The mitigating role of social capital on the association between food insecurity and psychological distress among Chinese adults

Abstract

Background:

Food insecurity, a pervasive global issue exacerbated by the COVID-19 pandemic, has been linked to adverse mental health outcomes. However, the role of social capital in mitigating this relationship remains understudied, particularly in the Chinese context.

Aims:

This study investigated the associations between food insecurity and psychological distress (depressive and anxiety symptoms) and examined the potential moderating effects of bonding and bridging social capital among Chinese adults in Shanghai.

Methods:

This cross-sectional study included 3,220 Chinese adults (mean age: 34.45; 51.5% male) in Shanghai. Food insecurity was assessed using the modified Household Food Insecurity Access Scale, psychological distress was measured using the Patient Health Questionnaire-9 and the Generalized Anxiety Disorder-7, and social capital was evaluated using the Revised Personal Social Capital Scale.

Results:

Multivariable linear regression analyses revealed that food insecurity was significantly positively associated with both depressive (β = 0.449, SE = 0.024) and anxiety symptoms (β = 0.391, SE = 0.022), after adjusting for sociodemographic characteristics, health status, and COVID-19-related factors. Higher levels of bonding and bridging social capital were significantly associated with fewer depressive and anxiety symptoms. Significant interactions (p < .001) between bonding social capital and food insecurity indicated that the associations between food insecurity and psychological distress were less pronounced among adults with higher bonding social capital.

Conclusions:

These findings highlight the critical role of food insecurity as a risk factor for psychological distress and the importance of bonding social capital in mitigating its impact on mental health. Policies and interventions targeting food insecurity prevention and bonding social capital enhancement may promote better mental health outcomes among Chinese adults.

Keywords

Food security social determinants of health mental health social Network COVID-19 Chinese

Introduction

Food insecurity, defined as a lack of social, physical, economic, and regular access to sufficient quality and adequate quantity of food to meet nutritional needs (Keenan et al., 2001), has been a persistent global problem, affecting various populations across different contexts (Pourmotabbed et al., 2020) According to the WHO Study on Global Aging and Adult Health, approximately 1.2% of China’s middle-aged and older adults experienced moderate or severe food insecurity in 2007 to 2010 (Smith et al., 2021). However, situations such as economic downturns, natural disasters, and public health crises can exacerbate food insecurity and disrupt social networks, potentially impacting mental well-beings.

The COVID-19 pandemic led to the implementation of lockdown measures across the globe, which have caused unintended consequences such as disrupted food supply chains and increased food insecurity (Laborde & Martin, 2020). The ‘dynamic zero-COVID’ policy issued by Chinese government mandated the closure of government and private offices, schools, agriculture, and commercial activities, as well as travel restrictions, forcing persons living in China to stay at home except for essential tasks (Brant, 2022). Food insecurity in China has risen dramatically in the context of the COVID-19 pandemic and related lockdowns (Dou et al., 2020; Niles et al., 2020). For example, one study conducted during the early phase of the COVID-19 pandemic in China found that 27.2% of adults experienced some levels of food insecurity (Dou et al., 2020). However, limited research exists on the impact of food insecurity on psychological distress among Chinese adults.

Food insecurity has been affecting various populations’ mental health across different contexts (Niles et al., 2020; Sabião et al., 2022). However, the impact of food insecurity on psychological distress may be influenced by social factors like social capital, which encompasses the resources and benefits derived from social networks. Social capital has been shown to have protective effects on mental health (Delaruelle et al., 2021; Li et al., 2023; Wu et al., 2016). For example, bonding social capital (strong ties within a homogeneous group) and bridging social capital (weaker ties across diverse groups) can potentially mitigate the relationship between food insecurity and mental health outcomes by providing resources, social support, and promoting resilience (Sseguya et al., 2018).

