Households with children under 5 years and older adults, and probability of expenditure on Food Away from Home (FAFH) in Peru in 2021: A Cross-Sectional Study

Michelle Lozada-Urbano; Aldo Alvarez-Risco; Amalia Flores-Montero; Juana Corzo-Ponte; Franklin Huamán; Joaquin Aguirre-Sosa; Rosa Maria Benavente Ayquipa; Oriana Rivera-Lozada; Jaime A. Yáñez

doi:10.12688/f1000research.140523.1

Home Browse Households with children under 5 years and older adults, and probability...

ALL Metrics

Views

Downloads

Get PDF

Get XML

Export

▬

✚

Research Article

Households with children under 5 years and older adults, and probability of expenditure on Food Away from Home (FAFH) in Peru in 2021: A Cross-Sectional Study

[version 1; peer review: 1 approved with reservations]

Michelle Lozada-Urbano ¹, Aldo Alvarez-Risco², Amalia Flores-Montero³, [...] Juana Corzo-Ponte³, Franklin Huamán⁴, Joaquin Aguirre-Sosa⁵, Rosa Maria Benavente Ayquipa⁶, Oriana Rivera-Lozada⁷, Jaime A. Yáñez⁸

Michelle Lozada-Urbano ¹, Aldo Alvarez-Risco², [...] Amalia Flores-Montero³, Juana Corzo-Ponte³, Franklin Huamán⁴, Joaquin Aguirre-Sosa⁵, Rosa Maria Benavente Ayquipa⁶, Oriana Rivera-Lozada⁷, Jaime A. Yáñez⁸

PUBLISHED 19 Dec 2023

Author details Author details

¹ South American Center for Education and Research in Public Health, Universidad Norbert Wiener, Lima District, Lima Region, 15046, Peru
² Facultad de Administración y Negocios, Universidad Tecnológica del Perú, Lima, Peru
³ Escuela de Nutrición y Dietética, Universidad Norbert Wiener, Lima District, Lima Region, Peru
⁴ Facultad de Ingeniería Económica, Estadística y Ciencias Sociales, Universidad de Ingeniería, Lima, Peru
⁵ Faculty of Business, School of Management, Universidad Privada del Norte, San Juan de Lurigancho 15434, Lima, Peru
⁶ Facultad de Educación, Escuela Profesional de Educación Inicial, Universidad Nacional Mayor de San Marcos, Lima, Peru
⁷ Facultad de Educación, Universidad Nacional Mayor de San Marcos, Lima, Peru
⁸ Facultad de Educación, Carrera de Educación y Gestión del Aprendizaje, Universidad Peruana de Ciencias Aplicadas, Lima, 15023, Peru

Michelle Lozada-Urbano
Roles: Conceptualization, Formal Analysis, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing

Aldo Alvarez-Risco
Roles: Funding Acquisition, Investigation, Writing – Review & Editing

Amalia Flores-Montero
Roles: Conceptualization, Investigation, Writing – Original Draft Preparation

Juana Corzo-Ponte
Roles: Conceptualization, Investigation, Writing – Original Draft Preparation

Franklin Huamán
Roles: Conceptualization, Formal Analysis, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing

Joaquin Aguirre-Sosa
Roles: Supervision, Writing – Original Draft Preparation, Writing – Review & Editing

Rosa Maria Benavente Ayquipa
Roles: Supervision, Writing – Original Draft Preparation

Oriana Rivera-Lozada
Roles: Funding Acquisition, Resources, Supervision, Writing – Review & Editing

Jaime A. Yáñez
Roles: Methodology, Software, Writing – Original Draft Preparation, Writing – Review & Editing

OPEN PEER REVIEW

REVIEWER STATUS

This article is included in the Global Public Health gateway.

Abstract

For the CFAFH study, it is necessary to analyze the economic variable of families and the way it is distributed, since it allows a broader perspective of what a household invests in food consumption and how it is associated with health. The aim of this research was to determine the probability of expenditure on households that consume food away from home, with the presence of children under five years (Ch<5y) and households with the presence of older adults (HOA). A cross-sectional, descriptive, and quantitative study was carried out based on the grouping and analysis of data obtained from the Peruvian National Household Survey (ENAHO) 2021 database. After joining the database, the household type variables were created. Households with and without children under five years, and households with and without older adults were selected, as well as other variables such as the area of origin, gender of the head of the household, ethnicity, education level, marital status, age of the head of the household, occupation of the head of the household, and poverty. Descriptive statistics of the expenses made according to the variables studied and on the effect of each variable on the probability of purchase was obtained a through the application of the Logit model. These results warrant that it is necessary to generate awareness in the population about the food that is sold and consumed away from home for an appropriate selection of healthy options.

Keywords

Food, Households, children, Older adult, ENAHO, Food away from home, expenditure

Corresponding author: Michelle Lozada-Urbano

Competing interests: No competing interests were disclosed.

Grant information: The author(s) declared that no grants were involved in supporting this work.

Copyright: © 2023 Lozada-Urbano M et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to cite: Lozada-Urbano M, Alvarez-Risco A, Flores-Montero A et al. Households with children under 5 years and older adults, and probability of expenditure on Food Away from Home (FAFH) in Peru in 2021: A Cross-Sectional Study [version 1; peer review: 1 approved with reservations]. F1000Research 2023, 12:1602 (https://doi.org/10.12688/f1000research.140523.1) First published: 19 Dec 2023, 12:1602 (https://doi.org/10.12688/f1000research.140523.1) Latest published: 24 Jul 2024, 12:1602 (https://doi.org/10.12688/f1000research.140523.2)

Introduction

Food consumption patterns have fluctuated because of changes in the access to processed foods, presence of genetically modified organisms (GMO),¹ urbanization and variations in income.²^,³ Despite the preferential consumption of processed foods and the recent alarming increase in overweight and obesity,⁴^–¹⁰ consumers are seeking functional foods because of their bioactive compound content,¹¹^–¹⁶ and with less pesticides,¹⁷ and that promote sustainability.¹⁸ On the other hand, there has been a reported increase of households that consume food away from home in part due to the increased offer of restaurants and food delivery services.³^,¹⁹^,²⁰ Eating in restaurants has been linked with an increase in overweight children,²¹ and overweight and obese adults.²²^,²³

