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Article

Investigating the Influence of Age-Friendly Community Infrastructure Facilities on the Health of the Elderly in China

by
Qingwen Chen
1,
Zhao Zhang
1,2,
Yihua Mao
1,2,3,*,
Ruyu Deng
1,
Yueyao Shui
1,
Kai Wang
1 and
Yuchen Hu
3
1
College of Civil Engineering & Architecture, Zhejiang University, Hangzhou 310027, China
2
Center for Balance Architecture, Zhejiang University, Hangzhou 310028, China
3
Binhai Industrial Technology Research Institute of Zhejiang University, Tianjin 300301, China
*
Author to whom correspondence should be addressed.
Buildings 2023, 13(2), 341; https://doi.org/10.3390/buildings13020341
Submission received: 23 November 2022 / Revised: 18 January 2023 / Accepted: 20 January 2023 / Published: 24 January 2023
(This article belongs to the Special Issue Housing as a Nexus of Unaffordability, Illegality and Livability)
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Versions Notes

Abstract

:
Global population aging has become a continuous and irreversible trend. Most of the elderly in China prefer “aging in place” owing to the influence of traditional concepts and social welfare, but many communities, as a basic place for the elderly to live, generally lack age-friendly infrastructure facilities. Based on the 2018 China Health and Retirement Longitudinal Study database, this study applies the propensity score matching method to empirically investigate the influence of the infrastructure facilities on the health of the elderly in China. The results show that adding elevators, installing bathing facilities, supplying gas or natural gas, and changing squatting toilets into sitting toilets, positively influence the health of the elderly, but there are some differences. The order of the degree of impact on self-rated health (SRH) was elevator > toilet type > kitchen gas supply > bathing facility, while that of activities of daily living (ADL) was bathing facility > toilet type > elevator > kitchen gas supply. Elderly people with different personal characteristics and family status have different degrees of ownership for the infrastructure facilities. It is suggested that age-friendly regeneration schemes be developed according to the different impacts and demands of the facilities.

1. Introduction

With the development of the social economy, people’s living and health standards, as well as medical standards, are constantly improving. The average life expectancy of humans is also increasing every year, which corresponds to the continuous decline of the total fertility rate. Population aging at the global level is rapidly increasing, and this trend is expected to continue for a long time [1]. Fundamentally speaking, the change in the age structure of the population is a historic progress in the improvement of medical care, health, education, living standards, and socio-economic development [2]. On the other hand, the unprecedented phenomenon in history of population aging has added many unknown factors to human life and future development. Providing safe and healthy life security to the elderly in the basic support fields, such as living environment and nursing, has become the focus of global attention [3,4].
China is no exception to the above trend. Since entering the 21st century, the degree of population aging in China has continued to increase. According to the data of the seventh population census, by the end of 2020, China’s population aged 60 years and above totaled 264 million, accounting for 18.70% of the total population, and the population aged 65 years and above totaled 191 million, accounting for 13.50% of the total population [5]. Compared with two decades ago, the proportion of the elderly population has nearly doubled. China has officially entered the stage of a “moderately aging society”, based on the statistics, and it is predicted that the degree of aging will continue to deepen rapidly [6].
Aging in place is the chief pension mode in China. On the one hand, under the influence of traditional concepts, many elderly people in China psychologically reject going to elderly care institutions and prefer to spend their old age in the original living environment and community neighborhood, maintaining the original pace of life and living habits [7]. On the other hand, considering the aging status of “getting old before getting rich,” it is unrealistic to provide the elderly with high-quality and inexpensive beds in social elderly care institutions. The constraints of economic conditions also make it difficult for ordinary elderly and their families to bear the enormous costs of living in specific high-end urban and rural elderly care communities. Therefore, providing a living environment consistent with the physical function and psychological needs of the elderly is the basic premise for reducing security risks and improving the acting ability and quality of life of the elderly [8,9]. It is necessary to optimize the community infrastructure to make the environment more suitable for the elderly to live in [10]. In particular, the most basic and commonly used community infrastructure facilities are crucial to the living health of the elderly [11]. However, in the process of rapid urbanization, many residential and public facilities in communities tend to be designed primarily for young and middle-aged people, overlooking the special residential needs of the elderly [12]. Notably, this situation is more serious in the old communities where more elderly people usually gather. It is imperative to implement the community age-friendly regeneration.
In the research process of community age-friendly facilities, scholars have constantly expanded the definition of an age-friendly community environment, from the internal structure of basic housing, including the its internal activity space, the special auxiliary configuration of housing, and the indoor barrier-free design, to the surrounding environment conditions, such as road traffic, environmental greening rate and air pollution people live in [13,14,15]. Physical functions of the elderly, such as vision, hearing, physical strength, mobility, and flexibility, are discernibly deteriorated compared with those of young people. Some scholars have paid more attention to the suitability of the housing interior for the elderly. They have argued that under the trend of population aging, the housing design adapted to the elderly’s living mode and life characteristics should be included in the scope of the investigation to solve the comfort problem of the elderly’s daily living environment [16]. Implementing a barrier-free treatment of the interior of the house accounting for the living habits of the elderly, adding living facilities, integrating the interior space, and improving the functions of the existing housing could increase the housing adaptability and can improve the elderly’s ability to live independently and thus accomplish the age-friendly regeneration of the interior of the housing [17]. In terms of community public facilities, it is essential to configure compact living service facilities, adjacent open spaces, and systematic community transportation structures so that the elderly can obtain convenient services, sufficient activity space, and reliable travel conditions in their daily life [18,19]. Based on the concept of environmental safety, applicability, convenience, comfort, and attribution, the community public space is designed to be conducive to the health of the elderly, enhancing their independent living ability and enthusiasm for social participation [20].
In terms of the impact of the community environment, the barrier-free environment inside the residence has been proved by some studies to affect the living safety and life quality of the elderly. After retirement, elderly people have more free time and are more dependent on housing. As a part of situated lifelong narratives and emotional sustenance for the elderly, housing has a profound impact on their physical health, life quality and enthusiasm to participate in society [21,22,23]. It is necessary to place the elderly in special housing to support them to enjoy their old age in peace [24]. Under the dual factors of declining physical function and an unsuitable housing environment, the risk of falls increases with the age of the elderly [25]. In particular, the elderly with a higher risk of falling who live alone or have physical barriers will face more mobility restrictions and home hazards [26,27]. The light environment directly affects people’s comfort and physical and mental health. Previous experiments have shown that light rich in blue light could reduce the anxiety of the elderly to a certain extent [28]. Different air quality also has a significant impact on the health of the elderly. Indoor air pollution is not conducive to the health of the elderly’s respiratory system, causing diseases, proving that timely natural ventilation is necessary [29,30]. Thermal comfort should also be considered, as high indoor temperature also causes greater damage to the health of the elderly, increasing the occurrence of discomfort symptoms such as thirst, sleep disorder, and excessive sweating [31]. Therefore, reasonable space planning and objective infrastructure facilities can improve the health and quality of life of the elderly [32]. The special design and renovation of living spaces, such as bedrooms and bathrooms, are of positive significance to the daily activities and physiological needs of the elderly [2,33]. The absence of a grab rail and anon-slip mat, and the distance to the toilet may increase the probability of injury in the elderly at home [34]. It is conducive to the physical and mental health and social relations of the elderly to increase lighting, the spaciousness of passageways, and the accessibility of furniture [35].
In addition, aging leads to the narrowing of the scope of daily activities of the elderly, thus the safety and perfection of the connecting passage between the residence and the outdoors affect the health and happiness of the elderly. Stairs, corridors, and step differences have been shown to increase the risk of falls in the elderly, while ramps enable physical activity [33,36]. It is recommended to install elevators to improve the travel safety of the elderly, which is beneficial to health [11]. Dense bus stops, easily accessible commercial facilities, traffic greenways for the elderly, and sufficient green spaces may effectively promote walking among the elderly, thus promoting their health as well [1,37]. However, some scholars have pointed out that younger elderly people may have good physical functions, so that they do not care about the obstacles in the living environment [38].
In summary, the existing relevant research has mainly focused on the construction and expansion of the meaning of an age-friendly community and the influence of some adaptive aging support systems in communities. However, the studies on the infrastructure facilities of the residential interior space are relatively insufficient, while the evidence for the impact of the outdoor environment and community service is well known [39]. In the daily life of the elderly, small-scale and specific infrastructure facilities are used most frequently in toilets, bathrooms, and kitchens almost every day to complete the most basic survival needs. At the same time, most elderly use elevators or stairs to go out or back home. Therefore, an elevator is one of the community facilities with the largest demand among the elderly.
Given the limitations of existing research, it is necessary to explore the influence of these age-friendly community infrastructure facilities on the health of the elderly. More importantly, in view of the practical problem mentioned at the beginning of this paper, that is, the serious lack of age-friendly infrastructure facilities in existing communities in China, it is urgent to assess the impact of various facilities to determine the priority of reconstruction under limited resources. Therefore, we selected the most typical community infrastructure facilities, namely, an elevator, a bathing facility, a kitchen gas supply, and a toilet type, as the research objects and explored their influence on the health of the elderly through propensity score matching. The importance of equipping and optimizing age-friendly infrastructure facilities in communities was quantitatively described. We aimed to show the urgency for age-friendly regeneration and point out the direction for it, contributing to active and healthy aging.