However, the interplay between food insecurity, social capital, and mental health is complex and varies across contexts and populations (Jessiman-Perreault & McIntyre, 2019; Niles et al., 2020; Pourmotabbed et al., 2020). Some studies found that the protective effects of social capital may be limited in extreme situations of food insecurity or in communities with high levels of social inequality (Maynard et al., 2018; Rayamajhee & Bohara, 2019). Shanghai, one of China’s most populous and economically developed cities, has experienced rapid growth and urbanization in recent decades. Despite its economic success, the city faces significant social inequality, with disparities in income, education, and access to resources among its residents (Ren & Guan, 2022). The COVID-19 pandemic and related lockdowns implemented in Shanghai in 2022 presented a unique context to investigate this relationship, as the COVID-19 not only exacerbated food insecurity but also disrupted social networks and support systems, potentially affecting the role of social capital in mitigating the mental health impacts of food insecurity.

Despite prior literature that indicates food insecurity and social capital play critical roles in shaping mental health, there is a lack of studies examining their associations with psychological distress among Chinese adults. Moreover, how different types of social capital may modify the impact of food insecurity on psychological distress is understudied. Using data from a large population-wide study enrolling more than 3,200 Shanghai residents, this study aims to determine the associations of food insecurity and social capital with psychological distress and examine the moderating role of social capital in these associations. We hypothesize that (1) greater food insecurity is associated with more depressive and anxiety symptoms; (2) higher bonding and bridging social capital are associated with less depressive and anxiety symptoms; and (3) the associations of food insecurity with depressive and anxiety symptoms will be mitigated by higher levels of bonding and bridging social capital. The conceptual framework for this study is presented in Figure 1.

Figure 1.

Conceptual framework.

Methods

Data and participants

This cross-sectional study collected data from Shanghai residents aged 18 and older using an online survey. Purposive sampling was used to recruit participants, aiming for a geographically representative sample of 200 individuals from each of Shanghai’s 16 districts, with an additional 10% recruited to account for potential invalid responses (Hall et al., 2023). The survey, administered in Chinese through the WenJuanXing platform (Changsha Haoxing Information Technology Co., Ltd., China) between April 29 and June 1, 2022, gathered information on participants’ sociodemographic characteristics, health-related behaviors, COVID-19-related factors, and overall well-being. Eligibility was restricted to adult Shanghai residents, which was verified using respondents’ IP addresses. Informed consent was obtained digitally, and participants received compensation of 6 Chinese Yuan (approximately 1 U.S. Dollar) upon completing the survey. The study protocol was reviewed and approved by the Institutional Review Board at NYU Shanghai (2022-008-NYUSH). Out of the 3,763 total responses received, a final analytical sample of 3,220 Shanghai residents was derived after excluding minors (n = 156), non-residents (n = 190), those who provided inappropriate answers to survey validity questions (n = 69), and those with missing demographic information (n = 128).

Measurements

Food insecurity

Food insecurity, the independent variable, was measured using a modified version of the Household Food Insecurity Access Scale (HFIAS) developed by the U.S. Department of Agriculture Food and Nutrition Service (Bickel et al., 2000). The HFIAS assesses the frequency of a family’s experiences related to uncertainty about future food availability, compromised food quality and quantity, and hunger over the past 2 weeks (Bickel et al., 2000). The scale consists of five items with response options categorized by frequency: no = 0 times (score 0), rarely = 1 to 2 times (score 1), sometimes = 3 to 5 times (score 2), and often = more than 5 times (score 3). A summary score (range: 0–15) was calculated by summing the responses, with higher scores indicating greater food insecurity. The modified-HFIAS has demonstrated acceptable validity and applicability across different cultural contexts (Pound & Chen, 2021; Sabião et al., 2022) and has been validated for use in Chinese populations (Y. Zhang et al., 2021). In the current study, the HFIAS showed good internal consistency (Cronbach’s α = .87).

