Neighborhood disadvantage and self-rated health in China: latent class analysis of community lifestyles

Neighborhood disadvantage and self-rated health in China: latent class analysis of community lifestyles | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Neighborhood disadvantage and self-rated health in China: latent class analysis of community lifestyles Jinfu Xu, Chunru Shang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7414975/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background Existing research on residents' health has focused predominantly on individual socioeconomic status (SES), which is often explained through the "social causality" theory of health inequality, with less attention given to neighborhood disadvantage in China. Furthermore, even within the same community context, the degree of association between different residents and the community varies. Therefore, this study aims to systematically examine the impact of neighborhood concentrated disadvantage and the moderating role of exposure to advance intervention strategies for addressing health inequalities at the community level and establish a scientific foundation for promoting health equity. Methods Based on data from the 2018 China Labor Data Study, this study first employed latent class analysis (LCA) to identify distinct lifestyle types related to community exposure among urban residents in China. Second, multilevel analysis was used to examine the association between community context and resident health. Results The LCA revealed three distinct lifestyle types among urban residents: passive, active, and mixed. Multilevel analysis demonstrated that residents in communities with higher levels of concentrated disadvantage reported significantly poorer self-rated health. The exposure levels of different resident groups within a community moderated the strength of the association between the community context (concentrated disadvantage) and resident health. Conclusion Community governance should consider not only the influence of the community social structure but also the varying associations that different groups have with the community. This highlights the need for targeted interventions that account for both structural and individual-level factors in health inequality. Neighborhood disadvantage Self-rated health Lifestyle exposure Latent class analysis Background Health, as one of the elements of human capital, is the basis for realizing people's freedom and comprehensive development and is an important condition for social and economic development. The Party and the state are deeply concerned about people's health and have issued the "Healthy China 2030" Outline Program and the "Healthy China Action (2019–2030) " and other documents. With the transformation of the human disease pedigree, the traditional view of health "centered on the treatment of illness" has been transformed into a "people-centered" holistic view. The World Health Organization defines health as "a state of complete physical, mental psychological and social well-being." Therefore, research on health problems both theoretically and practically is highly important, especially given the background of building a well-off society in an all-round way. Research on health determinants has largely focused on individual structural effects, whereas studies examining community structure, or "neighborhood effects," remain relatively scarce. Neighborhood effects investigate whether and how the community context influences residents' socioeconomic outcomes, adopting a structural analytical perspective. This perspective primarily concerns how different neighborhood characteristics impact individuals. For example, neighborhood socioeconomic disadvantage plays a role in health above and beyond individual measures of socioeconomic status [ 1 ] . This research emphasizes intergroup differences—the impact of differences between neighborhoods on their residents. However, intragroup differences—how residents within the same community are differentially affected by that community—have received insufficient attention. In reality, even within the same community context (shared environment, diversity of residents and occupations, safety levels, infrastructure and public resources, culture, organizational environment, etc.), the degree of connection and interaction between different residents and the community context varies. This means that the impact of the community on an individual may also depend on the individual's level of "exposure." Small et al. (2011) argued that neighborhood effects research is at a crossroads; understanding the extent of community influence requires not only addressing selection bias and impact mechanisms but also focusing on the heterogeneity of these effects. Much of the literature on neighborhood effects seeks average effects, with a generation of researchers preoccupied with answering yes/no questions or questions of magnitude rather than conditional questions (under what circumstances do they matter?) [ 2 ] . Jencks and Mayer contended that communities influence average life chances, and future research should shift from focusing on average effects to investigating and explaining heterogeneity: whether neighborhoods matter depends on individual, neighborhood, and city characteristics [ 3 ] . Therefore, this study focuses on the heterogeneous health outcomes of different residents within the same community context and the associations between this exposure and community structure (concentrated disadvantage). It makes a first attempt to use latent class analysis (LCA) to generate "community lifestyle" types as a measurement of exposure. Unlike previous approaches that treated community structure solely as a macrolevel structural characteristic, they emphasized the role of different forms of individual daily "exposure" within different structural communities while also considering how macrolevel institutional structures shape microlevel interaction processes. Neighborhood and Health Define boundaries Neighborhood refers to the people living in a district/area, as well as the surrounding region or a specific nearby location. A community refers to all the people living in a particular area, country, etc.; a group of people who share the same religion, ethnicity, profession, etc.; it also includes the sense of sharing things and belonging to a group in the place where one lives. In China's governance system, community committees represent the most granular level of political and administrative division. These committees maintain a standardized organizational structure across urban areas, where they operate under the formal designation of "Residents' Committees" (Juweihui). Since the pre-Qin period, China has practiced "organizing households into li", where the scope and functions of the li closely resembled those of modern communities. In recent years, large-scale social surveys in China have often adopted probability proportional to size (PPS) sampling, which defines urban communities at the administrative level on the basis of neighborhood committee (juweihui) boundaries. According to this framework, a "community" refers to a social life collective formed by people living within a certain geographical area. In the current Chinese urban context, the scope of a "community" generally corresponds to the jurisdiction of a residents' committee (juweihui), following reforms that adjusted the size and structure of these administrative units. Given this, what is referred to as the "neighborhood effect" in Western literature is essentially equivalent to the "community effect" in the Chinese context—where the administrative boundaries of a juweihui define the spatial and social unit of analysis. Research on neighborhood effects and health has grown rapidly since the early 21st century, mostly based on observational data using census tracts to define neighborhoods and focusing on the influence of community structural characteristics as context. The socioeconomic disadvantage of a community is that it has adverse effects on a range of health outcomes in adults, including self-rated health (SRH) [ 4 ] , mortality [ 5 ] , chronic diseases [ 6 ] , and obesity [ 7 ] . However, this field has been hampered by a lack of theoretical and empirical attention to the fundamental mechanisms implied by most neighborhood theories—exposure. Neighborhood effects theory assumes that the causal influence of the environment operates through exposure to neighborhood processes relevant to development. However, traditional neighborhood effects research has not theorized the collective impact of individual-level spatial exposure processes or patterns on neighborhood outcomes. Instead, it assumes that residing within a geographically defined neighborhood implies an equal degree of exposure for all residents. This neglect of exposure diverts attention away from the "person–environment dynamics" that actually shape contextual influences. Owing to differences in urbanization levels, Western countries have made initial explorations in this area. For instance, Roberts et al. noted that a key limitation in current research on neighborhood characteristics and health is the assumption that people are fixed at their residential addresses. In reality, people encounter multiple other environments in their daily lives (e.g., time spent within the neighborhood), which can expose different groups to different environments. This exposure affects the strength of the link between neighborhood characteristics and health outcomes. Harding et al. (2011) used the example of neighborhood effects on education and proposed a new theoretical framework, suggesting that E (exposure) represents the "dosage" of different neighborhood characteristics an individual receives. Sources of this heterogeneity may stem from individual differences in social networks ("for whom"), variations in family characteristics, and interactions between family characteristics and the social environment. They conceptualized the mechanisms of neighborhood effects into three logically sequential processes: "neighborhood context → degree of exposure → degree of vulnerability." The first stage involves the extent to which an individual is connected to their residential neighborhood context. The second stage concerns how different types of households, after interacting with various neighborhood contexts, resist negative consequences or leverage positive effects. They argued that lifestyle and time allocation patterns are the most important indicators for measuring neighborhood exposure [ 8 ] . Thus, Harding and colleagues focused on the micromechanisms of within-group heterogeneity in neighborhood effects, proposing a process model and offering a solution from the perspective of "exposure." Currently, the neighborhood, as a space of nonfamilial exposure, has become a new focus in community research. Community exposure varies due to factors such as crime (safety issues), deinstitutionalization, and school location (institutional resources). These neighborhood environments influence the time allocation patterns of adolescents in disadvantaged communities; adolescents' time spent in the neighborhood is closely related to neighborhood characteristics (including concentrated disadvantage and violence). Disadvantaged neighborhoods with safety issues such as violence and shootings alter time allocation patterns—either retreating into the home or moving beyond the neighborhood—significantly impacting adolescent development, resource access, and heterogeneity in daily social exposure. From a policy perspective, community-based intervention strategies must be rooted in accurate information about how young people actually utilize their neighborhood environments. Focusing on aggregate daily activity patterns in shaping community social organization also benefits neighborhood effects research. If adult residents in disadvantaged neighborhoods spend less time within community boundaries, opportunities for interaction in public spaces are limited [ 9 ] . Both violence and school institutional resources are closely tied to community structure, leading to less community exposure/contact for adolescents in disadvantaged neighborhoods. Browning et al. noted that exposure to organizations, institutions, and other settings characterized by personal activities is a key mechanism through which neighborhoods influence adolescent outcomes. They used the concept of "ecological networks" to describe the aggregated patterns of shared local exposure, which are influenced by a neighborhood's socioeconomic characteristics and exert independent effects on its adolescents. Residents who interact more broadly within the community space because of routine activities exhibit higher levels of social capital, including familiarity, beneficial (weak) social ties, trust, and prosocial behavior. Therefore, ecological networks have important implications for understanding the complexity of contextual exposure [ 10 ] . In short, the authors propose a new concept and theoretical framework for residents' exposure/contact within neighborhoods, which is influenced by neighborhood socioeconomic characteristics and extends beyond neighborhood boundaries. Wen Ming, studying neighborhood deprivation, social capital, and regular exercise among adults, noted that the interaction between neighborhood structure and individual-level factors deserves attention because neighborhood effects vary across subpopulations with different sociodemographic characteristics. Different groups may experience varying degrees of exposure to their residential environment. For example, children, elderly individuals, and women might spend more time in their local communities because of lower labor force participation rates and stronger attachment to residential areas, making them potentially more sensitive to neighborhood environments [ 11 ] . However, the authors focused primarily on the interaction analysis between neighborhood structure and individual characteristics, with the dependent variable being health behavior—regular exercise. The above studies link community structural characteristics with individual exposure/contact, conceptualizing and operationalizing this influence, thereby expanding beyond the previous limitations of neighborhood effects research, which focused solely on community structure. The impact of community structure on resident health varies on the basis of an individual's daily level of "exposure" within the community. Although this context exposure related to activity spaces and ecological networks may extend beyond neighborhood boundaries, the theoretical approach based on community spatial exposure elucidates the links between neighborhood disadvantage characteristics and the social processes underlying youth health and well-being. Residing in socioeconomically disadvantaged neighborhoods shapes the characteristics of individual-level activity spaces—the set of locations and settings where residents are frequently exposed/contacted. Therefore, this study conceptualizes community-related lifestyles as the "exposure" factor through which community structure influences individual health outcomes. From a community structure perspective, individuals are homogeneously influenced by the community context, but from an individual perspective, it involves how individuals utilize the community context. Thus, the individual use of the community is treated as a crucial channel of contact (exposure). From a symbolic interactionist perspective, individual participation in the community itself shapes community social processes (e.g., cohesion), thereby influencing individual socioeconomic outcomes such as health. In this sense, those deeply involved in community interactions gain more. Therefore, this section adopts a microinteraction analytical perspective, categorizing individual health behaviors within the community into three types: institutionalized, socialized, and everyday "community participation." Community Lifestyle as "Exposure" In lifestyle research, numerous studies have focused on the impact of individual objective SES on health, representing the "social causality" explanation of health inequality. However, with disease transition, modernity development, and changing social identities, healthy lifestyles are not solely the result of individual choice but are also influenced by structural factors [ 12 ] . Among structural factors, most research focuses on effects at the individual level—differences in lifestyle exist among different social groups. For example, Western empirical studies generally find that individuals with higher SES are more likely to adopt healthy lifestyles. In contrast, in developing countries, higher SES groups may be less healthy, which is related to social transition and economic development levels, where higher-status groups are often the first affected, sometimes leading to unhealthy lifestyles. Chinese empirical findings are more complex: on the one hand, lower-status groups tend to have less healthy lifestyles; on the other hand, even among higher SES groups, explanations involving both "status constraints" and "lifestyle transition" coexist [ 13 ] . In empirical lifestyle studies, alongside single behavioral indicators, latent class analysis (LCA), which reflects the intrinsic connections between various behaviors, has developed rapidly. For example, Zhao Li et al. were among the first in China to classify the lifestyles of residents aged 18 + in Guangzhou into healthy and subhealthy behavioral groups via LCA [ 14 ] . Wang Fuqin used CGSS2010 data to categorize public lifestyles into healthy, mixed, and risky types [ 15 ] . Zhang Yun et al.'s LCA of elderly lifestyles revealed four types: survival-oriented, health-oriented, risk-oriented, and mixed, with the survival-oriented type being both unique and widespread among Chinese seniors [ 16 ] . At a higher community structural