How Environmental Perception influence Depression and Anxiety: The Mediating Role of Social Interaction

doi:10.21203/rs.3.rs-4686926/v1

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How Environmental Perception influence Depression and Anxiety: The Mediating Role of Social Interaction

https://doi.org/10.21203/rs.3.rs-4686926/v1

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Background: With the economic and social transformations, depression and anxiety disorders are presenting an increasingly common trend globally, becoming a widely addressed issue in mental health. Research has shown that livable natural environments and high-quality social interactions can influence residents’ psychological health and happiness. However, Studies on Whether residents’ perception of the environment affects depression and anxiety, and the underlying mechanisms are remained limited.

Methods: This study relies on Health Life Satisfaction Survey of Yangtze River Phase II Delta (HLSSYRD II), collected during waves spanning 2021 and 2023. The primary objective is to examine the relationship between environmental perception, social interaction, and degree of depression and anxiety of individuals. Linear mixed-effects models were used to analyze the data.

Results: A total of 1752 observations were included in this paper. The “Depression and Anxiety (HAD)” variable has a mean score of 26.01 (SE = 6.99), with “Depression” scoring at 13.15 (SE = 3.78) and “Anxiety” scoring at 12.85 (SE = 3.75) among individuals in YRDC. There is a strong negative correlation between depression, anxiety and environmental perception (β = -0.187, SE = 0.03, p < 0.001; β = -0.152, SE = 0.03, p < 0.001). Moreover, there was significant mediating effects of social interaction in the association between environmental perception and HAD. In addition, gender, chronic disease，marital status, occupation, education level, monthly personal income were associated with depression and anxiety.

Conclusions: Individuals with worse environmental perception have more possibility to develop depressive and anxiety symptoms than those with better environmental perception YRDC, and such a relationship can be partly mediated by social interaction.

Environmental Perception

HAD

mental health

social interaction

depression and anxiety

YRDC

China

In recent years, depression and anxiety disorders have demonstrated an increasingly prevalent trend on a global scale, emerging as widespread mental health concerns. Depressive disorder (also known as depression) is a common mental disorder. It involves a depressed mood or loss of pleasure or interest in activities for long periods of time. Depression can happen to anyone(Evaluation, 2023; Organization, 2023). In 2023, the World Health Organization reported that an estimated 3.8% of the population experience depression, including 5% of adults (4% among men and 6% among women), and 5.7% of adults older than 60 years. Approximately 280 million people in the world have depression. More than 700,000 people die due to suicide every year. Suicide is the fourth leading cause of death in 15–29-year-olds(Organization, 2023). According to a mental health survey conducted in China, the lifetime prevalence of depression among Chinese adults is 6.8%, with major depressive disorder accounting for 3.4% of cases. It is estimated that there are currently 95 million individuals in China affected by depression. Alarmingly, approximately 280,000 suicides occur annually in China, with 40% of these cases being linked to individuals suffering from depression(Institute, 2023). Depression brings some somatic and anxiety-related symptoms, worsens physical diseases, and negatively affects the quality of life among individuals(Grover et al., 2019; Mitchell, 2023; Ren et al., 2024; Zhang et al., 2018). Therefore, identifying the factors related to depressive symptoms is essential for taking measures to improve the mental health of individuals.

A lot of studies have explored the associated factors of depression and anxiety. Specifically, there are different aspects of influencing factors, including biological, psychological, social, and environmental domains(Alshelleh et al., 2022; Brook & Schmidt, 2008; Deshmukh et al., 2017; Neuroscience et al., 2001; Schreier & Chen, 2013; Zajkowska et al., 2021). For instance, some authoritative research find that genetic predispositions and neurochemical imbalances, have long been recognized as significant contributors to the development of mood disorders(Caspi et al., 2003). Beck’s(1979) cognitive theory of depression highlights the impact of maladaptive thought patterns and schemas on the development and maintenance of depressive symptoms(Hollon & Beck, 1979). Similarly, studies on the role of rumination as a cognitive vulnerability factor in anxiety disorders (Nolen-Hoeksema, 2000) underscore the influence of cognitive processes on the experience of persistent worry and fear(Nolen-Hoeksema, 2000). Social factors, encompassing interpersonal relationships, social support networks, and socio-economic conditions, contribute significantly to the prevalence and severity of depression and anxiety(Alsubaie et al., 2019; Dong et al., 2024; Osborn et al., 2020). Moreover, the social determinants of health framework illuminate the role of social disparities and inequities in exacerbating risks for mental health disorders among vulnerable populations(Carey & Crammond, 2015; Organization, 2010; Pedrana et al., 2016; Shi, 2024; Solar & Irwin, 2010). Furthermore, the ecological model of Bronfenbrenner (1979) posits that individuals’ interactions with their environment at multiple levels, from the microsystem of immediate surroundings to the macrosystem of societal influences, shape their psychological well-being(Bronfenbrenner, 1979).