2. Materials and Methods

2.1. Data Source

The data for this study were taken from the China Health and Retirement Longitudinal Study (CHARLS) database, which was initiated by the National School of Development at Peking University. It is a high-quality health survey database of the middle-aged and elderly aged 45 years and above in China. The CHARLS national baseline survey started in 2011 and is tracked every two years. The survey covers 150 counties and 450 villages (communities) in 28 provinces (autonomous regions, municipalities directly under the Central Government) in China. The design of the questionnaire adopts multistage probability proportionate to size sampling, which is highly representative.
The database for 2018 was selected, excluding the samples with ages less than 48 years, 24 h of real sleep time, and missing key variables. Because the study aimed to discuss whether to install elevators live in buildings, it was necessary to delete the samples of the elderly living in bungalows for that aspect, while the discussion of bathrooms, kitchens, and toilets involved all the elderly. Consequently, in this study, the independent variable ‘elevator’ was extracted separately and sorted into the No. 1 database; while ‘bathing facility’, ‘kitchen gas supply’, and ‘toilet type’ were assembled into the No. 2 database. The sample sizes of the two databases were 1963 groups and 3950 groups respectively.

2.2. Variable Selection

2.2.1. Dependent Variables

Self-rated health (SRH) assessments comprehensively considered the health status of the elderly in this study, and the method truly reflected the psychological and physiological health level of the elderly along with the individual differentiated intergenerational support needs. In addition, this study selected 12 items of activities of daily living (ADL) to measure the health level of the elderly, such as: “Because of health and memory problems, do you have any difficulty with dressing?” No difficulty was assigned a value of 1, and other options were assigned a value of 0. The variable ADL was generated by adding the obtained values. The higher the score of ADL, the stronger the ability of the elderly to perform daily activities.

2.2.2. Independent Variables

The independent variables selected in this study were elevator, bathing facility, kitchen gas supply, and toilet type. For the elevator, the question was whether there was an elevator installed. If there was an elevator, the value was 1, and if there was no elevator, the value was 0. For the bathing facility, the question was: “Is there an in-house shower or bath facility? If yes, then what type?” We assigned the options “Concentration supply of hot water” and “Water heater installed by the household” to have bathing facilities, and the value was 1. If the answer was “No”, the value was 0. For the kitchen gas supply, the question item was: “Does your residence have coal gas or natural gas supply?”. If yes, the value was 1; otherwise, the value was 0. For the toilet type, the question item was “What is the type of toilet? Is it with or without a seat?” The selection of sitting type was assigned a value of 1, and the selection of squatting type was assigned a value of 0.