level, Li Xian used CLDS2014 data for LCA and multilevel analysis of contemporary healthy lifestyles, finding that community economic level had a significant positive impact on healthy lifestyles in both urban and rural areas, with spatial heterogeneity between them [ 17 ] . Building on existing individual lifestyle LCAs, this study expands the concept to "community lifestyles" related to individuals’ interactions with the community. This serves as an operationalized indicator of exposure, measuring the important micromechanism of the extent to which an individual is influenced by the community. Therefore, the research questions are as follows: Is the impact of concentrated disadvantage on resident health moderated by community exposure/contact (community lifestyle)? Does this effect vary by community structure? The research hypotheses are as follows: Hypothesis 1 The greater the individual's exposure to the community is, the stronger the impact of neighborhood disadvantage on self-rated health. Hypothesis 2 The lower the individual's exposure to the community is, the weaker the impact of neighborhood disadvantage on self-rated health. Hypothesis 3 An individual's exposure to the community is heterogeneously based on neighborhood disadvantage. Materials and methods Data sources The data used in this study are from the 2018 China Labor Force Dynamics Survey (CLDS2018). The CLDS is a nationally representative large-scale dynamic tracking survey of the labor force designed and implemented by the Social Science Survey Center of Sun Yat-sen University. It includes questionnaires at the individual, household, and community levels. These data have been widely recognized in academic research [ 18 , 19 ] . The 2018 CLDS sample covered 28 provinces, municipalities, and autonomous regions in China (excluding Hong Kong, Macao, Taiwan, Tibet, Hainan, and Xinjiang). The urban sample included 133 communities and 4,770 individual respondents (working-age population 15–64 years old and those over 64 still working). When clusters contain more than 25 individuals or households with an intraclass correlation (ICC) value exceeding 0.2, the bias in estimating aggregated variable effects remains below 10% [ 20 ] . In the CLDS survey, 35 households were randomly selected from each community. Both datasets involved relatively large samples of household and adult respondents, with intraclass correlation coefficients (ICCs) consistently exceeding 0.2. Therefore, using aggregated variables to characterize community social structure as population-level proxies introduces minimal bias. The final analytical sample consisted of 2,367 valid individuals nested within 133 communities. One core variable focuses on community-related health behavior indicators, particularly community social participation items newly added to the CLDS2018. Variable selection Dependent variable The independent variable in this study was self-rated health. It was measured on a five-point scale from "very healthy" to "very unhealthy." As this is an ordinal rather than continuous variable, it was dichotomized into "healthy" vs. "unhealthy" for analysis, yielding results similar to those of the ordinal approach. Independent variables One of the independent variables is neighborhood disadvantage. Owing to differences in the definitions of neighborhoods and communities, the measurement of community socioeconomic disadvantage varies across regions [ 21 , 22 , 23 , 24 ] . These indicators are critical in Western contexts, but due to differing social environments, they may not accurately reflect conditions in non-Western developing countries. For example, in China, racial composition and female-headed households are less common, whereas the influx of migrant populations due to urbanization is far more prominent. Moreover, China's community service and management system, which is primarily based on administrative divisions, differs significantly from that of Western countries. Therefore, it is necessary to develop community disadvantage indicators tailored to the Chinese context. Following Lei's approach [ 25 , 26 ] , this study constructs a composite socioeconomic status (SES) score for communities by randomly selecting households within each community and averaging key indicators: household net income, household wealth, average years of education among adult residents, and average occupational prestige score. It was constructed via community-level indicators: unemployment rate, poverty rate (World Bank standard), proportion of residents with education below high school, and median household income. These indicators constitute a comprehensive measurement of community socioeconomic status (SES) while also aligning with the construction of individual-level SES indicators. Principal component factor analysis was applied to these indicators. A factor with an eigenvalue > 1 was extracted to generate the "Community Concentrated Disadvantage" structural index. The factor loadings ranged from 0.60–0.84, and the cumulative explained variance was 52.28% [ 27 , 28 ] . Exposure was measured on the basis of community lifestyle factors. Lifestyle in social sciences includes risk behaviors (e.g., smoking, drinking) and health-promoting behaviors (e.g., exercise, sleep, diet). Chinese cities are experiencing a fifth disease transition characterized by sedentary lifestyles or a lack of exercise. Indicators of community-related lifestyles in CLDS2018 include smoking, drinking, regular exercise, community participation, and neighborly mutual assistance. While smoking, drinking, and exercise might occur outside the community, this study views these habits as daily, routinized behavioral patterns closely linked to one's living space, hence treating them as proxies for activity within the community life unit." Have you ever smoked for one year or more continuously? ", "Have you ever drunk alcohol at least once a week?, "Have you engaged in regular exercise in the past month?". Community participation categorized residents' participation in social organization activities into seven types: "recreation/arts," "sports/exercise," "seniors' associations," "skill training/correspondence," "knowledge learning," "volunteer groups," and "religious groups." The frequency of participation in each category ranged from "never" to "daily." The participants were further asked if these activities mainly took place within the community. For data processing, a resident was considered to have "community participation" if they participated in at least one of these seven types of activities and reported that participation occurred within the community. Neighborly mutual assistance is used as a behavioral indicator of neighborly interaction: "Do you and your neighbors/other residents in this community (village) help each other?" (five-point scale from "very little" to "very much"). This dichotomous approach, with questions coded in the same direction, further highlights the patterned characteristics of community lifestyles. Control Variable The study included several control variables: age, gender (female, male), marital status (married, unmarried), education level (primary school or below, junior high school, senior high school or equivalent, college or above), logannual income (personal annual income, missing values imputed with means, then log(income + 1)), occupational status index (ISEI score derived from occupational codes), and subjective social status (scale 1–10, low to high). Research methods Latent class analysis As a method to divide an average population into different homogeneous subgroups, it is particularly suitable for studying health lifestyles. Applied here, it identifies groups with distinct characteristics regarding community exposure/contact, characteristics obscured by average effects. It is a typological method. Multilevel Modeling Communities and individuals have a typical nested hierarchical structure. Multilevel models can distinguish between between between-group (community) and within-group (individual) differences, testing the effects at each level and the contribution of each level to the dependent variable. Since the dependent variable (self-rated health) is dichotomous, a multilevel logistic regression model (logit) was used for analysis. Research Results Descriptive characteristics Summary statistics for all variables are presented in Table 1 . The study sample (N = 2,367) exhibited a balanced gender distribution (51.58% male, 48.42% female), with predominantly married participants (82.59%). The population had a mean age of 42.93 years (SD = 11.52) and displayed educational polarization, with 30.29% attaining middle-level education and 32.83% attaining advanced-level education. The economic indicators revealed a log-transformed mean household income of 10.15 (SD = 2.78) and an average subjective social status of 4.63 (SD = 1.72) on a Likert-type scale, whereas neighborhood disadvantage was standardized (M = 0, SD = 1) for comparative analysis. Health outcomes showed that 69.41% of respondents self-reported themselves as healthy versus 30.59% as unhealthy, suggesting a 2:1 ratio favoring positive health assessments in this population." Table 1 Characteristics of the participants Mean or N SD or % Gender Male 1221 51.58 Female 1146 48.42 Marital status Married 1955 82.59 Unmarried 412 17.41 Education Primary level 315 13.31 Middle level 717 30.29 High level 558 23.57 Advance level 777 32.83 Age 42.93 11.52 Annual household income (log) 10.15 2.78 Isei 4.63 1.72 Subjective social status 34.27 23.54 Neighborhood disadvantage self-rated health Healthy Unhealthy 0 1643 724 1 69.41 30.59 Latent class analysis of exposure: community lifestyle An LCA was first performed on the five observed health behavior indicators to estimate patterns of community-related lifestyles among urban residents in China. Table 2 compares the fit statistics for models with one to four latent classes. The AIC and BIC values for the 3-class and 4-class models are relatively close. However, model fit statistics indicated that the 3-class solution had a significant p value (< 0.000), whereas the 4-class solution did not. The predicted probabilities for the last three classes in the 4-class model were similar (ranging from 0.10–0.16). Furthermore, the predicted means for behaviors such as regular exercise, community participation, and mutual assistance were similar and difficult to distinguish and name for classes 2 and 3 in the 4-class solution (e.g., for class 2: 0.63/0.99/0.44 vs. class 3: 0.75/0.99/0.55). Therefore, the 3-class solution was selected as the optimal solution. Table 2 Fit statistics for latent class models (1–4 classes) Latent Classes N AIC BIC Categorical Probability Class1 2367 14321 14408 0.36 Class2 2367 14108 14123 0.42/0.31 Class3 2367 13944 14042 0.63/0.23/0.12 Class4 2367 13925 14058 0.59/0.16/0.10/0.15 Table 3 reports the predicted probabilities of the five health behaviors within the three latent classes. Class 1 shows low predicted probabilities for smoking (8.9%) and drinking (18.7%), along with the lowest community participation (5.5%). The rates of regular exercise and neighborly mutual assistance are also relatively low. Thus, Class 1 is composed of residents who do not smoke, do not drink frequently, do not exercise regularly, and rarely participate in community activities. It is named the "passive" community lifestyle. Class 3 consists of residents with high rates of smoking (100%) and drinking (99%), alongside some community participation, mutual assistance, and moderate regular exercise (51.9%). This indicates the coexistence of health-promoting and risk behaviors, hence the "mixed" community lifestyle. Residents in Class 2 have the highest predicted rate of community participation (99.1%), the highest proportion engaging in regular exercise (67.4%), and the highest level of neighborly mutual assistance among the three classes (47.8%). This suggests good community interaction and a tendency toward health-promoting behaviors, leading to an "active" community lifestyle. Table 3 Sample distribution and predicted probabilities of community lifestyles Latent Classes Lifestyle Measure N % Class1 Negative Class2 Positive Class3 Mixed Smoke Yes 643 27.17 0.089 0.122 1 NO 1724 72.83 0.911 0.878 0.000 Drink Yes 488 20.62 0.187 0.135 0.99 NO 1879 79.38 0.813 0.865 0.01 Exercise Yes 1061 55.18 0.353 0.674 0.519 NO 1306 44.82 0.647 0.326 0.481 Neighborhood Participation Yes 725 30.63 0.055 0.991 0.303 NO 1642 69.37 0.945 0.009 0.697 Neighborhood reciprocity Yes 1466 61.93 0.348 0.478 0.364 NO 901 38.07 0.652 0.522 0.636 Categorical Probability 0.646 0.234 0.120 Note: Percentages for regular exercise appear miscalculated in the original (1061/2367 ≈ 44.82% Yes,1306/2367 ≈ 55.18% No). The predicted probabilities reflect the model estimates. On the basis of the posterior probabilities from the 3-class model, the estimated sample distribution across classes is shown in Table 4 . The negative type was the largest category, comprising 60.03% of the total sample, with a cumulative percentage of 60.03%. The positive type, the second-largest category, comprised 628 individuals, resulting in a cumulative percentage of 86.57%. The mixed type was the smallest group, at 13.43% of the sample." Overall, the positive lifestyle type in the community requires improvement. Table 4 Estimated probability sample values for latent class analysis fre % Cumulative odds Negative 1421 60.03 60.03 Positive 628 26.53 86.57 Mixed 318 13.43 100 N 2367 Demographic and socioeconomic characteristics by community lifestyle What are the profiles of residents with different community lifestyles? What demographic and socioeconomic characteristics do they exhibit? Descriptive statistical analysis was conducted for the three latent classes. The results of the chi-square tests and ANOVA indicated significant differences in these characteristics across community lifestyle types (Table 5 ). Table 5 Demographic and socioeconomic characteristics of residents by community lifestyle All Negative Positive Mixed Age(M) 42.93 42.86 42.18 44.75 Gender(%) Male 51.58 44.48 44.27 97.80 Female 48.42 55.52 55.73 2.21 Marital status(%) Unmarried 17.41 17.03 19.59 14.78 Married 82.59 82.97 80.41 85.22 Education(%) Primary level 13.31 16.64 7.48 10.38 Middle level 30.29 32.72 21.82 36.16 High level 23.57 22.52 22.77 29.87 Advance level 32.83 28.22 47.93 23.58 Annual household income (M) 10.15 10.04 10.36 10.23 Isei(M) 34.27 34.01 36.61 30.78 Subjective social status (M) 4.63 4.60 4.96 4.14 Note: "Chi-square tests for categorical variables (gender, marital status, education) showed significance at p < .000 for all except marital status. ANOVA for continuous variables (age, annual household income, income, and subjective social status) revealed that all variables were significant at p < .05." Table 5 shows that residents with the passive community lifestyle type had a mean age of 42.86 years, were predominantly female, and were mostly married. The highest proportion had junior high school education (32.72%), and both objective and subjective SES indicators were below the sample average. The active type had a similar gender distribution to the passive type but a slightly lower proportion of married individuals. The main difference lies in SES: the active group had the highest proportion of those with a college education or above (47.93%), far exceeding the sample average (32.83%), and the other two groups. Additionally, their log income, occupational status index, and subjective social status were significantly higher than the means. Residents with a mixed community lifestyle were slightly older than average and predominantly male, with the highest proportion having junior high education (36.16%). Their mean log income was slightly above average, but the occupational status index and subjective social status were below average, presenting a more complex profile. Neighborhood disadvantage and self-rated health in China T he role of community lifestyle exposure Previous studies often treat latent classes as dependent variables. This study incorporates community lifestyle type as a moderator variable. The results are presented in Table 7 . Table 7 Multilevel logit regression analysis of neighborhood disadvantage, lifestyle, and self-rated health Model1 Model2 Model3 Model4 High neighborhood disadvantage -0.434 ** -0.439 ** -0.340 * -0.489 ** (ref: Low neighborhood disadvantage) (0.146) (0.147) (0.149) (0.170) Positive -0.071 -0.181 -0.349 * (ref: negative) (0.115) (0.120) (0.154) Mixed -0.085 0.008 -0.148 (0.140) (0.155) (0.209) Gender(ref: female) -0.078 -0.080 (0.106) (0.106) Age -0.035 *** -0.035 *** (0.005) (0.005) Marital status (ref: married) 0.109 0.104 (0.143) (0.144) Education (ref: Primary level) Middle level 0.109 0.096 (0.157) (0.157) High level 0.028 0.012 (0.174) (0.174) Advance level 0.050 0.045 (0.189) (0.190) Annual household income (log) -0.014 -0.014 (0.019) (0.019) Isei 0.004 + 0.004 + (0.002) (0.002) Subjective social status 0.221 *** 0.220 *** (0.030) (0.030) Cross-level interaction High neighborhood disadvantage x Positive 0.414 + (ref: Low neighborhood disadvantage x Negative) (0.242) High neighborhood disadvantage x Mixed 0.317 (0.287) Constant 1.083 *** 1.115 *** 1.585 *** 1.672 *** (0.094) (0.103) (0.393) (0.396) Random part Var (neighborhood constant) 0.325 *** 0.324 *** 0.290 *** 0.293 *** (0.077) (0.077) (0.073) (0.074) Var (Residual) 0.000 (0.000) Number of individuals 2367 2367 2367 2367 Number of neighborhoods 133 133 133 133 Note: Standard errors in parentheses; significance levels: ⁺ p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001 Table 7 , Model 1 shows that, without considering other factors, residents in communities with higher concentrated disadvantage reported significantly poorer self-rated health (OR = e⁻⁰·⁴³⁴ ≈ 0.65, or 35% lower odds of being healthy). Controlling for concentrated disadvantage in Model 2, the main effects of