Edited by: Igor Pravst, Institute of Nutrition, Slovenia
Reviewed by: Shiri Shinan-Altman, Bar-Ilan University, Israel; Jayna Dave, Baylor College of Medicine, United States
This article was submitted to Eating Behavior, a section of the journal Frontiers in Nutrition
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The crisis related to the COVID-19 pandemic influenced food security and nutrition through both direct and indirect pathways. This ranged from short-term to long-term impacts, not only on health but also on food systems and thus on nutrition. This study aimed to identify how the observed constraints affected the food intake of populations across the globe. Here, special attention was paid to the consumption of vegetables and legumes and the diversity within these food groups. An online survey on Food and COVID-19 was conducted using a semi-structured questionnaire translated into several languages. Binary logistic regression models and Poisson regression models were calculated to evaluate changes in consumption patterns and to test potential determinants for the changes. For more detailed information on reasons for changes open ended questions were analysed qualitatively. Time spend at home, working from home, and mental stress were important drivers for changes in dietary intake according to the 1,042 respondents included in this analysis. The participants observed a change in food quantity (38%) and vegetable intake (27%). No changes were observed for the number of vegetable groups consumed, while significant reductions in diversity were detected within all vegetable groups. Moreover, associations between the number of consumed vegetable types during the COVID-19 pandemic and income regions as well as gender were found. The regression analysis showed that the level of decrease in vegetable diversity in the different vegetable groups were often depending on educational and occupational status, gender and household environment. Changes in food prices were related to changes in vegetable intake
COVID-19 (coronavirus disease 2019) refers to an infectious respiratory disease transmitted by SARS-CoV-II, which was first reported in Wuhan, China, in December 2019. Since then it was reported around the globe and was declared a global pandemic by the World Health Organisation (WHO) on 11th March 2020 (
Since COVID-19 has been declared a global pandemic, countries all over the world took measures such as contact and travel restrictions, store closures, curfews during day or at night or other general confinements to limit the further spread of the virus. These restrictions were in turn affecting the economic situation of many people and thus the purchasing power of these households (
The crisis related to the COVID-19 pandemic influenced food security and nutrition through both direct and indirect pathways. Direct pathways may be trade and transport restrictions which negatively impacted on food availability whereas indirect pathways include effects like no school feeding due to school closures or loss of income and thus reduced food purchasing power. This ranged from short-term to long-term impacts, not only on health but also on food systems and thus on nutrition. In this context, the High level of Panel Experts of the Committee on World Food Security (CFS) have emphasised that the initial situation of individual countries and regions and their resilience to such crises will play a decisive role in determining the severity of the disruption as the pandemic evolves (
The pandemic challenged the economic and physical access to sufficient and nutritious food, especially for already vulnerable groups and countries (
Before the Covid-19 pandemic began, a group of scientist looked at how current dietary practises impact planetary health (
This paper thus presents the analysis of the dietary changes (food quantity, overall vegetable consumption, and vegetable diversity) in relation to the restrictions and lockdown scenarios in diverse populations. Furthermore, individual and environmental characteristics as a possible cause of these changes were investigated to describe the groups most vulnerable to greatest reduction in diversity in vegetable consumption.
In close collaboration with members of the international research community from 12 different countries who were interested to join the COVID-Food systems project we developed an online semi-structured questionnaire. The transdisciplinary developed questionnaire asked for socio-demographic information, living environment of the participants, aspects of the participants food systems, food intake, and aid programs as well as the participants perceptions toward changes following the restrictions established in the respective countries. After a consensual validation process including two rounds of pre-testing the questionnaire consisted finally of 65 questions of which 15 were closed, 15 were open-ended, while 35 were designed as mixed questions. The closed-ended questions offered a list of predetermined responses. The open ended questions asked for observations made by the participants providing space for a text without limitation of characters. The mixed questions were offering space to comment and add information about the responses made to the question which had offered a predetermined response scale, e.g., “yes/no/don't know or other, please specify.” Changes in food consumption were assessed retrospectively using the same questions to assess the situations prior to and since the pandemic started. The question on price change was measured using a Likert scale (strong increase, little increase, no change, little decrease, and strong decrease).
Various translations of the questionnaire, originally designed in English were developed during the ongoing data collection, namely into Chinese, German, Polish, Russian, Spanish, and Vietnamese among others. The translations were back-translated to English for validation of the translation. The survey took place over a period of 4.5 months starting on 17.04.2020 with the English version.
