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1Original Manuscript
2Determinants of overweight and obesity among children between 5 to 11 years in
3Ecuador: a secondary analysis from the National Health Survey 2018
4Betzabé Tello1¶, José Ocaña2¶, Paúl García-Zambrano3&, Betsabé Enríque-Moreira3&,
5Iván Dueñas-Espín2¶*.
61 Center for Research in Health in Latin America CISeAL, Pontificia Universidad
7Católica del Ecuador, Quito, Ecuador
82 Instituto de Salud Pública, Facultad de Medicina, Pontificia Universidad Católica
9del Ecuador, Quito, Ecuador.
10 3 Postgrado de Medicina Familiar, Facultad de Medicina, Pontificia Universidad
11 Católica del Ecuador, Quito, Ecuador.
12
13 * Corresponding author:
14 E-mail: igduenase@puce.edu.ec (ID-E)
15
16 ¶These authors contributed equally to this work.
17 &These authors also contributed equally to this work.
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NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.
2
18 Abstract
19 Background: During the 1990s, global eating habits changed, affecting poorer and
20 middle-income nations, as well as richer countries. This shift, known as the "obesity
21 transition," led to more people becoming overweight or obese worldwide. In Ecuador,
22 this change is happening, and now, one in three children is affected by overweight or
23 obesity (OW/OB). This study explores the links between social, economic, and
24 demographic factors and childhood obesity in Ecuador, seeking to provide insights for
25 shaping future health policies in response to this intricate shift.
26 Methods: A cross-sectional study using 2018 National Health and Nutrition Survey
27 data from Ecuador. Weighted percentages were computed, and odds ratios for
28 OW/OB unadjusted and adjusted for each category of explanatory variables were
29 estimated using multilevel multivariate logistic regression models.
30 Results: Among 10,807 Ecuadorian school children aged 5 to 11, the prevalence of
31 OW/OB was 36.0%. Males exhibited 1.26 times higher odds than females (95% CI:
32 1.20 to 1.33), and each additional year of age increased the odds by 1.10 times (95%
33 CI: 1.09 to 1.10). Economic quintiles indicated increased odds (1.17 to 1.39) from the
34 2nd to 5th quintile (the richest) compared with the first quintile (the poorest). Larger
35 household size slightly reduced odds of OW/OB (adjusted odds ratio [aOR]=0.93,
36 95% CI: 0.91 to 0.95), while regular physical activity decreased odds ([aOR]=0.79,
37 95% CI: 0.75 to 0.82). The consumption of school-provided meals showed a non-
38 significant reduction (aOR: 0.93, 95% CI: 0.82 to 1.06). Children from families
39 recognizing and using processed food labels had a higher likelihood of being
40 overweight or obese (aOR=1.14, 95% CI: 1.02 to 1.26).
41 Conclusion: Age, male gender, and higher economic quintile increase OW/OB in
42 Ecuadorian school children. Larger households and physical activity slightly decrease
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43 risks. Ecuador needs policies for healthy schools and homes, focusing on health,
44 protection, and good eating habits.
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45 Introduction
46 Similar to high-income countries, low- and middle-income countries-initiated
47 processes of nutritional and food environment transition in the 1990s. These
48 transitions are characterized by an increase in the consumption of processed foods,
49 edible oils, and sugary beverages, as well as a greater tendency to eat outside the
50 home and an increased availability of ultra-processed products. All these changes
51 have come at the expense of healthy and traditional diets. Simultaneously, the
52 population in these countries has gradually reduced physical activity and increased
53 sedentary behavior [1–3].
54
55 These transitions have led to a gradual increase in overweight and obesity (OW/OB)
56 in all age groups, with a special increase in childhood OW/OB [1–4]. This global
57 increase follows a pattern known as the "obesity transition." This pattern is
58 characterized by a gradual shift in the burden of OW/OB from high-income to low-
59 and middle-income countries, from wealthy households to poor ones, from urban to
60 rural areas, and from adults to children. This changes affect several countries in Latin
61 America [5].
62
63 Childhood obesity is a precursor to adult cardiovascular diseases and cancer [6–8].
64 Understanding its determinants is vital for making informed public health policy
65 decisions.
66 This study aims to identify the independent factors associated with obesity and
67 overweight in Ecuadorian school-age children (5-11 years). By delving into
68 obesogenic environments and contextual sociodemographic conditions, this research
69 offers valuable territorial insights.
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70 In Ecuador, the prevalence of childhood OW/OB surged by almost 5 percentage
71 points from 2012 to 2018, reaching 35.4% [9]. This alarming increase predominantly
72 impacts urban areas, males, those with mixed and white ethnic backgrounds, and
73 wealthier households. Importantly, this trend extends beyond, affecting middle- and
74 low-income households and rural populations, highlighting the urgency for a thorough
75 exploration of its determinants [9].
