A comparative analysis of the impacts of objective versus subjective neighborhood environment on physical, mental, and social health

Health and Place 59 (2019) 102170

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A comparative analysis of the impacts of objective versus subjective neighborhood environment on physical, mental, and social health

T

Lin Zhanga,b, Suhong Zhoua,b,∗, Mei-Po Kwanc,d a

School of Geography and Planning, Sun Yat-sen University, Guangzhou, China Guangdong Provincial Engineering Research Center for Public Security and Disaster, Guangzhou, China c Department of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong, China d Department of Human Geography and Spatial Planning, Utrecht University, the Netherlands b

ARTICLE INFO

ABSTRACT

Keywords: Objective measures Subjective measures Neighborhood Three dimensions of health Hierarchical linear modeling

Research on the relationship between neighborhood context and health outcome has attracted notable attention. However, few studies examine and compare the associations between the objective and subjective neighborhood environment and different dimensions of health. To this end, high-resolution remote sensing images and pointsof-interest (POIs) data collected in Guangzhou, China, are used together with questionnaire survey data to measure the objective and subjective characteristics of the neighborhood environment. The sample includes 1029 adults selected from 34 communities in Guangzhou, China. Hierarchical linear modeling is then employed to analyze the associations between the objective and subjective neighborhood environment and three dimensions of health (physical health, mental health, and social health), as well as compare the relative strengths of and moderating mechanisms between these associations. The results indicate that significant variations in health outcomes are observed among neighborhoods, which can be explained by both personal attributes and the neighborhood environment. Although objective and subjective measures of the neighborhood environment are both linked to the three dimensions of health, physical health and social health are influenced more by objective measures, while mental health is affected more by subjective measures. Further, subjective measures have positive moderating effects on the relationship between objective measures and mental health but do not have significant moderating effects on the relationships between objective measures and physical and social health.

1. Introduction Over the past decades, there has been an increasing attention in health research on the factors that influence human health and how health outcomes could be improved by modifying these factors. Some factors that have been observed to be closely linked with health include demographic characteristics, socioeconomic status, social inequality (Haseda et al., 2018), physical activity (Eriksen et al., 2013), lifestyle (Ford et al., 2011), and diet (Rummo et al., 2015). Notably, environmental factors may exert distinctive effects on health in addition to the impacts of the above factors (Landrigan and Fuller, 2015). For example, the World Health Organization (WHO) reports that 92% of the world's population lives in places where air quality exceeds WHO guideline limits (World Health Organization, 2016a). Further, 23% of all deaths can be attributed to environmental factors (World Health Organization, 2016b). On the other hand, individual exposure to favorable environments may enhance people's health and mitigate some of the negative

∗

impacts of other factors. Therefore, much theoretical and methodological research has investigated the associations between the environment and health behaviors and outcomes. Specifically, these studies come from multiple domains (e.g., health geography, environmental science, urban planning, and sociology), and are based on different spatial scales (e.g., whole world, countries, cities, neighborhoods, and buildings). Among these studies, the neighborhood environment has been recognized to enhance or limit people's health not only during their daily lives but throughout their life courses. Neighborhood effects research to date has used objective, subjective, or mixed measures of environmental features to analyze individual environmental exposures and health outcomes (see Section 2 for details). In most studies, the emphasis has been on one or two dimension(s) of health (e.g., body mass index [BMI], disease, mental health) or general health (e.g., self-rated health). However, few studies to date address the different dimensions of health at the same time. According to a report by the WHO, “health” refers to a state of complete

Corresponding author. School of Geography and Planning, Sun Yat-sen University, Guangzhou, China. E-mail address: [email protected] (S. Zhou).

https://doi.org/10.1016/j.healthplace.2019.102170 Received 2 April 2019; Received in revised form 14 July 2019; Accepted 16 July 2019 1353-8292/ © 2019 Elsevier Ltd. All rights reserved.

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physical, mental and social well-being and not merely the absence of disease or infirmity (Huber et al., 2011). Among these three dimensions of health, physical health is defined as a state that the individual has a strong physique and a better self-protection ability to reduce harm and restore an equilibrium (Huber et al., 2011). Mental health refers to a state of emotional well-being, in which people can recognize their own potential, cope with stressful situations effectively, and work productively (Ettema and Schekkerman, 2016). Also, social health refers to the ability of people to have a good interpersonal relationship and social adaptation (Zhang et al., 2018). Prior research indicates that there are some interactions among these three dimensions of health. For example, people with physical disabilities and diseases tend to have adverse emotions or psychological problems, whereas those who have better physical health are less likely to suffer from mental disorders. People with poor mental health may have a weak physique as well as a worse self-protection ability (Diener and Chan, 2011; Kobau et al., 2013). People's physical and mental health may jointly influence their interpersonal relationships and social adaptation (Thoits, 2011). In addition, poor social health negatively affects people's physical and mental status (Santini et al., 2015; Tough et al., 2017). On the whole, these three dimensions of health are interactional and indispensable in improving overall health status. Hence, the outcome variables in this study are extended to the three dimensions of health (physical, mental, and social health), which can improve the analysis and further enhance our understanding of human health when compared to prior studies. Particularly, the inclusion of social health (beyond physical and mental health) is an important contribution of this study to existing literature. Although objective and subjective measures complement each other in reflecting neighborhood context, the relative strengths of their impacts on different dimensions of health may be different. Therefore, past studies may not provide adequate guidance as to what aspect of neighborhood characteristics should be used to examine what specific dimension of health and in what context, and it is difficult to improve the corresponding health outcomes by modifying the more critical and specific factors of the neighborhood environment in community planning and development. In addition, subjective measures have been found to moderate the relationship between objective measures and satisfaction. For example, as Liao et al. (2015) observed, perceived air quality has moderating effects on the association between objective air quality and people's life satisfaction. The possibility that the relationships between objective measures and individual health are moderated by some subjective neighborhood attributes thus cannot be ruled out (Weden et al., 2008). However, the moderating effects may be different for different dimensions of health, and this has not been adequately studied. In summary, few studies have explored the relationships between the objective and subjective neighborhood environment and different dimensions of health (physical, mental, and social health), as well as compare the relative strengths of and moderating mechanisms between these relationships. Therefore, this article seeks to fill some important gaps in the neighborhood effects and health literature.

