What Neighborhood Environment Configuration Can Alleviate Depressive Symptoms in Older Adults: A Fuzzy-set Qualitative Comparative Analysis

What Neighborhood Environment Configuration Can Alleviate Depressive Symptoms in Older Adults: A Fuzzy-set Qualitative Comparative Analysis | 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 What Neighborhood Environment Configuration Can Alleviate Depressive Symptoms in Older Adults: A Fuzzy-set Qualitative Comparative Analysis Yan Nan, Tingshuai Ge, Quanbao Jiang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4981256/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Jul, 2025 Read the published version in BMC Geriatrics → Version 1 posted 4 You are reading this latest preprint version Abstract Introduction: The neighborhood is a regular living and activity space for the elderly. It is important to identify neighborhood environmental factors that can alleviate depression in the elderly to improve their health. However, existing literature does not consider the complex interdependencies among key neighborhood environmental factors. Method: This study employs the Qualitative Comparative Analysis (QCA) method to explore how the configuration of neighborhood environmental conditions can help alleviate depressive symptoms in the elderly. The data is derived from the 2020 China Family Panel Study (CFPS) survey. Results: The results show that three different neighborhood environment configurations can help reduce depressive symptoms in older Chinese individuals. The first configuration requires a combination of neighborhood safety, good neighborhood relations, and neighborhood assistance. The second configuration involves amalgamating optimal community facilities, high-quality neighborhood relations, and neighborhood assistance. The third configuration encompasses sound community facilities, favorable housing surroundings, a secure communal atmosphere, and advanced neighborhood assistance. Furthermore, we identify neighborhood assistance as a core condition for alleviating depressive symptoms and find that the combined effects of neighborhood safety and housing surroundings on alleviating depressive symptoms are comparable to the effect of neighborhood relationships. Discussion: These research results deepen the current understanding of neighborhood environment configurations to alleviate depressive symptoms in older adults, offer important implications for theory and practice, and set new directions for the construction of age-friendly neighborhoods. Neighborhood environment Depressive symptoms Chinese older adults Fuzzy-set qualitative comparative analysis Figures Figure 1 Introduction The prevalence of depression is high in older adults, and numerous studies have consistently reported a moderate increase in depressive symptoms as age advances[ 1 , 2 ]. This is a matter of concern due to the significant relevance between depressive symptoms and disability, morbidity, and suicide risk, as well as the decline in physical, social, and cognitive functioning in older adults [ 3 , 4 ]. Moreover, it is noteworthy that older adults residing within neighborhoods exhibit higher rates of depressive symptoms. Specifically in community-dwelling older adults, the prevalence of clinically relevant depressive symptoms stands at approximately 13.5%, while the morbidity of depressive symptoms can reach as high as 49% [ 5 ]. Given China's rapidly growing older adults and substantial changes in family structures over time, it is particularly important to carry out community construction for the elderly group [ 6 ]. Consequently, Chinese older adults' psychological well-being within neighborhoods has attracted considerable attention. Specifically, identifying factors that exacerbate or alleviate depressive symptoms is of great importance for the health condition of older adults[ 7 ]. An increasing body of empirical research has highlighted the relevance between the neighborhood environment and depressive symptoms[ 8 , 9 ]. The association between neighborhood characteristics and the health of older adults can be elucidated by the heightened susceptibility of older adults to the influence exerted by their residential environment, owing to a multitude of factors[ 10 ]. After retirement, older adults are more likely to remain in their familiar neighborhoods due to limited mobility and a decrease in alternative contexts for relocation[ 10 ]. Consequently, they tend to spend an increasing amount of time within their neighborhood surroundings[ 11 ]. Research indicates that the mental well-being of older adults may be particularly influenced by the conditions present within their immediate neighborhood environment, as they rely heavily on local services and amenities while being less mobile[ 12 ]. The present research on the relationship between the neighborhood environment and depressive symptoms in older adults has had mixed results[ 13 ]. Previous studies have primarily focused on the impact of neighborhood disorder, poverty, and deprivation on depressive symptoms in adults[ 14 , 15 ]. It has been established that depressive symptoms in older adults are influenced by ethnic composition and physical environment[ 16 ]. One study examined two specific aspects of the neighborhood environment –the level of neighborhood poverty versus concentration, about depressive symptoms in older Mexican Americans[ 17 ]. Furthermore, it is important to note that the aforementioned research findings primarily originate from Western societies, and there remains a dearth of knowledge regarding the impact of neighborhood environment on depressive symptoms in Chinese neighborhoods. Given the cultural values in China, this relationship may differ significantly from that observed in Western contexts[ 18 ]. To address these gaps in knowledge, this study aims to explore the relationship between neighborhood environment and depressive symptoms in Chinese older adults. Neighborhood factors exhibit intricate interconnections in a complex and non-linear manner, with numerous interactions and reinforcing effects. It is conceptually challenging to solely consider one factor and attempt to partially exclude its influence. Qualitative Comparative Analysis (QCA) is a method specifically designed to unravel these intricate relationships[ 19 ]. However, most current studies on the determinants of depressive symptoms in older adults employ multi-level analysis, multi-level structural equation analysis, and other methodologies[ 20 ], while the application of QCA in this field remains limited. The present study utilizes QCA to examine the combinations of neighborhood environmental conditions that contribute to alleviating symptoms of depression in older adults. The objective is to identify distinct pathways for mitigating depressive symptoms at the neighborhood level. Subsequently, the paper proceeds as follows: Section 2 provides the theoretical background for this study; Section 3 presents the data and methodology; Section 4 outlines the data analysis process; Section 5 presents the results of fsQCA; and finally, Section 6 discusses these findings along with their implications. Literature Review and Theoretical Background The study is guided by the Ecological Theory of Aging and a theoretical framework that links neighborhood factors to mental health outcomes, specifically depressive symptoms[ 21 , 22 ]. The Ecological Theory of Aging posits that personal resources interact with environmental resources, and an individual's assessment of their environmental context can influence their psychosocial responses and shape subsequent behavior and health outcomes. The environment can be divided into various subsystems, including the microsystem, mesosystem, exosystem, and chronosystem. The neighborhood environment is considered an external system that significantly impacts the mental health of individuals. This study focuses on examining the relationship between neighborhoods and depressive symptoms in older adults based on this integrated theoretical background. Relevant characteristics of the neighborhood environment are selected based on previous studies identifying physical or social factors within neighborhoods that may influence depressive symptoms. Neighborhood physical environment The neighborhood environment encompasses both physical and social aspects[ 10 ]. In China, the terms 'neighborhood' and 'community' are used interchangeably. Officially, a neighborhood is defined as a social sphere comprising individuals residing within specific geographical boundaries under official administration[ 23 ]. This paper posits two crucial physical conditions within neighborhoods: community facilities and the surrounding housing environment. Community facilities play a crucial role in shaping the physical environment. In recent years, there has been an increasing emphasis on constructing leisure and recreational facilities in Chinese neighborhoods driven by government initiatives[ 24 ]. These amenities offer older adults direct access to leisure spaces for activities like morning exercises and post-meal walks, which contribute positively to their health and well-being[ 25 ]. Additionally, community facilities provide opportunities for social interaction in older adults by facilitating regular gatherings with their neighbors. This fosters social relationships that can help alleviate depressive symptoms[ 26 , 27 ]. Conversely, older adults who perceive inadequate community facilities as barriers to participation in social activities face an increased risk of depressive symptoms[ 28 ]. Furthermore, according to the neighborhood disorder model, housing surroundings characterized by dilapidated houses, abandoned buildings, environmental pollution, and noise are associated with depressive symptoms due to the prevailing sense of lack of control and societal disorder they reflect[ 29 , 30 ]. Research has consistently demonstrated a strong association between physical environmental factors such as pollution and noise with Cardiovascular disease caused by depressive symptoms[ 31 , 32 ]. Neighborhood social environment This paper posits three crucial neighborhood social conditions: neighborhood safety, neighborhood relations, and neighborhood assistance. The neighborhood social environment is a context that exposes individuals to factors that can either promote or hinder the development of depressive symptoms[ 33 ]. It may provide psychological support and guidance for older adults, helping them cope with challenges and prevent symptoms of depression. Additionally, cultivating a sense of purpose and belonging in this environment may directly impact one's mental health. Recent research has demonstrated the correlation between concepts such as perceived neighborhood safety, neighborhood cohesion (neighborhood relations, neighborhood assistance), and depressive symptoms in older adults[ 33 ]. Neighborhood safety pertains to the perception of safety in a community, typically assessed through the sense of security experienced in the local vicinity[ 34 ]. The potency