76
77 While there is extensive knowledge about the social, environmental, and clinical
78 determinants of excess malnutrition in school-age children, particularly in high-
79 income countries [10], few countries in the region have studied these determinants
80 [11,12]. In low- and middle-income countries such as Ecuador, there is a lack of
81 national information regarding the determinants of childhood obesity in school-age
82 children, except for some localized studies [13–15]. The existing knowledge gap
83 precludes the development of effective public health policies. Despite the bulk of
84 scientific evidence, the Ecuadorian government’s efforts are not focused on
85 improving school environments, increasing taxes on sugary beverages, or improving
86 the labeling of processed and ultra-processed products; rather, it has reduced taxes on
87 ultra-processed food [16]. Therefore, initiatives with proper implementation,
88 supervision, and robust evaluations are necessary to demonstrate their impact and
89 cost-effectiveness in school-age populations. These insights will serve as a compass
90 for evidence-based public policies and interventions, which are crucial for combating
91 childhood obesity in Ecuador.
92
93
94
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95 Materials and Methods
96 Study design:
97 This cross-sectional study involved a secondary analysis, utilizing data from the 2018
98 National Health and Nutrition Survey of Ecuador. To ensure methodological rigor and
99 transparency in both the study design and dissemination of findings, compliance with
100 the Strengthening the Reporting of Observational Studies in Epidemiology
101 (STROBE) [17] guidelines was scrupulously maintained, as detailed in S1 Table.
102 Population and sample:
103 In our study, we included data from children encompassing sociodemographic
104 information, anthropometric measurements, dietary habits at home and school, and
105 physical activity status. Additionally, we integrated information about whether the
106 children’s households identified, understood, and used the nutritional traffic light
107 labeling system for processed foods and beverages that was implemented in Ecuador.
108 These questions were incorporated into the National Health and Nutrition Survey of
109 2018 (see flowchart in Fig 1). We included study subjects who: (i) are ≥5 years old or
110 ≤11 years old; (ii) had complete anthropometric information, and (iii) had complete
111 data regarding age, ethnicity, economic quintile, schooling characteristics, dietary
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112 habits, and physical activity.
113
114 The ENSANUT 2018 survey
115 The Ecuadorian National Health and Nutrition Survey 2018 (ENSANUT 2018, for its
116 acronym in Spanish) was a cross-sectional study conducted in 2018 that involved
117 nationally representative samples from the Ecuadorian population [18].
118
119 In the ENSANUT 2018 study, a two-stage sampling strategy was employed to secure
120 a representative sample of the Ecuadorian populace. Initially, Primary Sampling Units
121 (PSU) were chosen through stratified sampling, incorporating proportional probability
122 to size. Subsequently, an average of 18 households per PSU were randomly selected
123 for investigation. Within these households, targeted demographic groups were
124 identified, including children under 5 years, women aged 12 to 49, men aged over 12
125 years, and individuals between 5 and 17 years. For households with children aged 5 to
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126 11 years, one individual between 5 and 17 years was selected for interview and
127 subjected to a specialized questionnaire. Anthropometric measurements were
128 obtained. The abovementioned sampling approach ensured the data quality and
129 representativeness of the study. Further information on the methodology, datasets, and
130 findings of ENSANUT 2018 is available at:
131 https://www.ecuadorencifras.gob.ec/institucional/home/.
132 Measurements:
133 We used the information that the survey collected about sex, age of the child,
134 ethnicity, education of the children, economic quintile, regular class attendance,
135 geographical regions of Ecuador, receiving the human developing bonus (BDH, for its
136 acronym in Spanish), number of people in the household, disposal of excreta, physical
137 activity, perception of consumption of vegetables, consumption of fast food, days per
138 week of school food consumption, buying food at school, consumption of the food
139 provided by the school, recognizing, understanding, and using the nutritional traffic
140 light labelling of processed foods; and, consumption of processed foods with a red
141 label. The Ecuadorian Nutritional Traffic Light Labelling system uses three colours -
142 red, yellow, and green - to indicate the levels of sugar, fat, and salt in processed foods.
143 Red signifies high concentrations, yellow denotes medium levels, and green indicates
144 low content [19]. This system empowers consumers to make healthier food choices.
145 Main outcome:
146 The main outcome variable was, overweight or obesity. The World Health
147 Organization (WHO) macro program Stata (WHO AnthroPlus) was used to establish
148 the nutritional status of children based on WHO 2007 standards for the classification
149 of children as overweight or obese between 5 and 19 years. Overweight was defined
150 as a Body-Mass-Index (BMI)-for-age greater than 1 standard deviation above the
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151 WHO Growth Reference median; and obesity as a greater than 2 standard deviations
152 above the WHO Growth Reference median [20].
153 Statistical analyses and sample considerations:
154 A priori, we calculated that a sample of 9759 individuals is enough to estimate, with
155 95% confidence and an accuracy of +/- 1 percentage units, a population percentage
156 that will predictably be around 35.38% [9]. The percentage of necessary replacements
157 is expected to be 10%.
158 In the analysis, the 'svy' command from Stata 16.1 (StataCorp. 2019. Stata Statistical
159 Software: Release 16. College Station, TX: StataCorp LLC.) was employed to
160 accommodate the expansion factors inherent in the survey design. This methodology
161 yield estimates that faithfully represent the complex nature of the survey’s structure.
162 Descriptive statistics for complex survey data involved calculating weighted
163 percentages for categorical variables and deriving means and standard errors for
164 discrete ones. Subsequently, the characteristics of non-overweight or obese children
165 were compared with those of overweight or obese children, adhering to the same 'svy'
166 command approach.