Social capital

Social capital was evaluated using the Revised Personal Social Capital Scale (PSCS-R; Chen & Yu, 2022), which measures the actual level of personal and organizational resources within a community (Scheffler & Brown, 2008). Bonding and bridging social capital were assessed using the neighborhood and social organization subscale of the PSCS-R. Bonding social capital refers to connections among individuals with similar backgrounds, such as neighbors, close relatives, and friends. In contrast, bridging social capital encompasses relationships across diverse groups, such as those formed through social organizations and leisure groups (Flora et al., 2018; Sseguya et al., 2018). Higher subscale scores indicate greater social resources and trusted people/organizations. Social capital scores were calculated by summing the subscale scores, with higher total scores (range: 4–20) reflecting greater social capital. The subscales demonstrated acceptable internal consistency (Cronbach’s α: bonding = .66, bridging = .80).

Depression Symptoms and Anxiety Symptoms

Depression symptoms were assessed using the Patient Health Questionnaire-9 (PHQ-9). The PHQ-9 is a self-report instrument with nine items rated from 0 (not at all) to 3 (nearly every day), reflecting the frequency of depressive symptoms experienced by participants in the past two weeks (Kroenke et al., 2001). The PHQ-9 has been previously validated in Chinese populations and has shown excellent reliability (W. Wang et al., 2014). In the current sample, the PHQ-9 exhibited good internal consistency (Cronbach’s α = .89). Anxiety symptoms were assessed using the Generalized Anxiety Disorder-7 (GAD-7) scale (Spitzer et al., 2006), which consists of seven items scored from 0 (not at all) to 3 (nearly every day), indicating the frequency of each symptom during the last 2 weeks. The GAD-7 has been validated and widely used in Chinese populations (C. Zhang et al., 2021) and demonstrated excellent internal consistency in the current sample (Cronbach’s α = .93).

Covariates

Several covariates related to food insecurity, social capital, and psychological distress were considered, including sociodemographic and health-related characteristics (Liao et al., 2024; Liu et al., 2023). Sociodemographic factors included age (in years), sex (male vs. female), marital status (married/partnered vs. divorced/widowed/never married), educational level (secondary school or lower, complete high school, complete college or higher), monthly family income (⩽4,000, 4,001–8,000, 8,001–15,000, ⩾15,001), and current employment status (employed, unemployed/retired, student). Health-related characteristics were assessed using self-rated health (continuous measure, 1 = poor to 5 = excellent) and the presence of at least one chronic condition (i.e., heart disease, high blood pressure, diabetes, asthmatic diseases, and cancer). As the data were collected online during the COVID-19 pandemic, we controlled for several COVID-19-related factors, including whether the participant had been infected with COVID-19, considered a close contact, or experienced quarantine.

Statistical analysis

Descriptive statistics were used to summarize the study variables, with means and standard deviations (SD) reported for normally distributed continuous variables and medians and interquartile ranges (IQR) for skewed distributions. Categorical variables were described using frequencies and percentages (%). Multivariable linear regression analyses were conducted to investigate the associations of food insecurity and social capital with depressive and anxiety symptoms, reporting regression coefficients (β) and their standard errors (SE). Six models were constructed to examine the relationships and interactions between food insecurity and social capital: an unadjusted model (Model 1), a model adjusted for all covariates (Model 2), models incorporating the effects of bonding (Model 3) and bridging social capital (Model 5), and models including interaction terms between bonding/bridging social capital and food insecurity (Models 4 and 6, respectively). All statistical analyses were performed using STATA MP 17.0 (Stata Corporation, College Station, TX, USA), with statistical significance set at a p value less than 0.05 (p < .05).

