Keywords coronavirus - nutrition - surveys - programs - policies - food security
Introduction
The pandemic associated with the coronavirus disease 2019 (COVID-19) compelled numerous
countries to implement emergency measures to mitigate the spread of the virus.[1 ] These measures included quarantines, isolations, and curfews aimed at curbing the
escalation of infections and deaths. Such actions induced changes in family dynamics
and lifestyles, impacting physical and psychological health, economic and job vulnerabilities,
which could, in turn, affect dietary habits.[1 ] Natural disasters, wars, migrations, displacements, strikes, economic downturns,
and job losses also influence food availability and feeding practices.[1 ] During the pandemic, the Colombian economy experienced significant repercussions;
according to the National Administrative Department of Statistics of Colombia (DANE),
the gross domestic product contracted by 6.8% in 2020.[2 ] Unemployment rose to 16.8% in May 2020, compared with 10.5% in the same month of
the previous year.[3 ] In terms of nutrition, the cost of the basic food basket increased by 8.8% in 2020,
as reported by DANE.[4 ] The COVID-19 quarantine potentially imposed barriers to food availability and access,
which could have multifaceted effects on society and individuals, leading to changes
in family dietary consumption, production and distribution of raw materials, availability,
access to markets, squares, stores, online purchases, and food choices, including
processed and ready-to-eat products or shifts in the consumption of fresh foods such
as vegetables, fruits, meats, cereals, and grains, as a result of the confinement's
impact.[1 ] This situation might have altered feeding practices, resulting in states of undernutrition
or excess weight due to the ease or difficulty in sourcing different foods, influenced
by the consequences of lockdown.[1 ] The current study aims to explore feeding practices and the associated determining
factors among individuals younger than 18 years during the COVID-19 quarantine period
in Colombia.
Materials and Methods
An observational, descriptive, cross-sectional study was conducted using a survey
aimed at families across Colombia. To determine the required sample size, the Colombian
population census was utilized, which reported a total of 15,454,633 individuals younger
than 18 years.[5 ] A margin of error of 4% and a confidence level of 99% were established, resulting
in a sample size of 1,026. Given that participant selection occurred during the study
months, a nonprobabilistic convenience sampling approach was employed. This method
ensured the efficiency and feasibility of participant recruitment within the context
of the COVID-19 pandemic.
Survey
A survey was designed by the researchers, taking into consideration information extracted
from the literature. A standardized data collection instrument was constructed using
Google Forms. The survey was electronically distributed through open invitations on
social media during the months of March and April 2020. It was intended to be self-administered
by the head of the household (parent/guardian) during the mandatory confinement period.
Participation was voluntary, and participants provided informed consent before proceeding.
The survey consisted of a combination of open-ended and closed-ended questions. Families
residing outside of Colombia, participants younger than 18 years, and families without
children younger than 18 years were excluded. The survey began with an informed consent
section, followed by an introductory paragraph explaining the primary objective of
the study. The subsequent sections contained questions pertaining to the variables
of interest. Each question allowed for multiple-choice responses. The study questions
encompassed demographic characteristics, age of the child (in cases where families
had multiple children younger than 18 years, they were requested to select the youngest
for data completion and assessment), meal-related aspects (breakfast, lunch, dinner,
snacks), food intake for each mealtime, weekly frequency of food consumption within
food groups (meats, eggs, dairy, vegetables, fruits, cereals and flours, legumes,
sugars, fats, beverages), with the option to include any additional foods not covered
in the list. Notably, the survey omitted precise weight or volume measurements of
foods to mitigate respondent fatigue during confinement. Furthermore, a section assessed
whether participants perceived changes in dietary habits compared with the prequarantine
period. If affirmative, the reasons justifying these changes were analyzed. Finally,
questions regarding the health status of the child younger than 18 years during the
specified period were included. Open responses regarding food groups were categorized
by an expert coauthor in nutrition and grouped for analysis.