of a neighborhood's safety perception lies in its capacity to interweave societal fabric, fostering interpersonal bonds that can mitigate the psychological tempests associated with depressive symptoms[ 9 , 35 ]. Following Social Security Theory, humans possess inherent preparedness for biological and physical threats in their environment; however, novel threats emerging from the social milieu may elicit similar stress responses, thereby augmenting susceptibility to mental and physical impairments[ 35 ]. Previous research has demonstrated that older adults who perceive higher levels of crime within their neighborhoods exhibit an increased vulnerability to depressive symptoms compared to those who perceive their neighborhoods as safe[ 20 , 36 ]. Furthermore, a heightened sense of neighborhood security may empower residents by fostering a greater perception of control and reducing feelings of powerlessness, thereby acknowledging their potential role in driving positive changes within neighborhoods[ 37 ]. The evaluation and coping responses of individuals are influenced by the quality of social relationships within neighborhoods (referred to as neighborhood relations ), making it a significant social determinant of health[ 38 ]. For instance, the presence of supportive and caring neighbors plays a crucial role in promoting good mental health[ 39 ]. This is associated with the protective impact of neighborliness on mental well-being. Research indicates that perceiving neighborliness positively can serve as a psychosocial coping factor, mitigating adverse mental health outcomes[ 40 ]. Recent research in Western societies increasingly acknowledges the pivotal role of neighborhood assistance in mitigating depressive symptoms in older adults[ 41 , 42 ]. The research revealed that individuals with higher levels of social support, encompassing emotional and practical aid from close acquaintances, as well as neighborhood social support involving neighborhood assistance, exhibit a reduced risk of depressive symptoms[ 43 ]. Older adults residing within neighborhoods experience depressive symptoms due to a dearth of neighborhood assistance[ 36 ]. Additionally, several studies have reported that neighborhood assistance can act as a buffer against the adverse impact of daily stressors on negative emotions[ 44 , 45 ]. This is attributed to the fact that neighborhood assistance serves not only as an instrumental resource but also provides emotional support[ 46 ]. In summary, this paper proposes five key neighborhood environmental conditions: community facilities, housing surroundings, neighborhood safety, neighborhood relations, and neighborhood assistance. Building upon previous studies, we have developed a research model that incorporates the neighborhood environment as an antecedent factor and the mitigation of depressive symptoms as an outcome. Figure 1 illustrates the proposed research framework. Methodology Method In this study, we employ Qualitative Comparative Analysis (QCA) to investigate the relevance between different configurations of neighborhood environments and lower levels of depressive symptoms in older adults. The QCA method, developed by Charles C. Ragin in 1987, diverges from regression analysis by focusing on case-based analysis and examining the configuration of variables linked to the outcome. This approach acknowledges that there can be diverse causal configurations leading to the same result[ 47 ]. Therefore, QCA has the potential to enhance our understanding of various factor configurations that alleviate depressive symptoms in older adults. Consequently, comprehending these intricate pathways may facilitate the development of more effective interventions for depressive symptoms in practice. The QCA method simplifies operations for identifying the paths leading to a result, utilizing set theory and Boolean algebra[ 47 ]. Consistency and coverage are employed to assess the relationships between conditions and outcomes when using this approach. Consistency refers to the extent to which a combination of causal conditions is reliably associated with an outcome, while coverage indicates how well a cause or causal combination explains an instance of an outcome. A minimum recommended threshold of 0.75 for consistency and 0.5 for coverage is suggested[ 48 ]. This method encompasses three types: crisp-set qualitative comparative analysis (csQCA), multi-value qualitative comparative analysis (mvQCA), and fuzzy-set qualitative comparative analysis (fsQCA). In fsQCA, condition variables can take any value between 0 and 1. This paper aims to identify different configurations of conditions that effectively alleviate depressive symptoms in older adults using the QCA method. However, assigning condition variables as either 0 or 1 is often difficult; therefore, we have chosen to use the fsQCA analysis technique. The key steps in utilizing fsQCA include variable measurement, calibration, and configuration analysis. Data The data utilized in this paper is derived from the 2020 survey conducted by the China Family Panel Studies (CFPS), which was published in 2022 and represents the most recent dataset available. This comprehensive survey was conducted by the Institute of Social Science Survey (ISSS) at Peking University. The CFPS sample covers 31 provinces in Mainland China, the target sample size is 16,000 households, and the survey objects include all the family members in the sample households. The CFPS dataset primarily captures and aggregates data at the individual, household, and community levels, encompassing various aspects of China's society, economy, population dynamics, education system, and public health. Notably, this extensively utilized dataset has been employed in numerous studies investigating the health of older adults in China[ 49 , 50 ]. The objective of this study is to explore the relevance between perceived neighborhood environment and depressive symptoms in Chinese older adults. Leveraging this invaluable dataset provides us with substantial data and information for conducting our research. In this study, we utilized the 2020 CFPS versions and employed adult and family questionnaires. By matching individuals with their respective families based on their unique IDs, we conducted data-cleaning procedures to eliminate missing values. Consequently, a total of 4,697 pieces of comprehensive data were obtained, encompassing essential information regarding the elderly participants, their perceptions of neighborhood environments, as well as their levels of depressive symptoms. The QCA method is suitable for sample scenarios ranging from small to large sizes; thus making it appropriate for our study's extensive sample range[ 47 ]. Each participant's combination of conditions has been coded as a case in QCA terminology, resulting in a total count of 4697 cases within this paper. The characteristics of respondents are presented in Table 1 . Descriptive statistics reveal that our sample includes older adults from diverse age groups, genders, marital statuses, and regions; thereby indicating its strong representativeness. Table 1 Respondents’characteristics Characteristics N Percentage Age 60–69 3004 63.96% 70–79 1471 31.32% 80–89 215 4.58% 90 or above 7 0.15% Gender Female 2258 48.07% Male 2439 51.93% Marital status Married 3895 82.93% Partner 20 0.43% Never married 37 0.79% Divorced/separated 69 1.47% Widowed 676 14.39% Place of residence Urban 2336 49.73% Rural 2361 50.27% Qualitative comparative analysis Variables and measures The outcome variable of this study is depressive symptoms, which are assessed using the Center for Epidemiological Studies Depression Scale (CESD), a widely used screening tool for depressive symptoms in older adults [ 51 ]. In our paper, we utilize the 8-item short version of CESD (CES-D 8), consisting of items related to feeling depressed, feeling happy, feeling lonely, enjoying life, feeling sad, everything being tasking, not being able to sleep, and feeling like life could not go on. Each item was rated on a four-point scale ranging from 0 (Rarely) to 3 (Most), with respondents indicating their experiences over the past week. The Cronbach's alpha coefficient for the CESD scale is calculated as 0.782, indicating satisfactory reliability. To ensure consistency in scoring interpretation across all items, we reverse-code two positively worded items: entry [ 2 ] and entry [ 4 ]. The total score range for each participant ranges from 0 to 24; higher scores indicate more severe depressive symptoms. In the QCA method, distinct conditional configurations leading to positive and negative outcomes are considered. Thus, the positive and negative aspects of the outcome variable are treated as separate variables. This study aims to identify perceived neighborhood environmental conditions that could result in lower levels of depressive symptoms in older adults. High levels of depressive symptoms in this population represented an undesirable outcome that was contrary to expectations and desires. Therefore, the outcome variable for this study is defined as low depression, defined as depression0. The condition variable represents the neighborhood environment, which encompasses the surrounding homes and neighborhood relations. The selected conditions of the neighborhood environment in this study are specifically related to the mental health of older adults, as supported by previous literature. Self-reported measures are considered more reliable for capturing the effects of various neighborhood characteristics[ 52 ]. Extensive evidence suggests that perceptions of neighborhoods largely mitigate the impact of socioeconomic indicators on health outcomes[ 15 , 53 ]. Therefore, in this study, we use the perceived neighborhood environment to measure the neighborhood environment. The physical neighborhood environment is assessed through community facilities and housing surroundings perceptions[ 54 ]. The social aspect of the neighborhood environment is measured by neighborhood safety, neighborhood relations, and neighborhood assistance, reflecting interpersonal interactions in the neighborhood context [ 55 , 56 ]. The community facilities are assessed through the inquiry, "What is the overall state of public amenities such as education, healthcare, and transportation in your locality?". The housing surroundings are evaluated using the question "What is the general condition of noise pollution and waste management in your vicinity?". The neighborhood safety is gauged via the query "How secure do you feel about your neighborhood?". The perceived neighborhood relations are measured using the question "Overall, how would you rate neighborly relations in your area?". The neighborhood assistance is determined based on responses to the question "If you required assistance from a neighbor, do you believe someone would be willing to help?". Each condition is assessed using an item, and the items for the initial four conditions were evaluated on a 5-point Likert Scale (1 = poor, 2 = inadequate, 3 = average, 4 = superior, 5 = outstanding). The perceived neighborhood assistance is measured utilizing a 5-point Likert Scale (1 = certainly not, 2 = probably not, 3 = uncertain, 4 = probably, 5 = certainly). Variable calibration The result variables are calibrated using the direct calibration method. A cutoff criterion of 9 is established for identifying clinically significant depressive symptoms on the CES-D 8 scale[ 51 ]. Consequently, respondents with scores exceeding 9 are assigned a calibrated depression score of 1, while those with scores equal to or below 9 are assigned a calibrated score of 0. A score of 1 indicates the presence of depressive symptoms in older adults, whereas a score of 0 signifies their absence. The fsQCA3.0 software is utilized to calibrate all condition variables on a scale ranging from 0 (fully outside a set) to 1 (fully in a set), with the point of maximum ambiguity at 0.5 determining the membership of the set. The determination of three threshold points (fully in, point of maximum ambiguity, fully out) is based on both theoretical and practical considerations. In this study, all condition variables were assessed using a 5-point Likert scale with ratings increasing positively from 1 to 5. We calibrated each condition variable by assigning thresholds: 1.5 was calibrated as 0.0, 3 as 0.5, and 4.5 as 1.0 [ 57 ]. Consequently, every condition variable is constructed as positive; thus, a score of 1 indicates its presence and positive impact on the outcome variable while a score of zero denotes the absence of any conditional variable for alleviating depressive symptoms. Results Analysis of single conditions The present study aims to investigate the impact of neighborhood environment on depressive symptoms in older adults. By the QCA method, it is essential to assess the necessity of each condition (community facilities, housing surroundings, neighborhood safety, neighborhood relations, and neighborhood assistance) before analyzing adequate conditional combinations. We examine the presence or absence of each antecedent condition in all samples where there is an absence of depressive symptoms in older adults to determine their indispensability for preventing depressive symptoms. A condition with a consistency score exceeding 0.90 is referred to as a necessary condition[ 58 ]. As depicted in Table 2 , the consistency scores for each condition are below 0.90. This implies that no individual condition can effectively alleviate depressive symptoms in older adults. Consequently, we incorporate all the conditions into the truth table to investigate the various configurations of neighborhood conditions contributing to alleviating depressive symptoms in older adults. Table 2 Consistency and coverage of single conditions Condition Type Depression0 Consistency Coverage Community facilities 0,1 [0.67,0.33] [0.83,0.79] Housing surroundings 0,1 [0.67,0.33] [0.83,0.79] Neighborhood safety 0,1 [0.76,0.24] [0.83,0.78] Neighborhood relations 0,1 [0.79,0.21] [0.82,0.79] Neighborhood assistance 0,1 [0.87,0.13] [0.79,0.82] Note: The consistency and overage values in square brackets correspond to the assignment of variables in turn. Depression0 indicates that depressive symptoms are assigned a value of 0. Configuration analysis of conditions By convention, the consistency and threshold are set at 0.8 and 1, respectively. The intermediate solutions are selected for presentation and analysis[ 58 ]. The results of the intermediate solution (Table 3 ) show that there are three pathways to alleviate depressive symptoms in Chinese older adults. Three configurations of the condition cover 70.2% of the cases and have a strong explanatory power. The consistency is 0.829, which is acceptable (consistency > = 0.75), and has strong consistency indicating the solution strongly relates to the outcome observed[ 58 ]. Robustness Tests By previous investigations[ 59 ], we conducted a robustness analysis by increasing the threshold value. If the resulting configuration, after elevating the frequency threshold, is a subset of the original configuration, it indicates the stability of our findings. By raising the case threshold from 1 to 5 (while retaining 98% of cases), we obtain a resulting configuration that is a subset of the original study group. This demonstrates that our configuration analysis identifies consistent main pathways. Discussion Three Configurations for successfully alleviating depressive symptoms Through qualitative comparative analysis, we obtain three configurations to alleviate depressive symptoms in Chinese older adults. According to Configuration 1, neighborhood safety, good neighborliness, and a high perception of neighborhood assistance are identified as core conditions in mitigating depressive symptoms in older adults in 68.4% of cases. This conclusion demonstrates a consistency level of 0.83. Neighborhood safety and assistance are the core conditions in this configuration. According to Configuration 2, findings from 27.0% of the cases indicate that neighborhoods with well-developed facilities, high-quality neighborhood relations, and neighborhood assistance play an effective role in alleviating depressive symptoms in older adults. The consistency level for this conclusion is reported as 0.82. Configuration 3 reveals that in 56.4% of the cases examined, neighborhoods effectively addressing depressive symptoms in older adults exhibit characteristics such as robust community facilities, favorable housing environments, safe surroundings, and high levels of neighborhood support. The consistency level associated with this conclusion is reported at 0.84. The core conditions in configurations 2 and 3 encompass community facilities and neighborhood assistance. Relationships between conditions The findings suggest that all attributes related to the neighborhood environment (i.e., community facilities, housing surroundings, neighborhood safety, neighborhood relations, and neighborhood assistance) significantly contribute to the mitigation of depressive symptoms in older adults; however, the attribute of neighborhood assistance emerges as particularly pivotal. Receiving neighborhood assistance is deemed crucial in effectively alleviating depressive symptoms. Previous research has demonstrated that neighborhood assistance can reduce stress and depressive symptoms by facilitating the sharing and transmission of adaptive behaviors[ 36 ]. Therefore, in Chinese society, it is important to consider allocating resources for elderly services directly provided by professional caregivers to enhance established social support networks and assistance relationships between elderly neighbors[ 18 ]. However, relying solely on the assistance of neighbors is insufficient; a robust physical neighborhood environment and social context are also necessary as complementary factors. The presence of community facilities can influence health behaviors, thereby impacting an individual's physical well-being and psychosocial stress levels, ultimately affecting the risk of depressive symptoms in older adults[ 60 ]. Favorable community facilities can serve as a buffer against stress and reduce the likelihood of depressive symptoms[ 30 ]. Several studies indicated that exposure to environmental risks associated with housing surroundings (including neighborhood noise and pollution) is linked to depressive symptoms in older adults[ 61 ]. In our study, neighborhood safety and neighborhood relations are integral components of the social environment within neighborhoods. These factors, in conjunction with other variables, are associated with depressive symptoms in older adults. Additionally, previous research has demonstrated significant associations between crime rates and higher levels of depressive symptoms, as well as between neighborhood safety and lower levels of depressive symptoms in older adults[ 20 , 36 , 62 ]. Furthermore, even after controlling for age, sex, and income influences, the significant relationship between perceptions of neighborhood safety and depressive symptoms remained robust[ 20 ]. These findings contribute to an expanding body of literature that examines the impact of both physical and social aspects of neighborhoods on mental health outcomes in older adults while also raising questions about how specific characteristics of neighborhoods relate to overall health. By comparing configuration 2 and configuration 3, we can find that the influence of neighborhood relationships on depressive symptom reduction in older adults is equivalent to the combined effects of neighborhood security and housing environment. According to the second and third configurations, it is observed that neighborhood safety is present in the second pathway but absent in the third pathway. Conversely, the residential surrounding environment and neighborhood safety are present in the third configuration but not in the second configuration. Therefore, it could be suggested that a socially connected neighborhood with supportive networks capable of buffering stress levels may exert a more favorable influence on the mental well-being of older adults[ 10 ]. Conversely, when neighborhood relations are suboptimal, increasing investments in enhancing both neighborhood safety and improving surrounding environments can help mitigate the risk of depressive symptoms in older adults within neighborhoods. In addition, we find that a combination of neighborhood social environmental factors can effectively alleviate depressive symptoms in older adults, whereas relying solely on physical environmental conditions may not produce a significant reduction unless supplemented by social environmental factors. Comparing Configuration 1 with Configuration 2 and 3, it is observed that all conditions in Configuration 1 pertain to the neighborhood social environment, whereas both physical and social conditions exist in Configurations 2 and 3. The underlying reason for this could be attributed to the influence of community facilities on older adults' mental health through their impact on social interaction and relationship quality. These findings align with previous research investigating the association between neighborhood support networks, depressive symptoms, and older adults[ 41 ]. Previous research has demonstrated that the physical environment (e.g., bicycle lanes, green spaces, housing/buildings) and the presence of amenities promoting social interactions (e.g., cafes, community centers, museums) can impact health behaviors and social interactions, influencing an individual's physical health and psychosocial stress levels, ultimately affecting their risk of depressive symptoms[ 63 , 64 ]. Simultaneously, neighborhood physical characteristics can influence the extent of supportive relationships among individuals which may subsequently affect depressive symptoms[ 65 ]. This finding aligns with a limited body of evidence suggesting that social context such as neighborhoods plays a more significant role in explaining depressive symptoms rather than serving as an indicator of socioeconomic disadvantage[ 30 , 66 ]. Declarations Acknowledgments We sincerely thank Peking University, the National School of Development, and the Institute of Social Science Survey for supporting this article, from where we obtained the data from China Family Panel Studies (CFPS). We also thank all the participants in this study. Funding This study was supported by the Social Science Foundation project of Shaanxi Province (2023F012). Availability of data and materials The datasets are publicly available from the project of China Family Panel Studies (CFPS) and can be downloaded after registration from: https://www.isss.pku.edu.cn/cfps/. Ethics approval and consent to participate Data for this study were obtained from CFPS. Ethical approval for all the CFPS waves was granted by the Institutional Review Board at Peking University. The IRB approval number for the main household survey, including anthropometrics, is IRB00001052-14010. All participants gave written informed consent. All methods were performed by the relevant guidelines and regulations. Clinical trial number: not applicable. Competing interests The authors declare that they have no competing interests. Consent for publication Not applicable. Authors’ contributions YN contributed to the research design, methodology, data analysis, and draft preparation. SG contributed to the literature review and problem analysis. BJ contributed to funding acquisition and revised the manuscript. All authors contributed to the article and approved the submitted version. Author Details School of Public Policy and Administration, Xi’an Jiaotong University, Xi’an, China References Davey A, Halverson CF Jr., Zonderman AB, Costa PT. Jr. Change in Depressive Symptoms in the Baltimore Longitudinal Study of Aging. Journals Gerontology: Ser B. 2004;59(6):270–7. Fiske A, Gatz M, Pedersen NL. Depressive Symptoms and Aging: The Effects of Illness and Non-Health-Related Events. Journals Gerontology: Ser B. 2003;58(6):320–8. 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Soc Psychiatry Psychiatr Epidemiol. 2012;47(2):175–84. Stafford MAI, McMunn A, De Vogli R. Neighbourhood social environment and depressive symptoms in mid-life and beyond. Aging Soc. 2011;31(6):893–910. Stirling K, Toumbourou JW, Rowland B. Community factors influencing child and adolescent depression: A systematic review and meta-analysis. Australian New Z J Psychiatry. 2015;49(10):869–86. Slavich GM. Social Safety Theory: A Biologically Based Evolutionary Perspective on Life Stress, Health, and Behavior. Annual Review of Clinical Psychology. 2020;16(Volume 16, 2020):265 – 95. Ivey SL, Kealey M, Kurtovich E, Hunter RH, Prohaska TR, Bayles CM, et al. Neighborhood characteristics and depressive symptoms in an older population. Aging Ment Health. 2015;19(8):713–22. Hawe P, Shiell A. Social capital and health promotion: a review. Soc Sci Med. 2000;51(6):871–85. Lam WWY, Loo BPY, Mahendran R. Neighbourhood environment and depressive symptoms among the elderly in Hong Kong and Singapore. Int J Health Geogr. 2020;19(1):48. Albor C, Uphoff EP, Stafford M, Ballas D, Wilkinson RG, Pickett KE. The effects of socioeconomic incongruity in the neighbourhood on social support, self-esteem and mental health in England. Soc Sci Med. 2014;111:1–9. Qin W, Erving CL, Nguyen AW. Trajectories of depressive symptoms among older African Americans: the influence of neighborhood characteristics and gender. Aging Ment Health. 2023;27(11):2220–8. Litwin H. The association between social network relationships and depressive symptoms among older Americans: what matters most? Int Psychogeriatr. 2011;23(6):930–40. Xiao Q, Wu M, Zeng T. Social support networks in Chinese older adults: health outcomes and health related behaviors: a path analysis. Aging Ment Health. 2019;23(10):1382–90. Kim J, Ross CE. Neighborhood-specific and general social support: which buffers the effect of neighborhood disorder on depression? J Community Psychol. 2009;37(6):725–36. Kingsbury M, Clayborne Z, Colman I, Kirkbride JB. The protective effect of neighbourhood social cohesion on adolescent mental health following stressful life events. Psychol Med. 2020;50(8):1292–9. Robinette JW, Charles ST, Mogle JA, Almeida DM. Neighborhood cohesion and daily well-being: Results from a diary study. Soc Sci Med. 2013;96:174–82. Choi YJ, Ailshire JA. Perceived neighborhood disorder, social cohesion, and depressive symptoms in spousal caregivers. Aging Ment Health. 2024;28(1):54–61. Ragin CC, Strand SI. Using Qualitative Comparative Analysis to Study Causal Order: Comment on Caren and Panofsky (2005). Sociological Methods & Research. 2008;36(4):431 – 41. Short K, Eadie P, Kemp L. Paths to language development in at risk children: a qualitative comparative analysis (QCA). BMC Pediatr. 2019;19(1):94. Chen H, Hou C, Zhang L, Li S. Comparative study on the strands of research on the governance model of international occupational safety and health issues. Saf Sci. 2020;122. Liang Y, Dong J. The impact of the send-down experience on the health of elderly Chinese women: Evidence from the China family panel studies. Int Rev Econ Finance. 2022;78:377–89. Briggs R, Carey D, O’Halloran AM, Kenny RA, Kennelly SP. Validation of the 8-item Centre for Epidemiological Studies Depression Scale in a cohort of community-dwelling older people: data from The Irish Longitudinal Study on Ageing (TILDA). Eur Geriatr Med. 2018;9(1):121–6. Julien D, Richard L, Gauvin L, Kestens Y. Neighborhood characteristics and depressive mood among older adults: an integrative review. Int Psychogeriatr. 2012;24(8):1207–25. Weden MM, Carpiano RM, Robert SA. Subjective and objective neighborhood characteristics and adult health. Soc Sci Med. 2008;66(6):1256–70. Engel L, Chudyk AM, Ashe MC, McKay HA, Whitehurst DGT, Bryan S. Older adults' quality of life – Exploring the role of the built environment and social cohesion in community-dwelling seniors on low income. Soc Sci Med. 2016;164:1–11. Chaudhury H, Mahmood A, Michael YL, Campo M, Hay K. The influence of neighborhood residential density, physical and social environments on older adults' physical activity: An exploratory study in two metropolitan areas. J Aging Stud. 2012;26(1):35–43. Chen YY, Wong GHY, Lum TY, Lou VWQ, Ho AHY, Luo H, et al. Neighborhood support network, perceived proximity to community facilities and depressive symptoms among low socioeconomic status Chinese elders. Aging Ment Health. 2016;20(4):423–31. Huarng K-H, Yu TH-K. Complexity theory of entrepreneur characteristics. Int Entrepreneurship Manage J. 2021;17(3):1037–48. Ragin CC, Fiss P. Net effects analysis versus configurational analysis: An empirical demonstration. Redesigning Social Inquiry: Fuzzy Sets beyond. 2008:190–212. Ma C, Dong X, Wang J. A Trade-off Between the Artistic Aesthetic Value and Market Value of Paintings With Naïve and Childlike Interest Complex. Empir Stud Arts. 2023;41(2):352–71. Kim D. Blues from the Neighborhood? Neighborhood Characteristics and Depression. Epidemiol Rev. 2008;30(1):101–17. Chen Y, Cui PY, Pan YY, Li YX, Waili N, Li Y. Association between housing environment and depressive symptoms among older people: a multidimensional assessment. BMC Geriatr. 2021;21(1):259. Moore KA, Hirsch JA, August C, Mair C, Sanchez BN, Roux AVD. Neighborhood Social Resources and Depressive Symptoms: Longitudinal Results from the Multi-Ethnic Study of Atherosclerosis. J Urb Health. 2016;93(3):572–88. Ewing R, Schieber RA, Zegeer CV. Urban sprawl as a risk factor in motor vehicle occupant and pedestrian fatalities. Am J Public Health. 2003;93(9):1541–5. Penedo FJ, Dahn JR. Exercise and well-being: a review of mental and physical health benefits associated with physical activity. Curr Opin Psychiatry. 2005;18(2):189–93. Sampson RJ, Morenoff JD, Gannon-Rowley T. Assessing Neighborhood Effects: Social Processes and New Directions in Research. Annual Review of Sociology. 2002;28(Volume 28, 2002):443 – 78. Hill TD, Ross CE, Angel RJ. Neighborhood Disorder, Psychophysiological Distress, and Health. J Health Soc Behav. 2005;46(2):170–86. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 24 Jul, 2025 Read the published version in BMC Geriatrics → Version 1 posted Editorial decision: Revision requested 11 Sep, 2024 Editor assigned by journal 06 Sep, 2024 Submission checks completed at journal 06 Sep, 2024 First submitted to journal 26 Aug, 2024 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4981256","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":352498270,"identity":"788d030c-ee64-4d0a-af32-e23ee47feb6e","order_by":0,"name":"Yan Nan","email":"","orcid":"","institution":"Xi’an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Nan","suffix":""},{"id":352498271,"identity":"5453c2cf-62fe-4b40-841f-3ae8d092101c","order_by":1,"name":"Tingshuai Ge","email":"","orcid":"","institution":"Xi’an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Tingshuai","middleName":"","lastName":"Ge","suffix":""},{"id":352498272,"identity":"7b4048ac-f245-48dd-a811-f2c56f34b9bc","order_by":2,"name":"Quanbao Jiang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYHACxgNgsoGH8UFChQ1xemBamA0enEkjQQsDAw+b5MO2Q4SVm7c3PzjM88eGgbk991hFAtsBBv727gS8WmTOHDM4zNuWxsDY8y7tRgLPHQaJM2c34NUiIZHDcJi34TAD44wcsxsJEs8YDCRyCWiRf8MAdNh/sJaCBIPDRGiR4AFqAXoBpIUhIYEYLTxpBgfntiWD/JIskXAgjYewX9gPP3zw5o8dg2F77sGPP//ZyPG39+LXAgP1GxsSwAweopSDgTxDAvGKR8EoGAWjYGQBABtXSfxj+Fr0AAAAAElFTkSuQmCC","orcid":"","institution":"Xi’an Jiaotong University","correspondingAuthor":true,"prefix":"","firstName":"Quanbao","middleName":"","lastName":"Jiang","suffix":""}],"badges":[],"createdAt":"2024-08-27 03:51:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4981256/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4981256/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12877-025-06193-0","type":"published","date":"2025-07-24T15:57:14+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":66321159,"identity":"ab9c78ac-6647-41c6-8c44-5669c8fbcaf0","added_by":"auto","created_at":"2024-10-10 11:50:00","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":50249,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual framework\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4981256/v1/f47580eea9643a8fda0878f6.jpg"},{"id":87756887,"identity":"6fcda8ba-0b39-4d1b-8d83-f4620d820553","added_by":"auto","created_at":"2025-07-28 16:10:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":542114,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4981256/v1/eb5257f5-146d-4da9-a3a1-f1bb2572ba9d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"What Neighborhood Environment Configuration Can Alleviate Depressive Symptoms in Older Adults: A Fuzzy-set Qualitative Comparative Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe prevalence of depression is high in older adults, and numerous studies have consistently reported a moderate increase in depressive symptoms as age advances[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This is a matter of concern due to the significant relevance between depressive symptoms and disability, morbidity, and suicide risk, as