The U-shape Association between Population Agglomeration and Individual Depression: the Role of Dialect Diversity

Abstract

Depression is a relevant mental illness affecting hundreds of millions of people worldwide. As urbanization accelerates, agglomeration of populations has altered individual social network distances and life crowding, which in turn affects depressive prevalence. However, the association between depression and population agglomeration (PA) remains controversial. This study aims to explore whether and how PA could influence individual depression. Based on the China Health and Retirement Longitudinal Study (CHARLS) 2018, the empirical results showed that there was a U-shaped association between PA and individual CES-D scores. As PA increases, the risk of depression first decreases and then increases. CES-D was lowest at moderate aggregation. Dialect diversity (DD) was positively related to the incidence of individual depression. The higher the DD, the higher the risk of depression. Meanwhile, DD also played a moderating role in the association between PA and individual depression. Our observations suggest that the optimistic level of agglomeration for individual mental health is within 1500 to 2000 persons per square kilometer.

Introduction

Depression is a prevalent mental disorder, currently impacting approximately 350 million individuals globally [1, 2]. This condition can disrupt daily life [3], threaten mental health, and significantly contribute to suicidal behaviors, thereby posing substantial public health challenges [4]. In recent years, population agglomeration (PA) has been increasing due to the accelerating process of urbanization worldwide [5]. While urbanization has brought about material wealth and spiritual civilization, it has also presented various challenges. The problems arising from urbanization have led urban dwellers to face significant psychological challenges. Some studies have suggested that high PA may have a negative impact on individuals’ mental health. However, the association between PA and depression remains controversial in current research.

Given the significant impact of increased PA, numerous studies have explored its correlation with depression. Despite extensive research, critical questions remain unresolved. For example, it is uncertain whether urbanization and its resultant increase in PA effectively reduce depression rates. Moreover, the extent of PA’s influence on depression is still not well defined. Researchers from social sciences and psychology have posited a potential link between PA and depression. Nonetheless, the relationship is intricate and influenced by a myriad of factors including economic [6, 7], cultural, historical, and social factors [8, 9]. Changes in PA modify the social network proximity, level of residential congestion, resource allocation, and psychological distance among individuals, subsequently impacting depression within the population.

While numerous studies have delved into the association between PA and depression, the results remain inconsistent [10]. Some research suggests that higher PA correlates with increased depression rates, as mental health issues tend to rise with population density. Although areas with dense populations provide greater opportunities for social interaction, they can paradoxically engender feelings of social isolation and loneliness. In large cities, for example, people may feel alienated from their neighbors and have fewer interactions with strangers, negatively impacting their mental health. Additionally, densely populated areas often feature environmental stressors and heightened competition, such as intense job markets, soaring housing costs, and urban congestion, all of which can amplify depression risks [11]. Conversely, other studies dispute this connection, with some even suggesting that depression rates decrease as PA increases. High-density areas typically offer extensive social opportunities and networks, which can enhance social support and mitigate depressive symptoms [12]. Furthermore, PA can affect the distribution and accessibility of healthcare resources, underscoring that the relationship between PA and depression is not strictly linear, as demonstrated by varying findings across different studies.

Through language, individuals express their emotions, needs, and experiences. It serves as a fundamental medium for social communication and carries human culture. Language reflects social realities and actively engages with society, influencing individuals’ conditions and psychological well-being. Dialect, as a unique cultural and linguistic characteristic, plays a significant role in social interactions, cultural diversity, and social support [13, 14], potentially affecting the onset and progression of depression. Consequently, diverse dialects may impact the relationship between PA and individual depression [15].

Dialect diversity (DD) may influence the formation and stability of social support networks. In areas with high DD, language differences can hinder communication among individuals, leading to smaller social networks and diminished access to social support. When coupled with high PA, these linguistic barriers can exacerbate social isolation and increase the risk of depression. Furthermore, DD can complicate communication and socialization, creating communication barriers that heighten the risk of depression [16]. In regions with significant DD, individuals might struggle to communicate with others, particularly outsiders, potentially undermining their sense of self-identity and social integration. This insecurity can intensify in settings with high PA, further increasing the risk of depression.