Specific indicators include the amount of, quality of, access to, or exposure to natural elements (e.g., green space) (Van den Berg et al., 2010; Ulmer et al., 2016), various institutions and facilities (e.g., health care facilities, recreational areas, shopping facilities) (Spring, 2017), public infrastructures (e.g., water, lighting, electricity), dwellings (e.g., housing quality, floor level) (Evans, 2003), walkability (land use mix, density, connectivity) (Frank et al., 2006; Berke et al., 2007; Li et al., 2009; Grasser et al., 2013), and social capital (Carpiano, 2007). These studies pay more attention to the influences of objective characteristics on physical health (e.g., BMI, disease) or general health, after controlling for other variables. Although objective measures reflect the actual neighborhood context, they are less likely to provide complete and accurate evaluations of how inhabitants are exposed to, experience, or interact with their neighborhoods in ways that influence their health behaviors or outcomes (Weden et al., 2008). As a result, the impacts of objective neighborhood characteristics on health may differ from influences that consider both objective and subjective neighborhood characteristics. In contrast, subjective neighborhood environment is based on residents' perceptions and assessments of neighborhood features. It entails the possibility that different individuals may perceive or respond to the same neighborhood context differently (Kwan, 2012, 2018). Subjective neighborhood characteristics include assessments of the quantity, quality, and experience of the natural environment (e.g., green space, litter, noise) (Ruijsbroek et al., 2017; Araya et al., 2006; Ma et al., 2018; Wen et al., 2006; Jones et al., 2014), the built environment (e.g., facilities, residential conditions, traffic conditions) (Parkes and Kearns, 2006; Leslie and Cerin, 2008; Echeverría et al., 2008; O'Campo et al., 2009; Ellaway et al., 2009), and social interaction (e.g., social cohesion, neighborhood communication, perceived safety and danger) (Tampubolon et al., 2013; Jones et al., 2014; Ruijsbroek et al., 2016; Tamayo et al., 2016; Robinette et al., 2017; Choi and Matz-Costa, 2018). Many studies suggest that subjective neighborhood characteristics remain strongly associated with people's mental health and life satisfaction (Toma et al., 2015; Oshio and Urakawa, 2012), after controlling for their sociodemographic characteristics. Subjective neighborhood environment may affect residents' health outcomes through behaviors and emotional responses that are triggered by their perceptions and satisfaction of the neighborhood environment. As indicated by decades of research, objective and subjective neighborhood characteristics are both important factors that affect individual health. To overcome the limitations of earlier studies that use only objective or subjective characteristics, a few recent studies have simultaneously taken into account the links between both objective and subjective measures with health behaviors or outcomes (Tilt et al., 2007; Weden et al., 2008; Yen et al., 2009; Parra et al., 2010; Gebel et al., 2011; Gale et al., 2011; Bell et al., 2014; Ettema and Schekkerman, 2016; Godhwani et al., 2019). Since objective and subjective neighborhood attributes constitute fundamentally different notions in terms of meaning and measurement, their relative impacts differ among studies. For instance, Lin and Moudon (2010) compare the strength of the relationships between the objective and subjective built environment and walking for individual health and conclude that the influences of objective characteristics are greater than that of subjective characteristics. More explicitly, objective measures are more likely to capture the actual structural aspects of neighborhoods that are the basis of the perceived environment. It could also contribute to clarifying or corroborating the meanings of the perceived environment and possibly promoting the translation of research findings directly into planning practice. Conversely, Gebel et al. (2011) show that promoting positive perceptions of walkability among residents who live in objectively determined walkable neighborhoods may considerably improve physical activity and maintain appropriate body weight. Such a result highlight a stronger relationship between subjective measures and health. Likewise, Ettema and Schekkerman (2016) and Weden et al. (2008) propose

2. Literature review This section reviews the literature on the neighborhood environment and health outcomes in order to summarize and compare the specific neighborhood characteristics as well as different health dimensions examined in prior research (Table 1). An important distinction among previous studies concerns the use of either objective or subjective characteristics. The neighborhood environment is often measured in past studies in an objective manner. Such studies assume that residents who live in the same contextual area are exposed to similar effects of their neighborhood context. Objective neighborhood characteristics are derived from different sources like data collected in the field, census statistics, land use databases, remote sensing images, and other surveys (Ettema and Schekkerman, 2016). 2

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Table 1 Summary of objective and subjective neighborhood environment and health literature. References

Neighborhood environment Objective measures

Van den Berg et al. (2010) Ulmer et al. (2016)

Green space Green space

Spring (2017)

Health care, recreational, and commercial facilities Dwelling (floor level, housing quality, and lighting) Walkability (net residential density, street connectivity, land use mix, and retail floor area ratio) Walkability (distance to closest grocery store, commercial and educational facilities) Fast-food restaurant density; Walkability (land use mix, street connectivity, public transit stations, green and open spaces) Walkability (intersection density, land use mix, connectivity) Social cohesion and support; Informal social control; Neighborhood organization participation

Evans (2003) Frank et al. (2006) Berke et al. (2007) Li et al. (2009) Grasser et al. (2013) Carpiano (2007) Ruijsbroek et al. (2017) Araya et al. (2006) Ma et al. (2018) Wen et al. (2006) Jones et al. (2014) Parkes and Kearns (2006) Leslie and Cerin (2008)

O'Campo et al. (2009) Ellaway et al. (2009)

Weden et al. (2008)

Accessibility to educational, recreational, and commercial spaces; Greenness (Normalized difference vegetation index (NDVI)) Neighborhood affluence and disadvantage

Yen et al. (2009)

Health-related resources; Traffic; Trash; Housing density; Land-use diversity; Availability of services

Parra et al. (2010)

Public park density; Transport stations

Gebel et al. (2011)

Walkability (dwelling density, street connectivity, land use mix, and retail density) Deprivation

Gale et al. (2011) Bell et al. (2014) Ettema and Schekkerman (2016) Godhwani et al. (2019)

Subjective measures Mental health; General health Obesity; High blood pressure; Type 2 diabetes; Mental health; General health Self-rated health Mental health BMI BMI Weight; Waist circumference Weight General health Green space Green space; Litter Noise Physical, social, and service environment Environmental incivilities (litter, noise); Cognitive social capital; Social incivilities (number of assaults/ muggings); Perceived safety Facilities and services Residential density; Land use mix; Access to services; Traffic facilities; Greenery; Safety Noise; Litter; Traffic problems; Commercial and recreational facilities; Violence; Social cohesion Human and social services; Neighborhood support; Green area and natural environment; Neighborhood affordability Nature environment incivilities (litter); Infrastructural incivilities (vacant/derelict buildings, overhead power lines); Absence of environmental goods Neighborhood trust, friendly, and deprivation Social cohesion, disorder, and unsafety Neighborhood safety; Violent crime Neighborhood cohesion and disorder Neighborhood Safety; Social Cohesion

Echeverría et al. (2008)

Tampubolon et al. (2013) Ruijsbroek et al. (2016) Tamayo et al. (2016) Robinette et al. (2017) Choi and Matz-Costa (2018) Toma et al. (2015) Oshio and Urakawa (2012) Tilt et al. (2007)