well as the decline in physical, social, and cognitive functioning in older adults [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Moreover, it is noteworthy that older adults residing within neighborhoods exhibit higher rates of depressive symptoms. Specifically in community-dwelling older adults, the prevalence of clinically relevant depressive symptoms stands at approximately 13.5%, while the morbidity of depressive symptoms can reach as high as 49% [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Given China's rapidly growing older adults and substantial changes in family structures over time, it is particularly important to carry out community construction for the elderly group [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Consequently, Chinese older adults' psychological well-being within neighborhoods has attracted considerable attention. Specifically, identifying factors that exacerbate or alleviate depressive symptoms is of great importance for the health condition of older adults[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAn increasing body of empirical research has highlighted the relevance between the neighborhood environment and depressive symptoms[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The association between neighborhood characteristics and the health of older adults can be elucidated by the heightened susceptibility of older adults to the influence exerted by their residential environment, owing to a multitude of factors[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. After retirement, older adults are more likely to remain in their familiar neighborhoods due to limited mobility and a decrease in alternative contexts for relocation[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Consequently, they tend to spend an increasing amount of time within their neighborhood surroundings[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Research indicates that the mental well-being of older adults may be particularly influenced by the conditions present within their immediate neighborhood environment, as they rely heavily on local services and amenities while being less mobile[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe present research on the relationship between the neighborhood environment and depressive symptoms in older adults has had mixed results[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Previous studies have primarily focused on the impact of neighborhood disorder, poverty, and deprivation on depressive symptoms in adults[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. It has been established that depressive symptoms in older adults are influenced by ethnic composition and physical environment[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. One study examined two specific aspects of the neighborhood environment \u0026ndash;the level of neighborhood poverty versus concentration, about depressive symptoms in older Mexican Americans[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Furthermore, it is important to note that the aforementioned research findings primarily originate from Western societies, and there remains a dearth of knowledge regarding the impact of neighborhood environment on depressive symptoms in Chinese neighborhoods. Given the cultural values in China, this relationship may differ significantly from that observed in Western contexts[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. To address these gaps in knowledge, this study aims to explore the relationship between neighborhood environment and depressive symptoms in Chinese older adults.\u003c/p\u003e \u003cp\u003eNeighborhood factors exhibit intricate interconnections in a complex and non-linear manner, with numerous interactions and reinforcing effects. It is conceptually challenging to solely consider one factor and attempt to partially exclude its influence. Qualitative Comparative Analysis (QCA) is a method specifically designed to unravel these intricate relationships[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. However, most current studies on the determinants of depressive symptoms in older adults employ multi-level analysis, multi-level structural equation analysis, and other methodologies[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], while the application of QCA in this field remains limited. The present study utilizes QCA to examine the combinations of neighborhood environmental conditions that contribute to alleviating symptoms of depression in older adults. The objective is to identify distinct pathways for mitigating depressive symptoms at the neighborhood level. Subsequently, the paper proceeds as follows: Section 2 provides the theoretical background for this study; Section 3 presents the data and methodology; Section 4 outlines the data analysis process; Section 5 presents the results of fsQCA; and finally, Section 6 discusses these findings along with their implications.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eLiterature Review and Theoretical Background\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe study is guided by the Ecological Theory of Aging and a theoretical framework that links neighborhood factors to mental health outcomes, specifically depressive symptoms[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The Ecological Theory of Aging posits that personal resources interact with environmental resources, and an individual's assessment of their environmental context can influence their psychosocial responses and shape subsequent behavior and health outcomes. The environment can be divided into various subsystems, including the microsystem, mesosystem, exosystem, and chronosystem. The neighborhood environment is considered an external system that significantly impacts the mental health of individuals. This study focuses on examining the relationship between neighborhoods and depressive symptoms in older adults based on this integrated theoretical background. Relevant characteristics of the neighborhood environment are selected based on previous studies identifying physical or social factors within neighborhoods that may influence depressive symptoms.\u003c/p\u003e \u003cp\u003eNeighborhood physical environment\u003c/p\u003e \u003cp\u003eThe neighborhood environment encompasses both physical and social aspects[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In China, the terms 'neighborhood' and 'community' are used interchangeably. Officially, a neighborhood is defined as a social sphere comprising individuals residing within specific geographical boundaries under official administration[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. This paper posits two crucial physical conditions within neighborhoods: community facilities and the surrounding housing environment. Community facilities play a crucial role in shaping the physical environment. In recent years, there has been an increasing emphasis on constructing leisure and recreational facilities in Chinese neighborhoods driven by government initiatives[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. These amenities offer older adults direct access to leisure spaces for activities like morning exercises and post-meal walks, which contribute positively to their health and well-being[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Additionally, community facilities provide opportunities for social interaction in older adults by facilitating regular gatherings with their neighbors. This fosters social relationships that can help alleviate depressive symptoms[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Conversely, older adults who perceive inadequate community facilities as barriers to participation in social activities face an increased risk of depressive symptoms[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Furthermore, according to the neighborhood disorder model, housing surroundings characterized by dilapidated houses, abandoned buildings, environmental pollution, and noise are associated with depressive symptoms due to the prevailing sense of lack of control and societal disorder they reflect[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Research has consistently demonstrated a strong association between physical environmental factors such as pollution and noise with Cardiovascular disease caused by depressive symptoms[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNeighborhood social environment\u003c/p\u003e \u003cp\u003eThis paper posits three crucial neighborhood social conditions: neighborhood safety, neighborhood relations, and neighborhood assistance. The neighborhood social environment is a context that exposes individuals to factors that can either promote or hinder the development of depressive symptoms[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. It may provide psychological support and guidance for older adults, helping them cope with challenges and prevent symptoms of depression. Additionally, cultivating a sense of purpose and belonging in this environment may directly impact one's mental health. Recent research has demonstrated the correlation between concepts such as perceived neighborhood safety, neighborhood cohesion (neighborhood relations, neighborhood assistance), and depressive symptoms in older adults[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNeighborhood safety pertains to the perception of safety in a community, typically assessed through the sense of security experienced in the local vicinity[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The potency of a neighborhood's safety perception lies in its capacity to interweave societal fabric, fostering interpersonal bonds that can mitigate the psychological tempests associated with depressive symptoms[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Following Social Security Theory, humans possess inherent preparedness for biological and physical threats in their environment; however, novel threats emerging from the social milieu may elicit similar stress responses, thereby augmenting susceptibility to mental and physical impairments[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Previous research has demonstrated that older adults who perceive higher levels of crime within their neighborhoods exhibit an increased vulnerability to depressive symptoms compared to those who perceive their neighborhoods as safe[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Furthermore, a heightened sense of neighborhood security may empower residents by fostering a greater perception of control and reducing feelings of powerlessness, thereby acknowledging their potential role in driving positive changes within neighborhoods[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe evaluation and coping responses of individuals are influenced by the quality of social relationships within neighborhoods (referred to as neighborhood relations ), making it a significant social determinant of health[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. For instance, the