Due to inconsistencies in previous studies, the contradictory results need resolution for a comprehensive understanding of how PA impacts individual depression. Thus, the primary goal of this study is to investigate the association between PA and individual depression, proposing a U-shaped curve as a unified theoretical framework to elucidate this relationship. Additionally, this study aims to enhance the theoretical model by examining the moderating effect of DD. To achieve these objectives, we analyze data from the China Health and Retirement Longitudinal Study (CHARLS) along with DD data. The findings will provide a scientific basis for further research on the regional distribution of depression and its correlates. The results may also inform the development of more effective mental health interventions, aiming to mitigate the adverse impacts of depression on individuals and society. Moreover, this study could offer valuable insights for formulating population policies and community management strategies across different regions to potentially reduce the incidence of depression.

Methods

Data Collection

The China Health and Retirement Longitudinal Study (CHARLS) provides high-quality microdata on households and individuals [8, 17]. This extensive dataset encompasses 150 counties and 450 communities (villages) across 28 provinces in China [18]. For our analysis, we utilized the 2018 CHARLS dataset, excluding any records with missing values. Ultimately, our experimental analysis incorporated data from 12,885 participants.

The Diversity index of Chinese Dialects dataset contains information on 2113 Chinese dialects spoken in observation units at the county level and above. Researchers constructed a diversity index of Chinese dialects for 278 prefecture-level and above cities [19]. The dialect diversity (DD) data is based on geographical regions rather than individuals [20]. In this study, we used diversity index data from this dataset corresponding to the individuals' residences in the CHARLS dataset.

Measurement

The characteristic variables in this study include age, gender, education, life satisfaction, long-term illness, residence type, PA, and DD. Of these, PA and DD required matching calculations. Initially, we obtained the CHARLS dataset and the DD dataset. We conducted data processing and cleansing of both the DD and CHARLS datasets, which included steps such as data format conversion, removal of missing values, and normalization. The variable used for matching was determined by selecting the area identifiers that appeared in both the DD dataset and the CHARLS dataset. The merging of the DD dataset with the CHARLS dataset, using the district matching variables, was accomplished through data linkage methods. PA was calculated by dividing the total population size of the district by the area of the region, and the PA for each individual was recorded. We also read the Chinese dialect diversity index for each CHARLS participant’s region in the linked dataset, which was noted as the value of their DD variable. After completing the data collection, the data were used for further analysis to explore the association between PA, DD, and depression.

Dependent Variables

Depression outcomes are assessed using the Center for Epidemiologic Studies Depression Scale (CES-D), provided by CHARLS. The CES-D is known for its adequate reliability and validity in Chinese depression studies [21]. The scale includes 10 questions about the participants’ feelings and behaviors over the past week. Participants are asked to select their responses from four provided options that indicate the frequency of these feelings and behaviors. The CES-D score for each participant is calculated by summing the scores from all the answers [22]. Higher scores indicate a greater risk of depression, with the CES-D score serving as a validated measure of depression. In this study, the depressive tendencies of the 12,885 participants were assessed based on their CES-D scores, which are treated as continuous variables.

Independent Variable

In our study, PA was considered as the independent variable. We utilized the population density of the participants’ locations to represent the level of local PA. Population density is defined as the number of individuals per unit of land area, reflecting the population denseness in a given area, and is widely used in demographic analysis [23]. This measure indicates the concentration of the population and is calculated solely based on the total population and total area of the location.

Moderating Variable

In our study, DD was employed as a moderating variable. The DD index ranges from 0 to 1, with higher values indicating greater diversity of dialects. DD specifically examines variations in dialect usage among the population and incorporates the population weight associated with each dialect in its calculations. By accounting for the number of individuals using different dialects, DD effectively mitigates potential errors that could arise when merely counting the number of dialect types as a measure. Simply counting dialect types does not fully capture the demographic weight of dialect usage. For instance, some dialects may be spoken by only a small number of people, whereas others may be widely used by a larger population. Ignoring population weights could lead to underestimating the influence of dialects spoken by fewer people and overestimating those spoken by more. By considering the number of speakers of different dialects, DD reduces the subjectivity inherent in dialect classification, thus providing a more accurate, comprehensive, and nuanced measure of dialect diversity.