Health outcomes

Neighborhood deprivation (housing, geographical access to services) Dwellings (housing types, age of housing); Accessibility of urban facilities (Basic, recreational, medical/educational, and transport facilities) Neighborhood deprivation

Mental Health Mental health Mental Health Self-rated health Mental Health Illness; Disability; Self-reported health Mental Health Mental Health Mental Health Mental Health; General health Self-rated health General health Diabetes; BMI Cardiometabolic risk Mental Health

Social relationship; Safety and trust Neighborhood satisfaction, safety, and trust Accessibility; Natural features; Satisfaction with greenness

Mental Health Self-rated health BMI

Air quality; Upkeep; Safety; Recreation; Abandon buildings; Litter Traffic; Trash; Safety; Access to or quality of commercial or public services; Social cohesion; Collective efficacy; Neighborliness Noise; Safety of parks, green space, and public recreational places; Traffic safety Walkability (dwelling density, street connectivity, land use mix, and retail density) Neighborhood problems (litter, unsafety, traffic, noise); Neighborhood cohesion Neighborhood disorder; Neighborhood attachment, trust, safety, and attractive Facilities and public space; Accessibility; Social safety

Self-rated health

Neighborhood dissatisfaction (trustworthiness, safety, friendliness, unkindness, cleanliness)

Self-rated health

3

Mortality; Morbidity; Mental health Physical and mental health; Selfrated health BMI Mental health BMI; Waist circumference Mental health

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Fig. 1. Conceptual framework.

Guangzhou city. Specifically, this study selects some demographic and socioeconomic indicators and housing conditions (e.g., age, hukou [household registration], education level, employment status, income level, housing type, housing age, and housing rent) from the Sixth National Census of China. Factorial ecology analysis and cluster analysis (Yeh et al., 1995; Gu et al., 2005) are then applied to classify the residential communities in Guangzhou into five categories, including commercial housing community, danwei community, historical block, informal housing community, and affordable housing community. Finally, a total of 34 typical communities with prominent eigenvalues are selected from the five types of social areas.

that objective and subjective neighborhood characteristics are both associated with self-reported health and well-being, while subjective characteristics are much more likely to influence these health dimensions. It should be noted that although subjective characteristics have greater influences on several health outcomes than objective characteristics, not all health dimensions have similar findings. Also, Godhwani et al. (2019) indicate the relationships between objective and subjective neighborhood deprivation and self-rated health, but do not highlight the relative strength of their relationships. This paper first analyzes the spatial variations in the three dimensions of health among the sampled communities. Then, a conceptual framework is presented for examining and comparing the relationships between the objective and subjective neighborhood environment and the three dimensions of health among adult residents in Guangzhou, China (Fig. 1). Specifically, this study aims to answer the following four questions:

3.2. Questionnaire survey A questionnaire survey of urban residents was conducted in January 2016 in Guangzhou, China. The main theme of the questionnaire survey concerns neighborhood contextual issues, including perceived neighborhood environment, as well as individual health behaviors and outcomes associated with the environment. Data on personal and household attributes were also collected. The data collection effort was approved by Sun Yat-sen University. When designing the questionnaire, we chose some reliable and validated scales or items which have been commonly used in previous surveys or studies. Before the main phase of the survey, a pilot study was conducted to test the feasibility of the questionnaire. Next, we hired some interviewers and they received training through our detailed explanation of the questionnaire and the reasons for asking those questions in the context of the study. According to the size of the adult population of each community reported in the Sixth National Census of China, a total of 1050 questionnaires were proportionally distributed to the 34 sampled communities (i.e., each community has a specific sample size). During the main phase of the survey, participants were randomly selected from the adult residents in each sampled community based on the given sample size. All participants were fully informed about the study design and provided informed consent. Each questionnaire was administered by an interviewer in a face-to-face interview with a participant, and each interview took about 20–25 min to complete. After checking the original data, questionnaires with incomplete and inconsistent responses were excluded from the study. Finally, 1029 questionnaires obtained from the participants are valid and useable.

Question 1. To what extent variations in the three dimensions of health can be simultaneously explained by the differences in individual-level attributes and neighborhood-level characteristics? Question 2. Is there any independent relationship between specific characteristics of the objective/subjective neighborhood environment and the different dimensions of health? Question 3. What are the relative strengths of the effects of the objective and subjective neighborhood environment on the three dimensions of health? Question 4. To what extent are the associations between objective measures and each dimension of health positively moderated by the subjective measures? Answers to these questions will shed new light on the roles of the objective and subjective neighborhood environment in relation to prospective changes in the three dimensions of health. Furthermore, this study has significant implications for enriching our knowledge of environmental health and informing the theory and practice of neighborhood planning and development. 3. Study design 3.1. Study area The paper selects Guangzhou as a representative megacity of China and investigates 34 communities in it (Fig. 2). These sampled communities are located in the central, transitional and marginal areas of 4

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Fig. 2. Study area and sampled communities.

3.3. Data description

affected by this distance (700–1200 m). Before designing this study, we collect information from some residents regarding how far (around 1 km) and how long (about 10–15 min) they are willing to travel to reach facilities and services in the study area. Further, prior studies in Guangzhou and other areas have shown that objective neighborhood characteristics can be effectively captured using a 1-km buffer based on the residential location (e.g., Zhao et al., 2018; Zhou et al., 2017; Lin and Moudon, 2010; Ulmer et al., 2016). Green space coverage is compiled and computed from high-resolution remote sensing images covering Guangzhou city. In addition, densities of various facilities are measured in numerical values using the data from the 2016 Points of Interest (which are spatial features with geographic [latitude and longitude] and other information, such as name and category) data of Guangzhou. Definitions of these variables are provided as follows.

3.3.1. Covariates: individual-level data Personal attributes (demographic and socioeconomic characteristics) collected via the questionnaire survey are taken as covariates in this study. Demographic characteristics include gender, age, marital status, hukou (household registration) and the length of residence. Furthermore, education level, employment status, and personal monthly income are used as measures of participants’ socioeconomic status. 3.3.2. Independent variables: neighborhood-level data 3.3.2.1. The objective neighborhood environment. Previous research on environmental health has identified multiple aspects of the objective neighborhood environment that are important in influencing health, including green space, housing conditions, and the built environment (Ulmer et al., 2016; Ettema and Schekkerman, 2016; Spring, 2017). These are mostly material resources or opportunity structures, which may improve or harm health directly or indirectly through the possibilities they provide to people for living a healthy life (Macintyre et al., 2002). Drawing upon prior literature, specific indicators are selected for measuring the objective environment. The objective neighborhood indicators in this study are derived using 1-km buffers based on Euclidean (straight-line) distance from each participant's residential location. The 1-km buffer is chosen based on the urban features of Guangzhou and information obtained from some adult residents. The straight-line distance between urban main roads in China should be set at 700–1200 m (Principles of Urban Planning (4th ed.) (Wu and Li, 2010)). It is recommended that people may obtain facilities and services they need or reach their destinations within a walking distance of 700–1200 m. In most Chinese cities (e.g., Guangzhou) to date, the spatial layouts of public facilities (e.g., health facilities, transport stations) and commercial facilities are significantly