community lifestyle type were not significant. Model 3 includes individual demographic and socioeconomic characteristics, as well as community lifestyle type. The significant negative effect of high concentrated disadvantage persists (β = -0.340, OR ≈ e⁻⁰·³⁴⁰ ≈ 0.71, or 29% lower odds), which is consistent with Western "neighborhood effects" research. Compared with residents in low disadvantage communities, those in high disadvantage communities have 29% lower odds of reporting good health, even after controlling for individual characteristics and lifestyle type. Without controls (Model 1), this disadvantage was 35%. This difference is partially explained by individual SES characteristics and community lifestyle type. Comparing Model 1 and Model 3, the variance in the community-level intercept decreased from 0.325 to 0.290, indicating that community structure (concentrated disadvantage) explained approximately 3.5% of the variance in urban residents' health. This is close to the 3.2% variance in youth lifestyle differences explained by community average income [14] , confirming that community concentrated disadvantage is a valid indicator for explaining resident health. Models 1–3 show that the main effect of community lifestyle type on resident health is not significant. This differs from individual lifestyle studies, such as Wang Fuqin's finding that healthy lifestyles (fitness/physical activity) directly impact health levels [12]. The effect of community-concentrated disadvantage changes slightly but remains significantly negative, demonstrating its robustness and independence. Both individual-level socioeconomic position and neighborhood disadvantage are associated with mental well-being [ 29 ] . Residents in communities with higher socioeconomic status (SES) are more likely to maintain an active community lifestyle (and thus have better health outcomes), which aligns with the relationship observed between individual SES groups and lifestyle. However, this finding differs from that of Ross's international study, which revealed that poorer communities exhibited more walking behavior and that higher community education levels were associated with increased walking—although not necessarily with other forms of exercise [ 30 ] . To examine how the different exposure levels of resident groups within communities moderate the relationship between community structure and health (i.e., whether differences in exposure/lifestyle type affect the impact of community disadvantage), Model 4 adds interaction terms between community lifestyle type and concentrated disadvantage. The results show that after controlling for other variables, the negative effect of high concentrated disadvantage (compared with low) on resident health changes from OR ≈ 0.71 (β = -0.340) in Model 3 to OR ≈ 0.61 (β = -0.489) in Model 4. In terms of the main effects, compared with the passive lifestyle, residents adopting the active community lifestyle had significantly lower odds of good health (β = -0.349, OR ≈ 0.71, or 29% lower). As Table 5 shows, this group has relatively high objective and subjective SES but resides in high-disadvantage communities. Despite higher levels of regular exercise, neighborly assistance, and community participation (indicating greater exposure/contact), they may experience greater dissatisfaction, which negatively impacts their health evaluation. This group exhibits strong "specificity." Recently, there has been growing scholarly interest in "gentrifying" communities, stemming largely from the post-2000 concentration of high-quality school districts in central urban areas, fueling demand for "school district housing" and real estate booms, leading to population shifts and socioeconomic/residential spatial differentiation. In the Chinese context, the residential community is closely tied to various citizen rights, with education being one. Policies linking public education resources to household registration (hukou) and residence location drive many residents to move into older urban areas with more concentrated disadvantages in pursuit of "school district housing." Research has revealed that when middle-class whites move into previously low-income neighborhoods (termed "gentrification"), neighborhoods become more diverse [ 31 ] . The interaction term shows that, compared with residents with a passive lifestyle in low-disadvantage communities (reference group), residents with an active lifestyle in high-disadvantage communities have significantly higher odds of good health (β = 0.414, p < 0.10, OR ≈ e⁰·⁴¹⁴ ≈ 1.51). This finding indicates that an active community lifestyle has a significant positive moderating effect on health for residents in highly disadvantaged communities. With respect to individual demographic and socioeconomic characteristics, older age is significantly associated with poorer health. The effect of education level is not significant. The occupational status index and subjective social status have significant positive effects and are relatively robust. Residents with higher occupational status have significantly better health. As the basis of modern social stratification, occupation is not only a bridge between education and income but also a source of prestige and power. The impact of education is not significant, possibly because the occupational status index was controlled for. Log income also had no significant effect, suggesting that it is not income per se. Zhou et al. (2012) reported that income inequality still significantly negatively impacts individual health after controlling for the "depression effect" of absolute income on health [ 32 ] . This suggests that different indicators of objective SES have distinct impact mechanisms. Furthermore, the influence of subjective social status (subjective class identification) cannot be ignored. Xu Yan, on the basis of CLDS 2012 data, reported that subjective class identification acts as a mediating variable linking objective economic status (income and education) with self-rated health and recent objective health. Subjective class identification is an important sociopsychological mechanism affecting physical health, with unique characteristics in the Chinese sociocultural context [ 33 ] . Although the aforementioned models controlled for as many individual and household characteristics affecting residential selection as possible, they may still face the greatest challenge in neighborhood effect research: the issue of selection bias. Adopting a quasiexperimental approach, this study employs propensity score matching (PSM) to examine the "net effect" of concentrated neighborhood disadvantage on residents' health. The analysis followed these steps. First, we estimated the probability of residing in a high-disadvantage neighborhood. The results indicate that residential selection across neighborhoods of different socioeconomic statuses is nonrandom but exhibits systematic selectivity (as demonstrated by mean comparisons). These findings confirm that neighborhood selection is selective rather than random. The full propensity score matching model specifications are presented in the Appendix. Discussion What is the most valuable knowledge sociology provides? structural analysis. This is its unique distinction from other disciplines. To uncover the causes and social consequences of a series of phenomena, structural thinking is essential. Structural analysis grounded in social facts is not only an analytical method but also a theoretical perspective, a specific worldview, and a way of understanding society [ 34 ] . Existing health research in sociology focuses on health inequalities stemming from social stratification, whereas epidemiology focuses on structural and agentic health lifestyles. However, both perspectives are based on individual-level analysis. Émile Durkheim, in the Rules of Sociological Method, stated, "A social fact is every way of acting, fixed or not, capable of exerting over the individual an external constraint; or again, every way of acting which is general throughout a given society, while at the same time existing in its own right independent of its individual manifestations" [ 35 ] . Social facts possess externalities and constraints. The community, as a place reflecting residents' social status, values, and connections with others, while influenced by residential preferences and purchasing power, is also the result of "structural constraints" (e.g., individual purchasing power constrained by market housing prices). In this sense, community influence represents a "social fact" from a structural analytical perspective. Although constrained by higher-level macrostructures and institutions, the community, as the primary social living space for individuals, represents the public sphere between the state and the family. On the one hand, individuals leave the family and connect with society and the state through community public life. On the other hand, the state connects with families through communities, embedding political sentiments, state responsibilities, and policy protections into the family—the fundamental unit supporting Chinese society and the state. This top-down and bottom-up interaction, which involves internal-external and external-internal linkages, constitutes the greatest "secret" of grassroots social governance in China. Within communities, people experience real China. In this sense, the state is within the community, materializing "China in the Community" [ 36 ] . Therefore, the community is a "small incision" reflecting national changes, embodying the modernization level of the national governance system and capacity. This study adopts a structural analytical perspective centered on the community. Building on the concept of "exposure" in recent neighborhood effects research, it moves beyond the traditional view of community structure as a homogeneous context influencing individual behavior and outcomes. Instead, it investigates how varying degrees of individual exposure influence the association between community structure and resident health, exploring heterogeneous effects. Operationally, it employs latent class analysis to generate "community lifestyle" types as indicators of individual exposure. First, the LCA revealed three current community lifestyle types among Chinese urban residents: passive, active, and mixed. The Active type exhibited the highest levels of social participation within the community, neighborly mutual assistance, and regular physical exercise. This group has the strongest "adhesion" to the community, indicating the highest degree of exposure. They possess relatively high objective and subjective SES, particularly the proportion with a college education or above, which far exceeds that of the other groups. The passive type is the opposite. The mixed group presented a complex profile; apart from log income slightly above average, other indicators (education above high school, occupational status, and subjective social status) were below average. This reflects the divergent impacts of different SES indicators on community lifestyles during social transition, highlighting the unique materialistic logic of income. Sampson noted that health-related issues are closely linked to the social characteristics of communities and neighborhoods. We cannot simplistically treat communities as attributes of individuals; the community context itself should be regarded as a crucial unit of analysis, requiring new measurement strategies and theoretical frameworks [ 37 ] . Research from the Project on Human Development in Chicago Neighborhoods confirms a direct link between the community context and health, even in experimental studies [ 38 ] . This study aligns with this community structural perspective. At the community level, higher community SES is associated with a greater tendency toward an active lifestyle but also coexists with the passive type. A similar trend exists at the individual level. Although three theoretical perspectives exist in community research—"community lost," "community saved," and "community liberated"—this study's findings support the "community saved" thesis. Despite urbanization, globalization, and networking potentially weakening ties to territorial communities, Sampson noted that, in reality, neighborhood inequalities in life chances have significantly increased and worsened due to globalization. Paradoxically, the concept of community has flourished more widely despite globalization [ 39 ] . Xie Guihua et al. (2021) argued that residential community characteristics largely reflect the effects of urbanization. Urbanization significantly impacts resident community interactions, having a distancing effect; higher urbanization correlates with looser ties within neighborhoods. However, this effect is not homogeneous and varies by community, meaning that the "community saved" thesis holds for certain communities. This study also revealed that resident community interaction (neighbor assistance) and formal community participation are distinct. Residents with higher education levels are more likely to volunteer in community activities. Community interaction might decline, but this does not mean that people stop participating in community affairs; the "community lost" thesis may better explain community participation [ 40 ] . Second, community structure significantly impacts resident health. Residents in communities with lower concentrated disadvantages reported better health. This effect persists even after controlling for individual and household characteristics, which is consistent with Western "neighborhood effects" research. Health inequalities stemming from individual structural factors are now extending to residential communities. Pickett, in a review of multilevel analyses of neighborhood structure and health outcomes, noted that 23 out of 25 studies reported at least one statistically significant association between a neighborhood indicator and a health outcome (context effect). After adjustment for individual-level socioeconomic status (compositional effect), the magnitude of context effects was moderate, smaller than that of compositional effects, but evidence for a moderate impact of neighborhood structure on health was fairly consistent [ 41 ] . Sampson more directly stated that existing health research neglects theories at the neighborhood level and measurement schemes such as "ecometrics" (focused on collective phenomena). Health research can leverage social science to creatively consider community-based intervention strategies. Neighborhood-level interventions attempting to alter places and social environments, rather than individuals, could complement traditional individual-focused approaches and prove fruitful [ 42 ] . Furthermore, the relationship between community structure and health depends on differences in individual community exposure/contact. The interaction effect shows that, compared with residents with a passive lifestyle in low-disadvantage communities, those with an active lifestyle in high-disadvantage communities report significantly better health. Research on health stratification from a sociological perspective identifies individual proximal factors such as socioeconomic status, explained by the "social causality" theory of health inequality. However, lifestyles depend on multiple social environments. The community, as a site of daily activities, is particularly important for those spending long hours at home. It also serves as an important consumption space, forming part of the class-based habitus described in Bourdieu's framework of class reproduction, transmitted through social and cultural capital within certain social ecologies (communities). In the Chinese context, the community is a "complex", integrating "political, service (administrative), and social" functions. Residents' needs for the community exist at the most basic level of daily life. The existing organizational system selectively channels social forces into the service sector, helping meet resident needs and reducing demand for political participation. In this sense, increasing individual exposure/contact with the community helps individuals connect with society and the state through community public life. Simultaneously, the state connects with families through communities, embedding political sentiments, state responsibilities, and policy protections into the family unit. This top-down and bottom-up interaction, which involves internal-external and external-internal linkages [ 43 ] , facilitates "good governance" in Chinese grassroots society. Policy recommendations Effective health promotion requires integrated interventions that address the physical infrastructure and social organizational structures of communities simultaneously. This approach is particularly critical in socioeconomically deprived neighborhoods exhibiting compounded disadvantages. Interventions should focus on transforming the physical environment to universally influence residents’ behaviors—a "place-based" approach centered on the principle of “changing the place to change the people.” Key actions include implementing urban renewal projects to develop community fitness trails, multifunctional sports facilities, and community gardens, ensuring equitable access to recreational infrastructure within walking distance. Planning interconnected greenway networks can link natural spaces, parks, and residential areas to create safe and attractive environments for walking and cycling. Additionally, establishing self-service health monitoring stations in accessible community locations provides free, convenient health management tools. Collaborating with local vendors to create “low-salt, low-oil” food zones with clear nutritional labels helps guide healthier food choices. Improving public safety through better lighting in alleys and parks encourages nighttime activity, especially among older adults, while transforming vacant lots into small green