Prevalence and determinants of food insecurity during the 2022 COVID-19 related lockdown in Shanghai

ABSTRACT

The SARS-CoV-2 coronavirus pandemic has led to increased food insecurity levels. This cross-sectional study examines the prevalence and determinants of food insecurity during the two-month (1 April to 1 June 2022) city-wide lockdown in Shanghai. The data was collected via an online questionnaire from 3230 adult Shanghai residents during the lockdown. Food insecurity was measured using an adapted version of the Household Food Insecurity Access Scale. Nearly 70% of participants reported being exposed to food insecurity. Using multivariable logistic regressions, we examined the associations between key correlates (i.e. age, income, lockdown-related income loss, migration, employment status, social capital, preparedness, and received social support) and overall food insecurity while adjusting for ethnicity, gender, education, household size, and marital status. Results showed that compared to local Shanghai residents, migrants (i.e, permanent migrants with Hukou (OR = 2.16), permanent migrants without Hukou (OR = 2.06), temporary migrants (OR = 2.74)), and participants with less than or greather than 50% lockdown-related income loss (OR = 2.60, OR = 3.09), were associated with higher odds of overall food insecurity. Participants with greater preparedness (OR = 0.66), greater bonding social capital (OR = 0.93), and greater bridging social capital (OR = 0.94), had lower odds of overall food insecurity. Targeted interventions are needed to enhance food resilience and health equity among vulnerable populations.

KEYWORDS:

• Food insecurity
• COVID-19
• health policy
• social capital
• health equity

Introduction

Throughout the novel SARS-CoV-2 coronavirus (COVID-19) pandemic, population-wide lockdown measures were adopted globally to minimise morbidity and mortality. In 2022, two years after the first identification of the virus in Wuhan, China, Shanghai endured a massive COVID-19 outbreak caused by the Omicron variant (Chen, 2022). In order to control the fast spread of the virus, Shanghai authorities imposed a two-month phased city-wide silent period, enforcing strict stay-at-home orders for all citizens (O’ Kane, 2022). The unprecedented Shanghai lockdown resulted in disruptions in the food system, exposing 26 million Shanghai citizens to food insecurity.

Food insecurity is defined as the lack of regular access to enough safe and nutritious food for an active and healthy life (Food and Agriculture Organization of the United Nations, n.d.). It is a global health issue that can lead to serious health complications (Gundersen & Ziliak, 2015). Food security contains four dimensions, including food availability, accessibility, utilisation, and stability (Ashby et al., 2016). Food availability is defined as the physical presence of a reliable and consistent supply of quality food. Food accessibility reflects whether people have adequate economic and physical access to food, given that food availability is already achieved (Ashby et al., 2016). In an emergency context, food availability and accessibility are the two most relevant dimensions (Smith & Lawrence, 2018).

A body of research has demonstrated the profound negative impact that COVID-19 and its related social and economic responses (e.g. stay-at-home mandates, work disruptions, and income losses) have had on global food security (Dou et al., 2021; Fitzpatrick et al., 2020; Hamadani et al., 2020; Head et al., 2022). Inflexible food supply chains were stretched-thin by increased consumer purchasing volume, resulting in food shortages. These situations were compounded by lockdown-related changes in consumer purchasing behaviours (e.g. panic buying, hoarding), resulting in systematic failures to adequately distribute limited resources. Concomitantly, other factors, such as work disruptions and financial instability, further undermined physical and economic food accessibility, exacerbating disparities in food access (Clay & Rogus, 2021).

Prior to COVID-19, considerable disparities in food insecurity were apparent among individuals characterised by varying demographic attributes and socioeconomic status. Vulnerable populations at higher risk of food insecurity included ethnic minorities, women, divorced or widowed individuals, the unemployed, migrants, those with low income and educational attainment, and individuals from larger households (Clay & Rogus, 2021; Fitzpatrick et al., 2020; Ihabi et al., 2013; Lee et al., 2020; Neter et al., 1996; O'Reilly et al., 2020; Pakravan-Charvadeh et al., 2022; Tarasuk et al., 2019; Zhang et al., 2021). While all segments of the population faced some level of food insecurity risk during the pandemic, the extent of vulnerability may not have been equally distributed among groups.

Under the context of the pandemic, individual and community preparedness, social support, and social capital may also influence the availability and accessibility of food, promote behaviours leading to food insecurity, and enhance resilience (Fitzpatrick et al., 2020; Willis & Fitzpatrick, 2016; Zhong et al., 2022). Received social support is defined as receiving assistance from others and belonging to a supportive network (Bhandari & Yasunobu, 2009; Chen et al., 2015). Social capital is a multidimensional concept that can be broadly defined as a collective asset in the form of shared norms, values, beliefs, trust, networks, social relations, and institutions that facilitate cooperation and collective action for mutual benefit (Bhandari & Yasunobu, 2009).