In recent years, there has been a growing recognition of the intricate connections between environmental factors and mental health outcomes such as depression and anxiety(Chen et al., 2020; X. Wang et al., 2024). The way individuals perceive and interact with their surroundings can have a significant impact on their emotional well-being. The concept of ecosystem services theory was first introduced by economist Robert Costanza and his colleagues in 1997. Ecosystem services theory posits that ecosystems provide a wide array of valuable services to humanity, both tangible and intangible, which are crucial for human well-being and sustainable development. Additionally, ecosystem services play a role in fostering social cohesion and enhancing social support networks(Costanza et al., 1997; Costanza et al., 2017; El Serafy, 1998). One seminal study by Ulrich (1983) demonstrated the restorative effects of nature exposure on stress reduction and emotional well-being(Huang et al., 2020; Ulrich, 1983). This study, commonly known as the “Attention Restoration Theory,” suggests that natural environments have the ability to restore cognitive function and alleviate symptoms of anxiety and depression. The biophilia hypothesis, proposed by Wilson (1984), posits that humans possess an innate affinity for nature and natural environments, and exposure to green spaces can have restorative effects on emotional well-being. Additionally, research by Kaplan and Kaplan (1989) introduced the concept of “soft fascination” in natural environments, which can enhance mood and reduce negative emotions(Kaplan et al., 1989). Moreover, findings from a meta-analysis by McEwen et al. (2020) indicated that exposure to natural environments was linked to reduced symptoms of depression and anxiety, suggesting a protective effect of green spaces on psychological well-being(McEwen & Akil, 2020).Conversely, living in environments characterized by noise pollution, crowding, pollution, or lack of access to green spaces has been associated with elevated levels of stress, anxiety, and depressive symptoms. A longitudinal study by Van den Berg et al. (2010) found that individuals living in urban neighborhoods with limited green spaces were at a higher risk of developing mental health problems over time, emphasizing the importance of environmental quality in promoting emotional well-being(Van den Berg et al., 2010).

Furthermore, social interaction plays a crucial role in shaping individuals’ mental health outcomes. The quality of social relationships and the level of social support received can either buffer or exacerbate the negative effects of environmental stressors on mental health. Individuals with strong social networks may be better equipped to cope with environmental challenges and may experience lower levels of depression and anxiety as a result. A study by Lederbogen et al. (2011) revealed that social integration and social network size were associated with reduced perceived stress and lower activation of the amygdala, a key brain region involved in processing emotions(Lederbogen et al., 2011). A meta-analysis by Ozbay et al. (2007) indicated that social support plays a crucial role in moderating the effects of traumatic events on the development of depression and anxiety disorders, suggesting that social connections are essential in coping with environmental challenges(Ozbay et al., 2007). Research by Jones et al. (2022) indicated that social cohesion and connectedness were associated with lower levels of perceived environmental stress and reduced symptoms of depression and anxiety, suggesting that strong social ties can enhance emotional well-being in adverse environmental contexts(Jones et al., 2022). S. Ding (2023) found that elders who receive visits from relatives have lower levels of anxiety compared to those who do not. Personal annual average income and self-rated health status are negatively correlated with anxiety levels(Ding et al., 2023).

These findings underscore the complex interplay between environmental perception, social interaction, and mental health outcomes, especially depression and anxiety. Environmental perception plays a significant role in shaping individuals’ mental health, particularly in relation to depression and anxiety. Numerous studies have explored the intricate connection between how individuals perceive their environment and the impact it has on their psychological well-being. By synthesizing the latest research findings on environmental perception, social interaction, depression, and anxiety, this paper seeks to advance our understanding of the complex pathways through which environmental factors influence mental health outcomes. Understanding the complex interplay between environmental perception, social interaction, and mental health is essential for developing effective interventions and policies to support individuals at risk of developing depression and anxiety. By elucidating the mediating role of social interaction in this relationship, this study aims to provide valuable insights that can inform strategies to promote mental well-being and resilience in the face of environmental stressors.

Based on existing literature and theory, this study proposes the following research hypotheses.

Hypothesis

a: Individual’s better perception of the environment may reduce their depression and anxiety.

Hypothesis

b: Individual’s poorer perception of the environment may exacerbate their depression and anxiety.

Hypothesis

a: Individual who perceive the environment well will significantly increase the frequency of social interactions and develop good interpersonal relationships.

Hypothesis

b: Individual who perceive the environment poorly will decrease the frequency of social interactions and will not be conducive to interpersonal relationships.

Hypothesis 3

The effect of individual’s environmental perceptions on depression and anxiety may be mediated by social interactions and interpersonal relationships.

Data source

The data used in this study were from the ‘Health Life Satisfaction Survey of Yangtze River Phase II Delta (HLSSYRD II)’, implemented by Shanghai Jiao Tong University in 2023. The survey used all urban residents in the Yangtze River Delta in China (YRDC) as the basic sampling frame and used a stratified, multistage and population size-proportional method for sampling. The survey comprised 10 sections, including basic demographic information, health insurance, choice of medical institutions, healthy environment and healthy society of residents in the YRDC.