Results

Characteristics of the study participants

The study included 3,220 eligible participants (Table 1), with a mean age of 34.45 years (SD, 10.93) and 1,563 (48.5%) being female. The majority of the participants were married or had a partner (62.9%), were employed (75.2%), had completed college or higher education (63.3%), and had a monthly income exceeding ¥8,000 (61.2%). Among the participants, 4.9% had been infected with COVID-19, 14.1% had experienced quarantine, 9.9% were considered close contacts, and 12.9% had at least one chronic condition. The participants reported moderate levels of food insecurity (median score = 5, IQR: 2, 8), depressive symptoms (median [IQR], 6 [3, 10]), and anxiety symptoms (median [IQR], 5 [2, 9]).

Table 1.

Descriptive analysis of Chinese adults in Shanghai, China, 2022.

Characteristics	Total (n = 3,220)	Range
Age (years), M (SD)	34.45 (10.93)	18–89
Sex, n (%)
Male	1,657 (51.5)
Female	1,563 (48.5)
Marital status, n (%)
Married/partnered	2,026 (62.9)
Divorced/widowed/never married	1,194 (37.1)
Education level, n (%)
Secondary school or lower	471 (14.6)
Complete high school	712 (22.1)
Complete college of higher	2,037 (63.3)
Monthly household income (¥), n (%)
⩽4,000	349 (10.8)
4,001–8,000	902 (28.0)
8,001–15,000	992 (30.8)
⩾15,001	977 (30.4)
Employment status, n (%)
Employed	2,421 (75.2)
Unemployed/retired	481 (14.6)
Student	327 (10.2)
Ever infected with COVID-19,^a n (%)	157 (4.9)
Ever experienced quarantine, n (%)	453 (14.1)
Close contact to someone with COVID-19, n (%)	320 (9.9)
Self-rated health, M (SD)	3.1 (1.0)	1–5
Have one or more chronic conditions,^b n (%)	416 (12.9)
Food insecurity (HFIAS),^c median (IQR)	5 (2, 8)	0–15
Depressive symptoms (PHQ-9),^c median (IQR)	6 (3, 10)	0–27
Anxiety (GAD-7),^c median (IQR)	5 (2, 9)	0–21
Bonding social capital (R-PSCS), mean (SD)	11.37 (2.93)	4–20
Bridging social capital (R-PSCS), mean (SD)	10.60 (3.30)	4–20

Note. SD = standard deviation; IQR = interquartile range; HFIAS = Household Food Insecurity Access Scale; PHQ-9 = Patient Health Questionnaire-9; GAD-9 = Generalized Anxiety Disorder-7; R-PSCS = Revised Personal Social Capital Scale.

Diagnosed by nucleic acid test.

Chronic conditions include heart disease, high blood pressure, diabetes, asthmatic diseases, and cancer.

Non-normal distribution continuous variable, median (interquartile range).

Associations of food insecurity with depressive and anxiety symptoms

In the unadjusted Model 1, higher levels of food insecurity were associated with increased depressive symptoms (β coefficient, 0.533; SE, 0.023) and anxiety symptoms (β, 0.468; SE, 0.020). After controlling for sociodemographic characteristics, health status, and COVID-19-related factors in Model 2, the strength of the associations decreased but remained statistically significant (depressive symptoms: β, 0.449; SE, 0.024; anxiety symptoms: β, 0.391; SE, 0.022). Participants who were younger, married or partnered, had higher education, were employed, and reported better self-rated health experienced lower levels of depressive and anxiety symptoms. Conversely, being considered a close contact and having at least one chronic condition were linked to increased depressive and anxiety symptoms. These results support Hypothesis 1 (Tables 2 and 3).

Table 2.

Results for the stepwise multivariable linear regression analysis for depressive symptoms (N = 3,220).

