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Article

Exploring the Influence of the Perceived Neighborhood Built Environment on the Fall Risks among Older Adults in China

by
Shichao Du
1,
Chunyu Wang
2,3,* and
Longjian Mao
2
1
Department of sociology, Fudan University, Shanghai 200433, China
2
School of Architecture and Urban Planning, Chongqing University, Chongqing 400044, China
3
Key Laboratory of New Technology for Construction of Cities in Mountain Area, Chongqing University, Chongqing 400044, China
*
Author to whom correspondence should be addressed.
Buildings 2024, 14(9), 2631; https://doi.org/10.3390/buildings14092631
Submission received: 24 July 2024 / Revised: 20 August 2024 / Accepted: 22 August 2024 / Published: 25 August 2024
(This article belongs to the Special Issue Advances of Healthy Environment Design in Urban Development)
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Abstract

:
Falls are the leading cause of accidental injury-related deaths among older adults, with approximately 50% of them occurring in the neighborhood built environment. This longitudinal study investigated the influence of the perceived built environment (PBE) on fall risks among Chinese older adults residing in neighborhoods. We utilized data from the 2018 and 2020 waves of the China Longitudinal Aging Social Survey (CLASS), comprising a sample size of 8686 respondents. A complementary log–log (cloglog) regression was used to effectively model falls because of their infrequent occurrence. The results revealed a significant U-shaped non-linear relationship between PBE and falls. As the PBE score increased from relatively low levels, there was a decrease in the probability of falls, indicating that enhancing PBE can effectively protect against fall risks. However, once an average PBE score threshold was reached (around a turning point score of 22), this association may slightly reverse. Living alone and living in urban areas are two major factors that increase the vulnerability of older adults to PBE, resulting in higher fall risks within their neighborhoods. The study enhances the understanding of how PBE affects fall risks among older adults.

1. Introduction

Falls are the leading cause of accidental injury-related deaths among older adults, with approximately 30% of individuals aged 65 and above experiencing falls annually worldwide [1]. This has become a significant global public health challenge. It is worth noting that China bears the greatest burden of fall-related injuries in the world [2,3,4]. Falls can lead to a series of physiological and psychological problems, which can decrease the quality of life in later years and increase personal, familial, and societal burdens [5]. Consequently, it is crucial to implement corresponding strategies for preventing falls among older adults.
The occurrence of falls among older adults is attributed to multiple factors, primarily classified as individual-level and environmental-level factors [6]. Extensive research has focused on the individual-level risk factors associated with falls among older adults, such as socioeconomic status, health status, and lifestyle. However, there is an increasing emphasis on the contribution of environmental factors to fall risks, particularly within the built environment [7]. Scholars worldwide have conducted relevant studies demonstrating the impact of home environments on falls among older adults [8,9,10]. Specifically, it has been found that neighborhood-dwelling older adults are predominantly exposed to both their home environment (indoor) and the neighborhood built environment (outdoor), with approximately a 50% likelihood of experiencing falls in the latter setting [11]. Nevertheless, compared with studies conducted within home environments, less attention has been given to investigating the role of the neighborhood built environment in relation to fall risks.
Meanwhile, the majority of current studies on the factors of fall risk in the neighborhood built environment primarily have relied on measuring the objective built environment (OBE). The OBE primarily refers to the physical attributes of the built environment. To evaluate it, objective methodologies such as using geographic information system (GIS) technology or conducting on-site observations by auditors are required [12]. However, some existing literature suggests that these objective measurements may not align with older adults’ subjective perceptions of their surrounding built environment [13,14]. This indicates that a model solely based on researchers’ objective audits may be insufficient. The concept of the perceived built environment (PBE) offers a new perspective. PBE mainly refers to an individual’s overall attitudes, behaviors, and satisfaction regarding their access to and utilization of the built environment [15]. We need to reveal whether and to what extent the quality of PBE can influence the fall risks of older adults. This would facilitate a closer alignment with older adults’ subjective experiences.
Most residential areas in Chinese urban areas are gated neighborhoods, which provide an interesting context for study due to their high density, fenced boundaries, and unified property management. This distinguishes them from previous studies conducted in low-density residential areas in Europe and America. Meanwhile, the residential areas in rural China, despite being relatively dispersed in planning, still adhere to the principle of centralized community management by organizations such as village committees. The top-down environmental modification approach used in both urban and rural neighborhoods in China is more feasible and efficient, contributing to prevention of falls among older adults. Furthermore, it is essential to consider various factors, such as living arrangements and gender, when examining this issue. For instance, the existing literature indicates that older adults living alone are particularly vulnerable to the risks associated with the neighborhood built environment [16,17].
To fill the knowledge gaps mentioned above, the study focused on Chinese older adults residing in neighborhoods and revealed whether and to what extent PBE influences the fall risk among them. Furthermore, we explore the heterogeneity of this influence by examining the factors that may reinforce it. We utilized data from the 2018 and 2020 waves of China Longitudinal Aging Social Survey (CLASS). This study contributes to a deeper understanding of the role played by PBE in the fall risks among older adults and provides recommendations for environmental intervention measures targeting the prevention of falls.