It is estimated that in the world there are more than 41 million children under 5 years of age (Ch<5y) who are overweight or obese, situations that predispose them to health problems.²⁴ In Peru, according to the demographic and family health survey (ENDES) of 2021, it was estimated that 9.6% of children under five years of age are overweight and obese.²⁵ Likewise, 24.8% of older adults are overweight and 16.8% obese, figures that are higher than those of the previous decade.²⁵ Overweight and obese children tend to continue to suffer from these conditions in adulthood and are more likely to suffer from non-communicable diseases such as diabetes, hypertension, heart disease, among others, at an increasingly younger age.²⁶^–³⁰ This has been linked with a higher incidence of overweight and obese children and adults.²¹^–²³

Consumption of food away from home (CFAFH) is a common eating habit and it has been reported that 42% of families in Peru do it and rank second in Latin America. As a result of globalization, fast food chains are a common option to purchase food away from home, which have been growing and improving the menu repertoire. In most cases, they offer food with low nutritional value, low in protein, vitamins, minerals, fiber and with high content of carbohydrates, saturated fats, cholesterol, showing a positive association with overweight,³¹^–³³ obesity,³⁴^–³⁶ hypertension,³⁷^–³⁹ diabetes,⁴⁰^–⁴² cardiovascular diseases⁴³^–⁴⁵ and metabolic syndrome in adults⁴⁶^–⁴⁸ and children.⁴⁹^–⁵¹ Furthermore, it has been reported that when dinner is consumed away from home, it is positively correlated with high body mass index (BMI).⁵²^,⁵³

The amount and frequency of consumption of food away from home (CFAFH) depends on factors such as knowledge, beliefs, advertising and social acceptance of products, eating habits, and the purchasing power of households.⁵⁴^,⁵⁵ Other factors such as the consumption patterns continuously fluctuate because of changes in food choice.¹^,²^,¹⁸^,⁵⁶^,⁵⁷ On the other hand, there has been an increase in food delivery apps that have increased the consumption of food away from home (CFAFH)¹⁹^,²⁰ that are typically characterized for their poor nutritional quality.⁵⁸^–⁶⁰

Children under 5 years old constitute a vulnerable population and are in a crucial stage of development, in which good nutrition and correct eating habits are imperative. In this sense, older adults are also affected by changes in diet and CFAFH because they need diet with adequate quantity and quality of nutrients. On the other hand, it is important to consider the socioeconomic level of the families, since the purchase of food away from home depends on the net economic income. This information will allow to observe the behavior of the families according to the variables studied.

The purpose of this study was to assess what are the characteristics of households that could have the probability of purchasing food away from home with the presence of children under five years of age (Ch<5y) and households with the presence of older adults (HOA). Our working hypothesis is that the expenditure of households with Ch<5y and households with the presence of older adults (HOA) will present at least one variable related to the probability of purchasing or not purchasing food away from home.

Literature review

The sustained growth of the Gross Domestic Product (GDP) in Peru between 1990 and 2018 was on average 4.90% per year, causing poverty to be reduced to 33.50% and the population in extreme poverty to 21.4%, and the Human Development Index (HDI) to improve from 0.61 to 0.75.⁶¹ Poverty is measured according to the people’s spending, so they will spend on goods or services that increase their well-being. The Poverty and Extreme Poverty Line has the cut-off point with the cost of the minimum basket that exclusively includes food, which was calculated for a person at 183 Peruvian Soles (19.28 US Dollars) per month and at 732 Peruvian Soles (193.29 US Dollars) per month for a family of 4 members in 2017.⁶²

Per capita spending on food in Peru in 2021 reached 40%, with 30.1% destined for food consumed at home (226 Peruvian Soles or 59.68 US Dollars) and 9.7% for food consumed away from home (73 Peruvian Soles or 19.28 US Dollars), thus implying that 40% of the expenses of Peruvians goes only to food. In the year 2022, the percentage has increased to 42.5% (INEI); however, the segments with less income, such as C, D and E spend approximately 45% of their salaries.⁶³ It is important to consider that inflation is also related to consumption. In February 2022, Peru had a reported inflation of 6.15% that increased to 8.09% on May and it continues to increase.⁶⁴ In households with lower incomes, there is a greater destination of money to food, being these households more sensitive to changes in the price of food.⁶⁴

It has been reported that as the income increases there is an increase in the consumption of processed food with high amount of saturated fats,⁶⁵^,⁶⁶ which correlates with the less time to prepare food at home because of increasing age and increasing responsibilities at the income increases.⁶⁶^,⁶⁷ When there is less time to prepare food at home there is an increase in CFAH because of the quick access to food.⁵⁶^,⁶⁸^,⁶⁹ During the pandemic, various countries provided financial support to the population, which increased income allowing for a higher food purchasing power.⁷⁰ Food consumption patterns are directly linked with income based on the General Theory of Employment, Interest and Money.⁷¹ In this sense, the variability of local food prices is greater compared to foreign foods, which can mean that the population in cities far away from the capital or a major airport or from important roads do not cover their nutritional requirements. This has been observed in the city of Junin, that is located in the central highlands in Peru, and where 27.3% of the population do not cover their nutritional requirements with the food they have locally available.⁷² According to Corzo and Flores,⁷³ in a study using the ENAHO database from 2017, households with children under five years of age and in households with older adults, the expense of consuming food away from home represents 8.29% and 6.83% of income, respectively. In another study published by our research group, it was observed that extremely poor households spend 4.14% or their income, non-extremely poor 6.45% and non-poor 7.20% based on the ENAHO database from 2019.³

Methods

Ethics

This research work took a free access database (secondary data). No names or documentation of the people are shown. This study had the approval of the Ethics Committee of the Norbert Wiener Private University (Registration No. 222-2020).

Database and construction of variables

A quantitative, cross-sectional, descriptive, and quantitative study was conducted using secondary source analysis from the National Household Survey (ENAHO) 2021 database, compiled by the National Institute of Statistics and Information (Instituto Nacional de Estadística e Informática - INEI).⁷⁴ The survey was conducted at national level, in urban and rural areas in the 24 departments of Peru and in the Constitutional Province of Callao. The sampling was probabilistic of areas, stratified, multi-stage and independent in each department. The survey is freely accessible and is divided into five modules: education, employment and income, income from household work, characteristics of household members, and consumption of food away from home (CFAFH).

The population was composed of families in Peru, the sample was defined as the families in the urban and rural areas of Peru with the presence of children under five years of age (Ch<5y) and households with the presence of older adults (HOA). The total population corresponded to 34,584 households and after applying the expansion factor, we obtained 9,903,824 households. The processing of the National Household Survey (ENAHO) includes the use of an expansion factor, which is applied to each record and multiplied by all the data included in the record.⁷⁴ The expansion factors are adjusted considering the population projections, including age and gender groups for each survey month and inference levels that are established with the sample design.

