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Examining dynamic developmental trends: the interrelationship between age-friendly environments and healthy aging in the Chinese population—evidence from China Health and Retirement Longitudinal Study, 2011–2018

Abstract

Background

The objective of this research is to investigate the dynamic developmental trends between Age-Friendly Environments (AFE) and healthy aging in the Chinese population.

Methods

This study focused on a sample of 11,770 participants from the CHARLS and utilized the ATHLOS Healthy Aging Index to assess the level of healthy aging among the Chinese population. Linear mixed model (LMM) was used to explore the relationship between AFE and healthy aging. Furthermore, a cross-lagged panel model (CLPM) and a random-intercept cross-lagged panel model (RI-CLPM) were used to examine the dynamic developmental trends of healthy aging, taking into account both Between-Person effects and Within-Person effects.

Results

The results from LMM showed a positive correlation between AFE and healthy aging (β = 0.087, p < 0.001). There was a positive interaction between the geographic distribution and AFE (central region * AFE: β = 0.031, p = 0.038; eastern region * AFE: β = 0.048, p = 0.003). In CLPM and RI-CLPM, the positive effect of healthy aging on AFE is a type of Between-Person effects (β ranges from 0.147 to 0.159, p < 0.001), while the positive effect of AFE on healthy aging is Within-Person effects (β ranges from 0.021 to 0.024, p = 0.004).

Conclusion

Firstly, individuals with high levels of healthy aging are more inclined to actively participate in the development of appropriate AFE compared to those with low levels of healthy aging. Furthermore, by encouraging and guiding individuals to engage in activities that contribute to building appropriate AFE, can elevate their AFE levels beyond the previous average level, thereby improving their future healthy aging levels. Lastly, addressing vulnerable groups by reducing disparities and meeting their health needs effectively is crucial for fostering healthy aging in these populations.

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Introduction

To tackle the challenges posed by the rapidly aging population, the World Health Organization (WHO) has introduced the concept of healthy aging. Healthy aging is defined as the process of developing and maintaining the functional ability that enables well-being in older age [1]. It emphasizes the crucial role of a harmonious relationship between individuals and their environment in achieving healthy aging. The environment comprises all the factors in the extrinsic world that form the context of an individual’s life, such as the built environment, people and their relationships, attitudes and values, health and social policies, the systems that support them, and the services that they implement.

Previous research has shown that factors such as good health, a regular lifestyle, and a higher socioeconomic status (SES) are crucial for healthy aging. Firstly, individual health status is positively associated with healthy aging. Past research has found a positive correlation between the number of remaining teeth [2] and the level of healthy aging, while individuals with complex combinations of diseases [3, 4] have the lower level of healthy aging. Secondly, lifestyle habits are also significant factors influencing healthy aging. Several studies [5, 6] have shown a positive association between moderate alcohol consumption, active physical activity, and healthy aging. Conversely, smoking [6, 7] is closely associated with poorer levels of healthy aging. Thirdly, there is a positive association between socioeconomic status and healthy aging, such as higher economic and educational levels [6, 8]. Additionally, research in China has found that experiencing various adverse childhood experience (ACE) is negatively correlated with the likelihood of achieving healthy aging [9]. Overall, researchers have explored the influencing factors of healthy aging from multiple dimensions.

However, there has been limited focus on the relationship between the environment and healthy aging. This is partly because while the definition of healthy aging acknowledges the potential impact of environmental factors, there is no specific comprehensive measure provided. Nevertheless, the WHO has recognized the importance of the environment in individuals' well-being. They have developed guidelines such as the "Global Age-friendly Cities: A Guide" [10] and accompanying AFE Features Checklist, as well as the "Measuring the age-friendliness of cities: a guide to using core indicators" [11]. The WHO has also introduced the concept of Age-Friendly Environments (AFE), which aims to create and maintain environment that support individuals' capabilities throughout their lives, enabling them to age in a healthy and positive way [12].

Previous studies have mainly explored the important roles of AFE in maintaining health status [13,14,15,16,17,18], promoting regular lifestyle habits [19, 20], enhancing life satisfaction [21,22,23,24,25,26,27,28,29], and facilitating social participation [30, 31]. However, there are still several gaps in the existing research. Firstly, there is a lack of studies investigating the relationship between AFE and healthy aging. Secondly, the WHO proposed AFE to examine whether the individual's environment is conducive to health. Therefore, does the correlation between AFE and healthy aging vary in different environments, such as urban or rural areas? Thirdly, WHO believes that healthy aging and the environment have a mutual interaction. So, how does this interaction develop over time?

The primary aim of this research is to investigate the dynamic developmental trends between AFE and healthy aging in the Chinese population, based on the concepts of healthy aging and AFE proposed by the WHO. This was accomplished by utilizing four waves of longitudinal data from a large and representative sample in China. A linear mixed model (LMM) was employed to preliminarily explore the relationship between AFE and healthy aging, while also assessing whether this relationship is influenced by urban–rural or regional disparities. Furthermore, both traditional cross-lagged panel model (CLPM) and random-intercept cross-lagged panel model (RI-CLPM) were utilized to examine the dynamic developmental trends between AFE and healthy aging in the Chinese population. Lastly, the underlying factors contributing to these dynamic trends were analyzed by considering Between-Person effects and Within-Person effects.

Method

Data sources and participants

Data were obtained from China Health and Retirement Longitudinal Survey (CHARLS) and Atmospheric Composition Analysis Group (ACAG) at Dalhousie University. CHARLS aims to gather high-quality microdata that represent Chinese individuals and households aged 45 and above. These data are crucial for analyzing the challenges posed by an aging population in China and promoting interdisciplinary research on aging. The survey was conducted in four waves: 2011 (Wave 1), 2013 (Wave 2), 2015 (Wave 3), and 2018 (Wave 4), covering 150 counties and 450 communities across 28 provinces, autonomous regions, and municipalities. By 2018, the survey had reached a total of 19,000 participants from 12,400 households. Additionally, the CHARLS Life History Survey was conducted in 2014, which covered the same areas as the CHARLS survey [32]. The study also includes PM2.5 data obtained from the ACAG [33], which used satellite and ground monitoring stations to provide detailed information on PM2.5 levels. The research was utilized the combined data from CHARLS W1-W4, the 2014 Life History Survey, the Harmonized CHARLS (Version D), and the ACAG for further empirical analysis.