Statistical Analysis
The data from each questionnaire were tabulated in an Excel database for subsequent
analysis using the statistical program IBM SPSS version 26. The analysis was conducted
in three steps: the first step involved descriptive statistics. The second step comprised
a multiple correspondence analysis, which included all types of foods for breakfast,
lunch, dinner, and beverages. Selection criteria were applied: (1) foods chosen by
more than 10% of the sample and (2) foods with a discrimination coefficient greater
than 0.10. These criteria were used to construct indices that estimate a summarized
rate of food consumption for each main meal. The breakfast index consisted of six
foods (flours, eggs, dairy, fruit, processed meats, and juices). The lunch index included
flours, meats (including chicken, beef, or pork), fish, cooked vegetables, raw vegetables,
legumes, and healthy fats. The dinner index was constructed using seven food groups:
fish, meats (chicken, beef, or pork), cooked vegetables, raw vegetables, legumes,
grains, and healthy fats. To aid interpretation, these scores were rescaled into an
index from 0 to 100, where 100 represented a higher score on the index. Specific foods
for each age group (such as breast milk and formula) with consumption frequency below
10% of the sample were excluded from these index estimations. For the analysis of
respondents who perceived changes in their diet, a series of Pearson's chi-square
tests were performed, with results having a p -value of less than 0.05 considered statistically significant. For variables such
as socioeconomic level and age group, post hoc tests were conducted for pairwise comparisons
of group averages, using Bonferroni correction. In the third and final steps, to examine
the relationship between the breakfast, lunch, and dinner indices and the reasons
cited for dietary changes during quarantine, a generalized linear model was employed
with each index as a dependent variable. This analysis was conducted exclusively with
participants who confirmed changes in their diet during quarantine. An initial null
model without independent variables was estimated to assess comparison parameters,
followed by a model including variables of interest. In the final model, the variables
added were socioeconomic level, age in years, and the six reasons for dietary changes
during quarantine (model with predictors). The relative reduction of goodness-of-fit
criteria, such as corrected Akaike information criterion (AIC), Bayesian information
criterion, and consistent AIC, between the null model and the model with predictors,
was calculated as a percentage. Reductions in these parameters indicate a well-fitting
model. Finally, the regression coefficients of each independent variable were evaluated
using a statistical significance test.
Ethical Considerations
Respondents willingly participated in the survey, and this study was classified as
research without risk in accordance with Colombian Resolution 8430/1993. Data collection
was conducted anonymously. The research was presented to and approved by the scientific
committee board of Gastronutriped. This research adheres to international guidelines
concerning recommendations for research involving human subjects as outlined in the
Declaration of Helsinki.
Results
The final sample consisted of 1,419 participants from Colombia, with the majority
residing in the city of Bogotá (49.2%) and the remainder from various other parts
of the country. The socioeconomic distribution was as follows: 28.1% had a low socioeconomic
status (strata 1 and 2), 49.3% had a medium socioeconomic status (strata 3 and 4),
and 11.5% had a high socioeconomic status (strata 5 and 6). In terms of the age of
the surveyed child, the distribution was as follows: infants younger than 12 months
accounted for 6.1%, infants aged 12 to 24 months accounted for 7.5%, preschoolers
aged 2 to 5 years accounted for 30.8%, school-aged children aged 6 to 9 years accounted
for 23.4%, teenagers aged 10 to 18 years accounted for 30.8%, and 1.3% did not respond.
The majority of respondents indicated that their child did not have a diagnosed illness
at the time of completing the survey (80.1%), and 80.6% reported adhering to regular
meal schedules. Regarding changes in the health of children younger than 18 years
during the initial phase of the COVID-19 quarantine, the following alterations were
reported by parents: sleep disturbances were noted in 884 cases (62.3%), an increased
need for groceries due to higher consumption was observed in 118 cases (8.3%), changes
in schedules were reported by 97 cases (6.8%), alterations in eating behavior (selective
eating, rejecting foods, or loss of appetite) were identified in 86 cases (6.1%),
and other changes were reported by 234 cases (16.5%).
Feeding Practices
During breakfast, the most common foods are flours, cereals, eggs, dairy, and beverages
(>60%). Breast milk was offered most frequently (61%) to infants younger than 12 months,
followed by fruits (51%) and flours/cereals (43%). For infants aged 12 to 24 months,
the most common foods were flours and cereals (81%), eggs (77%), and fruits (68%).