The inclusion criterion was that the household reported paying and consuming food away from home and met the characteristics of a household with the presence of children under five years of age and older adults. The independent variable was households with the presence of children under five years of age and older adults, and the sociodemographic characteristics of the head of household (area of origin, education, marital status, age of the head of household, gender, ethnicity, main occupation of the head of household, and poverty). The poverty variable was calculated in the ENAHO, and was divided into three groups: extremely poor, non-extremely poor, and non-poor. The dependent variable was expenditure on food consumed away from home.

Construction of indicators

The modules used for the development of this research correspond to modules 01 (characteristics of housing and household), 02 (characteristics of household members), 03 (education), 04 (health), 05 (employment) and 34 (module of calculated variables called summary) of the National Household Survey (ENAHO) 2021 database.⁷⁴ The modules were separated; it was necessary to unite them through the STATA merge command. A first selection was made of households with children under five years of age and those without children. In the same way, households with older adults were selected, and those households without the presence of older adults in their composition. Household income was used to calculate how much was spent on food consumed away from home.

The average monthly expenditure on food consumption away from home was calculated and a first selection of households with children under five years of age and households without children was made. In the same way, households with older adults and households without the presence of older adults in their composition were selected. The variables used were the education level of the head of the household, the type of dwelling, the marital status of the head of the household, the age of the head of the household, the occupation of the head of the household, the average income of the household, the household income and the household poverty level calculated by the National Institute of Statistics and Information (INEI).

Data analysis

The household, presence of children under five years of age (lower end) and older adults (higher end) and area of origin, education, marital status, age of the head of household, gender, ethnicity, main occupation of the head of household, and poverty were used as variables. The main exposure variable of quantitative nature was the household that spent on food consumption away from home to conduct calculations considering a confidence level of 95%. Then, to construct the binary variable for this study, a dummy or dichotomous variable was used for the variable “expenditure on food consumed away from home - g05hd”, where 1 implied that the household purchased food away from home, and 0 when the household did not. It should be noted that this indicator was chosen for its ease of interpretation and modeling with other variables from the same survey. Households with and without children under five years of age, and households with and without older adults were selected.

The ENAHO 2021 data structure contains the weight variable, also known as expansion factor, in addition to the variable conglomerate or primary sampling unit and the sample stratum. These variables allowed us to define the sampling design of the survey, including corrections for finite populations and specify weights; for this, we used the svyset command in the STATA program. In this sense, the average expense was estimated using sample design framework, the command “svy: mean” and the option “over” (that measures the average expense by type of variable) for the detail at the category level of average spending. In these cases, the standard deviation and confidence intervals of each estimated category of spending was calculated. To know the variables that are part of the probability of purchase, first the means were estimated using the svy command, then the Logit command was used to obtain the probability of purchase of food consumed away from home. The primary sampling unit was the expenditure between the household with and without children under five years of age and older adults to obtain a population-level estimator based on the survey sample. The Stata Statistical Software, version 16 (StataCorp LLC) was used with statistical significance at p<0.05.

Results

The data analyzed was obtained from the National Household Survey (ENAHO) 2021 version based on household records in Peru. These results include 9,903,824 families that consumed food away from home. Table 1 shows the average expenses per month, where households without children under 5 years of age spent on average 175.2 Peruvian Soles per month (approximately 46.26 US Dollars), households with children under 5 years of age spent 38.2 Peruvian Soles (approximately 10.09 US Dollars) more. In the case of households without any older adult, they spent 209.9 Peruvian Soles (approximately 55.43 US Dollars) on average per month, households with older adults spent 131.50 Peruvian Soles (approximately 34.72 US Dollars) less (Table 1).

Table 1. Participation of expenditure and per capita expenditure in food consumption away from home, in households with and without children under five years and with and without older adults, ENAHO 2021.

Over	Expenditure per household (month)				Number of households
Over	PEN (USD)	SD	[95% Conf. Interval]		Freq.	Percent
Household with children under 5 years
Total population					9,903,824	100.00
Yes	213.40 (56.35)	4.95	203.66	223.05	2,331,045	23.54
No	175.20 (46.26)	2.72	169.82	180.48	7,572,779	76.46
Difference	38.20 (10.09)
Household with older adults
Total population					9,903,824	100.00
Yes	131.50 (34.72)	3.58	124.45	138.50	3,248,792	32.80
No	209.90 (55.43)	3.12	203.74	215.98	6,655,032	67.20
Difference	78.40 (20.70)

Table 2 according to the averaging comparison analysis, the result in the group of households with children under 5 years regarding the group of households without children indicates that the averages are statistically different, also for homes with older adults with respect to the group of households without Older adults indicates that averages are statistically different.

Table 2. Mean diference.

Diff. Mean	PEN (USD)	Std. Err.	t	P>t	[95% Conf.	Interval]
Household with children under 5 years	-38.20 (10.09)	5.425	-7.040	0.000	-48.835	-27.563
Household with older adults	78.40 (20.70)	4.632	16.920	0.000	69.299	87.461

Household with children under 5 years

_subpop_1: tipo_household_children = Household_without children <= 5 años

_subpop_2: tipo_ household_children = Household with children <= 5 años

Ho: [GAFH]_subpop_1 - [GAFH]_subpop_2 = 0

Household with older adults

_subpop_1: tipo_household_older adults = Household without older adults

_subpop_2: tipo_ household_older adults = Household with older adults

Ho: [GAFH]_subpop_1 - [GAFH]_subpop_2 = 0

Table 3 shows the coefficients of the Logit model for households with Ch<5y and older adults. In households with children, the variables included in the expenditure model are the number of household members, when they increase by 1, there is a 4% higher probability of incurring in expenditure on FAFH. When the household is from a rural area, it has a 9% lower probability of incurring in expenditure on food consumed away from home. When the head of the household is a female, there is a 3% lower probability of incurring in expenditure on food consumed away from home. Likewise, if the head of the household is Aymara, there is a 12% probability of incurring in expenditure on FAFH. However, if the head of the household is a native person from the Amazon region, there is a 18% lower probability of incurring in expenditure on FAFH. If the head of the household is African Descendant, there is 11% lower probability of incurring in expenditure on FAFH. Head of households of mixed heritage have 8% lower probability of incurring in expenditure on FAFH. The educational level of the head of the household shows that there is a higher probability of incurring in expenditure on FAFH when the individual has: early education 26%, incomplete primary education 9%, complete primary education 10%, complete secondary education 5%, incomplete non-university higher education 5%. The head of the household in the 30-39 age range has 4% lower probability to spend on FAFH. Besides, when the head of the household is older than 60 years of age, there is also a 12% lower probability. When the head of the household is a dependent worker, there is a 3% lower probability of incurring in expenditure on FAFH. When the head of the household is non-extreme poor, there is a 15% probability of spending on FAFH. Non-poor heads of household have a 33% probability of spending on FAFH.