Shanghai, a highly populous and urbanised city in China, boasts the highest Gross Domestic Product (GDP) in 2022, driven by its advanced manufacturing and service industries. Agricultural production only accounts for 0.3% of the city’s GDP (Pan et al., 2022). Shanghai announced its plan to adopt strict control measures on 27 March 2022, in response to the rapid community spread of COVID-19, which extended throughout the city in early April. People were given little time to prepare for what became a two-month lockdown, also the largest known city-wide lockdown in the world (Hall et al., 2023; Kuo et al., 2022). During this period, Shanghai citizens relied on intermediary means, often deemed insufficient, for food access. The lockdown disproportionately affected vulnerable populations, further increasing food and health inequities caused by COVID-19 (Shadmi et al., 2020). Given the uniqueness of this lockdown and the magnitude of food insecurity it inflicted on the local population, our objective is to examine the factors that can determine the risk of food insecurity and identify precarious populations during a pandemic or similar crisis.

We chose correlates that encompass the interplay of individual, family, and community-level factors, which may directly influence the availability and accessibility of food during the lockdown. Our hypothesis posits that populations facing cultural, economic, and social vulnerabilities would exhibit higher odds of food insecurity during the Shanghai lockdown compared to their counterparts who were younger, had higher income, experienced fewer lockdown-related income loss, were born locally, employed, reported greater preparedness, received better social support, and possessed greater social capital.

Methods

Participants

Data from Shanghai residents (ages 18 and over) were collected and used in this cross-sectional study. Chinese adults who resided in Shanghai during the lockdown were identified based on their network IP address and were considered potentially qualified participants. A 10% oversampling was applied to account for invalid responses. An online Chinese questionnaire, delivered through Wenjuanxing (Ranxing Information Technology Co., LTD. Changsha), was distributed to participants from 29 April to 1 June 2022. A link to the questionnaire was sent to registered Wenjuanxing platform users whose registration location was in Shanghai. We included the first 220 valid responses (200 plus 10% oversampling) from participants in each district and stopped accepting further responses from participants once the geographic target sample size was reached in each of the 16 districts. The Pudong district was an exception because it has a relatively high population compared with other districts, consequently, we conducted a 30% oversampling for this distrct to improve representativeness of the sample. Participants were asked to provide digital informed consent before participation, and those who completed the survey received an incentive of 6 Chinese Yuan (roughly equivalent to 0.8 US Dollar). This study was approved by the Institutional Review Board at New York University Shanghai.

Measures

Food insecurity

The outcome, food insecurity, was measured via five items from the Household Food Insecurity Access Scale (HFIAS) (Coates et al., 2007). The HFIAS is an internationally used cross-cultural experience-based metric developed by the USAID-funded Food and Nutritional Technical Assistance II project (FANTA) to assess the prevalence of household food insecurity and changes in food insecurity over time (INDDEX Project, 2018; Zhang et al., 2021; Zhong et al., 2022). The HFIAS has demonstrated good psychometric characteristics in the Chinese population (Zhang et al., 2021). The original nine-question HFIAS was adapted into a brief version of five items in this study and measured food insecurity experience within the past two weeks. We retained one question that measured insufficient quality of food (or limitation in food choices), and one question that measured insufficient food intake and its physical consequences (or the lack of any food). The brief HFIAS demonstrated excellent reliability in our sample (Cronbach’s α = 0.87).

Within the context of food insecurity, the five items mapped three domains of food access issues, as shown in Table 1. Participants were asked to respond to the five questions with ‘never,’ ‘rarely (one to two times),’ ‘sometimes (three to five times),’ and ‘often (more than five times).’ Individual responses were scored on a four-point Likert-type scale from 0 ‘never,’ 1 ‘rarely,’ 2 ‘sometimes,’ and 3 ‘often’ for each question. The official HFIA categorisation method was adapted based on five items (see Supplementary Table 1) (Coates et al., 2007). Considering the high prevalence of students living in dormitories and migrant workers sharing living arrangements as flatmates, we redefined the term ‘household’ to refer to an individual rather than a traditional household unit. Individuals were categorised into four levels: food secure, mild food insecure, moderately food insecure, and severely food insecure. A dichotomised food security variable, in which individuals belonging to food secure and mild food insecure levels were considered food secure, and individuals belonging to moderate food insecure and severe food insecure were considered food insecure, was created. Three subscale scores were also created by summing items corresponding to each domain of food insecurity (i.e. anxiety and uncertainty about household food supply, insufficient quality, and insufficient food intake and its physical consequences) and were used to dichotomise participants into secure or insecure regarding each domain by the weighted medians of the sample.