Control Variables

In this study, two primary categories of control variables were incorporated into all models based on their relevance to the research questions and potential impact on the outcomes. The first category includes demographic characteristics such as age, gender, education level, and residence type. Age is considered a crucial demographic characteristic due to its strong association with health status and behavior. Gender is another significant demographic characteristic that may influence health status and behavior. The education level represents an individual’s socioeconomic status and psychological resources, with higher education levels potentially associated with better mental health. Residence type is acknowledged as an important environmental factor influencing mental health.

The second category comprises health-related characteristics, including long-term illness and life satisfaction. Long-term illness is included due to its strong correlation with overall health status, which could affect the study’s results. Life satisfaction measures how satisfied individuals are with their living situation, reflecting their subjective well-being and mental health status. Including these specific control variables helps to eliminate potential confounding factors, enhancing the reliability of the findings and providing a more comprehensive understanding of the relationship between PA and individual depression. Detailed information about these variables is available in Table 1.

Table 1.

Descriptive statistics of characteristic variables

Variables	Group	Number	Mean	%	S.D	Maximum	Minimum
Age: continuous variable		12,885	60.82		9.530	108	20
Gender	1: male	6242	1.52	48.44	0.500	2	1
	2: female	6643		51.56
Education: education level	0	2230	1.23	17.31	0.737	3	0
	1	5552		43.09
	2	4984		38.68
	3	119		0.92
Life satisfaction	1: not satisfied at all	312	3.26	2.42	0.772	5	1
	2: less satisfied	1025		7.95
	3: more satisfied	7134		55.37
	4: very satisfied	3784		29.37
	5: extremely satisfied	630		4.89
Long-term illness: number of chronic illnesses	0	7357	0.68	57.10	1.006	8	0
	1–8	5528		42.90
Residence type	0: rural	10,072	0.22	78.17	0.413	1	0
	1: urban	2813		21.83

S.D., standard deviation

Statistical analysis

The main characteristic variables analyzed in this study are presented descriptively in Table 1. Continuous variables are accompanied by their standard deviation (SD), while categorical variables are represented by frequencies or percentages (%). The results indicate that the mean age of the sample is 60.82. The sample composition by gender includes 51.56% females and 48.44% males, with females slightly outnumbering males. Regarding education, the largest proportion of the sample, accounting for 81.77%, has a secondary education level (levels 1 and 2 combined). The mean education level is 1.23, and the average life satisfaction score is 3.26, suggesting that a significant portion of the population, 55.37% or 7134 individuals, are more satisfied with their life. As for health conditions, the average number of chronic illnesses per participant is 0.68, with 57.10% of participants reporting no chronic diseases. In terms of residence type, 78.17% of the sample resides in rural areas, while the remaining 21.83% live in urban areas.

Results

The Association between PA and Individual Depression

We employed a multiple regression model, controlling for several potential influencing factors, such as age, gender, education level, residence type, long-term illness, and life satisfaction, to more accurately investigate the effects of PA and DD on individual depression. In this model, PA is treated as the independent variable, while DD is introduced as a moderating variable. This approach allows us to examine the relationship between PA and individual depression while also considering the influence of other variables, providing insights into how PA and DD interact and affect depression.

The analysis began with a regression model incorporating control variables. We subsequently added variables incrementally to construct more comprehensive multiple regression models. In model 1, which included age, gender, education, residence type, long-term illness, and life satisfaction, all variables were found to be associated with depression. As shown in Table 2, there were statistically significant differences in the effects of socio-demographic factors such as age, gender, education, and residence type on individual depression (p < 0.01). Additionally, health-related factors such as long-term illness and life satisfaction also showed a statistically significant effect on individual depression (p < 0.01).

Table 2.