Table 3. Logit model, coefficients and standard deviations of the variable expenditure on households with children under five years of age and older adults according to selected variables.

	Households with children under 5 years of age			Households with older adults
	Beta Coefficient	SD	P>z	Beta Coefficient	SD	P>z
Number of household members	0.045	0.005	0.000	0.052	0.004	0.000
Rural area	-0.093	0.017	0.000	-0.061	0.014	0.000
Gender
Female	-0.036	0.022	0.097	-0.094	0.016	0.000
Ethnicity
Aymara	0.119	0.028	0.000	0.138	0.031	0.000
Native from Amazon region	-0.177	0.051	0.000	-0.110	0.056	0.052
African-Peruvian or African Descendant	-0.110	0.027	0.000	-0.066	0.026	0.011
White	-0.027	0.035	0.453	-0.064	0.030	0.032
Mixed heritage	-0.088	0.017	0.000	-0.087	0.016	0.000
Other	-0.034	0.037	0.350	-0.089	0.035	0.011
Do not know/No answer	-0.008	0.039	0.848	-0.034	0.028	0.223
Belonging or part of another native people	0.182	0.063	0.004	-0.440	0.057	0.000
Educational level
Early education	0.265	0.107	0.013	0.110	0.207	0.593
Incomplete primary	0.093	0.047	0.047	0.051	0.021	0.014
Complete primary	0.101	0.048	0.037	0.062	0.023	0.008
Incomplete secondary	0.061	0.049	0.209	0.046	0.029	0.116
Complete secondary	0.056	0.048	0.238	0.069	0.026	0.008
Incomplete higher non-university	0.059	0.059	0.318	0.082	0.053	0.122
Complete higher non-university	-0.002	0.053	0.970	0.054	0.034	0.108
Incomplete university	-0.053	0.064	0.411	0.121	0.048	0.011
Complete university	-0.025	0.056	0.660	-0.033	0.032	0.308
Master’s degree/doctorate	-0.013	0.081	0.873	0.044	0.057	0.436
Marital status
Married	-0.034	0.017	0.048	-0.082	0.020	0.000
Widowed	-0.005	0.040	0.901	-0.002	0.024	0.939
Divorced	-0.003	0.180	0.985	0.071	0.057	0.216
Separated	-0.008	0.026	0.748	0.085	0.023	0.000
Single	-0.032	0.043	0.460	-0.019	0.028	0.499
Age range (years)
30 to 39	-0.044	0.022	0.039	0.036	0.078	0.643
40 to 49	-0.041	0.025	0.100	0.096	0.076	0.207
50 to 59	-0.045	0.029	0.127	0.090	0.075	0.233
Over 60	-0.116	0.035	0.001	0.024	0.073	0.738
Type of work
Dependent	0.033	0.016	0.035	0.072	0.017	0.000
No information	-0.389	0.047	0.000	-0.265	0.019	0.000
Poverty
Non-extreme poor	0.153	0.031	0.000	0.084	0.032	0.009
Non-poor	0.332	0.031	0.000	0.227	0.030	0.000

In households with older adults, the variables that are part of the expenditure model are the number of household members, when they increase by 1, there is a 5% higher probability of incurring in expenditure on FAFH. When the household is from a rural area, it has a 6% lower probability of incurring in expenditure on food consumed away from home. When the head of the household is a female, there is a 9% lower probability of incurring in expenditure on food consumed away from home. Likewise, if the head of the household is Aymara, there is a 14% probability of incurring in expenditure on FAFH. If the head of the household is African Descendant, there is 11% lower probability of incurring in expenditure on FAFH. If the head of the household is White, there is a 6% lower probability of spending on FAFH. Head of households of mixed heritage have 9% lower probability of incurring in expenditure on FAFH. The educational level of the head of the household shows that there is a higher probability of incurring in expenditure on FAFH when the individual has: incomplete primary education 5%, complete primary education 6%, complete secondary education 5%, incomplete non-university higher education 7%. When the head has incomplete university higher education, there is a 12% probability of spending on FAFH. When the head of the household is married, there is a 8% lower probability of incurring in expenditure on FAFH, and when the head is separated, there is a 8% probability of spending on FAFH. When the head of the household is a dependent worker, there is a 2% probability of incurring in expenditure on FAFH. When the head of the household is non-extreme poor, there is a 8% probability of spending on FAFH. Non-poor heads of household have a 23% probability of spending on FAFH.

Discussion

This paper assesses which variables are part of the probability of expenditure or non-expenditure on FAFH. In this study, it was found that households with Ch<5y spent an average of 213.4 Peruvian Soles (approximately 56.35 US Dollars) per month in food consumed away from home, which represents 4.94% of the income. In households with older adults (HOA), the expenditure on food consumed away from home was 131.5 Peruvian Soles (approximately 34.72 US Dollars), which represents 3.58% of income. This information in comparison with the available data in ENAHO 2017⁷⁵ indicates that the expenditure for Ch<5y, for the year 2021 decreased by 3.35%, while in the HOAs, the expenditure in the year 2017 was 6.83%, which shows a decrease for the year 2021 by 3.25%.

Queiroz and Coelho⁷⁶ found that hiring domestic help decreases expenditure on FAFH, especially on lunches and dinners, and that the presence of children in the household decreases consumption of most categories of FAFH. This alternative against FAFH is possible in Peru in families in socioeconomic levels A and B mainly due to the associated salary cost for the hired person. In this study, the definition of the variables non-extreme poor and non-poor favored the probability of expenditure on FAFH. In contrast to what was found in this study, Aguila-Lopez and Kuhar⁷⁷ reported that in Mexico the household share of food expenditure away from home decreased in all income deciles and in all regions, but the impact on household participation is far from uniform. Furthermore, they highlight that an increase or decrease in income will lead to a corresponding change in expenditure on FAFH, but the change will be less than proportional.⁷⁷

Crespo et al.⁶⁸ reported that although the frequency of FAFH was similar across all levels of food security (FS), adults with high FS spent more dollars ($213.60) and a greater proportion (29.4%) of their food budget on FAFH compared to adults with marginal, low and very low FS ($133.00, $116.20, $103.30 and 21.4%, 19.7% and 20.0%, respectively). They also reported that the prevalence of obesity was higher in adults with a low (42.9%) and very low (41.5%) FS, and lower in adults with a high FS (33.7%).⁶⁸ These are similar to those reported since expenditure on FAFH is related to family income.