As children grow older, hot beverages and juices become more common than in infants
([Table 1 ]). At lunchtime, the most common foods are flours and cereals (95%), meats (93%),
cooked vegetables (59%), and legumes and grains (66%). Regarding raw vegetables, their
consumption increases significantly starting at 12 months of age (infants younger
than 12 months = 8%, other ages = around 30%). In the school-aged and adolescent groups,
flours, cereals, and meats were the most consumed foods. Only 21 participants across
the entire sample (1.4%) reported offering any kind of beverage during this meal.
Finally, for dinner, flours/cereals (88%) and meats (97%) are the most frequently
provided foods. On the other hand, the consumption of legumes, grains, and vegetables
(cooked and raw) is lower during dinner compared with lunch (19 and 66%, respectively)
([Table 1 ]). For infants younger than 12 months, some type of meat is offered (60%), while
foods such as soups (32%) and flours/cereals (36%) were observed to a lesser extent
during dinner. Only 41 caregivers mentioned offering a beverage during dinner, with
juice and chocolate being more common among adolescents than among preschool and school-aged
children.
Table 1
Foods offered to children younger than 18 years in Colombia during the initial phase
of COVID-19 quarantine: Breakfast, lunch, dinner, and beverages Breakfast: proportion
of responses
Foods
All (N = 1,419)
Infant younger (n = 87)
Infant older (n = 107)
Preschooler (n = 437)
School-aged (332)
Adolescent (437)
Cereals and grains
0.89
0.43
0.81
0.90
0.93
0.95
Eggs
0.86
0.40
0.77
0.89
0.92
0.90
Dairy products
0.74
0.17
0.58
0.82
0.79
0.80
Beverages
0.60
0.22
0.51
0.62
0.62
0.68
Fruits
0.58
0.51
0.68
0.65
0.56
0.53
Cold cuts
0.39
0
0.15
0.43
0.45
0.46
Soups or broth
0.27
0.13
0.25
0.27
0.25
0.32
Unhealthy fats
0.14
0.00
0.08
0.14
0.18
0.16
Sweets
0.10
0.00
0.07
0.12
0.12
0.09
Plant-based beverages
0.04
0
0.08
0.04
0.02
0.03
Meats
0.01
*
0.03
*
0
*
Vegetables
0.01
*
0.05
0
0
*
Others
0.03
*
0.07
0.04
0.02
0.02
Note: The numbers represent proportions; multiply by 100 to obtain percentages. The
0 indicates that no participants in the age group offered the food. The asterisk (*)
represents proportions less than 0.01. Additionally, the table do not add up to 100%
because participants could choose more than one reason, and they were not mutually
exclusive. Infant younger: younger than 12 months; infant older: younger than 24 months;
preschooler: 2 to 5 years; school-aged: 5 to 12 years; adolescent: older than 12 years.
Lunch: proportion of responses
Foods
All (N = 1,419)
Infant younger (n = 87)
Infant older (n = 107)
Preschooler (n = 437)
School-aged (332)
Adolescent (437)
Cereals and grains
0.95
0.54
0.94
0.98
0.99
0.99
Meats1
0.93
0.58
0.90
0.98
0.96
0.96
Fish
0.56
0.31
0.49
0.65
0.53
0.55
Cooked vegetables
0.59
0.44
0.70
0.67
0.55
0.56
Raw vegetables
0.35
0.08
0.33
0.35
0.35
0.39
Legumes and grains
0.66
0.32
0.69
0.66
0.68
0.69
Healthy fats
0.52
0.33
0.56
0.53
0.53
0.55
Soup
0.50
0.48
0.65
0.57
0.48
0.41
Breast milk
0.02
0.20
0.03
0
0
0
Beverages
0.01
0.03
0.04
0.02
*
*
Infant formula
*
0.06
0
0
0
0
Others
0.09
*
0
0.02
0
*
Notes: 1 does not include fish. The numbers represent proportions; multiply by 100 to obtain
percentages. The 0 indicates that no participants in the age group offer the food.
The asterisk (*) represents proportions less than 0.01. Additionally, the table do
not add up to 100% because participants could choose more than one reason and they
were not mutually exclusive.