2. Literature Review and Research Framework

2.1. Neighborhood Built Environment and Fall Risks

There has been an increasing number of studies addressing the impact of the neighborhood built environment on falls. Studies have identified various factors within the neighborhood built environment that contribute to increased fall risks, including the quality of pavements, the design of facilities, the transportation system, and the combined effects of natural environmental factors (Table 1). Among these factors, the pavement’s quality stands out, and extensive evidence has suggested that the characteristics of the pavement, such as unevenness, slipperiness, the presence of obstacles, and the material’s composition directly influence the walking patterns of older adults. This consensus has been supported by multiple studies [5,8,18]. Inadequate facilities can also contribute to falls among older adults, frequently occurring in seating areas, stairs, utility poles, and other locations. For instance, stairs with uneven heights tend to disrupt the stability of older adults’ gait, causing them to lose balance and subsequently fall [19,20]. Furthermore, inadequate transportation systems contribute to falls due to factors such as high traffic speeds, the volume of vehicles, pedestrian density, etc. For example, congested pedestrian traffic not only increases the risk of older adults being knocked down but also compromises the stability of their gait, leading to an increased likelihood of falling [21].
In addition, the natural environment exhibits a superposition effect on the risks related to the neighborhood built environment. For instance, weather conditions that increase the probability of falls among older adults in rainy and snowy settings have been documented [22,23,24]. However, only a limited number of studies have quantitatively analyzed this phenomenon [25].
Other studies have explored potential variations in the fall risks among older adults based on gender, age, physical condition, and educational level within the context of the same neighborhood built environment. Moreover, examining how different living arrangements modulate the impact of the neighborhood built environment on fall risks among older adults represents an emerging research area. The literature suggests that older adults who live alone exhibit greater vulnerability to such hazards [26].

2.2. Perceived Built Environment

Lawton et al. (1982) proposed the person–environment (P-E) fit framework, which states that there needs to be a fit between persons (P) and their environment (E) (Figure 1) [27,28]. This implies that the environment encompasses not only OBE but also PBE, with the impact of objective environmental factors on older adults often being mediated by their subjective perceptions [29]. PBE is an essential yet frequently overlooked measure in studies investigating environmental behaviors.
The significance of PBE is further reflected by the potential inconsistency between OBE and individuals’ subjective perceptions [13], leading to a disparity in measuring the built environment’s actual impact. This discrepancy is particularly pronounced among older adults. For instance, when researchers conduct an audit of the brightness of lighting in neighborhoods at night, what they think to be sufficient lighting may still be insufficient for older adults with visual impairments. In contrast, older adults’ ratings of the neighborhood lighting’s quality obtained through questionnaires and interviews offer a more precise evaluation of the PBE’s quality and align closely with the genuine impact of built environment on older adults. De Souza Moreira et al. (2020) discovered that the subjective perception of dimmer night lighting among older adults was associated with an increased frequency of falls. However, this study’s limitations included its reliance on cross-sectional data and an analysis solely focused on falls in older adults from the previous year, which might have hindered an accurate assessment of the long-term cumulative impact of the built environment on fall risks in this population [31].
Additionally, the PBE is more closely associated with psychological effects on older adults compared with the OBE [32,33], which often manifest negatively [34,35]. For instance, a lower quality of the PBE leads to a fear of falling (FOF) among older adults, who express concerns about potential falls due to environmental factors [18,19]. Some studies have further explored the impact of the PBE on incidents of falls in older adults from a FOF perspective. For example, Lee et al. (2018) demonstrated that specific aspects of the PBE, such as adequate night lighting, enhance older adults’ perceptions of their surroundings and reduce the likelihood of experiencing FOF [36].