On average for Peru in 2021, the average expenditure on food consumed away from home reaches between 4.94 and 3.58% for households with Ch<5y and HOAs, respectively. In that sense, although a reduction in four years is notorious, there is continuity in the consumption of food bought on the street, without regulation; this could be an indication that in these homes children and older adults are not being fed with healthy food. Reuter found a positive correlation between higher rates of overweight and obesity and higher consumption of fast food in restaurants, with children being the most affected.²³ In other studies in Peru on food consumed in the home, Lozada-Urbano et al. found that the highest expenditure was made by households defined as composite.³ However, although in this study the same classification was not made in households with children where the head of household is widowed who spends more, and in households with older adults more is spent when the head of household is a cohabitant.³ In contrast to these findings, the separated marital status in households with older adults was a factor that favors the probability of expenditure on FAFH. Likewise, Jia et al. also found in their study with children under 18 years of age, a positive association between eating away from home and overweight and obesity.⁶⁰ Some other variables that favor the probability of expenditure and therefore consumption in households with children under five years of age are the increase in the number of household members, being native Aymara or other native people, having early education, complete primary education, being a dependent worker.

Regarding the occupation of the head of household, in households with children and adults, we observed that the head of household with dependent work is the one who spends the most on food consumed away from home and households with children spend more (36.8 Peruvian Soles, approximately 9.72 US Dollars) than households with older adults. Similarly, heads of households with dependent work was a factor that favored the probability of expenditure on FAFH for both households with children and adults. It has been reported that with every 10% increase in average per capita income, there is a 3.5% increase in the proportion of food consumed away from home.⁷⁸ In order to achieve better nutrition at home and avoid spending on eating out requires action on the part of parents but as reported by Eck et al.,⁷⁹ lack of time and busy schedules are the most common barriers. Also, a major obstacle to ensure that children eat healthy is that parents are not present to monitor children’s intake.⁷⁹ Importantly, children were also surveyed and reported to understand that frequent eating away from home resulted in less healthy behaviors.⁷⁹ This last data is of great value to generate studies that address the children’s point of view for these eating patterns and strategies focused on adults, adolescents and children can be generated.

Limitations

This study does not allow us to know the name of the foods or preparations consumed by the respondents and therefore we cannot calculate the calories consumed. It is also not known whether the food purchased was shared by all the members in the household. The differences shown in other studies show that those with a high socioeconomic level spend more and purchase healthy foods, while the poorest households purchase unhealthy foods.

Practical implications

For society, it is important to create and maintain awareness of the quality of the food consumed away from home due to the exposure to trans fats, sodium and sugar that leads to chronic diseases. For the government, it is critical to create policies that support educating the population on nutrition and healthy eating issues considering sociodemographic characteristics. A good and timely education should be aimed at those who consume and those who prepare food. A consumption of healthy foods can help in preventing diseases that would make investment in health more expensive and would alter the quality of life. For the university, nutrition schools can invest in research on nutritional health issues, and this study can help to propose new state policies. Nutrition professionals can work on practical proposals for the development of counseling and training spaces on consumption of food away from home issues in children under 5 years of age and older adults. Menu labeling can reduce the energy content of foods consumed away from home and make it easier for consumers to choose healthier items.

Conclusions

Households with children under 5 years of age spend more on food consumed away from home than households with older adults. In households with children, the variables that favor the probability of expenditure on FAFH are: the number of household members, being Aymara or from another native people, having early education, complete primary education, being a dependent worker, being non-extreme poor, and being non-poor. Variables that decrease the probability of expenditure are: when the head of the household is from a rural area, is native from the Amazon region, is African descendant, has mixed heritage, is between 30 and 39 years of age, and is over 60 years of age.

In households with older adults, the variables that favor the probability of expenditure on FAFH are: the number of household members, being Aymara or from another native people, having incomplete primary education, complete primary education, complete secondary education, incomplete higher university education, being a dependent worker, being separated, being non-extreme poor, and being non-poor. Variables that decrease the probability of expenditure are: when the head of the household is from a rural area, is native from the Amazon region, is African descendant, is White, has mixed heritage, is native from an indigenous people, is married.

Families with children and older adults need to know which are the foods and meals that favor the nutrition of these age groups, to ensure good habits and good nutrition in them. Likewise, it is necessary to have a control of the quality of food in restaurants and street vendors, to offer healthy consumption options.

Data availability

Underlying data

Older adults and children (Food consumed outside the home) https://doi.org/10.6084/m9.figshare.23935221.v1.⁸⁰

This project contains the following underlying data:

• DATA_niño&adultomayor_FIGSHARE 11 08.xlsx

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

References

1. Delgado-Zegarra J, Alvarez-Risco A, Cárdenas C, et al.: Labeling of Genetically Modified (GM) Foods in Peru: Current Dogma and Insights of the Regulatory and Legal Statutes. Int. J. Food Sci. 2022; 2022: 3489712–3489785. PubMed Abstract | Publisher Full Text | Free Full Text
2. Zhai FY, Du SF, Wang ZH, et al.: Dynamics of the Chinese diet and the role of urbanicity, 1991–2011. Obes. Rev. 2014; 15: 16–26. PubMed Abstract | Publisher Full Text | Free Full Text
3. Lozada-Urbano M, Huamán F, Xirinachs Y, et al.: Poverty, Household Structure and Consumption of Foods Away from Home in Peru in 2019: A Cross-Sectional Study. Foods. 2022; 11: 2547. PubMed Abstract | Publisher Full Text | Free Full Text
4. Keramat SA, Alam K, Al-Hanawi MK, et al.: Trends in the prevalence of adult overweight and obesity in Australia, and its association with geographic remoteness. Sci. Rep. 2021; 11: 11320. PubMed Abstract | Publisher Full Text | Free Full Text
5. Spinelli A, Buoncristiano M, Nardone P, et al.: Thinness, overweight, and obesity in 6- to 9-year-old children from 36 countries: The World Health Organization European Childhood Obesity Surveillance Initiative—COSI 2015–2017. Obes. Rev. 2021; 22: e13214. PubMed Abstract | Publisher Full Text
6. Agustina R, Meilianawati, Fenny, et al.: Psychosocial, eating behavior, and lifestyle factors influencing overweight and obesity in adolescents. Food Nutr. Bull. 2021; 42: S72–S91. PubMed Abstract | Publisher Full Text
7. Hecker J, Freijer K, Hiligsmann M, et al.: Burden of disease study of overweight and obesity; the societal impact in terms of cost-of-illness and health-related quality of life. BMC Public Health. 2022; 22: 46. PubMed Abstract | Publisher Full Text | Free Full Text
8. Chew HSJ, Gao Y, Shabbir A, et al.: Personal motivation, self-regulation barriers and strategies for weight loss in people with overweight and obesity: a thematic framework analysis. Public Health Nutr. 2022; 25: 2426–2435. PubMed Abstract | Publisher Full Text | Free Full Text
9. Stoś K, Rychlik E, Woźniak A, et al.: Prevalence and Sociodemographic Factors Associated with Overweight and Obesity among Adults in Poland: A 2019/2020 Nationwide Cross-Sectional Survey. Int. J. Environ. Res. Public Health. 2022; 19. PubMed Abstract | Publisher Full Text | Free Full Text
10. Zervos K, Koletsi M, Mantzios M, et al.: An Eight-Week Mindful Eating Program Applied in a Mediterranean Population With Overweight or Obesity: The EATT Intervention Study. Psychol. Rep. 2021; 125: 1011–1040. PubMed Abstract | Publisher Full Text
11. Bermudez-Aguirre D, Yáñez J, Dunne C, et al.: Study of strawberry flavored milk under pulsed electric field processing. Food Res. Int. 2010; 43: 2201–2207. Publisher Full Text
12. Yáñez JA, Miranda ND, Remsberg CM, et al.: Stereospecific high-performance liquid chromatographic analysis of eriodictyol in urine. J. Pharm. Biomed. Anal. 2007; 43: 255–262. PubMed Abstract | Publisher Full Text
13. Vega-Villa KR, Remsberg CM, Ohgami Y, et al.: Stereospecific high-performance liquid chromatography of taxifolin, applications in pharmacokinetics, and determination in tu fu ling (Rhizoma smilacis glabrae) and apple (Malus x domestica). Biomed. Chromatogr. 2009; 23: 638–646. PubMed Abstract | Publisher Full Text
14. Ramos-Escudero F, Santos-Buelga C, Pérez-Alonso JJ, et al.: HPLC-DAD-ESI/MS identification of anthocyanins in Dioscorea trifida L. yam tubers (purple sachapapa). Eur. Food Res. Technol. 2010; 230: 745–752. Publisher Full Text
15. Roupe KA, Helms GL, Halls SC, et al.: Preparative enzymatic synthesis and HPLC analysis of rhapontigenin: applications to metabolism, pharmacokinetics and anti-cancer studies. J. Pharm. Pharm. Sci. 2005; 8: 374–386. PubMed Abstract
16. Yáñez JA, Remsberg CM, Takemoto JK, et al.: Polyphenols and Flavonoids: An Overview. Flavonoid Pharmacokinetics: Methods of Analysis, Preclinical and Clinical Pharmacokinetics, Safety, and Toxicology. Davies NM, Yáñez JA, eitors. Hoboken, NJ USA: John Wiley & Sons; 2012; pp. 1–69.
17. Delgado-Zegarra J, Alvarez-Risco A, Yáñez JA: Indiscriminate use of pesticides and lack of sanitary control in the domestic market in PeruUso indiscriminado de pesticidas e falta de controle sanitário do mercado interno no Peru. Rev. Panam. Salud Publica. 2018; 42: e3. PubMed Abstract | Publisher Full Text | Free Full Text
18. Alvarez-Risco A, Delgado-Zegarra J, Yáñez JA, et al.: Predation Risk by Gastronomic Boom - Case Peru. J. Landsc. Ecol. 2018; 11: 100–103. Publisher Full Text
19. Penney TL, Burgoine T, Monsivais P: Relative Density of Away from Home Food Establishments and Food Spend for 24,047 Households in England: A Cross-Sectional Study. Int. J. Environ. Res. Public Health. 2018; 15: 2821. PubMed Abstract | Publisher Full Text | Free Full Text
20. Tian X, Zhong L, von Cramon-Taubadel S , et al.: Restaurants in the Neighborhood, Eating Away from Home and BMI in China. PLoS One. 2016; 11: e0167721. PubMed Abstract | Publisher Full Text | Free Full Text
21. Machado-Rodrigues AM, Gama A, Mourão I, et al.: Eating away from home: a risk factor for overweight in children. Eur. J. Clin. Nutr. 2018; 72: 1724–1727. PubMed Abstract | Publisher Full Text
22. Lachat C, Nago E, Verstraeten R, et al.: Eating out of home and its association with dietary intake: a systematic review of the evidence. Obes. Rev. 2012; 13: 329–346. PubMed Abstract | Publisher Full Text
23. Kim TH, Lee E-K, Han E: Food away from home and body mass outcomes: Taking heterogeneity into account enhances quality of results. Nutrition (Burbank, Los Angeles County, Calif.). 2014; 30: 1015–1021. PubMed Abstract | Publisher Full Text
24. Alshahrani A, Shuweihdi F, Swift J, et al.: Underestimation of overweight weight status in children and adolescents aged 0-19 years: A systematic review and meta-analysis. Obes. Sci. Pract. 2021; 7: 760–796. PubMed Abstract | Publisher Full Text | Free Full Text
