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Introduction
Malnutrition (undernutrition, overweight/obesity, and poor diets) is a major health and social problem worldwide.1 Moreover, malnutrition during childhood and adolescence is associated with adverse health consequences. The estimated prevalence of childhood overweight and obesity is about 25% in Europe; however, the burden is not homogeneously distributed across the region.2 Indeed, estimated prevalences range from about 11% in Belgium to about 40% in Greece.2 According to the most recent national health survey in Spain, about 25% of children (aged 2 to 17 years) presented overweight or obesity in Madrid in 2017.3 These prevalence rates are partially attributed to a gradual shift in these countries, from a Mediterranean Diet to a more Westernized diet. Thus, at the forefront of malnutrition and poor children's health is the obesogenic environment.4
Swinburn et al.4 defined the obesogenic environment as “the sum of the influences that the surroundings, opportunities, or conditions of life have on promoting obesity”. This obesogenic environment contributes to overweight/obesity by encouraging unhealthy diets and sedentarism.5 For children, this corresponds to an increased access to unhealthy food and sugar-sweetened beverages.6 Furthermore, accessibility to recreational facilities (e.g., playgrounds), walkability, sports facilities, and active school transportation opportunities are associated with increased physical activity.7,8
Yet, scarce research has simultaneously examined more than one dimension of the obesogenic environment concerning children's obesity.9,10 Also, most supporting evidence comes from the US, Australia, and the UK; however, the structure of the built and the food environment differ across geographic contexts.11-13 Despite the recognition that childhood obesity in Europe is highest within Mediterranean countries, no study has been conducted in this context.14,15 This lack of evidence is hindering effective translation into policy.
The SUECO study aims to assess the relationships between residential- and school-level urban environment features and individual-level anthropometrics, eating behaviors, physical activity among young children in Madrid, Spain.
Method
Study design
This is a multilevel study conducted in Madrid, Spain. We will use de-identified individual data from the 2017 dataset of the Encuesta del estado nutricional de la población infantil de la ciudad de Madrid, a city-wide survey commissioned by the city council to assess the nutritional status of Madrid school-age children (3 to 12 years).16 Contextual measures will be developed based on earlier work (Fig. 1). 6,17,18
Participants and sample
Participants were selected using a stratified cluster sampling design (see fig. I in online Appendix). First, schools were stratified according to area-level factors, and 84 were randomly selected. Out of these, 60 agreed to participate (response rate: 71.4%). Second, school classes were randomly selected in each school. The study population comprised 7,740 children from which 6,157 provided parental consent to participate in the study (recruitment rate: 79.5%).
The actual number of children for each analysis will vary because individuals could consent to single components of the study (e.g., 5298 for anthropometrics) while abstaining from others (e.g., 5201 completed the survey).
Study variables
Table 1 shows an overview of the study variables. The main outcome variables encompass anthropometrics, healthy and unhealthy consumption measures, and physical activity measures.
Table 1. Variables included in the study
| Outcomes | Measures |
|---|---|
| Anthropometrics | Age- and sex-standardized body mass index (BMI z-scores), overweight, obesity. Waist circumference. Body fat percentage. |
| Healthy and unhealthy consumption measures | Fruit and vegetables intake. Sweet-sweetened beverages. Sweets. Fast-food intake |
| Physical activity measures | Active travel to school. Physical activity |
| Exposures | Measures |
| Food environment | Unhealthy food retailers' density |
| Walkability | Walkability index |
| Surrounding physical activity opportunities | Density of playgrounds. Density of and proximity to exercise facilities. Green spaces' density |
Main outcome variables
1) Anthropometrics
Objectively measured heights and weights were used to calculate body mass index (BMI, weight in kg / height in m2) for each child. To allow for international comparisons, this study will use the WHO BMI growth reference which is an age-sex specific BMI z-score to classify overweight and obesity.19 We will also use as adiposity measures waist circumference and body fat percentage.
Measurements were made by trained staff using standard procedures and regularly calibrated instruments: height was measured with a stadiometer (MZ 10042) and weight with a digital weight scale (TANITA UM-076), both without shoes and with light clothing. Waist circumference was measured to the nearest 0.1cm in a standing position with the use of a measuring tape.