A total of 17,596 participants were included in Wave 1 of this study. In the follow-up surveys of Waves 2 to 4, 2,557, 1,603, and 1,567 participants were lost to follow-up or deceased, respectively. Additionally, we excluded 99 participants with missing values greater than 25% for the healthy aging assessment indicators. Finally, a total of 11,770 participants were included in the analysis (Fig. 1).

Fig. 1
figure 1

Flowchart of participant selection

Healthy aging (Outcome)

Among the various comprehensive indicators for evaluating healthy aging, the Aging Trajectories of Health: Longitudinal Opportunities and Synergies (ATHLOS) project has developed the most widely used comprehensive indicator for healthy aging. The scale consists of 31 items for CHARLS and is scored using a unidimensional, 2-parameter logistic model (2PLM) of Item Response Theory (IRT) [34], which has been effectively validated [35,36,37]. In this study, we followed the same method and excluded the Telephone and Walking speed items from the CHARLS survey, as they were not assessed in Wave 1 and Wave 4, respectively. The final scale used in this study was determined based on the aforementioned criteria: Root Mean Square Error of Approximation (RMSEA) < 0.06, Comparative Fit Index (CFI) > 0.95, and Tucker-Lewis Index (TLI) > 0.95, which assessed the adequacy of the measurement scale [34, 38]. Finally, the IRT scores were transformed into T-scores with a mean of 50 and a standard deviation of 10 for further research.

AFE (Exposure)

Based on the AFE Feature Checklist from the "Global Age-friendly Cities: A Guide" and the "Measuring the Age-Friendliness of Cities: A Guide to Using Core Indicators," as well as previous research findings, we matched 8 indicators from CHARLS to construct the comprehensive evaluation index for AFE (Appendix 1 in the Supplement). All variables are set as binary variables, with a value of 0 for "unfriendly" and a value of 1 for "friendly". Finally, following the method of previous research, we summed up the scores of the 8 indicators to obtain the AFE score, which ranges from 0 to 8. A higher score indicates a more friendly environment [39].

Covariates

We selected the established factors that have been clearly identified in previous research as control variables for healthy aging (Appendix 2 in the Supplement), including: physical condition (chronic diseases, teeth) [2,3,4, 34, 35, 38, 40], SES (education, ACE, household income) [3, 4, 6, 8, 9, 34, 35, 40,41,42,43,44], lifestyle habits (smoke, drink) [3, 6, 7, 34, 35, 41, 43, 45], and demographic factors (geographical distribution, urban–rural distribution, age, gender, and marital status) [3, 4, 6, 9, 34, 35, 40, 42].

Statistical analyses

Continuous variables are described using the mean and standard deviation (SD), while categorical variables are described using frequency and percentage.

We constructed LMM to investigate the relationship between AFE and healthy aging, with participant ID as a random intercept and survey time points as random slopes [46]. Considering the potential influence of covariates on the effect size, three models were considered in this study. Model 1a included only the core variable AFE. Model 1b adjusted for chronic diseases, teeth, household income, education, ACE, smoke, drink, age, gender, marital status, urban–rural distribution, and geographic distribution, based on Model 1a. Model 1c further plus the interaction between AFE and urban–rural distribution, as well as AFE and geographic distribution, based on Model 1b. In order to better explain the interaction effect, we conducted a simple slope analysis. Furthermore, we conducted Variance Inflation Factor (VIF) tests to examine the issue of multicollinearity in the models. The VIF test results showed that the VIF values for all variables in the models were much lower than the critical value of 10, indicating the absence of severe multicollinearity issues (Table S1 in the Supplement).

In our study, we used a traditional CLPM to explore the Between-Person effects [47, 48]. This model includes autoregressive paths, concurrent associations, and bidirectional lagged effects (i.e., the effects from Healthy Aging to AFE and vice versa). To examine the Within-Person effects, we employed the RI-CLPM [47, 48]. Unlike the conventional CLPM, the RI-CLPM distinguishes Within-Person effects and Between-Person effects, allowing us to characterize the Within-Person effects [49].

For CLPM and RI-CLPM, we first estimated unconstrained models where all paths were allowed to vary freely (Model 2a and Model 3a). Then, we imposed constraints on the cross-lagged paths to have the same values across different time points (Model 2b and Model 3b). In the third step, we imposed constraints on the autoregressive paths to have the same values across time (Model 2c and Model 3c). In the fourth step, we imposed constraints on the concurrent paths to have the same values across time (Model 2d and Model 3d). Finally, we imposed constraints on the cross-lagged paths, autoregressive paths, and concurrent paths to have the same values across time (Model 2e and Model 3e). It is important to note that when we impose these equalities across time, the non-standardized coefficients for each path will be identical, but the standardized coefficients will differ. Therefore, in presenting the results, we provided both the non-standardized coefficients and the standardized coefficients.

In order to evaluate the overall fit of the models, we used several indicators including RMSEA, chi-square difference test, CFI, and SRMR (Standardized Root Mean Square Residual). An adequate model fit is indicated when the CFI is greater than or equal to 0.90, RMSEA is less than or equal to 0.08, and SRMR is less than or equal to 0.10. A good model fit is indicated when the CFI is greater than or equal to 0.95, RMSEA is less than or equal to 0.06, and SRMR is less than or equal to 0.08 [50]. We use ΔCFI, ΔRMSEA, and ΔSRMR to compare the differences between the constrained model and the baseline model. When ΔCFI ≤ 0.010 and ΔRMSEA ≤ 0.015 or ΔSRMR ≤ 0.030, we choose the constrained model [51].

Both the CLPM and RI-CLPM models consider covariates. However, incorporating multiple covariates would lead to a more complex model, making it challenging to interpret [52]. Hence, in this study, we included only non-time-varying control variables, including ACE, education, gender, urban–rural distribution, and regional distribution. It is worth noting that in the RI-CLPM model, these control variables are controlled at the random intercept level.

To ensure accurate measurement of healthy aging, we excluded participants who had more than 25% missing values in the comprehensive assessment questionnaire on healthy aging [6]. Assuming that the missingness occurred randomly (Missing at Random, MAR), we employed multiple imputation (MI) to fill in the missing data [3].