presence of supportive and caring neighbors plays a crucial role in promoting good mental health[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. This is associated with the protective impact of neighborliness on mental well-being. Research indicates that perceiving neighborliness positively can serve as a psychosocial coping factor, mitigating adverse mental health outcomes[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecent research in Western societies increasingly acknowledges the pivotal role of neighborhood assistance in mitigating depressive symptoms in older adults[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The research revealed that individuals with higher levels of social support, encompassing emotional and practical aid from close acquaintances, as well as neighborhood social support involving neighborhood assistance, exhibit a reduced risk of depressive symptoms[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Older adults residing within neighborhoods experience depressive symptoms due to a dearth of neighborhood assistance[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Additionally, several studies have reported that neighborhood assistance can act as a buffer against the adverse impact of daily stressors on negative emotions[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. This is attributed to the fact that neighborhood assistance serves not only as an instrumental resource but also provides emotional support[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn summary, this paper proposes five key neighborhood environmental conditions: community facilities, housing surroundings, neighborhood safety, neighborhood relations, and neighborhood assistance. Building upon previous studies, we have developed a research model that incorporates the neighborhood environment as an antecedent factor and the mitigation of depressive symptoms as an outcome. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the proposed research framework.\u003c/p\u003e "},{"header":"Methodology","content":"\u003cp\u003eMethod\u003c/p\u003e \u003cp\u003eIn this study, we employ Qualitative Comparative Analysis (QCA) to investigate the relevance between different configurations of neighborhood environments and lower levels of depressive symptoms in older adults. The QCA method, developed by Charles C. Ragin in 1987, diverges from regression analysis by focusing on case-based analysis and examining the configuration of variables linked to the outcome. This approach acknowledges that there can be diverse causal configurations leading to the same result[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Therefore, QCA has the potential to enhance our understanding of various factor configurations that alleviate depressive symptoms in older adults. Consequently, comprehending these intricate pathways may facilitate the development of more effective interventions for depressive symptoms in practice.\u003c/p\u003e \u003cp\u003eThe QCA method simplifies operations for identifying the paths leading to a result, utilizing set theory and Boolean algebra[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Consistency and coverage are employed to assess the relationships between conditions and outcomes when using this approach. Consistency refers to the extent to which a combination of causal conditions is reliably associated with an outcome, while coverage indicates how well a cause or causal combination explains an instance of an outcome. A minimum recommended threshold of 0.75 for consistency and 0.5 for coverage is suggested[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. This method encompasses three types: crisp-set qualitative comparative analysis (csQCA), multi-value qualitative comparative analysis (mvQCA), and fuzzy-set qualitative comparative analysis (fsQCA). In fsQCA, condition variables can take any value between 0 and 1. This paper aims to identify different configurations of conditions that effectively alleviate depressive symptoms in older adults using the QCA method. However, assigning condition variables as either 0 or 1 is often difficult; therefore, we have chosen to use the fsQCA analysis technique. The key steps in utilizing fsQCA include variable measurement, calibration, and configuration analysis.\u003c/p\u003e \u003cp\u003eData\u003c/p\u003e \u003cp\u003eThe data utilized in this paper is derived from the 2020 survey conducted by the China Family Panel Studies (CFPS), which was published in 2022 and represents the most recent dataset available. This comprehensive survey was conducted by the Institute of Social Science Survey (ISSS) at Peking University. The CFPS sample covers 31 provinces in Mainland China, the target sample size is 16,000 households, and the survey objects include all the family members in the sample households. The CFPS dataset primarily captures and aggregates data at the individual, household, and community levels, encompassing various aspects of China's society, economy, population dynamics, education system, and public health. Notably, this extensively utilized dataset has been employed in numerous studies investigating the health of older adults in China[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. The objective of this study is to explore the relevance between perceived neighborhood environment and depressive symptoms in Chinese older adults. Leveraging this invaluable dataset provides us with substantial data and information for conducting our research.\u003c/p\u003e \u003cp\u003eIn this study, we utilized the 2020 CFPS versions and employed adult and family questionnaires. By matching individuals with their respective families based on their unique IDs, we conducted data-cleaning procedures to eliminate missing values. Consequently, a total of 4,697 pieces of comprehensive data were obtained, encompassing essential information regarding the elderly participants, their perceptions of neighborhood environments, as well as their levels of depressive symptoms. The QCA method is suitable for sample scenarios ranging from small to large sizes; thus making it appropriate for our study's extensive sample range[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Each participant's combination of conditions has been coded as a case in QCA terminology, resulting in a total count of 4697 cases within this paper. The characteristics of respondents are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Descriptive statistics reveal that our sample includes older adults from diverse age groups, genders, marital statuses, and regions; thereby indicating its strong representativeness.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\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\u003eRespondents\u0026rsquo;characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\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\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u0026ndash;69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63.96%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70\u0026ndash;79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31.32%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e80\u0026ndash;89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.58%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e90 or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.15%\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48.07%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51.93%\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e82.93%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePartner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.43%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.79%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced/separated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.47%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.39%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlace of residence\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49.73%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50.27%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eQualitative comparative analysis\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eVariables and measures\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe outcome variable of this study is depressive symptoms, which are assessed using the Center for Epidemiological Studies Depression Scale (CESD), a widely used screening tool for depressive symptoms in older adults [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. In our paper, we utilize the 8-item short version of CESD (CES-D 8), consisting of items related to feeling depressed, feeling happy, feeling lonely, enjoying life, feeling sad, everything being tasking, not being able to sleep, and feeling like life could not go on. Each item was rated on a four-point scale ranging from 0 (Rarely) to 3 (Most), with respondents indicating their experiences over the past week. The Cronbach's alpha coefficient for the CESD scale is calculated as 0.782, indicating satisfactory reliability. To ensure consistency in scoring interpretation across all items, we reverse-code two positively worded items: entry [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] and entry [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The total score range for each participant ranges from 0 to 24; higher scores indicate more severe depressive symptoms.\u003c/p\u003e \u003cp\u003eIn the QCA method, distinct conditional configurations leading to positive and negative outcomes are considered. Thus, the positive and negative aspects of the outcome variable are treated as separate variables. This study aims to identify perceived neighborhood environmental conditions that could result in lower levels of depressive symptoms in older adults. High levels of depressive symptoms in this population represented an undesirable outcome that was contrary to expectations and desires. Therefore, the outcome variable for this study is defined as low depression, defined as depression0.\u003c/p\u003e \u003cp\u003eThe condition variable represents the neighborhood environment, which encompasses the surrounding homes and neighborhood relations. The selected conditions of the neighborhood environment in this study are specifically related to the mental health of older adults, as supported by previous literature. Self-reported measures are considered more reliable for capturing the effects of various neighborhood characteristics[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Extensive evidence suggests that perceptions of neighborhoods largely mitigate the impact of socioeconomic indicators on health outcomes[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Therefore, in this study, we use the perceived neighborhood environment to measure the neighborhood environment. The physical neighborhood environment is assessed through community facilities and housing surroundings perceptions[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. The social aspect of the neighborhood environment is measured by neighborhood safety, neighborhood relations, and neighborhood assistance, reflecting interpersonal interactions in the neighborhood context [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. The community facilities are assessed through the inquiry, \"What is the overall state of public amenities such as education, healthcare, and transportation in your locality?