Dinner: proportion of responses
Foods
All (N = 1,419)
Infant younger (n = 87)
Infant older (n = 107)
Preschooler (n = 437)
School-aged (332)
Adolescent (437)
Cereals and grains
0.88
0.36
0.82
0.92
0.94
0.92
Meat1
0.97
0.60
0.96
0.99
1.00
1.00
Fish
0.17
0.16
0.27
0.22
0.14
0.12
Cooked vegetables
0.22
0.25
0.39
0.26
0.17
0.18
Raw vegetables
0.15
0.15
0.27
0.18
0.13
0.12
Eggs
0.34
0.05
0.31
0.38
0.36
0.35
Soup
0.29
0.32
0.49
0.36
0.24
0.21
Dairy
0.22
0.02
0.14
0.23
0.29
0.22
Cold cuts/deli meats
0.22
0.01
0.08
0.25
0.25
0.25
Legumes and grains
0.19
0.14
0.22
0.20
0.17
0.19
Healthy fats
0.13
0.10
0.29
0.13
0.11
0.09
Beverages
0.03
0.01
0.03
0.03
0.02
0.04
Note: 1 includes fish. The numbers represent proportions; multiply by 100 to obtain percentages.
0 indicates that no participants in the age group offer the food. The asterisk (*)
represents proportions less than 0.01. Additionally, the table do not sum up to 100%
because participants could choose more than one reason, and they were not mutually
exclusive.
Beverages for breakfast and dinner: proportion of responses Breakfast
Beverage—breakfast
Age group
Socioeconomic level
All (N = 1419)
Infant younger (n = 87)
Infant older (n = 107)
Preschooler (n = 437)
School-aged (332)
Adolescent (437)
Low (n = 399)
Middle (n = 700)
High (n = 163)
Breast milk
0.08
0.61
0.36
0.05
0.01
0.00
0.08
0.09
0.07
Infant formula
0.07
0.32
0.26
0.09
0.00
0.00
0.03
0.08
0.07
Juice
0.32
0.08
0.23
0.38
0.29
0.36
0.26
0.35
0.37
Water
0.10
0.14
0.24
0.14
0.05
0.05
0.04
0.12
0.15
Hot beverage
0.32
0.02
0.12
0.23
0.39
0.45
0.41
0.31
0.15
Breast milk
0.08
0.61
0.36
0.05
0.01
0.00
0.08
0.09
0.07
Dinner
Beverage—dinner
All (N = 41)
Age group
Socioeconomic level
Preschooler and school-aged (n = 18)
Adolescent (n = 19)
Low (n = 9)
Middle (n = 25)
High (n = 4)
Coffee
0.54
0.50
0.53
0.44
0.60
0.50
Juice
0.17
0.11
0.26
0.22
0.16
0
Hot chocolate
0.15
0.11
0.21
0.11
0.12
0.25
Hot beverages or panela water
0.24
0.28
0.26
0.33
0.28
0
Water
0
0
0
0
0
0.25
Notes: The numbers represent proportions; multiply by 100 to obtain percentages. 0
indicates that no participants in the age group/socioeconomic level offer the food
item. The asterisk (*) represents proportions less than 0.01.
Reasons for Changes in Diet
During the quarantine, 654 participants (46%) reported that their children's diet
did change. Among this group, the most common reasons cited were lack of money (34%),
portion restriction to make food last longer (32%), difficulty in shopping (32%),
and perception of a lack of variety in the market (29%). Statistically significant
differences were observed by age group and socioeconomic level. The reason “portion
restriction to make food last longer” was more common among infants younger than 12
months and adolescents, while “lack of money” and “portion restriction to make food
last longer” were more frequently cited in lower socioeconomic levels ([Table 2 ]).