There were no exclusion criteria for participants. The questionnaire was accessible to anyone with a device with internet access; resulting in a convenience sampling. The SoSci Survey platform was used to create and conduct the online survey tool (
The link to the survey was uploaded on the project website on “Sustainable Food systems—going beyond Food Security” and on the institutional website of the Centre for International Development and Environmental Research of the University of Giessen (
In total, the link to the survey was used 7,566 times. The klicks included any use of the link whether it was done on purpose or by accident or by a search engine. Thus, no conclusions can be made on how many people were interested in the study but rejected their participation after reading the introduction. Out of the total klicks, 1,528 were counted as completed questionnaires (participants responded to the final question). Participants who did not confirm their willingness to participate were excluded in the data analysis.
As we assume that the data has a risk to be blurred we used an exploratory approach for the analysis. Consequently, we did not follow a specific hypothesis and abstained to do a sample size calculation prior to the data collection.
There were several peaks in responses following the various promotion campaigns. The completed responses came from individuals living in 62 different countries. Mid July 2020 a special data collection campaign was started in Poland which resulted in a series of new responses from Polish citizens (
Response pattern of the data collection over time (17.4.-31.8.2020) (x-axis, date of data collection; y-axis, number of responses; orange, completed questionnaires; grey, total responses but incomplete or klicks; black vertical line, end of data collection for this study).
The responses given in the option “other” in the responses to questions related to
Respondents were asked to evaluate the amount of food they were eating at the time they filled the questionnaire in comparison to the before-pandemic time. Three answers were available: (a) just as much than before the pandemic, (b) less than before the pandemic, and (c) more than before the pandemic.
Binary logistic regression models were used to calculate Odds Ratios (OR) for the change. To obtain more precise information on the amount of food, food items, and reasons for changes the open-ended parts of the question were evaluated qualitatively based on summaries of the provided responses. All comments given in any other language than English were translated to English. Quoted comments were corrected for spelling mistakes. Country of residence, age, and gender were indicated in parentheses for each direct quote given in quotation marks. In the case of indirect quotes only the country of residence was reported in parentheses.
The impact of COVID-19 pandemic on the consumption of vegetables was evaluated based on the respective questions (a) “Prior to Covid-19 pandemic: Did you consume any of the vegetables listed below over a period of 4 weeks (1 month) prior to Covid-19?” and (b) “Since the pandemic started: Did you consume any of the vegetables listed below in the last 4 weeks?”. It was distinguished between the periods of time the change occurred looking at 4, 8, and 12 weeks retrospectively starting at the time of the interview. A list of 96 types of vegetables was stratified into 5 groups:
To analyse if perceived changes in food prices had an influence on the amount of food, vegetable consumption and vegetable diversity consumed, a price index was calculated. A perceived price change was assessed based on the 10 food groups of the minimum dietary diversity score for women (staple foods, legumes, nuts and seeds, milk and milk products, meat and fish, eggs, dark green leafy vegetables, vitamin-A rich vegetables and fruits, other vegetables, other fruits) (
Binary logistic regression models were used to analyse which factors have an influence on changes in food intake. A Poisson regression was calculated for food intake and in particular vegetable diversity to determine the time effect. Estimated marginal means are presented to visualise effects. Binary logistic regression and Poisson regression models were also calculated and adjusted for age, gender, and income regions in order to test the effects of lockdown and restriction scenarios. The models were created with the procedure Genlinmixed in SPSS and robust standard errors were used. Control variables were listed below the tables presenting our findings. The
During the survey period 17.4.-15.7.2020, 1,083 participants completed the questionnaire, of whom 1,042 gave their consent that the data from the questionnaire may be used for research purposes. More than 3/4 of the participants were females (77%), while 22% were male and 0.7% responded as non-binary. Two thirds of the participants (62%) were between 20 and 39 years old. The group younger than 15 and all groups from the age of 70 and above accounted for <1% each (
Distribution of age groups within the study population indicated in percent (
More general characteristics are presented in
General characteristics of the participants.