A total of 15600 valid questionnaires were collected in HLSSYRD II. Since the questions on HAD were optional, the total number of questionnaires that received complete responses to the HAD questions was 1,797. According to research needs of this study, we cleaned the data as follows (see Fig. 1):

Variable measurement

Dependent variable

Based on existing research and questionnaire design, this study used the Hospital Anxiety and Depression Scale (HAD) to measure residents’ levels of depression and anxiety. HAD is a self-assessment questionnaire commonly used to measure levels of anxiety and depression in individuals. HAD was found to perform well in assessing the symptom severity and casernes of anxiety disorders and depression in both somatic, psychiatric and primary care patients and in the general population(Bjelland et al., 2002; Norton et al., 2013). It consists of 14 items, seven of which relate to anxiety symptoms and the other seven to depressive symptoms. The scale is designed to be easy to administer and can be completed quickly by the individual. Each item is scored on a scale of 1 to 4, with higher scores indicating greater severity of symptoms. The total scores for anxiety and depression can provide valuable insights into an individual’s mental health status and help healthcare professionals in diagnosing and monitoring anxiety and depression. We conducted a Cronbach’s Alpha test on these fourteen items, with the test result of 0.87, which is greater than 0.7. The Cronbach’s Alpha test was passed, demonstrating that the assessment measurement tool for HAD can reliably measure the degree of depression and anxiety.

Independent variable and mediating variable

The core explanatory variable of this study was the environmental perception. Based on existing research(Nahar et al., 2023; Van den Berg et al., 2010) and the actual situation in China, we designed five questions in the questionnaire to measure citizens’ overall perception of the environment: ‘Are you satisfied with the local air quality?’; ‘Are you satisfied with the local green environment?’; ‘Are you satisfied with the local water quality?’; ‘Are you satisfied with the local waste sorting and disposal practices?’; ‘How safe do you feel about the local food/dining (medicine)?’. Firstly, we conducted a Cronbach’s Alpha test on these five items, with the test result of 0.78, which is greater than 0.7. The Cronbach’s Alpha test was passed, demonstrating that the assessment measurement tool for environmental perception can reliably measure the variable of environmental perception. Secondly, we used entropy-based methods for variable selection is that they can capture non-linear and complex relationships between variables, and are able to handle high-dimensional data effectively. By using entropy-based methods, we can identify important variables and reduce the dimensionality of the dataset, leading to improved model performance and interpretability.

Regardless of the mediating variable, we used the question to identify residents’ social interaction level: ‘how would you rate your social activities and social relationships over the past two weeks?’

Control variables

Potential confounders consisted of demographic and socioeconomic characteristics and health conditions. Demographic and socioeconomic variables included age, gender, family Population, marital status, birth children, education level, occupation, working status, monthly personal income, health insurance and whether have chronic disease to measure the objective health status of respondents.

Analytical plan

Stata 15.0 was used to analyze the relationship between environmental perceptions and HAD among urban residents in YRDC. Firstly, we used descriptive statistics to describe the overall sociodemographic characteristics of the samples. Secondly, we conducted linear mixed-effects models to explore the association between environmental perceptions and HAD. Thirdly, we employed the three-step method of mediation analysis to examine the mediating effect of social interaction, and utilized the bootstrap method with 1000 resamples to estimate the confidence interval of the mediation effect, enhancing the reliability and robustness of the analysis results. Finally, we conducted robustness tests on the regression results by replacing measurement of independent variables.

Descriptive results

Table 1 presented the depression and anxiety, environmental perception, social interaction and control variables of the study samples. The “Depression and Anxiety (HAD)” variable has a mean score of 26.01 (SE = 6.99), with “Depression” scoring at 13.15 (SE = 3.78) and “Anxiety” scoring at 12.85 (SE = 3.75). The “Environmental Perception” variable, which summarizes perceptions of local environmental factors such as air quality, green spaces, water quality, waste disposal practices, and food/dining conditions, has an average score of 18.67 (SE = 3.04). More than 36.64% of residents’ social interaction were very poor, poor and average, the ratio of residents with good social interaction was 31.79%, and the ratio of residents with excellent social interaction was 13.41%.

In terms of control variables, male and female accounted for 48.86% and 51.14% of the total sample, respectively. More than 85.50% of total adults were married with a spouse, and only 13.6% of people remained unmarried, divorced and widowed. More than 82.37% individuals have 1 or more children, and 17.64% individuals have never had a child. The education attainment of all residents was extremely optimistic, more than 69.69% of all urban residents had attended college and above, and followed by three-year college, accounting for 21.92%, the lower were High school/Vocational school and middle school and below, with only 6.22% and 2.17% of total samples respectfully.