spaces enhances aesthetics, reduces urban heat, and supports social interaction. In addition, “people-based” strategies should aim to directly empower individuals and groups by enhancing their knowledge, skills, motivations, and social networks—essentially “changing the people to better utilize the place.” This can be achieved through health education initiatives such as chronic disease self-management workshops and practical healthy cooking classes tailored to at-risk groups. Building social support is also vital; facilitating community organizations such as senior dance groups and parent‒child activity clubs promotes physical activity while strengthening mental well-being and social cohesion. For vulnerable populations, personalized support—such as a “Community Health Steward” program with in-home assessments and tailored activity plans—can foster engagement. Mobile health applications with gamified challenges and incentives offer innovative means to encourage sustained healthy behaviors. The greatest public health benefits emerge from strategically combining place-based and people-based approaches. For example, new parks should be activated with organized activities, and community groups should be supported with improved facilities. Such synergistic, dual-dimensional interventions maximize health promotion impacts. To translate these recommendations into action, we urge policymakers to allocate at least 30% of public health funding toward structural community interventions and to institutionalize a health impact assessment system to ensure policy coherence and effectiveness across sectors. Strengths and limitations The strengths of this study lie in its adoption of a community structure analysis perspective and its systematic examination, which is based on the nationwide large-scale CLDS database, of the direct impact of neighborhood disadvantage on the self-rated health of urban community residents, as well as the moderating effect of community lifestyle (latent class analysis). This approach transcends the limitations of traditional individual-level behavioral interventions and validates the cost-effectiveness advantages of the "community as an intervention platform"—highlighting the long-term health benefits of interventions targeting both environmental and social dimensions. This study provides an evidence base for the implementation of the "Healthy China" initiative at the community level and recommends that future policies allocate specific public health funds toward structural community improvements while establishing a "health impact assessment" system to ensure policy coherence. Although the research strives for rigor, the following limitations need to be addressed in future studies: definitions of neighborhood and community; the scale of a community is larger than that of a neighborhood, and there may be internal heterogeneity. If available, more precise census data should be utilized. Subjective data bias: Reliance on self-reported health data may introduce subjective bias. Future studies should incorporate objective health indicators, such as activities of daily living and the number of chronic diseases, to increase data accuracy. Incompletely controlled confounding factors: Although propensity score matching (PSM) was used to control for selection bias and as many control variables as possible were included, potential confounding factors may not have been fully accounted for, particularly the influence of length of residence. Longitudinal data should be employed to strengthen the robustness of the results. Conclusion Neighborhood disadvantage in urban China significantly impacts residents' health. Residents in communities with lower concentrated disadvantages exhibit better health outcomes. This association persists even after controlling for individual and household characteristics, which is consistent with western research on "neighborhood effects." Latent class analysis revealed that urban residents in China currently fall into three community lifestyle categories: "negative type," "positive type," and "mixed type." The relationship between community structure and health further depends on differences in individuals’ level of "exposure" (i.e., community lifestyle). Compared with residents with a negative lifestyle in low-disadvantage communities, those adopting a positive lifestyle in high-disadvantage communities demonstrate significantly better health. Studies indicate that it is necessary to implement dual-dimensional "environmental‒social" interventions at the community level to promote the adoption of positive and healthy community lifestyles among older adults in China, thereby advancing the development of a healthy aging society. Declarations Author details 1 Journal Center, Guangzhou Sport University, Guangzhou, China 2 School of Leisure and Digital Sport, Guangzhou Sport University, Guangzhou, China Funding This study was supported by the Guangdong Philosophy and Social Sciences Planning (Grant No. GD25YTY04) and the Young Doctor Project of Guangzhou Sport University (Project No. 5250180628). Data availability The datasets(questionnaire, individual and neighborhood data) used and/or analyzed during the current study are available from the official website of the China Labor-force Dynamics Survey (CLDS) at: https://isg.sysu.edu.cn . Declarations Ethics approval and consent to participate This study used data from the China Labor-force Dynamics Survey (CLDS). All procedures were conducted in accordance with relevant guidelines and regulations involving human participants (e.g., the Declaration of Helsinki or similar). The project was approved by the Ethics Committee of Sun Yat-sen University. Jinfu served as one of the staff members for the survey and received permission from the CLDS team to use this. Consent for publication Not applicable. Competing interests The authors declare no competing interests Author Contribution The study was conceptualized by Jinfu Xu and Chunru Shang. Jinfu Xu was responsible for the conceptualization, methodology, formal analysis, investigation, original draft preparation, review, editing, and supervision of the study. Chunru Shang contributed to the conceptualization. All the authors have read and agreed to the published version of the manuscript. Acknowledgement We appreciate Social Science Survey Center of Sun Yat-sen University for providing the China Labor Force Dynamics Survey (CLDS) data. Data Availability The datasets used and/or analyzed in the current study are available from the official website of CLDS, https://isg.sysu.edu.cn References Chamberlain AM, Finney Rutten LJ, Wilson PM, et al. 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Wu X, Qin W. Moving beyond the "Tönnies Myth": The construction and theoretical justification of the western concept of community over the past century. Fudan Journal (Social Sciences Edition).2022; 64(1): 134–147. Additional Declarations No competing interests reported. Supplementary Files Supplementaryfile.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 20 Oct, 2025 Reviews received at journal 13 Oct, 2025 Reviewers agreed at journal 06 Oct, 2025 Reviewers invited by journal 06 Oct, 2025 Editor invited by journal 08 Sep, 2025 Editor assigned by journal 24 Aug, 2025 Submission checks completed at journal 24 Aug, 2025 First submitted to journal 20 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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The Party and the state are deeply concerned about people's health and have issued the \"Healthy China 2030\" Outline Program and the \"Healthy China Action (2019\u0026ndash;2030) \" and other documents. With the transformation of the human disease pedigree, the traditional view of health \"centered on the treatment of illness\" has been transformed into a \"people-centered\" holistic view. The World Health Organization defines health as \"a state of complete physical, mental psychological and social well-being.\" Therefore, research on health problems both theoretically and practically is highly important, especially given the background of building a well-off society in an all-round way.\u003c/p\u003e\u003cp\u003eResearch on health determinants has largely focused on individual structural effects, whereas studies examining community structure, or \"neighborhood effects,\" remain relatively scarce. Neighborhood effects investigate whether and how the community context influences residents' socioeconomic outcomes, adopting a structural analytical perspective. This perspective primarily concerns how different neighborhood characteristics impact individuals. For example, neighborhood socioeconomic disadvantage plays a role in health above and beyond individual measures of socioeconomic status\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e1\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. This research emphasizes intergroup differences\u0026mdash;the impact of differences between neighborhoods on their residents. However, intragroup differences\u0026mdash;how residents within the same community are differentially affected by that community\u0026mdash;have received insufficient attention.\u003c/p\u003e\u003cp\u003eIn reality, even within the same community context (shared environment, diversity of residents and occupations, safety levels, infrastructure and public resources, culture, organizational environment, etc.), the degree of connection and interaction between different residents and the community context varies. This means that the impact of the community on an individual may also depend on the individual's level of \"exposure.\" Small et al. (2011) argued that neighborhood effects research is at a crossroads; understanding the extent of community influence requires not only addressing selection bias and impact mechanisms but also focusing on the heterogeneity of these effects. Much of the literature on neighborhood effects seeks average effects, with a generation of researchers preoccupied with answering yes/no questions or questions of magnitude rather than conditional questions (under what circumstances do they matter?)\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e2\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Jencks and Mayer contended that communities influence average life chances, and future research should shift from focusing on average effects to investigating and explaining heterogeneity: whether neighborhoods matter depends on individual, neighborhood, and city characteristics\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e3\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTherefore, this study focuses on the heterogeneous health outcomes of different residents within the same community context and the associations between this exposure and community structure (concentrated disadvantage). It makes a first attempt to use latent class analysis (LCA) to generate \"community lifestyle\" types as a measurement of exposure. Unlike previous approaches that treated community structure solely as a macrolevel structural characteristic, they emphasized the role of different forms of individual daily \"exposure\" within different structural communities while also considering how macrolevel institutional structures shape microlevel interaction processes.\u003c/p\u003e\n\u003ch3\u003eNeighborhood and Health\u003c/h3\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eDefine boundaries\u003c/h2\u003e\u003cp\u003eNeighborhood refers to the people living in a district/area, as well as the surrounding region or a specific nearby location. A community refers to all the people living in a particular area, country, etc.; a group of people who share the same religion, ethnicity, profession, etc.; it also includes the sense of sharing things and belonging to a group in the place where one lives.\u003c/p\u003e\u003cp\u003eIn China's governance system, community committees represent the most granular level of political and administrative division. These committees maintain a standardized organizational structure across urban areas, where they operate under the formal designation of \"Residents' Committees\" (Juweihui). Since the pre-Qin period, China has practiced \"organizing households into li\", where the scope and functions of the li closely resembled those of modern communities. In recent years, large-scale social surveys in China have often adopted probability proportional to size (PPS) sampling, which defines urban communities at the administrative level on the basis of neighborhood committee (juweihui) boundaries. According to this framework, a \"community\" refers to a social life collective formed by people living within a certain geographical area. In the current Chinese urban context, the scope of a \"community\" generally corresponds to the jurisdiction of a residents' committee (juweihui), following reforms that adjusted the size and structure of these administrative units. Given this, what is referred to as the \"neighborhood effect\" in Western literature is essentially equivalent to the \"community effect\" in the Chinese context\u0026mdash;where the administrative boundaries of a juweihui define the spatial and social unit of analysis.\u003c/p\u003e\u003cp\u003eResearch on neighborhood effects and health has grown rapidly since the early 21st century, mostly based on observational data using census tracts to define neighborhoods and focusing on the influence of community structural characteristics as context. The socioeconomic disadvantage of a community is that it has adverse effects on a range of health outcomes in adults, including self-rated health (SRH)\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e4\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e, mortality\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e5\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e, chronic diseases\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e6\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e, and obesity\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e7\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. However, this field has been hampered by a lack of theoretical and empirical attention to the fundamental mechanisms implied by most neighborhood theories\u0026mdash;exposure. Neighborhood effects theory assumes that the causal influence of the environment operates through exposure to neighborhood processes relevant to development. However, traditional neighborhood effects research has not theorized the collective impact of individual-level spatial exposure processes or patterns on neighborhood outcomes. Instead, it assumes that residing within a geographically defined neighborhood implies an equal degree of exposure for all residents. This neglect of exposure diverts attention away from the \"person\u0026ndash;environment dynamics\" that actually shape contextual influences.\u003c/p\u003e\u003cp\u003eOwing to differences in urbanization levels, Western countries have made initial explorations in this area. For instance, Roberts et al. noted that a key limitation in current research on neighborhood characteristics and health is the assumption that people are fixed at their residential addresses. In reality, people encounter multiple other environments in their daily lives (e.g., time spent within the neighborhood), which can expose different groups to different environments. This exposure affects the strength of the link between neighborhood characteristics and health outcomes. Harding et al. (2011) used the example of neighborhood effects on education and proposed a new theoretical framework, suggesting that E (exposure) represents the \"dosage\" of different neighborhood characteristics an individual receives. Sources of this heterogeneity may stem from individual differences in social networks (\"for whom\"), variations in family characteristics, and interactions between family characteristics and the social environment. They conceptualized the mechanisms of neighborhood effects into three logically sequential processes: \"neighborhood context \u0026rarr; degree of exposure \u0026rarr; degree of vulnerability.\" The first stage involves the extent to which an individual is connected to their residential neighborhood context. The second stage concerns how different types of households, after interacting with various neighborhood contexts, resist negative consequences or leverage positive effects. They argued that lifestyle and time allocation patterns are the most important indicators for measuring neighborhood exposure \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e8\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Thus, Harding and colleagues focused on the micromechanisms of within-group heterogeneity in neighborhood effects, proposing a process model and offering a solution from the perspective of \"exposure.