Study area
Belgium consists of three major regions: Flanders, Brussels and Wallonia. Flanders is an area in the northern part of Belgium. The region inhabits 6.629.143 people (57.5% of Belgian population), of which 1.123.000 are of school going age (2.5 to 17 years) [21]. The region is strongly urbanized with a high population density (383.9 inhabitants/km2). This study comprised the entire territory of Flanders, including all schools and food stores and services in Flanders.
Data sources
Food outlets
The geographical coordinates of all food retailers (stores and services) in Flanders were obtained from the commercial Locatus database (www.locatus.com). It covers the entire territory of Flanders and includes information such as the name and address of the retailer, the type of outlet and the size of the retail space. Since 2008, Locatus systematically performs regular field audits in Belgium to map the locations, sizes and types of retailers for commercial purposes. The frequency of field audits varies from once a year—in shopping areas—to once every 2 or 3 years in locations outside shopping areas.
Locatus has its own field service staff, who visit, inventorize and check all points of sale in the Benelux on a yearly basis. For this study the databases from the years 2008, 2013 and 2020 were acquired. The original database included 33 types of retailers whose primary purpose is to sell food, which was too detailed for the purposes of this study. Based on a consultation with an expert committee in Flanders consisting of 15 dieticians, food scientists and food policy advisors, a reclassification was performed into nine different classes of food outlets for the purposes of this study. The nine resulting classes of food retailers were: supermarkets, confectionary stores, convenience stores, fast food/takeaway/delivery outlets, shops that primarily sell animal products, full service restaurants, greengrocers, bakeries and other shops. The complete reclassification can be consulted in Additional file 1.
Road network and urbanicity
The road network of Flanders was sourced from the ‘Grootschalig Referentie Bestand Vlaanderen (GRB)’, which is a freely available data source, managed by the Flemish government, that continually maps and updates spatial entities such as roads, buildings and waterways [22].
The Degree of urbanization (DEGURBA) dataset, provided by Eurostat, was used to divide Flanders in regions that are ‘essentially urban’, ‘intermediate’ and ‘essentially rural’ [23].
Schools
The school data was acquired from the Department for Education Flanders and included the schools’ unique ID, name, type and geographical coordinates. The food environment was analyzed for both primary (n = 3404, children aged 2.5–12 years) and secondary (n = 1195, children aged 13–18 years) schools.
An anonymized dataset with aggregated socio-economic status (SES) indicators for the students of every school in Flanders was provided by the Flemish government. The SES indicators available included the proportion of pupils/students for which the level of education of the mother was low (i.e. defined as not completing a secondary school education) and the proportion of pupils/students for which the home language was not Dutch. Previous research has demonstrated that these indicators are strong predictors for children’s mental wellbeing, cognitive function and adiposity [24,25,26]. The schools were divided into terciles (‘low’, ‘medium’, ‘high’) for both SES indicators. The upper tercile (‘high’ score) means that the proportion of the schools’ children with a low educated mother or for whom the home language is not Dutch is high.
Weight status
A dataset including the percentage of children with overweight for each school and for the years 2011–2016 was provided to the researchers by the Flemish government agency ‘Agentschap Zorg en Gezondheid (AZG)’.
These data were stratified by sex and age group (< 6 years, 6-12 years, 13–14 years and 14–18 years) (See Additional file 2). Due to privacy reasons it was not possible to obtain the data on weight status, including BMI z-scores, from the individual children, only aggregated data (% of children with overweight) at the school level were accessible for research. In the analyses, therefore, the percentage of children with overweight at the school level was used.
The height and weight of all children were measured on a yearly basis when they visit the centers for pupils support (CLBs). These measures are obligatory as determined by a government decree, so parents or children cannot opt out unless they are sick on the day of the measurements. Height was measured barefoot, in light clothing (no jumper, shirt or jacket) with a SECA 213 mobile stadiometer. Weight was measured in underwear at a precision of at least 100 g with a SECA877 or Seca Quadra 808 scale. The percentage of children with overweight for each of the schools was calculated by AZG through comparing the BMI of the individual children with the IOTF thresholds for overweight and obesity [27] and those of the WHO (for low BMI for age).
Selection of indicators of the food environment
The first indicator that was calculated was the absolute density (number) of each food retail type within a 500 m and 1000 m road network distance from the main entrance of the school. The 500 m and 1000 m road network distances were chosen based on common walking distances that most children can do fairly easily. At an average walking speed of 5 km/h, a person would need approximately 6 or 12 min to walk 500 m or 1000 m respectively. Other studies in the same context, such as Cant et al. [28], assumed the same common walking distances.
The second indicator that was computed was the percentage of schools that had at least one food retailer of a certain type within a walking distance of 500 m or 1000 m from the entrance of the school.
The final indicator that was calculated was the shortest distance from each school entrance to the nearest fast food outlet, supermarket and convenience- or confectionary store. These types of food retailers were identified to be the most probable types of food shops that children would visit during lunchbreak or outside of school hours.
Geographical analyses
The analyses were performed in QGIS 3.16.5 and PostGIS 3.1. The service area algorithm in QGIS created a road distance network of 500 m and 1000 m around the entrance of every primary and secondary school. The absolute density of each retail type and the percentage of schools that had at least one food retailer of a certain type in their food environment were calculated based on these road distance networks. The Dijkstra algorithm, implemented in the pgRouting package (PostGIS), was used to calculate the shortest network distances needed for the third indicator. A custom SQL query was written to automate the process and perform the analyses for all schools simultaneously.
Statistical analyses
The statistical analyses were performed with SAS9.4. Associations between the density of fast food, takeaway, delivery outlets around schools, and the percentage of children with overweight was determined through a generalized linear model adjusted for the level of urbanization (DEGURBA), socio-economic status of children at school level (proportion of children for whom education level of the mother is low) and sex. The same analysis was repeated for convenience stores instead of fast food, takeaway and delivery outlets. All analyses were stratified by age group (< 6 years, 6–12 years, 13–14 years and 15–18 years) and by buffer size (500 m/1000 m road network distance from the school). A lag time of zero, one and two years was considered for assessing the association between exposure to fast food, takeaway, delivery outlets as well as convenience stores and the percentage of children with overweight.
Children’s data on weight status from the school years 2010–11, 2013–14, 2014–15 and 2015–16 were used and linked with Locatus data from the years 2008 and 2013.
In view of the many analyses conducted, a Bonferroni correction was applied, where a p-value of < 0.0006 (0.05/88) was considered statistically significant.