From socio-economic indicators, the ratio of people having a job was 92.12%. More than 48.23% of residents’ personal household income ranging from 5001 to 10000 CNY, the ratio of personal household income ranging from 10001–20000 CNY was 29.28%, and followed by 2001–5000 CNY and 20001 CNY and above. Only 3.17% of total samples’ personal household income was below 2000 CNY. In terms of health insurance participation, BMISUE had the highest participation rate of 69.86%, followed by BMISURR, accounting for 22.09%, and the lowest was PFH, with only 8.05% of total samples. In terms of health status, the ratio of people with chronic disease was 31.62%, and more than 68.38% of total samples did not have chronic disease.

Table1 Definition and descriptive results of variables

Variables	Definitions	Frequency/Mean		Percent/SE
Depression	Summarize depression questions		13.15	3.78
Anxiety	Summarize anxiety questions		12.85	3.75
Environmental Perception	Summarize ‘the local air quality, the local green environment, the local water quality, the local waste sorting and disposal practices, the local food/dining (medicine)’		18.67	3.04
Social Interaction	Very poor=1		8	0.46%
	poor= 2		77	4.39%
	Average=3		557	31.79%
	Good=4		875	49.94%
	Excellent=5		235	13.41%
Age	Continuous variable		36.98	10.26
Gender	Female = 0		896	51.14%
	Male = 1		856	48.86%
Family Population	Continuous variable		3.43	1.03
Marital status	Unmarried = 1		223	12.73%
	Married = 2		1498	85.50%
	Divorced = 3		18	1.03%
	Widowed = 4		13	0.74%
Birth children	0 child = 1		309	17.64%
	1 child = 2		1178	67.24%
	2 children = 3		258	14.73%
	3 or more children = 4		7	0.40%
Education level	Middle school and below = 1		38	2.17%
	High school/Vocational school = 2		109	6.22%
	Three-year college = 3		384	21.92%
	Four-year college and above = 4		1221	69.69%
Occupation	Worker = 1		218	12.44%
	Famer = 2		18	1.03%
	Civil servant and Public institution staff = 3		515	29.39%
	Business/Service sector/Self-employed = 4		778	44.41%
	Student and other occupations = 5		223	12.73%
Working status	No work = 0		138	7.88%
	Having a work = 1		1614	92.12%
Monthly personal income	0-2000 CNY = 1		65	3.17%
	2001-5000 CNY = 2		226	12.90%
	5001-10000 CNY = 3		845	48.23%
	10001-20000 CNY = 4		513	29.28%
	20001 CNY and above = 5		103	5.88%
Health insurance	BMISURR=1		387	22.09%
	BMISUE=2		1224	69.86%
	PFH=3		141	8.05%
Chronic disease	No = 0		1198	68.38%
	Yes = 1		554	31.62%

Regression results

We adopted the maximum likelihood method to evaluate the association between environmental perception and depression and anxiety. Column (1) in Table 2 evaluate the association between environmental perception and depression without any other variables. Column (2) evaluate the association between environmental perception and depression including all controls. Column (3) evaluate the association between environmental perception and anxiety without any controls. Column (4) evaluate the association between environmental perception and anxiety including all controls. Column (1)-Column (4) showed that there is a strong negative correlation between depression, anxiety and environmental perception (β = -0.187, SE = 0.03, p < 0.001; β = -0.152, SE = 0.03, p < 0.001; β = -0.339, SE = 0.056, p < 0.001).

Among controlled variables, gender was male (β = 0.655, SE = 0.179, p < 0.001; β = 0.349, SE = 0.179, p < 0.1; β = 1.004, SE = 0.331, p < 0.001), having chronic disease (β = 1.484, SE = 0.195, p < 0.001; β = 1.778, SE = 0.19, p < 0.001; β = 3.261, SE = 0.353, p < 0.001) was positively associated with depression and anxiety. In terms of socio-economic conditions, married individuals negatively associated with anxiety relative to those unmarried individuals (β = -0.627, SE = 0.367, p < 0. 1). Conversely, divorced individuals positively associated with depression and anxiety relative to those unmarried individuals (β = 2.172, SE = 0.735, p < 0.001; β = 1.754, SE = 0.988, p < 0.1; β = 3.962, SE = 1.486, p < 0.001). In addition, higher education level was negatively associated with depression and anxiety (β = -0.251, SE = 0.152, p < 0.1; β = -0.36, SE = 0.152, p < 0.05; β = -0.611, SE = 0.28, p < 0.05). Furthermore, famers were positively associated with depression (β = 1.831, SE = 0.708, p < 0.001; β = 2.849, SE = 1.348, p < 0.05) relative to worker. Conversely, civil servant and public institution staff, Business/Service sector/Self-employed, student and other occupations negatively associated with depression and anxiety relative to worker.