Results of regression

	Model 1		Model 2		Model 3		Model 4		Model 5
Variable	CES-D		CES-D		CES-D		CES-D		CES-D
	Coefficient (SE)	P value	Coefficient (SE)	P value	Coefficient (SE)	P value	Coefficient (SE)	P value	Coefficient (SE)	P value
Age	0.038 (0.005)	< 0.001	0.038 (0.005)	< 0.001	0.038 (0.005)	< 0.001	0.039 (0.005)	< 0.001	0.039 (0.005)	< 0.001
Gender	1.538 (0.103)	< 0.001	1.551 (0.103)	< 0.001	1.550 (0.103)	< 0.001	1.558 (0.103)	< 0.001	1.561 (0.103)	< 0.001
Education	− 1.213 (0.075)	< 0.001	− 1.193 (0.074)	< 0.001	− 1.198 (0.074)	< 0.001	− 1.184 (0.075)	< 0.001	− 1.175 (0.075)	< 0.001
Life satisfaction	− 3.152 (0.064)	< 0.001	− 3.113 (0.064)	< 0.001	− 3.108 (0.064)	< 0.001	− 3.114 (0.064)	< 0.001	− 3.108 (0.064)	< 0.001
Long-term illness	0.778 (0.049)	< 0.001	0.774 (0.049)	< 0.001	0.774 (0.049)	< 0.001	0.774 (0.049)	< 0.001	0.776 (0.049)	< 0.001
Residence type	− 1.504 (0.124)	< 0.001	− 1.579 (0.124)	< 0.001	− 1.610 (0.124)	< 0.001	− 1.593 (0.124)	< 0.001	− 1.556 (0.125)	< 0.001
PA			− 15.795 (1.533)	< 0.001	− 19.461 (1.881)	< 0.001	− 18.274 (1.938)	< 0.001	− 16.396 (2.037)	< 0.001
PA2					70.324 (20.926)	0.001	61.593 (21.201)	0.004	50.460 (22.159)	0.023
DD							0.557 (0.219)	0.011	0.600 (0.220)	0.006
PA × DD									− 24.479 (8.311)	0.003
PA2 × DD									191.006 (87.023)	0.028
$R^2$	0.237		0.244		0.244		0.245		0.245

PA, population agglomeration; DD, dialect diversity

The regression results presented in Table 2 show that individual depression increases with age [24]. Among other control variables, there is a significant positive association between gender and depression, with females more likely to suffer from depression than males [25]. Education level also has a favorable effect on depression, as individual depression decreases with increasing education levels. Additionally, regarding residence type, people living in rural areas are more prone to depression compared to those in urban areas. The presence of a long-term illness significantly increases personal depression. Moreover, life satisfaction is inversely correlated with depression, indicating that higher levels of satisfaction are associated with lower levels of depression.

The depression score (CES-D score) of the participants was included in the multiple regression analysis model as the dependent variable and PA as the independent variable. PA was included in the model with the control variables to obtain model 2. On the basis of model 2, PA² was added to form model 3. The results in Table 2 show that PA was negatively correlated with the CES-D score (p < 0.01); PA² was positively correlated with the CES-D score (p < 0.01).

We identified a U-shaped association between individual depression and PA. To confirm the existence of this “U”-shaped correlation, we conducted a U-test. The U-test was chosen because it is a nonparametric test that does not necessitate specific distributional assumptions about the data. This makes it particularly suitable for the characteristics of our data compared to other tests like the t-test. The U-test requires that the sample be independent, and it can be applied to ordinal or continuous variables, conditions that our sample meets.

Despite the U-test’s sensitivity to sample size, which can affect its statistical power, it proved to be an effective method for analyzing the impact of PA and DD on individual depression in our study. The PA values ranged from 0.004 to 0.249, and the critical point of the U-shape was within this range. The overall test confirmed the presence of a U-shaped relationship, with a significant result (p < 0.05), allowing us to reject the null hypothesis at the 5% significance level.

The slope analysis indicated a negative sign in the interval, supporting the U-shaped correlation. As PA increases, depression initially decreases until reaching a low point at a PA value of approximately 0.19. Beyond this point, as PA continues to rise, the risk of developing depression significantly increases. This pattern suggests that while a moderate level of PA can be beneficial in reducing depression, excessively high levels lead to an increase in depression risks. The graphical representation of this U-shaped association is detailed in Fig. 1, illustrating how the risk of depression changes with varying levels of PA.

Fig. 1.

U-shaped association of population agglomeration (PA) and individual depression

There are several plausible explanations for the observed U-shaped association between PA and depression. First, from the perspective of interpersonal social interactions, increased PA may enhance social connectivity among individuals. Improved interpersonal communication is known to act as a buffer against negative emotions, potentially reducing the incidence of depression [26]. This connection suggests that as people engage more within their communities, the supportive interactions can mitigate feelings of loneliness and isolation, common precursors to depression.

Second, an increase in PA often correlates with economic advancement. Moderate rises in PA can spur economic growth, leading to more efficient labor markets, enhanced technological exchange, and overall improvements in living standards. These changes can have a direct positive impact on mental health by reducing stress and anxiety associated with economic instability and poor living conditions.