Factors	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
	β coefficient (SE)
Food insecurity	0.533 (0.023)***	0.449 (0.024)***	0.395 (0.024)***	0.616 (0.079)***	0.403 (0.025)***	0.495 (0.068)***
Bonding social capital			−0.326 (0.032)***	−0.218 (0.048)***
Food insecurity × bonding social capital				−0.021 (0.007)**
Bridging social capital					−0.194 (0.029)***	−0.144 (0.044)**
Food insecurity × bridging social capital						−0.010 (0.007)
Age		−0.062 (0.011)***	−0.053 (0.011)***	−0.053 (0.011)***	−0.065 (0.011)***	−0.065 (0.011)***
Female (ref: male)		−0.012 (0.184)	0.022 (0.181)	0.033 (0.180)	0.033 (0.182)	0.039 (0.182)
Marital status (ref: married/partnered)
Divorced/widowed/never married		0.443 (0.222)*	0.355 (0.219)	0.330 (0.219)	0.475 (0.221)*	0.469 (0.221)*
Education level (ref: secondary school of lower)
Complete high school		0.335 (0.299)	0.362 (0.294)	0.345 (0.294)	0.351 (0.297)	0.357 (0.297)
Complete college of higher		1.074 (0.282)***	1.185 (0.278)***	1.216 (0.278)***	1.197 (0.281)***	1.214 (0.281)***
Monthly household income (Ref: ⩽ ¥4,000)
4,001–8,000		−0.914 (0.322)**	−0.857 (0.317)**	−0.846 (0.316)**	−0.994 (0.320)**	−0.983 (0.320)**
8,001–15,000		−0.787 (0.328)*	−0.594 (0.323)	−0.583 (0.323)	−0.784 (0.325)*	−0.778 (0.325)*
⩾15,001		−0.587 (0.348)	−0.250 (0.344)	−0.257 (0.344)	−0.500 (0.346)	−0.494 (0.346)
Employment status (ref: employed)
Unemployed/retired		1.123 (0.264)***	1.044 (0.260)***	1.012 (0.260)***	1.091 (0.262)***	1.071 (0.263)***
Student		0.861 (0.348)*	1.283 (0.344)***	1.284 (0.344)***	1.044 (0.346)**	1.034 (0.346)**
Ever infected with COVID-19 (ref: never)		0.785 (0.472)	0.595 (0.465)	0.560 (0.464)	0.698 (0.469)	0.704 (0.469)
Ever experienced quarantine (ref: never)		−0.461 (0.324)	−0.276 (0.319)	−0.250 (0.319)	−0.319 (0.322)	−0.304 (0.322)
Ever been considered as close contact (ref: never)		1.022 (0.360)**	0.911 (0.354)*	0.886 (0.354)*	0.969 (0.357)**	0.956 (0.357)**
Self-rated health		−0.088 (0.012)***	−0.085 (0.012)***	−0.086 (0.012)***	−0.086 (0.012)***	−0.087 (0.012)***
Have 1+ chronic conditions (ref: no)		1.611 (0.281)***	1.534 (0.277)***	1.501 (0.277)***	1.556 (0.280)***	1.557 (0.280)***
Model fit statistics
R²	.147	.205	.231	.233	.217	.217
Adjusted R²	.146	.201	.227	.229	.212	.212
Root-mean-square deviation	5.143	4.975	4.896	4.890	4.941	4.941

Note. Model 1: unadjusted; Model 2: Model 1 adjusted for sociodemographic characteristics (age, sex, marital status, education, income, and employment status), health status (self-rated health and having 1+ chronic conditions [including heart disease, high blood pressure, diabetes, asthmatic diseases, and cancer]), and COVID-19-related characteristics (COVID-19 infection history, ever experienced quarantine, and ever been considered as close contact); Model 3: Model 2 + bonding social capital; Model 4: Model 3 + interaction terms between food insecurity and bonding social capital; Model 5: Model 2 + bridging social capital; Model 6: Model 3 + interaction terms between food insecurity and bridging social capital.

p < .05. **p < .01. ***p < .001 from multivariable linear regression.

Table 3.

Results for the stepwise multivariable linear regression analysis for anxiety symptoms (N = 3,220).