\". The housing surroundings are evaluated using the question \"What is the general condition of noise pollution and waste management in your vicinity?\". The neighborhood safety is gauged via the query \"How secure do you feel about your neighborhood?\". The perceived neighborhood relations are measured using the question \"Overall, how would you rate neighborly relations in your area?\". The neighborhood assistance is determined based on responses to the question \"If you required assistance from a neighbor, do you believe someone would be willing to help?\". Each condition is assessed using an item, and the items for the initial four conditions were evaluated on a 5-point Likert Scale (1\u0026thinsp;=\u0026thinsp;poor, 2\u0026thinsp;=\u0026thinsp;inadequate, 3\u0026thinsp;=\u0026thinsp;average, 4\u0026thinsp;=\u0026thinsp;superior, 5\u0026thinsp;=\u0026thinsp;outstanding). The perceived neighborhood assistance is measured utilizing a 5-point Likert Scale (1\u0026thinsp;=\u0026thinsp;certainly not, 2\u0026thinsp;=\u0026thinsp;probably not, 3\u0026thinsp;=\u0026thinsp;uncertain, 4\u0026thinsp;=\u0026thinsp;probably, 5\u0026thinsp;=\u0026thinsp;certainly).\u003c/p\u003e \u003cp\u003eVariable calibration\u003c/p\u003e \u003cp\u003eThe result variables are calibrated using the direct calibration method. A cutoff criterion of 9 is established for identifying clinically significant depressive symptoms on the CES-D 8 scale[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Consequently, respondents with scores exceeding 9 are assigned a calibrated depression score of 1, while those with scores equal to or below 9 are assigned a calibrated score of 0. A score of 1 indicates the presence of depressive symptoms in older adults, whereas a score of 0 signifies their absence.\u003c/p\u003e \u003cp\u003eThe fsQCA3.0 software is utilized to calibrate all condition variables on a scale ranging from 0 (fully outside a set) to 1 (fully in a set), with the point of maximum ambiguity at 0.5 determining the membership of the set. The determination of three threshold points (fully in, point of maximum ambiguity, fully out) is based on both theoretical and practical considerations. In this study, all condition variables were assessed using a 5-point Likert scale with ratings increasing positively from 1 to 5. We calibrated each condition variable by assigning thresholds: 1.5 was calibrated as 0.0, 3 as 0.5, and 4.5 as 1.0 [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Consequently, every condition variable is constructed as positive; thus, a score of 1 indicates its presence and positive impact on the outcome variable while a score of zero denotes the absence of any conditional variable for alleviating depressive symptoms.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eAnalysis of single conditions\u003c/p\u003e\n\u003cp\u003eThe present study aims to investigate the impact of neighborhood environment on depressive symptoms in older adults. By the QCA method, it is essential to assess the necessity of each condition (community facilities, housing surroundings, neighborhood safety, neighborhood relations, and neighborhood assistance) before analyzing adequate conditional combinations. We examine the presence or absence of each antecedent condition in all samples where there is an absence of depressive symptoms in older adults to determine their indispensability for preventing depressive symptoms. A condition with a consistency score exceeding 0.90 is referred to as a necessary condition[\u003cspan class=\"CitationRef\"\u003e58\u003c/span\u003e]. As depicted in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, the consistency scores for each condition are below 0.90. This implies that no individual condition can effectively alleviate depressive symptoms in older adults. Consequently, we incorporate all the conditions into the truth table to investigate the various configurations of neighborhood conditions contributing to alleviating depressive symptoms in older adults.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eConsistency and coverage of single conditions\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCondition\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eType\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eDepression0\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eConsistency\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoverage\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCommunity facilities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.67,0.33]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.83,0.79]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHousing surroundings\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.67,0.33]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.83,0.79]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeighborhood safety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.76,0.24]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.83,0.78]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeighborhood relations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.79,0.21]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.82,0.79]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeighborhood assistance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.87,0.13]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.79,0.82]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\u003cem\u003eNote: The consistency and overage values in square brackets correspond to the assignment of variables in turn. Depression0 indicates that depressive symptoms are assigned a value of 0.\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eConfiguration analysis of conditions\u003c/p\u003e\n\u003cp\u003eBy convention, the consistency and threshold are set at 0.8 and 1, respectively. The intermediate solutions are selected for presentation and analysis[\u003cspan class=\"CitationRef\"\u003e58\u003c/span\u003e]. The results of the intermediate solution (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e) show that there are three pathways to alleviate depressive symptoms in Chinese older adults. Three configurations of the condition cover 70.2% of the cases and have a strong explanatory power. The consistency is 0.829, which is acceptable (consistency\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;0.75), and has strong consistency indicating the solution strongly relates to the outcome observed[\u003cspan class=\"CitationRef\"\u003e58\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eRobustness Tests\u003c/p\u003e\n\u003cp\u003eBy previous investigations[\u003cspan class=\"CitationRef\"\u003e59\u003c/span\u003e], we conducted a robustness analysis by increasing the threshold value. If the resulting configuration, after elevating the frequency threshold, is a subset of the original configuration, it indicates the stability of our findings. By raising the case threshold from 1 to 5 (while retaining 98% of cases), we obtain a resulting configuration that is a subset of the original study group. This demonstrates that our configuration analysis identifies consistent main pathways.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThree Configurations for successfully alleviating depressive symptoms\u003c/p\u003e \u003cp\u003eThrough qualitative comparative analysis, we obtain three configurations to alleviate depressive symptoms in Chinese older adults. According to Configuration 1, neighborhood safety, good neighborliness, and a high perception of neighborhood assistance are identified as core conditions in mitigating depressive symptoms in older adults in 68.4% of cases. This conclusion demonstrates a consistency level of 0.83. Neighborhood safety and assistance are the core conditions in this configuration. According to Configuration 2, findings from 27.0% of the cases indicate that neighborhoods with well-developed facilities, high-quality neighborhood relations, and neighborhood assistance play an effective role in alleviating depressive symptoms in older adults. The consistency level for this conclusion is reported as 0.82. Configuration 3 reveals that in 56.4% of the cases examined, neighborhoods effectively addressing depressive symptoms in older adults exhibit characteristics such as robust community facilities, favorable housing environments, safe surroundings, and high levels of neighborhood support. The consistency level associated with this conclusion is reported at 0.84. The core conditions in configurations 2 and 3 encompass community facilities and neighborhood assistance.\u003c/p\u003e \u003cp\u003eRelationships between conditions\u003c/p\u003e \u003cp\u003eThe findings suggest that all attributes related to the neighborhood environment (i.e., community facilities, housing surroundings, neighborhood safety, neighborhood relations, and neighborhood assistance) significantly contribute to the mitigation of depressive symptoms in older adults; however, the attribute of neighborhood assistance emerges as particularly pivotal. Receiving neighborhood assistance is deemed crucial in effectively alleviating depressive symptoms. Previous research has demonstrated that neighborhood assistance can reduce stress and depressive symptoms by facilitating the sharing and transmission of adaptive behaviors[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Therefore, in Chinese society, it is important to consider allocating resources for elderly services directly provided by professional caregivers to enhance established social support networks and assistance relationships between elderly neighbors[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, relying solely on the assistance of neighbors is insufficient; a robust physical neighborhood environment and social context are also necessary as complementary factors. The presence of community facilities can influence health behaviors, thereby impacting an individual's physical well-being and psychosocial stress levels, ultimately affecting the risk of depressive symptoms in older adults[\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Favorable community facilities can serve as a buffer against stress and reduce the likelihood of depressive symptoms[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Several studies indicated that exposure to environmental risks associated with housing surroundings (including neighborhood noise and pollution) is linked to depressive symptoms in older adults[\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. In our study, neighborhood safety and neighborhood relations are integral components of the social environment within neighborhoods. These factors, in conjunction with other variables, are associated with depressive symptoms in older adults. Additionally, previous research has demonstrated significant associations between crime rates and higher levels of depressive symptoms, as well as between neighborhood safety and lower levels of depressive symptoms in older adults[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Furthermore, even after controlling for age, sex, and income influences, the significant relationship between perceptions of neighborhood safety and depressive symptoms remained robust[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. These findings contribute to an expanding body of literature that examines the impact of both physical and social aspects of neighborhoods on mental health outcomes in older adults while also raising questions about how specific characteristics of neighborhoods relate to overall health.