\"\u003c/p\u003e\u003cp\u003eCurrently, the neighborhood, as a space of nonfamilial exposure, has become a new focus in community research. Community exposure varies due to factors such as crime (safety issues), deinstitutionalization, and school location (institutional resources). These neighborhood environments influence the time allocation patterns of adolescents in disadvantaged communities; adolescents' time spent in the neighborhood is closely related to neighborhood characteristics (including concentrated disadvantage and violence). Disadvantaged neighborhoods with safety issues such as violence and shootings alter time allocation patterns\u0026mdash;either retreating into the home or moving beyond the neighborhood\u0026mdash;significantly impacting adolescent development, resource access, and heterogeneity in daily social exposure. From a policy perspective, community-based intervention strategies must be rooted in accurate information about how young people actually utilize their neighborhood environments. Focusing on aggregate daily activity patterns in shaping community social organization also benefits neighborhood effects research. If adult residents in disadvantaged neighborhoods spend less time within community boundaries, opportunities for interaction in public spaces are limited\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e9\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Both violence and school institutional resources are closely tied to community structure, leading to less community exposure/contact for adolescents in disadvantaged neighborhoods. Browning et al. noted that exposure to organizations, institutions, and other settings characterized by personal activities is a key mechanism through which neighborhoods influence adolescent outcomes. They used the concept of \"ecological networks\" to describe the aggregated patterns of shared local exposure, which are influenced by a neighborhood's socioeconomic characteristics and exert independent effects on its adolescents. Residents who interact more broadly within the community space because of routine activities exhibit higher levels of social capital, including familiarity, beneficial (weak) social ties, trust, and prosocial behavior. Therefore, ecological networks have important implications for understanding the complexity of contextual exposure\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e10\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. In short, the authors propose a new concept and theoretical framework for residents' exposure/contact within neighborhoods, which is influenced by neighborhood socioeconomic characteristics and extends beyond neighborhood boundaries. Wen Ming, studying neighborhood deprivation, social capital, and regular exercise among adults, noted that the interaction between neighborhood structure and individual-level factors deserves attention because neighborhood effects vary across subpopulations with different sociodemographic characteristics. Different groups may experience varying degrees of exposure to their residential environment. For example, children, elderly individuals, and women might spend more time in their local communities because of lower labor force participation rates and stronger attachment to residential areas, making them potentially more sensitive to neighborhood environments\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e11\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. However, the authors focused primarily on the interaction analysis between neighborhood structure and individual characteristics, with the dependent variable being health behavior\u0026mdash;regular exercise.\u003c/p\u003e\u003cp\u003eThe above studies link community structural characteristics with individual exposure/contact, conceptualizing and operationalizing this influence, thereby expanding beyond the previous limitations of neighborhood effects research, which focused solely on community structure. The impact of community structure on resident health varies on the basis of an individual's daily level of \"exposure\" within the community. Although this context exposure related to activity spaces and ecological networks may extend beyond neighborhood boundaries, the theoretical approach based on community spatial exposure elucidates the links between neighborhood disadvantage characteristics and the social processes underlying youth health and well-being. Residing in socioeconomically disadvantaged neighborhoods shapes the characteristics of individual-level activity spaces\u0026mdash;the set of locations and settings where residents are frequently exposed/contacted. Therefore, this study conceptualizes community-related lifestyles as the \"exposure\" factor through which community structure influences individual health outcomes. From a community structure perspective, individuals are homogeneously influenced by the community context, but from an individual perspective, it involves how individuals utilize the community context. Thus, the individual use of the community is treated as a crucial channel of contact (exposure). From a symbolic interactionist perspective, individual participation in the community itself shapes community social processes (e.g., cohesion), thereby influencing individual socioeconomic outcomes such as health. In this sense, those deeply involved in community interactions gain more. Therefore, this section adopts a microinteraction analytical perspective, categorizing individual health behaviors within the community into three types: institutionalized, socialized, and everyday \"community participation.\"\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eCommunity Lifestyle as \"Exposure\"\u003c/h3\u003e\n\u003cp\u003eIn lifestyle research, numerous studies have focused on the impact of individual objective SES on health, representing the \"social causality\" explanation of health inequality. However, with disease transition, modernity development, and changing social identities, healthy lifestyles are not solely the result of individual choice but are also influenced by structural factors\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e12\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Among structural factors, most research focuses on effects at the individual level\u0026mdash;differences in lifestyle exist among different social groups. For example, Western empirical studies generally find that individuals with higher SES are more likely to adopt healthy lifestyles. In contrast, in developing countries, higher SES groups may be less healthy, which is related to social transition and economic development levels, where higher-status groups are often the first affected, sometimes leading to unhealthy lifestyles. Chinese empirical findings are more complex: on the one hand, lower-status groups tend to have less healthy lifestyles; on the other hand, even among higher SES groups, explanations involving both \"status constraints\" and \"lifestyle transition\" coexist\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e13\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn empirical lifestyle studies, alongside single behavioral indicators, latent class analysis (LCA), which reflects the intrinsic connections between various behaviors, has developed rapidly. For example, Zhao Li et al. were among the first in China to classify the lifestyles of residents aged 18\u0026thinsp;+\u0026thinsp;in Guangzhou into healthy and subhealthy behavioral groups via LCA\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e14\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Wang Fuqin used CGSS2010 data to categorize public lifestyles into healthy, mixed, and risky types\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e15\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Zhang Yun et al.'s LCA of elderly lifestyles revealed four types: survival-oriented, health-oriented, risk-oriented, and mixed, with the survival-oriented type being both unique and widespread among Chinese seniors\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e16\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. At a higher community structural level, Li Xian used CLDS2014 data for LCA and multilevel analysis of contemporary healthy lifestyles, finding that community economic level had a significant positive impact on healthy lifestyles in both urban and rural areas, with spatial heterogeneity between them\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e17\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eBuilding on existing individual lifestyle LCAs, this study expands the concept to \"community lifestyles\" related to individuals\u0026rsquo; interactions with the community. This serves as an operationalized indicator of exposure, measuring the important micromechanism of the extent to which an individual is influenced by the community. Therefore, the research questions are as follows: Is the impact of concentrated disadvantage on resident health moderated by community exposure/contact (community lifestyle)? Does this effect vary by community structure? The research hypotheses are as follows:\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis 1\u003c/strong\u003e\u003cp\u003e\u003cem\u003eThe greater the individual's exposure to the community is, the stronger the impact of neighborhood disadvantage on self-rated health.\u003c/em\u003e\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis 2\u003c/strong\u003e\u003cp\u003e\u003cem\u003eThe lower the individual's exposure to the community is, the weaker the impact of neighborhood disadvantage on self-rated health.\u003c/em\u003e\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis 3\u003c/strong\u003e\u003cp\u003e\u003cem\u003eAn individual's exposure to the community is heterogeneously based on neighborhood disadvantage.\u003c/em\u003e\u003c/p\u003e\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eData sources\u003c/h2\u003e\u003cp\u003eThe data used in this study are from the 2018 China Labor Force Dynamics Survey (CLDS2018). The CLDS is a nationally representative large-scale dynamic tracking survey of the labor force designed and implemented by the Social Science Survey Center of Sun Yat-sen University. It includes questionnaires at the individual, household, and community levels. These data have been widely recognized in academic research\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e18\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e19\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. The 2018 CLDS sample covered 28 provinces, municipalities, and autonomous regions in China (excluding Hong Kong, Macao, Taiwan, Tibet, Hainan, and Xinjiang). The urban sample included 133 communities and 4,770 individual respondents (working-age population 15–64 years old and those over 64 still working). When clusters contain more than 25 individuals or households with an intraclass correlation (ICC) value exceeding 0.2, the bias in estimating aggregated variable effects remains below 10%\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e20\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. In the CLDS survey, 35 households were randomly selected from each community. Both datasets involved relatively large samples of household and adult respondents, with intraclass correlation coefficients (ICCs) consistently exceeding 0.2. Therefore, using aggregated variables to characterize community social structure as population-level proxies introduces minimal bias. The final analytical sample consisted of 2,367 valid individuals nested within 133 communities. One core variable focuses on community-related health behavior indicators, particularly community social participation items newly added to the CLDS2018.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eVariable selection\u003c/h3\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eDependent variable\u003c/h2\u003e\u003cp\u003eThe independent variable in this study was self-rated health. It was measured on a five-point scale from \"very healthy\" to \"very unhealthy.\" As this is an ordinal rather than continuous variable, it was dichotomized into \"healthy\" vs. \"unhealthy\" for analysis, yielding results similar to those of the ordinal approach.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eIndependent variables\u003c/h3\u003e\n\u003cp\u003eOne of the independent variables is neighborhood disadvantage. Owing to differences in the definitions of neighborhoods and communities, the measurement of community socioeconomic disadvantage varies across regions \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e21\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e \u003csup\u003e22\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e \u003csup\u003e23\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e \u003csup\u003e24\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. These indicators are critical in Western contexts, but due to differing social environments, they may not accurately reflect conditions in non-Western developing countries. For example, in China, racial composition and female-headed households are less common, whereas the influx of migrant populations due to urbanization is far more prominent. Moreover, China's community service and management system, which is primarily based on administrative divisions, differs significantly from that of Western countries. Therefore, it is necessary to develop community disadvantage indicators tailored to the Chinese context. Following Lei's approach\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e25\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e26\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e, this study constructs a composite socioeconomic status (SES) score for communities by randomly selecting households within each community and averaging key indicators: household net income, household wealth, average years of education among adult residents, and average occupational prestige score. It was constructed via community-level indicators: unemployment rate, poverty rate (World Bank standard), proportion of residents with education below high school, and median household income. These indicators constitute a comprehensive measurement of community socioeconomic status (SES) while also aligning with the construction of individual-level SES indicators. Principal component factor analysis was applied to these indicators. A factor with an eigenvalue \u0026gt; 1 was extracted to generate the \"Community Concentrated Disadvantage\" structural index. The factor loadings ranged from 0.60–0.84, and the cumulative explained variance was 52.28%\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e27\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e28\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eExposure was measured on the basis of community lifestyle factors. Lifestyle in social sciences includes risk behaviors (e.g., smoking, drinking) and health-promoting behaviors (e.g., exercise, sleep, diet). Chinese cities are experiencing a fifth disease transition characterized by sedentary lifestyles or a lack of exercise. Indicators of community-related lifestyles in CLDS2018 include smoking, drinking, regular exercise, community participation, and neighborly mutual assistance. While smoking, drinking, and exercise might occur outside the community, this study views these habits as daily, routinized behavioral patterns closely linked to one's living space, hence treating them as proxies for activity within the community life unit.\" Have you ever smoked for one year or more continuously? \", \"Have you ever drunk alcohol at least once a week?, \"Have you engaged in regular exercise in the past month?\". Community participation categorized residents' participation in social organization activities into seven types: \"recreation/arts,\" \"sports/exercise,\" \"seniors' associations,\" \"skill training/correspondence,\" \"knowledge learning,\" \"volunteer groups,\" and \"religious groups.\" The frequency of participation in each category ranged from \"never\" to \"daily.\" The participants were further asked if these activities mainly took place within the community. For data processing, a resident was considered to have \"community participation\" if they participated in at least one of these seven types of activities and reported that participation occurred within the community. Neighborly mutual assistance \u003cb\u003eis\u003c/b\u003e used as a behavioral indicator of neighborly interaction: \"Do you and your neighbors/other residents in this community (village) help each other?\" (five-point scale from \"very little\" to \"very much\"). This dichotomous approach, with questions coded in the same direction, further highlights the patterned characteristics of community lifestyles.\u003c/p\u003e\n\u003ch3\u003eControl Variable\u003c/h3\u003e\n\u003cp\u003eThe study included several control variables: age, gender (female, male), marital status (married, unmarried), education level (primary school or below, junior high school, senior high school or equivalent, college or above), logannual income (personal annual income, missing values imputed with means, then log(income + 1)), occupational status index (ISEI score derived from occupational codes), and subjective social status (scale 1–10, low to high).\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eResearch methods\u003c/h2\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003eLatent class analysis\u003c/h2\u003e\u003cp\u003eAs a method to divide an average population into different homogeneous subgroups, it is particularly suitable for studying health lifestyles. Applied here, it identifies groups with distinct characteristics regarding community exposure/contact, characteristics obscured by average effects. It is a typological method.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eMultilevel Modeling\u003c/h2\u003e\u003cp\u003eCommunities and individuals have a typical nested hierarchical structure. Multilevel models can distinguish between between between-group (community) and within-group (individual) differences, testing the effects at each level and the contribution of each level to the dependent variable. Since the dependent variable (self-rated health) is dichotomous, a multilevel logistic regression model (logit) was used for analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Research Results","content":"\u003ch2\u003eDescriptive characteristics\u003c/h2\u003e\u003cp\u003eSummary statistics for all variables are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The study sample (N = 2,367) exhibited a balanced gender distribution (51.58% male, 48.42% female), with predominantly married participants (82.59%). The population had a mean age of 42.93 years (SD = 11.52) and displayed educational polarization, with 30.29% attaining middle-level education and 32.83% attaining advanced-level education. The economic indicators revealed a log-transformed mean household income of 10.15 (SD = 2.78) and an average subjective social status of 4.63 (SD = 1.72) on a Likert-type scale, whereas neighborhood disadvantage was standardized (M = 0, SD = 1) for comparative analysis. Health outcomes showed that 69.41% of respondents self-reported themselves as healthy versus 30.59% as unhealthy, suggesting a 2:1 ratio favoring positive health assessments in this population.