Table 1. Items and domains of Food Insecurity measured in the study.

Questions	Domains
1. Did you worry that your household would not have enough food?	Anxiety and uncertainty about household food supply
2. Were you or any household member not able to eat the kinds of foods you preferred because of a lack of resources?	Insufficient Quality (includes variety and preference of the type of food)
3. Did you or any household member have to eat a smaller meal than you felt you needed because there was not enough food?	Insufficient food intake and its physical consequences
4. Did you or any household member have to eat fewer meals in a day because there was not enough food?	Insufficient food intake and its physical consequences
5. Did you or any household member go a whole day and night without eating anything because there was not enough food?	Insufficient food intake and its physical consequences

Key correlates of food insecurity

Several sociodemographic variables related to food insecurity were measured. These include age (19–25 years, 26–35 years, 36–45 years, 46–55, and 55 years or older), household income after tax (less than 4000 CNY, 4001–8000 CNY, 8001–15,000 CNY, 15,001–30,000 CNY, 30,001 CNY or higher), loss of income since lockdown (none, less than 50%, and greater than 50%), employment status (employed, unemployed, retired, student), and migration status (Shanghai local, and permanent migrant with Hukou [i.e. household registration for local residents that determine a range of social benefits and services], permanent migrant without Hukou, temporary migrant).

Preparedness for the lockdown period was measured. Participants were provided four statements regarding preparedness for at least one week of supplies for household food, daily necessities, water, and medication, to which they were asked to respond with whether they were prepared or not. A preparedness score is calculated from the summed average for the four items, with higher scores indicating better preparedness.

Received social support was assessed by four questions asking the frequency of receiving food, economic, emotional, and other support from sources (e.g. the government, workplace, neighbours, family, etc.) during the lockdown period. Each question was scored on a four-point Likert-type scale from 0 ‘never,’ 1 ‘once,’ 2 ‘twice,’ and 3 ‘more than three times.’ An overall social support score was calculated for each household by summing and averaging the individual scores.

Social capital was assessed using the revised version of the Petris Social Capital Index (PSCI-R) (Chen & Yu, 2022). The PSCI measures the actual level of personal and organisational resources within a community (Scheffler & Brown, 2008). We administered the neighbourhood and social organisation subscale from PSCI-R to assess bonding and bridging social capital. Bonding social capital refers to the connections among individuals with similar backgrounds (e.g. neighbour, close relatives, friends), whereas bridging social capital is the relationships that exist across groups of people (e.g. social organisations, leisure groups) (Flora et al., 2018; Sseguya et al., 2018). Cronbach’s α for bonding social capital was 0.65 and 0.80 for bridging social capital.

Covariates

The covariates included in this analysis were gender (male, female, others), education (secondary or lower, complete high school, complete college or higher), ethnicity (Han Chinese vs. minorities), marital status (single, married/cohabitating, divorced/widowed), and household size (living alone, 2–3 people, 4–6 people, more than 6 people). The covariates have been widely recognised as risk factors for food insecurity and were thus not of primary interest in this study (Ihabi et al., 2013; Lee et al., 2020; O'Reilly et al., 2020).

Statistical analysis

Weights were calculated by utilising logistic regression models to create an inverse probability of sampling weights to account for the differences in the distribution of covariates (i.e. district and age) between the study population and the 2020 Shanghai Census data. Survey weights were incorporated in all analyses to adjust for deviations between the study population and the 2020 Shanghai Census data.

First, multicollinearity was checked between variables. Next, descriptive statistics were calculated using raw frequencies, weighted percentages, means, and standard deviations. Chi-square tests were performed to examine the unadjusted association between categorical variables and food insecurity. The unadjusted statistical significance of continuous variables was analysed using t-tests at p < 0.05. Last, a set of multivariable logistic regression models adjusted for all study covariates were conducted. Models examined the adjusted relationship between the overall food insecurity status for each of the three sub-domains of food insecurity, and independently significant key correlates. The 95% confidence intervals (CI) for prevalence and odds ratio were calculated. We further explored overall food insecurity as a continuous outcome using linear regression and examined the significance of the correlates, adjusted for all covariates as a sensitivity analysis. All statistical analyses were conducted using Stata, Version 15 (StataCorp LP, College Station, Texas).