(i) Green space Green space coverage: The proportion of the area of green space in the 1-km buffer based on a participant's residential location. (ii) Housing conditions Age of buildings: The number of buildings constructed before 2000 divided by the total number of buildings. According to the national housing policy and results of our field survey, “constructed before 2000” and “constructed after 2000” are used to distinguish the age of buildings. First, the State Council of the People's Republic of China issued a housing-reform notice in July 1998, marking the beginning of comprehensive housing marketization (Chen and Han, 2014). Afterward, there are a large number of new buildings as a result of the rapid development of Chinese real estate industry. Compared with buildings constructed before the “housing reform”, these new buildings of better quality and with larger residential area have greatly improved 5

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residents' housing conditions and met their housing demands. Second, according to the field survey, the buildings constructed before and after 2000 in the sample communities are found to be considerably different. Specifically, the building types before 2000 mainly include self-established buildings, welfare housing, informal and affordable housing, whereas buildings constructed after 2000 mainly belong to commercial housing. The quality, architectural style, and the associated public facilities and services are significantly different between buildings constructed before and after 2000. Per capita residential area: The total land area in residential use divided by the number of residents.

3.3.2.2. The subjective neighborhood environment. Subjective neighborhood features that affect health outcomes include participants’ evaluations of the natural environment, built environment and social interaction (Ruijsbroek et al., 2017; Liao et al., 2015; Ma et al., 2018; Ettema and Schekkerman, 2016; Bell et al., 2014; Robinette et al., 2017). We thus select specific indicators from the reliable and validated neighborhood environmental scale in the questionnaire survey. These indicators are rated from 1 (extremely poor) to 5 (extremely good). Note that “litter” and “noise” are both negative indicators and are rated from “extremely good” to “extremely poor” with a score that increases from 1 to 5. The subjective neighborhood indicators used in this study are as follows.

(iii) Built environment

(i) Natural environment Water quality Green space Air quality Litter Noise (ii) Built environment Public service facilities (e.g., health facilities, fitness facilities, dining facilities, shopping facilities, recreational facilities, and other related facilities) Living facilities (e.g., power supply, network, and traffic condition) (iii) Social interaction Neighborhood communication

Security features: The proportion of the number of security features (e.g., security doors, security windows) that is within the 1-km buffer based on a participant's residential location. Public services and living facility density: The total number of public services and living facilities (e.g., health facilities, fitness facilities, and other related facilities) divided by the total area of the 1-km buffer. Land use mix: According to Rajamani et al. (2003), the distributions of four land uses are assessed to measure the land use mix within the 1km buffer as follows.

Land use mix = 1

n n+T

× 1

r T

1 3

+

c T

1 3

+

o T

1 3

4/3 (1)

3.3.3. Outcome variables: health status The outcome variables are the three dimensions of health (physical, mental, and social health). To measure physical health, we employ the MOS 36-Item Short-Form Health Survey (SF-36, items 1, 4, and 7) (Mchorney et al., 1993), which has been commonly used in the health literature. As all items are rated on a 1–5 scale, total scores vary between 3 and 15, and people with a score of 15 have an extremely strong and healthy physique at all times. Mental health is measured using the World Health Organization's Five Well-Being Indexes (WHO-5) (Bech et al., 2003), which has been widely used in prior studies. Its total score spans between 5 and 25 since the five self-reported items are rated on a 1–5 scale. Specifically, a score between 5 and 8 suggests that a person's mental status is extremely poor, whereas a score between 21 and 25 means that a person has an extremely healthy mental status. In addition, a score ranging from 9 to 12 indicates that a person has some mild mental illness in his or her daily life, while a score between 17 and 20 means that a person has good mental health. Social health is assessed by an adapted version of the Social Cohesion and Support Scale (Kempen and Van Eijk, 1995; Sampson et al., 1997; Völker et al., 2007). This scale is a reliable and valid measure to assess to what extent residents experience social cohesion and have access to social support from neighbors. Participants were asked to rate their agreement on five items from 1 (extremely disagree) to 5 (extremely agree). Socially unhealthy participants are those with a score of under 14.88 (the average score of the five items), whereas a higher score denotes a better social health status. Please see a detailed description of all items in Zhang et al. (2018). The descriptive statistics of all variables used to explore the respective relationships between objective and subjective measures with individual health are provided in Table 2.

(2)

T=r+c+o

where r , c , o and n represent residential land, commercial land, other construction land (e.g., recreational land and so on) and non-constructive land, respectively. Road network density: The total length of roads divided by the total area of the 1-km buffer. Accessibility to commercial facilities: Drawing upon the notions of Reilly's law of retail gravitation (Reilly, 1931) and Converse's new laws of retail gravitation (Converse, 1949), a commercial center attracts or influences a community approximately in inverse proportion to the square of the distance between them. Similarly, the convenience of a community to a commercial center is inversely proportional to the square of the distance between them (Qi and Zhou, 2018). In this paper, the shortest path over the transportation network between a participant's residential location and a commercial center1 is identified using network analysis. Based on this method, the distances from a participant's residential location to the three nearest commercial centers are used to measure a participant's accessibility to commercial facilities (e.g., dining facilities, shopping facilities, and recreational facilities) within the 1-km buffer around his or her home. The following equation has been validated and used in previous studies for measuring the accessibility to commercial facilities (Zhou et al., 2017; Qi and Zhou, 2018; Song et al., 2018). 3

Accessibility to commercial facilities = i=1

1 di2

(3)

where di is walking distance (i.e., network distance based on walking as the travel mode) from a participant's residential location to the nearest commercial center i . Public transport station density: The total number of public transport stations (e.g., bus stations, subway stations) divided by the total area of the 1-km buffer.