2.3. Research Framework

We proposed a research framework to investigate the association between the neighborhood PBE and fall risks, as illustrated in Figure 2. Prior to data analysis, we explored how to evaluate PBE at the neighborhood scale. In the existing literature, the factors of PBE primarily encompass accessibility to destinations, the absence of sidewalks, high-speed traffic, heavy traffic, neighborhood safety, land use mix, infrastructure, aesthetics, etc. [30,37,38,39]. These factors constitute the fundamental framework of the PBE. Upon these, researchers have developed assessment tools such as the Neighborhood Environment Walkability Scale (NEWS) and the Senior Walking Environmental Assessment Tool (SWEAT). These tools use questionnaires, interviews, and other methods to evaluate subjective perceptions of the built environment within the neighborhood [14,19,29].
Considering that some of the factors of the PBE at the macro- and meso-scales appear to have limited relevance to events of falling among older adults, we conducted further refinement and screening of the PBE framework from a micro-perspective. In terms of infrastructural factors, the existing literature indicated that infrastructure such as streetlights and activity spaces significantly contribute to older adults’ walkability [3,40]. Therefore, we incorporated this measure into our framework. Meanwhile, the NEWS does not specifically evaluate accessibility facilities. China is a developing country with limited awareness of the construction of accessibility facility, inadequate supervision over accessibility facilities, and plenty of neighborhoods neglecting their accessible design. These problems pose risks to the walking activities of older adults with functional impairments [41]. Consequently, in addition to infrastructural factors, we also took into account the availability of accessibility facilities.
Additionally, we redefined the aesthetic indicators of the PBE as environmental cleanness, which more closely refers to pathology. Environments with poor cleanness are more likely to contribute to physical diseases and can also lead to depression [42,43], both of which may increase the fall risk. Moreover, plenty of old neighborhoods in China have problems with cleanness [44]. Therefore, it was imperative to assess the factor of environmental cleanness.
Finally, the evaluation of the quality of the PBE lay across six dimensions: road conditions, fitness and recreational spaces, neighborhood safety, environmental cleanness, road lighting, and accessibility facilities. The questionnaire settings of the CLASS comprehensively covered all six variables identified. Detailed information regarding the questionnaire’s setup is provided in Section 3.
Data analysis was conducted on the basis of the framework of the PBE. Primarily, we hypothesized that there might be an association between the PBE and fall risks. Furthermore, we hypothesized that heterogeneity in terms of place of residence, living arrangement, gender, age group, etc., could strengthen this influence. The detailed procedures are described in Section 3.

3. Methods

3.1. Data

We utilized two waves of data (2018 and 2020) from the China Longitudinal Aging Social Survey (CLASS), a comprehensive nationwide social survey project that collects socioeconomic information on the aging population in China and their respective neighborhoods. The CLASS targets Chinese citizens aged over 60 years and has been officially implemented since 2014. The baseline wave encompassed 11,511 older adult respondents residing in 462 neighborhoods across 28 provinces or municipalities in China. Starting from 2018, the CLASS began gathering more detailed information on the neighborhood built environments, including the subjective perceptions of older adult respondents. Given our aims to examine the causal relationship between the perceived built environment and fall risks among older adults, we used panel information from CLASS, restricting our analysis to individuals who participated in both the 2018 and 2020 waves. We also excluded those who relocated residences between these observations. As the outcome variable for this study was the fall risk, individuals without walking abilities were excluded from the analysis. These data restrictions resulted in a final sample size of 8686 older adult respondents.