We also conducted a series of sensitivity analyses. Firstly, we performed subgroup analyses based on factors such as urban–rural differences, geographical regions, and gender [53]. Secondly, for the assessment of healthy aging, we used a simple summation method instead of IRT [54].

Descriptive statistics and LMM were conducted using R software (Version 4.2.2, R Foundation for Statistical Computing, Vienna, Austria). The mirt package was utilized to score the healthy aging of the Chinese population [55], while the lme4 package was used to build the LMM [56]. The CAR package was used to compute the VIF [57],the mice package was used for MI [58], the interaction package was utilized to plot simple slope graphs [59], and the bruceR package was used to obtain estimates (β), p-values, and 95% confidence intervals for fixed and random effects [60]. The CLPM and RI-CLPM models were constructed using Mplus software (Version 8.3, Muthén & Muthén, Los Angeles, USA). A significance level of p < 0.05 was used to indicate statistical significance of differences.

Results

Baseline characteristics

The IRT model converged successfully with an excellent fit (RMSEA = 0.05, TLI = 0.95, CFI = 0.95 and had a marginal reliability of 0.80). Table 1 summarizes the baseline characteristics of the participants. The study included participants with an average age of 57.57 ± 9.21 years. Among them, males accounted for 46.3% of the total sample, while the urban population represented 34.5% of the participants. In terms of regional distribution, the highest proportion was observed in the West at 32.8%, whereas the lowest proportion was found in the Northeast at 6.8%. On average, the participants had 3.88 ± 1.09 appropriate AFE indicators.

Table 1 Baseline descriptive statistics of variables

The associations between AFE and healthy aging

Table 2 provides an initial assessment of the relationship between AFE and healthy aging. Consistent positive associations between AFE and healthy aging were observed across different models (Models 1a-c). According to the multivariable adjusted model (Model 1b), a higher number of appropriate AFE indicators was associated with a higher level of healthy aging (β = 0.087, p < 0.001). We also investigated the interaction between urban–rural distribution, geographic distribution, and AFE (Model 1c). No significant interaction was found between urban–rural distribution and AFE (β = 0.014, p = 0.318), while an interaction was observed between the geographic distribution and AFE (central region * AFE: β = 0.031, p = 0.038; eastern region * AFE: β = 0.048, p = 0.003). To further explain the interaction between regional distribution and AFE, we conducted a simple slope test (Fig. 2). The results indicate that in the central region (green dashed line) and the eastern region (red dashed line), as the level of AFE increases, the corresponding level of healthy aging also increases, with a greater upward trend observed in the eastern region.

Table 2 Association between AFE and healthy aging of LMM
Fig. 2
figure 2

Simple slope analysis

Lagged association between AFE and healthy aging

The fitting results of all CLPM models (Table S2 in the Supplement) are relatively mediocre. The main reason is that the RMSEA values are all above 0.08. For example, in Model 2e, the RMSEA is 0.115. However, according to Orth [48], it is acceptable for some fit indices in CLPM models to be below the threshold. Similarly, in previous studies [61], there are examples of accepting models with RMSEA values exceeding 0.1 for further research. Therefore, we kept the above-mentioned models. After comparing Model 2a-e, Model 2e, which imposes equality constraints on all paths across time, is more favored. Thus, we retained this model for further analysis. The CLPM model results (Table 3) indicate that AFE has a moderate positive predictive effect on healthy aging (β values range from 0.077 to 0.089, p < 0.001). Similarly, healthy aging also has a significant positive predictive effect on AFE (β values range from 0.147 to 0.159, p < 0.001).

Table 3 Cross-Lagged Coefficients of CLPM

The fitting results of all RI-CLPM models (Table S2 in the Supplement) are good. After comparing Model 3a-e, Model 3e, which imposes equality constraints on cross-lagged paths and autoregressive paths across time, is more preferred. Thus, we kept this model for further analysis. The RI-CLPM model results (Table 4) indicate that there is only a significant positive promoting effect of AFE on healthy aging (β values range from 0.021 to 0.024, p = 0.004).

Table 4 Cross-Lagged Coefficients of RI-CLPM

The above results show that the promoting effect of healthy aging on AFE exists in both CLPM and RI-CLPM, while the promoting effect of AFE on healthy aging exists only in RI-CLPM. Consistent with previous research [47, 62], we consider the promoting effect of healthy aging on AFE to be Between-Person effects rather than Within-Person effects, and the promoting effect of AFE on healthy aging to be interpreted as Within-Person effects rather than Between-Person effects.

Sensitivity analysis

In order to examine the robustness of the findings, a series of sensitivity analyses (Table S3-12 in the Supplement) were conducted. The majority of the results are consistent with the main findings. It is noteworthy that in the subgroups of individuals aged over 65, females, rural residents, individuals from the western, northeastern, and central regions, the results of LMM and CLPM are completely consistent with the main results, but the results of RI-CLPM are no longer significant. For these subgroups, we interpret the effects of AFE on healthy aging and healthy aging on AFE as Between-Person effects.

Discussion

This study is the first to use microdata from the CHARLS database to assess the dynamic developmental trends between AFE levels and healthy aging in the Chinese population. The results indicate a positive correlation between AFE and healthy aging, with significant positive interactions existing in the central and eastern regions, respectively. Additionally, we found that the promoting effect of healthy aging on AFE is a type of Between-Person effect, while the promoting effect of AFE on healthy aging is a type of Within-Person effect.

The results of the LMM analysis revealed a positive correlation between AFE and healthy aging in the Chinese population. Although there is a lack of research specifically examining the relationship between AFE and healthy aging, previous studies have explored the association between single dimensions of AFE, such as employment and social participation, and healthy aging. These studies consistently found a positive link between employment, social participation, and healthy aging [6, 41, 63]. This may be because work and social engagement contribute to improved physical health, reducing the risk of illness. Moreover, employment and social participation can enhance individuals' social status, promoting psychological well-being and a sense of dignity. Regarding other variables within AFE, although their impact on healthy aging has not been extensively studied, their significance in terms of health should not be disregarded. For instance, PM2.5 pollution may impede the psychological well-being of older adults [64], accelerate cognitive decline in middle-aged and older individuals [65], and increase the risk of premature death. Conversely, a favorable outdoor environment can increase the frequency of social engagement and physical activity, thereby fostering overall well-being [66]. The American Medical Association recognizes the pivotal role of Broadband Internet Access (BIA) in six health domains [67]. Furthermore, the WHO highlights that nearly 2 billion people globally face catastrophic or impoverishing health expenditures, underscoring the fundamental challenge of health inequities in achieving universal health coverage.