Results

Correlation testing

There is a low risk of multicollinearity (see Supplementary Table 2). Overall, the magnitude of the correlation coefficients is within the range of – 0.5 to 0.5, suggesting low strength of association between the key correlates (Berry et al., 1985). One exception is the correlation coefficient of 0.51 between social capital sub-scales. We further checked for collinearity between the continuous variables by calculating the variance inflation factors, which were all under 1.5, smaller than the cut-off value for VIF of 5 (Neter et al., 1996).

Sample characteristics

A total of 3763 potential participants responded to the invitation to participate in the study. The final analytic sample contained 3230 Shanghai residents after the removal of participants under 18 years old (n = 156), people not currently in Shanghai (n = 190), those who responded inappropriately to three survey validity questions (e.g. please select the second option for this question, n = 69), and repeated responses to the questionnaire idenfied by same physical and IP address (n = 118).

Table 2 provides a descriptive overview of the sample (N = 3230). Most of the participants were Han Chinese. This slightly younger sample was nearly evenly split in terms of gender, with over 60% of the participants having completed college or higher. Approximately 60% of the participants lived in small families of two to three people, close to 70% of the participants were married, and 70% experienced some form of lockdown-related income loss. Roughly one-third of the participants were Shanghai locals, and most of the sample was employed.

Table 2. Characteristics of the sample and bivariate analyses.

	Overall		Food secure		Food insecure
	N	%	N	%	N	%	χ^2 (df)	p
Total	3230	100%	980	33.28%	2250	66.72%
Covariate
Ethnicity							8.45 (1)	0.009
Han	3126	96.94%	953	33.71%	2173	66.29%
Other	104	3.06%	27	19.72%	77	80.27%
Gender							25.16 (2)	0.018
Male	1657	55.37%	533	37.01%	1124	62.99%
Female	1563	44.37%	443	28.64%	1120	71.36%
Other	10	0.26%	4	30.65%	6	69.35%
Educational level							16.93 (2)	0.277
Secondary school or lower	471	14.74%	121	32.21%	350	67.79%
Complete high school	714	22.54%	226	39.57%	488	60.43%
Complete college or higher	2045	62.73%	633	31.27%	1412	68.73%
Household size							19.96 (3)	0.116
Living alone	330	9.66%	100	25.95%	230	74.05%
2–3 people	1887	60.29%	576	34.79%	1311	65.21%
4–6 people	924	27.51%	285	34.06%	639	65.94%
>6 people	89	2.55%	19	16.71%	70	83.29%
Marital status							24.54 (3)	0.032
Single	1079	27.34%	331	27.96%	748	72.04%
Married/cohabitating	2032	68.87%	601	34.59%	1431	65.41%
Divorced	106	3.27%	43	48.17%	63	51.83%
Widowed/other	13	0.53%	5	44.494%	8	55.06%
Key Correlates
Age categories							196.18 (4)	0.000
9–25 years	671	16.14%	217	28.85%	454	71.15%
26–35 years	1346	36.93%	321	23.95%	1025	76.05%
36–45 years	703	18.97%	223	30.59%	480	69.41%
46–55 years	354	9.72%	141	38.33%	213	61.67%
55 years or older	156	18.26%	78	56.17%	78	43.83%
Income categories							8.35 (4)	0.570
<4000 CNY	351	8.81%	99	26.83%	252	73.17%
4000–8000 CNY	904	31.39%	248	34.35%	656	65.65%
8001–15000 CNY	996	32.75%	302	34.42%	694	65.58%
15001–30000 CNY	685	19.07%	227	31.34%	458	68.66%
30000 CNY +	294	8.08%	104	36.01%	190	63.99%
Lockdown-related loss in income							342.43 (2)	0.000
None	729	30.76%	346	56.00%	383	44.00%
<50%	1393	38.59%	413	25.82%	980	74.18%
>50%	1108	30.65%	221	19.86%	887	80.14%
Migration status							279.32 (3)	0.000
Shanghai local	1004	38.22%	445	50.16%	559	49.84%
Permanent migrant with Hukou	436	11.90%	134	28.68%	302	71.32%
Permanent migrant without Hukou	929	25.00%	235	24.42%	694	75.58%
Temporary migrant	861	24.89%	166	18.44%	695	81.56%
Employment status							277.81 (3)	0.000
Employed	2430	67.24%	736	28.66%	1694	71.34%
Unemployed	369	10.50%	58	15.84%	311	84.16%
Retired	101	15.36%	57	63.90%	44	36.10%
Student	328	6.90%	129	36.75%	199	63.25%
Missing	2	0.00%	-	-	-
	Mean	SD	Mean	SD	Mean	SD	t	p
Bonding social capital	11.45	2.87	12.24	2.54	11.05	2.94	6.29	0.000
Bridging social capital	10.49	3.37	11.30	3.18	10.09	3.39	4.09	0.000
Preparedness	2.92	1.11	3.22	0.80	2.77	1.22	7.33	0.000
Received social support	6.22	2.73	6.30	2.58	6.18	2.80	0.68	0.496

Note: Data shown are raw numbers and weighted percentages as N(%) for categorical variables and weighted mean and standard deviations for continuous variables. There were two missing values for employment status. Bonding capital, bridging capital, preparedness, and social support scores were calculated as the average scores for each participant’s response to all related questions multiplied by the number of questions.