3.2. Measurement of Variables

3.2.1. Falling History

The respondents were asked to report any histories of falls within the past 12 months during both the 2018 and 2020 surveys. The response options included “none”, “fell once”, and “fell at least twice”. To predict fall risks in 2020 while accounting for potential reversed causality, we dichotomized this measure into two categories: individuals with history of falling and those without. We utilized data from both waves to incorporate the lagged dependent variable approach, ensuring control over potential confounding factors. Further details regarding our analytical strategy are provided below.

3.2.2. PBE Score

The predicting variable, PBE, was measured by older adults’ self-reported satisfaction with six dimensions of their neighborhood built environment. These six dimensions include road conditions, fitness and recreational spaces, neighborhood safety, environmental cleanness, road lighting, and accessibility facilities available in the neighborhood. The respondents were asked to rate their level of satisfaction with each item on a 5-point Likert scale ranging from “1” (very dissatisfied) to “5” (very satisfied). The alpha coefficient of the six dimensions was 0.83, showing a high consistency. Consequently, we added them up as a continuous variable representing the PBE. Additional details regarding the items are provided in Table 2. Given our hypothesis of a non-linear association between PBE and falls among older adults, we also included the squared term of PBE in our model’s estimations.

3.2.3. Covariates

The models were adjusted for individual-level covariates, including age, gender, marital status, retirement status, education, living arrangements (1 = living alone), and health status. Additionally, the respondents’ indoor home environment was controlled by considering factors including brightness levels and the flatness of the flooring in their homes. Furthermore, we accounted for the necessity of using stairs; if the participants resided on the second floor or higher without access to an elevator, it was assumed that they would need to use stairs. These indoor environmental features have been identified as potential triggers for falls among older adults [45,46].

3.3. Statistical Analysis

Given that falls are rare events even among the aging population (in our sample, only around 7% of the respondents had experienced a fall in the past 12 months), we used complementary log–log (cloglog) regression to examine the relationship between the PBE and falls among older adults. The cloglog regression is more appropriate for rare events than regular logistic regression [47,48]. Specifically, we assumed that the hazard of falls takes the following form
P r y i x i = 1 − e x p ⁡ { − e x p ⁡ ( β x i ) }
where y i is the occurrence of a fall, x i is a vector of the variables for individual i, and β is a vector of the parameters. The cloglog transformation of Equation (1) yielded
log ⁡ − log ⁡ P r y i x i = α t + β x i
where α t is the cloglog transformation of the baseline hazard or the intercept. Since the relationship between PBE and falls can be reciprocal, we also controlled for the falling history in the previous survey year to fix the problem of reverse causality. This strategy is also known as lagged dependent variable cloglog regression.

4. Results

4.1. Descriptive Statistics

The descriptive statistics are presented in Table 3. The average PBE score was 22.21 on a scale of 6–30. The incidence of falls (occurring once or more than once in the past year) was 7.19% in the 2018 wave and increased to 7.71% in the 2020 wave. On average, respondents were aged 70.84 years (in 2018), with urban residents accounting for 50.07%. The low percentage of respondents living alone (11.73%) can be attributed to the survey’s methodology, which involved visiting both spouses in older adult couples’ households, resulting in a higher proportion of non-single older individuals being included. The respondents’ health status was indicated by an average self-reported health score of 3.37 out of 5. Most respondents lived in well-equipped residential settings characterized by elevators or ground-floor locations, sufficient lighting, and flat flooring within their homes.