25. INEI: Encuesta Demográfica y de Salud Familiar, 2021 [Demographic and Family Health Survey, 2021].2021.
26. Makni E, Elloumi A, Ben Brahim M, et al.: Six-minute walk distance equation in children and adolescents with obesity. Acta Paediatr. 2020; 109: 2729–2737. PubMed Abstract | Publisher Full Text
27. Sanyaolu A, Okorie C, Qi X, et al.: Childhood and Adolescent Obesity in the United States: A Public Health Concern. Glob. Pediatr. Health. 2019; 6: 2333794X19891305. PubMed Abstract | Publisher Full Text | Free Full Text
28. Caprio S, Santoro N, Weiss R: Childhood obesity and the associated rise in cardiometabolic complications. Nat. Metab. 2020; 2: 223–232. PubMed Abstract | Publisher Full Text | Free Full Text
29. Safaei M, Sundararajan EA, Driss M, et al.: A systematic literature review on obesity: Understanding the causes & consequences of obesity and reviewing various machine learning approaches used to predict obesity. Comput. Biol. Med. 2021; 136: 104754. Publisher Full Text
30. Knebusch V, Williams J, Yordi Aguirre I, et al.: Effects of the coronavirus disease 2019 pandemic and the policy response on childhood obesity risk factors: Gender and sex differences and recommendations for research. Obes. Rev. 2021; 22: e13222. PubMed Abstract | Publisher Full Text | Free Full Text
31. Canhada SL, Luft VC, Giatti L, et al.: Ultra-processed foods, incident overweight and obesity, and longitudinal changes in weight and waist circumference: the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). Public Health Nutr. 2020; 23: 1076–1086. PubMed Abstract | Publisher Full Text | Free Full Text
32. Askari M, Heshmati J, Shahinfar H, et al.: Ultra-processed food and the risk of overweight and obesity: a systematic review and meta-analysis of observational studies. Int. J. Obes. 2020; 44: 2080–2091. PubMed Abstract | Publisher Full Text
33. Sartorelli DS, Crivellenti LC, Baroni NF, et al.: Effectiveness of a minimally processed food-based nutritional counselling intervention on weight gain in overweight pregnant women: a randomized controlled trial. Eur. J. Nutr. 2022; 62: 443–454. PubMed Abstract | Publisher Full Text | Free Full Text
34. Passos CM, Maia EG, Levy RB, et al.: Association between the price of ultra-processed foods and obesity in Brazil. Nutr. Metab. Cardiovasc. Dis. 2020; 30: 589–598. Publisher Full Text
35. Nardocci M, Leclerc B-S, Louzada M-L, et al.: Consumption of ultra-processed foods and obesity in Canada. Can. J. Public Health. 2019; 110: 4–14. PubMed Abstract | Publisher Full Text | Free Full Text
36. Harb AA, Shechter A, Koch PA, et al.: Ultra-processed foods and the development of obesity in adults. Eur. J. Clin. Nutr. 2022; 77: 619–627. PubMed Abstract | Publisher Full Text | Free Full Text
37. Barbosa SS, Sousa LC, de Oliveira Silva DF , et al.: A Systematic Review on Processed/Ultra-Processed Foods and Arterial Hypertension in Adults and Older People. Nutrients. 2022; 14. PubMed Abstract | Publisher Full Text | Free Full Text
38. Wang M, Du X, Huang W, et al.: Ultra-Processed Foods Consumption Increases the Risk of Hypertension in Adults: A Systematic Review and Meta-Analysis. Am. J. Hypertens. 2022; 35: 892–901. PubMed Abstract | Publisher Full Text
39. Scaranni POS, Cardoso LO, Chor D, et al.: Ultra-processed foods, changes in blood pressure and incidence of hypertension: the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). Public Health Nutr. 2021; 24: 3352–3360. PubMed Abstract | Publisher Full Text | Free Full Text
40. Leone A, Martínez-González MÁ, Craig W, et al.: Pre-Gestational Consumption of Ultra-Processed Foods and Risk of Gestational Diabetes in a Mediterranean Cohort. The SUN Project. Nutrients. 2021; 13. Publisher Full Text
41. Llavero-Valero M, Escalada-San Martín J, Martínez-González MA, et al.: Ultra-processed foods and type-2 diabetes risk in the SUN project: A prospective cohort study. Clin. Nutr. 2021; 40: 2817–2824. PubMed Abstract | Publisher Full Text
42. Nardocci M, Polsky JY, Moubarac J-C: Consumption of ultra-processed foods is associated with obesity, diabetes and hypertension in Canadian adults. Can. J. Public Health. 2021; 112: 421–429. PubMed Abstract | Publisher Full Text | Free Full Text
43. Juul F, Vaidean G, Lin Y, et al.: Ultra-Processed Foods and Incident Cardiovascular Disease in the Framingham Offspring Study. J. Am. Coll. Cardiol. 2021; 77: 1520–1531. PubMed Abstract | Publisher Full Text
44. Bonaccio M, Costanzo S, Di Castelnuovo A, et al.: Ultra-processed food intake and all-cause and cause-specific mortality in individuals with cardiovascular disease: the Moli-sani Study. Eur. Heart J. 2022; 43: 213–224. PubMed Abstract | Publisher Full Text
45. Matos RA, Adams M, Sabaté J: Review: The Consumption of Ultra-Processed Foods and Non-communicable Diseases in Latin America. Front. Nutr. 2021; 8. PubMed Abstract | Publisher Full Text | Free Full Text
46. Crimarco A, Landry MJ, Gardner CD: Ultra-processed Foods, Weight Gain, and Co-morbidity Risk. Curr. Obes. Rep. 2022; 11: 80–92. PubMed Abstract | Publisher Full Text | Free Full Text
47. Ivancovsky-Wajcman D, Fliss-Isakov N, Webb M, et al.: Ultra-processed food is associated with features of metabolic syndrome and non-alcoholic fatty liver disease. Liver Int. 2021; 41: 2635–2645. PubMed Abstract | Publisher Full Text
48. Juul F, Deierlein AL, Vaidean G, et al.: Ultra-processed Foods and Cardiometabolic Health Outcomes: from Evidence to Practice. Curr. Atheroscler. Rep. 2022; 24: 849–860. PubMed Abstract | Publisher Full Text
49. Filgueiras AR, Pires de Almeida VB, Koch Nogueira PC, et al.: Exploring the consumption of ultra-processed foods and its association with food addiction in overweight children. Appetite. 2019; 135: 137–145. PubMed Abstract | Publisher Full Text
50. Sirkka O, Fleischmann M, Abrahamse-Berkeveld M, et al.: Dietary Patterns in Early Childhood and the Risk of Childhood Overweight: The GECKO Drenthe Birth Cohort. Nutrients. 2021; 13. PubMed Abstract | Publisher Full Text | Free Full Text