Bivariable analyses

Nearly 70% of the study participants reported experiencing food insecurity during the Shanghai lockdown. The bivariable associations between food insecurity and key correlates are presented in Table 2. Food secure and food insecure participants were significantly different in age, lockdown-related loss in income, migration status, employment status, bonding social capital, bridging social capital, and lockdown preparedness.

Multivariable logistic regression analyses

Correlates independently associated with food insecurity were included in multivariable analyses that adjusted for covariates (see Table 3). In model one, the overall state of food insecurity was examined as the outcome. Participants with any losses in income due to the lockdown had increased odds of experiencing food insecurity. Compared to participants who did not experience income loss, the odds of experiencing food insecurity was higher among participants with greater than 50% lockdown-related income loss (OR = 3.09, CI 2.18, 4.39) and those with less than 50% lockdown-related income loss (OR = 2.60, CI 1.95, 3.47). Also significant were the differences in food insecurity and migration status. Permanent migrants with Hukou (OR = 2.16, CI 1.53, 3.06), permanent migrants without Hukou (OR = 2.06, CI 1.51, 2.81), and temporary migrants (OR = 2.74, CI 1.97, 3.80) had higher odds of being food insecure than Shanghai locals.. Higher bonding social capital (OR = 0.93, CI 0.88, 0.99), bridging social capital (OR = 0.94, CI 0.89, 0.99), and preparedness (OR = 0.66, CI 0.57, 0.76) were associated with lower odds of food insecurity.

Table 3. Adjusted logistic models for overall state of food insecurity and three sub-domains among 3230 Shanghai residents from 1 April 2022 to 1 June 2022.

	Overall Food Insecurity		Anxiety and uncertainty about household food supply		Insufficient quality		Insufficient food intake and its physical consequences
	OR	95% CI	OR	95% CI	OR	95% CI	OR	95% CI
Age categories
19–25 years	ref		ref		ref		ref
26–35 years	0.97	0.66, 1.41	0.92	0.67, 1.28	0.89	0.64, 1.22	1.02	0.71, 1.45
36–45 years	0.95	0.61, 1.49	0.77	0.52, 1.14	0.89	0.61, 1.31	0.85	0.57, 1.29
46–55 years	0.82	0.49, 1.35	0.89	0.56, 1.40	1.12	0.70, 1.78	0.58	0.36, 0.93
55 years or older	1.20	0.59, 2.43	2.24	1.11, 4.53	2.11	1.04, 4.27	0.56	0.25, 1.25
Lockdown-related loss in income
None	ref		ref		ref		ref
<50%	2.60	1.95, 3.47	2.31	1.71, 3.12	1.87	1.38, 2.52	2.71	1.98, 3.70
>50%	3.09	2.18, 4.39	2.81	2.00, 3.95	1.28	1.64, 3.17	2.75	1.95, 3.86
Migration status
Shanghai local	ref		ref		ref		ref
Permanent migrant with Hukou	2.16	1.53, 3.06	1.34	0.98, 1.83	1.26	0.91, 1.75	1.67	1.17, 2.37
Permanent migrant without Hukou	2.06	1.51, 2.81	1.65	1.23, 2.21	1.92	1.44, 2.57	2.04	1.52, 2.75
Temporary migrant	2.74	1.97, 3.80	1.82	1.34, 2.47	2.66	1.97, 3.61	2.51	1.83, 3.43
Employment status
Employed	ref		ref		ref		ref
Unemployed	1.43	0.97, 2.09	1.13	0.83, 1.56	1.36	0.98, 1.88	1.34	0.92, 1.96
Retired	0.69	0.33, 1.44	0.57	0.24, 1.35	0.59	0.26, 1.32	1.06	0.43, 2.62
Student	1.01	0.65, 1.55	0.83	0.56, 1.24	1.25	0.83, 1.87	0.87	0.57, 1.32
Bonding social capital	0.93	0.88, 0.99	0.95	0.92, 0.99	0.95	0.90, 0.99	0.93	0.89, 0.97
Bridging social capital	0.94	0.89, 0.99	0.93	0.89, 0.97	0.91	0.87, 0.95	0.93	0.89, 0.98
Preparedness	0.66	0.57, 0.76	0.66	0.60, 0.73	0.68	0.61, 0.76	0.62	0.55, 0.71

Note: Odds ratio and 95% confidence intervals from logistic regression analyses are reported. Bolded numbers indicate significant findings at p < 0.05 level. Models are adjusted for ethnicity, gender, educational level, household size, and marital status.