Last, regions with higher PA typically boast better healthcare infrastructure, including more hospitals, higher quality of care, and greater availability of medical resources. This enhanced healthcare environment facilitates better prevention, diagnosis, and treatment of mental health issues, including depression. The accessibility to quality healthcare means individuals are more likely to receive the support they need before mental health challenges escalate into severe depression.

However, when PA exceeds a reasonable density, the relationship with individual depression changes, leading to an increase in depression rates as PA continues to rise. Excessive PA can lead to overpopulation in a region, which introduces several challenges and can heighten the incidence of depression. Firstly, excessive PA might force people to interact socially beyond their comfort levels, negatively impacting the living environment and quality of life. Problems such as metro congestion and traffic jams, typical of overpopulated areas, can adversely affect individual behaviors and moods, potentially increasing depression rates. Secondly, too high a level of PA can actually hinder economic development rather than promote it. An overstretched infrastructure can lead to declining economic levels, reduced per capita income, exacerbated human resource scarcity, resource depletion, and environmental degradation. These factors collectively contribute to increased stress and anxiety, thereby raising the risk of depression. Additionally, excessive PA can strain medical resources. Overpopulated areas might face shortages in healthcare facilities, leading to inadequate diagnosis and treatment for depression and other mental health issues. This lack of adequate healthcare support can further exacerbate the prevalence of depression among the population.

The Role of Dialect Diversity

DD refers to the existence of multiple different dialects or language variations within a particular region or community, which can be indicative of the quantity of dialect types. Human society is a complex system of dynamic change, and as the degree of PA changes, different social groups are formed. This diversity may stem from various influences such as geographic, historical, cultural, and social factors, closely intertwined with the organization of populations, leading individuals to employ different dialects or language forms during communication. DD involves the coordination and changes in culture, society, and economy, manifesting in diverse interpersonal communication, social interactions, and economic development. These coordinated changes are vital in appropriately modulating individuals’ emotions within different PAs. Furthermore, PA shapes social networks, and social networks, in turn, influence individuals’ psychological well-being [15]. In areas with high DD, people face relatively more language barriers and communication difficulties. In areas with low DD, it is easier for people to communicate and understand. DD can have an impact on economic opportunities and employment [27], which can also have a negative effect on an individual’s mental health. In addition, DD can lead to cultural and identity differences within the community, and dialect community pride and identity produce individual differences, which can alter one’s psychological well-being. When individuals feel a lack of belonging and support systems, it increases the risk of depression [28]. Therefore, DD is a significant factor for varying degrees of PA, regardless of the area’s density. It serves as a moderating influence on the relationship between PA and individual depression, highlighting how linguistic and cultural diversity within communities can impact mental health outcomes.

Model 4 adds DD to model 3. As shown in Table 2, DD is positively correlated with CES-D score (p < 0.05). That is, as DD increases, individual depression increases. Our study found that the higher the DD, the more individual depression. The higher the DD, the greater the variety of dialects spoken locally, with a high probability of using different dialects and a low probability of using the same dialect. This also means that there is a low consistency of language. And language affects the psychological distance between people when they interact with each other. As shown in Table 2, model 5 is obtained by adding PA × DD and PA² × DD to model 4. PA × DD is negatively correlated with CES-D (p < 0.01); PA² × DD is positively correlated with CES-D (p < 0.05).

Figure 2 illustrates the varying moderating effects of low and high DD on the relationship between PA and depression. Under conditions of low DD, where local language barriers are minimal and communication is smoother, the graph shows a decrease in depression with increasing PA, specifically within the range of 0 to 0.25. In this same PA range, under high DD conditions, the graph depicts an initial decrease followed by an increase in depression levels as PA continues to rise. Notably, the curve’s lowest point—indicating the least depression—occurs earlier (shifted to the left) in high DD conditions, with the inflection point appearing at values greater than 0.15.

Fig. 2.

Association of population agglomeration (PA), dialect diversity (DD), and individual depression

Under low DD, fewer language barriers facilitate better communication interactions, which in turn support a reduction in depression as the population density increases. This setting allows the social, economic, and medical benefits of higher PA to be more effectively realized, contributing to lower depression rates.