Female | 76.7 |
Male | 22.3 |
Non-binary | 0.7 |
Preferred not to say | 0.3 |
No degree or below the level of high school | 5.6 |
Finished high school | 14.8 |
Completed apprenticeship or vocational baccalaureate diploma | 8.9 |
University degree | 70.7 |
Student in school | 2.0 |
University student or training | 27.7 |
Unemployed | 3.7 |
Employee | 28.8 |
Self-employed | 7.2 |
Civil servant | 27.7 |
Retirement/Pension | 2.8 |
Asia and the Pacific | 14.8 |
Latin America and the Caribbean | 4.5 |
North America | 3.8 |
Africa | 4.2 |
Europe | 72.7 |
Low income countries | 1.5 |
Lower middle income countries | 11.9 |
Upper middle income countries | 9.1 |
High income countries | 77.5 |
Rural area | 21.4 |
Peri urban area | 14.0 |
Small town (<1 h walking distance from farmland) | 16.8 |
Small town (1–4 h walking distance from farmland) | 10.9 |
Big town (1–4 h walking distance from farmland) | 3.5 |
Big town (province capital) | 11.8 |
City | 10.1 |
Mega city | 2.8 |
Capital city | 8.8 |
Alone | 15.6 |
With my partner | 29.6 |
2 generation family | 18.3 |
3 generation family | 5.7 |
1 generation shared flat | 14.3 |
2 generation shared flat | 7.6 |
Single parents with children of different age | 1.9 |
Other | 1.4 |
Different family types with children of unknown age | 5.6 |
Contact restrictions | 72.6 |
Travel restrictions | 74.8 |
Only food retailers/supermarkets, drugstores and pharmacies are open | 53.1 |
Curfew during day | 4.8 |
Curfew at night | 6.8 |
You are not allowed to leave your house but only to buy food | 11.7 |
Other restrictions | 10.7 |
Not that I know | 3.5 |
Eat less food (any) | 15.1 |
Amount of food (any) did not change | 62.0 |
Eat more food (any) | 22.9 |
The majority (91%) reported no change in the occupational status due to COVID-19, 4% claimed that their working hours had decreased, their job had been temporarily suspended or they had experienced economic losses due to COVID-19 which might affect the level of food expenditure. Still, loss of their jobs due to the pandemic was reported by 5% of the respondents. Any support from the government, associations, religious communities, or individuals was received by 8%.
Overall, 62 countries were covered in this study, but the countries were unevenly represented. Nearly all geographical regions were covered, however the majority of the respondents resided in Europe (73%) at the time of their participation. The majority of participants lived in Germany (67%), followed by Vietnam (8%), China (4%), USA (4%), Colombia (1.4%), Poland (1.3%), and Kenya (1.3%); the remaining 14% of the respondents live in 53 different countries (
The majority of the respondents (62%) did not observe any change in the amount of food they consumed. The proportion of people who observed an increase over time was higher than those who observed a decrease (23 vs. 15%) (
Perceived change in food quantity in relation to lockdown scenarios indicated in percent in reference to prior to the pandemic.
Low income countries |
25.0 | 68.8 | 6.3 | 28.6 |
Lower middle income countries |
19.5 | 63.4 | 17.1 | 21.1 |
Upper middle income countries |
18.1 | 64.9 | 17.0 | 23.3 |
High income countries |
14.0 | 61.1 | 24.9 | 28.6 |
No lockdown ( |
12.6 | 67.4 | 20.0 | 22.7 |
No lockdown anymore ( |
8.6 | 68.5 | 22.8 | 26.5 |
Lockdown ( |
17.4 | 58.7 | 23.9 | 28.9 |
To obtain more information about the possible reasons for a change we asked the respondents to give a more detailed explanation for reported change in food intake. Most frequently mentioned reasons for an increase in the consumed amount of food were isolation, boredom, more home-cooked meals, more free time, spending more time at home, working from home, having meals together with the family, and mental stress. All of these reasons might be direct or indirect result of the restrictions implemented by the governments.
The binary logistic regression on the decrease in the amount of food eaten confirmed a significant influence of the lockdown scenarios. The proportion of people who ate less in the group that experienced a lockdown was higher than in the group that was no longer in lockdown, with an average difference of 14.2% (−0.142, 95% CI [−0.257, −0.027],
Odds ratios for perceived changes in food and vegetable intake.