Factors	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
	β coefficient (SE)
Food insecurity	0.468 (0.020)***	0.391 (0.022)***	0.349 (0.022)***	0.572 (0.071)***	0.363 (0.022)***	0.452 (0.062)***
Bonding social capital			−0.252 (0.028)***	−0.146 (0.043)***
Food insecurity × bonding social capital				−0.021 (0.006)***
Bridging social capital					−0.118 (0.026)***	−0.071 (0.040)
Food insecurity × bridging social capital						−0.009 (0.006)
Age		−0.036 (0.010)***	−0.028 (0.009)**	−0.028 (0.009)**	−0.037 (0.009)***	−0.037 (0.009)***
Female (ref: male)		0.288 (0.165)	0.315 (0.163)	0.325 (0.162)*	0.316 (0.164)	0.321 (0.164)
Marital status (ref: married/partnered)
Divorced/widowed/never married		0.116 (0.199)	0.048 (0.197)	0.023 (0.197)	0.136 (0.199)	0.130 (0.199)
Education level (ref: secondary school of lower)
Complete high school		0.284 (0.268)	0.305 (0.265)	0.287 (0.264)	0.294 (0.267)	0.299 (0.267)
Complete college of higher		0.962 (0.253)***	1.049 (0.250)***	1.079 (0.250)***	1.037 (0.253)***	1.054 (0.253)***
Monthly household income (ref: ⩽¥4,000)
4,001–8,000		−0.738 (0.289)*	−0.695 (0.285)*	−0.683 (0.285)*	−0.787 (0.288)**	−0.777 (0.288)**
8,001–15,000		−0.506 (0.294)	−0.357 (0.291)	−0.347 (0.290)	−0.505 (0.293)	−0.499 (0.293)
⩾15,001		−0.209 (0.312)	0.051 (0.310)	0.044 (0.310)	−0.156 (0.312)	−0.150 (0.312)
Employment status (ref: employed)
Unemployed/retired		0.734 (0.237)**	0.673 (0.234)**	0.642 (0.234)**	0.715 (0.236)**	0.696 (0.237)**
Student		1.021 (0.312)**	1.347 (0.310)***	1.348 (0.310)***	1.132 (0.312)***	1.123 (0.312)***
Ever infected with COVID-19 (ref: never)		−0.105 (0.423)	−0.252 (0.418)	−0.287 (0.418)	−0.158 (0.422)	−0.153 (0.422)
Ever experienced quarantine (ref: never)		0.274 (0.290)	0.417 (0.287)	0.443 (0.287)	0.361 (0.290)	0.375 (0.290)
Ever been considered as close contact (ref: never)		0.836 (0.323)**	0.750 (0.319)*	0.724 (0.318)*	0.804 (0.322)*	0.791 (0.322)*
Self-rated health		−0.102 (0.011)***	−0.100 (0.011)***	−0.101 (0.011)***	−0.101 (0.011)***	−0.102 (0.011)***
Have 1+ chronic conditions (Ref: No)		1.034 (0.252)***	0.974 (0.250)***	0.941 (0.249)***	1.000 (0.252)***	1.002 (0.252)***
Model fit statistics
R²	.142	.200	.219	.222	.205	.206
Adjusted R²	.141	.196	.215	.217	.201	.201
Root-mean-square deviation	4.613	4.464	4.411	4.404	4.450	4.449

Note. Model 1: unadjusted; Model 2: Model 1 adjusted for sociodemographic characteristics (age, sex, marital status, education, income, and employment status), health status (self-rated health and having 1+ chronic conditions [including heart disease, high blood pressure, diabetes, asthmatic diseases, and cancer]), and COVID-19-related characteristics (COVID-19 infection history, ever experienced quarantine, and ever been considered as close contact); Model 3: Model 2 + bonding social capital; Model 4: Model 3 + interaction terms between food insecurity and bonding social capital; Model 5: Model 2 + bridging social capital; Model 6: Model 3 + interaction terms between food insecurity and bridging social capital.

p < .05. **p < .01. ***p < .001 from multivariable linear regression.

