| Literature DB >> 35328924 |
Katherine Kent1,2, Laura Alston3,4, Sandra Murray2, Bonnie Honeychurch2, Denis Visentin2.
Abstract
Prior to the COVID-19 pandemic, rural-dwelling people in high-income countries were known to have greater challenges accessing healthy food than their urban counterparts. The COVID-19 pandemic has impacted food supplies across the world, and public health restrictions have changed the way people shop for food, potentially exacerbating food insecurity. This systematic literature review aimed to synthesize the available evidence on the impact of the COVID-19 pandemic on aspects of food insecurity in rural populations residing in high-income countries. Five electronic databases were searched, identifying 22 articles that assessed food insecurity prevalence or data on food availability, access, utilization and the stability of the food supply in rural populations during the COVID-19 pandemic. Ten studies examined the prevalence of food insecurity in rural populations, with the reported prevalence ranging from 15% to 95%. Where rural/urban comparisons were presented, most studies (n = 5; 71%) reported that food insecurity was significantly higher in rural regions. Five studies examined the availability of food and eight studies examined access to food, identifying that rural populations often had lower food availability and access to food during the pandemic. In contrast, two studies identified positive effects such as more gardening and increased online access to food. Rural populations experienced multiple changes to food utilization, such as reduced diet quality and food safety observed in eight studies, but this was not shown to be different from urban populations. Additionally, the food supply in rural regions was perceived to be affected in two studies. The results of this review may be used to inform region-specific mitigation strategies to decrease the impact of the current COVID-19 pandemic and future global events on food security. However, the lack of consistency in study outcomes in research on rural populations limits the identification of priority areas for intervention at a global-scale.Entities:
Keywords: COVID-19; food access; food availability; food security; high-income; rural
Mesh:
Year: 2022 PMID: 35328924 PMCID: PMC8954908 DOI: 10.3390/ijerph19063235
Source DB: PubMed Journal: Int J Environ Res Public Health ISSN: 1660-4601 Impact factor: 3.390
Overview of the inclusion and exclusion criteria that guided the study screening.
| Section | Criteria | Include If: |
|---|---|---|
| Language | Publication reported in English | Yes |
| Design | Observational studies including prospective and retrospective cohort and cross-sectional studies; or baseline data from intervention studies. | Yes |
| Qualitative research including in-depth interviews, focus groups, ethnographic research, content analysis and case studies. | Yes | |
| Follow-up data from randomised or non-randomised trials, case reports, reviews, editorials, letter to the editor | No | |
| Population | Any age | Yes |
| Those living in rural or remote communities as classified by any regional or remote scales | Yes | |
| Urban dwelling populations, or both urban and rural dwelling populations that have not been stratified by rurality | No | |
| Content | Food security status, including the prevalence of food insecurity, as determined by any valid and reliable screening tool at an individual or population level | Yes |
| The experience of food insecurity or hunger | Yes | |
| Availability of food, including concepts of panic buying, food hoarding and food transport issues | Yes | |
| Physical access to food such as restrictions on shopping, closure of food outlets and loss of public transport. | Yes | |
| Financial access to food such as higher food prices, loss of income and lack of social support during the COVID-19 pandemic | Yes | |
| Utilization of food, including challenges and opportunities around the skills and knowledge surrounding food and food preparation throughout the pandemic | Yes | |
| Stability of the food supply such as disruptions to the labour or transport needed to maintain the food supply, including apparent consumption data. | Yes | |
| Access | Full-text article accessible | Yes |
Figure 1PRISMA flow diagram of search strategy resulting in included studies.
General study overview of included studies, including a checkbox of whether they explored the prevalence of food insecurity, access to food, availability of food, utilization of food or the stability of the food supply.