OR | |||||||||
95% CI | |||||||||
OR | 0.547 | 1.038 | 1.019 | 1.387 | 0.981 | 1.072 | 1.057 | 1.109 | |
0.102 | 0.892 | 0.476 | 0.216 | 0.779 | 0.543 | 0.552 | 0.158 | ||
95% CI | 0.265/1.127 | 0.605/1.782 | 0.968/1.072 | 0.825/2.332 | 0.861/1.119 | 0.857/1.341 | 0.880/1.269 | 0.961/1.280 | |
OR | 1.315 | 1.133 | 1.036 | 1.349 | 1.126 | 1.083 | 1.161 | 1.107 | 1.040 |
0.310 | 0.587 | 0.091 | 0.167 | 0.061 | 0.104 | 0.123 | 0.172 | 0.493 | |
95% CI | 0.775/2.233 | 0.722/1.776 | 0.994/1.080 | 0.882/2.064 | 0.994/1.275 | 0.984/1.192 | 0.960/1.404 | 0.957/1.281 | 0.930/1.163 |
OR | 1.063 | 0.977 | 0.768 | 1.033 | 1.030 | 1.067 | 1.135 | 1.068 | 1.079 |
0.804 | 0.907 | 0.171 | 0.106 | 0.598 | 0.173 | 0.140 | 0.344 | 0.156 | |
95% CI | 0.654/1.728 | 0.656/1.453 | 0.527/1.120 | 0.993/1.075 | 0.924/1.147 | 0.972/1.171 | 0.959/1.343 | 0.932/1.224 | 0.971/1.198 |
OR | 0.998 | 0.852 | 0.859 | 1.020 | 0.947 | 0.912 | 0.954 | 0.949 | |
0.992 | 0.399 | 0.409 | 0.698 | 0.191 | 0.216 | 0.436 | 0.277 | ||
95% CI | 0.625/1.591 | 0.587/1.237 | 0.598/1.234 | 0.923/1.128 | 0.872/1.028 | 0.789/1.055 | 0.848/1.074 | 0.863/1.043 | |
OR | 1.230 | 1.351 | 1.308 | 0.991 | 0.993 | 1.023 | 0.982 | 1.059 | 1.046 |
0.296 | 0.068 | 0.083 | 0.535 | 0.870 | 0.524 | 0.766 | 0.285 | 0.273 | |
95% CI | 0.833/1.816 | 0.978/1.866 | 0.965/1.773 | 0.964/1.019 | 0.915/1.079 | 0.953/1.098 | 0.871/1.107 | 0.953/1.177 | 0.965/1.132 |
OR | 1.131 | 1.356 | 1.454 | 0.936 | 1.021 | 0.941 | 0.940 | 0.872 | 0.904 |
0.813 | 0.584 | 0.408 | 0.341 | 0.894 | 0.679 | 0.716 | 0.442 | 0.539 | |
95% CI | 0.408/3.135 | 0.455/4.038 | 0.598/3.536 | 0.815/1.074 | 0.755/1.379 | 0.706/1.256 | 0.671/1.315 | 0.614/1.238 | 0.654/1.249 |
OR | 1.802 | 1.002 | 1.700 | 1.034 | 0.925 | 1.129 | 1.128 | 0.982 | |
0.162 | 0.997 | 0.182 | 0.543 | 0.560 | 0.325 | 0.367 | 0.899 | ||
95% CI | 0.788/4.122 | 0.371/2.704 | 0.780/3.704 | 0.929/1.152 | 0.710/1.204 | 0.886/1.438 | 0.868/1.468 | 0.739/1.305 | |
OR | 1.014 | 0.943 | 0.819 | 1.023 | 1.043 | 0.949 | 1.027 | 0.921 | 1.061 |
0.966 | 0.824 | 0.470 | 0.458 | 0.644 | 0.479 | 0.800 | 0.425 | 0.478 | |
95% CI | 0.541/1.900 | 0.559/1.590 | 0.475/1.410 | 0.963/1.086 | 0.871/1.249 | 0.820/1.098 | 0.834/1.264 | 0.751/1.128 | 0.901/1.248 |
Overall, the change in food quantity in relation to income regions was lowest in the low income countries and highest in the high income countries. The group of people who said they ate more than before the pandemic was represented most frequently in the high income countries (25%) and least frequently in the low income countries (6%). In contrast, the prevalence of participants reporting to eat less was lowest in high income countries (14%) and highest in low income countries (25%) (
With an increase by one age-group (
Out of the 1,042 participants included in this study, 995 reported in detail on their vegetable consumption. Out of these, 27% indicated a change in their vegetable consumption which was not associated with age (
Results of binary logistic regression models for correlations between personal factors and housing situations and
The change in vegetable consumption occurred in both directions: increase and decrease which resulted in an overall “no change” for all respondents. Reasons for decrease were “reduced access and availability” as reported from Bangladesh, Ecuador, Guatemala, Ireland, Kenya, New Zealand, Poland, Vietnam, Spain, Tanzania, and USA, “increased prices” reported from Ecuador, Fiji, Kenya, and Germany or because respondent went “less shopping” (Germany and USA) or “those who provided the meals, do not make balanced dishes and you have to eat what they are offering” as mentioned by a respondent from Columbia (35–39 year old woman). “Children do not eat as diversely” or “my parents buy less vegetables than I would” were mentioned by women from Germany (35–39 years old and 20–24 years old, respectively) indicating new household settings due to students staying at home. But also, time constraints and stress were pointed out by a man as factor influencing vegetable consumption: “Less vegetables, [because] less time to cook (work and childcare), more emotional stress” (Germany, 45–49 years, male).