3.4. Hierarchical linear modeling In this paper, individuals are nested within their neighborhoods which leads to the nested data structures. Compared with traditional OLS (Ordinary Least Squares) regression, hierarchical linear modeling (HLM) with shrinkage estimation can effectively address the nested data structures and eliminate the problem that OLS estimation may

1 Commercial centers are selected from The Development Planning of Large Retail Outlets in Guangzhou (2011–2020).

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Table 2 Descriptive statistics of all variables (N = 1029). Level

Variable

Individual-level

Gender

Mean/% Female Male 19–44 45–59 ≥60 Married Single Non-Guangzhou Guangzhou

Age Marital status Hukou Length of residence (years) Education level

Primary school or lower Junior high school degree Senior high school degree Bachelor degree Master degree or higher Unemployed Employed ≤2999 3000–4999 5000–8999 9000–12000 ≥12000 Green space coverage Age of buildings (buildings constructed before 2000) Per capita residential area (m2) Security features Public services and living facility density (number/km2) Land use mix Road network density (km/km2) Accessibility to commercial facilities Public transport station density (number/km2) Water quality Green space Air quality Litter Noise Public service facilities Living facilities Neighborhood communication Physical health Mental health Social health

Employment status Personal monthly income (Yuan)

Neighborhood-level

Objective neighborhood environment

Subjective neighborhood environment

Health status

underestimate the true standard error, and thus obtain more stable and accurate regression results (Raudenbush and Bryk, 2002; Subramanian et al., 2003; Goldstein, 2011). On the other hand, HLM allows modeling of both within-neighborhood variation (i.e., individual-level) and between-neighborhood variation (i.e., neighborhood-level) (Frohlich et al., 2007). Hence, it helps us to test to what extent variations in health outcomes can be simultaneously explained by the differences in personal attributes and neighborhood context. Additionally, hierarchical modeling has been widely used in the neighborhood effects and individual health literature (Carpiano, 2007; Yen et al., 2009; Li et al., 2009; Oshio and Urakawa, 2012; Tampubolon et al., 2013; Haseda et al., 2018). Given these advantages, a two-level (individual-level and neighborhood-level) hierarchical linear model is constructed for exploring and comparing the associations between objective and subjective neighborhood environment and the three dimensions of health. To be specific, the stepwise modeling starts from a ‘null’ variance component model (Null Model). Model 1 is then fitted with the individual-level variables in which health status is influenced by only personal attributes. Next, objective and subjective neighborhood characteristics are added in separate models (Model 2a and Model 2b). Finally, objective and subjective neighborhood characteristics are both entered into the full model (Model 3).2 Variance component changes

49.85% 50.15% 61.32% 23.91% 14.77% 78.48% 21.52% 22.00% 78.00% 13.21 2.53% 12.93% 37.12% 46.94% 0.48% 15.86% 84.14% 9.04% 38.48% 44.31% 3.11% 5.06% 27.14% 73.37% 24.94 99.22% 1880.84 0.66 16.81 1.64 17.24 3.27 3.36 3.19 3.05 3.16 3.73 4.11 3.86 12.65 12.08 14.88

from the Null Model to Model 3 are identified. Model 1 (individual-level variables):

Yij =

0j

+

1j (Gender )

+

+

5j (Length

+

7j (Employment

2j (Age )

of residence ) + status ) +

+

3j (Marital

6j (Education 8j (Personal

status ) +

4j (Hukou )

level)

monthly income ) + rij (4)

Varrij =

2 ij

(5)

where individual “i” (i = 1, …, 1029) is nested within the neighborhood “j” (j = 1, …, 34, note that more than 30 samples in the neighborhood-level can ensure the robustness of hierarchical linear modeling); Y is the health status (physical, mental, and social health, respectively); 0j is the random intercept of individual-level; nj (n = 1, …, 8) is the impact of individual-level variables on health status; rij is the random effect of individual-level. Model 2a (individual-level variables + objective neighborhood environment):

2 For visual clarity, we do not show the regression of each variables on the health, while the variance component and χ2 are depicted in Table 4 (Model 3).

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0j

=

00

+

01 (Green

+

03 (Per

space coverage) +

02 (Age

capita residential area) +

the Qingfuli Community (14.667) in Yuexiu District, the Xincun Community (14.303) in Tianhe District, and the Sanyuanxiang Community (14.000) in Yuexiu District, and the lowest level is found in the Mingyuan Community (10.161) in Haizhu District. These results indicate that most people in the study area rate their physical health as good or excellent (75.02%). In general, participants with better physical health are more likely to live in Yuexiu District and Tianhe District, since these two districts have good residential environment and service facilities (e.g., aesthetic and safe environment, convenient facilities, and comfortable life). Participants’ mental health is rated the highest in the Heyixiang Community (18.857) in Yuexiu District and the lowest in the Qiaocheng Community (7.419) in Haizhu District. Although there are no remarkable spatial variations in mental health level in Fig. 3b, nearly 58.79% of the participants reported poor mental status or mental illness in their daily lives. Additionally, some residents with good physical status live in Tianhe District, while their mental health level is slightly poor. The spatial variations in social health level are illustrated in Fig. 3c. Residents with good interpersonal relationship and social adaptation tend to live in Liwan District and Yuexiu District, both are old downtown areas of Guangzhou city. Also, the scores of social health range from 10.871 (Qiaocheng Community in Haizhu District) to 19.667 (Qingfuli Community in Yuexiu District), indicating that people's social health vary greatly at the neighborhood level. Given the variations in residents’ physical, mental, and social health among the neighborhoods, it can be argued that neighborhood is an appropriate geographic scale to explore the impacts of the environment on health behaviors and outcomes.

of buildings )

04 (Security

+

05 (Public

+

07 (Road network density )

+

08 (Accessibility

+

09 (Public transport station density ) + µ0j

features )

services and living facility density ) +

06 (Land

use mix )

to commercial facilities ) (6)

Model 2b (individual-level variables + subjective neighborhood environment): 0j

=

00

+

01 (Water

+

quality ) +

+

04 (Litter )

+

07 (Living facilities ) +

05 (Noise )

02 (Green

+

space ) +

06 (Public

03 (Air

quality )

service facilities )

08 (Neighborhood communication ) + µ 0j

(7) Model 3 (individual-level variables + neighborhood-level variables): 0j

=

00

+

01 (Green

space coverage)

+ …+

09 (Public

+ …+

17 (Neighborhood

Varµ 0j =

transport station density ) + communication) + µ 0j

2 0j

10 (Water

quality ) (8) (9)

where 0j (the random intercept of individual-level) is treated as the linear function of neighborhood-level explanatory variables; n (n = 01, …, 17) is the impact of neighborhood-level variables on health status. In the paper, the index of the proportional reduction in variance is used to measure the variance component changes in the models before and after adding the neighborhood-level variables. It reflects the proportion of variance in the outcome variables explained by the neighborhood-level variables (Raudenbush and Bryk, 2002; Wang et al., 2011). The larger the value of this index is, the stronger the explanatory powers or effect of neighborhood-level variables on outcome variables are.