| Reference | Author and Year | Setting | Rural Sample Size | Key Demographics | Food Security Outcome Assessed | ||||
|---|---|---|---|---|---|---|---|---|---|
| Food Insecurity Prevalence | Access | Availability | Utilization | Stability | |||||
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| [ | Giacoman et al., 2021 | Adults in rural, regional, and urban Chile | Total sample demographics not presented | √ | |||||
| [ | Kent et al., 2020 | Households in Tasmania, Australia. During lockdown. | 77% female, 68% aged 46+ y; 67% had a university education | √ | |||||
| [ | Niles et al., 2020 | Households in Vermont, USA | 79% female; 96% white; 65% had a university education | √ | √ | √ | √ | ||
| [ | Parekh et al., 2021 | Households in rural, suburban and urban USA | Households with children (62% female; 60% 40–59 years old; 70% employed) and without children (58% female; 44% 40–59 years old; 56% employed); | √ | |||||
| [ | Men et al., 2021 | Adults in Canada | 50.8% were female; 48% household with children, 43% immigrants; 50% not working | √ | |||||
| [ | Steimle et al., 2021 | Socioeconomically disadvantaged parents and their elementary school-aged children in rural Pennsylvania, USA | Parents (90% mothers; mean age = 35 years) youngest child aged 4–11, 49% female | √ | √ | ||||
| [ | Mui et al., 2021 | Adults in rural and urban USA | Total sample demographics not presented for rural group | √ | √ | √ | |||
| [ | Niles et al., 2021 | Households in Vermont, USA | 43.8% were aged 55 y+; 67% female | √ | √ | √ | √ | ||
| [ | Kar et al., 2021 | Store data in Franklin County, OH, USA. During and after the state-wide stay-at-home period | 7 stores in rural areas | Store characteristics included number of employees, sales, volumes. | √ | ||||
| [ | Sherbuk et al., 2020 | Patients at a HIV/AIDS clinic in the nonurban southern USA who had low income | 53.5% were men, | √ | |||||
| [ | Simmet et al., 2021 | Food banks in Germany throughout the pandemic | Total sample demographics not presented | √ | |||||
| [ | Taylor et al., 2021 | Farmers markets in Michigan, USA | Rural markets had a mean of 189 customers/week and 12.4 years in current location. Mean age of market managers was 55.4 years | √ | |||||
| [ | Cohn-Schwartz et al., 2021 | Adults in rural and urban Israel | Adults aged 50+, mean age 63 years, 47% women. | √ | |||||
| [ | Jordan et al., 2021 | Adults; international; 62 different countries | 77% were female, 62% aged between 20 and 39 years. Study also explored influence of perceive price on intake but did not report by rurality. | √ | |||||
| [ | Sidor and Rzymski | Adults not working regularly in Poland during lockdown (under stay-at-home orders) | Of total sample: mean age 27.7 (SD = 9.0), 1043 (95.1%) female. 10% unemployed, 47.2% students and 42.8% full time workers. 51.7% tertiary educated | √ | |||||
| [ | Luckstead et al., 2021 | Adults, low-skilled domestic workers, USA | Survey 1: | Not reported; but respondents likely to have income below USD 50,000, without a college degree, and who are below the retirement age of sixty-five | √ | ||||
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| [ | Barr et al., 2021 | Adults in Kentucky, USA (a largely rural state) | 72% female, mean age 43.5 ± 15 years, 37% white | √ | √ | ||||
| [ | Jackson et al., 2022 | Adults in rural and urban USA | Adults aged 18–78, 52% middle aged, 51.1% female | √ | √ | ||||
| [ | Snuggs et al., 2021 | Adults in UK | Of total sample, 208 (86.7%) female; 213 (88.9%) lived in close proximity to a supermarket. | √ | |||||
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| [ | Barr et al., 2021 | Laurel County, Kentucky, USA | Mean age 54.9 ± 12.6 years | √ | √ | ||||
| [ | Pyle et al., 2021 | A single neighborhood in Oconee County, South Carolina, USA | 55% female, 65% white, 58% high school graduate | √ | |||||
| [ | Whelan et al., 2021 | Regional community in Victoria, Australia | 55% female, did not report other statistics on participants | √ | √ | √ | |||
Overview of included studies that reported the prevalence of food insecurity in the respective study settings.