Table2 The regression results for the association between environmental

perception and HAD

	(1)	(2)	(3)	(4)
	Depression	Depression	Anxiety	Anxiety
environmental	-0.194^***	-0.187^***	-0.164^***	-0.152^***
perception	(0.029)	(0.030)	(0.031)	(0.031)
age		0.018		-0.024^**
		(0.011)		(0.011)
gender		0.655^***		0.349^*
		(0.179)		(0.179)
Family Population		0.010		0.206^**
		(0.088)		(0.095)
Marital status
(Ref: unmarried)
Married		-0.391		-0.627^*
		(0.373)		(0.367)
Divorced		2.172^***		1.754^*
		(0.735)		(0.988)
Widowed		0.259		-0.031
		(0.677)		(0.953)
Birth children		-0.066		-0.133
		(0.209)		(0.215)
Education level		-0.251^*		-0.360^**
		(0.152)		(0.152)
Working status		0.472		-0.484
		(0.415)		(0.394)
Occupation	(Ref:Worker)
Famer		1.831^***		1.018
		(0.708)		(0.780)
Civil servant and Public institution staff		-0.610^**		-0.622^**
Civil servant and Public institution staff		(0.289)		(0.292)
Business/Service sector/Self-employed		-0.288		-0.587^**
Business/Service sector/Self-employed		(0.282)		(0.274)
Student and other occupations		-0.358		-0.906^***
Student and other occupations		(0.357)		(0.344)
Monthly personal income		-0.418^***		0.038
		(0.128)		(0.128)
Health insurance		0.110		-0.252
		(0.173)		(0.178)
Chronic disease		1.484^***		1.778^***
		(0.195)		(0.190)
_cons	16.769^***	17.543^***	15.914^***	18.568^***
	(0.549)	(1.072)	(0.598)	(1.085)
N	1752	1752	1752	1752
adj. R²	0.024	0.085	0.017	0.084

Standard errors in parentheses

^* p < 0.1, ^** p < 0.05, ^*** p < 0.01

Mediating effects analysis

Table 3 illustrated the mediating effects of social interaction in the association between environmental perception and depression. Column (1) displays the effect of environmental perception on social interaction, revealing a significant positive association (β = 0.085, SE = 0.006, p < 0.001). Column (2) represents the direct effect of environmental perception on depression, showing a significant negative relationship (β = -0.187, SE = 0.030, p < 0.001). In Column (3), the indirect effect of environmental perception on depression through social interaction is analyzed. The results suggest a significant negative indirect effect (β = -1.410, SE = 0.120, p < 0.01), indicating that social interaction partially mediates the relationship between environmental perception and depression.

Table 3: the mediating effects of social interaction (Depression)

	(1)	(2)	(3)
	X
environmental	0.085***	-0.187***	-0.068**
perception	(0.006)	(0.030)	(0.030)
social interaction			-1.410*** (0.120)

Control variables	Yes	Yes	Yes
_cons	1.870***	17.543***	20.180***
	(0.220)	(1.072)	(1.074)
N	1752	1752	1752
adj. R²	0.161	0.085	0.153

Standard errors in parentheses

^* p < 0.1, ^** p < 0.05, ^*** p < 0.01

Table 4 illustrated the mediating effects of social interaction in the association between environmental perception and anxiety. Column (2) represents the direct effect of environmental perception on anxiety, showing a significant negative relationship (β = -0.152, SE = 0.031, p < 0.001). In Column (3), the results suggest a significant negative indirect effect (β = -1.107, SE = 0.124, p < 0.01), indicating that social interaction partially mediates the relationship between environmental perception and anxiety.

Table 4: the mediating effects of social interaction (Anxiety)

	(1)	(2)	(3)
environmental	0.085***	-0.152***	-0.058*
perception	(0.006)	(0.031)	(0.032)
social interaction			-1.107*** (0.124)

Control variables	Yes	Yes	Yes
_cons	1.870***	18.568***	20.637***
	(0.220)	(1.085)	(1.096)
N	1752	1752	1752
adj. R²	0.161	0.084	0.127

Standard errors in parentheses

^* p < 0.1, ^** p < 0.05, ^*** p < 0.01

Table5 showed that a significant indirect effect, with a coefficient of -1.410 (SE = 0.122, z = -11.540, p < 0.001). The 95% confidence interval for the indirect effect ranges from -1.650 to -1.171, indicating a strong and significant relationship between the variables.

Table5 Bootstrap test for depression

		Observed			Bootstrap			Normal-based
	Coef.		Std.Err.	z		P>z	[95%Conf.		Interval]
_bs_1	-1.410		0.122	-11.540		0.000	-1.650		-1.171

Table6 showed that a significant indirect effect, with a coefficient of -1.107 (SE = 0.121, z = -9.130, p < 0.001). The 95% confidence interval for the indirect effect ranges from -1.345 to -0.869, indicating a strong and significant relationship between the variables.