4.2. Variance component analysis in the Null Model First, the Null Model without any variables is applied to examine to what extent variations in physical, mental, and social health can be simultaneously explained by the differences in individual-level attributes and neighborhood-level characteristics. Table 3 presents the random variations in the three dimensions of health, respective 75.7%, 60.1% and 72.4% of variations in physical health, mental health, and social health occurred at the individual level with the remaining 24.3%, 39.9% and 27.6% of variations occurred at the neighborhood level. Since the Intraclass Correlation Coefficient (ICC) ≥ 0.059,3 the variations in health outcomes are significantly attributable to the differences in neighborhood-level characteristics, and the health effects of neighborhood context should be highlighted in this study (Cohen, 1988).

Proportional reduction in variance Variance component (Model 1) – Variance component (specified model) = Variance component (Model 1) (10) where specified model refers to Model 2a, Model 2b, and Model 3, respectively. Note that the multi-collinearity of independent variables is tested before constructing the hierarchical linear models. The results show that there is no significant collinearity among the independent variables (Variance Inflation Factor (VIF) < 5). In addition, the outcome variables have high reliability (Cronbach's Alpha is 0.789 (≥0.700)) and validity (Kaiser-Meyer-Olkin (KMO) is 0.799 (>0.700) and Sig. is 0.000 (<0.05)).

4.3. Effects of the neighborhood environment on health Next, this study examines the independent associations between the neighborhood environment and the three dimensions of health. Models 2a and 2b are applied to analyze and compare the extent to which the objective and subjective measures are associated with physical, mental, and social health separately. 4.3.1. Effects of the objective neighborhood environment on health As shown in Table 4 (Model 2a), the results suggest that different aspects of the objective neighborhood environment influence different dimensions of health. Green space coverage has the strongest association with mental health (β = 3.120, p < 0.01), followed closely by social health (β = 2.022, p < 0.05). This finding indicates that ample green space is an effective way to promote people's psychosocial health

4. Results 4.1. Spatial variations in participants’ health level Fig. 3 shows the spatial variations in the three dimensions of health among the 34 selected communities. It intends to capture the relationships between the neighborhood context and individual health. Further, some participants are selected from the sampled communities for a qualitative interview to obtain more detailed information about their perceptions and interpretations of the neighborhood environment. As shown in Fig. 3a, there are significant variations in physical health level among the 34 communities. The average level of physical health is 12.65 for these communities. The top three levels are found in

3

According to the Cohen (1988), 0.01 ≤ ICC <0.059 is characterized as low correlation, 0.059 ≤ ICC <0.138 is considered to represent moderate correlation, and ICC ≥ 0.138 as high correlation. Thus, it is necessary to consider the between-group variance based on the hierarchical modeling, especially the effect of high-level (such as neighborhood-level in the study) when ICC ≥ 0.059. 8

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Fig. 3. Spatial variations in participants' health level in Guangzhou.

operate in the opposite direction as vulnerable groups with health problems tend to live in older neighborhoods. Similarly, an increase in the per capita residential area improves residents' physical health since larger rooms and more comfortable environments can better meet their living needs (β = 0.018, p < 0.01). Such a result is seldom mentioned in existing studies. The objective built environment has the strongest relationship with physical health, followed by mental and social health. More explicitly, residents' social health declines sharply with an increase in the number of security features (β = −12.756, p < 0.01). This finding suggests that security features (e.g., anti-theft doors and windows) may hinder the communication and interaction between neighbors through reducing the accessibility and approachability of dwellings to potential visitors. Land use mix is positively correlated with physical health, indicating that higher level of land use mix may induce more active travel (e.g., walking), and thereby positively influence residents’ physical health (β = 1.081, p < 0.05). Notably, road network density is strongly and positively associated with mental health (β = 0.071, p < 0.01), and accessibility to commercial facilities (β = 0.042, p < 0.10) has a marginally positive correlation with mental health. Conversely, both of these two indicators have obvious and negative relationships with physical health (β = −0.057, p < 0.01; β = −0.077, p < 0.01). It might be the case that although convenient access to such facilities may be a pleasant experience, living close to them may have some adverse impacts due to more crowding and noise associated with these facilities. On the other hand, residents who live in neighborhoods with high accessibility to commercial facilities (including a large number of dining facilities) tend to go out for dining, which causes their exposure to unhealthy foods. In addition, residents are more likely to engage to travel by public transportation if they live in a neighborhood with a higher density of public transport stations, which enhances physical health via reducing motorized travel and promoting physical activity level (β = 0.047, p < 0.01).

Table 3 Variance component analysis in the Null Model.

Physical health Mental health Social health

Level

Variance component

Intraclass correlation coefficient (ICC)

Individual-level Neighborhood-level Individual-level Neighborhood-level Individual-level Neighborhood-level

0.290 0.093 0.326 0.216 0.239 0.091

0.757 0.243 0.601 0.399 0.724 0.276

Chi-square

374.988∗∗∗ 771.492∗∗∗ 477.255∗∗∗

∗∗∗p < 0.01.

and relieve their stress. Also, green spaces provide various spaces for physical and recreational activities, which encourage people to enjoy the outdoors where they may encounter neighbors and interact with each other. However, the relationship between green space coverage and physical health is not statistically significant. Housing conditions are closely related to physical health, followed by mental and social health. To be specific, residents who live in buildings constructed before 2000 have a much lower health level (unhealthy physical, mental and social status) than residents who live in buildings constructed after 2000 (β = −0.227, p < 0.05; β = −3.788, p < 0.01; β = −0.463, p < 0.01). An explanation for this may be that new dwellings are usually safer, of higher quality, and have better surroundings (e.g., better maintenance, diversified architectural style, beautiful landscaping, and adequate public services), while old buildings are much more likely to be worn down and result in adverse impacts on people's health. Moreover, it may reflect that people who live in newer dwellings are more positive in their sense of life satisfaction and well-being. However, the causal relationship may also 9

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Table 4 Modeling associations between neighborhood environment and health. Variables

Physical health

Model 1 (individual-level variables) Intercept Gender Age Marital status Hukou Length of residence Education level Employment status Personal monthly income Variance component χ2

Mental health

Coef.

S.E.

Coef.

S.E.

Coef.

S.E.