| Reference | Method of Data Collection | Food Security Outcome Measures | Analysis Method Used | Interpretation of Results or Key Finding Relating to Rurality Only |
|---|---|---|---|---|
|
| ||||
| [ | Telephone survey; country-wide | UN FAO Food Insecurity Experience Scale; eight questions; recall period of 30 days | Weighted descriptive statistics; multinomial logistic regression model | 53.5% of people in rural Chile were food insecure (combining mild (27.2%) and moderate-severe food insecurity (26.3%)), an increase from 29.9% pre-COVID-19 (mild = 15.7%, moderate-severe 14.2%) |
| [ | Online survey; state-wide | U.S. Household Food Security Survey Module: Six-Item Short Form with recall period of 30 days | Descriptive statistics; univariate and multivariate binary logistic regression | 33% of rural respondents in Tasmania, Australia were classified into marginal, low and very low food security groups compared to 23% of their urban-dwelling counterparts. After adjusting for other characteristics, authors reported an 82% increase in experiencing food insecurity among respondents in rural areas (AOR: 1.82; SE = 0.34; 95%CI [1.28, 2.62]; |
| [ | Online survey; state-wide | U.S. Household Food Security Survey Module: Six-Item Short Form with recall periods “in the year before the coronavirus outbreak” and “since the coronavirus outbreak.” | Kruskal–Wallis tests, Wilcoxon rank sum tests, | |
| [ | Online survey; country-wide | U.S. Household Food Security Survey Module: Six-Item Short Form with recall period of 90 days | Descriptive statistics; multivariable logistic regression | |
| [ | Online survey; emergency food program recipients | Hager two-item screener | Descriptive statistics | 95% of the sample (participants of a rural emergency food program) were classified as food insecure. |
| [ | Online survey; convenience sample of Vermont households from August and September 2020 | U.S. Department of Agriculture’s (USDA) Household Food Security Survey Module: Six-Item Short Form, recall periods both “in the year before the coronavirus outbreak” and “since the coronavirus outbreak. | Multivariate logistic regression | 29% ( |
| [ | Daily text-messaged surveys of families in a food assistance program during and after school closures located in rural Pennsylvania | Four daily survey questions assessed families’ levels of FI, all adapted for daily use from the Current Population Survey Food Security Supplement | Multilevel, mixed-effects models | For families with children in rural Pennsylvania, all indicators of daily FI significantly increased when schools closed in their region, and gradually decreased in the months that followed. The mean sum of FI question increased from 0.77 before closures to 0.84 after closures ( Worry about running out of food (33.5 vs. 38.7%, Parent ate less than should (22.5 vs. 22.2%) Child ate less than should (9.95 vs. 10.0%) Parent or child skipped meal (11.2 vs. 12.8% |
| [ | Online, cross-sectional survey | Food insecurity determined using the Hager 2-item food insecurity screener with a recall period of “since the COVID-19 pandemic” began. | Multivariate analysis of covariance (MANCOVA) followed by pairwise univariate tests | 40% of participants reported food insecurity. Rurality was not significantly associated with food insecurity. |
| [ | Online survey, Canada wide | Household food insecurity in the past 30 days determined by a six-item questionnaire adapted from the 18-item questionnaire that is routinely used to monitor 12-month food insecurity in Canada | Prevalence of outcomes | 14.9% of rural dwelling Canadian adults were food insecure, |
| [ | Online survey, USA Country-wide | Food insecurity was determined by adapting the 2-item Hunger Vital Sign screening tool with a recall period of the past 30 days of the COVID-19 pandemic. | Chi-square tests | Food insecurity was significantly higher in rural adults (40.5 %; |
Data presented in bold and italics have been calculated by the review authors from data presented in tables and figures.
Overview of included studies that reported the impact of the COVID-19 pandemic on the availability of food in rural areas in the respective study settings.
| Food Availability | ||||
|---|---|---|---|---|
| Reference | Method of Data Collection | Outcome Measures | Analysis Method Used | Interpretation of Results or Key Finding Relating to Rurality Only |
| [ | Online survey; state-wide | Close-ended question developed for the study asked respondents if food was unavailable to them. | Kruskal–Wallis tests, Wilcoxon rank sum tests, | Food-insecure respondents were more likely to report that food was unavailable to them during the pandemic than food-secure households, regardless of rurality. |
| [ | Online survey; USA wide | One question of food availability in food in retailers | Chi square test | 35% of rural participants reported that there was limited availability of food in retailers, compared to urban dwelling (~40%) and suburban (~30%) areas. |
| [ | Online survey of food banks in one organization | Days of operation during the COVID-19 pandemic | Chi square test | Of the 401 food banks for which data were available, 58.6% were closed at some point from 16 March to 3 May 2020. On average, food banks were closed 48.1 days (SD 28.7). There were no differences between closed and open food banks concerning the size of the municipality the food bank was located in. |
| [ | Telephone or self-administered surveys of Farmer’s Market managers | Participation in subsidized nutrition programs to reduce FI and Impact of COVID-19 on operations | Descriptive statistics | Rural farmers markets: Were more likely to make a profit, with 57.9% of the rural markets making a profit, compared with only 35% in urban clusters and 30.3% in urban areas. Less likely to report a reduction in staff than markets in urban areas. Less likely to report that the number of customers declined. In urban areas it was 43.8% compared to 31.3% of the rural markets. |
| [ | Focus groups and group model building | Group model building was undertaken to map issues impacting on the food supply and consumer behaviour | Thematic analysis and causal loop diagrams to describe the system | Rural supermarket managers described ‘empty shelves’ due to panic buying, product unavailability, and community fear generated from the media/mixed messaging. Customers reported fear of not being able to access the food they needed during lockdowns. |
Overview of included studies that reported on the impact of the COVID-19 pandemic on access to food in rural areas in the respective study settings.