Associations of bonding and bridging social capital with depressive and anxiety symptoms

Model 3 revealed that higher levels of bonding social capital were associated with reduced depressive symptoms (β, −0.326; SE, 0.032) and anxiety (β, −0.252; SE, 0.028). Likewise, in Model 5, higher levels of bridging social capital were associated with fewer depressive symptoms (β, −0.194; SE, 0.029) and anxiety symptoms (β, −0.118; SE, 0.026). These findings support Hypothesis 2 (Tables 2 and 3).

The moderating effect of social capital

Models 4 and 6 (Tables 2 and 3) tested the moderating effects of bonding and bridging social capital on the associations between food insecurity and psychological distress. For depressive symptoms (Table 2), a significant interaction between bonding social capital and food insecurity was observed (Model 4; β for interaction, −0.021; SE, 0.007). Similarly, the interaction between bonding social capital and food insecurity was significant for anxiety symptoms (Table 3, Model 4; β, 0.021; SE, 0.006). However, bridging social capital did not significantly moderate the associations between food insecurity and depressive symptoms (Table 2, Model 6; β, −0.010; SE, 0.007; p = .145) or anxiety symptoms (Table 3, Model 6; β, −0.009; SE, 0.006; p = .121). These findings partially support Hypothesis 3, emphasizing the more crucial role of bonding social capital compared to bridging social capital in protecting mental health when adults face food insecurity.

Discussion

The present study aimed to investigate the associations between food insecurity and psychological distress, specifically depressive and anxiety symptoms, while also examining the potential moderating role of social capital among Chinese adults in Shanghai. The findings revealed that food insecurity was significantly associated with increased levels of depressive and anxiety symptoms, even after controlling for sociodemographic characteristics, health status, and COVID-19-related factors, supporting Hypothesis 1. Additionally, both bonding and bridging social capital were found to be associated with lower levels of depressive and anxiety symptoms, providing support for Hypothesis 2. Interestingly, bonding social capital was found to mitigate the associations between food insecurity and psychological distress, while bridging social capital did not, partially supporting Hypothesis 3.

The positive association between food insecurity and psychological distress observed in this study is in line with prior research conducted across various populations and contexts (Niles et al., 2020; Pourmotabbed et al., 2020; Sabião et al., 2022). This relationship can be explained by several factors. Firstly, the challenges and feelings of powerlessness associated with obtaining adequate food may contribute to a higher prevalence of negative psychological states among adults (Pourmotabbed et al., 2020). Secondly, food insecurity can lead to insufficient nutrient intake and poor nutritional status, which may adversely affect brain function and increase the risk of psychological distress (Na et al., 2020; Pourmotabbed et al., 2020; Sabião et al., 2022).

The protective effects of bonding and bridging social capital on psychological distress found in this study align with existing literature (Elgar et al., 2020; Maynard et al., 2018; Rayamajhee & Bohara, 2019). Social capital can provide access to resources and support, buffer against stress and anxiety, and facilitate the sharing coping strategies and positive health behaviors (Aldrich & Meyer, 2015; Sseguya et al., 2018). Notably, our study found that bonding social capital moderated the associations between food insecurity and psychological distress while bridging social capital did not. This suggests that strong ties within homogeneous groups (e.g., family, friends, neighbors) may be more effective in buffering against food insecurity’s mental health impacts than weaker ties across diverse groups (e.g., social organizations, leisure groups). This finding aligns with previous studies that have demonstrated the differential effects of bonding and bridging social capital on mental health outcomes (Murayama et al., 2013, 2015; Simons et al., 2023).