Table 2
Reasons for changes in feeding practices in children younger than 18 years in Colombia
during the first phase of COVID-19 quarantine
Reason
All (N = 654)
Age group
Socioeconomic level
Infant younger (n = 33)
Infant older (n = 55)
Preschooler (n = 188)
School-aged (n = 170)
Adolescent (242)
Low (n = 267)
Medium (n = 312)
High (n = 45)
Lack of money
0.34
0.46
0.27
0.28
0.36
0.36
0.57
0.21
0.07
χ2 (4, 688) = 6.84, p = 0.14
χ2 (2, 624) = 99.71,
p
= < 0.001
Lack of variety when shopping
0.29
0.27
0.4
0.30
0.29
0.26
0.27
0.32
0.18
χ2 (4, 688) = 4.10, p = 0.39
χ2 (2, 624) = 4.70, p = 0.09
Difficulty in going shopping
0.32
0.52
0.29
0.30
0.34
0.29
0.30
0.36
0.20
χ2 (4, 688) = 7.87, p = 0.10
χ2 (2, 624) = 4.70, p = 0.09
Difficulty in cooking/we do not like cooking
0.06
0.09
0.04
0.10
0.04
0.04
0
0.09
8.9
–
–
We try to restrict portions to make our food last
0.32
0 .45
0.2
0.26
0.27
0.43
0.46
0.26
0.11
χ2 (4, 688) = 24.66,
p
= < 0.001
χ2 (2, 624) = 35.99,
p
< 0.001
Fear of leaving the house
0.20
0.30
0.20
0.17
0.20
0.20
0.20
0.21
0.20
χ2 (4, 688) = 3.24, p = 0.52
χ2 (2, 624) = 0.17, p = 0.92
Notes: The table shows the distribution of responses, and below each row is a nonparametric
chi-square test to estimate differences by age and socioeconomic level. Due to the
low proportion of responses for the “difficulty in cooking/we do not like cooking”
reason (less than 5% in some cells), it was not possible to estimate differences by
age and socioeconomic level using a nonparametric chi-square test. The survey allowed
marking more than two options, which is why the percentages do not add up to 100%.
Relationship between Dietary Practices and Reasons for Dietary Changes during the
Pandemic
Lack of variety in shopping significantly predicted all three indices (breakfast,
lunch, and dinner). For the breakfast index (p = 0.037), the lack of variety in shopping and its interactions with socioeconomic
level/age were statistically significant ([Fig. 1 ] and [Table 3 ]). The lack of variety in shopping seems to have a greater impact on higher socioeconomic
levels compared with middle and lower levels. For the lunch index, the lack of variety
also showed differences (p = 0.037), as it was positively associated with the index. This suggests that those
who mentioned this reason had higher scores compared with those who did not cite it
as an explanation for dietary changes. Finally, the lack of variety in shopping significantly
predicted the dinner index (p = 0.001).
Table 3
General linear model for predicting food indices and interaction terms during the
COVID-19 quarantine in Colombia in families with children younger than 18 years
Breakfast index and interaction terms
Predictor variable
B
95% Wald confidence interval
Statistical significance test
Lower 3
Upper 3
p -Value 2
(Intercept)
39.48
12.06
66.90
0.005
Socioeconomic level
16.87
3.73
30.01
0.012
Age (y)
−0.60
−2.19
1.00
0.462
Lack of money
−7.99
−20.78
4.79
0.220
Lack of variety when shopping
15.27
0.90
29.64
0.037
Socioeconomic level and lack of money
2.28
−4.88
9.44
0.533
Socioeconomic level and lack of variety
−9.52
−16.49
−2.54
0.008
Age and lack of money
0.07
0-67
0.82
0.849
Age and lack of variety
0.88
0.04
1.72
0.039
Lunch index and interaction terms
Predictor variable
B
95% Wald confidence interval
Statistical significance test
Lower
Upper
p
-Value
(Intercept)
39.480
12.062
66.898
0.005
Socioeconomic level
16.871
3.733
30.008
0.012
Age of child/children being responded about (y)
−0.598
−2.193
0.997
0.462
Lack of money
−7.993
−20.779
4.793
0.220
Lack of variety when shopping
15.272
0.903
29.641
0.037
Socioeconomic level and lack of money
2.277
−4.882
9.437
0.533
Socioeconomic level and lack of variety when shopping
−9.515
−16.494
−2.535
0.008
Age of child/children being responded about (y) and lack of money
0.072
−0.674
0.819
0.849
Age of child/children being responded about (y) and lack of variety when shopping
0.882
0.044
1.721
0.039
Dinner index and interaction terms
Predictor variable
B
95% Wald confidence interval
Statistical significance test
Lower
Upper
p
-Value
(Intercept)
11.971
−3.231
27.174
0.123
Socioeconomic level 1
−6.958
−14.636
0.719
0.076
Socioeconomic level 2
0.474
−6.797
7.744
0.898
Socioeconomic level 3
0a
Age of child/children being responded about (y)
−0.350
−0.712
0.012
0.058
Lack of money
0.885
−3.326
5.096
0.680
Lack of variety when shopping
6.793
2.608
10.978
0.001
Difficulty in going shopping
3.144
−0.899
7.187
0.127
Difficulty in cooking/we do not like cooking
−1.966
−10.363
6.431
0.646
We try to restrict portions to make our food last
−1.405
−5.491
2.681
0.500
Fear of leaving the house
1.338
−3.348
6.023
0.576
Model: (Intercept), socioeconomic level, age of child/children being responded about
(years), lack of money, lack of variety when shopping, difficulty in going shopping,
difficulty in cooking/we do not like cooking, we try to restrict portions to make
our food last, fear of leaving the house.