51. Noubiap JJ, Nansseu JR, Lontchi-Yimagou E, et al.: Global, regional, and country estimates of metabolic syndrome burden in children and adolescents in 2020: a systematic review and modelling analysis. Lancet Child Adolesc. Health. 2022; 6: 158–170. PubMed Abstract | Publisher Full Text
52. Seguin RA, Aggarwal A, Vermeylen F, et al.: Consumption Frequency of Foods Away from Home Linked with Higher Body Mass Index and Lower Fruit and Vegetable Intake among Adults: A Cross-Sectional Study. J. Environ. Public Health. 2016; 2016: 3074212–3074241. PubMed Abstract | Publisher Full Text | Free Full Text
53. Salleh R, Ganapathy SS, Ibrahim Wong N, et al.: Is socio-demographic status, body mass index, and consumption of food away from home associated with high sodium intake among adults in Malaysia?: findings from the Malaysian Community Salt Survey (MyCoSS). J. Health Popul. Nutr. 2021; 40: 12. PubMed Abstract | Publisher Full Text | Free Full Text
54. Binkley JK: The Effect of Demographic, Economic, and Nutrition Factors on the Frequency of Food Away from Home. J. Consum. Aff. 2006; 40: 372–391. Publisher Full Text
55. Landais E, Miotto-Plessis M, Bene C, et al.: Consumption of food away from home in low- and middle-income countries: a systematic scoping review. Nutr. Rev. 2022; 81: 727–754. Publisher Full Text
56. García Arancibia R, Depetris-Guiguet E, Rossini G: Consumo de alimentos fuera del hogar en Argentina. Relevancia de la composición demográfica y tipología de los hogares. Población y Salud en Mesoamérica. 2013; 10: 1–19.
57. Delgado-Zegarra J, Alvarez-Risco A, Yáñez JA: Indiscriminate use of pesticides and lack of sanitary control in the domestic market in Peru. Revista Panamericana de Salud Publica/Pan American Journal of Public Health. 2018; 42: e3.
58. Abegunde DO, Mathers CD, Adam T, et al.: The burden and costs of chronic diseases in low-income and middle-income countries. Lancet. 2007; 370: 1929–1938. Publisher Full Text
59. Villacis C, Zazpe I, Santiago S, et al.: Frecuencia de comidas fuera de casa y calidad de hidratos de carbono y de grasas en el Proyecto SUN. Nutr. Hosp. 2015; 31: 466–474.
60. Jia X, Liu J, Chen B, et al.: Differences in nutrient and energy contents of commonly consumed dishes prepared in restaurants v. at home in Hunan Province, China. Public Health Nutr. 2018; 21: 1307–1318. PubMed Abstract | Publisher Full Text | Free Full Text
61. Abelardo Hurtado V, Wadson P: Crecimiento económico, pobreza y desarrollo humano en el Perú. Revista Científica Pakamuros. 2019; 7: 68–79. Publisher Full Text
62. Parodi C: ¿Cómo se mide la pobreza? (accessed on 28 December 2022). Reference Source
63. A pesar de la inflación, el 40% del gaasto de los hogares peruanos se destina a alimentos. (accessed on 20 December 2022). Reference Source
64. El González D : 40% del gasto de los hogares peruanos se destina a alimentos. (accessed on 20 December 2022). Reference Source
65. López de Blanco M, Carmona A: La transición alimentaria y nutricional: Un reto en el siglo XXI. Anales Venezolanos de Nutrición. 2005; 18: 90–104.
66. Venn D, Dixon J, Banwell C, et al.: Social determinants of household food expenditure in Australia: the role of education, income, geography and time. Public Health Nutr. 2018; 21: 902–911. PubMed Abstract | Publisher Full Text | Free Full Text
67. Taillie LS, Afeiche MC, Eldridge AL, et al.: The contribution of at-home and away-from-home food to dietary intake among 2–13-year-old Mexican children. Public Health Nutr. 2017; 20: 2559–2568. PubMed Abstract | Publisher Full Text | Free Full Text
68. Crespo-Bellido MS, Grutzmacher SK, Takata Y, et al.: The Association Between Food-Away-From-Home Frequency and a Higher BMI Varies by Food Security Status in US Adults. J. Nutr. 2021; 151: 387–394. PubMed Abstract | Publisher Full Text
69. Smith LP, Ng SW, Popkin BM: Trends in US home food preparation and consumption: analysis of national nutrition surveys and time use studies from 1965–1966 to 2007–2008. Nutr. J. 2013; 12: 45. PubMed Abstract | Publisher Full Text | Free Full Text
70. Muhammad A, Seale JL, Meade B, et al.: International Evidence on Food Consumption Patterns: An Update Using 2005 International Comparison Program Data. U.S. Department of Agriculture, Economic Research Service; 2011.
71. Jean De L: A Note on the “General Theory of Employment, Interest and Money”. J. Post Keynes. Econ. 1979; 1: 6–15. Publisher Full Text
72. Rosales G, Mercado W: Efecto de los cambios en el precio de los alimentos sobre el consumo de la quinua y la seguridad alimentaria rural en el Perú. Sci. Agropecu. 2020; 11: 83–93. Publisher Full Text
73. Corzo Ponte JM, Flores Montero AE: Gasto en el consumo de alimentos fuera del hogar, y cuánto representa del ingreso familiar, en hogares con presencia de niños menores de cinco años y hogares con presencia de adultos mayores-2017.2020.
74. INEI: Encuesta Nacional de Hogares ENAHO [National Household Survey ENAHO]. Peru: INEI; 2021.
75. INEI: Encuesta Nacional de Hogares ENAHO [National Household Survey ENAHO]. Peru: INEI; 2017.
76. Queiroz P, Coelho AB: Food away from home in Brazil: the role of sociodemographic factors and family structure. Int. J. Soc. Econ. 2019; 46: 503–522. Publisher Full Text
77. Aguilar-Lopez A, Kuhar A: Food-Away-from-Home Expenditure in Mexico during the COVID-19 Pandemic: A Micro-Econometric Analysis. Agriculture. 2022; 12. Publisher Full Text
78. Cunha DB, Bezerra IN, Pereira RA, et al.: At-home and away-from-home dietary patterns and BMI z-scores in Brazilian adolescents. Appetite. 2018; 120: 374–380. PubMed Abstract | Publisher Full Text
79. Eck KM, Delaney C, Olfert MD, et al.: Parents’ and kids’ eating away from home cognitions. Br. Food J. 2019; 121: 1168–1182. Publisher Full Text
80. Lozada-urbano M, Huamán F: Older adults and children (Food consumed outside the home). Dataset. figshare. 2023. Publisher Full Text

Comments on this article Comments (0)

Version 2

VERSION 2 PUBLISHED 19 Dec 2023