Several factors may explain the differential effects of bonding and bridging social capital. First, bonding social capital may offer more direct and practical support that can immediately alleviate the stress and anxiety associated with food insecurity (Sseguya et al., 2018). Second, the COVID-19 pandemic and related lockdowns may have disrupted traditional forms of bridging social capital (e.g., in-person interactions, community gatherings), making it more difficult for individuals to access resources and support from diverse social networks (Aldrich & Meyer, 2015). Third, the effectiveness of bridging social capital may depend on the specific context and population, and its protective effects may be limited in extreme situations of food insecurity or in communities with high levels of social inequality (Maynard et al., 2018; Rayamajhee & Bohara, 2019). The COVID-19 pandemic and related lockdowns may have severely disrupted the formation and maintenance of bridging social capital by hindering the development of weak ties across diverse groups. Moreover, the COVID-19 pandemic and related lockdowns exacerbated existing high social inequalities in Shanghai, with vulnerable populations, such as migrant workers and low-income households, facing disproportionate challenges in accessing food and other essential resources (Q. Wang et al., 2022).

This study has several implications for policy and practice. Firstly, it emphasizes the need for targeted interventions and policies to tackle food insecurity and its mental health impacts, especially during public health emergencies or crises. This may include measures to ensure access to affordable and nutritious food, such as food assistance programs, community food purchase channels, and home delivery services. Secondly, it underscores the importance of strengthening social capital, particularly bonding social capital, as a potential buffer against the negative mental health impacts of food insecurity. This may involve initiatives to foster community social connections and support networks, such as neighborhood support groups, online social platforms, and peer support programs. Thirdly, our findings suggest that interventions and policies should consider the differential effects of bonding and bridging social capital and tailor approaches accordingly, with a focus on strengthening bonding social capital in communities with high levels of social inequality.

This is one of the first studies to examine the associations of food insecurity with psychological distress and the moderating role of social capital in the Chinese context. Our study used a large sample size and validated measures while controlling for a wide range of potential confounders. However, we acknowledge that this study has some limitations. First, the cross-sectional design and purposive sampling method limit causal inference and generalizability. Second, residual confounding cannot be precluded entirely by unmeasured variables, such as family farming or receiving basic food baskets from the neighborhood committee. Third, our food insecurity and psychological distress measures were based on self-report, which may be subject to recall and social desirability biases. Fourth, the results may not represent the reality in other parts of China or other populations, as Shanghai is one of China’s most developed cities. The problems studied could be much worse in rural and inland regions of China.

Conclusions

The present study revealed that food insecurity was significantly associated with increased levels of depressive and anxiety symptoms among residents of Shanghai. Notably, these associations were moderated by bonding social capital but not by bridging social capital. These findings underscore the crucial role of addressing food insecurity and fostering social capital, especially bonding social capital, in order to mitigate the adverse mental health consequences of food insecurity, particularly among socially disadvantaged populations. To further advance our understanding of these relationships, future research should employ longitudinal designs to investigate the prospective impact of food insecurity and social capital on psychological distress. Moreover, it is essential to assess the effectiveness of interventions and policies designed to tackle these issues across diverse contexts and populations.

Footnotes

Acknowledgements

The authors express sincere appreciation to all participants and to each member of the study.

Author contributions

XQ: Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing – original draft, Writing – review & editing. YP: Data curation, Methodology, Writing – review & editing. GL: Data curation, Methodology, Writing – review & editing. YL: Data curation, Methodology, Writing – review & editing. WT: Data curation, Methodology, Writing – review & editing. BH: Funding acquisition, Data curation, Methodology, Writing – review & editing. BW: Conceptualization, Funding acquisition, Project administration, Writing – review & editing.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Funding was provided by the NYU Shanghai Center for Global Health Equity.

Ethics approval

The study procedures were approved by NYU Shanghai Institutional Review Board on April 21, 2022. All participants provided digital informed consent. The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008.

Informed consent

All participants provided written informed consent. No experimental interventions were performed.

ORCID iD

Xiang Qi

Data availability statement

The data supporting this study’s findings are available on request from the corresponding author (BJH). The data are not publicly available because they contain information that could compromise the privacy of research participants.

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