In the study of interactions, there is no significant interaction between urban–rural distribution and AFE. This may be due to the continuous improvement of China's urbanization level since the 16th National Congress of the Communist Party of China in 2002, which proposed the concept of "taking the path of urbanization with Chinese characteristics". The urbanization rate has increased from 36.21% in 2000 to 52.57% in 2012, with an average annual growth rate of 1.36 percentage points [68]. By 2018, China's urbanization level reached 59.58% [69]. Additionally, there has been a significant improvement in the living standards of rural residents. The per capita disposable income has risen from 7,394 yuan [70] in 2011 to 14,617 yuan [71] in 2018, and the per capita rural healthcare expenditure has increased from 436.8 yuan [72] to 1,240 yuan [73]. Therefore, although cities have more abundant social and economic resources, and urban populations can better enjoy social security, pensions, healthcare, and other services, the gap between urban and rural areas is narrowing. In terms of the interaction between geographical distribution and AFE, the positive correlation between AFE and healthy aging is stronger in the eastern and central regions than in the western region, with the eastern region showing a stronger correlation than the central region. This may be because, although China has achieved remarkable economic and social development since the reform and opening-up policy, there are still issues of imbalanced and insufficient development. Taking GDP as an example, in 2018, the GDP of the eastern region alone accounted for 53% of the national total [74]. Furthermore, there are significant disparities between the western, northeastern, central regions and the eastern region in terms of healthcare, public resources, and infrastructure [75].

Our results indicate that the promotion of AFE by healthy aging is a Between-Person effect, suggesting that older individuals with higher levels of healthy aging are more likely to experience higher levels of AFE at subsequent time points compared to those with lower levels of healthy aging. This finding can be attributed to several factors. Individuals with higher levels of healthy aging often possess greater intrinsic capabilities, better SES, and healthier lifestyle habits. This enables them to afford expenses related to transportation, healthcare, and retirement, thereby maintaining optimal physical functioning in the long term and exhibiting enhanced learning abilities. Consequently, they have more energy and capacity to actively engage in paid work, social participation, and environmental preservation, thereby fostering an AFE that is conducive to their individual well-being.

Based on our findings, the promotion of healthy aging by AFE is a Within-Person effect, suggesting that encouraging and guiding individuals to engage in building suitable AFE (e.g., paid work, social participation, environmental protection, and fostering a respectful attitude towards the elderly) to achieve AFE levels higher than the previous average (Within-Person effect) can enhance their future level of healthy aging. The reason for this result may be that AFE encompasses multiple key factors from both the physical environment (e.g., accessible public facilities) and the social environment (e.g., active engagement in volunteer activities), providing sufficient support to the Chinese population in multiple aspects. This helps to compensate for or even reverse the gradual loss of intrinsic abilities that occur with age, ultimately achieving a higher level of healthy aging.

The results of the RI-CLPM for subgroups aged over 65, females, rural residents, individuals from the western, northeastern, and central regions did not show significance, indicating that the mutual promotion effect between AFE and healthy aging is a Between-Person effect for these subgroups. Therefore, compared to encouraging and guiding participation in building AFE to achieve levels higher than the previous average level, eliminating age, gender, and regional differences among the population, meeting the diverse health needs of different elderly populations, and steadily improving the health levels of vulnerable groups may be more effective in enhancing the healthy aging levels of the population mentioned above.

However, this study also has several limitations. Firstly, although we combined the "Global Age-friendly Cities: A Guide", the "Measuring the age-friendliness of cities: a guide to using core indicators" and previous research to construct a comprehensive evaluation index for AFE by selecting matching indicators from the CHARLS database, it should be noted that CHARLS is a comprehensive database focusing on the health and elderly care of middle-aged and elderly people in China, rather than a specific survey on AFE. Therefore, some indicators may not fully capture the essence of AFE. Secondly, although the comprehensive evaluation index for healthy aging developed by ATHLOS has been widely validated, it primarily relies on self-assessment and lacks quantitative, objective evaluation indicators, which may introduce recall bias. Thirdly, despite controlling for established factors influencing healthy aging based on existing literature, residual confounding from unmeasured variables cannot be completely ruled out.

Conclusions

Firstly, individuals with high levels of healthy aging are more inclined to actively participate in the development of appropriate AFE compared to those with low levels of healthy aging. Furthermore, by encouraging and guiding individuals to engage in activities that contribute to building appropriate AFE, such as paid work, social engagement, environmental protection, and fostering a society that respects and values the elderly, can elevate their AFE levels beyond the previous average level (Within-Person effect), thereby improving their future healthy aging levels. Lastly, it is crucial to address vulnerable groups such as the elderly, women, rural residents, and individuals in the western regions by gradually reducing age, gender, urban–rural, and regional disparities, meeting their health needs effectively, enhancing their health status steadily, and fostering healthy aging within these vulnerable populations.

Statement

All methods in this study on humans described in the manuscript were performed in accordance with national law and the Helsinki Declaration of 1975 and its later amendments.

Availability of data and materials

The datasets used and analyzed during the current study are available in the official website of CHARLS (https://charls.pku.edu.cn/) and ACAG (https://sites.wustl.edu/acag/datasets/surface-pm2-5/).

Abbreviations

AFE:

Age-friendly environments

LMM:

Linear mixed model

CLPM:

Cross-lagged panel model

RI-CLPM:

Random-intercept cross-lagged panel model

WHO:

The World Health Organization

SES:

Socioeconomic status

CHARLS:

China Health and Retirement Longitudinal Survey

ACAG:

The Atmospheric Composition Analysis Group

2PLM:

2-Parameter logistic model

IRT:

Item Response Theory

RMSEA:

Root Mean Square Error of Approximation

SRMR:

Standardized Root Mean Square Residual

CFI:

Comparative Fit Index

TLI:

Tucker-Lewis Index

VIF:

Variance Inflation Factor

MI:

Multiple imputation

ACE:

Adverse childhood experience

References

  1. World Health Organization. World report on ageing and health. Geneva Switzerland: World Health Organization; 2015.

    Google Scholar 

  2. Matsuyama S, Lu Y, Aida J, Tanji F, Tsuji I. Association between number of remaining teeth and healthy aging in Japanese older people: The Ohsaki Cohort 2006 Study. Geriatr Gerontol Int. 2022;22:68–74. https://doi.org/10.1111/ggi.14320.