2.2.3. Covariates

Covariates were similar to the control variables in the general regression model. Generally, during propensity score matching, it is critical to select covariates that may affect both dependent and independent variables, and these variables should be as close to exogenous as possible. Referring to the experience of variable selection in previous literature, we selected the covariates of this study from two aspects, namely, the personal characteristics and family status of the elderly. Gender, age, residential category, education level, marital status, real sleep time, exercise score, disability score, chronic disease score, family expenditure, and family members were selected as the covariates. Among them, we used 2018 to subtract the date of birth to generate the age variable. The exercise score was divided into three grades according to the intensity in the database, and then, the amount of exercise of three kinds of intensities for the elderly was estimated every week. We assigned the value of exercise to 1 and the value of no exercise to 0. The sum of the three generated the exercise score variable. The highest value was 3, and the lowest value was 0. The higher the score, the greater the amount of exercise per week. For the disability score, we selected whether there were problems in the body, brain (dementia), eyes (blindness), ears (deafness), mouth (muteness), and other aspects to assign values. If there was a certain type of disability, the assigned value was 1, and if there was no disability, the assigned value was 0. The variable disability score was generated by adding, with a maximum value of 5 and the minimum value of 0. The higher the score, the more disabilities the elderly had. The number of family members was not reflected in the database directly, so we posed the question: “How many people in your family ate in the last week, excluding guests?” to determine the number of family members.
The specific assignment of variables is shown in Table 1, and the descriptive statistics of variables are shown in Table 2.
To accurately explain the distribution of the number of dependent variables in the database, SRH and ADL were classified. The SRH scores of 1 and 2 were classified as low, 3 as medium, and 4 and 5 as high. Those with an ADL score of 0–3 were classified as being at a low health level, 4–9 as being at a medium health level, and 10–12 as being at a high health level. The specific population distribution is shown in Figure 1 and Figure 2.
From Table 2, it is evident that most of the elderly’s SRH scores were at a medium level, while most of the elderly’s ADL scores were at a relatively high level, which indicated that the real physical condition of the elderly was better than what they thought. Most homes of the elderly were equipped with bathing facilities, while the elevator and kitchen gas supply were available to relatively few, and most toilets were the squatting type. The education level of the elderly was generally low. Most of the elderly were in good marital status, and most did a moderate level of exercise, while the elderly with disabilities or chronic diseases were in the minority. Regarding the economic situation, the average monthly family expenditure of the elderly was about 2000 yuan. Each family had three members on average.

2.3. Research Methodology

We used the method of propensity score matching to quantitatively analyze the influence of aging infrastructure facilities in communities on the health of the elderly. Propensity score matching was first proposed by Paul Rosenbaum and Donald Rubin in 1983 [40]. It is a statistical method mainly used to process the data from observation research and conduct research. To assess the influence of certain factors, we usually set up a treatment group and a control group for comparison. However, in many social science studies, it is difficult to set up randomized grouping experiments, and most of them are based on observation and quasi-experiment. Therefore, researchers often use observational data rather than randomized controlled trial data. This ultimately leads to the observation that research cannot be based on the role of the large number theorem to weaken the influence of hybrid variables between the treatment group and the control group. Because of the existence of selection bias and a counterfactual framework, it is easy to generate systematic bias. Propensity score matching is used to adjust the observation data, reduce the influence of data bias and confounding variables, and eliminate the interference factors between groups to make a more reasonable comparison between the treatment group and the control group.
Propensity score matching (PSM) aims to reduce the impact of data bias and confounding variables. As the method can eliminate the errors caused by the improper setting of the model function form and does not require the explanatory variables to be strictly exogenous, it also has advantages in solving endogenous problems. In view of the high matching degree between propensity score matching and the research content of this study, as well as its wide application and good effect in public health, medicine, economics and other fields, this method was selected in this study. The specific steps are shown in Figure 3.
In step 1, we selected the covariates affecting the elderly health including personal characteristics and family status. Then the logit model was applied to calculate the propensity score value for the individual.
In step 2, three methods for score matching were selected: nearest neighbor matching (NNM), radius matching (RM), and kernel matching (KM). Nearest neighbor matching finds the object closest to the score of the treatment group sample forward or backward in the control group sample based on the tendency score and forming a pair. The Radius matching matches the samples whose difference between the scores in the treatment group and the scores in the control group is within the set constant r. Kernel matching matches the sample of the treatment group with the estimated effect. The estimated effect is obtained by the weighted average of the individual scores of the treatment group and the scores of all samples of the control group. The weight is calculated by the kernel function.
In step 3, the overlap test and balance test were carried out to assess the quality and accuracy of the matching.
In step 4, the average treatment effect on treatment group, that is, the average change in the health status of the elderly in the treatment group, was calculated to test the influence of the age-friendly community infrastructure facilities on the health of the elderly.

3. Results

3.1. Overlap Test

Through the nuclear density map of the infrastructure facilities in each community, as shown in Figure 4, we intuitively found that the probability densities of the treatment group and the control group were relatively close after the samples were matched. The common support area was wide, which indicated that most sample values were within the common value range, and the two groups of samples had good matching quality.

3.2. Balance Test

As shown in Table 3, t-tests were conducted on the mean values of variables. From the test results, most variables in the treatment group and the control group had significant t-values before matching, indicating that the elderly with different personal characteristics and family status, including age, education level, marital status, real sleep time, exercise score, disability score, chronic disease score, monthly family expenditure, and the number of family members, lived in communities with different levels of age-friendly infrastructure facilities. However, after matching, the t-values became insignificant, indicating that there existed no systematic difference in the covariates between the matched treatment group and the matched control group.

3.3. Average Treatment Effect on Treatment Group

We measured and calculated the average treatment effects of different age-friendly community infrastructure facilities (elevator, bathing facility, kitchen gas supply, and toilet type) on the elderly’s SRH and ADL using NNM, RM, and KM.
The results, summarized in Table 4 and Table 5, show that the measurements obtained by the three matching methods were basically the same, indicating that the sample data in this study were highly robust. Theoretically, we found no significant difference between the variables of the samples in the processing group and the control group after matching. The only difference was whether the age-friendly community infrastructure facilities were installed. Then, the difference in the average treatment effect between the treatment group and the control group (ATT) was calculated. If ATT was significant, it indicated that age-friendly infrastructure facilities would influence the health of the elderly.
The higher the score of SRH and ADL, the better the physical condition of the elderly. According to the results in Table 4 and Table 5, the four age-friendly infrastructure facilities would affect not only the elderly’s SRH but also the ADL. However, the impact of the infrastructure facilities on SRH and ADL of the elderly was different. On the one hand, it proved the robustness of the data in this study. On the other hand, it more comprehensively demonstrated the impact of age-friendly infrastructure facilities for the elderly on the health from both subjective and objective perspectives. Specifically, the order of impact degree on SRH was noted as elevator (ATT = 0.178) > toilet type (ATT = 0.163) > kitchen gas supply (ATT = 0.139) > bathing facility (ATT = 0.122), while the order of impact degree on ADL was found to be bathing facility (ATT = 0.527) > toilet type (ATT = 0.504) > elevator (ATT = 0.386) > kitchen gas supply (ATT = 0.384).