| Food Access | ||||
|---|---|---|---|---|
| Reference | Method of Data Collection | Outcome Measures | Analysis Method Used | Interpretation of Results or Key Finding Relating to Rurality Only |
| [ | Online survey; convenience sample of Vermont households from August and September 2020 | Thirteen home food procurement variables developed about home food procurement (local food, gardening, fishing, foraging, hunting, livestock, and canning) examining current practices and changes during the COVID-19 pandemic, | Descriptive statistics | A third of all respondents (34.5%; |
| [ | Screening patients during standard care. Developed a database of food bank/home delivered meals services provided by the clinic during April 2020 and the preceding 12 months (March 2019 to February 2020). | The variables used to assess food insecurity included: change in employment among patients at risk based on self-report; food support provided through gift cards or delivery of food boxes. | Descriptive statistics | Support for food services increased 66% during April 2020, from 131 average monthly services to 218 services. Home-delivered meals were the most common source of support for patients. |
| [ | Daily text messaged surveys of families in a food assistance program during and after school closures located in rural Pennsylvania | Weekly frequency of receiving a “Power Pack” and frequency of use of “Grab and Go” meal options. | Multilevel, mixed-effects models | Families who experienced a greater increase in food insecurity during school closures were more likely to rely upon emergency food relief parcels (“power pack”). Using the power pack services was associated with greater recovery from food insecurity throughout the pandemic. Use of Grab and Go meals was not associated with changes in food insecurity. |
| [ | Online survey; state-wide | Close-ended questions developed for the study explored food access challenges and concerns; use of food assistance programs | Kruskal–Wallis tests, Wilcoxon rank sum tests, | Food-insecure households reported more food access challenges compared to food-secure households, including trouble affording food, getting food through a food pantry, and which services were available to help getting food, but this was not affected by rurality. |
| [ | Online survey; emergency food program recipients | Open-ended response survey data | Thematic analysis | An emergency food program help alleviate issues of food access (physical and financial) during the pandemic, due to reduced income by “keeping food on the table” and “reducing the frequency of leaving the house”. |
| [ | Focus groups; community residents | Exploring experiences in the changes to the food environment during the pandemic | Grounded theory | Respondents described how COVID-19 increased emergency food assistance while other health resources (such as through a library) were restricted. Positive experiences were found with the expansion and utilization of online food ordering, which increased access to food. |
| [ | In-depth interviews; community residents | Exploring experiences and the effects of the crises (disasters and pandemics) on community members’ access to food. | A post-positivist theoretical frame | Rural respondents described how food insecurity existed prior to COVID-19, but was exacerbated by the pandemic and disasters in the rural community. Others faced only short-term food insecurity depending on their social networks. A major recurring issue was the scarcity of healthy foods and an increase in food costs during the pandemic. |
| [ | Focus groups and group model building | Group model building was undertaken to map issues impacting on the food supply and consumer behaviour | Thematic analysis and causal loop diagrams to describe the system | The nearest larger grocery retailers for one rural community were approximately 70 km by road, inaccessible to many residents during ‘lockdown’. Fear of COVID-19 and of not being able to access food drove panic buying in a rural community. |
| [ | Changes in food access based on observed travel data for all grocery shopping trips during and after the state-wide stay-at-home period. | Data in this study were store locations and characteristics store visits (weekly count of customers) assumed characteristics of incoming shoppers as inferred from their origins, and characteristics of store locations | Variables explored was difference in average weekly store visits to during lockdown and initial reopening phases of the pandemic. | During lockdown, traffic declined 2.5 times more in urban stores than those located in rural areas. Stores in urban area experienced a decline in traffic 7.5 times greater than that of a store located in a rural area during the initial reopening phases. |
| [ | Online survey | Barriers to food access and transport mode to obtain food | Chi-square tests | Transportation as a barrier did not vary significantly between rural and urban regions. However, food-insecure adults in urban areas faced more barriers to food access and issues obtaining culturally appropriate foods than those in rural areas |
Overview of included studies that reported the impact of the COVID-19 pandemic on the utilisation of food in rural areas in the respective study settings.