    Article  PubMed  Google Scholar 

  3. Nguyen H, Wu Y-T, Dregan A, Vitoratou S, Chua K-C, Prina AM. Multimorbidity patterns, all-cause mortality and healthy aging in older English adults: Results from the English Longitudinal Study of Aging. Geriatr Gerontol Int. 2020;20:1126–32. https://doi.org/10.1111/ggi.14051.

    Article  PubMed  Google Scholar 

  4. La Fuente J, de, Caballero FF, Sánchez-Niubó A, Panagiotakos DB, Prina AM, Arndt H, et al. Determinants of Health Trajectories in England and the United States: An Approach to Identify Different Patterns of Healthy Aging. J Gerontol A Biol Sci Med Sci. 2018;73:1512–8. https://doi.org/10.1093/gerona/gly006.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Moreno-Agostino D, Daskalopoulou C, Wu Y-T, Koukounari A, Haro JM, Tyrovolas S, et al. The impact of physical activity on healthy ageing trajectories: evidence from eight cohort studies. Int J Behav Nutr Phys Act. 2020;17:92. https://doi.org/10.1186/s12966-020-00995-8.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Caballero FF, Soulis G, Engchuan W, Sánchez-Niubó A, Arndt H, Ayuso-Mateos JL, et al. Advanced analytical methodologies for measuring healthy ageing and its determinants, using factor analysis and machine learning techniques: the ATHLOS project. Sci Rep. 2017;7:43955. https://doi.org/10.1038/srep43955.

    Article  PubMed  Google Scholar 

  7. Daskalopoulou C, Koukounari A, Wu Y-T, Terrera GM, Caballero FF, La Fuente J, de, et al. Healthy ageing trajectories and lifestyle behaviour: the Mexican Health and Aging Study. Sci Rep. 2019;9:11041. https://doi.org/10.1038/s41598-019-47238-w.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Wu Y-T, Daskalopoulou C, Muniz Terrera G, Sanchez Niubo A, Rodríguez-Artalejo F, Ayuso-Mateos JL, et al. Education and wealth inequalities in healthy ageing in eight harmonised cohorts in the ATHLOS consortium: a population-based study. Lancet Public Health. 2020;5:e386–94. https://doi.org/10.1016/S2468-2667(20)30077-3.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Hu B. Childhood adversity and healthy ageing: a study of the Chinese older population. Eur J Ageing. 2021;18:523–35. https://doi.org/10.1007/s10433-021-00608-8.

    Article  PubMed  PubMed Central  Google Scholar 

  10. World Health Organization. Global Age-friendly Cities: A Guide. 2007. https://www.who.int/publications/i/item/9789241547307. Accessed 25 May 2023.

  11. World Health Organization. Measuring the age-friendliness of cities: a guide to using core indicators. 2023/5/12. https://www.who.int/china/publications-detail/9789241509695. Accessed 12 May 2023.

  12. World Health Organization. National programmes for age-friendly cities and communities A guide. 2023. https://www.who.int/publications/i/item/9789240068698. Accessed 29 Oct 2023.

  13. Lei P, Feng Z. Age-friendly neighbourhoods and depression among older people in China: Evidence from China Family Panel Studies. J Affect Disord. 2021;286:187–96. https://doi.org/10.1016/j.jad.2021.02.081.

    Article  PubMed  Google Scholar 

  14. Yu R, Wong M, Woo J. Perceptions of Neighborhood Environment, Sense of Community, and Self-Rated Health: an Age-Friendly City Project in Hong Kong. J Urban Health. 2019;96:276–88. https://doi.org/10.1007/s11524-018-00331-3.

    Article  PubMed  Google Scholar 

  15. Choi YJ. Age-Friendly Features in Home and Community and the Self-Reported Health and Functional Limitation of Older Adults: the Role of Supportive Environments. J Urban Health. 2020;97:471–85. https://doi.org/10.1007/s11524-020-00462-6.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Park S-Y, Kim M, Chung S. Age-friendly environments and depressive symptoms among Korean adults: The mediating effects of loneliness. Aging Ment Health. 2021;25:1060–70. https://doi.org/10.1080/13607863.2020.1755827.

    Article  PubMed  Google Scholar 

  17. Tang JYM, Chui CHK, Lou VWQ, Chiu RLH, Kwok R, Tse M, et al. The Contribution of Sense of Community to the Association Between Age-Friendly Built Environment and Health in a High-Density City: A Cross-Sectional Study of Middle-Aged and Older Adults in Hong Kong. J Appl Gerontol. 2021;40:1687–96. https://doi.org/10.1177/0733464821991298.

    Article  PubMed  Google Scholar 

  18. Kim K, Buckley T, Burnette D, Kim S, Cho S. Measurement Indicators of Age-Friendly Communities: Findings From the AARP Age-Friendly Community Survey. Gerontologist. 2022;62:e17–27. https://doi.org/10.1093/geront/gnab055.

    Article  PubMed  Google Scholar 

  19. Ward M, Gibney S, O’Callaghan D, Shannon S. Age-Friendly Environments, Active Lives? Associations Between the Local Physical and Social Environment and Physical Activity Among Adults Aged 55 and Older in Ireland. J Aging Phys Act. 2020;28:140–8. https://doi.org/10.1123/japa.2019-0012.

    Article  PubMed  Google Scholar 

  20. Jagroep W, Cramm JM, Denktaș S, Nieboer AP. Age-friendly neighbourhoods and physical activity of older Surinamese individuals in Rotterdam, the Netherlands. PLoS ONE. 2022;17: e0261998. https://doi.org/10.1371/journal.pone.0261998.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Mullen N, Stinchcombe A, Seguin C, Marshall S, Naglie G, Rapoport MJ, et al. Perceived Community Age-friendliness is Associated With Quality of Life Among Older Adults. J Appl Gerontol. 2022;41:1274–82. https://doi.org/10.1177/07334648211065431.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Menec VH, Nowicki S. Examining the relationship between communities’ ‘age-friendliness’ and life satisfaction and self-perceived health in rural Manitoba, Canada. Rural Remote Health. 2014;14:2594. https://doi.org/10.22605/RRH2594.