4.2. The Relationship between the PBE and Fall Risk among Older Adults

The results of the model’s estimation are presented in Table 4. In Model 1, which included only the PBE score, there appeared to be no significant linear relationship between PBE and falls among older adults. However, this null association very likely obscured the true non-linear relationship between the two variables. In Model 2, where squared PBE scores were included, a significantly non-linear relationship between PBE and falls was revealed. To better illustrate the curvilinear relationship, Figure 3 plotted fall risks among older adults across different PBE scores. Fall risks decreased as PBE increased when it was relatively low. After reaching a turning point around 22, there seemed to be a slightly positive relationship between PBE and fall risks. However, the magnitude of this positive relationship after the turning point was smaller than that of the negative relationship before it.
Three batteries of robustness checks were conducted to verify the findings (Table 5). Firstly, we performed regular logistic regression modeling, and the results were consistent with those in our main analysis. Secondly, we also used Firth logit regression modeling, another popular estimation method for rare events, using a penalized maximum likelihood estimator to overcome potential bias. Compared with the cloglog approach, Firth logit regression is more suitable for small samples with rare events. The results obtained using this approach were also consistent with those in our main analysis. Thirdly, we used threshold regression to detect whether a significant turning point existed. On the basis of our hypothesis and findings, we set the number of thresholds as 1, and the results indicated that a significant threshold existed at a PBE score of 18.74. This score was very close to the estimated turning point of 22 in our main analysis. These three batteries of robustness tests collectively confirmed the U-shaped relationship between PBE and falls among older adults in China.

4.3. Heterogeneity

Four types of heterogeneity were tested in this study, as illustrated in Figure 4. Given the urban–rural dualism in China, we first examined the heterogeneity on the basis of the older adults’ place of residence. As shown in the figure, there was a more pronounced curvilinear relationship between PBE and falls in urban areas compared with rural areas. For urban older adults, there existed a U-shaped relationship between their PBE and falls; whereas for rural older adults, their PBE was negatively associated with falls, indicating that increasing their PBE can significantly protect them against fall risks.
The relationship was also heterogeneous when assessed by the older adults’ living arrangements. The impact of PBE on fall risks was more pronounced among older adults who live alone compared with their counterparts living with other family members. Increasing the PBE could significantly reduce the fall risks of older adults living alone, especially when their PBE was relatively low. However, for older adults not living alone, there was no significant association between their PBE and falls.
We did not detect any heterogeneity by gender or age. The curvilinear relationship between PBE and falls existed among both male and female older adults, although it was slightly more pronounced among the former. Similarly, this curvilinear relationship also existed among older adults aged 60–75 and those above 75, although the overall risk was higher among the latter group.
In summary, the findings indicated that older adults who live alone in urban neighborhoods are more vulnerable to PBE, particularly with regard to their fall risks.