167 Then, multilevel logistic regression models were built to analyze the relationships
168 between the explanatory variables and the outcome. We estimated crude and adjusted
169 odds ratios (OR and aOR) of OW/OB for each explanatory variable and/or their
170 categories. In that sense, we built multilevel multivariate logistic regression models to
171 evaluate the independent association between each explanatory variable and health
172 self-perception. The multilevel variable was geographical region, considering that
173 there are important differences in OW/OB across those regions. Moreover, expansion
174 factors were used to align estimates with the stratified sampling method and primary
175 sampling units, managing variability and correlations within groups.
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176 We built a saturated model that included all the individual covariates. Then, based on
177 the researchers’ criteria, we eliminated covariates with p>0.25 from significant
178 covariates that were retained in the model [21]. Confidence intervals (95%CI) of the
179 adjusted and unadjusted ORs and their corresponding p-values were calculated. Once
180 the parsimonious model was obtained, we compared both models and chose the
181 “final” model, according to its level of significance from the likelihood ratio test. The
182 final model was stratified by sex. Given the small number of missing data (there were
183 missing values in <1% of the whole database), we employed complete case analysis
184 to estimate statistical associations.
185 To test for potential effect modification, we performed several secondary analyses to
186 assess the sensitivity of our estimates with our assumptions regarding biases, and to
187 test for model misspecifications. We ran the final model excluding: (i) children
188 categorized in the highest income quintile, (ii) children whose parents received the
189 BDH, and (iii) children within the upper third of the highest number of people per
190 household.
191 Ethical issues:
192 The research protocol was thoroughly reviewed and approved by the Ethics
193 Subcommittee for Research in Human Beings of the Faculty of Medicine of the
194 Pontifical Catholic University of Ecuador under the code SB-CEISH-POS-691. The
195 committee determined that informed consent was not required for this study.
196
197 Results
198 A comprehensive demographic and health snapshot of children aged 5 to 11 years is
199 presented in Table 1. In the study sample comprising 10807 children aged 5 to 11
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200 from Ecuador, it was found that the mean age of the children was 8.0 years (standard
201 error [SE]: 0.03). About ethnicity, Mestizo (mixed ethnic background) children were
202 predominant, making up 80.8% of the sample. When stratified by economic quintiles,
203 approximately a quarter of the children (25.4%) belonged to the lowest income
204 quintile. Educationally, it was noted that the vast majority (98.3%) were currently
205 attending elementary school. Regarding dietary habits, a significant proportion of the
206 sample (64.3%) reported consuming food provided by the school, and nearly half
207 (48.4%) indicated a reduction in the consumption of processed foods with a red label.
208 Notably, 36.0% of the children were identified as being overweight or obese,
209 according to the Body Mass Index (BMI) adjusted for age and sex, using the WHO
210 Growth References.
211 Table 1. Description of the sample.
Variable
Whole sample
(n= 10807)
No.
Weighted
percentage or
mean (SE)
Sex (Male)
5541
50.3
Age of the children
10807
8.0 (0.03)
Ethnicity
Ethnicity (Indigenous)
1252
7.2
Ethnicity (Afroecuadorian)
459
4.8
Ethnicity (Mestizo-mixed ethnic background)
8536
80.8
Ethnicity (White)
141
1.3
Ethnicity (Montubio or other)
419
5.9
Economic quintiles by incomea
Economic quintile by income (1st quintile)
2865
25.4
Economic quintile by income (2nd quintile)
2344
23.6
Economic quintile by income (3rd quintile)
2096
20.4
Economic quintile by income (4th quintile)
1767
16.7
Economic quintile by income (5th quintile)
1608
13.9
Education of children (Elementary school ongoing)
10618
98.3
Regular class attendance (Yes)
10764
99.6
The head of the household receives the BDH (Yes)
455
4.4
Number of people in the household
10807
5.0 (0.03)
Inadequate disposal of excreta (Yes)
2570
24.1
Regular physical activity (Yes)
1389
13.5
Perception of low consumption of vegetables (Yes)
5218
47.1
Days per week of consumption in fast food restaurants
10807
0.9 (0.02)
Days per week of school food consumption
7930
4.4 (0.03)
The child buys food at school (Yes)
7271
64.1
The child eats the food provided by the school (Yes)
7103
64.3
Family members recognize, understand, and use the labeling of
processed foods (Yes)
6635
65.4
In the family, they reduced the consumption of processed foods with a
red label (Yes)
4256
48.4
Overweight or obesityb
3931
36.0
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BDH=Human Development Voucher, by its Spanish spelling.
SE= Standard error.
a Income quintiles are calculated at the household level using monetary labour income per capita, first calculating the total
income for each income earner. This total income includes earnings from work, income from investments, transfers, and other
benefits, such as cash social transfers. Once we add all these up, we obtain the total household income. Then, we determine
the average income per person (per capita income) by dividing the total household income by the number of people in each
household. Subsequently, the population is systematically arranged on the basis of the per capita income variable. The
calculation of the quintiles was performed by dividing the population into five equal groups, known as quintiles. The first
quintile includes the percentage of households with the lowest income, the second quintile includes the next percentage, and
so on until the fifth quintile, which includes the percentage of households with the highest income.
b Overweight and obesity were determined by calculating the Body Mass Index (BMI), adjusted for age and sex, according to
the WHO growth references. Overweight is BMI-for-age greater than 1 standard deviation above the WHO Growth
Reference median; and obesity is greater than 2 standard deviations above the WHO Growth Reference median.