Conversely, in high DD scenarios, despite the richer local linguistic diversity, the increased communication barriers can complicate social interactions. As PA increases, the initial benefits, such as enhanced social, economic, and medical opportunities, help lower depression rates. However, once PA reaches a certain threshold, the negative effects of overcrowding and reduced effective communication begin to outweigh the positives, resulting in increased depression rates.

The study’s findings indicate that incorporating DD as a moderating variable reveals that low DD can help reduce depression as PA increases, while high DD decreases the PA threshold at which depression begins to increase again. High DD does not alter the overall U-shaped relationship between PA and depression but does shift the inflection point to the left and increases the risk of depression under similar conditions. Thus, low DD can have a positive moderating effect by reducing depression, whereas high DD can exacerbate the risk of depression under high PA conditions.

Discussion

Our study makes significant contributions to understanding the effects of PA on individual depression and expands existing research on mental health. Utilizing the 2018 CHARLS data and the DD index data, we conducted multiple regression analyses to quantitatively explore the impact of PA on individual depression. It is crucial to thoroughly consider the association between PA and individual depression, incorporating the influence of DD, in order to promote the construction of public mental health infrastructures and reduce the incidence of depression.

We provide actionable insights for organizers and policymakers, enabling them to offer public mental health services in a manner tailored to local needs. Our findings facilitate the translation of experimental research into practical applications and policy initiatives. This approach not only enhances the understanding of the complex interactions between population density and mental health but also guides the development of strategies that can effectively mitigate depression in diverse population settings.

Our study has identified a U-shaped association between PA and individual depression, providing a unique theoretical perspective and empirical evidence that enhances our understanding of PA’s role. The relationship between PA and depression is not linear but U-shaped, with depression initially decreasing and then increasing as PA intensifies. This nuanced perspective helps to reconcile previous conflicting findings regarding the effects of PA on mental health [29].

PA varies across different regions, with urbanized areas typically exhibiting higher levels of agglomeration compared to rural areas [30]. By considering population distribution and interpersonal interactions in urban planning strategies, it is possible to mitigate the adverse consequences of dense populations, such as reduced living space, declining resource availability, and deteriorating quality of life [31]. A reasonable level of agglomeration can allow individuals to satisfy their psychological needs through social interactions, thereby reducing anxiety, stress, and preventing mental disorders.

Furthermore, regional variations in PA significantly influence the prevalence of depression among local populations. Population policies can be crafted to regulate PA levels while incorporating mental health considerations. However, it is crucial to acknowledge that the outcomes of PA are not universally beneficial for mental health. At lower levels, an increase in PA generally corresponds with reduced depression, suggesting that a moderate increase in PA can be beneficial. Conversely, excessive PA heightens the risk of depression. Thus, maintaining a balanced approach to PA and avoiding excessive agglomeration are essential for promoting optimal mental health outcomes.

Second, we explored the moderating role of DD in the relationship between PA and individual depression, uncovering the mechanisms through which DD impacts this association. Our findings reveal that low DD positively influences the relationship between PA and individual depression, effectively reducing the occurrence of depression at similar levels of PA. Conversely, high DD exacerbates the risk of depression and necessitates a lower threshold of reasonable PA. [32]. High DD can lead to communication difficulties.

In areas with low DD, larger PA thresholds can be established to enhance human interaction, facilitate effective communication, and subsequently decrease depression rates. On the other hand, in high DD areas, which are often ethnically and linguistically diverse, establishing lower PA thresholds and implementing population control measures can help mitigate communication barriers and reduce the incidence of depression [32]. High DD is associated with several challenges including communication difficulties [32], language inconsistencies, social isolation, and cultural disparities, which can hinder educational, employment, and community development opportunities.

To mitigate the negative effects of high DD in areas with significant PA, interventions such as educational programs, communication strategies, and policy adjustments are vital. For instance, incorporating dialect education alongside Mandarin education can improve communication skills and inclusiveness. Integrating artificial intelligence technology to facilitate language translation between dialects can further break down communication barriers. By emphasizing and valuing linguistic and cultural diversity, smoother communication and more effective social activities can be promoted, ultimately contributing to reduced depression rates [33].