5. Discussion

This study represents a pioneering effort to investigate the impact and extent of the PBE on fall risks among older adults residing in Chinese neighborhoods. The findings demonstrated a significant non-linear relationship between the probability of falling and the PBE score; specifically, fall risks decreased as PBE improved when the PBE is relatively low. However, after reaching an average PBE score threshold, there appeared to be a slight positive association between PBE and fall risks. These results suggest that improving older adult residents’ overall PBE can effectively reduce their fall risks when general PBE levels are not high. However, when older adult residents have excessively high levels of PBE, they are more likely to underestimate the potential risks of walking and exercising in their neighborhoods, thus increasing their probability of falls [10,18].
The results demonstrate a significant association between the neighborhood built environment and falls, which is consistent with previous studies [49,50]. However, by incorporating perception scales to assess respondents’ neighborhood built environment, we have discovered that the relationship between PBE and fall risks is not simply linear but exhibits a distinct non-linear U-shaped pattern. This novel finding contributes significantly to the existing literature. The advantage of using PBE scales lies in their ability to uncover implicit information that combines both the physical and social factors of the built environment, which are challenging to obtain through systematic observations or geographic information systems [31]. Moreover, defining a neighborhood’s boundaries objectively poses challenges due to variations in neighborhood activities among different older adults. Therefore, utilizing PBE scales provides a closer approximation of the true impact of the factors of the neighborhood built environmental on respondents [31,51].
In the heterogeneity analysis, there were two novel findings. Firstly, the impact of PBE on falls was more pronounced in urban areas compared with rural areas. This could be attributed to the high density in most urban neighborhoods in China, which leads older adults to walk more frequently rather than relying on transportation. Consequently, the built environment has a greater influence on the probability of falling. Conversely, rural areas in China have lower neighborhood density, resulting in a relatively smaller impact of the built environment on falls. The results from rural areas in China are similar to studies conducted in low-density cities in Western countries [18,19]. Secondly, improving the PBE can significantly reduce fall risks among older adults living alone, especially in neighborhoods with a low PBE. In contrast, there was no significant association between PBE and falls for older adults who do not live alone. This may be because family members serve as effective protection against falls for older adults. These conclusions are consistent with previous research utilizing OBE measurements [16,17,26].
The findings make several significant contributions to research, public policy, and clinical practice. Firstly, while current studies have primarily focused on the indoor home environment as a contributing external factor to fall risks among older adults, this study specifically highlights the impact of the neighborhood built environments. By utilizing nationwide longitudinal survey data, we provide evidence on the cumulative effects of factors of the built environmental on fall risks among older adults. These findings are crucial for informing future efforts to prevent falls targeting older adults, particularly through improvements in the neighborhood built environment. Secondly, this study introduced the innovative concept of perceived scales and revealed the non-linear impact of the PBE on fall risks among older adults. In line with the P-E fit theory, enhancing the OBE was identified as a prerequisite for improving the PBE. This established a theoretical foundation for future investigations on enhancing specific aspects of the OBE through design and renovation, thereby promoting the PBE for older adults and preventing falls. Efforts should be made to improve poor environments to reach an average level, while in excellent environments, educating older adults about fall prevention is important to prevent carelessness during physical activities. Thirdly, the heterogeneity analysis identified the higher fall risks among urban adults living alone. Greater policy attention, such as the provision of community-based care services and wearable fall detection devices, is imperative to mitigate falls among this vulnerable group.
Additionally, this study specifically focused on Chinese older adults living in neighborhoods with clearly defined boundaries. Among them, urban older adults predominantly reside in gated communities with community environments managed by developers or property management companies; whereas rural older adults live in villages under centralized management by village committees or other organizations. This centralized community governance model facilitates unified implementation of renewal of the built environment, thus rendering the policy implications of this study highly feasible.
Despite the contributions to the existing literature, the limitations of this study should be acknowledged. While the impact of the PBE on older adults was revealed, we were unable to measure the OBE in their neighborhoods due to confidentiality constraints regarding specific geographic information of the survey. Consequently, we were unable to further compare the differential effects of the fall risk between the OBE and PBE among older adults. This limited the discovery of how the OBE and PBE can predict each other. Future studies could address this issue by using alternative methodologies and datasets. For instance, conducting surveys within the same neighborhood to assess older adults’ PBE, and integrating it with the researchers’ observations to audit their OBE would be a valuable research direction.

6. Conclusions

This study innovatively introduced the concept of perceived scales and revealed the influence of the PBE on fall risks among Chinese older adults residing in neighborhoods. The results obtained from a cloglog regression model indicated a significant non-linear U-shaped relationship between the PBE and falls. Specifically, when the PBE is relatively low, improving it can effectively decrease the fall risk. However, once an average PBE score threshold is reached, this association may slightly reverse. Furthermore, the heterogeneity analysis identified that living alone and living in urban areas are two major factors that render older adults more vulnerable to the PBE, resulting in higher fall risks within their neighborhoods. These findings provide valuable evidence and insights for developing environmental intervention measures and policies aimed at preventing falls in China and in other countries with similar contexts. Future efforts should be made to improve poor environments to reduce falls, particularly through neighborhood renewal schemes. Meanwhile, educating older adults on fall prevention is important to prevent carelessness during physical activities. Policy attention should also be given to the higher fall risks among urban adults living alone, and should provide them with adequate community-based care services.