    Article  PubMed  Google Scholar 

  23. Au A, Lai DWL, Yip H-M, Chan S, Lai S, Chaudhury H, et al. Sense of Community Mediating Between Age-Friendly Characteristics and Life Satisfaction of Community-Dwelling Older Adults. Front Psychol. 2020;11:86. https://doi.org/10.3389/fpsyg.2020.00086.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Park S, Lee S. Age-friendly environments and life satisfaction among South Korean elders: person-environment fit perspective. Aging Ment Health. 2017;21:693–702. https://doi.org/10.1080/13607863.2016.1154011.

    Article  PubMed  Google Scholar 

  25. Nieboer AP, Cramm JM. Age-Friendly Communities Matter for Older People’s Well-Being. J Happiness Stud. 2018;19:2405–20. https://doi.org/10.1007/s10902-017-9923-5.

    Article  Google Scholar 

  26. Gibney S, Zhang M, Brennan C. Age-friendly environments and psychosocial wellbeing: a study of older urban residents in Ireland. Aging Ment Health. 2020;24:2022–33. https://doi.org/10.1080/13607863.2019.1652246.

    Article  PubMed  Google Scholar 

  27. Garner IW, Holland CA. Age-friendliness of living environments from the older person’s viewpoint: development of the Age-Friendly Environment Assessment Tool. Age Ageing. 2020;49:193–8. https://doi.org/10.1093/ageing/afz146.

    Article  PubMed  Google Scholar 

  28. Yu J, Ma G, Ding W, Mao J, Wang J. Structural model for the relationships between age-friendly communities and quality of life of older adults in Hefei. China ECAM. 2021;29:1376–95. https://doi.org/10.1108/ECAM-08-2020-0647.

    Article  Google Scholar 

  29. Amoah PA, Mok KH, Wen Z, Li LW. Achieving the age-friendly city agenda: an interventional study in Hong Kong’s Islands District. J Asian Public Policy. 2021;14:333–52. https://doi.org/10.1080/17516234.2019.1663981.

    Article  Google Scholar 

  30. Lai M-M, Lein S-Y, Lau S-H, Lai M-L. Modeling Age-Friendly Environment, Active Aging, and Social Connectedness in an Emerging Asian Economy. J Aging Res. 2016;2016:2052380. https://doi.org/10.1155/2016/2052380.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Aung MN, Koyanagi Y, Ueno S, Tiraphat S, Yuasa M. A Contemporary Insight into an Age-Friendly Environment Contributing to the Social Network, Active Ageing and Quality of Life of Community Resident Seniors in Japan. J Aging Environ. 2021;35:145–60. https://doi.org/10.1080/26892618.2020.1813232.

    Article  Google Scholar 

  32. Zhao Y, John Strauss, Xinxin Chen, Yafeng Wang, Jinquan Gong, Qinqin Meng, et al. China Health and Retirement Longitudinal Study Wave 4 User’s Guide. 2020. https://charls.charlsdata.com/Public/ashelf/public/uploads/document/2018-charls-wave4/application/CHARLS_2018_Users_Guide.pdf. Accessed 12 May 2023.

  33. van Donkelaar A, Hammer MS, Bindle L, Brauer M, Brook JR, Garay MJ, et al. Monthly Global Estimates of Fine Particulate Matter and Their Uncertainty. Environ Sci Technol. 2021;55:15287–300. https://doi.org/10.1021/acs.est.1c05309.

    Article  CAS  PubMed  Google Scholar 

  34. Sanchez-Niubo A, Forero CG, Wu Y-T, Giné-Vázquez I, Prina M, La Fuente J, de, et al. Development of a common scale for measuring healthy ageing across the world: results from the ATHLOS consortium. Int J Epidemiol. 2021;50:880–92. https://doi.org/10.1093/ije/dyaa236.

    Article  PubMed  Google Scholar 

  35. Li H, Zeng Y, Gan L, Tuersun Y, Yang J, Liu J, Chen J. Urban-rural disparities in the healthy ageing trajectory in China: a population-based study. BMC Public Health. 2022;22:1406. https://doi.org/10.1186/s12889-022-13757-x.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Critselis E, Panaretos D, Sánchez-Niubò A, Giné-Vázquez I, Ayuso-Mateos JL, Caballero FF, et al. Ageing trajectories of health-longitudinal opportunities and synergies (ATHLOS) Healthy Ageing Scale in adults from 16 international cohorts representing 38 countries worldwide. J Epidemiol Community Health. 2020;74:1043–9. https://doi.org/10.1136/jech-2020-214496.

    Article  PubMed  Google Scholar 

  37. Chen J, Zeng Y, He W, Yang J, Xu D, Li H. How Healthy Aging and Contact With Children Are Associated With Satisfaction in Middle-Aged and Older Parents in China: A Mediation Analysis. Front Public Health. 2022;10: 836558. https://doi.org/10.3389/fpubh.2022.836558.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Nguyen H, Moreno-Agostino D, Chua K-C, Vitoratou S, Prina AM. Trajectories of healthy ageing among older adults with multimorbidity: A growth mixture model using harmonised data from eight ATHLOS cohorts. PLoS ONE. 2021;16: e0248844. https://doi.org/10.1371/journal.pone.0248844.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Yang Y, Cao L, Xia Y, Li J. The Effect of Living Environmental Factors on Cardiovascular Diseases in Chinese Adults: Results from a Cross-sectional and Longitudinal Study. Eur J Prev Cardiol. 2022. https://doi.org/10.1093/eurjpc/zwac304.

    Article  PubMed  Google Scholar 

  40. Gao J, Xu J, Chen Y, Wang Y, Ye B, Fu H. Development and Validation of a Multidimensional Population-Based Healthy Aging Scale: Results From the China Health and Retirement Longitudinal Study. Front Med (Lausanne). 2022;9: 853759. https://doi.org/10.3389/fmed.2022.853759.