Our research provides compelling experimental evidence supporting the association between PA and depression, as well as the crucial moderating role of DD. First, it is essential to consider local population and environmental factors when examining different regions [34]. Local organizers and policymakers can implement judicious population clustering systems and make necessary adjustments to population distribution within reasonable bounds. Moderate PA contributes significantly to improving individuals' mental health. The optimistic level of agglomeration should be within 1500 to 2000 persons per square kilometer. Second, concerted efforts should be made to mitigate the detrimental effects stemming from high DD in order to reduce the incidence of depression. Facilitating social integration and bolstering social support networks is paramount [35]. These measures foster individuals’ sense of social participation, diminish psychological distance, and stimulate community cohesion and collective growth. Cultivating respect and understanding for diverse dialects and cultures is essential. Strengthening multicultural exchanges plays a pivotal role in this endeavor. Communicating the significance and value of DD to the public can improve inter-dialectal and intercultural communication and comprehension. Governments and organizers should prioritize the provision of enhanced education and employment opportunities. Offering a diverse array of educational initiatives encompassing dialectal and cultural knowledge, as well as language training and translation services for individuals requiring communication with diverse dialect backgrounds, will effectively reduce communication barriers and promote collaboration and rapport. Furthermore, it is incumbent upon governments and relevant organizations to formulate and implement appropriate policies and regulations safeguarding and promoting DD. These measures can incentivize the preservation and usage of dialects, protect language rights, amplify personal and social identities, and foster social connectivity and community cohesion.

Conclusions and Limitations

We considered PA and the impact of DD, analyzed their association with individual depression, and provided corresponding measures and suggestions. Our study aims to promote the construction of public mental health and reduce the incidence rate of depression. Overcoming the adverse effects of high DD is a complex and ongoing process that requires concerted efforts and collaboration across all of society. Through a comprehensive approach encompassing education, communication, collaboration, and policy support, we can advance the understanding and recognition of DD, foster an inclusive and diverse social environment, create a safer and more comfortable social atmosphere, and ultimately uphold public mental health. Special attention should be given to mental health, and public mental health services should be provided in a locally tailored manner.

We offer organizers and policymakers effective measures that can translate experimental research into practical applications and policy work. These measures can have positive implications for people’s mental health and the prevention of depression while also informing public health researchers. By addressing the challenges posed by high DD through targeted strategies, we can enhance social integration, reduce communication barriers, and support the well-being of diverse communities.

There are several limitations to this study. First, the analysis was based on the 2018 CHARLS data. Future research should aim to incorporate multi-temporal and multi-modal data to enhance the robustness of the findings. Additionally, the CHARLS dataset primarily consists of middle-aged and older individuals, so it is important to include a more diverse age range in future studies to ensure a comprehensive sample that better represents the population.

Second, it is worth noting that the CHARLS data only covers information from 28 provinces in China, while the DD index data includes 278 administrative districts at the prefecture level and above. To provide a more comprehensive representation, future studies should consider geographical supplementation to include additional regions.

Third, despite the relatively large sample size used in this study, caution should be taken when generalizing the findings to other regions or populations. Historical and cultural differences between regions and countries may influence the results, and these factors should be considered in future research to ensure the findings are broadly applicable.

Finally, it is important to recognize that the pathogenesis of depression is a complex phenomenon influenced by various factors, such as demographics, health-related risk factors, family economic status, and residential environment. Further longitudinal studies are needed to investigate the interplay of these factors and their impact on the development of individual depression. Understanding these interactions will provide a more comprehensive view of how PA and DD influence mental health and inform more effective interventions and policies.

Author Contribution

Conceptualization: J.T. Han, K. Zhang, H. Lin, and Q. Mai. Data collection: J.T. Han, K. Zhang, and H. Lin. Methodology: L. Chang and J. Tu. Writing—original draft: J.T. Han, K. Zhang, and L. Chang. Writing—review and editing: H. Lin, J. Tu, and Q. Mai. All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded by the National Natural Science Foundation of China (72271126, 12227808), the Qinglan Project of Jiangsu Province, and the Postgraduate Research and Practice Innovation Program of Jiangsu Province (KYCX23_2343).

Declarations

Certification of Exemption from Ethical Review

Ethical review and approval were not required for this study on human participants in accordance with the local legislation and institutional requirements. Written informed consent from the patients/participants or patients/participants legal guardian/next of kin was not required to participate in this study in accordance with the national legislation and the institutional requirements.