\"\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCharacteristics of the participants\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean or N\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSD or %\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1221\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e51.58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1146\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48.42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1955\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e82.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnmarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e412\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17.41\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrimary level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e315\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMiddle level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e717\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e558\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAdvance level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e777\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.52\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnnual household income (log)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIsei\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSubjective social status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeighborhood disadvantage\u003c/p\u003e\u003cp\u003eself-rated health\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHealthy\u003c/p\u003e\u003cp\u003eUnhealthy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003cp\u003e1643\u003c/p\u003e\u003cp\u003e724\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e69.41\u003c/p\u003e\u003cp\u003e30.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003eLatent class analysis of exposure: community lifestyle\u003c/h2\u003e\u003cp\u003e\u003cb\u003eAn\u003c/b\u003e LCA was first performed on the five observed health behavior indicators to estimate patterns of community-related lifestyles among urban residents in China. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e compares the fit statistics for models with one to four latent classes. The AIC and BIC values for the 3-class and 4-class models are relatively close. However, model fit statistics indicated that the 3-class solution had a significant p value (\u0026lt; 0.000), whereas the 4-class solution did not. The predicted probabilities for the last three classes in the 4-class model were similar (ranging from 0.10–0.16). Furthermore, the predicted means for behaviors such as regular exercise, community participation, and mutual assistance were similar and difficult to distinguish and name for classes 2 and 3 in the 4-class solution (e.g., for class 2: 0.63/0.99/0.44 vs. class 3: 0.75/0.99/0.55). Therefore, the 3-class solution was selected as the optimal solution.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFit statistics for latent class models (1–4 classes)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLatent Classes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAIC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBIC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCategorical Probability\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClass1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2367\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14321\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14408\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClass2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2367\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.42/0.31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClass3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2367\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13944\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.63/0.23/0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClass4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2367\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13925\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14058\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.59/0.16/0.10/0.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e reports the predicted probabilities of the five health behaviors within the three latent classes. Class 1 shows low predicted probabilities for smoking (8.9%) and drinking (18.7%), along with the lowest community participation (5.5%). The rates of regular exercise and neighborly mutual assistance are also relatively low. Thus, Class 1 is composed of residents who do not smoke, do not drink frequently, do not exercise regularly, and rarely participate in community activities. It is named the \"passive\" community \u003cb\u003elifestyle.\u003c/b\u003e Class 3 consists of residents with high rates of smoking (100%) and drinking (99%), alongside some community participation, mutual assistance, and moderate regular exercise (51.9%). This indicates the coexistence of health-promoting and risk behaviors, hence the \"mixed\" community lifestyle. Residents in Class 2 have the highest predicted rate of community participation (99.1%), the highest proportion engaging in regular exercise (67.4%), and the highest level of neighborly mutual assistance among the three classes (47.8%). This suggests good community interaction and a tendency toward health-promoting behaviors, leading to \u003cb\u003ean\u003c/b\u003e \"active\" community lifestyle.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSample distribution and predicted probabilities of community lifestyles\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c5\" namest=\"c4\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" morerows=\"1\" nameend=\"c8\" namest=\"c6\" rowspan=\"2\"\u003e\u003cp\u003eLatent Classes\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e\u003cp\u003eLifestyle\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMeasure\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eClass1\u003c/p\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eClass2\u003c/p\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eClass3\u003c/p\u003e\u003cp\u003eMixed\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eSmoke\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e643\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e27.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.089\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.122\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1724\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e72.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.911\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.878\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eDrink\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e488\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.187\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1879\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e79.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.813\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.865\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eExercise\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1061\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e55.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.353\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.674\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.519\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1306\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e44.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.647\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.326\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.481\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eNeighborhood Participation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e725\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e30.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.055\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.991\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.303\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1642\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e69.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.945\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.697\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eNeighborhood reciprocity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1466\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e61.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.348\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.478\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.364\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e901\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e38.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.652\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.522\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.636\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eCategorical Probability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.646\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.234\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.120\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote: Percentages for regular exercise appear miscalculated in the original (1061/2367 ≈ 44.82% Yes,1306/2367 ≈ 55.18% No). The predicted probabilities reflect the model estimates.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eOn the basis of the posterior probabilities from the 3-class model, the estimated sample distribution across classes is shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The negative type was the largest category, comprising 60.03% of the total sample, with a cumulative percentage of 60.03%. The positive type, the second-largest category, comprised 628 individuals, resulting in a cumulative percentage of 86.57%. The mixed type was the smallest group, at 13.43% of the sample.\" Overall, the positive lifestyle type in the community requires improvement.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEstimated probability sample values for latent class analysis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003efre\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCumulative odds\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1421\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e60.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e60.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e628\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e86.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMixed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e318\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2367\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003eDemographic and socioeconomic characteristics by community lifestyle\u003c/h2\u003e\u003cp\u003eWhat are the profiles of residents with different community lifestyles? What demographic and socioeconomic characteristics do they exhibit? Descriptive statistical analysis was conducted for the three latent classes. The results of the chi-square tests and ANOVA indicated significant differences in these characteristics across community lifestyle types (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographic and socioeconomic characteristics of residents by community lifestyle\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAll\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMixed\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eAge(M)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e42.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e44.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e44.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e44.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e97.80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e55.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e55.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital status(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnmarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14.78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e82.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e82.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e80.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e85.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrimary level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMiddle level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e36.16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e29.87\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAdvance level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e47.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e23.58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eAnnual household income (M)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eIsei(M)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e36.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e30.78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eSubjective social status\u0026nbsp;(M)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: \"Chi-square tests for categorical variables (gender, marital status, education) showed significance at p \u0026lt; .000 for all except marital status. ANOVA for continuous variables (age, annual household income, income, and subjective social status) revealed that all variables were significant at p \u0026lt; .05.\"\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows that residents with the passive community lifestyle type had a mean age of 42.86 years, were predominantly female, and were mostly married. The highest proportion had junior high school education (32.72%), and both objective and subjective SES indicators were below the sample average. The active type had a similar gender distribution to the passive type but a slightly lower proportion of married individuals. The main difference lies in SES: the active group had the highest proportion of those with a college education or above (47.93%), far exceeding the sample average (32.83%), and the other two groups. Additionally, their log income, occupational status index, and subjective social status were significantly higher than the means. Residents with a mixed community lifestyle were slightly older than average and predominantly male, with the highest proportion having junior high education (36.16%). Their mean log income was slightly above average, but the occupational status index and subjective social status were below average, presenting a more complex profile.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eNeighborhood disadvantage and self-rated health in China\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eT\u003cb\u003ehe role of community lifestyle exposure\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ePrevious studies often treat latent classes as dependent variables. This study incorporates community lifestyle type as a moderator variable. The results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultilevel logit regression analysis of neighborhood disadvantage, lifestyle, and self-rated health\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModel1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModel2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModel3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eModel4\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh neighborhood disadvantage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.434\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.439\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.340\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.489\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(ref: Low neighborhood disadvantage)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.146)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.147)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.149)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.170)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.071\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.181\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.349\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(ref: negative)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.115)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.120)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.154)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMixed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.148\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.140)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.155)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.209)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender(ref: female)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.080\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.106)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.106)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.035\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.035\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.005)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.005)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital status\u003c/p\u003e\u003cp\u003e(ref: married)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.104\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.143)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.144)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003cp\u003e(ref: Primary level)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.096\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.157)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.157)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.174)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.174)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdvance level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.189)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.190)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnnual household income (log)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.014\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.019)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.019)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIsei\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.004\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.004\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.002)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSubjective social status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.221\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.220\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.030)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.030)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCross-level interaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh neighborhood disadvantage x Positive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.414\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(ref: Low neighborhood disadvantage x Negative)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.242)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh neighborhood disadvantage x Mixed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.317\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.287)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.083\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.115\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.585\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.672\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.094)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.103)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.393)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.396)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRandom part\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVar (neighborhood constant)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.325\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.324\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.290\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.293\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.077)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.077)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.073)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.074)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVar (Residual)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.000)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of individuals\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2367\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2367\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2367\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2367\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of neighborhoods\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e133\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: Standard errors in parentheses; significance levels: ⁺ p \u0026lt; 0.10, * p \u0026lt; 0.05, ** p \u0026lt; 0.01, *** p \u0026lt; 0.001\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e7\u003c/span\u003e, Model 1 shows that, without considering other factors, residents in communities with higher concentrated disadvantage reported significantly poorer self-rated health (OR = e⁻⁰·⁴³⁴ ≈ 0.65, or 35% lower odds of being healthy). Controlling for concentrated disadvantage in Model 2, the main effects of community lifestyle type were not significant. Model 3 includes individual demographic and socioeconomic characteristics, as well as community lifestyle type. The significant negative effect of high concentrated disadvantage persists (β = -0.340, OR ≈ e⁻⁰·³⁴⁰ ≈ 0.71, or 29% lower odds), which is consistent with Western \"neighborhood effects\" research. Compared with residents in low disadvantage communities, those in high disadvantage communities have 29% lower odds of reporting good health, even after controlling for individual characteristics and lifestyle type. Without controls (Model 1), this disadvantage was 35%. This difference is partially explained by individual SES characteristics and community lifestyle type. Comparing Model 1 and Model 3, the variance in the community-level intercept decreased from 0.325 to 0.290, indicating that community structure (concentrated disadvantage) explained approximately 3.5% of the variance in urban residents' health. This is close to the 3.2% variance in youth lifestyle differences explained by community average income \u003csup\u003e[14]\u003c/sup\u003e, confirming that community concentrated disadvantage is a valid indicator for explaining resident health.\u003c/p\u003e\u003cp\u003eModels 1–3 show that the main effect of community lifestyle type on resident health is not significant. This differs from individual lifestyle studies, such as Wang Fuqin's finding that healthy lifestyles (fitness/physical activity) directly impact health levels [12]. The effect of community-concentrated disadvantage changes slightly but remains significantly negative, demonstrating its robustness and independence. Both individual-level socioeconomic position and neighborhood disadvantage are associated with mental well-being\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e29\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Residents in communities with higher socioeconomic status (SES) are more likely to maintain an active community lifestyle (and thus have better health outcomes), which aligns with the relationship observed between individual SES groups and lifestyle. However, this finding differs from that of Ross's international study, which revealed that poorer communities exhibited more walking behavior and that higher community education levels were associated with increased walking—although not necessarily with other forms of exercise\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e30\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTo examine how the different exposure levels of resident groups within communities moderate the relationship between community structure and health (i.e., whether differences in exposure/lifestyle type affect the impact of community disadvantage), Model 4 adds interaction terms between community lifestyle type and concentrated disadvantage. The results show that after controlling for other variables, the negative effect of high concentrated disadvantage (compared with low) on resident health changes from OR ≈ 0.71 (β = -0.340) in Model 3 to OR ≈ 0.61 (β = -0.489) in Model 4. In terms of the main effects, compared with the passive lifestyle, residents adopting the active community lifestyle had significantly lower odds of good health (β = -0.349, OR ≈ 0.71, or 29% lower). As Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows, this group has relatively high objective and subjective SES but resides in high-disadvantage communities. Despite higher levels of regular exercise, neighborly assistance, and community participation (indicating greater exposure/contact), they may experience greater dissatisfaction, which negatively impacts their health evaluation. This group exhibits strong \"specificity.\" Recently, there has been growing scholarly interest in \"gentrifying\" communities, stemming largely from the post-2000 concentration of high-quality school districts in central urban areas, fueling demand for \"school district housing\" and real estate booms, leading to population shifts and socioeconomic/residential spatial differentiation. In the Chinese context, the residential community is closely tied to various citizen rights, with education being one. Policies linking public education resources to household registration (hukou) and residence location drive many residents to move into older urban areas with more concentrated disadvantages in pursuit of \"school district housing.\" Research has revealed that when middle-class whites move into previously low-income neighborhoods (termed \"gentrification\"), neighborhoods become more diverse \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e31\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. The interaction term shows that, compared with residents with a passive lifestyle in low-disadvantage communities (reference group), residents with an active lifestyle in high-disadvantage communities have significantly higher odds of good health (β = 0.414, p \u0026lt; 0.10, OR ≈ e⁰·⁴¹⁴ ≈ 1.51). This finding indicates that an active community lifestyle has a significant positive moderating effect on health for residents in highly disadvantaged communities.\u003c/p\u003e\u003cp\u003eWith respect to individual demographic and socioeconomic characteristics, older age is significantly associated with poorer health. The effect of education level is not significant. The occupational status index and subjective social status have significant positive effects and are relatively robust. Residents with higher occupational status have significantly better health. As the basis of modern social stratification, occupation is not only a bridge between education and income but also a source of prestige and power. The impact of education is not significant, possibly because the occupational status index was controlled for. Log income also had no significant effect, suggesting that it is not income per se. Zhou et al. (2012) reported that income inequality still significantly negatively impacts individual health after controlling for the \"depression effect\" of absolute income on health\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e32\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. This suggests that different indicators of objective SES have distinct impact mechanisms. Furthermore, the influence of subjective social status (subjective class identification) cannot be ignored. Xu Yan, on the basis of CLDS 2012 data, reported that subjective class identification acts as a mediating variable linking objective economic status (income and education) with self-rated health and recent objective health. Subjective class identification is an important sociopsychological mechanism affecting physical health, with unique characteristics in the Chinese sociocultural context\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e33\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAlthough the aforementioned models controlled for as many individual and household characteristics affecting residential selection as possible, they may still face the greatest challenge in neighborhood effect research: the issue of selection bias. Adopting a quasiexperimental approach, this study employs propensity score matching (PSM) to examine the \"net effect\" of concentrated neighborhood disadvantage on residents' health. The analysis followed these steps. First, we estimated the probability of residing in a high-disadvantage neighborhood. The results indicate that residential selection across neighborhoods of different socioeconomic statuses is nonrandom but exhibits systematic selectivity (as demonstrated by mean comparisons). These findings confirm that neighborhood selection is selective rather than random. The full propensity score matching model specifications are presented in the Appendix.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWhat is the most valuable knowledge sociology provides? structural analysis. This is its unique distinction from other disciplines. To uncover the causes and social consequences of a series of phenomena, structural thinking is essential. Structural analysis grounded in social facts is not only an analytical method but also a theoretical perspective, a specific worldview, and a way of understanding society\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e34\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Existing health research in sociology focuses on health inequalities stemming from social stratification, whereas epidemiology focuses on structural and agentic health lifestyles. However, both perspectives are based on individual-level analysis. \u0026Eacute;mile Durkheim, in \u003cem\u003ethe\u003c/em\u003e Rules of Sociological Method, stated, \"A social fact is every way of acting, fixed or not, capable of exerting over the individual an external constraint; or again, every way of acting which is general throughout a given society, while at the same time existing in its own right independent of its individual manifestations\"\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e35\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Social facts possess externalities and constraints.\u003c/p\u003e\u003cp\u003eThe community, as a place reflecting residents' social status, values, and connections with others, while influenced by residential preferences and purchasing power, is also the result of \"structural constraints\" (e.g., individual purchasing power constrained by market housing prices). In this sense, community influence represents a \"social fact\" from a structural analytical perspective. Although constrained by higher-level macrostructures and institutions, the community, as the primary social living space for individuals, represents the public sphere between the state and the family. On the one hand, individuals leave the family and connect with society and the state through community public life. On the other hand, the state connects with families through communities, embedding political sentiments, state responsibilities, and policy protections into the family\u0026mdash;the fundamental unit supporting Chinese society and the state. This top-down and bottom-up interaction, which involves internal-external and external-internal linkages, constitutes the greatest \"secret\" of grassroots social governance in China. Within communities, people experience real China. In this sense, the state is within the community, materializing \"China in the Community\" \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e36\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Therefore, the community is a \"small incision\" reflecting national changes, embodying the modernization level of the national governance system and capacity.\u003c/p\u003e\u003cp\u003eThis study adopts a structural analytical perspective centered on the community. Building on the concept of \"exposure\" in recent neighborhood effects research, it moves beyond the traditional view of community structure as a homogeneous context influencing individual behavior and outcomes. Instead, it investigates how varying degrees of individual exposure influence the association between community structure and resident health, exploring heterogeneous effects. Operationally, it employs latent class analysis to generate \"community lifestyle\" types as indicators of individual exposure.\u003c/p\u003e\u003cp\u003eFirst, the LCA revealed three current community lifestyle types among Chinese urban residents: passive, active, and mixed. The Active type exhibited the highest levels of social participation within the community, neighborly mutual assistance, and regular physical exercise. This group has the strongest \"adhesion\" to the community, indicating the highest degree of exposure. They possess relatively high objective and subjective SES, particularly the proportion with a college education or above, which far exceeds that of the other groups. The passive type is the opposite. The mixed group presented a complex profile; apart from log income slightly above average, other indicators (education above high school, occupational status, and subjective social status) were below average. This reflects the divergent impacts of different SES indicators on community lifestyles during social transition, highlighting the unique materialistic logic of income. Sampson noted that health-related issues are closely linked to the social characteristics of communities and neighborhoods. We cannot simplistically treat communities as attributes of individuals; the community context itself should be regarded as a crucial unit of analysis, requiring new measurement strategies and theoretical frameworks \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e37\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Research from the Project on Human Development in Chicago Neighborhoods confirms a direct link between the community context and health, even in experimental studies \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e38\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. This study aligns with this community structural perspective. At the community level, higher community SES is associated with a greater tendency toward an active lifestyle but also coexists with the passive type. A similar trend exists at the individual level. Although three theoretical perspectives exist in community research\u0026mdash;\"community lost,\" \"community saved,\" and \"community liberated\"\u0026mdash;this study's findings support the \"community saved\" thesis. Despite urbanization, globalization, and networking potentially weakening ties to territorial communities, Sampson noted that, in reality, neighborhood inequalities in life chances have significantly increased and worsened due to globalization. Paradoxically, the concept of community has flourished more widely despite globalization\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e39\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Xie Guihua et al. (2021) argued that residential community characteristics largely reflect the effects of urbanization. Urbanization significantly impacts resident community interactions, having a distancing effect; higher urbanization correlates with looser ties within neighborhoods. However, this effect is not homogeneous and varies by community, meaning that the \"community saved\" thesis holds for certain communities. This study also revealed that resident community interaction (neighbor assistance) and formal community participation are distinct. Residents with higher education levels are more likely to volunteer in community activities. Community interaction might decline, but this does not mean that people stop participating in community affairs; the \"community lost\" thesis may better explain community participation \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e40\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eSecond, community structure significantly impacts resident health. Residents in communities with lower concentrated disadvantages reported better health. This effect persists even after controlling for individual and household characteristics, which is consistent with Western \"neighborhood effects\" research. Health inequalities stemming from individual structural factors are now extending to residential communities. Pickett, in a review of multilevel analyses of neighborhood structure and health outcomes, noted that 23 out of 25 studies reported at least one statistically significant association between a neighborhood indicator and a health outcome (context effect). After adjustment for individual-level socioeconomic status (compositional effect), the magnitude of context effects was moderate, smaller than that of compositional effects, but evidence for a moderate impact of neighborhood structure on health was fairly consistent\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e41\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Sampson more directly stated that existing health research neglects theories at the neighborhood level and measurement schemes such as \"ecometrics\" (focused on collective phenomena). Health research can leverage social science to creatively consider community-based intervention strategies. Neighborhood-level interventions attempting to alter places and social environments, rather than individuals, could complement traditional individual-focused approaches and prove fruitful\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e42\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eFurthermore, the relationship between community structure and health depends on differences in individual community exposure/contact. The interaction effect shows that, compared with residents with a passive lifestyle in low-disadvantage communities, those with an active lifestyle in high-disadvantage communities report significantly better health. Research on health stratification from a sociological perspective identifies individual proximal factors such as socioeconomic status, explained by the \"social causality\" theory of health inequality. However, lifestyles depend on multiple social environments. The community, as a site of daily activities, is particularly important for those spending long hours at home. It also serves as an important consumption space, forming part of the class-based habitus described in Bourdieu's framework of class reproduction, transmitted through social and cultural capital within certain social ecologies (communities). In the Chinese context, the community is a \"complex\", integrating \"political, service (administrative), and social\" functions. Residents' needs for the community exist at the most basic level of daily life. The existing organizational system selectively channels social forces into the service sector, helping meet resident needs and reducing demand for political participation. In this sense, increasing individual exposure/contact with the community helps individuals connect with society and the state through community public life. Simultaneously, the state connects with families through communities, embedding political sentiments, state responsibilities, and policy protections into the family unit. This top-down and bottom-up interaction, which involves internal-external and external-internal linkages \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e43\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e, facilitates \"good governance\" in Chinese grassroots society.\u003c/p\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003ePolicy recommendations\u003c/h2\u003e\u003cp\u003eEffective health promotion requires integrated interventions that address the physical infrastructure and social organizational structures of communities simultaneously. This approach is particularly critical in socioeconomically deprived neighborhoods exhibiting compounded disadvantages.\u003c/p\u003e\u003cp\u003eInterventions should focus on transforming the physical environment to universally influence residents\u0026rsquo; behaviors\u0026mdash;a \"place-based\" approach centered on the principle of \u0026ldquo;changing the place to change the people.\u0026rdquo; Key actions include implementing urban renewal projects to develop community fitness trails, multifunctional sports facilities, and community gardens, ensuring equitable access to recreational infrastructure within walking distance. Planning interconnected greenway networks can link natural spaces, parks, and residential areas to create safe and attractive environments for walking and cycling. Additionally, establishing self-service health monitoring stations in accessible community locations provides free, convenient health management tools. Collaborating with local vendors to create \u0026ldquo;low-salt, low-oil\u0026rdquo; food zones with clear nutritional labels helps guide healthier food choices. Improving public safety through better lighting in alleys and parks encourages nighttime activity, especially among older adults, while transforming vacant lots into small green spaces enhances aesthetics, reduces urban heat, and supports social interaction.\u003c/p\u003e\u003cp\u003eIn addition, \u0026ldquo;people-based\u0026rdquo; strategies should aim to directly empower individuals and groups by enhancing their knowledge, skills, motivations, and social networks\u0026mdash;essentially \u0026ldquo;changing the people to better utilize the place.\u0026rdquo; This can be achieved through health education initiatives such as chronic disease self-management workshops and practical healthy cooking classes tailored to at-risk groups. Building social support is also vital; facilitating community organizations such as senior dance groups and parent‒child activity clubs promotes physical activity while strengthening mental well-being and social cohesion. For vulnerable populations, personalized support\u0026mdash;such as a \u0026ldquo;Community Health Steward\u0026rdquo; program with in-home assessments and tailored activity plans\u0026mdash;can foster engagement. Mobile health applications with gamified challenges and incentives offer innovative means to encourage sustained healthy behaviors.\u003c/p\u003e\u003cp\u003eThe greatest public health benefits emerge from strategically combining place-based and people-based approaches. For example, new parks should be activated with organized activities, and community groups should be supported with improved facilities. Such synergistic, dual-dimensional interventions maximize health promotion impacts. To translate these recommendations into action, we urge policymakers to allocate at least 30% of public health funding toward structural community interventions and to institutionalize a health impact assessment system to ensure policy coherence and effectiveness across sectors.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eStrengths and limitations\u003c/h2\u003e\u003cp\u003eThe strengths of this study lie in its adoption of a community structure analysis perspective and its systematic examination, which is based on the nationwide large-scale CLDS database, of the direct impact of neighborhood disadvantage on the self-rated health of urban community residents, as well as the moderating effect of community lifestyle (latent class analysis). This approach transcends the limitations of traditional individual-level behavioral interventions and validates the cost-effectiveness advantages of the \"community as an intervention platform\"\u0026mdash;highlighting the long-term health benefits of interventions targeting both environmental and social dimensions. This study provides an evidence base for the implementation of the \"Healthy China\" initiative at the community level and recommends that future policies allocate specific public health funds toward structural community improvements while establishing a \"health impact assessment\" system to ensure policy coherence.\u003c/p\u003e\u003cp\u003eAlthough the research strives for rigor, the following limitations need to be addressed in future studies: definitions of neighborhood and community; the scale of a community is larger than that of a neighborhood, and there may be internal heterogeneity. If available, more precise census data should be utilized. Subjective data bias: Reliance on self-reported health data may introduce subjective bias. Future studies should incorporate objective health indicators, such as activities of daily living and the number of chronic diseases, to increase data accuracy. Incompletely controlled confounding factors: Although propensity score matching (PSM) was used to control for selection bias and as many control variables as possible were included, potential confounding factors may not have been fully accounted for, particularly the influence of length of residence. Longitudinal data should be employed to strengthen the robustness of the results.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eNeighborhood disadvantage in urban China significantly impacts residents' health. Residents in communities with lower concentrated disadvantages exhibit better health outcomes. This association persists even after controlling for individual and household characteristics, which is consistent with western research on \"neighborhood effects.\" Latent class analysis revealed that urban residents in China currently fall into three community lifestyle categories: \"negative type,\" \"positive type,\" and \"mixed type.\" The relationship between community structure and health further depends on differences in individuals\u0026rsquo; level of \"exposure\" (i.e., community lifestyle). Compared with residents with a negative lifestyle in low-disadvantage communities, those adopting a positive lifestyle in high-disadvantage communities demonstrate significantly better health. Studies indicate that it is necessary to implement dual-dimensional \"environmental‒social\" interventions at the community level to promote the adoption of positive and healthy community lifestyles among older adults in China, thereby advancing the development of a healthy aging society.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eAuthor details\u003c/h2\u003e\u003cp\u003e\u003csup\u003e1\u003c/sup\u003e Journal Center, Guangzhou Sport University, Guangzhou, China\u003c/p\u003e\u003cp\u003e\u003csup\u003e2\u003c/sup\u003e School of Leisure and Digital Sport, Guangzhou Sport University, Guangzhou, China\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis study was supported by the Guangdong Philosophy and Social Sciences Planning (Grant No. GD25YTY04) and the Young Doctor Project of Guangzhou Sport University (Project No. 5250180628).\u003c/p\u003e\u003cp\u003eData availability\u003c/p\u003e\u003cp\u003eThe datasets(questionnaire, individual and neighborhood data) used and/or analyzed during the current study are available from the official website of the China Labor-force Dynamics Survey (CLDS) at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://isg.sysu.edu.cn\u003c/span\u003e\u003cspan address=\"https://isg.sysu.edu.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eDeclarations\u003c/p\u003e\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\u003cp\u003eThis study used data from the China Labor-force Dynamics Survey (CLDS). All procedures were conducted in accordance with relevant guidelines and regulations involving human participants (e.g., the Declaration of Helsinki or similar). The project was approved by the Ethics Committee of Sun Yat-sen University. Jinfu served as one of the staff members for the survey and received permission from the CLDS team to use this.\u003c/p\u003e\u003cp\u003eConsent for publication\u003c/p\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003cp\u003eCompeting interests\u003c/p\u003e\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThe study was conceptualized by Jinfu Xu and Chunru Shang. Jinfu Xu was responsible for the conceptualization, methodology, formal analysis, investigation, original draft preparation, review, editing, and supervision of the study. Chunru Shang contributed to the conceptualization. All the authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe appreciate Social Science Survey Center of Sun Yat-sen University for providing the China Labor Force Dynamics Survey (CLDS) data.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analyzed in the current study are available from the official website of CLDS, https://isg.sysu.edu.cn\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e Chamberlain AM, Finney Rutten LJ, Wilson PM, et al. Neighborhood socioeconomic disadvantage is associated with multimorbidity in a geographically defined community.\u0026nbsp;BMC public health. 2020;20(1), 13.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Small, Mario, Luis, et al. Urban poverty after the truly disadvantaged: The rediscovery of the Family, the neighborhood, and culture. 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Moving beyond the \"T\u0026ouml;nnies Myth\": The construction and theoretical justification of the western concept of community over the past century.\u0026nbsp;Fudan Journal (Social Sciences Edition).2022; 64(1): 134\u0026ndash;147.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Neighborhood disadvantage, Self-rated health, Lifestyle exposure, Latent class analysis","lastPublishedDoi":"10.21203/rs.3.rs-7414975/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7414975/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eExisting research on residents' health has focused predominantly on individual socioeconomic status (SES), which is often explained through the \"social causality\" theory of health inequality, with less attention given to neighborhood disadvantage in China. Furthermore, even within the same community context, the degree of association between different residents and the community varies. Therefore, this study aims to systematically examine the impact of neighborhood concentrated disadvantage and the moderating role of exposure to advance intervention strategies for addressing health inequalities at the community level and establish a scientific foundation for promoting health equity.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eBased on data from the 2018 China Labor Data Study, this study first employed latent class analysis (LCA) to identify distinct lifestyle types related to community exposure among urban residents in China. Second, multilevel analysis was used to examine the association between community context and resident health.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe LCA revealed three distinct lifestyle types among urban residents: passive, active, and mixed. Multilevel analysis demonstrated that residents in communities with higher levels of concentrated disadvantage reported significantly poorer self-rated health. The exposure levels of different resident groups within a community moderated the strength of the association between the community context (concentrated disadvantage) and resident health.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eCommunity governance should consider not only the influence of the community social structure but also the varying associations that different groups have with the community. This highlights the need for targeted interventions that account for both structural and individual-level factors in health inequality.\u003c/p\u003e","manuscriptTitle":"Neighborhood disadvantage and self-rated health in China: latent class analysis of community lifestyles","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-17 11:57:17","doi":"10.21203/rs.3.rs-7414975/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"191820797792677586758094049857675166800","date":"2025-10-20T11:38:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-13T05:20:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"2705162553470447685013425619997347064","date":"2025-10-06T22:30:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-06T12:26:02+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-08T14:48:07+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-25T00:32:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-25T00:30:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-08-20T08:02:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5069ee75-beec-4e91-879e-a350100ddc0e","owner":[],"postedDate":"October 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-17T11:57:17+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-17 11:57:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7414975","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7414975","identity":"rs-7414975","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}