    Article  PubMed  Google Scholar 

  41. Sowa A, Tobiasz-Adamczyk B, Topór-Mądry R, Poscia A, La Milia DI. Predictors of healthy ageing: Public health policy targets. BMC Health Serv Res. 2016. https://doi.org/10.1186/s12913-016-1520-5.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Rivadeneira MF, Mendieta MJ, Villavicencio J, Caicedo-Gallardo J, Buendía P. A multidimensional model of healthy ageing: proposal and evaluation of determinants based on a population survey in Ecuador. BMC Geriatr. 2021;21:615. https://doi.org/10.1186/s12877-021-02548-5.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Kollia N, Caballero FF, Sánchez-Niubó A, Tyrovolas S, Ayuso-Mateos JL, Haro JM, et al. Social determinants, health status and 10-year mortality among 10,906 older adults from the English longitudinal study of aging: the ATHLOS project. BMC Public Health. 2018;18:1357. https://doi.org/10.1186/s12889-018-6288-6.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Lu W, Pikhart H, Sacker A. Comparing socio-economic inequalities in healthy ageing in the United States of America, England, China and Japan: evidence from four longitudinal studies of ageing. Ageing Soc. 2021;41:1495–520. https://doi.org/10.1017/S0144686X19001740.

    Article  Google Scholar 

  45. Tyrovolas S, Panaretos D, Daskalopoulou C, Gine-Vazquez I, Niubo AS, Olaya B, et al. Alcohol Drinking and Health in Ageing: A Global Scale Analysis of Older Individual Data through the Harmonised Dataset of ATHLOS. Nutrients. 2020. https://doi.org/10.3390/nu12061746.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Ochoa-Rosales C, van der Schaft N, Braun KVE, Ho FK, Petermann-Rocha F, Ahmadizar F, et al. C-reactive protein partially mediates the inverse association between coffee consumption and risk of type 2 diabetes: The UK Biobank and the Rotterdam study cohorts. Clin Nutr. 2023;42:661–9. https://doi.org/10.1016/j.clnu.2023.02.024.

    Article  CAS  PubMed  Google Scholar 

  47. Waddell JT, Sternberg A, Grimm KJ, Chassin L. Do Alcohol Consequences Serve as Teachable Moments? A Test of Between- and Within-Person Reciprocal Effects From College Age to Adulthood. J Stud Alcohol Drugs. 2021;82:647–58. https://doi.org/10.15288/jsad.2021.82.647.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Orth U, Clark DA, Donnellan MB, Robins RW. Testing prospective effects in longitudinal research: Comparing seven competing cross-lagged models. J Pers Soc Psychol. 2021;120:1013–34. https://doi.org/10.1037/pspp0000358.

    Article  PubMed  Google Scholar 

  49. Mulder JD, Hamaker EL. Three Extensions of the Random Intercept Cross-Lagged Panel Model. Struct Equ Modeling. 2021;28:638–48. https://doi.org/10.1080/10705511.2020.1784738.

    Article  Google Scholar 

  50. Israel ES, Gibb BE. Transactional Relations Between Peer Victimization and Depressive Symptoms Among Youth at Risk of Developing Depression: Evidence for Gender Differences. Res Child Adolesc Psychopathol. 2023;51:625–37. https://doi.org/10.1007/s10802-023-01032-5.

    Article  PubMed  Google Scholar 

  51. Zhu S, Kong X, Han F, Tian H, Sun S, Sun Y, et al. Association between social isolation and depression: Evidence from longitudinal and Mendelian randomization analyses. J Affect Disord. 2024;350:182–7. https://doi.org/10.1016/j.jad.2024.01.106.

    Article  PubMed  Google Scholar 

  52. Stanyon D, DeVylder J, Yamasaki S, Yamaguchi S, Ando S, Usami S, et al. Auditory Hallucinations and Self-Injurious Behavior in General Population Adolescents: Modeling Within-Person Effects in the Tokyo Teen Cohort. Schizophr Bull. 2023;49:329–38. https://doi.org/10.1093/schbul/sbac155.

    Article  PubMed  Google Scholar 

  53. Jiang H, Zhang S, Yao X, Meng L, Lin Y, Guo F, et al. Does physical activity attenuate the association between ambient PM2.5 and physical function? Sci Total Environ. 2023;874:162501. doi:https://doi.org/10.1016/j.scitotenv.2023.162501.

  54. Daskalopoulou C, Prince M, Koukounari A, Haro JM, Panagiotakos DB, Prina AM. Healthy ageing and the prediction of mortality and incidence dependence in low- and middle- income countries: a 10/66 population-based cohort study. BMC Med Res Methodol. 2019;19:225. https://doi.org/10.1186/s12874-019-0850-5.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Chalmers RP. mirt : A Multidimensional Item Response Theory Package for the R Environment. J Stat Soft. 2012. https://doi.org/10.18637/jss.v048.i06.

    Article  Google Scholar 

  56. Bates D, Mächler M, Bolker B, Walker S. Fitting Linear Mixed-Effects Models using lme4. 2014/6/23. https://arxiv.org/pdf/1406.5823.pdf. Accessed 12 May 2023.

  57. John Fox. car: Companion to Applied Regression. 2023/5/4. https://cran.r-project.org/web/packages/car/index.html. Accessed 12 May 2023.

  58. Stef van Buuren. mice: Multivariate Imputation by Chained Equations. 2023/5/5. https://cran.r-project.org/web/packages/mice/. Accessed 12 May 2023.

  59. Long JA. interactions: Comprehensive, User-Friendly Toolkit for Probing Interactions. https://cran.r-project.org/web/packages/interactions/interactions.pdf. Accessed 7 Apr 2024.

  60. Han-Wu-Shuang Bao. bruceR: Broadly Useful Convenient and Efficient R Functions. https://cran.r-project.org/web/packages/bruceR/index.html. Accessed 12 May 2023.

  61. Zheng X, Ruan C, Zheng L. Money or love? The impact of the COVID-19 pandemic on consumer life goals and subjective well-being. J Bus Res. 2021;137:626–33. https://doi.org/10.1016/j.jbusres.2021.08.044.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Sasser J, Waddell JT, Doane LD. Family dynamics and adjustment across Latino/a students’ transition to college: Disentangling within- and between-person reciprocal associations. Dev Psychol. 2023;59:487–502. https://doi.org/10.1037/dev0001474.