The mean number of vegetable groups covered in the diets was 4.5 out of 5 for the two time points: prior to and since the pandemic started. No significant association was found between age and diversity within the observed vegetable groups excluding “other vegetables.” The latter was associated with a small increase over “time” by age (
The diversity of the “dark green leafy vegetable” consumption reduced since the beginning of the pandemic with an average decrease of 0.71 vegetable types (0.706, 95% CI [0.579, 0.832], max = 18 types,
The OR of the basic model estimated that individuals had a lower chance of eating a greater number of different “dark green leafy vegetables” (16.8%), “provitamin A rich vegetables” (13.4%), “starchy vegetables” (12.2%), “legumes” (15.2%), and “other vegetables” (6.2%) since the onset of the pandemic (calculated based on OR) (
The Poisson regressions presented in
Reduction in vegetable diversity was associated in this study with “income region,” gender, education level, occupation, household type, and the living environment of the respondent. Hence, respondents living in lower middle income countries, being a woman, having a university degree, being unemployed, living in a 3-generational family and living in a small town were in general associated with the greatest reduction in diversity in each five vegetable groups. The most pronounced reductions were found for dark green leafy vegetables, legumes, and other vegetables, the lowest reductions in the vegetable groups “starchy vegetables” and “provitamin A rich vegetables” (
Results of Poisson regressions for changes since the onset of the pandemic in the diversity of vegetable categories, dark green leafy vegetables, and provitamin A rich vegetables.
Low income | 0.003 | 0.993 | −0.644/0.650 | 0.027 | 0.822 | −0.211/0.266 | |||
Lower middle income | 0.180 | 0.140 | −0.059/0.418 | ||||||
Upper middle income | 0.060 | 0.697 | −0.244/0.365 | 0.278 | 0.159 | −0.109/0.666 | |||
High income | 0.004 | 0.975 | −0.230/0.237 | 0.103 | 0.438 | −0.158/0.365 | |||
Female | 0.050 | 0.712 | −0.215/0.315 | 0.227 | 0.058 | −0.008/0.461 | |||
Male | 0.078 | 0.536 | −0.168/0.323 | 0.171 | 0.083 | −0.022/0.365 | |||
No degree/degree below level of | 0.173 | 0.412 | −0.241/0.588 | 0.132 | 0.512 | −0.263/0.527 | |||
high school | |||||||||
High school/A-level degree | −0.004 | 0.975 | −0.254/0.246 | 0.077 | 0.487 | −0.141/0.295 | |||
Apprenticeship/vocational | 0.041 | 0.774 | −0.238/0.320 | ||||||
baccalaureate diploma | |||||||||
Vocational university diploma | 0.046 | 0.671 | −0.167/0.260 | ||||||
Student in school | −0.047 | 0.885 | −0.677/0.584 | 0.426 | 0.502 | −0.830/1.683 | −0.320 | 0.290 | −0.914/0.273 |
University student/Trainee | 0.189 | 0.101 | −0.037/0.414 | ||||||
Unemployed | 0.109 | 0.593 | −0.292/0.510 | ||||||
Employee | 0.105 | 0.378 | −0.128/0.337 | ||||||
Self-employed | −0.069 | 0.625 | −0.346/0.208 | 0.087 | 0.598 | −0.237/0.411 | |||
Civil servant | 0.087 | 0.497 | −0.163/0.336 | ||||||
Retirement/Pension | 0.080 | 0.683 | −0.304/0.463 | 0.269 | 0.170 | −0.116/0.655 | |||
Living alone | 0.056 | 0.664 | −0.196/0.307 | 0.171 | 0.117 | −0.043/0.386 | |||
With partner | −0.014 | 0.913 | −0.269/0.241 | 0.125 | 0.253 | −0.089/0.338 | |||
2 generation family (underage children) | −0.023 | 0.861 | −0.281/0.235 | 0.141 | 0.235 | −0.092/0.373 | |||
3 generation family | 0.240 | 0.175 | −0.107/0.587 | ||||||
1 generation shared flat | 0.022 | 0.884 | −0.270/0.313 | 0.047 | 0.704 | −0.197/0.292 | |||
2 generation shared flat | 0.247 | 0.284 | −0.205/0.699 | 0.269 | 0.255 | −0.194/0.732 | |||
Other types | −0.075 | 0.601 | −0.356/0.206 | 0.050 | 0.699 | −0.204/0.305 | |||
Rural area | 0.041 | 0.737 | −0.196/0.278 | 0.164 | 0.102 | −0.033/0.361 | |||
Peri urban area | 0.162 | 0.216 | −0.095/0.420 | ||||||
Small town (<1 h from farmland) | 0.163 | 0.211 | −0.092/0.419 | ||||||
Small town (1–4 h from farmland) | 0.106 | 0.487 | −0.192/0.403 | ||||||
Big town (<4 h from farmland) | 0.007 | 0.958 | −0.263/0.278 | 0.048 | 0.791 | −0.304/0.399 | |||
Big town (province capital) | 0.134 | 0.373 | −0.161/0.429 | 0.159 | 0.222 | −0.096/0.413 | |||
City | 0.287 | 0.090 | −0.044/0.618 | ||||||
Mega city | −0.319 | 0.197 | −0.804/0.166 | −0.422 | 0.176 | −1.034/0.189 | |||
Capital city | −0.009 | 0.949 | −0.270/0.253 | 0.108 | 0.462 | −0.181/0.397 | |||
Low income | −0.087 | 0.825 | −0.859/0.685 | 0.680 | 0.065 | −0.043/1.404 | 0.582 | 0.341 | −0.617/1.781 |
Lower middle income | |||||||||
Upper middle income | 0.106 | 0.435 | −0.160/0.371 | 0.399 | 0.077 | −0.044/0.842 | 0.387 | 0.314 | −0.366/1.140 |