4. Discussion

Based on the analysis of the average treatment effect on treatment group, we can conclude that the four age-friendly infrastructure facilities in the communities have positive influence on the SRH of the elderly. Among them, the elevator has the greatest impact, and the bathing facility has the smallest impact. With age increasing, the joint synovial fluid of the elderly decreases due to the degenerative changes of the tissues in the joints and the atrophy of the synovium. This will cause abnormal friction inside the knee joint, causing pain, deformation, and swelling of the knee, placing the elderly at risk of falling [41]. All these knee joint changes and possible related diseases will limit the walking function of the elderly especially when going up and down stairs, the knee joint bears more strength than walking on a flat road, which aggravates the wear of the knee [42]. On the other hand, many old people report that a lack of access to an elevator seriously affects their daily lives, and they are likely to greatly reduce the frequency of going out in order to avoid the trouble of going up and down the stairs [12,43]. Therefore, lack of access to an elevator may trap the elderly at home, counting against the proper exercise and social participation, which greatly reduces the physical and mental health of the elderly [6].
From the results presented in Table 5, we can see that each age-friendly infrastructure facility in the communities positively influenced the ADL score of the elderly, and the ranking of influence degree was bathing facility > toilet type > elevator > kitchen gas supply. Among them, the degree of influence of elevator (ATT = 0.386) and kitchen (ATT = 0.384) is almost the same, far less than that of bathing facility (ATT = 0.527) and toilet type (ATT = 0.504). As shown in previous studies, bathrooms and toilets are common places for falls and injuries among the elderly [36,44]. If the home lacks a bathing facility, the elderly will have to take a bath with a basin or go to a public bathhouse. This will not only bring inconvenience to the daily life of the elderly, but also increase the risk of falling over because of the lack of barrier-free special facilities for bathing, such as safety armrests, bath seats, and anti-skid tiles [45]. An in-house bathing facility will increase the frequency of bathing for the elderly and enable them to carry out barrier-free regeneration according to their own needs more freely, which will help improve sleep, improve cognitive health, and reduce the incidence rate of cardiovascular diseases [5]. When it comes to the impact of toilet style on ADL of the elderly, most elderly people have insufficient muscle strength and joint injuries, and it is easy to induce temporary cerebral ischemia when they stand up after squatting for a long time. Therefore, the use of squatting toilets by the elderly will not only increase the burden on knee joints but also lead to dizziness, falls, and sudden cardiovascular diseases [46]. It is added that sitting toilets are conducive to cultivating the health awareness and health behavior of the elderly as well [5]. Consequently, it is clear that toilet type greatly affects the health of the elderly from both long-term and short-term perspectives. When the conditions for installing all age-friendly community infrastructure facilities are not available, the installation sequence shall be arranged corresponding to the influence of different infrastructure facilities on the health of the elderly.
The comparative analyses in Table 4 and Table 5 shows that the impact of age-friendly infrastructure facilities on SRH and ADL of the elderly differs. This can be attributed to the different focus of SRH and ADL, that is, compared with SRH mixed with subjective thoughts, ADL can more accurately and objectively measure the actual physical health of the elderly. The reverse order of the influence of the bathing facility in SRH and ADL may be because the data for SRH is relatively vulnerable to subjective factors, reflecting that the elderly do not pay much attention to whether they have bathing facilities at home, while the data for ADL is relatively objective, which proves that bathing facility has a high influence on the health of the elderly. It is suggested that the government should not only focus on the subjective and obvious needs of the elderly but also pay attention to the objective indoor facilities that affect the elderly health such as bathing facilities when promoting age-friendly regeneration. It was found that the impact of elevators on SRH of the elderly ranks higher than that of ADL, which may reflect the strong will of the elderly themselves to get exposure to the outdoor environment, enrich their daily activities, and integrate into society [47]. The influence of the toilet type on the health of the elderly was noted to be relatively stable, which indicates that the influence of the age-friendly regeneration of toilets on the health of the elderly exists and has been paid attention to by the elderly. Comparatively speaking, the effect of the age-friendly regeneration of the kitchen on the health of the elderly is relatively weak although it cannot be ignored. Therefore, under the condition of limited resources, priority should be given to the improvement of toilet style rather than the increase of kitchen gas supply in the age-friendly regeneration.
Another point that needs to be clarified is that, whether SRH or ADL is taken as the standard, the elevator has a positive impact on the elderly at high health levels. This is inconsistent with some previous research conclusions, which were that the stairs in a building are not obstacles for the healthy elderly without disabilities but can provide a good opportunity for the elderly to maintain physical activity and function on the contrary [48,49]. This may be because, with the development of society, the elderly have more scientific and interesting outdoor exercise activities to choose from, instead of the less suitable daily exercise up and down stairs. On the other hand, outdoor activities can improve the health of the elderly, relatively, the elderly with better health also prefer to go out for activities [50,51]. Consequently, healthier elderly people may need to use elevators more frequently. However, the elderly in relatively poorer health condition may tend to spend most of their time at home. Therefore, in terms of the community with a low overall health level of the elderly, the managers and designers should first meet the regeneration of the interior of the residence such as adding bathing facilities, changing toilet style and providing kitchen gas supply.
Besides, the balance test, as an auxiliary step of this study, also shows the existing tendency of the four age-friendly infrastructure facilities in the living environment of the elderly with different characteristics such as age, education level, real sleep time, chronic disease, disability, marital status, etc. For example, the communities where most elderly of venerable age live lack elevators in buildings and lack bathing facilities and kitchen gas supply at home. Therefore, the needs for elevators, bathing facilities and kitchen gas supply should be a priority in the age-friendly regeneration of these communities. In terms of family status, it is necessary to conduct a multi-dimensional regeneration needs assessment on the family situation of the elderly, including family members, marital status, and family economic status, whether there is a temporary residence during the regeneration, demand content, regeneration willingness, regeneration cost expectation, etc. It will help to obtain the key needs of the elderly and point toward the direction of aging adaptation. As an example, we should focus on elderly families with difficult economic conditions, who often live in communities with inadequate age-friendly infrastructure and have a low ability to reform at their own expense. It is possible to establish a special evaluation system for elderly families suitable for age-friendly regeneration, provide different degrees of help and regeneration design direction for different elderly families, and provide a part or all the financial subsidies for elderly families with poor economic situations and urgent needs for regeneration.