Table6 Bootstrap test for anxiety

		Observed			Bootstrap			Normal-based
	Coef.		Std.Err.	z		P>z	[95%Conf.		Interval]
_bs_1	-1.107		0.121	-9.130		0.000	-1.345		-0.869

Robust test

In the regression analysis examining the relationship between the five dimensions of the independent variable and depression, results indicate a significant negative association between perceived satisfaction with air quality, greenery, water quality, and food and drug safety, and levels of depression in table7. Notably, the dimension of waste classification did not show a significant relationship with depression. These findings suggest that perceived satisfaction with environmental factors such as air quality (β = -0.767, SE = 0.117, p < 0.001), greenery (β = -0.606, SE = 0.112, p < 0.001), water quality (β = -0.528, SE = 0.104, p < 0.001), and food and drug safety (β = -0.563, SE = 0.113, p < 0.001) may be important predictors of depression levels, while waste classification may not significantly impact depression levels in this analysis.

Table 7 The regression results for the association between different environmental perception types and depression

	(1)	(2)	(3)	(4)	(5)
	Air	Greenery	Water	Waste	Food/medicine
Depression	-0.767^***	-0.606^***	-0.528^***	-0.106	-0.563^***
	(0.117)	(0.112)	(0.104)	(0.097)	(0.113)
age	0.014	0.018	0.019	0.016	0.016
	(0.011)	(0.011)	(0.012)	(0.012)	(0.012)
gender	0.683^***	0.675^***	0.631^***	0.627^***	0.648^***
	(0.179)	(0.181)	(0.180)	(0.182)	(0.180)
Family Population	-0.026	0.029	-0.005	0.012	0.018
Family Population	(0.088)	(0.089)	(0.088)	(0.089)	(0.090)
Marital status (Ref: unmarried)
	-0.371	-0.432	-0.513	-0.582	-0.495
	(0.373)	(0.373)	(0.368)	(0.373)	(0.371)
	2.152^***	2.341^***	1.954^***	2.139^***	2.178^***
	(0.777)	(0.777)	(0.731)	(0.776)	(0.736)
	0.415	0.355	0.025	0.186	0.483
	(0.672)	(0.682)	(0.644)	(0.647)	(0.658)
Birth children	-0.034	-0.033	-0.044	-0.026	-0.055
	(0.211)	(0.211)	(0.211)	(0.213)	(0.212)
Education level	-0.264^*	-0.227	-0.231	-0.215	-0.197
	(0.152)	(0.151)	(0.152)	(0.153)	(0.154)
Working status	0.455	0.540	0.642	0.647	0.535
	(0.410)	(0.413)	(0.414)	(0.420)	(0.414)
Occupation (Ref: Worker)
	1.812^**	1.737^**	2.068^***	1.745^**	1.872^***
	(0.743)	(0.727)	(0.727)	(0.705)	(0.703)
	-0.580^**	-0.572^**	-0.553^*	-0.516^*	-0.607^**
	(0.292)	(0.287)	(0.288)	(0.289)	(0.289)
	-0.244	-0.251	-0.272	-0.204	-0.240
	(0.285)	(0.280)	(0.282)	(0.282)	(0.280)
	-0.335	-0.196	-0.228	-0.192	-0.271
	(0.359)	(0.353)	(0.353)	(0.352)	(0.353)
Monthly personal income	-0.453^***	-0.458^***	-0.453^***	-0.481^***	-0.442^***
	(0.126)	(0.127)	(0.128)	(0.128)	(0.129)
Health insurance	0.108	0.176	0.129	0.154	0.119
	(0.173)	(0.174)	(0.175)	(0.177)	(0.175)
Chronic disease	1.519^***	1.552^***	1.502^***	1.585^***	1.522^***
	(0.193)	(0.193)	(0.194)	(0.195)	(0.195)
_cons	17.181^***	16.122^***	15.872^***	14.304^***	16.015^***
	(1.035)	(0.995)	(0.995)	(1.012)	(1.005)
N	1752	1752	1752	1752	1752
adj. R²	0.087	0.079	0.078	0.064	0.078

Standard errors in parentheses

^* p < 0.1, ^** p < 0.05, ^*** p < 0.01

In table8, the dimension of waste classification did not show a significant relationship with anxiety. These findings suggest that perceived satisfaction with environmental factors such as air quality (β = -0.565, SE = 0.119, p < 0.001), greenery (β = -0.607, SE = 0.121, p < 0.001), water quality (β = -0.549, SE = 0.103, p < 0.001), and food and drug safety (β = -0.353, SE = 0.115, p < 0.001) may be important predictors of anxiety levels, while waste classification may not significantly impact anxiety levels in this analysis.