4.243∗∗∗ 0.082∗ −0.206∗∗∗ 0.182∗∗∗ −0.029 −0.002 0.061∗ 0.203∗∗ 0.022 0.097 450.223∗∗∗

0.055 0.042 0.039 0.042 0.052 0.002 0.031 0.076 0.018

2.518∗∗∗ 0.107∗∗ 0.008 0.188∗∗∗ 0.132∗ −0.000 0.072∗∗ 0.095 0.017 0.219 409.197∗∗∗

0.081 0.049 0.034 0.061 0.077 0.001 0.032 0.067 0.021

2.996∗∗∗ 0.044 0.030 0.054 0.113∗∗ 0.001 0.000 0.025 −0.026 0.091 317.473∗∗∗

0.054 0.037 0.035 0.058 0.054 0.002 0.024 0.067 0.026

1.773∗

1.032

15.745∗∗∗

3.923

3.120∗∗∗

0.793

2.022∗∗

0.792

−3.788∗∗∗ −0.014

0.952 0.010

−0.463∗∗∗ 0.000

0.105 0.007

0.000∗∗∗ 0.001∗∗ −0.912 0.071∗∗∗ 0.042∗ −0.039 0.175 550.019∗∗∗

0.000 0.000 1.682 0.022 0.024 0.027

−12.756∗∗∗ 0.000∗∗∗ −0.771 0.464 0.002 −0.005 0.049 137.370∗∗∗

3.521 0.000 0.841 0.547 0.007 0.013

2.227∗∗∗

0.511

2.314∗

0.601

0.659∗∗∗ 0.373∗ 0.374∗∗∗ −0.538∗∗∗ −0.369∗∗∗

0.123 0.200 0.123 0.137 0.125

0.328∗∗ 0.211∗∗ −0.066 −0.088 −0.086

0.119 0.079 0.140 0.154 0.115

0.610∗∗∗ 0.374∗∗

0.196 0.160

0.400∗∗ 0.345∗∗

0.169 0.138

0.513∗∗ 0.100 635.607∗∗∗

0.215

0.469∗∗ 0.054 264.896∗∗∗

0.202

1.644∗∗ 0.069 208.670∗∗∗

0.712

12.366∗∗∗ 0.051 148.870∗∗∗

4.064

Model 2a (individual-level variables + objective neighborhood environment) Intercept 3.431∗∗∗ 0.321 Green space Green space coverage 0.510 0.431 Housing conditions Age of buildings −0.227∗∗ 0.102 0.005 Per capita residential area 0.018∗∗∗ Built environment ∗∗∗ Security features 0.000 0.000 Public services and living facility density −0.000 0.000 Land use mix 1.081∗∗ 0.512 0.012 Road network density −0.057∗∗∗ Accessibility to commercial facilities −0.077∗∗∗ 0.012 0.014 Public transport station density 0.047∗∗∗ Variance component 0.047 2 ∗∗∗ χ 244.245 Model 2b (individual-level variables + subjective neighborhood environment) Intercept 2.163∗∗∗ 0.426 Natural environment Water quality 0.198∗∗ 0.075 Green space 0.238∗∗∗ 0.064 Air quality −0.174 0.121 Litter −0.139 0.087 Noise 0.089 0.101 Built environment Public service facilities 0.413∗∗∗ 0.132 Living facilities 0.235∗∗∗ 0.079 Social interaction Neighborhood communication 0.116 0.166 Variance component 0.058 χ2 272.948∗∗∗ Model 3 (individual-level variables + neighborhood-level variables) Intercept 2.772∗∗ Variance component 0.054 χ2 235.841∗∗∗ ∗

p < 0.10,

p < 0.05,

∗∗

∗∗∗

Social health

0.998

p < 0.01.

4.3.2. Effects of the subjective neighborhood environment on health As observed in Table 4 (Model 2b), the subjective natural environment is strongly associated with mental health, followed by physical and social health. Specifically, the evaluations of water quality (β = 0.198, p < 0.05; β = 0.659, p < 0.01; β = 0.328, p < 0.05) and green space (β = 0.238, p < 0.01; β = 0.373, p < 0.10; β = 0.211, p < 0.05) are positively correlated with physical, mental, and social health. Perceived air quality has a highly significant and positive association with mental health (β = 0.374, p < 0.01). Notably, the higher assessments of litter (β = −0.538, p < 0.01) and noise (β = −0.369, p < 0.01) are both significantly correlated with worse mental health, indicating that litter and noise may invoke adverse emotional responses and harm people's psychological well-being. The subjective built environment is closely related to physical, mental and social health. The evaluations of public service facilities (β = 0.413, p < 0.01; β = 0.610, p < 0.01; β = 0.400, p < 0.05) and

living facilities (β = 0.235, p < 0.01; β = 0.374, p < 0.05; β = 0.345, p < 0.05) have strong associations with the three dimensions of health, suggesting that living in a more convenient and attractive neighborhood with abundant amenities may enhance residents’ health. Also, people who enjoy interactions with neighbors tend to have better mental and social health (β = 0.513, p < 0.05; β = 0.469, p < 0.05). Overall, the objective/subjective neighborhood environment is independently related to the three dimensions of health. The specific features of the objective and subjective neighborhood environment have different impacts on different health outcomes. 4.4. Differences in the effects of the objective versus subjective neighborhood environment on health Given the fact that various objective and subjective characteristics have different associations with the three dimensions of health, and to 10

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on people's physical, mental, and social health. Objective and subjective measures are complementary to each other in providing information on the neighborhood environment and can both be used in a study that examines the influence of neighborhood environment on health behaviors or outcomes. Some features (e.g., neighborhood communication) may be measured by one measurement (e.g., subjective measurement) but cannot be measured by another (e.g., objective measurement) fully and readily. Thus, there is a poor level of agreement between objective and subjective neighborhood characteristics in this paper (i.e., some specific indicators of objective and subjective environment do not necessarily correspond to each other), which is consistent with past studies (Weden et al., 2008; Bell et al., 2014; Ettema and Schekkerman, 2016; Godhwani et al., 2019). As the results indicated, the relative strengths of the relationships between both objective and subjective measures with the three dimensions of health are highly different. This finding has some practical implications for policy makers and planners when designing healthy neighborhoods. It may contribute to supporting healthy behaviors and improving corresponding health outcomes by highlighting the more critical and specific features of the neighborhood environment. For instance, the objective neighborhood environment has a stronger association with physical and social health than the subjective neighborhood environment. This suggests that the importance of specific objective indicators should be emphasized in the construction of healthy neighborhoods in order to improve residents' physical health and social health. Specifically, larger and newer dwellings, as well as an increase in security features, transportation facilities, and land use mix, are more likely to enhance people's physical health, while a decrease in road network density and accessibility to commercial facilities may promote physical health. Notably, neighborhood characteristics that encourage physical activity are highly related to better physical health. Also, an increase in the quality and quantity of green space, public service facilities, and new dwellings contribute to strengthening social cohesion and stimulate communication with neighbors. Also, subjective measures are more highly related to individual mental health than objective measures. However, studies that only explore the influences of the subjective characteristics on mental health are partial, because they ignore the effects of the objective characteristics on health which is also a basis for the formation of perceived neighborhood environment (Weden et al., 2008). Road network density is negatively associated with physical health, which is in contrast with past studies (Smith et al., 2008; Zhang, 2004). For instance, previous studies have typically reported that an increase in transportation facility density (e.g., bus, subway, sidewalk, road) may reduce traffic-related pollution and promote physical activity (e.g., walking and bicycling) by decreasing car travel, thereby promoting health outcomes. However, most of these studies are conducted in western developed countries, whose neighborhood environments may differ from the high density of transportation facilities in the developing country (e.g., China) (Xu et al., 2010). Specifically, further increasing road network density in the already dense environments in China's megacities (e.g., Guangzhou) may not reduce car travel. Conversely, car ownership and usage have increased gradually with economic growth and rapid urbanization. This finding indicates that the effects of some environmental characteristics (e.g., road network density) on health behaviors and outcomes may be positive and significant in one context (e.g., low-density areas in western developed countries), whereas these effects may be negative or not significant in another context (e.g., highdensity areas in China). The impacts of neighborhood environment on health are thus context specific. Another noteworthy finding is that public transport station (e.g. bus stations, subway stations) density has a positive association with physical health, which is quite different from the negative correlation between road network density and physical health as discussed above. It might be the case that “density” by itself does not influence health behaviors and outcomes that much, but elements (e.g., road network or public transport stations) that make up the