\u003c/p\u003e \u003cp\u003eBy comparing configuration 2 and configuration 3, we can find that the influence of neighborhood relationships on depressive symptom reduction in older adults is equivalent to the combined effects of neighborhood security and housing environment. According to the second and third configurations, it is observed that neighborhood safety is present in the second pathway but absent in the third pathway. Conversely, the residential surrounding environment and neighborhood safety are present in the third configuration but not in the second configuration. Therefore, it could be suggested that a socially connected neighborhood with supportive networks capable of buffering stress levels may exert a more favorable influence on the mental well-being of older adults[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Conversely, when neighborhood relations are suboptimal, increasing investments in enhancing both neighborhood safety and improving surrounding environments can help mitigate the risk of depressive symptoms in older adults within neighborhoods.\u003c/p\u003e \u003cp\u003eIn addition, we find that a combination of neighborhood social environmental factors can effectively alleviate depressive symptoms in older adults, whereas relying solely on physical environmental conditions may not produce a significant reduction unless supplemented by social environmental factors. Comparing Configuration 1 with Configuration 2 and 3, it is observed that all conditions in Configuration 1 pertain to the neighborhood social environment, whereas both physical and social conditions exist in Configurations 2 and 3. The underlying reason for this could be attributed to the influence of community facilities on older adults' mental health through their impact on social interaction and relationship quality. These findings align with previous research investigating the association between neighborhood support networks, depressive symptoms, and older adults[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Previous research has demonstrated that the physical environment (e.g., bicycle lanes, green spaces, housing/buildings) and the presence of amenities promoting social interactions (e.g., cafes, community centers, museums) can impact health behaviors and social interactions, influencing an individual's physical health and psychosocial stress levels, ultimately affecting their risk of depressive symptoms[\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Simultaneously, neighborhood physical characteristics can influence the extent of supportive relationships among individuals which may subsequently affect depressive symptoms[\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. This finding aligns with a limited body of evidence suggesting that social context such as neighborhoods plays a more significant role in explaining depressive symptoms rather than serving as an indicator of socioeconomic disadvantage[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e].\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eWe sincerely thank Peking University, the National School of Development, and the Institute of Social Science Survey for supporting this article, from where we obtained the data from\u0026nbsp;China Family Panel Studies (CFPS). We also thank all the participants in this study.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis study was supported by the\u0026nbsp;Social Science Foundation project of Shaanxi Province (2023F012).\u003c/p\u003e\n\u003cp\u003eAvailability\u0026nbsp;of\u0026nbsp;data\u0026nbsp;and materials\u003c/p\u003e\n\u003cp\u003eThe datasets are publicly available from the project of China Family Panel Studies (CFPS) and can be downloaded after registration from:\u0026nbsp;https://www.isss.pku.edu.cn/cfps/.\u003c/p\u003e\n\u003cp\u003eEthics\u0026nbsp;approval\u0026nbsp;and\u0026nbsp;consent\u0026nbsp;to\u0026nbsp;participate\u003c/p\u003e\n\u003cp\u003eData for this study were obtained from\u0026nbsp;CFPS. Ethical approval for all the CFPS waves was granted by the Institutional Review Board at Peking University. The IRB approval number for the main household survey, including anthropometrics, is IRB00001052-14010. All participants gave written informed consent. All methods were\u0026nbsp;performed by the relevant guidelines and regulations. Clinical trial number: not applicable.\u003c/p\u003e\n\u003cp\u003eCompeting\u0026nbsp;interests\u003c/p\u003e\n\u003cp\u003eThe\u0026nbsp;authors declare\u0026nbsp;that\u0026nbsp;they\u0026nbsp;have no\u0026nbsp;competing\u0026nbsp;interests.\u003c/p\u003e\n\u003cp\u003eConsent\u0026nbsp;for\u0026nbsp;publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026rsquo;\u0026nbsp;contributions\u003c/p\u003e\n\u003cp\u003eYN contributed to the research design, methodology, data analysis, and draft preparation. SG contributed to the literature review and problem analysis. BJ contributed to funding acquisition and revised the manuscript. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003eAuthor\u0026nbsp;Details\u003c/p\u003e\n\u003cp\u003eSchool of Public Policy and Administration, Xi\u0026rsquo;an Jiaotong University,\u0026nbsp;Xi\u0026rsquo;an, China\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDavey A, Halverson CF Jr., Zonderman AB, Costa PT. Jr. Change in Depressive Symptoms in the Baltimore Longitudinal Study of Aging. Journals Gerontology: Ser B. 2004;59(6):270\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFiske A, Gatz M, Pedersen NL. Depressive Symptoms and Aging: The Effects of Illness and Non-Health-Related Events. Journals Gerontology: Ser B. 2003;58(6):320\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlazer DG. Depression in late life: Review and commentary. 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Exercise and well-being: a review of mental and physical health benefits associated with physical activity. Curr Opin Psychiatry. 2005;18(2):189\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSampson RJ, Morenoff JD, Gannon-Rowley T. Assessing Neighborhood Effects: Social Processes and New Directions in Research. Annual Review of Sociology. 2002;28(Volume 28, 2002):443\u0026thinsp;\u0026ndash;\u0026thinsp;78.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHill TD, Ross CE, Angel RJ. Neighborhood Disorder, Psychophysiological Distress, and Health. J Health Soc Behav. 2005;46(2):170\u0026ndash;86.\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":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Neighborhood environment, Depressive symptoms, Chinese older adults, Fuzzy-set qualitative comparative analysis","lastPublishedDoi":"10.21203/rs.3.rs-4981256/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4981256/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction:\u003c/strong\u003eThe neighborhood is a regular living and activity space for the elderly. It is important to identify neighborhood environmental factors that can alleviate depression in the elderly to improve their health. However, existing literature does not consider the complex interdependencies among key neighborhood environmental factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod:\u003c/strong\u003eThis study employs the Qualitative Comparative Analysis (QCA) method to explore how the configuration of neighborhood environmental conditions can help alleviate depressive symptoms in the elderly. The data is derived from the 2020 China Family Panel Study (CFPS) survey.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe results show that three different neighborhood environment configurations can help reduce depressive symptoms in older Chinese individuals. The first configuration requires a combination of neighborhood safety, good neighborhood relations, and neighborhood assistance. The second configuration involves amalgamating optimal community facilities, high-quality neighborhood relations, and neighborhood assistance. The third configuration encompasses sound community facilities, favorable housing surroundings, a secure communal atmosphere, and advanced neighborhood assistance. Furthermore, we identify neighborhood assistance as a core condition for alleviating depressive symptoms and find that the combined effects of neighborhood safety and housing surroundings on alleviating depressive symptoms are comparable to the effect of neighborhood relationships.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiscussion:\u003c/strong\u003eThese research results deepen the current understanding of neighborhood environment configurations to alleviate depressive symptoms in older adults, offer important implications for theory and practice, and set new directions for the construction of age-friendly neighborhoods.\u003c/p\u003e","manuscriptTitle":"What Neighborhood Environment Configuration Can Alleviate Depressive Symptoms in Older Adults: A Fuzzy-set Qualitative Comparative Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-10 11:49:55","doi":"10.21203/rs.3.rs-4981256/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-11T10:17:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-09-06T07:51:27+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-09-06T07:50:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Geriatrics","date":"2024-08-27T03:49:53+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"13658b8d-89e6-4ad9-bd49-d49238b407a8","owner":[],"postedDate":"October 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-07-28T16:06:56+00:00","versionOfRecord":{"articleIdentity":"rs-4981256","link":"https://doi.org/10.1186/s12877-025-06193-0","journal":{"identity":"bmc-geriatrics","isVorOnly":false,"title":"BMC Geriatrics"},"publishedOn":"2025-07-24 15:57:14","publishedOnDateReadable":"July 24th, 2025"},"versionCreatedAt":"2024-10-10 11:49:55","video":"","vorDoi":"10.1186/s12877-025-06193-0","vorDoiUrl":"https://doi.org/10.1186/s12877-025-06193-0","workflowStages":[]},"version":"v1","identity":"rs-4981256","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4981256","identity":"rs-4981256","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}