Table 8 The regression results for the association between different environmental perception types and anxiety

	(1)	(2)	(3)	(4)	(5)
	Air	Green	Water	Rubbish	Food
Anxiety	-0.565^***	-0.607^***	-0.549^***	-0.016	-0.353^***
	(0.119)	(0.121)	(0.103)	(0.103)	(0.115)
age	-0.027^**	-0.023^**	-0.023^**	-0.026^**	-0.026^**
	(0.011)	(0.011)	(0.011)	(0.011)	(0.011)
gender	0.369^**	0.374^**	0.329^*	0.330^*	0.341^*
	(0.179)	(0.180)	(0.179)	(0.182)	(0.180)
Family Population	0.180^*	0.226^**	0.191^**	0.206^**	0.211^**
	(0.095)	(0.095)	(0.095)	(0.096)	(0.096)
Marital status (Ref: unmarried)
	-0.628^*	-0.629^*	-0.706^*	-0.796^**	-0.732^**
	(0.368)	(0.369)	(0.363)	(0.370)	(0.368)
	1.737^*	1.932^**	1.538	1.721^*	1.750^*
	(0.996)	(0.975)	(1.019)	(1.020)	(0.996)
	0.082	0.071	-0.266	-0.065	0.103
	(0.949)	(0.965)	(0.916)	(0.887)	(0.912)
Birth children	-0.105	-0.110	-0.121	-0.094	-0.117
	(0.216)	(0.216)	(0.216)	(0.217)	(0.216)
Education level	-0.367^**	-0.345^**	-0.350^**	-0.325^**	-0.318^**
	(0.151)	(0.151)	(0.150)	(0.152)	(0.153)
Working status	-0.481	-0.454	-0.352	-0.323	-0.406
	(0.392)	(0.394)	(0.394)	(0.395)	(0.396)
Occupation (Ref: Worker)
	1.001	0.932	1.275	0.976	1.035
	(0.807)	(0.817)	(0.787)	(0.779)	(0.760)
	-0.593^**	-0.604^**	-0.587^**	-0.540^*	-0.602^**
	(0.295)	(0.287)	(0.289)	(0.292)	(0.294)
	-0.547^**	-0.569^**	-0.593^**	-0.511^*	-0.539^**
	(0.278)	(0.271)	(0.272)	(0.275)	(0.274)
	-0.873^**	-0.784^**	-0.819^**	-0.741^**	-0.812^**
	(0.348)	(0.338)	(0.341)	(0.344)	(0.342)
Monthly personal income	0.006	0.012	0.019	-0.022	0.009
	(0.127)	(0.127)	(0.128)	(0.128)	(0.129)
Health insurance	-0.249	-0.196	-0.244	-0.210	-0.236
	(0.179)	(0.178)	(0.179)	(0.180)	(0.180)
Chronic disease	1.812^***	1.824^***	1.771^***	1.868^***	1.823^***
	(0.190)	(0.190)	(0.191)	(0.191)	(0.191)
_cons	18.023^***	17.838^***	17.662^***	15.652^***	16.930^***
	(1.033)	(1.007)	(0.996)	(1.010)	(0.995)
N	1752	1752	1752	1752	1752
adj. R²	0.083	0.086	0.085	0.070	0.075

Standard errors in parentheses

^* p < 0.1, ^** p < 0.05, ^*** p < 0.01

Figure1-figure5 illustrated the mediating effects of social interaction in the association between different dismissions of environmental perception and HAD. Figure1 showed that mediating analysis of the air quality perception and HAD, with findings social interaction partly mediated the association between air perception and HAD(β = -1.393; β = -1.112). Figure2 showed that mediating analysis of the greenery perception and HAD, with findings social interaction partly mediated the association between greenery perception and HAD(β = -1.433; β = -1.105). Figure3 showed that mediating analysis of the water quality perception and HAD, with findings social interaction partly mediated the association between water quality perception and HAD(β = -1.441; β = -1.094). Figure5 showed that mediating analysis of the food/medicine quality perception and HAD, with findings social interaction partly mediated the association between food/medicine quality perception and HAD(β = -1.443; β = -1.171).

Utilizing data from the Health Life Satisfaction Survey of Yangtze River Phase II Delta, this study delved into the potential relationship between environmental perceptions and HAD among YRDC. A particular focus of this study was the exploration of the mediating effect of social interaction on the individual’s relationship between environmental perceptions and HAD. While previous research has extensively examined the link between environmental perceptions and HAD, there is also a growing body of literature investigating the influence of environmental perceptions on an individual’s mental health(Chen et al., 2020; X. Wang et al., 2024; Y. Wang et al., 2024). However, to the best of our knowledge, this study represents the first attempt to bridge these two strands of literature by examining how environmental perception can influence their social interaction, subsequently leading to reduction in depression and anxiety level.

Our study disclosed that environmental perception was negatively associated with the level of depression and anxiety. This was consistent with the literature which investigated the relationship between environmental stress and mental health. For instance, Bratman et al. (2015) provided neuroscientific evidence supporting the restorative effects of nature exposure on reducing rumination and improving mood, thereby highlighting the potential role of natural environments in mitigating symptoms of depression and anxiety(Bratman et al., 2015). Moreover, studies on the impact of urban environments on mental health (Evans, 2003) underscore how factors like noise, pollution, and lack of green spaces can contribute to heightened levels of stress, anxiety, and depressive symptoms among urban dwellers(Evans, 2003).