5. Conclusions

Population aging poses a major challenge to personal and social health. How to promote active aging by promoting the health and well-being of the elderly is a crucial topic. This study further explored the relationship between age-friendly community infrastructure facilities and the health of the elderly through the propensity score matching method, using the survey data of CHARLS in 2018. The study found whether an elevator was installed in the community, whether a bathing facility was installed in the bathroom, whether there was a gas or natural gas supply in the kitchen, and whether the toilet was sitting type, all had an influence on the health of the elderly. The influence of different infrastructure facilities on the health of the elderly was found to differ. In the more subjective SRH, the ranking of impact degree was found to be elevator > toilet type > kitchen gas supply > bathing facility. In the more objective ADL, the ranking was bathing facility > toilet type > elevator > kitchen gas supply, and the impact of bathing facility and toilet type was very close, far greater than that of elevator and kitchen gas supply. It may be that the elderly have different subjective needs and concerns for each infrastructure facility, which leads to the difference. The balance test results also reflected that the elderly with different personal and family status lived in communities with different levels of age-friendly infrastructure. Through the study on the influence of age-friendly infrastructure on the health of the elderly, the effect of age-friendly regeneration in communities was tested to a certain extent. The potential needs of different elderly people for different age-friendly regeneration of infrastructure facilities were also explored. Only by effectively matching the internal demand characteristics of residential areas with the external resources can age-friendly regeneration bring greater social benefits.
Finally, we suggest that the government should make reasonable and specific policy standard segmentation for different types of the elderly and their families and that enterprises product safer and healthier age-friendly community infrastructure facilities, responding to the existing needs of the elderly. We hope that the study results will improve the understanding and attention of all social parties on the age-friendly regeneration and stimulate them to participate in it together so that more elderly can have a healthier living and living environment.
However, this study also has some limitations. Owing to the limited time and energy, we only selected the data from one year in the CHARLS database for research, and the research data are only available in China. The conclusions drawn from this may vary in countries or regions with different cultural and historical backgrounds and social development conditions. In future research, we can further expand the sample scope and explore the differences and reasons for conclusions in different time points and regions. In addition, this study only examined the influence of four typical age-friendly community infrastructure facilities. In the future, the scope of infrastructure can be expanded, especially the effects of small facilities, such as walking aids, handrails, bedside railings, and emergency call devices, for the elderly.

Author Contributions

Conceptualization, Y.M.; methodology, R.D.; software, Z.Z.; formal analysis, Q.C.; data curation, Y.S. and K.W.; writing—original draft preparation, Q.C. and Z.Z.; writing—review and editing, Y.M. and Q.C.; project administration, Y.H.; funding acquisition, Y.M. All authors have read and agreed to the published version of the manuscript.

Funding

This work is funded by the Center for Balance Architecture, Zhejiang University (Project No.20203512-28C) and the MOE Youth Funded Project of Humanities and Social Sciences (Project No.20YJC840008).