Table 5 Relative strengths of the relationships between neighborhood environment and health.

Model 1 Model 2a Model 2b Model 3

Variance component Variance component Proportional reduction in variance Variance component Proportional reduction in variance Variance component Proportional reduction in variance

Physical health

Mental health

Social health

0.097 0.047 51.55%

0.219 0.175 20.09%

0.091 0.049 46.15%

0.058 40.21%

0.100 54.34%

0.054 40.66%

0.054 44.33%

0.069 68.49%

0.051 43.96%

answer Question 3, we thus employ the pair-wise model (Table 5) to compare the relative strengths of these associations. Comparing Models 1 and 2a, the index of the proportional reduction in variance of the relationships between objective characteristics and physical, mental, and social health are 51.55%, 20.09%, and 46.15%. This suggests that objective characteristics have the strongest association with physical health (51.55%), followed closely by social health (46.15%), and its association strength with mental health is the lowest (20.09%). In contrast, and comparing Models 1 and 2b, the index of the proportional reduction in variance of the relationships between subjective neighborhood environment and physical, mental, and social health are 40.21%, 54.34%, and 40.66%. Subjective characteristics are much more likely to influence mental health (54.34%) and have nearly similar strength in influencing social health (40.66%) and physical health (40.21%). Such results indicate that the relative strengths of the associations between the objective and subjective neighborhood environment and the three dimensions of health are different. Physical health and social health are more likely to be influenced by objective measures, while mental health is affected more by subjective measures. 4.5. Positive moderating effects of the subjective neighborhood environment Finally, we also use the pair-wise model illustrated in Table 5 to examine the positive moderating effects of the subjective neighborhood environment on the relationships between the objective neighborhood environment and different dimensions of health. By comparing the index of the proportional reduction in variance among Models 2a, 2b, and 3, we observe that the objective neighborhood environment has a larger association with physical health than the subjective neighborhood environment, while subjective characteristics may not exert positive moderating effects between objective characteristics and physical health (Model 2a (51.55%) > Model 3 (44.33%) > Model 2b (40.21%)). In a similar vein, the objective neighborhood environment has a stronger association with social health than the subjective neighborhood environment. Also, subjective measures have no significant moderating effects on the relationship between objective measures and social health (Model 2a (46.15%) > Model 3 (43.96%) > Model 2b (40.66%)). On the contrary, the impacts of subjective measures on mental health are greater than that of objective measures. Notably, the inclusion of subjective characteristics substantially increases the strength of the association between objective measures and mental health, suggesting that subjective characteristics may play a positive and important moderating role in their association (Model 3 (68.49%) > Model 2b (54.34%) > Model 2a (20.09%)). 5. Discussion The findings of this paper have significant implications for how to properly measure neighborhood characteristics and assess their impacts 11

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“density” are essential to people's health status. Also, proper “density” is probably more attractive and promotes urban sustainable development and individual health.

Conflicts of interest

6. Conclusion

Funding

The main purpose of this study is to explore the associations between the objective and subjective neighborhood environment and the three dimensions of health (physical, mental, and social health), as well as to compare the relative strengths of and moderating mechanisms between these relationships. The results suggest that, as Kwan (2018) highlights, neighborhood effects are multidimensional and can be contradictory, where some neighborhood characteristics may have health-promoting effects, while other characteristics within the same neighborhood may have health-constraining effects. Objective and subjective neighborhood environments have diverse effects on the three dimensions of health. More explicitly, the influences of the objective neighborhood environment on social health are less than those on physical health but much greater than those on mental health. The impacts of the subjective neighborhood environment on social health are nearly equal to those on physical health but less than the effects of subjective measures on mental health. In addition, although subjective measures may not exert positive moderating effects on the respective relationships between the objective measures with physical health and social health, they positively moderate the association between objective measures and mental health. This paper makes meaningful theoretical contributions to the limited body of literature examining objective versus subjective neighborhood environment in relation to different health outcomes. Compared with previous studies that investigate the influences of neighborhood environment only on one or two dimension(s) of health or general health, this study focuses on three dimensions of health (physical, mental, and social health) and comprehensively examines their associations with objective and subjective neighborhood environment. The findings not only deepen the knowledge of environmental health but also enrich the theoretical research in existing literature. Moreover, results from this study provide quantitative evidence and practical implications for policy makers and planners in building healthy cities and communities. Future planning of healthy communities should consider modifying specific neighborhood characteristics to promote the corresponding dimension of health. Specifically, improving the objective neighborhood environment (including increasing green space and improving housing conditions and the built environment) is highly effective for enhancing residents' physical and social health. Meanwhile, as the promotion of individual mental status could be facilitated by fostering residents' positive perceptions of the neighborhood environment, residents’ perceptions and assessments of environmental characteristics should be considered when designing healthy communities. The article has several limitations which should be addressed in future studies. For instance, this research is based on a cross-sectional dataset, which renders an examination of the cumulative effects of neighborhood context on health over time nearly impossible, since their long-term associations may occur over a resident's life course. Conducting longitudinal surveys in the future will provide more robust evidence on these associations. Additionally, improving our understanding of how objective and subjective characteristics overlap and interact as well as how specific pathways moderate or mediate the relationships between the objective and subjective neighborhood environment with three dimensions of health is also of interest. Although this paper has examined whether subjective measures moderate the associations between objective measures and different health outcomes, the specific pathways of moderating effects are not explicitly discussed. Therefore, further research should aim to explore the various moderating mechanisms or mediating pathways that link the objective and subjective neighborhood environment to different dimensions of health.

This research was supported by the National Natural Science Foundation of China (41871148, 41529101, and 71961137003) and International Program for Ph.D. Candidates of Sun Yat-sen University.

The authors declare no conflict of interest.

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