This study additionally contributed evidence supporting the mediating role of social interaction in the relationship between environmental perceptions and HAD. Our findings revealed a significant ‘social interaction effect’. Specifically, the mediating effect of social interaction underscores the importance of social support networks and community connections in mitigating the adverse psychological effects of environmental challenges, highlighting the protective role of positive social relationships in mental health resilience. This finding was consistent with the literature disclosed that environmental perception can impact individual’s mental health, through various mechanisms. For instance, Jielu Lin(2009) used structural equation modeling to test the association between low quality of community environment and increased psychological distress was accounted for by the mediating role of social support(Lin et al., 2009). By examining the pathways through which social relationships influence the impact of environmental stressors on mental health, we provide novel insights into the complex interplay between external environments and interpersonal dynamics.

Furthermore, we found that different aspects of environmental perceptions were closely associated with HAD. Residents’ perceptions of air quality, water quality, green spaces, and food and medicine safety all have an impact on their levels of depression and anxiety, this finding is consistent with the study by Lim(2012), Baird(2015), Agnes(2010) et al(Agnes et al., 2010; Baird et al., 2015; Lim et al., 2012; McEwen & Akil, 2020). Furthermore, these environmental factors are found to decrease residents’ levels of depression and anxiety through social interactions. However, our research found that residents’ satisfaction with community waste sorting does not impact their levels of depression and anxiety. In the Yangtze River Delta region of China, including the Shanghai metropolitan area, significant efforts have been made to promote waste sorting and recycling since 2019. Residents are encouraged to separate their household waste into categories such as recyclables, food waste, hazardous waste, and general waste. The local government has implemented various initiatives, including educational campaigns, community workshops, and the establishment of recycling stations to facilitate the sorting process. These efforts aim to improve environmental sustainability, reduce landfill waste, and promote a culture of waste reduction and resource conservation in the region. Yuanchao Gong(2023) confirmed that the positive link between waste sorting behaviors and subjective well-being(Gong et al., 2023). However, our study did not find evidence that waste sorting affects residents’ mental health. On one hand, it may be due to residents’ resistance to the complex rules of waste sorting. On the other hand, it could also be explained by the ineffective outcomes of waste sorting, leading residents to not perceive this environmental factor.

Overall, Individuals with worse environmental perception have more possibility to develop depressive and anxiety symptoms than those with better environmental perception YRDC, and such a relationship can be partly mediated by social interaction. the results of our study underscore the need for integrated approaches that address both environmental factors and social dynamics in promoting mental health and well-being. Strategies aimed at improving environmental quality, enhancing social support systems, and fostering community cohesion can play a critical role in mitigating the negative impact of environmental stressors on mental health outcomes. By recognizing the interconnectedness of environmental perception, social interaction, and mental health, policymakers, public health practitioners, and urban planners can collaborate to create environments that support emotional resilience and contribute to the overall well-being of individuals and communities. However, our study design might introduce bias, and it cannot establish causality between the tested variables.

YRDC, Yangtze River Delta in China; HLSSYRD, Health Life Satisfaction Survey of Yangtze River Delta; HAD, the Hospital Anxiety and Depression Scale; BMISURR, Basic Medical Insurance System for Urban and Rural Residents; BMISUE, Basic Medical Insurance System for Urban Employees; PFH, Publicly Funded Healthcare;

Author Contributions: All authors contributed to the writing of the final manuscript and have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work. Contributor Guang Yang was responsible for the conception, study design, execution, data analysis , or in all these areas. Contributors Ying Zuo and Haoxuan Cheng took part in revising or critically reviewing the article. Contributor Lufa Zhang was responsible for the organization and coordination of the research, gave final approval of the version to be published, and agree to be accountable for all aspects of the work.

Role of the Funding source: This research was supported by the National Social Science Fund of China (22AZD082) .

Ethics Approval: The data was collected using a semi-structured questionnaire contained participants’ socio-demographics and healthy lifestyle information in Yangtze River Delta. Obtained informed consent from all study participants before the start of the study. The study protocol was approved by the ethics review committee of Shanghai University of Medicine & Health Sciences, China based on the assessment of design and process of the study and was conducted in accordance with the principles of the Declaration of Helsinki. The approval number is 2021-gskyb-02-372424198012222511. Before starting the research study, the respondents were informed about the anonymity of this study with relevant details. The questionnaire-related information is as follows: “In order to ensure the authenticity and confidentiality of the research, this research is anonymous, and the content of the survey is only used for relevant research, and the content of your answers will be kept confidential in accordance with the requirements of the Statistical Law of the People’s Republic of China”.

This study is not a clinical trial.

Availability of data and materials declarations: not applicable

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No competing interests reported.

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How Environmental Perception influence Depression and Anxiety: The Mediating Role of Social Interaction

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