212
213 We found distinct differences between non-overweight or non-obese children and
214 their overweight or obese counterparts (S2 Table). In a comparison of characteristics
215 between non-overweight/non-obese children (n=6876) and those identified as
216 overweight or obese (n=3931) the mean age of overweight or obese children was
217 slightly higher at 8.3 years (SE: 0.05) compared to 7.9 years (SE: 0.04) for their non-
218 overweight counterparts. Ethnic distribution showed that 81.7% of the overweight or
219 obese groups were Mestizo (mixed ethnic background), compared with 80.3% in the
220 non-overweight group. Economic stratification revealed that children from the lowest
221 income quintile were less represented (20.6%) among the overweight or obese
222 compared to those non-overweight (28.2%). Furthermore, a greater proportion of
223 overweight or obese children (65.7%) consumed food provided by the school, in
224 contrast to 63.2% of the non-overweight children. Finally, a difference was detected
225 in the perception of reduced consumption of processed foods with a red label, with
226 50.4% of overweight or obese children indicating consumption, compared to 47.2% of
227 their non-overweight peers.
228 After running multivariate logistic regression models, we found that, several factors
229 were significantly associated with childhood overweight and obesity (Table 2).
230 According to the final adjusted model, several variables exhibited statistically
231 significant associations between several explanatory variables and being overweight
232 or obese. Notably, male children exhibited a higher likelihood of being OW/OB, with
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233 1.26 times increased adjusted odds (95% CI: 1.20 to 1.33) compared to female
234 children. In addition, for every yearly increase in a child’s age, the odds of being
235 overweight or obese increased by 1.10 times (95% CI: 1.09 to 1.10). When broken
236 down by ethnicity, compared to Indigenous children, the Afroecuadorian ethnicity
237 presented a slightly elevated but not statistically significant odds of 1.12 (95% CI:
238 0.99 to 1.26), while Mestizo children showed 1.14 times increased odds (95% CI:
239 1.04 to 1.25). White children and those from Montubio (mixed ethnic background of
240 coastal Ecuador) or other ethnicities did not demonstrate statistically significant
241 differences in this model. When considering economic quintiles by income, children
242 in the 2nd quintile demonstrated 1.17 times higher odds (95% CI: 1.07 to 1.31), those
243 in the 3rd quintile showed 1.33 times (95% CI: 1.11 to 1.59), in the 4th quintile it was
244 1.39 times (95% CI: 1.18 to 1.65), and in the 5th quintile, the odds were 1.39 times
245 higher (95% CI: 1.29 to 1.51) compared to those in the 1st quintile. An increase in the
246 number of household members corresponded to a slight reduction in odds by 0.93
247 times for each additional person (95% CI: 0.91 to 0.95). Moreover, children with
248 inadequate disposal of excreta exhibited 0.82 times lower odds of being overweight or
249 obese (95% CI: 0.76 to 0.90). Similarly, regular physical activity was associated with
250 reduced odds, at 0.79 times (95% CI: 0.75 to 0.82). Interestingly, children from
251 families that recognized and used processed food labels exhibited a higher likelihood
252 of being overweight or obese, with an adjusted odds ratio (aOR) of 1.14 (95% CI:
253 1.02 to 1.26). Conversely, the consumption of food provided by schools was linked
254 with a non-significant reduction in the risk of overweight or obesity, with an aOR of
255 0.93 (95% CI: 0.82 to 1.06).
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256 Table 2. Crude and adjusted Odds Ratios of overweight or obesity from each explanatory variable using multilevel and logistic regression models.
Crude models
Adjusted models
Variable
OR (IC95%)
p-value
Saturated
aOR (IC95%)
p-value
Parsimonious
aOR (IC95%)
p-value
Male sex (female is the ref.)
1.24 (1.18 to 1.31)
<0.001
1.23 (1.08 to 1.40)
0.002
1.26 (1.20 to 1.33)
<0.001
Age of the child (per each increase in one year)
1.10 (1.08 to 1.12)
<0.001
1.09 (1.08 to 1.10)
<0.001
1.10 (1.09 to 1.10)
<0.01
Ethnicity
Ethnicity (Indigenous is the ref.)
1
-
1
-
1
-
Ethnicity (Afroecuadorian)
1.09 (0.90 to 1.32)
0.381
0.99 (0.86 to 1.15)
0.917
1.12 (0.99 to 1.26)
0.062
Ethnicity (Mestizo - mixed ethnic background)
1.25 (1.06 to 1.49)
0.009
1.06 (0.88 to 1.27)
0.560
1.14 (1.04 to 1.25)
0.004
Ethnicity (White)
1.39 (1.05 to 1.85)
0.021
1.17 (0.80 to 1.71)
0.412
1.26 (0.95 to 1.67)
0.114
Ethnicity (Montubio or other)
1.01 (0.83 to 1.24)
0.903
0.82 (0.76 to 0.89)
<0.001
0.97 (0.83 to 1.14)
0.718
Basic education of the children (no formal education is the ref.)