Data Availability Statement

The data that support the findings of this study are openly available in the China Health and Retirement Longitudinal Study at: http://charls.pku.edu.cn/ (accessed on 21 January 2023). The study design and protocol were approved by the Biomedical Ethics Review Committee of Peking University (IRB00001052-11015). Each participant was voluntary, who was informed of the study objective and context and signed written informed consent.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Yu, S.; Liu, Y.; Cui, C.; Xia, B. Influence of outdoor living environment on elders’ quality of life in old residential communities. Sustainability 2019, 11, 6638. [Google Scholar] [CrossRef] [Green Version]
  2. Li, M.; Cai, Q.; Li, C.; Wu, X.; Wang, T.; Xu, J.; Wu, Z. A Study in Bedroom Living Environment Preferences of the Urban Elderly in China. Sustainability 2022, 14, 13552. [Google Scholar] [CrossRef]
  3. Mulliner, E.; Riley, M.; Maliene, V. Older people’s preferences for housing and environment characteristics. Sustainability 2020, 12, 5732. [Google Scholar] [CrossRef]
  4. Means, R. Safe as houses? Ageing in place and vulnerable older people in the UK. Soc. Policy Admin. 2007, 41, 65–85. [Google Scholar] [CrossRef]
  5. Gao, Y.; Wang, Y.; Rao, T. The Effect of the Dwelling Environment on Rural Elderly Cognition: Empirical Evidence from China. Sustainability 2022, 14, 16387. [Google Scholar] [CrossRef]
  6. Chu, Y.; Shen, S. Adoption of Major Housing Adaptation Policy Innovation for Older Adults by Provincial Governments in China: The Case of Existing Multifamily Dwelling Elevator Retrofit Projects. Int. J. Environ. Res. Public Health 2022, 19, 6124. [Google Scholar] [CrossRef]
  7. Jiang, N.; Lou, V.W.Q.; Lu, N. Does social capital influence preferences for aging in place? Evidence from urban China. Aging Ment. Health 2016, 22, 405–411. [Google Scholar] [CrossRef]
  8. Fänge, A.; Iwarsson, S. Changes in ADL Dependence and aspects of usability following housing adaptation—A longitudinal perspective. Am. J. Occup. Ther. 2005, 59, 296–304. [Google Scholar] [CrossRef] [Green Version]
  9. Lamb, S.E.; Jørstad-Stein, E.C.; Hauer, K.; Becker, C. Development of a common outcome data set for fall injury prevention trials: The prevention of falls network Europe consensus. J. Am. Geriatr. Soc. 2005, 53, 1618–1622. [Google Scholar] [CrossRef]
  10. Buffel, T.; Phillipson, C. Can global cities be ‘age-friendly cities’? Urban development and ageing populations. Cities 2016, 55, 94–100. [Google Scholar] [CrossRef]
  11. Lyu, Y.; Forsyth, A.; Worthington, S. Built Environment and Self-Rated Health: Comparing Young, Middle-Aged, and Older People in Chengdu, China. HERD: Health Environ. Res. Des. J. 2021, 14, 229–246. [Google Scholar] [CrossRef] [PubMed]
  12. Chu, Y. City Government’s Adoption of Housing Adaptation Policy Innovation for Older Adults: Evidence From China. J. Gerontol. B 2022, 77, 429–434. [Google Scholar] [CrossRef] [PubMed]
  13. Cesaroni, G.; Badaloni, C.; Romano, V.; Donato, E.; Perucci, C.A.; Forastiere, F. Socioeconomic position and health status of people who live near busy roads: The Rome Longitudinal Study (RoLS). Environ. Health 2010, 9, 41. [Google Scholar] [CrossRef] [Green Version]
  14. Kemperman, A.; Timmermans, H. Green spaces in the direct living environment and social contacts of the aging population. Landsc. Urban Plann. 2014, 129, 44–54. [Google Scholar] [CrossRef]
  15. Xia, T.; Nitschke, M.; Zhang, Y.; Shah, P.; Crabb, S.; Hansen, A. Traffic-related air pollution and health co-benefits of alternative transport in Adelaide, South Australia. Environ. Int. 2015, 74, 281–290. [Google Scholar] [CrossRef] [PubMed]
  16. Fadda, G.; Cortes, A.; Olivi, A.; Tovar, M. The perception of the values of urban space by senior citizens of Valparaiso. J. Aging Stud. 2010, 24, 344–357. [Google Scholar] [CrossRef]
  17. Collerton, J.; Kingston, A.; Bond, J.; Davies, K.; Eccles, M.P.; Jagger, C.; Kirkwood, T.B.L.; Newton, J.L. The personal and health service impact of falls in 85 year olds: Cross-sectional findings from the Newcastle 85+ cohort study. PLoS ONE 2012, 7, e33078. [Google Scholar] [CrossRef]
  18. Choi, Y.J. Understanding Aging in Place: Home and community features, perceived age-friendliness of community, and intention toward aging in place. Gerontologist 2022, 62, 46–55. [Google Scholar] [CrossRef]
  19. Lee, C.; Zhu, X.; Lane, A.P.; Portegijs, E. Editorial: Healthy aging and the community environment. Front. Public Health. 2021, 9, 737955. [Google Scholar] [CrossRef]
  20. Campbell, N. Designing for social needs to support aging in place within continuing care retirement communities. J. Hous. Built Environ. 2015, 30, 645–665. [Google Scholar] [CrossRef]
  21. Bowling, A.; Stafford, M. How do objective and subjective assessments of neighbourhood influence social and physical functioning in older age? Findings from a British survey of ageing. Social Sci. Med. 2007, 64, 2533–2549. [Google Scholar] [CrossRef] [PubMed]
  22. Gobbens, R.J.J.; van Assen, M.A.L.M. Associations of Environmental Factors With Quality of Life in Older Adults. Gerontologist 2018, 58, 101–110. [Google Scholar] [CrossRef] [PubMed]
  23. Severinsen, C.; Breheny, M.; Stephens, C. Ageing in Unsuitable Places. Housing Stud. 2016, 31, 714–728. [Google Scholar] [CrossRef]
  24. Forsyth, A.; Molinsky, J.; Kan, H.Y. Improving housing and neighborhoods for the vulnerable: Older people, small households, urban design, and planning. Urban Des. Int. 2019, 24, 171–186. [Google Scholar] [CrossRef] [Green Version]
  25. Timsina, L.R.; Willetts, J.L.; Brennan, M.J.; Marucci-Wellman, H.; Lombardi, D.A.; Courtney, T.K.; Verma, S.K. Circumstances of fall-related injuries by age and gender among community-dwelling adults in the United States. PLoS ONE 2017, 12, e0176561. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  26. Ekbrand, H.; Ekman, R.; Thodelius, C.; Moller, M. Fall-related injuries for three ages groups—Analysis of Swedish registry data 1999–2013. J. Saf. Res. 2020, 70, 143–152. [Google Scholar] [CrossRef] [PubMed]