High income | 0.767 | 0.126 | −0.216/1.750 | ||||||
Female | 0.188 | 0.168 | −0.079/0.455 | ||||||
Male | 0.151 | 0.242 | −0.102/0.405 | ||||||
No degree/degree below level of | 0.219 | 0.261 | −0.163/0.600 | 1.038 | 0.080 | −0.125/2.202 | |||
high school | |||||||||
High school/A-level degree | 0.002 | 0.991 | −0.283/0.286 | 0.554 | 0.155 | −0.210/1.317 | |||
Apprenticeship/vocational | 0.265 | 0.093 | −0.044/0.573 | 0.708 | 0.075 | −0.072/1.487 | |||
baccalaureate diploma | |||||||||
Vocational university diploma | 0.187 | 0.121 | −0.049/0.424 | ||||||
Student in school | 0.212 | 0.352 | −0.234/0.657 | 0.452 | 0.460 | −0.755/1.659 | −0.611 | 0.470 | −2.269/1.048 |
University student/Trainee | 0.180 | 0.110 | −0.041/0.401 | 0.388 | 0.244 | −0.265/1.040 | |||
Unemployed | 0.354 | 0.168 | −0.150/0.859 | ||||||
Employee | 0.105 | 0.424 | −0.152/0.361 | ||||||
Self-employed | 0.039 | 0.868 | −0.417/0.495 | 0.328 | 0.096 | −0.058/0.714 | |||
Civil servant | 0.082 | 0.552 | −0.189/0.354 | 0.551 | 0.088 | −0.083/1.186 | |||
Retirement/Pension | 0.206 | 0.252 | −0.147/0.558 | ||||||
Living alone | 0.175 | 0.154 | −0.065/0.415 | 0.679 | 0.051 | −0.003/1.361 | |||
With partner | 0.112 | 0.430 | −0.167/0.391 | 0.623 | 0.106 | −0.132/1.378 | |||
2 generation family (underage children) | 0.082 | 0.524 | −0.170/0.333 | 0.464 | 0.218 | −0.275/1.203 | |||
3 generation family | |||||||||
1 generation shared flat | 0.041 | 0.842 | −0.362/0.444 | 0.471 | 0.234 | −0.305/1.248 | |||
2 generation shared flat | 0.110 | 0.608 | −0.311/0.531 | 1.328 | 0.067 | −0.091/2.748 | |||
Other types | 0.166 | 0.318 | −0.160/0.491 | 0.326 | 0.091 | −0.052/0.703 | 0.468 | 0.216 | −0.273/1.210 |
Rural area | 0.055 | 0.682 | −0.207/0.317 | 0.620 | 0.084 | −0.083/1.323 | |||
Peri urban area | 0.167 | 0.258 | −0.122/0.455 | ||||||
Small town (<1 h from farmland) | 0.133 | 0.351 | −0.146/0.412 | ||||||
Small town (1–4 h from farmland) | 0.254 | 0.170 | −0.109/0.616 | ||||||
Big town (<4 h from farmland) | 0.214 | 0.253 | −0.153/0.581 | 0.284 | 0.179 | −0.130/0.698 | 0.621 | 0.211 | −0.353/1.595 |
Big town (province capital) | 0.056 | 0.697 | −0.228/0.341 | 0.558 | 0.182 | −0.262/1.377 | |||
City | 0.210 | 0.192 | −0.106/0.526 | ||||||
Mega city | 0.224 | 0.263 | −0.169/0.617 | 0.302 | 0.454 | −0.488/1.092 | 0.488 | 0.595 | −1.312/2.288 |
Capital city | 0.216 | 0.239 | −0.143/0.575 | 0.227 | 0.152 | −0.084/0.537 | 0.621 | 0.130 | −0.183/1.425 |
Poisson models that examined the effect of perceived price changes on the diversity of vegetable consumption showed no significant association for dark green leafy, starchy vegetables, legumes, and other vegetables. For provitamin A rich vegetables as well as for the diversity of the vegetable groups, a significant correlation was found with a negative coefficient of −0.011 and −0.006, respectively. The odds ratio showed that with a one unit increase in the price index, the chance of consuming a greater number of different provitamin A rich vegetables decreases by 1.1% when adjusted for age, gender, and income region (adOR = 0.989, 95% CI [0.980, 0.999],
Results of binary logistic and Poisson regressions for the independent variable “perceived price changes”
Decrease in food quantity |
0.022 | 0.280 | 1.022 | 0.982 | 1.064 |
Increase in food quantity |
0.015 | 0.656 | 1.015 | 0.950 | 1.084 |
Vegetable consumption |
|||||
Vegetable categories |
|||||
Dark green leafy vegetables |
−0.007 | 0.202 | 0.993 | 0.981 | 1.004 |
Provitamin A rich vegetables |
|||||
Starchy vegetables |
−0.014 | 0.062 | 0.986 | 0.972 | 1.001 |
Legumes |
−0.012 | 0.067 | 0.988 | 0.975 | 1.001 |
Other vegetables |
−0.008 | 0.195 | 0.993 | 0.981 | 1.004 |
In this study, one out of five persons ate more than prior to the Pandemic whereas fewer people reported to eat less. At the same time, the findings of this study showed that the restrictions and lockdown events negatively impacted on the level of diversity in vegetable consumption. The reduced consumption of different vegetable types was only partly due to lockdown scenarios but mainly due to individual factors which became probably more pronounced by the side effects of the pandemic.
Decreased appetite or feeling of hunger, lower caloric needs due to less physical effort, losing, or stabilising weight, mental stress, reduction of out of home consumption, and price increases were described as the reasons of a reduction of quantity of food consumed since the pandemic started. Besides general reasons for controlling one's eating habits such as the caloric intake, most of the given reasons were related to the implemented restrictions. Overall, the reasons given for the reduction in food quantity were more diverse than the ones for the increase since the onset of the pandemic.