2.52 (0.69 to 9.18)
0.160
0.82 (0.50 to 1.35)
0.433
Economic quintiles by incomea
Economic quintile by income (1st quintile is the ref.)
1
-
1
-
1
-
Economic quintile by income (2nd quintile)
1.21 (1.14 to 1.29)
<0.001
1.13 (1.07 to 1.19)
<0.001
1.17 (1.12 to 1.21)
<0.001
Economic quintile by income (3rd quintile)
1.47 (1.24 to 1.75)
<0.001
1.32 (1.05 to 1.64)
0.015
1.33 (1.11 to 1.59)
0.002
Economic quintile by income (4th quintile)
1.59 (1.36 to 1.86)
<0.001
1.34 (1.10 to 1.63)
0.004
1.39 (1.18 to 1.65)
<0.001
Economic quintile by income (5th quintile)
1.67 (1.50 to 1.85)
<0.001
1.41 (1.19 to 1.67)
<0.001
1.39 (1.29 to 1.51)
<0.001
p-for-trend
1.15 (1.10 to 1.19)
<0.001
1.09 (1.05 to 1.14)
<0.001
1.09 (1.05 to 1.14)
<0.001
Regular class attendance (otherwise is the ref)
1.48 (0.94 to 2.34)
0.091
0.67 (0.26 to 1.71)
0.403
-
-
The head of the household receives the BDH (otherwise is the ref.)
0.77 (0.74 to 0.81)
<0.001
0.97 (0.86 to 1.08)
0.563
-
-
Number of people in the household (per each extra person)
0.92 (0.91 to 0.93)
<0.001
0.94 (0.91 to 0.97)
<0.001
0.93 (0.91 to 0.95)
<0.001
Inadequate disposal of excreta (otherwise is the ref.)
0.73 (0.68 to 0.78)
<0.001
0.87 (0.75 to 1.00)
0.058
0.82 (0.76 to 0.90)
<0.001
Regular physical activity (otherwise is the ref.)
0.76 (0.75 to 0.78)
<0.001
0.77 (0.76 to 0.78)
<0.001
0.79 (0.75 to 0.82)
<0.001
Perception of low consumption of vegetables (otherwise is the ref.)
0.91 (0.79 to 1.05)
0.196
1.01 (0.91 to 1.13)
0.824
-
-
Days per week of consumption in fast food restaurants (per each extra
day of consumption)
1.05 (1.03 to 1.07)
<0.001
1.01 (0.96 to 1.06)
0.810
-
-
Days per week of school food consumption (per each extra day of
consumption)
0.98 (0.94 to 1.02)
0.318
0.99 (0.93 to 1.06)
0.751
-
-
The child buys food at school (otherwise is the ref.)
1.17 (0.95 to 1.44)
0.139
1.10 (0.90 to 1.36)
0.349
-
-
Consumption of food provided by the school (otherwise is the ref.)
0.83 (0.73 to 0.95)
0.007
0.82 (0.74 to 0.90)
<0.001
0.93 (0.82 to 1.06)
0.288
Family members recognize, understand, and use the labeling of
processed foods (otherwise is the ref.)
1.24 (1.14 to 1.35)
<0.001
1.14 (1.09 to 1.19)
<0.001
1.14 (1.02 to 1.26)
0.019
In the family, they reduced the consumption of processed foods with a
red label (otherwise is the ref.)
1.14 (1.10 to 1.18)
<0.001
1.08 (0.94 to 1.25)
0.260
-
-
BDH=Human Development Voucher, by its Spanish spelling
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a Income quintiles are calculated at the household level using monetary labour income per capita, first calculating the total income for each income earner. This total income includes earnings from work,
income from investments, transfers, and other benefits, such as cash social transfers. Once we add all these up, we obtain the total household income. Then, we determine the average income per person (per capita
income) by dividing the total household income by the number of people in each household. Subsequently, the population is systematically arranged on the basis of the per capita income variable. The calculation of
the quintiles was performed by dividing the population into five equal groups, known as quintiles. The first quintile includes the percentage of households with the lowest income, the second quintile includes the
next percentage, and so on until the fifth quintile, which includes the percentage of households with the highest income.
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16
258 In analyzing the determinants of overweight and obesity in children, notable
259 differences emerged between genders when running the final parsimonious model
260 (Table 3). For each incremental year in age, a significant association with overweight
261 or obesity was noted in both genders, with the odds of being overweight or obese
262 increasing by an adjusted odds ratio (aOR) of 1.09 (95% CI: 1.07 to 1.11) for women
263 and 1.10 (95% CI: 1.09 to 1.11) for men. Among the ethnicities, White ethnicity was
264 associated with the highest risk in women, with an aOR of 1.57 (95% CI: 1.34 to
265 1.83). With respect to economic quintiles, women in the 5th quintile exhibited the
266 greatest risk, with aOR of 1.38 (95% CI: 1.13 to 1.70). In relation to other
267 determining factors, it was observed that inadequate disposal of excreta had
268 significant associations with overweight or obesity in both genders. For women, the
269 risk was reduced with an adjusted odds ratio (aOR) of 0.86 (95% CI: 0.80 to 0.92),
270 whereas for men, the reduction in risk was slightly more pronounced with an aOR of
271 0.81 (95% CI: 0.74 to 0.89). Regular physical activity appeared to be protective
272 against overweight or obesity. Women who engaged in regular physical activity
273 presented a reduced risk, as indicated by an aOR of 0.84 (95% CI: 0.80 to 0.88),
274 whereas men benefitted slightly more from such activity, displaying an aOR of 0.76
275 (95% CI: 0.70 to 0.83). Notably, in households where processed food labeling was
276 recognized, understood, and utilized, the risk of overweight or obesity increased in
277 both women and men. This association, for women, was statistically significant with
278 an aOR of 1.16 (95% CI: 1.09 to 1.24), and for men, it was not significant (aOR=
279 1.11; 95% CI: 0.95 to 1.29).
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280 Table 3. Adjusted Odds Ratios of overweight or obesity from each explanatory variable using the parsimonious logistic regression model of Table 2
281 between women and men.
282
Variable
Parsimonious model in women
aOR (IC95%)
p-value
Parsimonious model in men
aOR (IC95%)
p-value
Age of the child (per each increase in one year)
1.09 (1.07 to 1.11)
<0.001
1.10 (1.09 to 1.11)
<0.001
Ethnicity
Ethnicity (Indigenous is the ref.)
1
-
1
-
Ethnicity (Afroecuadorian)
1.19 (0.82 to 1.72)
0.374
1.07 (0.93 to 1.23)
0.325
Ethnicity (Mestizo – mixed ethnic background)
1.32 (1.09 to 1.60)
0.005
1.01 (0.97 to 1.06)
0.653
Ethnicity (White)
1.57 (1.34 to 1.83)
<0.001
1.05 (0.64 to 1.70)
0.858
Ethnicity (Montubio or other)
0.94 (0.67 to 1.32)
0.716
0.99 (0.97 to 1.00)
0.196
Economic quintiles by incomea
Economic quintile by income (1st quintile is the ref.)
1
-
1
-
Economic quintile by income (2nd quintile)
1.13 (1.10 to 1.17)
<0.001
1.19 (1.14 to 1.24)
<0.001
Economic quintile by income (3rd quintile)
1.27 (0.88 to 1.83)
0.203
1.38 (1.32 to 1.44)
<0.001
Economic quintile by income (4th quintile)
1.31 (1.07 to 1.60)
0.009
1.48 (1.26 to 1.73)
<0.001
Economic quintile by income (5th quintile)
1.38 (1.13 to 1.70)
0.002
1.40 (1.33 to 1.48)
<0.001
p-for-trend
1.09 (1.01 to 1.17)
0.021
1.10 (1.09 to 1.12)
<0.001
Number of people in the household (per each extra person)
0.92 (0.90 to 0.95)
<0.001
0.93 (0.88 to 1.00)
0.041
Inadequate disposal of excreta (otherwise is the ref.)
0.86 (0.80 to 0.92)
<0.001
0.81 (0.74 to 0.89)
<0.001
Regular physical activity (otherwise is the ref.)
0.84 (0.80 to 0.88)
<0.001
0.76 (0.69 to 0.83)
<0.001
Consumption of food provided by the school (otherwise is the ref.)
1.01 (0.98 to 1.05)
0.499
0.86 (0.69 to 1.09)
<0.001
Family members recognize, understand, and use the labeling of
processed foods (otherwise is the ref.)
1.16 (1.09 to 1.24)
<0.001
1.11 (0.95 to 1.29)
0.179
BDH=Human Development Voucher, by its Spanish spelling
a Income quintiles are calculated at the household level using monetary labour income per capita, first calculating the total income for each income earner. This total income includes earnings from work, income
from investments, transfers, and other benefits, such as cash social transfers. Once we add all these up, we obtain the total household income. Then, we determine the average income per person (per capita income)
by dividing the total household income by the number of people in each household. Subsequently, the population is systematically arranged on the basis of the per capita income variable. The calculation of the
quintiles was performed by dividing the population into five equal groups, known as quintiles. The first quintile includes the percentage of households with the lowest income, the second quintile includes the next
percentage, and so on until the fifth quintile, which includes the percentage of households with the highest income.
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perpetuity.
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18
284 After conducting an analysis excluding children in the highest quintile, children with
285 parents receiving the BDH, and those residing in households with a larger number of
286 individuals, the observed associations maintained similar trends. However, the
287 relationships were less statistically significant, as detailed in S3 Table.
288 Discussion
289 Principle Findings
290 Increasing age, male gender, mestizo (mixed ethnic background) ethnicity, higher
291 economic quintiles, inadequate disposal of excreta, and lack of physical activity are
292 factors associated with a higher likelihood of overweight or obesity in children aged 5
293 to 11 years in Ecuador. The impact of consuming school-provided meals was
294 inconclusive. Children from families with a higher number of individuals in the
295 household and with families that recognize and use processed food labels exhibited a
296 higher likelihood of being overweight or obese.
297 Comparison with the Literature
298 The prevalence of OW/OB in school children places the country in the eighth position
299 in the Americas, following countries such as Mexico, Chile, Panama, and the United
300 States, and surpassing the prevalences in Colombia and Peru [22]. International data
301 reported in 2016 ranked the country 15th in the same region [23]. This highlights the
302 drastic increase in prevalence in the absence of effective public policies, compared
303 with other countries that have taken strong measures against childhood overweight
304 and obesity [24,25].
305 The study reveals that children who purchase food at school are at a greater risk of
306 being overweight or obese than those who consume meals provided by the school, but
307 this difference is not statistically significant. Despite that, it is important to mention
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19
308 that prior research from the ENSANUT 2018 survey, indicated a link between school
309 foods, particularly those sold in stores (73%), and elevated BMI [26]. A plausible
310 explanation is that 60% of these stores provide unhealthy products labeled with a “red
311 traffic light”. The lack of significant results may stem from the Ecuadorian school
312 feeding program, which relies on processed and packaged products mandated to bear
313 nutritional labeling (traffic light system) [1]. These products contain high levels of
314 sugar, salt, and fats similar to the ultra-processed items distributed by food industries
315 in formal markets.
316
317 The study underscores the crucial role of the school food environment in addressing
318 childhood obesity. Despite robust evidence[27], many Latin American schools,
319 including those in Ecuador[28]. lack effective public health policies. Practical
320 recommendations include prohibiting the advertisement and sale of processed foods in
321 and around schools, imposing higher taxes on sugar-rich, ultra-processed products,
322 and promoting the consumption of fresh fruits and vegetables, along with education
323 on food labeling[29].
324
325 In a broader epidemiological context, the gradual increase in the prevalence of
326 OW/OB in the population of this study, along with a population of adults who have
327 been overweight and obese for decades, coupled with a distribution of excess
328 malnutrition across all socioeconomic strata in both children and adults [9], places
329 Ecuador in a second stage of transitioning to obesity. It is noteworthy that the richest
330 quintile of households has a lower prevalence of OW/OB than the quintile
331 immediately below, which could indicate that Ecuador is entering the third stage of
332 transitioning to obesity, where excess malnutrition decreases in wealthier households
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333 and becomes concentrated in socioeconomically vulnerable households [5].
334 Additionally, the observation that children residing in homes with inadequate excreta
335 disposal are less likely to be OW/OB underscores the presence of a socioeconomic
336 gradient in obesity and overweight prevalence in Ecuador. This characteristic is
337 compounded by the fact that the country has not overcome malnutrition in early
338 childhood [9], further complicating the issue of overall malnutrition, resulting in a
339 country experiencing a double burden of malnutrition (malnutrition due to deficiency
340 and excess).
341 The prevalence patterns of OW/OB in Ecuadorian school children are similar to those
342 in other low and middle-income countries in the region. Similar to a study of school
343 children in Mexico, there is a higher prevalence of OW/OB in boys than in girls, in
344 the non-indigenous population, and in higher-income households, and OW/OB also
345 increases with age [30,31]. However, the results of this study differ from patterns seen
346 in high-income countries in the region such as Chile, where OW/OB is more prevalent
347 in socioeconomically vulnerable families [32]. It is challenging to compare the
348 determinants found in this study with those in other countries in the region because of
349 to the scarcity of published studies [33].
350 The fact that children from households self-identified as mestizo have higher rates of
351 OW/OB may be explained by the fact that these children tend to live in urban
352 environments where they are more exposed to obesogenic environments such as
353 advertisements for sugary beverages, ultra-processed foods, and junk food. This
354 would also explain why indigenous children are protected from obesity and
355 overweight, in addition to socioeconomic vulnerabilities in this population, along with
356 household size, which is more strongly linked to malnutrition due to deficiency rather
357 than excess in the country [15,34]. Therefore, further research is needed on the
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358 circumstances of social vulnerability and food insecurity in households of school
359 children with OW/OB to explain why certain negative socioeconomic factors seem to
360 protect against OW/OB.
361 Physical activity among schoolchildren acts as a protective factor against OW/OB
362 [35,36]. However, the percentage of children in the sample who engage in regular
363 physical activity is very low, which aligns with previously published findings [37].
364 There is limited research linking physical activity to excess malnutrition in this group.
365 Therefore, further research on this topic is necessary.
366
367 Regarding gender differences, our findings support the current understanding of
368 gender-based dietary segregation, both in Ecuador and globally. In Ecuador,
369 prevailing dietary customs, shaped by a patriarchal and sexist societal framework,
370 often prioritize feeding boys. This may partly account for why physical activity
371 appears to benefit boys more significantly in reducing the likelihood of being
372 overweight or obese, as compared to girls, where such activities do not seem to
373 diminish the probability of overweight or obesity as effectively [38]. This is
374 exacerbated by the inclination to maintain purely aesthetic standards, which results in
375 food restrictions for girls, especially as they enter adolescence [39]. Additionally,
376 there are existing and differential unhealthy exposures based on socioeconomic strata
377 [40].
378 Given Ecuador's extensive history of childhood chronic malnutrition, a pivotal risk
379 factor for overweight, obesity, and non-communicable diseases, coupled with the
380 severe impacts of climate change linked to environmental factors, it is imperative to
381 focus research and public policies on understanding the global syndemic of
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382 malnutrition. This approach should incorporate triple-duty actions, considering the
383 distinctive aspects of diverse life stages. [41].
384 Our findings suggest that public health policies should place greater focus on
385 improving the quality of available foods within schools to mitigate the risks of
386 childhood overweight and obesity.
387 In the context of Ecuador and the wider region, our study underscores the criticality of
388 scrutinizing policy missteps that deregulate the marketing of unhealthy foods and
389 other harmful products, thereby hindering the enhancement of food environments.
390 Remarkably, the President of Ecuador enacted Presidential Decree No. 645 on
391 January 10, 2023, which aims to slash taxes on known health hazards, such as alcohol,
392 tobacco, sugary beverages, and firearms, in stark contrast to advancing public health
393 policies [16]. This decree is poised to adversely affect collective health. This
394 underscores a significant lacuna in public health strategies, potentially exacerbating
395 the obesity and overweight crisis among school children. This situation urgently
396 demands the attention of authorities and policymakers to realign regulations with
397 public health principles, ensuring that economic interests and conflicts of interest do
398 not undermine the development of sound public health policies [42,43]. In this
399 context, it becomes imperative for Ecuadorian authorities to implement and
400 strengthen comprehensive policies that address both the quality of food offered within
401 schools and those sold in their vicinity.
402 The congruence between the primary and sensitivity analyses bolsters the robustness
403 of our findings. Although the consumption of food purchased at school and food
404 provided by the school did not attain statistical significance in the model adjusted for
405 expansion factors, the overall pattern of results remained consistent. Importantly, the
406 existing scientific literature supports our findings concerning these variables. It is also
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407 crucial to emphasize that there are additional food environments associated with
408 formal markets and marketing targeted at children, which were not considered in this
409 study but significantly contribute to childhood OW/OB. Consequently, it is imperative
410 that national health and nutrition surveys not only possess sensitivity to growth
411 retardation but also to excess malnutrition. While the study delves into various
412 sociodemographic factors, some potentially relevant variables, such as cultural
413 practices or parental education, are not thoroughly examined. It is worth mentioning
414 that not all information from different forms can be cross-referenced for analysis
415 because of to variations in the design and objectives of the national survey.
416 We believe that our findings necessitate authorities to contemplate public policies
417 aimed at reducing the burden of OW/OB from an early age, thereby diminishing
418 future disabilities and deaths, which evidently result in heavier economic costs and
419 social burdens.
420
421 Conclusions
422 Increasing age, male gender, mestizo (mixed ethnic background) ethnicity, higher
423 economic quintiles, inadequate disposal of excreta, and lack of physical activity are
424 factors associated with a higher likelihood of overweight or obesity in children aged 5
425 to 11 years in Ecuador. The impact of consuming school-provided meals was
426 inconclusive. Children from families with a higher number of individuals in the
427 household and with families that recognize and use processed food labels exhibited a
428 higher likelihood of being overweight or obese. It is imperative for the Ecuadorian
429 government to implement public policy actions aimed at safeguarding the right to
430 health of school-age children, addressing both social protection within households and
431 evidence-based dietary policies for promoting healthy school food environments.
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432 Acknowledgements
433 We extend our sincere gratitude to David Grijalva, an economics student from the
434 Faculty of Economics at the Pontifical Catholic University of Ecuador, for his
435 invaluable assistance in the preparation of the tables for this manuscript. Additionally,
436 we would like to thank Natali Mendoza and Margoth Herrera from the Department of
437 Sociodemographic Statistics at the National Institute of Statistics and Censuses for
438 their specific technical guidance on the online database.
439 Author Contribution
440 Conceptualization: Betzabé Tello, Iván Dueñas-Espín, José Ocaña, Paúl García-
441 Zambrano, Betsabé Enríquez-Moreira.
442 Data curation: Betzabé Tello, Iván Dueñas-Espín, José Ocaña
443 Formal analysis: Iván Dueñas-Espín
444 Funding acquisition: Betzabé Tello
445 Investigation: Betzabé Tello, Iván Dueñas-Espín
446 Methodology: Betzabé Tello, Iván Dueñas-Espín, José Ocaña
447 Project administration: Betzabé Tello
448 Resources: Betzabé Tello, Iván Dueñas-Espín, José Ocaña
449 Software: Iván Dueñas-Espín, José Ocaña
450 Supervision: Betzabé Tello
451 Validation: Betzabé Tello, Iván Dueñas-Espín, José Ocaña, Paúl García-Zambrano,
452 Betsabé Enríquez-Moreira.
453 Visualization: Betzabé Tello, Iván Dueñas-Espín
454 Writing – original draft: Betzabé Tello, Iván Dueñas-Espín, José Ocaña
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455 Writing – review & editing: Betzabé Tello, Iván Dueñas-Espín, José Ocaña, Paúl
456 García-Zambrano, Betsabé Enríquez-Moreira.
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