2) Healthy and unhealthy consumption measures
We will calculate fruit and vegetable intake based on the sum of two questions from the questionnaire. Consumption of sugar-sweetened beverages will include carbonated sugar-sweetened drinks and non-carbonated sugar-sweetened drinks. Fast-food consumption will include hamburgers and pizza. Possible answer categories were: never, 1-3 times a week, 4-6 times per week, 7 times a week. Consumption frequencies will be then summed to calculate consumption measures as frequencies per week. Eating at fast-food outlets will be calculated based on the specific question “In a normal week, how many times does your visit a fast-food outlet?”. Possible answer categories were: never, less than one time a week, and one or more times per week.
3) Physical activity
Moderate-to-vigorous physical activity levels will be estimated based on a measure of weekly moderate-to-vigorous physical activity (total amount in minutes) that includes all types of activity out of school hours. We will measure active travel to school based on the following question; “On a typical day is the main part of your child's journey to school made by walking/bicycle, bus/train/subway, car/motorcycle or other means?” Response “walking/bicycle” was coded 1 signifying active travel, and all other responses coded 0.
Main exposure variables
We will construct a geodatabase using Geographic Information Systems, which will allow us to geocode children's home and school addresses, and estimate exposure measurements (e.g., food environment, walkability, and exercise opportunities) around both home and school environment. Data will be spatially joined to census tracts to merge databases obtained at different geographic unit levels.
1) Food environment
Assessment of the retail food environment will be based on earlier work6. We will measure the density of food retailers offering unhealthy food and sugar-sweetened beverages within a 400 m shortest network path buffer around each children's residence and school address. Buffers of 800m will be considered in sensitivity analyses. Retailers' addresses will be collected (as of 2017) using a publicly available administrative dataset (Censo de locales y actividades) which was previously validated.20
2) Walkability
We will measure area-level walkability, at the census tract level, using a previously validated walkability index.18 This index comprises four indicators: residential density, population density, retail destinations, and street connectivity. Data will be obtained from different secondary sources.
3) Surrounding physical activity opportunities
We will use three measures: 1) density of surrounding playgrounds; 2) density of green spaces,21 and 3) density of exercise facilities (including both free outdoor sports courts and public sports centers). We obtained green spaces data combining all green land use cover categories from the City Council of Madrid for the year 2016 and all green spaces under mainteinance by the Madrid City Council in 2020.
Potential confounders
We will identify potential confounders for each of the environmental exposures and include individual sociodemographic variables and area-level characteristics: age (years), sex (male or female), country of origin, household composition, Family Affluence Score (FAS)22 as a measure of the family's socioeconomic status, area-level socioeconomic status,18 and population density.
Statistical analysis
The study has a multilevel structure, where children are nested within schools (Fig. 1). Therefore, multilevel regression models will be used to examine the cross-sectional associations between each outcome measure and each areal level factor. All regression models will be adjusted for a common set of potential confounders and checked for effect modification (e.g., by sex to obtain sex-specific estimates). Effect modification will be tested by including interaction terms in the models. We will design sensitivity analyses to test the robustness of our findings to different model specifications.
Ethical considerations
The study will be conducted according to the guidelines laid down by the Declaration of Helsinki and ethical approval was granted by the Ethics Committee of the Universidad de Alcalá (CEI/HU/2019/35). Participants were assured of anonymity and confidentiality and written consent was obtained from the parents or legal guardians of all children. All data exchanges will adhere to the most up-to-date EU and national data protection regulations.
Discussion
To the best of our knowledge, no research projects on this topic have been conducted or are in progress in Spain. Thus, it will provide significant evidence. First, its multi-level study design will allow for assessing possible area-level effects over and above individual-level effects. Second, it focuses on school-age children which is a current priority given the magnitude of excess body weight, physical inactivity, and sedentarism among this population group. Third, it will measure the obesogenic environment across several domains and locations (both residence and school location).10 Fourth, it will assess the impact of individual- and area-level SES, which are potentially important covariates of eating habits and physical activity and potential effect modifiers of the associations between environmental determinants and eating habits or physical activity.
This study presents several methodological limitations. First, eating and physical activity data are based on self-report, which is prone to bias and measurement error (e.g., parents may be reluctant to disclose the frequency at which their children consume sugar-sweetened beverages). Second, it may be the case that fast-food retailers preferentially locate in areas with greater demand resulting in a causal pathway that is in the opposite direction to that hypothesized.
Conclusions
The study offers a unique opportunity to evaluate the health consequences of obesogenic environments for school-age children in a large Southern European city like Madrid. This study will also provide relevant evidence to influence urban policies to promote children's well-being while addressing social inequities.