Food insecurity sub-domain analyses

A set of logistic models, including all independently significant correlates, was used to test the three sub-domains of food insecurity. Any form of lockdown-related loss in income increased the odds of anxiety and uncertainty about household food supply, insufficient quality, and insufficient food intake and its physical consequences. Permanent migrants without Hukou and temporary migrants had higher odds of being food insecure in the three sub-domains compared to Shanghai locals. Permanent migrants with Hukou (OR = 1.67, CI 1.17, 2.37) had 67% higher odds of experiencing insufficient food intake and its physical consequences. Bonding and bridging social capital, and preparedness were all associated with lower odds of being food insecure in the three sub-domains. Participants 55 years or older were more likely to experience anxiety and uncertainty about household food supply (OR = 2.24, CI 1.11, 4.53) and insufficient food quality (OR = 2.11, CI 1.04, 4.27) compared to participants in the 19–25 age group. In the model with insufficient food intake and its physical consequences as the outcome, the odds of experiencing insufficient food intake and its physical consequences were reduced by approximately 40% among participants in age group 46–55 years (OR = 0.58, CI 0.36, 0.93) than those in the 19–25 years age group.

Sensitivity analyses

We further explored if our main findings changed when treating food insecurity as a continuous outcome using a linear regression model and examined the significance of the correlates, adjusted for all covariates (see Table 4). Consistent with the logistic regression model, lockdown-related loss in income, and migration status were positively associated with increased food insecurity. Bonding and bridging social capital, and preparedness were associated with reduced food insecurity. In addition, the linear regression showed less food insecurity among older (older than 46 years) compared to younger participants, and this can be directly linked to the significance of the sub-domain insufficient food intake and its physical consequences (see Table 3). Additionally, the sensitivity analysis showed greater food insecurity for participants who were unemployed than those who maintained employment during the lockdown.

Table 4. Result from multiple linear regression analysis with overall food insecurity as a continuous variable.

	Coefficient	t	95% Confidence Interval
Age categories
19–25 years	ref	–	–
26–35 years	−0.10	−0.38	−0.62, 0.42
36–45 years	−0.55	−1.79	−1.16, 0.05
46–55 years	−0.81	−2.23	−1.52, −0.10
55 years or older	−0.97	−2.23	−1.83, −0.12
Lockdown-related loss in income
None	ref	–	–
<50%	1.61	5.48	1.04, 2.19
>50%	1.97	7.04	1.42, 2.51
Migration status
Shanghai local	ref	–	–
Permanent migrant with Hukou	0.75	3.50	0.33, 1.18
Permanent migrant without Hukou	1.21	5.08	0.74, 1.67
Temporary migrant	1.58	6.12	1.07, 2.08
Employment status
Employed	ref	–	–
Unemployed	1.02	3.84	0.50, 1.54
Retired	0.69	1.26	−0.38, 1.77
Student	−0.36	−1.13	−0.99, 0.27
Bonding social capital	−0.15	−4.22	−0.22, −0.08
Bridging social capital	−0.17	−4.97	−0.24, −0.10
Preparedness	−0.90	−11.58	−1.05, −0.74

Note: Bolded numbers indicate significant findings at p < 0.05 level. Models are adjusted for ethnicity, gender, educational level, household size, and marital status.

Discussion

This study examined the prevalence and determinants associated with food insecurity among Shanghai residents during a two-month COVID-related lockdown in 2022. After accounting for covariates, migration status, lockdown-related loss in income, low preparedness, and low social capital were associated with a higher odds of overall food insecurity.

In alignment with previous studies, this research shows a potentially significant increase in food insecurity during the the pandemic (Clay & Rogus, 2021; Dou et al., 2021; Fitzpatrick et al., 2020; Hamadani et al., 2020; Head et al., 2022; Zhang et al., 2021). Shanghai is considered a Tier-1 city and one of the most prosperous regions in China. In a 2015 household survey conducted in Nanjing, a Tier-2 and comparatively less developed city, only 7.4% of the study population was food insecure (Si & Zhong, 2018). Although there is no directly comparable data for Shanghai, the notable negative impact of the lockdown measure on food insecurity is evident. Restrictive measures also exacerbated food insecurity in already vulnerable populations.