    Article  PubMed  Google Scholar 

  63. Lu YK, Matsuyama S, Tanji F, Otsuka T, Tomata Y, Tsuji I. Social Participation and Healthy Aging Among the Older Japanese: The Ohsaki Cohort 2006 Study. J Gerontol Series A-Biol Sci Med Sci. 2022;77:106–13. https://doi.org/10.1093/gerona/glab101.

    Article  Google Scholar 

  64. Chen F, Zhang X, Chen Z. Air pollution and mental health: Evidence from China Health and Nutrition Survey. J Asian Econ. 2023;86: 101611. https://doi.org/10.1016/j.asieco.2023.101611.

    Article  Google Scholar 

  65. Mo S, Wang Y, Peng M, Wang Q, Zheng H, Zhan Y, et al. Sex disparity in cognitive aging related to later-life exposure to ambient air pollution. Sci Total Environ. 2023;886: 163980. https://doi.org/10.1016/j.scitotenv.2023.163980.

    Article  CAS  PubMed  Google Scholar 

  66. Kruize H, van Kamp I, van den Berg M, van Kempen E, Wendel-Vos W, Ruijsbroek A, et al. Exploring mechanisms underlying the relationship between the natural outdoor environment and health and well-being - Results from the PHENOTYPE project. Environ Int. 2020;134: 105173. https://doi.org/10.1016/j.envint.2019.105173.

    Article  PubMed  Google Scholar 

  67. Benda NC, Veinot TC, Sieck CJ, Ancker JS. Broadband Internet Access Is a Social Determinant of Health! Am J Public Health. 2020;110:1123–5. https://doi.org/10.2105/AJPH.2020.305784.

    Article  PubMed  PubMed Central  Google Scholar 

  68. Xu S. Report on the Work of Urbanization Construction by the State Council - Delivered on June 26, 2013 at the Third Meeting of the Standing Committee of the Twelfth National People’s Congress. 2013. http://www.npc.gov.cn/zgrdw/npc/zxbg/czhjsgzqk/2013-06/27/content_1798667.htm. Accessed 22 Oct 2023.

  69. National Bureau of Statistics. Continuous Improvement in Urbanization Level, Steady Progress in Urban Development - Report Seventeen of the Series on Achievements in Economic and Social Development on the 70th Anniversary of the Founding of New China. 2019. https://www.gov.cn/xinwen/2019-08/15/content_5421382.htm. Accessed 22 Aug 2023.

  70. National Bureau of Statistics. Per Capita Disposable Income of Rural Residents in China in 2011. 2011. https://data.stats.gov.cn/search.htm?s=%E5%86%9C%E6%9D%91%20%E4%BA%BA%E5%9D%87%E5%8F%AF%E6%94%AF%E9%85%8D%20%E6%94%B6%E5%85%A5%202011. Accessed 25 May 2023.

  71. National Bureau of Statistics. Per Capita Disposable Income of Rural Residents in China in 2018. 2018. https://data.stats.gov.cn/search.htm?s=%E5%86%9C%E6%9D%91%20%E4%BA%BA%E5%9D%87%E5%8F%AF%E6%94%AF%E9%85%8D%20%E6%94%B6%E5%85%A5%202018. Accessed 25 May 2023.

  72. National Bureau of Statistics. Per Capita Healthcare Expenditure of Rural Residents in 2011. 2011. https://data.stats.gov.cn/search.htm?s=%E5%86%9C%E6%9D%91%20%E5%8C%BB%E7%96%97%E4%BF%9D%E5%81%A5%20%E6%94%AF%E5%87%BA%202011. Accessed 25 May 2023.

  73. National Bureau of Statistics. Per Capita Healthcare Expenditure of Rural Residents in 2018. 2018. https://data.stats.gov.cn/search.htm?s=%E5%86%9C%E6%9D%91%20%E5%8C%BB%E7%96%97%E4%BF%9D%E5%81%A5%20%E6%94%AF%E5%87%BA%202018. Accessed 25 May 2023.

  74. National Bureau of Statistics. Gross Regional Product (in RMB 100 million). 2023/8/22. https://data.stats.gov.cn/easyquery.htm?cn=E0103. Accessed 22 Aug 2023.

  75. Chai K-C, Zhang Y-B, Chang K-C. Regional Disparity of Medical Resources and Its Effect on Mortality Rates in China. Front Public Health. 2020;8:8. https://doi.org/10.3389/fpubh.2020.00008.

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We appreciate all participants who attended the China Health and Retirement Longitudinal Study (CHARLS). We appreciate Verena Menec from University of Manitoba and Nuno Marques de Paiva from Instituto Superior Miguel Torga for providing the questionnaires used in their previous research.

Funding

This work was supported by the National Natural Science Foundation of China (Grant No. 72074125), the Traditional Chinese Medicine Science and Technology Special Project of Nanjing City (Grant No. ZYQN202203), and the Natural Science Foundation Project of Nanjing University of Traditional Chinese Medicine (Grant No. XZR2021052), the Philosophy and Social Sciences Research in Universities in Jiangsu Province (Grant No. 2020SJB0218).

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Contributions

Shao-liang Tang: Conceptualization, Supervision, Writing – review & editing, Funding acquisition. Ning Gu: Conceptualization, Supervision, Writing – review & editing. Yan Cheng: Data curation, Formal analysis, Methodology, Writing – original draft, Writing – review & editing. Zhi-liang Chen: Data curation, Formal analysis, Funding acquisition, Writing – original draft. Yue Wei: Data curation, Writing – original draft, Funding acquisition. The authors read and approved the final manuscript.

Corresponding author

Correspondence to Shao-liang Tang.

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Ethics approval and consent to participate

Ethics approval was obtained from the Ethical Review Committee of Peking University, and all the participants provided signed informed consent at the time of participation. Ethical approval for all the CHARLS waves was granted from the Institutional Review Board at Peking University. The IRB approval number for the main household survey, including anthropometrics, is IRB00001052–11015; The IRB approval number for biomarker collection, was IRB00001052–11014.

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The authors declare no competing interests.

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Cheng, Y., Chen, Zl., Wei, Y. et al. Examining dynamic developmental trends: the interrelationship between age-friendly environments and healthy aging in the Chinese population—evidence from China Health and Retirement Longitudinal Study, 2011–2018. BMC Geriatr 24, 429 (2024). https://doi.org/10.1186/s12877-024-05053-7

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