After the pandemic has been declared, 22.9% of the respondents reported to have consumed more food and 15.1% less food. This rate was lower than in a Polish study which showed that the proportion of people eating more than before COVID-19 was 43.5%—almost twice as high as in this study (
Food intake changes were not associated with differences between lockdown scenarios or specific restrictions. This might be related to one's mental state, personal coping strategies, and individual reaction to governmental regulations (
A significant effect of age on food intake was found in our study with respect to increase in food quantity. The younger the participants were, the more likely they reported an increase in the amount of food they had eaten since the COVID-19 pandemic. Results of the Bavarian study mentioned above also indicated that younger people more likely changed the amount of food they consumed (
Similar to Sidor and Rzymski (
A multi-country study conducted from mid-April to end of May using the same method as in this study showed a strong relation between country of residence and the mean food intake since the onset of the pandemic (
The hypothesis that mental stress and anxious feelings could be one reason for a change in food quantity was supported by the study of Di Renzo et al. which showed that anxious feelings were likely to occur during the pandemic due to isolation (
Any change in overall vegetable consumption was reported by 27% of the participants in this study. Almost the same number of persons stated to have increased their vegetable intake to those who reported a reduction since the beginning of the COVID-19 pandemic. Moreover, a self-reported shift from fresh and perishable vegetables toward canned, frozen, and storable vegetables emerged. Reasons given for the decline in vegetable intake included reduced availability and access, rise in prices, reduced shopping frequency, seasonality, and changes in work situations. In the case of the increase in vegetable consumption, reasons mentioned by the respondents included more home-cooked meals, for better health and immunity, more time to cook, seasonality, switching to a vegetarian diet and for a higher variation of meals. Vegetable intake is associated with habit, motivation, knowledge, and goals (
Whereas, agrobiodiversity loss has already caused production losses and food insecurity, the current Covid-19 pandemic and related food crisis has in addition contributed to an increase in food insecurity (
The evaluation of all potential factors influencing the change in vegetable consumption showed no significant correlations in our models. In contrast, Ruiz-Roso et al. (
Being a woman was indicated to be a risk factor toward feeling challenged to eat healthy foods, while older respondents were more likely to face no such obstacles (
To date, no comparable studies are available that address the changes of vegetable diversity due to the COVID-19 pandemic. A study in the United States using data from a digital behaviour change weight loss program observed a decrease in the consumption of salads while the consumption of starchy vegetables increased, which indicates a shift in vegetable selection but not whether less vegetable types were consumed (
To identify potential vulnerable groups, we tested changes in vegetable diversity over time for different social groups and for different living environments. Our results suggest that the region where people reported from, the “income regions,” played a crucial role for diversity of all vegetable groups and the overall diversity consumed in both time periods. The same effect was observed for gender except for the starchy vegetables and legumes. Household types had a significant effect on the overall diversity prior to COVID-19 and on the category other vegetables for both time periods. The fact that in several cases pre-COVID-19 effects disappeared since the COVID-19 outbreak indicates that the food environment has converged between the different groups. This may reflect that overall supply and availability were important factors but also that individuals had in most cases fewer opportunities for out of home eating than before. Moreover, the change may be caused by more than one predictor, as especially in the global context it is likely that potential reasons differ in certain regions. However, this would need to be confirmed in further studies.