This study identified loss in income and migration status as important correlates for determining overall food insecurity, consistent with previous studies on the general population (Etana & Tolossa, 2017; Nickanor et al., 2016; Swann, 2017). Our findings also align with much of the work conducted on food insecurity and access during the COVID-19 pandemic, suggesting the underlying associations of these correlates with physical and economic accessibility of food (Clay & Rogus, 2021; Fitzpatrick et al., 2020; Zhang et al., 2021). Even after the lockdown, the impact of losing income would still exist until the income returned to the pre-lockdown level. Vulnerable populations lacking adequate economic and social resources have had difficulty obtaining food in an already resource-scarce environment.

Extending existing literature, the current analysis explored risk factors for each sub-domain of food insecurity and examined the associations of preparedness and social capital on overall food insecurity in a pandemic context. The sub-domain analyses revealed that older age groups had higher stress of food vulnerability and significant limitation in food choices. They reported lower odds of insufficient food intake and its physical consequences. Older populations may be less prone to report food insecurity overall because they had better psychological resilience, coping strategies, and adaptation than younger adults during the pandemic (Fuller & Huseth-Zosel, 2021). Our results further revealed that food security among older populations may be more related to sufficient food intake and its physical consequences. Though this may be viewed as counterintuitive as older populations were in a disadvantaged position during the lockdown because of the digital divide. This was somewhat compensated by the additional attention of neighbourhoods, communities, or families to provide food for the older populations, most of whom were Shanghai locals.

The Shanghai lockdown introduced strict control measures that forced rapid and complex changes within the city’s food system. The availability of food was severely compromised by extensive disruptions in the national food supply chain, coupled with the imposition of travel restrictions to Shanghai. Most residential compounds in Shanghai had sealed gates that restricted mobility within the compounds or sometimes within individual buildings. Depending on the specific district, residents were provided with a window of half to three days before the full implementation of the lockdown. The majority of people were unprepared for the two-month lockdown, which rendered them unable to procure food through conventional means. The high prevalence of adult food insecurity in Shanghai is attributable, in part, to this lack of preparedness. Our findings highlight a clear association, wherein individuals who were prepared had 34% lower odds to experience food insecurity than their counterparts. To improve preparedness and enhance resiliency in similar situations, a viable approach is to provide training programmes focusing on essential aspects such as health and nutrition education for emergencies, coping strategies during times of food scarcity, and practical guidance on food preparation in emergency situations, tailored to the local context and circumstances. Research has shown a strong correlation between nutrition knowledge and dietary diversity, and improvement in the level of nutrition knowledge can help families identify behaviours and reactions appropriate for emergencies and allow them to prepare (Pakravan-Charvadeh et al., 2021). Additionally, providing information on where to access safe food is crucial. Directing individuals to relevant community resources, such as social support services and local food kitchens, can further bolster their preparedness and ability to navigate food insecurity challenges.

Many residents resorted to alternative food purchasing methods, with social capital playing a crucial role in acquiring food. Within social networks, interpersonal relationships fostered bonding social capital, enabling individuals to receive social support and engage in actions such as sharing food supplies among neighbours. The interconnectivity between social networks, known as bridging social capital, facilitated the exchange of information and resources across communities with diverse social ties. Neighbourhood committees (Juweihui) and volunteer groups emerged as important forces in supporting residents’ livelihoods during the Shanghai lockdown, actively forming and coordinating social networks. Volunteer groups collaborated with neighbourhood committees to facilitate group-based bulk food purchasing, further strengthening the role of social capital in ensuring food availability and accessibility. Our finding that bonding and bridging social capital had a nearly equal effect on food insecurity supports the notion that both types of social capital improve food insecurity by promoting product and information sharing. Social capital can also increase community members’ resilience, decrease vulnerability to expected shocks, and enhance food stability (Nosratabadi et al., 2020).

Furthermore, the significance of social capital extends beyond its immediate role in food procurement during the lockdown; it can also have implications that potentially exacerbate the precarious situation faced by marginalised groups. Social capital plays a pivotal role in the adjustment and integration process for migrants, who often encounter challenges in forming bonding and bridging social capital (H. Cheong, 2006; Zhu et al., 2019). Our findings revealed that migrant workers comprise a substantial proportion, exceeding 60% of the Shanghai population. Given this context, fostering social capital becomes increasing crucial as it can yield substantial benefits in addressing food insecurity during times of crisis and increasing community resilience in the face of extreme events. The existing literature examining the intersection of social capital and food insecurity has been limited regarding COVID disruptions. Therefore, this study offers valuable insights into this understudied area. Our results underscore the significance of investigating the relationship between social capital and food insecurity as a means of identifying the most vulnerable populations and developing community-based programmes aimed at mitigating food insecurity challenges. By elucidating this pathway, future interventions and initiatives should prioritise the facilitation of network and community building involving stakeholders at various stages of the food supply chain, from production to consumption.