Author Contributions

Conceptualization, S.D. and C.W.; methodology, S.D. and C.W.; software, S.D.; validation, S.D. and C.W.; formal analysis, S.D. and C.W.; investigation, S.D. and C.W.; resources, S.D. and C.W.; data curation, S.D. and C.W.; writing—original draft preparation, S.D., L.M. and C.W.; writing—review and editing, S.D. and C.W.; visualization, S.D., L.M. and C.W.; supervision, C.W.; project administration, C.W.; funding acquisition, C.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research study was supported by the National Natural Science Foundation of China (52308010), the Chongqing Municipal Construction Science and Technology Program (2023—No. 6-7), and the Fundamental Research Funds for the Central Universities (2024CDJXY014). The funding bodies did not influence this study in any way prior to circulation.

Data Availability Statement

The original data presented in the study are openly available in the China Longitudinal Aging Social Survey (CLASS) repository at http://class.ruc.edu.cn/ (accessed on 21 August 2024).

Acknowledgments

The authors acknowledge the support from the Institute of Gerontology and the National Survey Research Center at Renmin University of China for providing the CLASS data.

Conflicts of Interest

The authors declare no conflicts of interest.

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Buildings 14 02631 g001
Figure 1. The objective built environment (OBE) and perceived built environment (PBE) in the person–environment fit model, adapted from Kahana et al. (2003) and Veeroja et al. (2024) [28,30].
Figure 1. The objective built environment (OBE) and perceived built environment (PBE) in the person–environment fit model, adapted from Kahana et al. (2003) and Veeroja et al. (2024) [28,30].
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Figure 2. Research framework: the influence of the neighborhood PBE on fall risk.
Figure 2. Research framework: the influence of the neighborhood PBE on fall risk.
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Figure 3. The predicted relationship between PBE and falls among older adults.
Figure 3. The predicted relationship between PBE and falls among older adults.
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Figure 4. Heterogeneous effects of the perceived built environment on fall risk among older adults.
Figure 4. Heterogeneous effects of the perceived built environment on fall risk among older adults.
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Table 1. Key findings regarding the impact of factors of the neighborhood built environment on falls.
Table 1. Key findings regarding the impact of factors of the neighborhood built environment on falls.
Major FactorsKey FindingsReferences
Quality of pavementsUnevenness, slipperiness, the presence of obstacles, and the material’s composition directly influence the walking patterns of older adults[5,8,18]
Design of facilitiesInadequate facilities such as seating areas, stairs, utility poles, and other locations contribute to falls[19,20]
Transportation systemHigh traffic speeds, the volume of vehicles, pedestrian density, etc. contribute to falls[21]
Natural environmentA superposition effect on risks related to the neighborhood built environment[22,23,24,25]
Table 2. PBE scores assessed by respondents across six dimensions of the neighborhood built environment.
Table 2. PBE scores assessed by respondents across six dimensions of the neighborhood built environment.
DimensionsItemsResponsesOperationalization
Road conditionsWhat is the level of satisfaction regarding road conditions within your neighborhood?Each item used a 5-point Likert-like scale:
1 = very dissatisfied
2 = relatively dissatisfied
3 = fair
4 = relatively satisfied
5 = very satisfied		Adding up the responses from the six dimensions to derive a composite score that reflected respondents’ PBE of their neighborhood.
The range of the PBE score was from 6 to 30.
Fitness and recreational spacesWhat is the level of satisfaction regarding fitness and recreational spaces within your neighborhood?