Our findings showed that perceived changes in food prices are significantly correlated with the change in vegetable consumption. The stronger the increase in perceived prices or the more food groups were affected by a rise in prices, the more likely was a change in vegetable consumption. An increase in prices can lead to issues in affordability, especially in combination with loss of income (
In the case of perceived price changes for a basic food basket, our study showed that there was a significant association with the number of vegetable groups consumed and the number of different provitamin A rich vegetable types. Within all other vegetable groups, no effect of price changes on the variety was observed which maybe also due to only about 5% of respondents experiencing a loss of their job. Due to increased prices, especially in combination with loss of income, respondents may have had to compromise on their vegetable diversity (
In our international survey on Food and COVID-19 more increase than decrease of general food consumption was detected from April to July 2021 compared to the period prior to the pandemic. The reaction on the COVID-19 restrictions on a personal level were more decisive influencing food consumption than the specific restrictions themselves. The increase in vegetable consumption was reported by as many participants as the decrease and a clear shift from fresh and perishable vegetables toward canned, frozen, and storable vegetables was observed. The restrictions and lockdown events negatively impacted the diversity in vegetable consumption but mainly due to individual factors which became probably more pronounced by the side effects of the pandemic. The most vulnerable to greatest reduction in diversity in vegetable consumption were those living in lower middle income countries, being a woman, having a university degree, being unemployed, living in a 3-generational family and living in a small town. Perceived changes in food prices were significantly correlated with the change in vegetable consumption. The stronger the increase in perceived prices or the more food groups were affected by a rise in prices, the more likely was a change in vegetable consumption.
Food systems are not static and are transitioning quickly as could be observed during the Covid-19 pandemic. Consequently, a nutrition strategy is needed to strengthen the resilience of all households so that they can consume a balanced, diverse, and sustainable diet in sufficient quantities especially as regards highly perishable foods such as vegetables for planetary health (
The strength of this study are the sample size and the internationality of the study participants. This enabled us to provide a first overview about the impact of the Covid-19 pandemic at international level who responded to the same questions although the number of respondents from low income countries was limited. The latter are shown to complete the picture, yet, should be used with care. We also have to acknowledge that the chosen method, online survey, is a barrier for participation from most vulnerable populations, poor people, and/or elders who do not have access to the resources. Also, it was reported to us that the poor internet capacities in some countries hindered people to participate. Therefore, results should be interpreted with caution only, especially for the low income countries. Nevertheless, we think that our results can contribute to the ongoing debate on dietary diversity and serve as initial estimates that should be followed up by conducting representative studies.
The survey covered an important time during the first half year of the pandemic and allowed to observe different scenarios of restrictions. At the same time the long period may have biassed the recall of the participants in terms of dietary patterns prior to the lockdown. Like with food frequency questionnaires underestimation can be expected (
Despite the limitations of the study, this study is the first to look at the diversity of food intake at global level and the findings show that there is an urgent need to pay attention to vegetable diversity in local and global food systems and in research on the same.
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
The studies involving human participants were reviewed and approved by Review Board of the medical faculty of the Justus Liebig University Gießen, Germany. The patients/participants provided their written informed consent to participate in this study.
LS conducted the data cleaning and the statistical analysis under the lead of IJ. IJ prepared the manuscript based on the findings from LS with contributions from GK, KJ, IH, and EH. IJ, LS, KJ, IH, and EH developed and translated the questionnaire with the support of an international network. IJ was the principle investigator and responsible for the conceptualisation of the study design. All the authors read and approved the final manuscript.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
The authors greatly acknowledge the support from Carolin Schöler who supported the development and the translation of the questionnaire as well as the overall study. We also acknowledge the contributions from Dr. Johannes Herrmann to the statistical analyses and data interpretation. We thank all contributors during the development of the questionnaire and the respondents who participated in this study. We also appreciate the support of the soscisurvey support team who helped us in setting up the different languages on one platform.
The Supplementary Material for this article can be found online at: