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EDITED BY
Safarina G. Malik,
Mochtar Riady Institute for Nanotechnology,
Indonesia

REVIEWED BY
Harry Muhammad,
Gadjah Mada University,
Indonesia
Anestis Dougkas,
Institut Paul Bocuse,
France

*CORRESPONDENCE
Sara Ebrahimi
saraebrahimi123@gmail.com

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RECEIVED 07 November 2022

ACCEPTED 21 February 2023

PUBLISHED 17 April 2023

CITATION
Ebrahimi S, Leech RM, McNaughton SA,
Abdollahi M, Houshiarrad A and
Livingstone KM (2023) Dietary patterns derived
using principal component analysis and
associations with sociodemographic
characteristics and overweight and obesity: A
cross-sectional analysis of Iranian adults.
Front. Nutr. 10:1091555.
doi: 10.3389/fnut.2023.1091555

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# Dietary patterns derived using principal component analysis and associations with sociodemographic characteristics and overweight and obesity: A cross-sectional analysis of Iranian adults

Sara Ebrahimi¹*, Rebecca M. Leech¹, Sarah A. McNaughton¹, Morteza Abdollahi²,³, Anahita Houshiarrad³ and Katherine M. Livingstone¹

¹Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Geelong, VIC, Australia, ²Social Determinants of Health Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran, ³Department of Nutrition Research, School of Nutrition Sciences and Food Technology, National Nutrition and Food Technology Research Institute, Shahid Beheshti University of Medical Sciences, Tehran, Iran

**Introduction:** This study examined the cross-sectional association between household dietary patterns and sociodemographic characteristics and BMI in a nationally representative sample of Iranian adults.

**Methods:** Data on 6,833 households (n = 17,824 adults) from the National Comprehensive Study on Household Food Consumption Pattern and Nutritional Status 2001–2003 were used. Principal component analysis (PCA) was used to extract dietary patterns from three household 24-h dietary recalls. Linear regression analyses were used to examine associations between dietary patterns and sociodemographic characteristics and BMI.

**Results:** Three dietary patterns were identified: the first was characterized by high citrus fruit intake, the second by high hydrogenated fats intake and the third by high non-leafy vegetables intake. The first and third patterns were associated with household heads with higher education and living in urban areas, while the second was associated with household heads with lower education and living in rural areas. All dietary patterns were positively associated with BMI. The strongest association was found with the first dietary pattern (β: 0.49, 95% CI: 0.43, 0.55).

**Discussion:** While all three dietary patterns were positively associated with BMI, the sociodemographic characteristics of Iranian adults who consumed them differed. These findings inform the design of population-level dietary interventions to address rising obesity rates in Iran.

KEYWORDS

dietary patterns, principal component analysis, obesity, Iranians, adults
Introduction

Middle-Eastern countries, such as Iran, have faced an accelerated
nutrition transition over recent decades (1). Consequently, shifts in
dietary patterns from traditional Iranian diets towards Western diets
high in added sugars and fat have resulted in higher rates of diet-
related chronic diseases and obesity (1, 2). According to the latest
available national estimates in 2016, the prevalence of obesity and
overweight/obesity among Iranian adults was 23 and 59%, respectively
(3). As a poor diet is a significant risk factor for obesity (4) and diet
consists of a combination of foods and nutrients, (5) it is crucial to
understand the factors that influence overall dietary patterns, such as
sociodemographic characteristics, and their associations with
overweight and obesity. This will inform the design of strategies to
address the health impact of the Iranian nutrition transition.

In our previous study of a nationally representative sample of
Iranian adults, the use of overall diet quality indices was shown to
be appropriate for use in the population (6–8). However, as Iran is a
Middle-Eastern county with a culturally-specific diet, (1) data driven
approaches, such as principal component analysis (PCA), present an
opportunity to explore the patterns of dietary intake that exist in the
population, which diet quality indices may not adequately capture
(9–11). PCA has previously been used to assess the dietary patterns of
Iranian adults, (12–14) with most identifying an increasingly
westernized dietary pattern. For example, in a sample of 10,693
Iranian adults from Yazd, a large urban city, a dietary pattern was
identified that was characterized by high intakes of confectionary,
sugars and snacks, (12) while findings from a cross-sectional analysis
of 4,834 Iranian adults identified a Western dietary pattern, a fast-food
dietary pattern and an animal fat dietary pattern (15).

Little is known about the data-driven dietary patterns of Iranians
and their associations with sociodemographic characteristics and
overweight and obesity (16-19). Research to date has shown mixed
associations with BMI, (12, 15) and evidence is inconsistent for the
relationship between empirical dietary patterns and sociodemographic
characteristics, including age (12, 17, 20, 21) and education (12, 15,
17, 20, 21). Further, no studies have investigated associations with area
of residence. Previous research has been conducted in populations
from large Iranian cities, such as Shiraz, (22) Isfahan (15, 21) and
Tehran, (17, 23) thus limiting the ability to identify culturally specific
dietary patterns at a national level and by key sociodemographic
characteristics, such as sex and area of residence. Since Iran has
experienced rising rates of obesity and urbanization due to the
nutrition transition, (2) and obesity is more prevalent in Iranian
females than males, (24, 25) understanding the effects of sex and area
of residence on the association between dietary patterns and
overweight and obesity is of significance. Thus, this study aimed to
examine the associations between empirical dietary patterns,
sociodemographic characteristics, and overweight and obesity in the
National Comprehensive Study on Household Food Consumption
Pattern and Nutritional Status 2001-2003. As a secondary aim, this
study investigated interactions by sex and area of residence on
associations between dietary patterns and overweight and obesity.

Methods

Subjects and study design

Dietary, demographic, and anthropometric data were used from 7,248 households (36,014 individuals) from the National Comprehensive Study on Household Food Consumption Pattern and Nutritional Status 2001–2003 (26). Participants from all provinces of Iran (28 at that time) were recruited using a cluster sampling method between March 2001 and November 2003. Trained nutritionists working in the health sector collected information on dietary intake, sociodemographic characteristics, and anthropometry (27). Participants were excluded from the present analysis if (i) they were aged <18 years and (ii) any member of the household reported implausible body weight or height (as detailed later), (iii) households with 0 or >1 custodian to maximize comparability between household dietary recalls. Ethics for the study was approved by the Ethics Committee of Shahid Beheshti University of Medical Sciences and was exempted by the Deakin University Human Research Ethics Committee (reference number 2019-288). This manuscript is reported according to the Strengthening the Reporting of Observational Studies in Epidemiology—Nutritional Epidemiology (STROBE-Nut) reporting checklist (Supplementary Table 1) (28).

Study measures

Dietary intake

Three 24-h dietary recalls were used to collect data on household
food and beverage intake over consecutive days (27). The household
member who was responsible for cooking in the household provided
these data. Households with complete data for 2 days (129 households)
or 3 days (7,119 households) of these 24-h dietary recalls were
included in the survey. The dietary data were collected in every
province throughout the year (except for Ramadan and New Year
public holiday period) to capture seasonal variation in dietary intake.
Variation was also captured throughout the week by including week
days and weekends (27). Dietary data were collected in the household
using the *per capita* approach. This dietary assessment methodology
considers the equal distribution of intake of food between all members
in the household (29). Dietary surveys in low-middle-income
countries often collect data at the household-level due to the high cost
and complexity of dietary surveys at the individual-level (30–32). To
calculate the total food intake per day *per capita*, the total grams of the
food consumed in each meal was divided by the number of persons
present (including visitors). The intake for each food in each meal was
then summed up to calculate the total food intake (g/day/capita) (27).
The interviews to collect dietary intake were conducted at the house
of the participants. The interviewers used the conversion rate shown
on the labels of food products (created by Food and Drug
Administration of Iran). For example, for liquid vegetables oils, 1.1 ml
was used to convert from volume (cc) to weight (grams). For milk,
1 ml was considered as the conversion rate. Average food and nutrient
intake of the 2 or 3 days of 24-h dietary recalls was calculated. An
estimate of the ratio of household energy intake to household energy
requirement was calculated to adjust for energy intake misreporting.
The Iranian Food Composition Dataset was applied to evaluate food
items and their energy (kcal/day) and nutrient content. Nutrients

<footnote>Abbreviations: BMI, Body mass index; DAG, Directed acyclic graph; DQI-I, Diet quality index-international; HEI, Healthy eating index; PCA, Principal Component analysis; WHO, World Health Organization.</footnote>
examined included sodium (mg/day), fiber (g/day), iron (mg/day), calcium (mg/day), vitamin C (mg/day), and total fat (% of energy), protein (% of energy), and carbohydrate (% of energy) (27). The Iranian Food Composition Dataset was used to evaluate the average content of energy of food groups contributed in PCA to evaluate the percentage of energy contribution from each food groups.

## Principal component analysis

Dietary data were used to derive dietary patterns using PCA, using varimax rotation. Consistent with the PCA methodology, dietary patterns were constructed based on linear combinations of the food groups, reflecting combinations of foods frequently consumed together. The dietary pattern scores thus indicate the degree to which the participant's diet conforms to the pattern (33). A total of 43 food groups were created for use in PCA analyses (34). These groupings and the number of food groups were based on previous dietary pattern studies in Iranian adults and the similarity of the nutrient composition of foods, and were pilot tested to avoid low proportions of consumers in each food group (16–19). Input variables for the food groups were grams per capita per day. A list of the food groups used is provided in Supplementary Table 2.

The number of dietary patterns was identified based on factors with eigenvalues >1.5, breaks in the scree plots and the interpretability and meaningfulness of the patterns using previous Iranian surveys (16–19). Patterns were described in relation to the food groups that loaded most positively or negatively (factor loading >0.20) for the respective patterns. A factor loading of >0.20 was selected based on previous dietary pattern research in Iranian populations (17–19). The Stata postestimation predict command was used to generate standardized dietary pattern scores.

## Sociodemographic characteristics

Information on age, sex, education and area of residence were collected during in person interviews with each individual in the household. Area of residence was assessed based on location data provided by the National Statistics Centre, which was used to classify households as living in a rural or urban area (binary variable). For the purpose of regression analyses with household dietary patterns, age and education at the household level were created for the household head (household custodian). Age of adults, and the household heads, was categorized into 18 to ≤40 years (young adults), >40–≤60 years (mid-aged adults), >60 years (older adults) based on previous Iranian research on obesity (35, 36). Education level of adults, and household heads, was categorized into three levels based on the total number of the years of education: low (0–5 years; equivalent to completed primary school), moderate (6–12 years; equivalent to completed secondary school), and high (more than 12 years; equivalent to university education).

## Anthropometric characteristics

Body weight and height were measured for individuals in households. Weight was measured without shoes and twice to the nearest 100 g using a Seca scale and the mean of these values was used. The accuracy of the scale was adjusted with a control scale (5 kg). Height was measured without shoes and to the nearest 0.1 centimeter with tape measures. Adults with biologically implausible weight (<24.9 or >453.6 kg) and biologically implausible height (<111.8 or >228.6 cm) were excluded from the analysis based on established cut

points (37). Body Mass Index (BMI; weight kg/height m²) was calculated for every adult in the household. Using the World Health Organization (WHO) cut points, adults were grouped into non-overweight/obese (<25 kg/m²) or overweight/obese (≥25 kg/m²) (38).

## Data analysis

Data analysis was performed using Stata (version 16.0; StataCorp). The number and percentage of participants were reported for categorical variables and means and standard deviations were reported for continuous variables. The Xtile command was used to create tertiles of dietary patterns. To be able to easily quantify and understand the underlying composition of the dietary patterns, unadjusted linear regression analysis was used to examine nutrient intake across tertiles of each of the three dietary patterns. Furthermore, to evaluate the healthfulness of the identified dietary patterns, the Healthy Eating Index (HEI) and Diet Quality Index International (DQI-I) diet quality indices were examined across tertiles of the dietary patterns. The calculation of these indices has been described in full elsewhere (6, 7). The margins command was used to report the adjusted mean (SD) of food group intakes used in the PCA analysis. Linear regression analysis was used to assess how household dietary patterns (continuous dependent variables) varied across household sociodemographic characteristics (independent variables): the age of household heads (categorical), sex of household heads (binary), education level of household heads (categorical), and area of residence (binary). Multi-level linear regression analysis was used to assess how individual-level BMI (continuous dependent variable) varied by dietary patterns of households (continuous independent variable). Multi-level logistic regression analysis was used to assess the association between household dietary patterns (continuous independent variable) and individual-level obesity (binary and dependent variable). Confounders were identified using a Directed Acyclic Graph (DAG) and previous research (Supplementary Figure 1). DAGs are visual tools used to better understand the relationship between exposures, outcomes and potential confounders (39). A DAG was used to identify the confounders for the association between dietary patterns and overweight/obesity, where age, sex, education, area of residence and energy intake were identified as confounders available for use in this study. Analyses for the associations with BMI and overweight/obesity were adjusted for age (continuous), sex (binary), education (categorical), area of residence (binary) and energy intakes in adults. To examine interactions by sex and area of residence, interaction terms were added to the regression models. Considering the lack of available data on physical activity, and that 50% of the Iranian adult population has insufficient physical activity (40) a sensitivity analysis was conducted to examine whether adjusting analyses for the ratio of energy intake: energy requirement (instead of adjusting for energy intake) changed the direction and strength of associations. As this study used dietary data collected at the household level, a further sensitivity analysis was conducted to examine if adjusting for household size changed the direction or strength of the association between household dietary patterns and adults' individual-level BMI. Household size was operationalized as no children (<18 years), less than three children and more than three children. The number of children was based on the 1996 Iranian census - the latest
census in Iran at the time of the survey (41). A complete case analysis approach was used to manage any missing data. *p* value < 0.05 was considered to determine statistical significance.

## Results

Of the 36,014 individuals (n = 7,248 households) with available data from the National Comprehensive Study on Household Food Consumption Pattern and Nutritional Status 2001–2003, 797 adults were excluded for being ineligible (implausible weight and height and 0 or > 1 custodian) and 2,545 adults were excluded for missing data (Figure 1). A total of 17,824 adults were included in the present study. The mean age of adults was 37.3 (15.1) years, the mean energy intake of adults was 2,660 (692.27) kcal, and mean weight and BMI were 66.2 (13.7) kg and 25.2 (5.0) kg/m², respectively. Forty seven percent were overweight or obese. As shown in Table 1, the majority of adults were young (18–40 years; 62%), female (55%), had low education (50%) and lived in urban areas (65%), with a mean energy intake of 2,660 (696) kcal/day. At the household level, the mean age of household heads was 45.5 (13.3) years and the mean energy intake of household heads was 2,635 (694.45) kcal. The majority of household heads were males (95%), middle aged (44%), lived in urban areas (65%), and had low levels of education (57%).

Three dietary patterns were extracted using PCA. Factor loadings for the dietary patterns are presented in Figure 2. The first dietary pattern, referred to as mixed dietary pattern 1, was characterized by higher intake of citrus fruits, leafy vegetables, non-hydrogenated fats, nuts, root vegetables, rice, milk, cakes and poultry meat and low intake of breads. The second dietary pattern, referred to as traditional dietary pattern, was characterized by higher intake of hydrogenated fats, onions, potatoes, tomato pastes, legumes and breads. The third dietary pattern, referred to as mixed dietary pattern 2, was characterized by higher intake of non-leafy vegetables, fruits grown on the ground, soft drinks, cream and fruits grown on trees and low intake of citrus fruits and legumes. Nutrient intake (mean and standard error) and diet quality indices according to tertiles of each dietary pattern are presented in Table 2. Energy intake was significantly higher with increasing tertiles of the traditional dietary pattern. In contrast, energy intake was lower in the second tertile of the mixed dietary patterns, and higher in the third tertile. Intake of sodium, calcium, fiber, and vitamin C were higher across tertiles of all dietary patterns, while iron intake was higher across tertiles of the mixed dietary pattern 1 and traditional dietary pattern. The percentage of energy from fat was higher across tertiles of all three dietary patterns, while the percentage of energy from carbohydrates was lower. The percentage of energy from protein was higher across tertiles of mixed dietary pattern 1 but was lower across tertiles of the traditional dietary pattern. The mean HEI score was higher across tertiles of the mixed dietary patterns while the mean DQI-I score was higher across all three dietary patterns.

The mixed dietary pattern 1 and traditional dietary pattern were associated with household heads with higher education levels and living in urban areas, while the traditional dietary pattern was associated with household heads with lower education levels and living in rural areas (Table 3). The Iranian Food Composition Dataset was used to evaluate the percentage of energy contribution from each food group used in the PCA. The food groups that contributed most

to energy intakes were breads (36.7%), rice (14.7%), hydrogenated fats (12.5%), and sugars (7.4%) (Supplementary Table 3).

The adjusted intakes of food groups used in the PCA are presented stratified by weight status (non-overweight/obesity, overweight/obesity) in Supplementary Table 4. While intakes of breads and sugars intake were significantly higher in non-overweight/obese groups, the intake of all other food groups (rice, other grains, leafy vegetables, non-leafy vegetables, tomato paste, onions, root vegetables, citrus fruits, fruits grown on trees, fresh fruit juice, dried fruits, red meat, poultry meat, processed meats, fish and sea foods, eggs, cheese, cream, non-hydrogenated fats, cakes and desserts, sweet biscuits, soft drinks, sweetened beverages, condiments, and spices) was significantly higher in adults with overweight/obesity than adults with non-overweight/obesity.

The association between household dietary patterns and individual-level BMI and obesity are presented in Table 4. All three dietary patterns were positively associated with BMI. A one-unit increase in mixed dietary pattern 1, the traditional dietary pattern and mixed dietary patterns 2 was associated with a 0.49, 0.17, and 0.17 -unit increase in BMI, respectively. Furthermore, households with higher scores for all three dietary patterns were more likely to be overweight/obese.

The direction of associations was comparable for all patterns after adjusting models for the ratio of energy intake: energy requirement, instead of energy intake. However, the strength of the association for the traditional dietary pattern became larger (mixed dietary pattern 1: β 0.57, 95% CI: 0.52, 0.63; traditional dietary pattern: β 0.69, 95% CI: 0.62, 0.77; mixed dietary pattern 2: β 0.24, 95% CI: 0.17, 0.30). Furthermore, results showed that 29, 46, and 25% of adults came from households with no children, less than three children, and more than three children, respectively. The direction and strength of associations with BMI were comparable for all patterns after adjusting for household size: mixed dietary pattern 1: β 0.50, 95% CI: 0.44, 0.56; traditional dietary pattern: β 0.17, 95% CI: 0.09, 0.25; mixed dietary pattern 2: β 0.17, 95% CI: 0.11, 0.24.

Tests for interaction by area of residence and sex showed a significant interaction for area of residence on the association between mixed dietary pattern 1 and BMI (P-interaction ≤0.001) and overweight/obesity (P-interaction = 0.001), while the interactions for area of residence on the association between the traditional dietary pattern and mixed dietary pattern 2 and BMI and overweight/obesity were not significant (interaction *p*-values all >0.05). Sex did not modify the association between dietary patterns and BMI and overweight/obesity (interaction *p*-values all >0.05). As shown in Table 4, findings by area of residence for BMI and overweight/obesity showed strong evidence for larger effect sizes in rural areas for mixed dietary pattern 1.

## Discussion

The aim of this study was to examine the association between PCA-derived dietary patterns and sociodemographic characteristics and overweight and obesity in Iranian adults. Three dietary patterns were identified that demonstrated unique characteristics. The first and third dietary patterns were mixed, including both healthier and unhealthier foods, whereas the second dietary pattern was closer to a traditional Iranian dietary pattern. In turn, the mixed dietary patterns
<figure><img src="image_19.png" /><figcaption>FIGURE 1
Participants flow diagram.</figcaption></figure>

were associated with higher education and living in urban areas while the traditional pattern was associated with lower levels of education and living in rural areas. While all three dietary patterns were positively associated with obesity, mixed dietary pattern 1 showed the strongest association. Furthermore, the association was stronger in rural areas for mixed dietary pattern 1. This study provides insights into culturally-specific dietary patterns in a nationally representative sample of Iranian adults and will help inform the design of population-level dietary interventions and policies to address obesity.

The traditional dietary pattern identified in this study is similar to an Iranian dietary pattern identified by Esmailzadeh et al. (17).

The diet of Middle-Eastern countries, including Iran, is characterized by high consumption of refined grains and hydrogenated fats (17). These foods were observed in the traditional dietary pattern, which was comprised of higher amounts of hydrogenated fats, onions, potatoes, tomato pastes, legumes and breads. In addition, higher intake of this pattern was associated with the highest intake of iron and sodium of all three patterns. This may reflect the highest factor loading for intake of red meat in this pattern, as well as national data on sodium intake, where it is estimated that 41% of the Iranian population consume salt at least two times more than the WHO recommended level (42). However, Iran, like other Middle-Eastern
<table><caption>TABLE 1 Characteristics of Iranian adults overall and by household heads.</caption><thead><tr><th>Characteristics</th><th>Overall,<br>n = 17,824<br>adults N (%) or<br>Mean (SD)</th><th>Household<br>Heads, n = 6,833<br>households N<br>(%) or Mean (SD)</th></tr></thead><tbody><tr><td>Age<sup>1</sup>, N (%)</td><td></td><td></td></tr><tr><td>Young age</td><td>11,041 (61.94)</td><td>2,736 (40.04)</td></tr><tr><td>Middle-aged</td><td>4,463 (25.04)</td><td>2,979 (43.60)</td></tr><tr><td>Older</td><td>2,320 (13.02)</td><td>1,118 (16.36)</td></tr><tr><td>Sex, N (%)</td><td></td><td></td></tr><tr><td>Males</td><td>8,067 (45.30)</td><td>6,501 (95.14)</td></tr><tr><td>Females</td><td>9,757 (54.74)</td><td>332 (4.86)</td></tr><tr><td>Level of education<sup>2</sup>, N (%)</td><td></td><td></td></tr><tr><td>Low</td><td>8,891 (50.00)</td><td>3,869 (56.62)</td></tr><tr><td>Moderate</td><td>7,220 (40.51)</td><td>2,312 (33.84)</td></tr><tr><td>High</td><td>1,713 (9.61)</td><td>652 (9.54)</td></tr><tr><td>Area of residence, N (%)</td><td></td><td></td></tr><tr><td>Rural</td><td>6,289 (35.30)</td><td>2,386 (34.92)</td></tr><tr><td>Urban</td><td>11,535 (64.72)</td><td>4,447 (65.08)</td></tr><tr><td>Anthropometrics, mean<br>(SD), N (%)<sup>3</sup></td><td></td><td></td></tr><tr><td>Overweight/obesity, N (%)</td><td>8,381 (47.02)</td><td>3,999 (58.52)</td></tr><tr><td>Weight (kg)</td><td>66.2 (13.69)</td><td>71.1 (13.41)</td></tr><tr><td>BMI (kg/m<sup>2</sup>)</td><td>25.2 (5.02)</td><td>25.2 (4.34)</td></tr><tr><td>Energy intake (kcal), mean<br>(SD)</td><td>2,660 (692.27)</td><td>2,635 (694.45)</td></tr></tbody></table>

countries, has experienced a nutrition transition over the last three decades and diets have shifted towards unhealthier dietary patterns (1). Some unhealthier foods found in Western and unhealthy dietary patterns in other studies were also observed in the present study (17, 19). For example, mixed dietary pattern 1 included high intake of cakes, and the mixed dietary pattern 2 included high intake of soft drinks and creams and low intake of legumes and citrus fruits. This is comparable to a recent study that examined trends in dietary patterns of 6,508 Iranian adults aged 18 years and over from Tehran. A mixed dietary pattern, characterized by higher simple sugars intake was identified, for which adherence increased from 29.7 to 34.1% between the years 2006 to 2017 (43).

No studies have examined associations between overall dietary patterns and area of residence in Iran. In terms of associations with specific foods, previous research is inconsistent. While a study reported higher consumption of fruits and vegetables in provinces with higher rates of urbanization (44), another study did not find any difference in fruits and vegetable intake between rural and urban areas (45). The present study identified that the mixed dietary patterns were associated with living in urban areas, while the traditional dietary pattern was associated with living in rural areas. While the nutrition transition has been occurring across Iran, these findings, and that of others, suggest that rural regions may be more inclined to uphold traditional dietary habits (46). This may be due to limited availability of Western foods in rural areas, or differences in cultural and socio-economic determinants of diet, where adults who reside in rural areas are older and more inclined to prepare traditional meals (47). However, given no studies have examined dietary patterns in relation to area of residence, and this study identified stronger associations between mixed dietary pattern 1 and obesity in rural areas, prospective research is needed to confirm these findings and to determine which dietary factors should be targeted as part of obesity prevention interventions in these population groups.

<table><caption>TABLE 2 Household nutrient intakes and diet quality indices according to tertiles of dietary patterns (n = 6,833 households)<fnref n="1" />.</caption><thead><tr><th rowspan="2">Nutrients</th><th colspan="3">Mixed dietary pattern 1</th><th colspan="3">Traditional dietary pattern</th><th colspan="3">Mixed dietary pattern 2</th></tr><tr><th>T1</th><th>T2</th><th>T3</th><th>T1</th><th>T2</th><th>T3</th><th>T1</th><th>T2</th><th>T3</th></tr></thead><tbody><tr><td>Protein (%E)</td><td>10.74 (0.0)</td><td>10.96 (0.0)</td><td>11.48 (0.0)</td><td>11.4 (0.0)</td><td>11.0 (0.0)</td><td>10.8 (0.0)</td><td>11.08 (0.0)</td><td>11.06 (0.0)</td><td>11.07 (0.0) *</td></tr><tr><td>Fat (%E)</td><td>21.10 (0.2)</td><td>24.85 (0.1)</td><td>27.50 (0.2)</td><td>22.9 (0.2)</td><td>24.1 (0.1)</td><td>26.8 (0.2)</td><td>22.34 (0.2)</td><td>24.41 (0.2)</td><td>27.00 (0.2)</td></tr><tr><td>Carbohydrate (%E)</td><td>68.69 (0.1)</td><td>64.66 (0.1)</td><td>61.54 (0.2)</td><td>66.2 (0.2)</td><td>65.4 (0.1)</td><td>62.10 (0.2)</td><td>67.07 (0.2)</td><td>65.00 (0.2)</td><td>62.56 (0.2)</td></tr><tr><td>Sodium (mg/day)</td><td>1,730 (34.8)</td><td>2,196 (44.2)</td><td>2,812 (70.1)</td><td>1,649 (24.7)</td><td>2,112 (33.2)</td><td>3,042 (81.4)</td><td>2,084 (41.0)</td><td>2,147 (53.7)</td><td>2,551 (63.6)</td></tr><tr><td>Calcium (mg/day)</td><td>521.80 (4.8)</td><td>551.50 (4.4)</td><td>698.1 (5.4)</td><td>518.25 (4.5)</td><td>579.72 (4.7)</td><td>683.70 (5.6)</td><td>573.23 (5.2)</td><td>564.30 (4.8)</td><td>641.04 (5.4)</td></tr><tr><td>Iron (mg/day)</td><td>15.02 (0.1)</td><td>14.23 (0.1)</td><td>15.61 (0.1)</td><td>12.18 (0.1)</td><td>14.65 (0.1)</td><td>18.13 (0.1)</td><td>15.30 (0.1)</td><td>14.43 (0.1)</td><td>15.17 (0.1) *</td></tr><tr><td>Fiber (g/day)</td><td>11.2 (0.1)</td><td>11.3 (0.1)</td><td>13.1 (0.1)</td><td>9.5 (0.1)</td><td>11.6 (0.1)</td><td>14.7 (0.1)</td><td>11.90 (0.1)</td><td>11.30 (0.1)</td><td>12.55 (0.1)</td></tr><tr><td>Vitamin C (mg/day)</td><td>35.8 (0.7)</td><td>54.6 (0.7)</td><td>93.6 (1.2)</td><td>51.12 (0.9)</td><td>60.00 (0.9)</td><td>75.93 (1.1)</td><td>61.82 (1.2)</td><td>49.78 (0.8)</td><td>74.70 (1.0)</td></tr><tr><td>Energy intake (kcal)</td><td>2,611 (15.54)</td><td>2,530 (13.27)</td><td>2,758 (14.41)</td><td>2,136 (10.18)</td><td>2,580 (10.06)</td><td>3,202 (13.30)</td><td>2,611 (14.96)</td><td>2,563 (14.25)</td><td>2,728 (14.21)</td></tr><tr><td>Diet quality indices</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>HEI</td><td>29.9 (0.11)</td><td>33.9 (0.12)</td><td>37.5 (0.13)</td><td>34.5 (0.14)</td><td>33.8 (0.13)</td><td>33.5 (0.13)</td><td>32.7 (0.14)</td><td>33.1 (0.12)</td><td>35.9 (0.13)</td></tr><tr><td>DQI-I</td><td>37.2 (0.14)</td><td>36.5 (0.16)</td><td>39.3 (0.17)</td><td>36.5 (0.15)</td><td>37.6 (0.16)</td><td>39.0 (0.17)</td><td>36.9 (0.15)</td><td>36.7 (0.15)</td><td>39.5 (0.17)</td></tr></tbody></table>

Abbreviations: HEI, healthy eating index; DQI-I, diet quality index international¹Wald test in unadjusted linear regression analysis for the association between nutrient intakes (continuous dependent) and tertiles of dietary patterns (categorical independent). Values represent mean and standard errors.

*The trends of nutrient intakes and diet quality indices across dietary pattern tertiles are significant *p* < 0.001 apart from protein and iron intake across third dietary pattern.

<footnote n="1">¹Young aged (18- ≤ 40 years old), middle aged (>40- ≤ 60 years old), older (>60 years old).</footnote>

<footnote n="2">²Low (no formal schooling, less than primary school, the primary school completed), medium (secondary school completed, high school completed), and high (college/university completed).</footnote>

<footnote n="3">³Data on anthropometrics were available for 5,666 household heads only.</footnote>
<figure><img src="image_20.png" /><figcaption>FIGURE 2</figcaption></figure>

Factor loadings for the (A) mixed dietary pattern 1, (B) traditional dietary pattern, and (C) mixed dietary pattern 2 in Iranian household identified by principal component analysis.

The existing evidence for associations between dietary patterns and age, sex and education are mixed (15, 16, 18, 20–22). In line with our study, previous studies have found no evidence of associations between age and sex and dietary patterns (16, 18), however, other studies in Iranian populations suggest positive associations between age, being female and consuming a healthy dietary pattern (15, 20–22). A likely explanation for the lack of associations observed in this study is that age and sex of household head were assessed, where over 95% of household heads were male and were mostly middle-aged, compared to 5% female and mostly of younger age in the overall sample. As females are more likely to be the food providers in Middle Eastern countries, and males may be more likely to consume foods outside of the home, further research is needed to quantify the individual-level sociodemographic correlates of dietary patterns in Iran (15, 48, 49).

The current findings showed that all three dietary patterns were positively associated with BMI and overweight and obesity, despite being characterized by both healthy and unhealthy food groups and higher overall diet quality. All three dietary patterns comprise both healthy and less healthy food groups and share

some similarities, however each had some unique characteristics. For example, the mixed dietary pattern 1 comprises components including cakes and desserts, the traditional dietary pattern includes hydrogenated fats and the mixed dietary pattern 2 consists of cream and soft drink. Evidence for associations between dietary patterns and BMI and obesity in Iranian populations is inconsistent (12, 15–17, 22), with some studies reporting that healthier dietary patterns were associated with lower BMI (12, 16, 17), and others reporting that higher consumption of an unhealthier dietary pattern was associated with lower BMI and prevalence of obesity (15, 22). The large variation in sample size and representativeness in previous research may explain these differences, ranging from 10,693 adults from Yazd (12), 418 adults from Shiraz (22), 267 adults from Tehran (16), 486 women from Tehran (17) and 4,834 adults from Isfahan (15). In addition to the distinct sample differences, another possible explanation for these discrepancies could be the measurement of dietary intake and the availability of physical activity data. While this study collected dietary data at the household level, other studies measured dietary intakes at the
<table><caption>TABLE 3 Associations between sociodemographic characteristics of household heads and principal component dietary patterns (n=6,833 households).</caption><thead><tr><th rowspan="2">Characteristics</th><th colspan="3">Mixed dietary pattern 1<sup>4</sup></th><th colspan="3">Traditional dietary pattern<sup>4</sup></th><th colspan="3">Mixed dietary pattern 2<sup>4</sup></th></tr><tr><th>β - Coeff</th><th>95% CI</th><th>p-Value<sup>3</sup></th><th>β - Coeff</th><th>95% CI</th><th>p-Value<sup>3</sup></th><th>β - Coeff</th><th>95% CI</th><th>p-Value<sup>3</sup></th></tr></thead><tbody><tr><td>Age of household head<sup>1</sup></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Young age (reference)</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Middle-aged</td><td>-0.08</td><td>-0.20, 0.04</td><td>0.18</td><td>-0.01</td><td>-0.13, 0.11</td><td>0.89</td><td>0.04</td><td>-0.08, 0.15</td><td>0.54</td></tr><tr><td>Older</td><td>-0.21</td><td>-0.41, 0.03</td><td>0.09</td><td>0.13</td><td>-0.12, 0.37</td><td>0.30</td><td>-0.05</td><td>-0.27, 0.17</td><td>0.64</td></tr><tr><td>Sex of household head</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Males (reference)</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Females</td><td>-0.03</td><td>-0.18, 0.13</td><td>0.73</td><td>0.04</td><td>-0.12, 0.19</td><td>0.63</td><td>0.10</td><td>-0.04, 0.25</td><td>0.15</td></tr><tr><td>Level of education of household head<sup>2</sup></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Low (reference)</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Moderate</td><td>0.90</td><td>0.82, 0.98</td><td>&lt;0.001</td><td>-0.04</td><td>-0.12, 0.04</td><td>0.31</td><td>0.42</td><td>0.34, 0.49</td><td>&lt;0.001</td></tr><tr><td>High</td><td>1.46</td><td>1.34, 1.58</td><td>&lt;0.001</td><td>-0.19</td><td>-0.31, -0.07</td><td>0.002</td><td>0.68</td><td>0.57, 0.79</td><td>&lt;0.001</td></tr><tr><td>Area of residence</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Rural (reference)</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Urban</td><td>0.57</td><td>0.50, 0.64</td><td>&lt;0.001</td><td>-0.24</td><td>-0.31, -0.17</td><td>&lt;0.001</td><td>0.33</td><td>0.27, 0.40</td><td>&lt;0.001</td></tr></tbody></table>

individual level (12, 15–17, 22). Moreover, the present study did not adjust for physical activity, yet other studies did (12, 16, 17). Thus, the positive associations with BMI identified in this study should be interpreted with this in mind. Furthermore, this study has revealed that 47% of adults had overweight/obesity in 2002–2003, which was comparable to evidence from 2005 that showed the prevalence of overweight/obesity was 43 and 52% in Iranian men and women, respectively (50). However, the most recent national data on overweight and obesity in Iran (2016) indicates that the prevalence of overweight/obesity has risen to 59%, thus future research is needed to determine if associations between dietary patterns and BMI persist in more recent national data (3). Nevertheless, the mixed dietary patterns identified in the current study was similar to the unhealthy dietary patterns identified by previous studies. These dietary patterns were high in soft drinks, red meat, confectionary, and vegetable oils and were positively associated with obesity (16, 17, 19). Furthermore, high intakes of legumes, grains, and potatoes in the traditional dietary pattern of Sarkhosh-Khorasani et al. (12) are comparable to the traditional dietary pattern identified in the present study, which were both positively associated with obesity. Despite both traditional and mixed dietary patterns being positively associated with obesity, the larger effect size for dietary pattern 1 suggests that a more Western dietary pattern, particularly one high in soft drinks and low in vegetables, is most detrimental to risk of obesity. The possible explanation may be that, while all models were adjusted for total energy intake, this association was driven by high consumption of energy-dense foods and beverages. This dietary pattern was the only one characterized by intake of soft drinks,

and contributed to the highest percentage of energy from total fat. Given the percentage from saturated fats and added sugars was not available, it was not possible to determine whether these were highest in this dietary pattern, thus this warrants further investigation.

This study has several strengths. This study included a nationally representative sample, which allowed us to examine dietary patterns across key demographic variables. Three 24-h dietary recalls were used to collect data on foods and beverages consumed in the previous 24 h. These were collected in every province throughout the year (except for Ramadan and New Year public holiday period) to capture seasonal variation in dietary intake. Variation was also captured throughout the week by including week days and weekends. The 24-h dietary recall interview required at least 20 min to be completed and could be administered through a phone call or a face-to-face interview by a trained interviewer. Since trained interviewers administered the questionnaires, literate respondents were not required, and as the recall period was over the last 24-h, respondents are likely to mostly recall what they have consumed. However, recall biases may still be present (51–53). With limited information available regarding the characteristics of dietary patterns nationally and in rural areas, this study addresses this gap. In addition, this study uses a statistical dietary pattern method that creates patterns based on foods that are commonly consumed together, thus enabling investigation of culturally-specific combinations of foods as part of an overall dietary pattern.

This study acknowledges several limitations that should be considered in the interpretation of the present findings. Firstly, the household dietary data is likely to be less precise than

<footnote n="1">¹Young aged (18- ≤ 40 years old), middle aged (>40- ≤ 60 years old), older (>60 years old).</footnote>

<footnote n="2">²Low (no formal schooling, less than primary school, the primary school completed), medium (secondary school completed, high school completed), and high (college/university completed).</footnote>

<footnote n="3">³Wald test in linear regression analysis for the association between sociodemographic characteristics (categorical independent) and dietary patterns (continuous dependent) were adjusted for age, sex, education of household head, and area of residence.</footnote>

<footnote n="4">⁴The first dietary pattern was characterized by higher intake of citrus fruits, non-hydrogenated fats, leafy and root vegetables, rice and low intake of breads. The second dietary pattern was characterized by higher intake of hydrogenated fats, onions, potatoes, legumes and breads. The third dietary pattern was characterized by higher intake of fruits grown on trees, cream, soft drink, and lower intake of citrus fruits and legumes.</footnote>
<table><caption>TABLE 4 Associations between household principal component dietary patterns and adult BMI (continuous and binary) overall and by area of residence (n =17,824 adults).</caption><thead><tr><th rowspan="2">Dietary patterns</th><th colspan="3">BMI</th><th colspan="3">Overweight/Obesity<sup>1</sup></th></tr><tr><th>β - Coeff</th><th>95% CI</th><th>p-Value<sup>2</sup></th><th>OR</th><th>95% CI</th><th>p-Value<sup>3</sup></th></tr></thead><tbody><tr><td colspan="7"><strong>Mixed dietary pattern 1</strong></td></tr><tr><td>Overall</td><td>0.49</td><td>0.43, 0.55</td><td>&lt;0.001</td><td>1.28</td><td>1.24, 1.32</td><td>&lt;0.001</td></tr><tr><td>Rural (n = 6,289)</td><td>0.71</td><td>0.61, 0.82</td><td>&lt;0.001</td><td>1.40</td><td>1.35, 1.52</td><td>&lt;0.001</td></tr><tr><td>Urban (n = 11,535)</td><td>0.38</td><td>0.31, 0.45</td><td>&lt;0.001</td><td>1.20</td><td>1.17, 1.26</td><td>&lt;0.001</td></tr><tr><td colspan="7"><strong>Traditional dietary pattern</strong></td></tr><tr><td>Overall</td><td>0.17</td><td>0.09, 0.25</td><td>&lt;0.001</td><td>1.08</td><td>1.04, 1.13</td><td>&lt;0.001</td></tr><tr><td>Rural (n = 6,289)</td><td>0.33</td><td>0.18, 0.47</td><td>&lt;0.001</td><td>1.20</td><td>1.11, 1.29</td><td>&lt;0.001</td></tr><tr><td>Urban (n = 11,535)</td><td>0.08</td><td>-0.02, 0.18</td><td>0.105</td><td>1.02</td><td>0.97, 1.08</td><td>0.36</td></tr><tr><td colspan="7"><strong>Mixed dietary pattern 2</strong></td></tr><tr><td>Overall</td><td>0.17</td><td>0.11, 0.24</td><td>&lt;0.001</td><td>1.07</td><td>1.04, 1.11</td><td>&lt;0.001</td></tr><tr><td>Rural (n = 6,289)</td><td>0.16</td><td>0.04, 0.28</td><td>0.010</td><td>1.08</td><td>1.01, 1.15</td><td>0.025</td></tr><tr><td>Urban (n = 11,535)</td><td>0.17</td><td>0.10, 0.25</td><td>&lt;0.001</td><td>1.07</td><td>1.03, 1.11</td><td>0.001</td></tr></tbody></table>

individual dietary data because it assumes that each household member has consumed the foods relative to his/her energy and nutrient requirements (31). Although adjustment for household size did not change the direction or strength of the associations, the impact of excluding children when assessing household-level dietary data warrants further investigation. Secondly, the cross-sectional study design is susceptible to reverse causality when examining the association between dietary patterns and overweight and obesity, thus future prospective research is needed to confirm our findings. Thirdly, one of the limitations of studies conducted in the Middle East is the lack of data on alcohol intake (54). The present study did not collect data on alcohol intake, physical activity and smoking habits, which may be important confounders and should be considered in the interpretation of associations between dietary patterns and overweight and obesity. Fourthly, while the number of fast-foods outlets is likely to have increased in Iran over recent decades, data on foods consumed out of the home were not collected in this survey. This could be important for examining dietary patterns of Iranians in the advent of more Westernized diets. Fifthly, as the Iranian population, like other Middle Eastern countries, is susceptible to central obesity and the complications of this type of obesity (55), further research is needed to measure both waist circumference and BMI. Sixthly, there was evidence of underreporting in dietary intake as the effect size for the association between the traditional dietary pattern and BMI increased after adjusting for the ratio of energy intake to requirement. The lower levels of education and adults living in rural areas may be proxies for underreporting of energy intake as our results showed positive associations between these sociodemographic characteristics and the traditional dietary pattern. Seventhly, 2% of households had only two recalls, which may impact on the variety of foods consumed over two rather than 3 days. Lastly, although this study used data collected in

2001–2003, this period of time marks the beginning of the nutrition transition in Iran (2). Thus, this study will be important for future research aiming to understand how diets have changed during the nutrition transition. Moreover, as most research in national Iranian data has focused on single foods and nutrients (27), this study addressed an important gap by providing the most recent evidence on the dietary patterns of a national sample of Iranians.

## Conclusion

The present study identified three distinct PCA-derived dietary patterns in a large and nationally representative sample of Iranian adults. While all three patterns were associated with higher BMI and odds of overweight and obesity, the socio-demographic characteristics of Iranian adults who consumed them were different. As Iran is undergoing a nutrition transition, future research is needed to examine dietary patterns at the individual level, in both rural and urban areas of Iran. As all dietary patterns identified were positively associated with obesity, further research in Iranian adults is required to identify the foods that characterize a healthful dietary pattern and are protective with respect to obesity. Moreover, analysis of are prospective cohorts that include relevant confounding factors is needed to be able to understand and compare the current results.

## Data availability statement

The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.

<footnote n="1">¹BMI≥ 25 kg/m².</footnote>

<footnote n="2">²Wald test in multi-level linear regression analysis for the association between dietary patterns (continuous independent) and BMI (continuous dependent) adjusted for age (continuous), sex, education, area of residence and energy intake in adults, with a mean energy intake of 2,660 kcal (SD 696).</footnote>

<footnote n="3">³Wald test in multi-level logistic regression analysis for the association between dietary patterns (continuous independent) and obesity (categorical dependent), adjusted for age, sex, education, area of residence and energy in adults; non-obese considered as reference group.</footnote>
## Ethics statement

The studies involving human participants were reviewed and approved by the Ethics Committee of Shahid Beheshti University of Medical Sciences and was exempted by the Deakin University Human Research Ethics Committee (reference number 2019-288). Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.

## Author contributions

MA and AH designed and carried out the National Comprehensive Study on Household Food Consumption Pattern and Nutritional Status 2001–2003. SE, RL, SM, and KL designed the analysis. SE conducted the statistical analysis and drafted the manuscript. All authors contributed to the article and approved the submitted version.

## Funding

KL is supported by a National Health and Medical Research Council Emerging Leadership Fellowship (APP1173803). RL was supported by a National Heart Foundation Postdoctoral Research Fellowship (ID102109) and is currently supported by a National Health

and Medical Research Council Emerging Leadership Fellowship (APP1175250).

## Conflict of interest

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.

## Publisher's note

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.

## Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnut.2023.1091555/full#supplementary-material

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<figure><img src="image_21.png" /></figure>

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EDITED BY
Emmanuel Cohen,
UMR7206 Eco Anthropologie et Ethnobiologie
(EAE), France

REVIEWED BY
Jenny Vilchis-Gil,
Hospital Infantil de México Federico Gómez,
Mexico
Margarita Teran-García,
University of Illinois at Urbana-Champaign,
United States

*CORRESPONDENCE

Jaimie Davis

Jaimie.davis@austin.utexas.edu

RECEIVED 16 August 2023

ACCEPTED 27 November 2023

PUBLISHED 15 December 2023

CITATION

Hudson EA, Burgermaster M, Isis SM, Jeans MR, Vandyousefi S, Landry MJ, Seguin-Fowler R, Chandra J and Davis J (2023) School-based intervention impacts availability of vegetables and beverages in participants' homes.
*Front. Nutr.* 10:1278125.
doi: 10.3389/fnut.2023.1278125

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© 2023 Hudson, Burgermaster, Isis, Jeans, Vandyousefi, Landry, Seguin-Fowler, Chandra and Davis. This is an open-access article distributed under the terms of the [Creative Commons Attribution License (CC BY)](https://creativecommons.org/licenses/by/4.0/). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# School-based intervention impacts availability of vegetables and beverages in participants' homes

Erin A. Hudson<fnref n="1" />, Marissa Burgermaster<fnref n="1" />, Sophia M. Isis<fnref n="1" />, Matthew R. Jeans<fnref n="1" />, Sarvenaz Vandyousefi², Matthew J. Landry³, Rebecca Seguin-Fowler⁴, Joya Chandra⁵ and Jaimie Davis<fnref n="1" />*

As rates of metabolic syndrome rise, children consume too few vegetables and too much added sugar. Because children tend to eat what is available at home, the home environment plays a key role in shaping dietary habits. This secondary analysis evaluated the effects of a school-based gardening, cooking, and nutrition education intervention (TX Sprouts) compared to control on the availability of vegetables, fruit juice, and sugar-sweetened beverages (SSBs) at home. In the TX Sprouts cluster-randomized trial, 16 schools were randomized to TX Sprouts (n = 8 schools) or control (n = 8 schools) for one academic year. All schools served predominately Hispanic families with low incomes. TX Sprouts built school gardens and taught 18 lessons to all 3rd-5th grade students at intervention schools. TX Sprouts also offered monthly caregiver lessons before and/or after school. Caregivers completed questionnaires pre and post, providing demographics and information about home availability of vegetables, fruit juice, and SSBs. Summary statistics were used to describe the sociodemographic characteristics of participants. Linear regression assessed the change in scores (pre to post) for the food/ beverage availability question. The model was adjusted for the caregiver's education, employment status, child's grade, and free or reduced-price lunch eligibility. The analytic sample included 895 participants. Compared to control, the intervention positively changed the home availability of targeted foods and beverages, largely by improving the availability of vegetables and vegetable juice. This study showed that a school gardening, nutrition, and cooking program delivered to elementary children may positively influence the home food environment.

KEYWORDS

school garden, home food environment, home food availability, school-based intervention, nutrition education, Hispanic (demographic), low-income children

Introduction

A healthy dietary pattern that includes vegetables and whole fruits and limits added sugar helps maintain health and reduces the risk of metabolic disorders like type 2 diabetes (1-3). Early childhood dietary habits can shape lifelong food preferences and eating behavior (4, 5). In addition, unhealthy eating patterns can lead to metabolic syndrome in childhood (2, 3),

<footnote n="1">¹The University of Texas at Austin, Austin, TX, United States, ²Grossman School of Medicine, New York University, New York, NY, United States, ³Stanford Prevention Research Center, Department of Medicine, School of Medicine, Stanford University, Stanford, CA, United States, ⁴Texas A&M AgriLife Research, Temple, NH, United States, ⁵Pediatrics Division, University of Texas MD Anderson Cancer Center, Houston, TX, United States</footnote>
increasing the risk of lifelong metabolic disorders (6–8). Yet in the United States, children's diets fail to meet the recommendations for a healthy dietary pattern, with too few fruits and vegetables and too much added sugar, often in the form of sugar-sweetened beverages (SSBs) (9, 10). As children enter adolescence, they tend to consume even fewer vegetables and more added sugar (11, 12), making early childhood a crucial period to establish healthy dietary habits (4, 5).

Children's dietary habits are affected by environmental characteristics in their homes and school; therefore, both environments are important intervention points to promote healthy eating habits. At home, children may be influenced by parental modeling (i.e., children choose foods they see caregivers eat) (13). A child's food choices may also be shaped by their level of nutrition security at home, i.e., whether foods that fit a healthy dietary pattern are regularly available, accessible, and affordable in their homes (14). Nutrition security requires access to and availability of foods and beverages that promote well-being, prevent disease, and align with cultural, social, or dietary preferences (14). Children typically eat what is available in their homes. For example, higher availability of vegetables in the home is associated with higher vegetable consumption in children, and higher availability of SSBs is associated with increased SSB intake (15–17). Although caregivers largely determine the foods and beverages available in the home, a child's preferences can also influence the foods and beverages their caregiver purchases and brings into the home (15, 16, 18).

School-based gardening interventions are a promising approach to addressing food preferences and promoting healthy dietary patterns in children. Prior studies demonstrate that garden-centered interventions can increase child knowledge and preference for vegetables through exposure, education, and experience growing, harvesting, and tasting vegetables (19, 20) and improve vegetable consumption (6, 21, 22). Many gardening interventions with children also provide classes and resources to caregivers of the participating children, which teach the skills and knowledge to prepare these foods (23). Given their multilayered influence, school-based garden interventions have the potential to improve the home food environment, yet few studies have reported on such findings (24–26).

One such study, Texas! Go! Eat! Grow! (TGEG), was a school-based garden intervention that measured the availability of vegetables, fruit juice, and SSBs in participants' homes. In the 5-month TGEG pilot, the program improved the availability of vegetables in the homes of third-grade students (24). However, when the TGEG intervention was tested in a cluster randomized controlled trial (cRCT) across 28 schools, TGEG did not measurably change the vegetable, fruit juice, and SSB availability in the participants' homes (25). This result may be explained by the inconsistent principal and teacher commitment to the program resulting in lower student participation in the cRCT than in the pilot (24, 25).

The TX Sprouts cRCT examined the effect of its school-based gardening, cooking, and nutrition education program on home food environment changes among all 3rd-5th grade children in participating elementary schools (27). TX Sprouts provided a culturally tailored curriculum in elementary schools across central Texas, serving predominantly low-income Hispanic children and their families, and received strong support from teachers and school administrators. TX Sprouts was developed and tested with Hispanic stakeholders, whose feedback was incorporated into the curriculum (28). The nutrition curriculum targets included increasing vegetable

consumption, decreasing SSBs and fruit juice, and educating students and caregivers about preparing vegetables in ways aligned with cultural preferences. We previously found that TX Sprouts participants compared to control showed increased vegetable intake (6, 29) and decreased added sugar (6), which aligns with the intervention targets. Based on these nutrition targets and our previous findings, this study aims to determine whether the TX Sprouts intervention compared to control improved the availability of foods and beverages in the home.

# Methods

## Participants and recruitment

This study is a secondary analysis of data from the TX Sprouts program, a cluster-randomized school-based gardening, cooking, and nutrition education intervention. This study enrolled 3rd to 5th-grade children and their caregivers from 16 elementary schools in the greater Austin area. Methods and main outcomes for TX Sprouts are published elsewhere (27, 29–31). Briefly, schools were randomized to TX Sprouts intervention ($n=8$ schools) or control (delayed intervention; $n=8$ schools) using block randomization for one academic year. In each of the schools recruited, the majority of students were of Hispanic ethnicity (63.6%) and were eligible for the free- and reduced-price lunch (FRL) program (66.0%).

Children and caregivers at the recruited schools were contacted to participate during “Back to School” and “Meet the Teacher” events, via flyers sent home with children, and through teachers’ in-class announcements. All caregiver participants provided written informed consent, and assent was obtained from each participating child. The study was conducted according to the guidelines in the Declaration of Helsinki, and all procedures involving human subjects were approved by the Institutional Review Board of the University of Texas at Austin and the individual school district review boards. The trial is registered at [ClinicalTrials.gov](https://ClinicalTrials.gov) (NCT02668744).

## Description of TX Sprouts intervention

The TX Sprouts program was based on the social-ecological-transactional model, which suggests that child behavior and adaptation are shaped by bidirectional influences across levels of ecological systems (32, 33). This model treats the child as nested within micro-systems (e.g., school and family) that reciprocally interact with each other to shape development and behaviors (32, 33). The logic model for the TX Sprouts program proposed that by intervening at the child and school levels, the program could impact the family and home environment (29).

The intervention was implemented in three waves across three academic years from 2016 to 2019. In each wave, the intervention schools each received the same TX Sprouts intervention for one academic year, and the delayed intervention schools received the same intervention the following year. The TX Sprouts research team built a 0.25-acre outdoor teaching garden at each intervention school before the academic year of baseline measurements. At least 6 months before the intervention, Garden Leadership Committees (GLC) were formed and comprised key stakeholders, including teachers and school staff. The GLC frequently met during the planning year to prepare to implement the program
long-term. The program provided the schools with all supplies for maintaining the garden. Because the program was culturally tailored to Hispanic populations living in Texas, produce such as squash, peppers, and cilantro were planted and used in the TX Sprouts recipes.

Throughout the academic year, TX Sprouts nutrition and garden educators taught 18-one hour TX Sprouts lessons to each 3rd-5th grade class at the intervention school as part of the students' typical school day. TX Sprouts provided all material to teach each of these lessons. The 3rd-5th grade teachers attended the lessons, but did not deliver them. The TX Sprouts curriculum was adapted from LA Sprouts (28) and the Junior Master Gardener program developed by Texas A&M AgriLife Extension (34). The student curriculum was designed to be culturally tailored to Hispanic children in Texas, including culturally appropriate recipes, content, and activities.

TX Sprouts lessons aimed to improve a variety of diet-related psychosocial constructs, including increasing nutrition, gardening, and cooking knowledge, self-efficacy, and willingness to try and prefer fruits and vegetables. The curriculum included the following nutrition concepts: (a) cooking/preparing fruits and vegetables consistent with cultural preferences; (b) making nutritious food choices; (c) eating locally produced food; (d) choosing low-sugar beverages made with fresh fruits and vegetables, but not fruit juice, as alternatives to SSBs, like agua frescas; and (e) understanding health benefits of fruits and vegetables. Every lesson included either a garden taste test (7 lessons) or a cooking activity (11 lessons), and information and recipe cards were sent home to caregivers following the lessons. The control schools received a delayed intervention in the following academic year, which was identical to the TX Sprouts intervention described above.

During the trial, garden and nutrition educators also taught nine monthly, in-person, 60-min TX Sprouts lessons for caregivers at each school. At the beginning of the year, TX Sprouts educators met with caregivers and school administrators at each school to schedule these classes according to the caregiver preferences at each school. The dates and times varied widely, including mornings, after school, evenings, and weekends based on caregiver preferences. The caregiver lessons were delivered in person in English and Spanish.

The curriculum for the caregivers paralleled the nutrition and gardening topics and activities taught to the children and shared similar skills and knowledge with the caregivers as was previously taught in the student lessons. Each lesson included preparing culturally tailored recipes using fresh produce. These lessons also addressed family shopping, parent modeling, and positive parenting approaches. Incentives to attend the lessons included free meals, produce giveaways, groceries, and free childcare for children and siblings. In addition, children in the TX Sprouts program were invited to attend and encouraged to teach their caregivers how to cook meals with fresh produce, empowering the child to be the champion for healthy changes in the family.

## Instruments and measures

At the beginning and end of the academic school year (pre and post), caregivers were asked to complete a self-administered questionnaire packet, which took 20-30 min to complete. Questionnaires were provided in both English and Spanish and bilingual research assistants were available to assist caregivers in completing them. Caregivers were given a $15 gift card to a local grocery store for completing each

questionnaire. The questionnaires asked about the caregiver's and child's demographics, eligibility for FRL, caregiver-child grocery shopping behavior, and the availability of vegetables, fruit juice, and SSBs in the home, using the same scale administered in the TGEG trials (24). The questions addressing co-shopping behavior asked whether (yes or no) caregivers did the following activities with their child the previous week: "took your child to the store to get vegetables" or "chose vegetables to buy at the grocery store together."

The survey concerning home food/beverage availability included seven items that were specifically targeted in the TX Sprouts curriculum: (a) 100% fruit juice; (b) vegetable juice; (c) fresh vegetables; (d) canned, frozen, or dried vegetables; (e) salad; (f) cut up fresh vegetables in a place that is easy for kids to reach; and (g) soft drinks or sugar-sweetened beverages (15). Caregivers were asked to indicate the frequency that each item was available in their home the previous week on a 4-point Likert-type scale: never, some of the time, most of the time, or all of the time. The survey items aligned with the TX Sprouts curriculum, which emphasized eating more vegetables and reducing high-sugar beverage consumption. This same survey about home food/beverage availability has been used in other pediatric populations, reporting a Cronbach's alpha of 0.7, indicating satisfactory internal consistency (15, 35, 36). A previously published cross-sectional analysis from the TX Sprouts study also incorporated these questions (37). Another Texas-based school garden intervention previously used this survey, which allows clearer comparison of the results.

The responses for each item were converted to numeric values where "never" = 0 and "all of the time" = 3, except for two categories— (1) 100% fruit juice and (2) soft drinks or SSBs—which were coded such that "all of the time" = 0 and "never" = 3. Consistent with the TX Sprouts curriculum, these beverages were reverse-coded due to their deleterious effect on health outcomes when consumed in high amounts (38–42). Scores for each item were summed to yield a composite score for pre and post. A higher composite score indicated a greater availability of vegetables and lower availability of fruit juice and SSBs in the home. The availability scores ranged from 0 to 3 for each item and 0 to 19 for the composite score. A change variable was created representing the difference between pre and post in the composite score and for each of the seven items in the survey.

## Data analysis

Summary statistics were used to describe the sociodemographic characteristics of participants in the intervention and control groups of the analytical sample, using information from the caregivers' pre-intervention questionnaires. Texas Education Agency data was used to compare the demographics of the eligible students to those in the analytical samples. Chi-square tests and t-tests were used to determine differences in demographics between participants in the analytic sample and those excluded due to incomplete survey data. Chi-square tests and t-tests were also used to determine demographic differences between intervention and control. Participant characteristics that significantly differed between intervention and control groups were identified as covariates. Inter-factor correlations were calculated to ensure independence of covariates from each other. Descriptive statistics and t-tests were used to analyze the questions regarding caregiver-child co-shopping and evaluate differences pre and post and between intervention and control.
Because this was a cRCT, the necessity of a multilevel model was assessed. A random intercept model in which the intercept varied across schools was fitted and compared to the fixed intercept model. Residual variance at the school level was evaluated, and intraclass correlation coefficients (ICCs) were calculated for the composite score and the score for each of the seven survey items. The intercepts did not vary significantly across the schools, and calculated ICCs further suggested that a multilevel model was not warranted (Supplementary Table 1) (43).

Linear regression was used to assess the change from pre to post in the composite score between intervention and control. The change in each item of the food and beverage availability question was also analyzed with linear regression. The models were adjusted for covariates: caregiver's education, employment status, child's grade, and FRL eligibility. Results were considered significant when *p* < 0.05. All analyses were performed using R (version 4.2.0) and R Studio (version 2021.09.0 + 351) software.

# Results

## Fidelity of TX Sprouts intervention

Fidelity to the intervention has previously been described (27, 29). In brief, 100% of the classes were taught to each 3rd-5th grade

classroom, less than 1% of the 18 lessons were modified across the eight intervention schools due to school-related interruptions, and 34% of classes were taught indoors due to inclement weather. Caregiver classes were poorly attended; some had to be canceled because no caregivers attended. As a result, only 88.9% of the caregiver classes were taught. Only 7.1% (n = 106) of caregivers attended one or more classes, and less than 1% (n = 11) attended 50% or more of the nine classes.

## Study sample

Of the 4,353 eligible children at the 16 schools, 3,302 students consented to participate in TX Sprouts. Pre-intervention questionnaire packets were completed by 2,882 caregivers, and 1,153 caregivers completed the packet post-intervention. Given that the 13-page parent questionnaire packet included close to 200 questions and the home availability items included in this analysis were in the middle of the packet, there were many incomplete survey items at both pre- and post-intervention. A complete case analysis, in which inclusion required completion of the 7-item home availability by caregivers at pre- and post-intervention, resulted in an analytical sample of 895 participants (n = 414 in intervention; n = 481 in control) (Figure 1).

The sociodemographic characteristics of all children eligible to participate (n = 4,353), those enrolled in the clinical study

<figure><img src="image_22.png" /><figcaption>FIGURE 1
TX Sprouts CONSORT diagram.</figcaption></figure>
(n = 3,135), those enrolled in the study with incomplete survey data (n = 2,240), and the children in the analytic sample (n = 895) are presented in Table 1. There were significantly more children who were female (58%), identified as being Hispanic (62.8%) or Non-Hispanic White (20.2%), and eligible for FRL (66.0%) included in the study than those with incomplete survey data (p < 0.001 for all). There was a significant between-group difference in the children's grade levels between intervention and control, with a higher percentage of older students in the intervention schools because only 4th and 5th grade students were enrolled in some larger schools due to budgetary constraints (Table 1).

Caregivers were predominantly female (92.3%), and over half worked at least full-time outside the home (54.0%). Caregivers who completed the survey had higher rates of part-time or full-time jobs, had a higher education level, and were comprised of more females compared to those caregivers with incomplete survey data (p < 0.001). There were no significant differences between the caregivers' age or number of children at home in the analytical sample compared to those with incomplete survey data.

Table 1 also compares the sociodemographic characteristics of the caregivers in intervention and control groups. In this study, the intervention had a higher percentage of caregivers working least full-time outside the home than the control (56.8% vs. 51.6%, p = 0.004). In addition, the intervention had a lower rate of caregivers with college degrees (22.0% vs. 25.4%) but also a lower percentage of caregivers without at least a high school diploma or GED than the control (27.1% vs. 34.1%) (p = 0.024).

At pre-intervention, 70% of caregivers reported that in the previous week they had taken their child to the store to buy vegetables, and 66% of caregivers had chosen vegetables to buy with their child. At post-intervention, the percentage of caregivers reporting that they had co-shopped for vegetables with their child in the past week was similar to pre with 69.9% of caregivers reporting co-shopping with their children. There was no significant difference between intervention and control in either co-shopping question pre or post.

## Home food/beverage availability

The pre- and post-intervention means with standard deviations are shown in Table 2. The change in the composite score ranged from -11 to 15. The intervention increased home availability of the measured foods/beverages consistent with a healthy dietary pattern compared to control ($\beta$ = 0.528, 95% CI: 0.115, 0.941), as shown in Table 2 and further illustrated in Figure 2. After adjusting for education level and employment status of the caregiver, child's grade, and eligibility for FRL, the change in the composite score in the intervention remained significant compared to control ($\beta$ = 0.428, 95% CI: 0.009, 0.847).

In similar analyses considering each component item, the intervention significantly increased vegetable juice and fresh vegetable availability compared to control ($\beta$ = 0.269, 95% CI: 0.123, 0.415; $\beta$ = 0.126, 95% CI: 0.006, 0.247, respectively). However, after adjustment, the change in vegetable juice availability was the only individual item that significantly improved compared to control ($\beta$ = 0.246, 95% CI: 0.098, 0.394).

## Discussion

This study found that the TX Sprouts school-based gardening, nutrition, and education intervention improved home availability of foods/beverages consistent with a healthy dietary pattern compared to the control group. Specifically, compared to control, the intervention improved the availability of vegetable juice, and there was a trend toward more fresh vegetable availability at home.

As children age, they tend to prefer and consume more sugar-sweetened beverages and fewer vegetables (11, 12). These changes in preferences can influence their household food environment. Though much literature focuses on the role of caregivers in shaping the home food environment, this is not a unidirectional relationship (16, 18). Prior research confirms that children who co-shop with their parents influence their caregivers' buying behavior at grocery stores (44, 45). This is particularly true with Hispanic children, who co-shop more frequently with their parents than the general U.S. population (44). Similarly, marketing research has found that Hispanic families make more grocery trips per week, and their shopping behavior is more likely to be influenced by their children compared to the general U.S. population (16, 44, 46). In the present study, over two-thirds of all caregivers reported co-shopping with their children within the previous week. The high frequency of caregiver-child co-shopping provides more opportunities for children to influence the food purchased and, therefore, their home food environment (44).

The TX Sprouts program was designed to leverage this interconnectedness between children and the micro-systems to which they belong, such as schools and families using the socio-ecological model (33). The model acknowledges a dynamic, reciprocal influence among children and their micro-systems that influence behavior change (33, 47). Through exposing children to gardening, cooking, and hands-on nutrition education, TX Sprouts compared to control children had increased vegetable intake and decreased SSB consumption, as previously reported (6, 29). This may be explained, in part, by the changes in the child's home food environment. Similarly, by recognizing the child's influence on the home food environment, the intervention may have protected against greater adverse changes to the home food environment, such as larger increases in fruit juice availability.

These results may also be attributable to the high fidelity of the TX Sprouts intervention in the participating schools. In one of the few studies reporting the impact of a school-based garden program on the home availability of foods and beverages, the TGEG cRCT trial found no significant changes in the home food and beverage environment using the same survey as TX Sprouts (24, 25). The authors attributed this to low caregiver participation in the home component of the intervention (25). Like TGEG, TX Sprouts had limited caregiver involvement, with less than 7% of participating caregivers attending a single class (29). Though the caregiver involvement was low, the fidelity of the TX Sprouts intervention was high (27, 29). This contrasts with the TGEG cRCT, where student participation varied widely among schools, with only a mean participation rate of 55.7% (25). The high fidelity of the TX Sprouts intervention may explain the program's success despite the lack of caregiver involvement.

These findings may be further explained by the culturally-tailored recipes and handouts sent home to the intervention households following the student lessons. All schools served predominantly
<table><caption>TABLE 1 Sociodemographic characteristics of children eligible for TX Sprouts; sociodemographic characteristics of total participating children and caregivers, analytical sample, intervention, and control.</caption><thead><tr><th rowspan="2"></th><th colspan="2">Eligible participants<sup>a</sup> (n = 4,353)</th><th colspan="2">Enrolled participants (n = 3,135)</th><th colspan="2">Participants with incomplete data (n = 2,240)</th><th colspan="2">Total analytical sample (n = 895)</th><th rowspan="2">p-value<sup>b</sup></th><th colspan="2">Intervention (n = 414)</th><th colspan="2">Control (n = 481)</th><th rowspan="2">p-value<sup>c</sup></th></tr><tr><th>No</th><th>%</th><th>No</th><th>%</th><th>No</th><th>% or ±SD</th><th>No</th><th>% or ±SD</th><th>No</th><th>% or SD</th><th>No</th><th>% or ± SD</th></tr></thead><tbody><tr><td>Intervention</td><td>1,830</td><td>42%</td><td>1,412</td><td>45.0%</td><td>998</td><td>44.6%</td><td>414</td><td>46.3%</td><td>0.409</td><td>414</td><td>100%</td><td></td><td></td><td></td></tr><tr><td colspan="15">Children's characteristics</td></tr><tr><td>Sex</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>&lt;0.001</td><td></td><td></td><td></td><td></td><td>0.541</td></tr><tr><td>Male</td><td>2,237</td><td>51%</td><td>1,485</td><td>47.4%</td><td>1,111</td><td>49.6%</td><td>374</td><td>41.8%</td><td></td><td>196</td><td>40.7%</td><td>178</td><td>43.0%</td><td></td></tr><tr><td>Female</td><td>2,116</td><td>49%</td><td>1,650</td><td>52.6%</td><td>1,129</td><td>50.4%</td><td>521</td><td>58.2%</td><td></td><td>285</td><td>59.3%</td><td>236</td><td>57.0%</td><td></td></tr><tr><td>Grade</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.712</td><td></td><td></td><td></td><td></td><td>0.018*</td></tr><tr><td>3rd</td><td>1,239</td><td>28%</td><td>923</td><td>29.4%</td><td>650</td><td>29.0%</td><td>273</td><td>30.5%</td><td></td><td>108</td><td>56.1%</td><td>165</td><td>34.3%</td><td></td></tr><tr><td>4th</td><td>1,554</td><td>36%</td><td>1,128</td><td>36.0%</td><td>811</td><td>36.2%</td><td>317</td><td>35.4%</td><td></td><td>150</td><td>36.2%</td><td>167</td><td>34.7%</td><td></td></tr><tr><td>5th</td><td>1,560</td><td>36%</td><td>1,084</td><td>34.6%</td><td>779</td><td>34.8%</td><td>305</td><td>34.1%</td><td></td><td>156</td><td>37.7%</td><td>149</td><td>31.0%</td><td></td></tr><tr><td>Race or ethnicity<sup>d</sup></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>&lt;0.001</td><td></td><td></td><td></td><td></td><td>0.707</td></tr><tr><td>Black or African American</td><td>302</td><td>7%</td><td>264</td><td>8.4%</td><td>191</td><td>8.53%</td><td>73</td><td>8.16%</td><td></td><td>36</td><td>8.7%</td><td>37</td><td>7.7%</td><td></td></tr><tr><td>Hispanic</td><td>2,177</td><td>50%</td><td>1,869</td><td>59.6%</td><td>1,307</td><td>58.3%</td><td>562</td><td>62.8%</td><td></td><td>255</td><td>61.6%</td><td>307</td><td>63.8%</td><td></td></tr><tr><td>White (not Hispanic)</td><td>498</td><td>11%</td><td>562</td><td>17.9%</td><td>381</td><td>17.0%</td><td>181</td><td>20.2%</td><td></td><td>89</td><td>21.5%</td><td>92</td><td>19.1%</td><td></td></tr><tr><td>Eligible for FRL<sup>e</sup></td><td>3,371</td><td>77%</td><td>1,903</td><td>60.7%</td><td>1,312</td><td>58.6%</td><td>591</td><td>66.0%</td><td>&lt;0.001</td><td>276</td><td>66.7%</td><td>31</td><td>65.5%</td><td>0.235</td></tr><tr><td colspan="15">Caregivers' characteristics</td></tr><tr><td>Age</td><td></td><td></td><td>36.89</td><td>±6.85</td><td>36.8</td><td>±6.89</td><td>37.1</td><td>±6.76</td><td>0.181</td><td>37.2</td><td>±6.55</td><td>37.1</td><td>±6.95</td><td>0.942</td></tr><tr><td>Female</td><td></td><td></td><td>2,457</td><td>78.4%</td><td>1,631</td><td>72.8%</td><td>826</td><td>92.3%</td><td>&lt;0.001</td><td>389</td><td>94.0%</td><td>437</td><td>90.9%</td><td>0.328</td></tr><tr><td>Highest level of education</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>&lt;0.001</td><td></td><td></td><td></td><td></td><td>0.024*</td></tr><tr><td>College degree or higher</td><td></td><td></td><td>552</td><td>17.6%</td><td>339</td><td>15.1%</td><td>213</td><td>22.8%</td><td></td><td>91</td><td>22.0%</td><td>122</td><td>25.4%</td><td></td></tr><tr><td>Some college</td><td></td><td></td><td>642</td><td>20.5%</td><td>441</td><td>19.7%</td><td>201</td><td>22.5%</td><td></td><td>99</td><td>23.9%</td><td>102</td><td>21.2%</td><td></td></tr><tr><td>High school diploma or GED</td><td></td><td></td><td>551</td><td>17.6%</td><td>369</td><td>16.5%</td><td>182</td><td>20.3%</td><td></td><td>100</td><td>24.2%</td><td>82</td><td>17.0%</td><td></td></tr><tr><td>No high school diploma or GED</td><td></td><td></td><td>1,026</td><td>32.7%</td><td>750</td><td>33.5%</td><td>276</td><td>30.8%</td><td></td><td>112</td><td>27.1%</td><td>164</td><td>34.1%</td><td></td></tr><tr><td>Employment status</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>&lt;0.001</td><td></td><td></td><td></td><td></td><td>0.004*</td></tr><tr><td>Full-time or more</td><td></td><td></td><td>1,584</td><td>50.5%</td><td>1,101</td><td>49.2%</td><td>483</td><td>54.0%</td><td></td><td>235</td><td>56.8%</td><td>248</td><td>51.6%</td><td></td></tr><tr><td>Part-time</td><td></td><td></td><td>322</td><td>10.3%</td><td>213</td><td>9.5%</td><td>109</td><td>12.2%</td><td></td><td>62</td><td>15.0%</td><td>47</td><td>9.77%</td><td></td></tr><tr><td>Retired/not working outside home</td><td></td><td></td><td>814</td><td>26%</td><td>541</td><td>24.2%</td><td>273</td><td>30.5%</td><td></td><td>105</td><td>25.4%</td><td>168</td><td>34.9%</td><td></td></tr><tr><td>Number of children at home</td><td></td><td></td><td>2.75</td><td>±1.21</td><td>2.76</td><td>±1.22</td><td>2.72</td><td>±1.18</td><td>0.476</td><td>2.78</td><td>±1.21</td><td>2.74</td><td>±1.24</td><td>0.495</td></tr></tbody></table>

<footnote><sup>a</sup>Data gathered from the Texas Education Agency (TEA).</footnote>

<footnote><sup>b</sup>P-value for the difference between eligible participants with incomplete survey data and the analytic sample from chi-square tests or independent t-tests.</footnote>

<footnote><sup>c</sup>P-value for the difference between intervention and control from chi-square tests or independent t-tests.</footnote>

<footnote><sup>d</sup>Only three most prominent race and ethnicities were included in this sample to provide a comparison to school-wide data from TEA.</footnote>

<footnote><sup>e</sup>Free- or reduced-price lunches.</footnote>

<footnote>*Indicates a statistically significant value of *p* < 0.05.</footnote>
<table><caption>TABLE 2 Means and standard deviations of responses to home food/beverage availability question, change in score pre and post, and effect of TX Sprouts on that change using linear regression.</caption><thead><tr><th rowspan="2">Variable</th><th colspan="3">Intervention (n = 414)</th><th colspan="3">Control (n = 481)</th><th rowspan="2">Change analysis p-value</th><th rowspan="2">Adjusted<sup>1</sup> p-value</th></tr><tr><th>Pre Mean ± SD</th><th>Post Mean ± SD</th><th>Change Mean ± SD</th><th>Pre Mean ± SD</th><th>Post Mean ± SD</th><th>Change Mean ± SD</th></tr></thead><tbody><tr><td>Composite score<sup>2</sup></td><td>11.7 (3.19)</td><td>12.0 (3.02)</td><td>0.29 (3.07)</td><td>12.0 (3.38)</td><td>11.8 (3.33)</td><td>-0.24 (3.19)</td><td>0.012*</td><td>0.046*</td></tr><tr><td>Individual items</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Fruit juice</td><td>1.20 (1.06)</td><td>1.36 (1.00)</td><td>0.15 (1.05)</td><td>1.09 (1.04)</td><td>1.33 (1.07)</td><td>0.23 (1.04)</td><td>0.250</td><td>0.434</td></tr><tr><td>Vegetable juice</td><td>0.80 (0.95)</td><td>0.84 (1.00)</td><td>0.04 (1.01)</td><td>1.01 (1.10)</td><td>0.77 (0.98)</td><td>-0.23 (1.19)</td><td>&lt;0.001*</td><td>0.001*</td></tr><tr><td>Fresh vegetables</td><td>2.27 (0.85)</td><td>2.29 (0.84)</td><td>0.03 (0.94)</td><td>2.36 (0.84)</td><td>2.26 (0.87)</td><td>-0.10 (0.89)</td><td>0.039*</td><td>0.122</td></tr><tr><td>Canned, frozen, or dried vegetables</td><td>2.00 (1.03)</td><td>1.98 (1.00)</td><td>-0.02 (1.10)</td><td>1.95 (1.08)</td><td>1.88 (1.08)</td><td>-0.07 (1.06)</td><td>0.460</td><td>0.582</td></tr><tr><td>Salad</td><td>1.95 (0.97)</td><td>1.92 (0.91)</td><td>-0.03 (0.98)</td><td>2.02 (0.96)</td><td>1.94 (0.93)</td><td>-0.08 (0.95)</td><td>0.442</td><td>0.804</td></tr><tr><td>Cut up vegetables easy for kids to reach</td><td>1.84 (1.07)</td><td>1.77 (1.01)</td><td>-0.07 (1.16)</td><td>1.96 (1.01)</td><td>1.81 (1.05)</td><td>-0.15 (1.12)</td><td>0.295</td><td>0.617</td></tr><tr><td>Soft drinks or SSBs</td><td>1.65 (0.96)</td><td>1.84 (0.78)</td><td>0.19 (0.91)</td><td>1.61 (0.96)</td><td>1.77 (0.89)</td><td>0.16 (1.00)</td><td>0.636</td><td>0.480</td></tr></tbody></table>

Adjusted R² for adjusted model = 0.016.

*Indicates a statistically significant value of *p* < 0.05.

¹Adjusted for caregiver's education, caregiver's employment status, child's grade, and child's eligibility for free- or reduced-priced lunches.

²The Composite Score reflects the sum of participant responses to the 7-item food/beverage availability Likert-type question. Consistent with the TX Sprouts curriculum, fruit juice and soft-drinks or SSBs (sugar-sweetened beverages) were coded such that "never" = 3 and "all of the time" = 0; all other items were coded with "never" = 0.

<figure><img src="image_23.png" /><figcaption>FIGURE 2
Boxplot showing results of linear regression model examining the effect of TX Sprouts on home availability of vegetables and beverages.</figcaption></figure>

Hispanic families, and TX Sprouts gardens grew culturally-specific produce. In each lesson, students tasted culturally-familiar fresh produce and/or prepared culturally-tailored recipes and were given accompanying handouts and recipes to take home. Past research shows that incorporating culturally familiar foods and recipes can promote acceptance of dietary changes (48, 49). Thus, the resulting improvements in the home environment may have been driven by incorporating culturally tailored TX Sprouts recipes sent home to the caregivers.

The current study has some limitations that must be considered. First, the analytical sample in this complete case analysis (n = 895) represents less than one-third of the caregiver participants enrolled in the study, which means data was unavailable for many participants, reducing power and introducing potential bias. There were no significant differences between the caregivers' age, race/ethnicity, or the number of children or adults in the home in the analytical sample

compared to those with incomplete survey data, which mitigates some concerns raised by the attrition rate. However, children in the analytical sample were more likely to be female, Hispanic, and FRL eligible. In addition, the caregivers in the analytical sample had higher education and were more likely to work outside the home compared to those with incomplete survey data. These findings suggest that the analytic sample may have had higher risk factors than eligible participants without complete data. Thus, these variables were included in the adjusted model, minimizing the potential bias due to these differences.

Another weakness of this study is that the food and beverage question was limited to only seven items and did not capture all foods and beverages available in participants' homes. With over 200 questions in the survey packet, a more detailed examination of all foods available in the home was not possible. It was also not feasible to directly observe foods available in the homes of hundreds of students. Instead, the self-report survey focused on items covered in the curricula and mirrored that used in a similar Texas-based garden intervention, TGEG.

This study was also limited in its duration to one academic school year, and the long-term effects of the intervention on the home food environment were not measured. The improvement in the intervention compared to control was significant but small in this 9-month intervention. A multiple school-year intervention could amplify the at-home impact, particularly if caregivers were more involved. Given the acceptance of remote learning since 2020, online lessons could be a potential avenue to boost caregiver involvement in a longer-term study.

Another limitation was that the sample was predominantly low-income and Hispanic families in Central Texas, so the results may not be generalizable to other populations. However, the study was intentionally designed to target Hispanic children, who are at high risk of metabolic syndrome and similar chronic diseases. As a result, this
study sheds light on a potential avenue to improve the home food environment in this population.

In sum, compared to control, the TX Sprouts intervention demonstrated modest improvements in the availability of healthy foods and beverages in the home compared to control over the course of one school year, independent of direct caregiver participation in the program. These findings indicate that children can be agents for positive family nutrition changes. These results further suggest that a school-based program taught only to elementary children may protect nutrition security in families and homes by impacting the availability of foods and beverages that promote well-being, prevent disease, and align with cultural and dietary preferences. Future interventions may build on these findings and incorporate strategies to further empower children to influence the availability of healthier food options in their homes in longer-term education programs, particularly when serving predominantly Hispanic children and their families.

## Data availability statement

The data contains sensitive information concerning minors. De-identified data will be available by request to JD (Jaimie.davis@austin.utexas.edu), and a data sharing agreement will need to be completed. Protocols, analytics, and study material are also available upon request.

## Ethics statement

The studies involving humans were approved by Institutional Review Board at the University of Texas at Austin. The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation in this study was provided by the participants' legal guardians/next of kin.

## Author contributions

EH: Conceptualization, Formal analysis, Methodology, Writing – original draft. MB: Conceptualization, Methodology, Supervision, Writing – review & editing. SI: Conceptualization, Formal analysis, Methodology, Writing – review & editing. MJ:

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## Funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This research was funded by the National Institutes of Health, National Heart, Lung, and Blood Institute, grant number 1R01HL123865 (2015–2020). Whole Kids Foundation, Home Depot, and Sprouts Healthy Communities Foundation gave funding for garden builds and enhancements.

## Conflict of interest

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.

## Publisher's note

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.

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EDITED BY
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UMR7206 Eco Anthropologie et Ethnobiologie
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Federal University of Agriculture, Abeokuta,
Nigeria
Whadi-ah Parker,
Human Sciences Research Council,
South Africa

*CORRESPONDENCE

Collins John

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cchibunkem@yahoo.com

RECEIVED 17 August 2023

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PUBLISHED 05 January 2024

CITATION

John C, Poh BK, Jalaludin MY, Michael G, Adedeji I, Oyenusi EE, Akor B, Charles NC, Buthmanaban V and Muhardi L (2024) Exploring disparities in malnutrition among under-five children in Nigeria and potential solutions: a scoping review. *Front. Nutr.* 10:1279130. doi: 10.3389/fnut.2023.1279130

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© 2024 John, Poh, Jalaludin, Michael, Adedeji, Oyenusi, Akor, Charles, Buthmanaban and Muhardi. This is an open-access article distributed under the terms of the [Creative Commons Attribution License (CC BY)](https://creativecommons.org/licenses/by/4.0/). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Exploring disparities in malnutrition among under-five children in Nigeria and potential solutions: a scoping review

Collins John¹*, Bee Koon Poh², Muhammad Yazid Jalaludin³, Godpower Michael⁴, Idris Adedeji⁵, Elizabeth Eberechi Oyenusi⁶, Blessing Akor⁷,⁸, Nkwoala C. Charles⁹, Vanitha Buthmanaban¹⁰ and Leilani Muhardi¹¹

¹Department of Paediatrics, College of Health Sciences, University of Jos, Jos, Nigeria, ²Nutritional Sciences Programme and Centre for Community Health Studies (ReaCH), Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia, ³Department of Paediatrics, University Malaya Medical Centre, Kuala Lumpur, Malaysia, ⁴Department of Family Medicine, Aminu Kano Teaching Hospital, Kano, Nigeria, ⁵Department of Paediatrics, Abubakar Tafawa Balewa University Teaching Hospital, Bauchi, Nigeria, ⁶Department of Paediatrics, College of Medicine, University of Lagos/Lagos University Teaching Hospital, Lagos, Nigeria, ⁷Department of Family Medicine, University of Abuja, Abuja, Nigeria, ⁸Department of Community Medicine, University of Abuja, Abuja, Nigeria, ⁹Department of Human Nutrition and Dietetics, Michael Okpara University of Agriculture, Umudike, Nigeria, ¹⁰FrieslandCampina, Amersfoort, Netherlands, ¹¹FrieslandCampina, AMEA, Singapore

**Introduction:** Triple burden of malnutrition in children remains a significant public health issue. This scoping review aims to assess the information on undernutrition, micronutrient deficiencies and the quality of complementary feeding in various regions in Nigeria.

**Methods:** A literature search was conducted using PubMed and Google Scholar databases from January 1, 2018 to January 31, 2023 to include studies focusing on 0 to 5 years old children in Nigeria, reporting data on nutritional status, nutrient deficiencies, and published in English.

**Results:** 73 out of 1,545 articles were included. Stunting remained alarmingly high ranging from 7.2% (Osun, South West) to 61% (Kaduna, North Central), while wasting varied from 1% (Ibadan, South West) to 29% (FCT Abuja, Central) and underweight from 5.9% (Osun, South West) to 42.6% (Kano, North West) respectively. The overall prevalence of anemia and vitamin A deficiency ranged between 55.2 to 75.1 % and 5.3 to 67.6%, respectively. Low rates of achieving minimum dietary diversity and minimum meal frequency were reported across different states depicting the suboptimal quality of complementary feeding. The prevalence of overweight/obesity ranged from 1.5% (Rivers, South South) to 25.9% (Benue, North Central).

**Conclusion:** Multiple early childhood malnutrition issues exist with a wide disparity across states in Nigeria, particularly in the Northern region. Targeted nutrition interventions must be implemented to improve the situation.

KEYWORDS

children below 5 years, Nigeria, poor quality of complementary feedings, multiple undernutrition issues, targeted interventions
## Introduction

Malnutrition is defined as a “pathological state resulting from inadequate or excess nutrition” (1). According to the World Health Organization (WHO), ‘poor nutrition status’ is the sole important threat to the world’s health, with adequate nutrition being the critical element in helping individuals to live healthy and productive lives. Malnutrition impacts the intergenerational economic growth of a country. Morbidity related to malnutrition leads to a loss in human capital through an education gap and a resultant low-skilled workforce owing to poor cognitive development and reduced school attainment (2). Inadequate food intake and poor dietary quality are directly or indirectly responsible for causing poor health, with the top 6 of the 11 global risk factors associated with dietary imbalances (3).

Malnutrition is expressed through undernutrition (stunting, wasting, and underweight) and/or overnutrition, which is related to a high intake of protein and energy (1). The WHO in 2021 has defined complementary feeding indicators, including minimum dietary diversity (MDD), minimum meal frequency (MMF), and minimum acceptable diet (MAD) (4), as essential in the early years of life to the formation of eating habits that may eventually have short- and long-term implications on the child’s health (5). Severe acute malnutrition (SAM) or protein energy malnutrition (PEM) is a life-threatening condition requiring urgent attention. Over the years, it has been known by different names such as protein-calorie malnutrition, PEM, oedematous malnutrition, nutritional oedema, severe wasting, or based on clinical manifestations including marasmus, kwashiorkor, or marasmic kwashiorkor (6).

Globally there are over 150 million stunted children, 50 million wasted children, and 38 million underweight children (6). In addition, over 40 million children under the age of 5 years are overweight (6). While more countries are dealing with several forms of malnutrition indices, individual children are known to suffer from more than one form of malnutrition indicators across the world (7).

Nigeria is one of the top 10 countries with malnutrition in children aged under 5 years (7). It has the second-highest burden of stunted children in the world and a higher-than-the-world average child-wasting prevalence (8). Within Nigeria, nearly half of all under-5 children were stunted in the North East and North West geopolitical zones, while the rest of Nigeria contributed to 22.0 % of under-5 stunting prevalence (9).

Despite the availability of studies on stunting, there is a paucity of information on other nutritional issues across all the regions of Nigeria. Hence, the objective of this study was to review the current evidence on nutritional status, nutrient intake, and quality of complementary feeding in under-5 children across all the geopolitical zones and states of Nigeria.

## Methodology

### Search strategy

Literature searches were carried out using the PubMed and Google Scholar databases. The main search terms used in the literature search were malnutrition, nutritional deficiency, Nigeria, stunting, wasting, or underweight. The details of the search are provided in Table 1. The results are reported according to the PRISMA guidelines for scoping reviews (Figure 1).

### Screening of articles and selection of studies

For all the articles, the search results were extracted and imported into reference management software (Mendeley). All duplicates were removed, and a researcher screened the results based on manuscript’s title and abstract based on the following criteria:

### Inclusion criteria

* Studies including infants and children from 0 to 60 months of age of either gender living in Nigeria.

* Studies reporting data on nutritional status and nutrient deficiencies through dietary intake and measurement studies.

* Baseline data from intervention studies.

* Specific case reports if relevant.

* Review articles

* Articles published from 01-01-2018 to 31-01-2023 to focus on recent available data.

* Articles published in peer-reviewed journals in the English language.

<table><caption>TABLE 1 Summary of search strategy and results.</caption><thead><tr><th>Search engine</th><th>Search key string</th><th>Number of results returned</th><th>Articles screened</th></tr></thead><tbody><tr><td rowspan="3">Google Scholar</td><td>Nigeria* AND (child* OR toddler OR infant) AND (inadequate OR problem OR intake OR condition OR situation OR status) AND nutrition* AND ("under-five" OR "pre-school")</td><td rowspan="3">5,370</td><td>470</td></tr><tr><td>Nigeria* AND (child* OR toddler OR infant) AND (inadequate OR problem OR intake OR condition OR situation OR status) AND nutrition* AND ("under-five" OR "pre-school") -"</td><td>299</td></tr><tr><td>Nigeria* AND (child* OR toddler OR infant OR nursery OR baby) AND (inadequate OR condition OR status OR deficien* OR insufficient OR maln* OR undern* OR wast* OR stunt*) AND nutritional* AND ("pre-school" OR "under-five") -primary</td><td>343</td></tr><tr><td rowspan="2">PubMed</td><td>((Nigeria*[Title/Abstract]) AND (child*)) AND (nutrition)</td><td></td><td>235</td></tr><tr><td>Nigeria* AND (child* OR toddler OR infant) AND (malnutrition OR nutritional deficiency) AND nutrition* AND ("under-five" OR "pre-school")</td><td></td><td>198</td></tr></tbody></table>
<figure><img src="image_26.png" /><figcaption>FIGURE 1
PRISMA flow diagram of study search and selection process.</figcaption></figure>

## Exclusion criteria

* Studies including children with any medical conditions, such as sickle-cell anemia, human immunodeficiency virus (HIV), and diarrhea.

* Grey literature such as reports.

* Single case reports on a specific disease.

Two authors (CJ and LM) reviewed the retrieved full-text articles for final inclusion in the study. If there were any disagreements in the selection of the articles, a third author (PBK or MYJ) acted as a referee to decide whether the article under consideration should be excluded or included. LM extracted date from the eligible retrieved articles to provide extraction of information. No assessment on data quality was implemented.

## Data extraction

Data for the following outcomes of interest were extracted from relevant research articles: participant characteristics (age, sex, geographical location, socioeconomic status, weight, and height); nutritional status (stunting, wasting, underweight,) acute malnutrition, overweight, and obesity; nutrient deficiencies biochemical analysis (including iron deficiency anemia, vitamin A, and zinc deficiency); from dietary intake studies including energy; macronutrients (protein, total fat, total calorie); and micronutrients (iron, zinc, vitamin A, calcium, magnesium, phosphorus, vitamin D, B-vitamins, vitamin C, zinc, folate).

Results were pooled by region to facilitate regional comparison. Available data describing the estimation of malnutrition, nutrient status, and dietary quality, including differences by age, gender, and
setting (urban vs. rural), are also discussed in the narrative findings for each outcome. The data in the review is incorporated as a range from the lowest to the highest percentage values or as per geopolitical zones in Nigeria, namely North Central, North East, North West, Central, South East, South South, South West or the year of study or publication.

## Definitions of important terms

In this study, WHO definitions for various malnutrition status were used. Stunting in a child is defined with a height-for-age Z-score (HAZ) of below minus two standard deviations (−2SD) from the median; wasting as a weight-for-age Z-score of below minus two standard deviations (−2SD) from the median. An underweight is weight-for-height Z-score is less than minus two standard deviations (−2SD) from the median and could be a composite extraction of both stunting and wasting child's status; a child with severe stunting has HAZ < −3SD and moderately stunting as −3SD ≤ HAZ ≤ −2SD. The same classification also holds for other anthropometric indicators of undernutrition (1). The WHO in 2021 has defined complementary feeding indicators, including MDD (percentage of children 6–23 months of age who consumed foods and beverages from at least 5 out of 8 defined food groups during the previous day), MMF (percentage of children 6–23 months of age who consumed solid, semi-solid or soft foods the minimum number of times or more during the previous day), and MAD (percentage of children 6–23 months of age who consumed at least MDD and MMF during the previous day) (4). Inadequate energy intake used in the study is defined as energy intake less than 80% of recommended daily intake (570–1742 kcal). Similarly, inadequate intake of other micronutrients is also defined as less than 80% of recommended daily nutrient intake (10). Severe and moderate anaemia is defined as <7 g/dl and 7.0–9.9 g/dl, respectively (11).

## Results

Seventy-three articles were included in the study based on the inclusion-exclusion criteria. Along with the national prevalence, data was also available from 20 states covering all six geopolitical zones of Nigeria. Amongst the included articles, a majority of studies (84.9%) reported data for the prevalence of undernutrition status (stunting, wasting, and underweight), overweight, and obesity. Articles reporting stunting prevalence (61.6%) were the highest in number, while that reporting wasting (53.4%) and underweight (46.6%) prevalence was relatively less. Only six articles provided information on SAM/PEM (8.2%). Approximately 28.8% of articles reported nutrient intake data, 47.6% reported anemia prevalence, 33.3% reported vitamin A deficiency prevalence, 19.0% reported zinc deficiency, 14.3% reported iron and vitamin D deficiency, while only 1 article each was identified reporting the prevalence of vitamin B12, vitamin C, and potassium deficiency.

On average, only 15% of the articles mentioned the year the study was conducted. Some of the selected articles with year of publication after January 1, 2018, did however cite data from earlier date including those from 2003.

## Severe acute malnutrition/protein-energy malnutrition

The average national prevalence of SAM/PEM in Nigeria was 8.8% (12, 13); however, the prevalence data reported a wide region-wise variance in prevalence, ranging from 1.9 to 46.0% (14–17) (Supplementary Table 1A). The highest SAM/PEM prevalence was reported from the South West and North West geopolitical zone of Nigeria (15, 16). In the Osun state (South West), the prevalence of Marasmus, Kwashiorkor, and Marasmus-Kwashiorkor were as low as 2.0, 3.0, and 2.0%, respectively (15).

## Overall prevalence of undernutrition (stunting, wasting, and underweight)

From several retrieved literature, the national prevalence of undernutrition has been reported to decline from 2003 to 2013 and 2018 (45% vs. 37% vs. 36.8% for stunting, 13% vs. 8% vs. 6.7% for wasting, and 27.2% vs. 28.9% vs. 21.4% for underweight) (18–20). The regional prevalence of stunting, wasting, and underweight reported a difference from the national prevalence in Nigeria, as seen in the geopolitical zone mapping of Nigeria (Figure 2).

As per National Demographic Health Survey (NDHS) 2018, the national stunting prevalence among under-5 children in Nigeria was reported to be 36.1% (21). The prevalence by zone reported stunting to be the highest in Kaduna in North West (61.3%) (22) and Kwara in the North Central zone (55.8%; Supplementary Table 2) (23). The lowest prevalence of stunting was reported from Osun state in the South West region (7.2%) (24). The severity of stunting varied among states from 3.4% in Rivers (South West) (25) to 54.5% in Kaduna (North West) (26). The prevalence of moderate stunting ranged from 8.2% in Anambra (South East) (27) to 28.8% in Kaduna (North West) (26) (Supplementary Table 2). From 2003 to 2018, the national prevalence of severe stunting reduced from 22.8 to 16.9% (28).

Several states in Nigeria reported a high prevalence of wasting, which was above the national prevalence in Nigeria, including Anambra (South East; 30%) (29), FCT, Abuja (North Central; 29.3%) (30), Bida, Niger (North Central; 27.8%) (31), Ondo (South West; 25.3%) (32), Enugu (South East; 25.0%) (33), Osun (South West; 24.0%) (34), and Kano (North West; 22.8%) (35). The lowest prevalence of wasting (1.0%) was seen in Ifedore, Ondo state (South West) (36). However, none of these studies cited the year of data collection.

The underweight prevalence in Nigerian states ranged from 5.9% in the Osun state (South West) (34) to 42.6% in the North West zone of Nigeria (37). Several states reported underweight prevalence above the national prevalence, with the highest prevalence in North West including Kano State (around 41.9%-42.6%) (35, 37), followed by Enugu state (South East; 39.8%) (24).

There are more than 10 reported risk factors for undernutrition, among others: malaria and anemia (38), having more than four children and religious belief (39), having a respiratory or diarrheal infection (28, 40). However, immunization status, maternal education, parental income, maternal height and body mass index (BMI); as well as having antenatal care less than four times are among commonly reported risk factors (28, 39–43).
<figure><img src="image_27.png" /><figcaption>FIGURE 2
Malnutrition map of Nigeria for (A) stunting, (B) wasting, (C) underweight, (D) overweight and obesity.</figcaption></figure>

## Urban vs. rural prevalence of undernutrition

It was seen that the overall national prevalence of stunting, wasting, and underweight in urban areas of Nigeria ranged between 26.8–30.6%, 10.5–35.6%, and 23.0–31.2%, respectively, while in the rural regions, it was 44.8–72.8%, 10.5–69.9%, and 35.5–72.9%, respectively (Supplementary Table 3). The highest prevalence of stunting was reported in the urban Ibadan, Oyo state (44) and rural regions of Lagos (39). State-wide prevalence of wasting was almost similar in rural regions compared to urban regions, with the highest prevalence reported from Lagos state (urban: 8.7%; rural: 9.3%) (38). The Ibadan urban region in Oyo state showed a wasting prevalence of 1.8% which was much below the national prevalence (10). The prevalence of underweight was considerably higher in rural compared to urban (19, 37, 45–47) (Figure 3).

## Gender-based prevalence of undernutrition

There was no definitive pattern has been noticed in the prevalence of gender-based undernutrition (Supplementary Table 4). A few studies have reported higher stunting prevalence in males compared to females. The

regional prevalence differs from the national prevalence in that the males depict a higher prevalence of stunting and wasting but not underweight (48–51). In contrast, wasting prevalence was slightly higher amongst females than males (22, 50, 52, 53). There were no differences between males and females regarding underweight prevalence (18, 45) (Supplementary Table 4).

In males, wasting prevalence ranged from 2.5 to 50.0%; underweight ranged from 6.0 to 78.0%, while in females, wasting prevalence was reported from 8.0 to 61.0% and underweight from 7.0 to 61.4% (16, 22, 48, 50, 54, 55).

## Age-based prevalence of undernutrition

Between 2008 and 2018, the NDHS data reported that the prevalence of stunting among children 6–11 months ranged from 28.9 to 35.4%, while the prevalence of stunting among 0–23 months old was 39.1%, and among 24 to 59 months was 53.3% (55). The prevalence of underweight was stable of around 10% among infants 0–6 months and 6–11 months. However, this number doubled to around 20.0% in older children of 12–60 months.

The prevalence of wasting was the highest among children 12–35 months (Figure 4).
<figure><img src="image_28.png" /><figcaption>FIGURE 3
Urban-rural prevalence of undernutrition across a few states in Nigeria.</figcaption></figure>

## Overweight and obesity

The data collected across 13 states reported the national prevalence of obesity in 2013 to be 4.0% (20, 43), while in 2018, it declined to 2.0%. The overweight prevalence ranged from 1.5 to 25.2%, while obesity ranged from 0.6 to 25.9% among under-5 children in different regions of Nigeria (21, 25, 30, 56, 57). The prevalence of overweight in male children in Ondo and Benue states was 9.8 and 10.5%, while in female children, it was 10.7 and 8.4%, respectively (53, 58) (Supplementary Table 5).

## Dietary intake

The prevalence of inadequate energy intake ranged from 18.0 to 34.0% (36, 59). A study in Ibadan, Oyo State (South West), reported an inadequate intake of fat (92.0%), protein (46.0%), and carbohydrates (30.0%) (10, 11). Other studies reported that the prevalence of inadequate intake for different micronutrients in under-5 children including zinc 32.0–91.0%, potassium 91.0%, vitamin A 82.0%, vitamin C 80.0%, iron 74.0%, and folate 44.0% (59, 60).

## Anemia and iron deficiency

Data from the NDHS shows the national prevalence of anemia among children under 5 years of age in Nigeria has slightly declined from 67.0% reported in 2013 to 58% in 2018 (20). However, the anemia prevalence has shown an upward trend in urban regions from

55.2% in 2010 to 62.0% in 2018, while in rural regions; the prevalence remains similar which was reported around 75.1% in 2010 and 72.5% in 2018 (61).

The prevalence of severe and moderate anemia in males was reported to be 3.6 and 43.3%, respectively, and in females, the prevalence of severe and moderate anemia was 2.9 and 38.4%, respectively (11). The highest prevalence of severe and moderate anemia was reported in children under 1 year (11) (Figure 5A). Moderate iron deficiency ranged from 1.1 to 32.2%, while severe deficiency ranged from 1.1 to 67.8% (16). Having vitamin A deficiency and malaria are some of the risk factors reported to be associated with anemia (62, 63).

## Other micronutrient deficiencies

Vitamin A deficiency ranged from 5.3 to 67.6% (24, 64), with a severe deficiency of 77.0 and 20.7% of moderate deficiency (Figure 5B) (16). The prevalence of vitamin A deficiency in a study of children 3 to 5 years of age ranged from 8.0 to 10.0% (65). Factors reported to be associated with vitamin A deficiency are anaemia (16) and low consumption of vitamin A-rich foods (26).

Based on baseline data of an intervention study among toddlers in Lagos (South West), it was reported a deficiency of vitamin D (9.0–37.0%), folate (8.0–21.1%), and vitamin B12 (7.1%) (66). Moderate vitamin D deficiency ranged from 1.15 to 24.14%, while severe deficiency ranging from 2.3 to 50.6% was reported in Kaduna state (16). Low serum zinc was reported in 12.4–26.0%, being higher in males (32.1%) than in females (19.1%) (33, 67).
Prevalence of undernutrition in Nigeria

<figure><img src="image_29.png" /><figcaption>FIGURE 4
Prevalence of undernutrition among children according to age.</figcaption></figure>

Quality of complementary feed (MDD, MAF, and MAD)

The proportion of children achieving MDD ranged from 3.0 to 55.2% in Nigeria (37, 54, 68–71) (Table 2). Dietary diversity was classified into low (meals comprising 1–6 food groups) and high (meals comprising 6–12 food groups). The prevalence of low and high dietary diversity reported in one study was 68.5 and 31.5%, respectively (22). While another study reported a total of 85.5% among 6- to 24-month-old children fulfilled the MDD, with the highest prevalence seen in 9- to 11-month-old infants (36.0%) (70).

The proportion of children achieving MMF ranged from 33.4 to 76.3% (37, 54, 68–70). In a study by Samuel and Ibidapo, the highest prevalence of MMF was 30.0% in infants 9 to 11 months of age. A high prevalence of MAD (90.8%) was reported in children in Osun state (70). Several factors reported in this review to be associated with poor quality of complementary foods are antenatal care visit, child's welfare clinic visit, mother's work place and education, as well as household size (70).

Discussion

This scoping review summarizes available evidence on the burden of malnutrition in under-5 children in various regions of Nigeria as well as the nutrient deficiency and quality of

complementary feeding. It highlights the disparities across regions, age groups and gender across several indicators of malnutrition, such as stunting, wasting, underweight, and anemia. While a total of 73 studies were identified, most reported data on nutritional status; only 6 reported the prevalence of SAM/PEM, and a few studies (less than 30%) revealed information on nutrient intake/deficiency or quality of complementary feeding.

The prevalence of stunting, wasting, and underweight in under-5 children was high, exhibiting considerable variations across different zones. The North West and North Central zones have been reported to have the highest prevalence of SAM, stunting, wasting, underweight, iron deficiency anemia, and moderate/severe vitamin D deficiencies and the lowest proportion of children under 6 months who met the MDD and MAD. In contrast, the prevalence of undernutrition was found to be generally lower in South East Nigeria compared to other zones (72).

The high prevalence of poor nutritional status among under-5 children in the Northern zones could be attributed to the militant insurgency, banditry, and cattle rustling hindering a large number of farmers and livestock herders from accessing their farmlands for almost a decade. These disastrous scenarios not only lead to restrictions on agricultural produce but also severe food insecurity, affecting many households residing in these zones, particularly in the rural regions (9, 22, 37, 48, 55). This situation also drives more consumption of staple foods as accessible sources of energy (37). In addition, there were some food taboos for
A

46.50%

3.80%

0-12 months

42.90%

3.60%

13-36 months

34.30%

2.44%

37-59 months

B

Vitamin A deficiency

77.01

20.69

12.64

49.42

13-24 months

2.3

16.09

months

3.45 4.6

months

6.9

37-48 months

months

1.15 0

49-59 months

months

■ Moderate Vit A deficiency (%)

■ Severe Vit A deficiency (%)

<caption>FIGURE 5
(A) Anemia prevalence in different age groups. (B) Severe vs. moderate vitamin A deficiency in different age groups.</caption>

children and pregnant women that restrict their access to certain foods, such as eggs which were more prominent in the Northern region (37). It was also clearly seen that undernutrition prevalence was more pronounced in rural regions than in urban regions.

More than half of the under-5 children have anemia with a higher prevalence reported in rural areas than in urban regions. Studies have shown that anemia in children leads to poor cognition, school achievement, and more behavioral issues as they grow, particularly into middle childhood (73). In several cases, it may lead to irreversible damage to the child's development (73); hence, efforts to alleviate the anemia burden are necessary in Nigeria. This review also noted that a substantial prevalence of vitamin A deficiency in Nigeria.

Undernutrition particularly in the first 2 years of life, has been reported to lead to an impairment of their ability to resist disease

(74, 75), undertake physical activity, and progress in study and school (74, 75). The impact of not achieving growth potential impedes the cognitive development of the child, with enormous and adverse implications on well-being and economic prospects as the child grows into adulthood. Undernutrition in the early years of life leads to reduced educational completion, lower economic productivity, increased morbidity, and reduced life expectancy (9). Studies have shown that children who suffer from undernutrition are not able to complete their education and show deficiencies in vocabulary and mathematics, along with other learning or intellectual disabilities, which leads to reduced economic productivity in adulthood (73, 78). This depletion of human capital not only poses social and economic challenges in disadvantaged communities of Nigeria but also hinders the overall economic and social progress of the country in general.
<table><caption>TABLE 2 Proportion of children who meet the minimum dietary diversity, minimum meal frequency, and minimum acceptable diet across different regions in Nigeria.</caption><thead><tr><th>Region</th><th>State</th><th>Year of data collection</th><th>Minimum dietary diversity (%)</th><th>Minimum meal frequency (%)</th><th>Minimum acceptable diet (%)</th><th>Reference</th></tr></thead><tbody><tr><td>North East</td><td></td><td>2016-2017</td><td>31.7</td><td>47.0</td><td>15.8</td><td>(36)</td></tr><tr><td rowspan="3">North West</td><td></td><td>2016-2017</td><td>37.5</td><td>40.1</td><td>14.4</td><td>(36)</td></tr><tr><td>Kano</td><td>2019</td><td>31.3</td><td></td><td></td><td>(63)</td></tr><tr><td>Kaduna</td><td>2019</td><td>28.0</td><td></td><td></td><td>(63)</td></tr><tr><td>North Central</td><td></td><td>2016-2017</td><td>41.5</td><td>46.5</td><td>16.6</td><td>(36)</td></tr><tr><td rowspan="2">South East</td><td></td><td>2016-2017</td><td></td><td></td><td></td><td></td></tr><tr><td></td><td>2016-2017</td><td>52.3</td><td>48.1</td><td>16.9</td><td>(36)</td></tr><tr><td rowspan="4">South West</td><td></td><td>2016-2017</td><td>46.8</td><td>33.4</td><td>12.8</td><td>(36)</td></tr><tr><td>Ogun</td><td>2019</td><td>85.5</td><td>66.4</td><td>90.8</td><td>(65)</td></tr><tr><td>Ijero, Ekiti</td><td>NA</td><td></td><td></td><td>40.7</td><td>(66)</td></tr><tr><td>Ikole, Ekiti</td><td>NA</td><td></td><td></td><td>29.4</td><td>(66)</td></tr><tr><td rowspan="2">South South</td><td>Edo</td><td>NA</td><td></td><td>76.3</td><td></td><td>(51)</td></tr><tr><td></td><td>2016-2017</td><td>55.2</td><td>43.1</td><td>18.9</td><td>(36)</td></tr></tbody></table>

NA: not available.

In addition, this report also shows the high prevalence of overweight and obesity in several states in North Central and South West Nigeria. This confirms the presence of a triple burden of malnutrition among children under 5 years of age in Nigeria which further worsens the dire situation of these children as resources need to be allocated to address these three problems simultaneously (79, 80).

The review reported a high prevalence of underweight and wasting among children between 12-35 months while the highest prevalence of stunting was in children 24-59 months old. In addition, the highest prevalence of severe and moderate anemia was reported in children under 1 year of age. These three conditions could be the root cause of why the prevalence of stunting remained and will continue to be high in Nigeria as has been shown in other countries (81). Studies have shown that underweight/wasting in the early life can persist into failure to thrive and will continue to stunting at a later age (82, 83).

Undernutrition among under-5 children is driven by a mesh of influencing factors, including cultural, behavioral, and environmental factors. The quality of complementary feeding practices is one of the important factors in determining the child's nutritional status. The 6- to 24-month age group is characterized by increased energy expenditure and need for nutritional requirements through complementary feeding. Inappropriate complementary feeding practices significantly contribute to undernutrition among children contributing as a major cause of disease burden, especially in developing countries (84). Maternal characteristics such as education and BMI were frequently reported in this review as influencing factors for stunting and the quality of complementary foods. This points out the importance to have better education and nutrition status of the mothers to ensure a healthy period during the first 1,000 days. In addition, malaria and anemia seem to counter-influence each other, thus it is important to address both factors together whenever one of these health issues occurred.

## Potential solutions to overcome the disparities in nutritional issues

Possible interventions that have been shown to reduce the prevalence of malnutrition should be able to address the problem at different levels. These are itemized as follows: (1) at an individual level: increased uptake and attendance of antenatal care visits, lower average number of birth and mandatory vaccination for children and women (81, 85), and improvement of nutrition and health knowledge among girls and pre-pregnant women (81); (2) at the household level: change in behavior to include the use of clean fuel for cooking, encouragement for more hygienic behavior including a reduction in the practice of open defecation (81); (3) at regional level: food subsidies or food-based intervention in the northern region of Nigeria has been shown to simultaneously overcoming multiple nutritional challenges in the area; and (4) at the national level to support at least four times minimal antenatal care, address anemia and malaria (86), to enhance awareness of vaccination in young children (85), create more equitable household economic growth (81, 87), and build infrastructure for greater access to improve toilet facilities (81, 88).

It is crucial to prioritize efforts that enhance, among others, access to nutritious food, improve healthcare services, and implement effective, targeted interventions. Furthermore, there is a need for more monitoring efforts to evaluate the effectiveness of these interventions as a basis for further refinement of the interventions in the future.

## Strengths and limitations

This scoping review offers a comprehensive assessment of the nutritional status of children in Nigeria, as well as various related indicators, providing invaluable insights to guide future research endeavours. Furthermore, it highlights the need to invest in public
policies and socioeconomic strategies that can foster improved nutrition status and address the underlying issues. This study meticulously collates an array of essential undernutrition parameters, complementary feeding indicators, and dietary intake status pertaining to under-5 children in Nigeria. Drawing from recent data sourced from national health surveys, regional surveys, and other pertinent studies, a spectrum of prevalence rates spanning from low to high has been thoughtfully incorporated based on geopolitical zone. To enhance accessibility, the amassed data has been meticulously organized and presented in a user-friendly mapping format.

Due to the heterogeneity of studies, the absence of precise study years, and limited results for some regions, the undertaking of a comprehensive meta-analysis or applying statistical analysis to individual study results was not feasible.

## Areas for further research

There is a dearth of data available for micronutrient intake and deficiency. For example, in this review, there were no data retrieved on nutrients related to immunity, such as vitamin C and E (89), or nutrients that are important for bone growth, such as calcium, phosphorus, magnesium (90), and also nutrients that are commonly reported to be deficient in other populations (such as pregnant women in Nigeria) like thiamine and riboflavin (91, 92). For macronutrients, total protein and LCPUFA intake in under-5 children, for example, was notably absent in all of the selected studies.

## Conclusion

The culmination of this comprehensive analysis has brought to the forefront a multitude of pressing nutritional problems, prominently underscored by the pervasive triple burden of malnutrition encompassing stunting, overweight, and anemia. The northern zones in Nigeria have emerged as particularly vulnerable, grappling with a more acute undernutrition crisis as evidenced by the array of indicators examined.

Gender differentials further paint a nuanced picture, whereby stunting exhibited a higher prevalence among males, while the prevalence of wasting in several states, was skewed toward females. The widespread occurrence of anemia across diverse geographical regions, reveals noteworthy disparities between urban and rural areas. The high prevalence of vitamin A and D deficiency, along with inadequate nutrient intake, were also notable.

Compounding these challenges, a significant proportion of children were found to lack access to a diverse range of food groups, and fell short of consuming the recommended number of meals per day in their complementary diets.

In light of the far-reaching impact of malnutrition, encompassing the loss of human capital and reduced economic productivity across Nigeria, the imperative for targeted and expeditious nutrition interventions becomes evident. Swift and concerted action are crucial to rectify the prevailing disparities in nutrition status among young children in Nigeria, securing a healthier and more prosperous future for the nation at large.

## Data availability statement

The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.

## Author contributions

CJ: Conceptualization, Data curation, Investigation, Methodology, Writing - review & editing, Formal analysis. BP: Conceptualization, Data curation, Investigation, Writing - review & editing, Formal analysis. MJ: Conceptualization, Formal analysis, Writing - review & editing, Methodology. GM: Formal analysis, Methodology, Writing - review & editing, Data curation. IA: Formal analysis, Methodology, Writing - review & editing, Investigation. OE: Formal analysis, Investigation, Writing - review & editing, Data curation. BA: Formal analysis, Writing - review & editing, Methodology. NC: Formal analysis, Methodology, Writing - review & editing, Data curation. VB: Writing - review & editing. LM: Writing - review & editing, Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Supervision.

## Funding

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

## Acknowledgments

The authors would like to acknowledge and appreciate the medical writer, Pooja S. Banerjee, IJCP Group, New Delhi, India, for her support in the development of the first drafts of the manuscript.

## Conflict of interest

VB and LM are employees of FrieslandCampina.

The remaining 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.

## Publisher's note

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.

## Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnut.2023.1279130/full#supplementary-material
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