Results from this analysis add value to the literature as it examines correlates for food insecurity in times of crisis, providing an opportunity to develop more targeted, sustainable, and effective food insecurity response plans. Robustness to major public emergencies requires responsive and equitable interventions that can adequately meet the needs of vulnerable populations. This study identified migration status, financial stability, preparedness, and social capital as correlates significantly associated with overall food insecurity during the Shanghai lockdown. By identifying these key correlates, our study sheds light on the multifaceted nature of food insecurity and provides a foundation for targeted interventions and policy initiatives, which can enhance the effectiveness of food insecurity response plans, and promote greater equity and inclusivity in meeting the diverse needs of populations during crises. In addition to the pandemic or similar public health crisis, China is especially vulnerable to food insecurity due to natural disasters, climate change, resource and input constraints, and growth in food demand (Qi et al., 2015). Preventing food insecurity during crisis is an urgent issue that should be prioritised. Additionally, the study emphasises the need for improved monitoring of food insecurity at the city-level in Shanghai, as there was a lack of pre-pandemic data. Without data from studies using prospective designs, it remains unclear whether the public health emergency caused the reported food insecurity situation or merely exacerbated existing problems in the city. Future research should focus on developing robust systems for monitoring and assessing food insecurity to better understand its dynamics and inform timely interventions both for routine and emergency settings.

Although the study context was the Shanghai lockdown under a COVID outbreak, the uncovered fragility of the subnational and national food system should alarm other countries of the importance of being prepared, developing food shock resilience, and cultivating food security in response to other large-scale emergency events such as natural disasters. Food is central to the United Nation’s Sustainable Development Goals (SDGs) for the twenty-first century. The second of the 17 SDGs is to ‘end hunger, achieve food security and improve nutrition and promote sustainable agriculture’ by 2030 (United Nations, n.d.). The ideal solution to achieve the SDGs may not be ‘one plan for all.’ Rather, equitable interventions that incorporate considerations for physical, mental, and social well-being, and target vulnerable populations are desperately needed to reduce food insecurity.

This study has several limitations. First, the HFIAS was adapted to a shorter version with five questions. This may limit the comparability of the result with other studies using the full scale. Despite the change in available items, our scale reported strong reliability. Second, self-report bias may be present. More objective measures of food insecurity such as the variety of dishes consumed per meal and nutrition index can be included. Third, the study was distributed via an online survey platform, and probability-based sampling to identify participant households was impossible. However, the sample was purposively selected to spatially cover all districts in Shanghai to minimise selection bias caused by nonrandom sampling and survey weights were applied to improve the representativeness of the study population. Fourth, only received social support is measured in the study, but there are two distinct types of social support: received social support and perceived social support. Fifth, only food availability and accessibility were of focus in this study. The potential impact of poor food quality on food utilisation and subsequent consequences of long-term insufficient food intake on food stability are important aspects that warrant further investigation in future studies. Lastly, given the choice of an online delivery platform, lower-educated groups at high risk of food insecurity may need to be better presented in the sample. The omission of such populations may have resulted in an underestimation of the severity of food insecurity.

Conclusion

This study reported on the prevalence of food insecurity and its key correlates among Shanghai residents during the city’s two-month lockdown from 1 April 2022, to 1 June 2022. During this period, approximately 70% of households surveyed reported experiencing food insecurity (moderate or severe food insecurity). Socioeconomic factors such as migration status and lockdown-related loss in income were associated with higher odds of food insecurity, when adjusted for gender, ethnicity, educational level, household size, and marital status. Better preparedness and higher social capital were associated with lower food insecurity during the lockdown period. The study reports no significant associations between age, income, and received social support and overall food insecurity. Based on our findings and in preparation for future emergency planning, targeted, responsive, and equitable interventions are called for to enhance food system robustness and improve the food security of the population. Addressing the socioeconomic factors, as well as promoting preparedness and fostering social capital, can contribute to a more robust and inclusive food security framework.

Acknowledgements

We are grateful to Dr. Haidong Lu for developing the study weighting.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available on request from the corresponding author, BJH. The data are not publicly available due to their containing information that could compromise the privacy of research participants.

Additional information

Funding

This work was supported by the Center for Global Health Equity, NYU Shanghai.