  27. Pettersson, C.; Malmqvist, I.; Gromark, S.; Wijk, H. Enablers and Barriers in the Physical Environment of Care for Older People in Ordinary Housing: A Scoping Review. J. Aging Environ. 2020, 34, 332–350. [Google Scholar] [CrossRef] [Green Version]
  28. Hopkins, S.; Morgan, P.L.; Schlangen, L.J.M.; Williams, P.; Skene, D.J.; Middleton, B. Blue-enriched lighting for older people living in care homes: Effect on activity, actigraphic sleep, mood and alertness. Curr. Alzheimer Res. 2017, 14, 1053–1062. [Google Scholar] [CrossRef]
  29. Almeida-Silva, M.; Wolterbeek, H.T.; Almeida, S.M. Elderly exposure to indoor air pollutants. Atmos. Environ. 2014, 85, 54–63. [Google Scholar] [CrossRef]
  30. Fisk, W.J.; Singer, B.C.; Chan, W.R. Association of residential energy efficiency retrofits with indoor environmental quality, comfort, and health: A review of empirical data. Build. Environ. 2020, 180, 107067. [Google Scholar] [CrossRef]
  31. Van Loenhout, J.A.F.; le Grand, A.; Duijm, F.; Greven, F.; Vink, N.M.; Hoek, G.; Zuurbier, M. The effect of high indoor temperatures on self-perceived health of elderly persons. Environ. Res. 2016, 146, 27–34. [Google Scholar] [CrossRef] [PubMed]
  32. Leung, M.; Yu, J.; Chow, H. Impact of indoor facilities management on the quality of life of the elderly in public housing. Facilities 2016, 34, 564–579. [Google Scholar] [CrossRef]
  33. Thordardottir, B.; Fänge, A.M.; Chiatti, C.; Ekstam, L. Participation in Everyday Life Before and After a Housing Adaptation. J. Aging Environ. 2020, 34, 175–189. [Google Scholar] [CrossRef]
  34. Romli, M.H.; Tan, M.P.; Mackenzie, L.; Lovarini, M.; Kamaruzzaman, S.B.; Clemson, L. Factors associated with home hazards: Findings from the Malaysian Elders Longitudinal Research study: Factors associated with home hazards. Geriatr. Gerontol. Int. 2018, 18, 387–395. [Google Scholar] [CrossRef] [PubMed]
  35. Leung, M.Y.; Famakin, I.; Kwok, T. Relationships between indoor facilities management components and elderly people’s quality of life: A study of private domestic buildings. Habitat Int. 2017, 66, 13–23. [Google Scholar] [CrossRef]
  36. Mahmoodabad, S.S.M.; Zareipour, M.; Askarishahi, M.; Beigomi, A. Effect of the Living Environment on falls among the Elderly in Urmia. Open Access Maced. J. Med. Sci. 2018, 6, 2233. [Google Scholar] [CrossRef] [Green Version]
  37. Ottoni, C.A.; Sims-Gould, J.; Winters, M. Safety perceptions of older adults on an urban greenway: Interplay of the social and built environment. Health Place 2021, 70, 102605. [Google Scholar] [CrossRef]
  38. Kylén, M.; Ekström, H.; Haak, M.; Elmståhl, S.; Iwarsson, S. Home and Health in the Third Age—Methodological Background and Descriptive Findings. Int. J. Environ. Res. Public Health 2014, 11, 7060–7080. [Google Scholar] [CrossRef] [Green Version]
  39. Azim, F.T.; Ariza-Vega, P.; Gardiner, P.A.; Ashe, M.C. Indoor Built Environment and Older Adults’ Activity: A Systematic Review. Can. J. Aging 2022, 1–18. [Google Scholar] [CrossRef]
  40. Rosenbaum, P.R.; Rubin, D.B. The central role of the propensity score in observational studies for causal effects. J. Biometrika 1983, 70, 1–10. [Google Scholar] [CrossRef]
  41. Ippersiel, P.; Robbins, S.M.; Dixon, P.C. Lower-limb coordination and variability during gait: The effects of age and walking surface. Gait Posture 2021, 85, 251–257. [Google Scholar] [CrossRef] [PubMed]
  42. Shen, P.; Li, L.; Song, Q.; Sun, W.; Zhang, C.; Fong, D.T.P.; Mao, D. Proprioceptive Neuromuscular Facilitation Improves Symptoms Among Older Adults With Knee Osteoarthritis During Stair Ascending: A Randomized Controlled Trial. Am. J. Phys. Med. Rehabil. 2022, 101, 753–760. [Google Scholar] [CrossRef] [PubMed]
  43. Yu, Y.; Chen, Z.; Bu, J.; Zhang, Q. Do Stairs Inhibit Seniors Who Live on Upper Floors From Going Out? HERD Health Environ. Res. Des. J. 2020, 13, 128–143. [Google Scholar] [CrossRef] [PubMed]
  44. Wang, K.; Chen, M.; Zhang, X.; Zhang, L.; Chang, C.; Tian, Y.; Wang, X.; Li, Z.; Ji, Y. The Incidence of Falls and Related Factors among Chinese Elderly Community Residents in Six Provinces. Res. Public Health 2022, 19, 14843. [Google Scholar] [CrossRef]
  45. Whitehead, P.J.; Golding-Day, M.R. The lived experience of bathing adaptations in the homes of older adults and their carers (BATH-OUT): A qualitative interview study. Health Soc. Care Community 2019, 27, 1534–1543. [Google Scholar] [CrossRef] [Green Version]
  46. Fang, M.; Mirutse, G.; Guo, L.; Ma, X. Role of socioeconomic status and housing conditions in geriatric depression in rural China: A cross-sectional study. BMJ Open 2019, 9, e024046. [Google Scholar] [CrossRef] [Green Version]
  47. Palmberg, L.; Portegijs, E.; Rantanen, T.; Aartolahti, E.; Viljanen, A.; Hirvensalo, M.; Rantakokko, M. Neighborhood Mobility and Unmet Physical Activity Need in Old Age: A 2-Year Follow-Up. J. Aging Phys. Act. 2020, 28, 442–447. [Google Scholar] [CrossRef] [Green Version]
  48. Ekström, H.; Schmidt, S.M.; Iwarsson, S. Home and health among different sub-groups of the ageing population: A comparison of two cohorts living in ordinary housing in Sweden. BMC Geriatr. 2016, 16, 90. [Google Scholar] [CrossRef] [Green Version]
  49. Tomioka, K.; Kurumatani, N.; Hosoi, H. Association between stairs in the home and instrumental activities of daily living among community-dwelling older adults. BMC Geriatr. 2018, 18, 132. [Google Scholar] [CrossRef]
  50. Matud, M.P.; Díaz, A.U. Gender, exercise, and health: A life-course cross-sectional study. J. Nurs. Health Sci. 2020, 22, 812–821. [Google Scholar] [CrossRef]
  51. Li, Y.; Liu, L.; Diao, Y.; Dong, H. Using a Service Ontology to Understand What Influences Older Adults’ Outdoor Physical Activities in Nanjing. J. Aging Phys. Act. 2022, 1–10. [Google Scholar] [CrossRef] [PubMed]
Buildings 13 00341 g001 550
Figure 1. Health distribution of the No. 1 database samples.
Figure 1. Health distribution of the No. 1 database samples.
Buildings 13 00341 g001
Buildings 13 00341 g002 550
Figure 2. Health distribution of the No. 2 database samples.
Figure 2. Health distribution of the No. 2 database samples.
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Buildings 13 00341 g003 550
Figure 3. Research steps of PSM.
Figure 3. Research steps of PSM.
