting healthy lifestyles emerges as a potential avenue to mitigate the socioeconomic disparities in cognitive impairment. This highlights the significance of LBs modification in as a strategy to alleviate the disease burden across all population, particularly among those with low SES in the elderly Chinese demographic.

 

Funding: This work was supported by the Platform for Dynamic Monitoring and Comprehensive Evaluation of Healthy Central Plains Action (20220134B), the series of cohort studies on healthy lifestyle in tertiary hypertension population (20230013B/20230014B/20230015B), the Collaborative Innovation System Research on Drug Intervention&Non-drug Intervention in Proactive Health Context (20220518A), and the 2024 Graduate Independent Innovation Project of Zhengzhou University (20240332). Research on the system and mechanism of the development of public hospitals in China: historical evolution, practical logic and future approach (ZYYC202302ZD)

Competing interests: The authors declare that they have no competing interests.

Author contributions: Jian Wu and Yudong Miao: design of this study and formal analysis. Yifei Feng: data analysis and manuscript drafting. Mingze Ma and Xinghong Guo: method application. Shiyu Jia, Weijia Zhao, Xiaoman Wu, Yibo Zuo, Saiyi Wang, Lipei Zhao, and Clifford Silver Tarimo: manuscript revision.

Ethics approval: The study was conducted in line with the Helsinki Declaration. The current study received approval from the research ethics committees of Peking University (IRB00001052-13074).

 

SUPPLEMENTARY MATERIAL

 

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