| Utilization of Food | ||||
|---|---|---|---|---|
| Reference | Method of Data Collection | Outcome Measures | Analysis Method Used | Interpretation of Results or Key Finding Relating to Rurality Only |
| [ | Online survey; state-wide | Close-ended question developed for the study asked respondents about concerns of food safety. | Kruskal–Wallis tests, Wilcoxon rank sum tests, | Food-insecure respondents were more concerned about the safety of food during the COVID-19 pandemic than food-secure households, regardless of rurality. |
| [ | Online survey; convenience sample of Vermont households from August and September 2020 | Authors developed new questions to measure perceived change in fruit/vegetable and red meat/processed meat consumption since the onset of the COVID-19 pandemic. | Descriptive statistics | Of the sample of rural adults, nearly one in four (23.3%) respondents indicated that ate fewer fruits and vegetables during the pandemic as compared to before, most (65.5%) reported eating the same as before COVID-19, and 11.2% reported eating more. A quarter (25.9%) of respondents reported eating less red and/or processed meat since the start of the COVID-19 pandemic, and 7.9% reported eating more. |
| [ | Telephone surveys; country-wide | A single item-question about stocking up food for emergency. | Descriptive data and bivariate analyses | Rural adults were less likely to report stocking up on food than adults living in urban localities. |
| [ | Focus groups; community residents | Exploring changes to the food environment during the pandemic | Grounded theory | Rural respondents described how COVID-19 changed the home food environment, including spending more time cooking and eating at home, and increased produce gardening. |
| [ | Online cross-sectional survey | Change in food concussion during quarantine; Frequency of consumption of selected food products, Frequency of breakfast consumption during quarantine; Level of fear of contracting SARS-CoV-2 during grocery shopping and contact with food products. | Correlation analysis | Changes in food consumption, snacking, and cooking, eating breakfast, during the quarantine were not differentiated by rurality ( |
| [ | Online survey through social media; close and open-ended questions | The Food Choice Questionnaire and The Family Mealtime Goals Questionnaire. Open-ended questions. | Repeated measures ANOVA | There was no significant effect of suburban/rural location on any of the food choices made by participants in the study. |
| [ | Online survey using open-ended and close-ended questions | Diet quality was measured using the dietary screener questionnaire (DSQ), which was modified to assess intake over the past week. Individual nutrient intakes were then combined into an overall measure of diet quality | Multivariate analysis of covariance (MANCOVA) followed by pairwise univariate tests | Over 60% of participants scored a 2 or lower on the 6-point scale of diet quality. Rurality was not linked to dietary quality. However, social connection and changes in dietary behaviour occurred during the pandemic, with food-insecure adults reporting a reduction in diet quality. |
| [ | Online survey. | Changes in food quantity, fruit and vegetable consumption as a result of pandemic restrictions. Study also explored the influence of perceive price on intake using open-ended questions but did not report by rurality for this outcome. | Binary logistic regression models and Poisson regression models were calculated to evaluate changes in consumption patterns and to test associations with COVID-19 related factors | The overall effect of living environment was not a significantly influencing vegetable. Regarding consumption, however, among residents of ‘small towns’ (not defined clearly as rural in this study), 20% more (compared to mega cities) reported either an increase/decrease in vegetable intakes. Living in a small town was associated with a reduction in diversity in each of the five vegetable groups reported to be consumed. |
Overview of included studies that reported the impact of the COVID-19 pandemic on the stability of the food supply rural areas in the respective study settings.
| Stability | ||||
|---|---|---|---|---|
| Reference | Method of Data Collection | Food Security Outcome Measures | Analysis Method Used | Interpretation of Results or Key Finding Relating to Rurality Only |
| [ | Online survey of a representative sample of low-skilled domestic workers’ attitudes; USA country wide | Perceived importance of agriculture food production and concern about having a food shortage due to the effect of COVID-19 amid the coronavirus crisis. | Descriptive statistics; Logistic and Order Logistic regression | On average, the total sample of respondents perceived that agricultural production was more important than before the crisis and were more concerned about a food shortage than prior to the pandemic. There was no difference between rural and urban respondents regarding the perceived importance of agricultural production and concern for food shortage. |
| [ | Focus groups and group model building | Group model building was undertaken to map issues impacting on the food supply and consumer behaviour | Thematic analysis and causal loop diagrams to describe the system | Supermarket managers described an unpredictability in consumer behaviour as well as supply chain issues as a result of COVID-19, that led to a lack of stability in the local food supply. The supply chain struggled to re-orient itself in a tight time frame. |