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Buildings 13 00341 g004 550
Figure 4. Kernel density.
Figure 4. Kernel density.
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Table
Table 1. The assignment of the variables.
Table 1. The assignment of the variables.
VariableAssignment
Dependent variables:
SRHVery poor = 1, Poor = 2, Fair = 3, Good = 4, Very good = 5
ADLNo difficulty = 1, Otherwise = 0
Independent variables:
ElevatorYes = 1, No = 0
Bathing facilityYes = 1, No = 0
Kitchen gas supplyYes = 1, No = 0
Toilet typesitting type = 1, squatting type = 0
Covariates:
GenderMale = 1, Female = 0
Age2018 subtracts the Year of Birth
Residential categoryFamily residence = 1, Otherwise = 0
Education levelFrom illiterate to doctor degree, assign 0 to 10
Marital statusMarried living with spouse = 1, Otherwise = 0
Real sleep time0~24 (hour)
Exercise scoreYes = 1, No = 0; Added
Disease score Yes = 1, No = 0; Added
Chronic disease scoreYes = 1, No = 0; Added
Family expenditureTake its logarithm into the model
Family membersNumber of family members
Table
Table 2. Descriptive statistics of the databases.
Table 2. Descriptive statistics of the databases.
VariableThe No. 1 Database (1963 Samples)The No. 2 Database (3950 Samples)
MeanStd. Dev.Min.Max.MeanStd. Dev.Min.Max.
Dependent variables:
SRH2.8430.943152.8010.96515
ADL10.7592.13401210.4422.387012
Independent variables:
Elevator0.0900.28701----
Bathing facility----0.6320.48201
Kitchen gas supply----0.2840.45101
Toilet type----0.3420.47401
Covariates:
Gender0.1640.370010.1610.36801
Age67.5427.830489868.0187.3864898
Residential category0.9900.100010.9850.12301
Education level2.2632.071081.8911.94508
Marital status0.7330.442010.7400.43901
Real sleep time5.8372.0391155.8802.130115
Exercise score1.5740.828031.5240.86203
Disease score 0.1760.459040.1930.48304
Chronic disease score0.9001.188080.9071.180011
Family expenditure222727771580,00017872248280,000
Family members3.2381.8530153.0191.786018
Table
Table 3. Balance test.
Table 3. Balance test.
VariableMatchingElevatorBathing FacilityKitchen Gas SupplyToilet Type
D-ValuetD-ValuetD-ValuetD-Valuet
GenderUnmatched0.0250.86−0.017−1.38−0.001−0.07−0.003−0.23
Matched0.0000.000.0111.110.0010.090.0060.41
AgeUnmatched−1.533−2.49 **−2.102−8.71 ***−1.256−4.83 ***−0.366−1.48
Matched-0.124-0.14-0.265-1.280.0350.10-0.037-0.12
Residential categoryUnmatched−0.001−0.150.0061.480.0051.230.0020.50
Matched0.0030.240.0020.640.0010.20−0.001−0.28
Education levelUnmatched1.3578.46 ***0.93414.96 ***1.40321.61 ***1.05516.73 ***
Matched−0.006−0.020.0641.110.0030.03−0.018−0.22
Marital statusUnmatched0.0270.760.0422.88 ***0.0211.350.0140.92
Matched0.0180.400.0312.47−0.006−0.340.0030.15
Real sleep timeUnmatched0.3292.05 **0.0040.06−0.018−0.240.1452.03 **
Matched0.0510.270.0310.530.0060.070.0290.38
Exercise scoreUnmatched−0.073−1.110.1174.12 ***0.0140.47−0.098−3.39 ***
Matched0.0861.09−0.003−0.13−0.003−0.070.0401.25
Disease scoreUnmatched−0.045−1.24−0.099−6.25 ***−0.054−3.19 ***−0.030−1.85 *
Matched−0.006−0.12−0.006−0.46−0.001−0.080.0010.08
Chronic disease scoreUnmatched0.1781.90 *−0.051−1.300.0942.26**0.0942.38 **
Matched0.0170.120.0080.230.0000.000.0471.01
Family expenditureUnmatched0.6508.65 ***0.82325.30 ***0.77621.89 ***0.67219.75 ***
Matched0.0070.090.0552.130.0140.370.0330.88
Family membersUnmatched0.1420.970.71512.36 ***0.0881.39−0.066−1.11
Matched−0.026−0.14−0.087−1.60−0.089−1.18−0.030−0.46
*** Indicates significance at the 0.001 level; ** indicates significance at the 0.01 level, and * indicates significance at the 0.05 level.
Table
Table 4. The influence on SRH.
Table 4. The influence on SRH.
VariableMatching MethodTreatment GroupControl GroupATTStd.
Dev.		t
ElevatorNNM (k = 4)3.0512.9280.1230.0841.46
RM (r = 0.2502)3.0512.8350.2150.0732.95 ***
KM3.0512.9110.1400.0761.85 *
Mean3.0512.8730.178--
Bathing
facility		NNM (k = 4)2.8662.7780.0870.0491.78 *
RM (r = 0.2665)2.8662.6910.1750.0364.85 ***
KM2.8662.7630.1030.0452.28 **
Mean2.8662.7440.122--
Kitchen gas supplyNNM (k = 4)2.9462.8180.1280.0452.86 ***
RM (r = 0.2634)2.9462.7740.1710.0354.88 ***
KM2.9462.8270.1190.0402.94 ***
Mean2.9462.8070.139--
Toilet typeNNM (k = 4)2.9242.7800.1440.0423.43 ***
RM (r = 0.2221)2.9242.7440.1800.0345.27 ***
KM2.9242.7580.1660.0384.31 ***
Mean2.9242.7610.163--
*** Indicates significance at the 0.001 level; ** indicates significance at the 0.01 level, and * indicates significance at the 0.05 level.
Table
Table 5. The influence on ADL.
Table 5. The influence on ADL.
VariableMatching MethodTreatment GroupControl GroupATTStd.
Dev.		t
ElevatorNNM (k = 4)11.33911.0480.2910.1501.94 *
RM (r = 0.2502)11.33910.7530.5860.1264.65 ***
KM11.33911.0580.2810.1332.11 **
Mean11.33910.9530.386--
Bathing
facility		NNM (k = 4)10.75910.2960.4630.1243.73 ***
RM (r = 0.2665)10.75910.1150.6440.0917.04 ***
KM10.75910.2840.4750.1154.12 ***
Mean10.75910.2320.527--
Kitchen gas supplyNNM (k = 4)10.96810.6500.3190.1053.05 ***
RM (r = 0.2634)10.96810.4520.5170.0826.27 ***
KM10.96810.6540.3150.0973.25 ***
Mean10.96810.5850.384--
Toilet typeNNM (k = 4)10.88810.4510.4360.1014.31 ***
RM (r = 0.2221)10.89010.3120.5770.0807.20 ***
KM10.89010.3900.4990.0935.40 ***
Mean10.88910.3850.504--
*** Indicates significance at the 0.001 level; ** indicates significance at the 0.01 level, and * indicates significance at the 0.05 level.
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Chen, Q.; Zhang, Z.; Mao, Y.; Deng, R.; Shui, Y.; Wang, K.; Hu, Y. Investigating the Influence of Age-Friendly Community Infrastructure Facilities on the Health of the Elderly in China. Buildings 2023, 13, 341. https://doi.org/10.3390/buildings13020341

AMA Style

Chen Q, Zhang Z, Mao Y, Deng R, Shui Y, Wang K, Hu Y. Investigating the Influence of Age-Friendly Community Infrastructure Facilities on the Health of the Elderly in China. Buildings. 2023; 13(2):341. https://doi.org/10.3390/buildings13020341

Chicago/Turabian Style

Chen, Qingwen, Zhao Zhang, Yihua Mao, Ruyu Deng, Yueyao Shui, Kai Wang, and Yuchen Hu. 2023. "Investigating the Influence of Age-Friendly Community Infrastructure Facilities on the Health of the Elderly in China" Buildings 13, no. 2: 341. https://doi.org/10.3390/buildings13020341

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