Note: In order to facilitate the estimation and interpretation of interaction terms,
socioeconomic level was included as a continuous variable in this model.
Fig. 1 Perception of lack of variety when shopping, socioeconomic level, and breakfast and
lunch intake in children younger than 18 years in Colombia during the initial phase
of the COVID-19 quarantine. Note: The graph displays the expected scores on the breakfast
index (A) and lunch index (B) among those who reported perceiving a lack of variety
when shopping and those who did not, differentiated by socioeconomic level. Scores
are calculated based on the calculation obtained using the coefficients from the linear
regression model, including the intercept and corresponding interaction terms.
Socioeconomic Level
The results presented in [Table 3 ] suggest that for lower socioeconomic levels, the breakfast index (p = 0.012) and lunch index (p = 0.012) are lower compared with higher levels, and these differences are statistically
significant. However, no differences were found regarding the dinner index and socioeconomic
level.
Discussion
Upon evaluating dietary practices in Colombia during the initial COVID-19 lockdown
period, it was found that nearly half of Colombian families experienced changes in
their eating habits. These changes were related to limited variety in grocery shopping
and socioeconomic status, likely influenced by government directives to restrict market
outings and purchases. The World Health Organization (WHO) implemented various measures
to control COVID-19 transmission, including public transportation suspension, restricted
community access, and space closures. More than 40 countries and regions, including
Italy, some parts of the United States, and Latin America, implemented lockdown and
shelter-in-place measures similar to China's early 2020 approach.[1 ]
Although most supermarkets and grocery stores remained open during the isolation period,
concerns arose regarding the supply and safe access to adequate food.[6 ]
[7 ] Foods contain essential nutrients and important phytochemicals that support biological
functions, exert protective and complementary effects to prevent and treat diseases,
including infections.[8 ] Significant dietary changes occur during the first year of life, and dietary patterns
solidify by the age of 2 years. Survey results revealed that infants younger than
2 years primarily consumed flour and cereals, followed by proteins, which contributed
important nutrients, vitamins, and trace elements crucial for growth and development
at this age. This critical period provides an opportunity for parents, caregivers,
and health care personnel to establish lifelong healthy eating habits.[9 ]
[10 ]
[11 ] According to the Food and Agriculture Organization (FAO), departments such as La
Guajira, Boyacá, and Nariño display elevated rates of malnutrition, deficient protein
intake, and excessive consumption of saturated fats.[12 ] Analyzing the survey, about 50% lived in Bogotá with a medium socioeconomic status
(strata 3 and 4), possibly explaining the frequent consumption of various food groups.