Neighborhood safetyWhat is the level of satisfaction regarding neighborhood safety in the environment within your neighborhood?
Environmental cleannessWhat is the level of satisfaction regarding environmental cleanness within your neighborhood?
Road lightingWhat is the level of satisfaction regarding road lighting within your neighborhood?
Accessibility facilitiesWhat is the level of satisfaction regarding accessibility facilities within your neighborhood?
Table 3. Descriptive statistics.
Table 3. Descriptive statistics.
Mean/%S.D.Range
PBE score22.213.61[6, 30]
Incidence of falls (≥1) (wave 2018) %7.19
Incidence of falls (≥1) (wave 2020) %7.71
Age70.846.25[60, 98]
Gender (1 = male) %50.34
Place of residence (1 = urban) %50.07
Marital status (1 = married) %72.86
Retirement status (1 = retired) %35.31
Education
Illiteracy %25.13
Primary school %40.89
Junior high school %24.22
Senior high school and above %9.75
Living arrangement (1 = living alone) %11.73
Health status (1 = poor, to 5 = excellent)3.370.86[1, 5]
Necessity of using stairs (1 = not necessary to use stairways in the home) %70.93
Brightness at home (1 = bright) %87.75
Flatness of the flooring at home (1 = flat) %92.47
N8686
Data source: CLASS2018 and 2020.
Table 4. Results of the complementary log–log model: the relationship between PBE and fall risks among older adults.
Table 4. Results of the complementary log–log model: the relationship between PBE and fall risks among older adults.
Model 1Model 2
PBE score−0.003−0.212 *
(0.012)(0.085)
PBE score squared 0.005 *
(0.002)
Falls in the previous survey3.134 ***3.137 ***
(0.087)(0.087)
Age0.040 ***0.041 ***
(0.007)(0.007)
Gender (1 = male)0.0070.005
(0.087)(0.087)
Place of residence (1 = urban)0.213 *0.215 *
(0.106)(0.107)
Marital status (1 = married)0.0270.024
(0.105)(0.105)
Retirement status (1 = retired)0.285 *0.301 **
(0.113)(0.114)
Education (rf. illiteracy)
Primary school−0.289 **−0.281 **
(0.103)(0.104)
Junior high school−0.025−0.025
(0.131)(0.132)
Senior high school and above−0.235−0.233
(0.173)(0.172)
Living arrangement (1 = living alone)−0.185−0.196
(0.139)(0.139)
Health status (1 = poor, to 5 = excellent)−0.189 ***−0.189 ***
(0.048)(0.048)
Necessity of using stairs (1 = not necessary to use stairways in the home)−0.051−0.053
(0.102)(0.102)
Brightness in the home (1 = bright)0.0670.068
(0.117)(0.117)
Flatness of the flooring in the home (1 = flat)−0.409 **−0.405 **
(0.140)(0.140)
Intercept−5.295 ***−3.081 **
(0.609)(1.064)
Log-likelihood−1535.11 ***−1532.40 ***
N86868686
Data source: CLASS 2018 and 2020. Note: standard errors are shown in parentheses; * p < 0.05, ** p < 0.01, *** p < 0.001.
Table 5. Results of the robustness tests.
Table 5. Results of the robustness tests.
Logit RegressionFirth Logit RegressionThreshold Regression
PBE score−0.227 *−0.236 *
(0.102)(0.101)
PBE score squared0.005 *0.005 *
(0.002)(0.002)
Threshold point 18.74
ControlsYESYESYES
N868686868686
Data source: CLASS 2018 and 2020. Note: standard errors are shown in parentheses; * p < 0.05. The results of the threshold regression model are based on bootstrapping of 6950 regressions.
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Du, S.; Wang, C.; Mao, L. Exploring the Influence of the Perceived Neighborhood Built Environment on the Fall Risks among Older Adults in China. Buildings 2024, 14, 2631. https://doi.org/10.3390/buildings14092631

AMA Style

Du S, Wang C, Mao L. Exploring the Influence of the Perceived Neighborhood Built Environment on the Fall Risks among Older Adults in China. Buildings. 2024; 14(9):2631. https://doi.org/10.3390/buildings14092631

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Du, Shichao, Chunyu Wang, and Longjian Mao. 2024. "Exploring the Influence of the Perceived Neighborhood Built Environment on the Fall Risks among Older Adults in China" Buildings 14, no. 9: 2631. https://doi.org/10.3390/buildings14092631

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