Most respondents did not have a diagnosed illness at the time of the survey and adhered
to meal schedules. The World Food Programme designed diverse strategies to enhance
nutrition, including school feeding programs, food security networks, technological
innovations, and a focus on the first thousand days of life.[12 ] A Latin American survey of older individuals during the initial phase of the COVID-19
pandemic concluded that women exhibited healthier dietary habits than men, with greater
contributions of fruits and vegetables. However, there was an observable increase
in obesity among women, potentially linked to increased intake.[13 ] COVID-19 continues to devastate global economic and health indicators, including
child nutrition, due to increased maternal and infant inequality gaps. This is directly
and indirectly related to the impact of poverty and reasons for dietary changes, such
as limited variety in shopping, affecting families differently based on their socioeconomic
status.[14 ]
[15 ]
[16 ]
[17 ]
[18 ]
[19 ]
[20 ] Furthermore, access to nutritious foods appears compromised,[21 ]
[22 ] and critical sectors at risk of collapse include food systems, education, health
care services for women and children, and access to clean water and sanitation.[23 ]
[24 ]
[25 ]
[26 ] Resilient food systems are proposed during the COVID-19 pandemic, featuring innovative
context-specific supply-demand initiatives and supported food supply chains, such
as community-supported agriculture (CSAs). CSAs, which provide 80% of food consumption
in Africa and Asia, now depend on these markets, necessitating exemption from lockdowns
and taxes.[27 ]
[28 ] Identified risks include CSA closures, restaurant shutdowns, urban food system disruptions,
unemployment, reduced income for farmers and industry workers, production and delivery
restrictions, increased food and staple costs, reduced family income, vulnerability
to price spikes and food shortages, low agricultural productivity, and disruptions
in food import and export systems.[29 ]
[30 ] Limited access to fresh produce may lead to increased consumption of processed and
packaged, yet less nutritious, affordable foods, with adverse health consequences.[31 ]
[32 ] Various countries globally have demonstrated dietary changes, such as a Spanish
study that analyzed shifts in eating habits and lifestyles during confinement.[33 ] The study found increased consumption of fruits (27%), eggs (25.4%), legumes (22.5%),
vegetables (21%), and fish (20%), alongside reduced intake of processed meats (35.5%),
lamb or rabbit (32%), pizza (32.6%), distilled alcoholic beverages (44.2%), sugary
drinks (32.8%), and chocolate (25.8%), with age-related variations.[33 ] This indicates greater consumption of healthy foods, reduced intake of low-nutrient
foods, and increased homemade preparations. Quarantine may also condition the consumption
of low-nutrient and ultra-processed foods.[34 ] Coupled with reduced physical activity, these factors may lead to positive energy
balance, resulting in overweight or weight gain. The FAO highlighted COVID-19's disruptions
to CSAs worldwide, impacting both supply and demand. These disruptions exacerbate
inequalities, disproportionately affecting poorer families.[8 ] Following the WHO's declaration of the end of the health emergency in May 2023,
establishing policies ensuring favorable nutritional status in all individuals and
preparing for future public health emergencies is crucial. Ensuring affordable food
access for vulnerable communities is vital. The FAO, International Fund for Agricultural
Development, and World Bank have urged exporting countries to reduce taxes, prevent
trade interruptions, and ensure smooth food and agricultural input flow across borders.[5 ]
[8 ] These institutions recommend facilitating investment in agriculture to maintain
primary food production despite restrictions. Social support programs for families
with lost income or limited food purchasing capability are necessary. Promoting optimal
nutrition through increased consumption of high-nutrient foods, addressing comorbidity
risks, promoting local agriculture, reinforcing food safety policies, nutritional
counseling, breastfeeding promotion, and campaigns for high-nutrient, low-cost food
consumption against COVID-19 and diet-related diseases are essential strategies.[13 ]
[14 ]
[15 ] Public nutrition investment, with government support for small-scale farmers, is
crucial to guarantee basic food availability, reduce vegetable and fruit costs, and
manage healthy and unhealthy food taxation and regulation.[13 ]
[22 ] Finally, sponsoring and strengthening CSAs would aid populations at risk of food
scarcity, requiring public social programs to support the vulnerable, marginalized
households, rural communities, and underserved neighborhoods through community health
worker interventions.[28 ] Limitations of the present study include the absence of prequarantine period characterization
for dietary change comparison. Additionally, the assessment of reasons behind dietary
changes relied solely on caregiver responses. Finally, quantitative consumption frequency
questionnaires and portion size parameters (sizes, grams, or milliliters) were not
used. Qualitative food element inquiries were preferred due to the potential impact
of a more extensive survey on response rates.
Conclusion
This study delineates dietary practices during the COVID-19 pandemic in Colombian
families. It was observed that nearly half of the families experienced alterations
in their eating habits, which were linked to socioeconomic status and limited variety
in grocery shopping. This reality underscores the necessity for well-defined strategies
ensuring food availability during times of disaster and the implementation of a nutritional
education system for the population to effectively navigate risk situations.
