A comparison of the spatial dependence of body mass index among adults and children in a Swiss general population

Abstract

Background:Body mass index (BMI) may cluster in space among adults and be spatially dependent. Whether BMI clusters among children and how age-specific BMI clusters are related remains unknown. We aimed to identify and compare the spatial dependence of BMI in adults and children in a Swiss general population, taking into account the area's income level.Methods:Geo-referenced data from the Bus Santé study (adults, n=6663) and Geneva School Health Service (children, n=3601) were used. We implemented global (Moran's I) and local (local indicators of spatial association (LISA)) indices of spatial autocorrelation to investigate the spatial dependence of BMI in adults (35–74 years) and children (6–7 years). Weight and height were measured using standardized procedures. Five spatial autocorrelation classes (LISA clusters) were defined including the high–high BMI class (high BMI participant's BMI value correlated with high BMI-neighbors' mean BMI values). The spatial distributions of clusters were compared between adults and children with and without adjustment for area's income level.Results:In both adults and children, BMI was clearly not distributed at random across the State of Geneva. Both adults' and children's BMIs were associated with the mean BMI of their neighborhood. We found that the clusters of higher BMI in adults and children are located in close, yet different, areas of the state. Significant clusters of high versus low BMIs were clearly identified in both adults and children. Area's income level was associated with children's BMI clusters.Conclusions:BMI clusters show a specific spatial dependence in adults and children from the general population. Using a fine-scale spatial analytic approach, we identified life course-specific clusters that could guide tailored interventions.

Análisis espacial del sobrepeso y la obesidad infantil en el Perú, 2014

“…Otras investigaciones realizadas en una cohorte sobre migrantes adultos rural-urbanos en el país señalan que la prevalencia de obesidad es mayor en los residentes urbanos, y migrantes rurales-urbanos (27,28) , además que la antigüedad de la migración hacia el área urbana incrementa el riesgo de obesidad (27) . Este tipo de análisis ha sido empleado en diversos estudios con el objetivo de determinar la presencia de clústeres de obesidad en adultos y niños, así como también, para establecer la relación prevalencia de obesidad y ambientes obesogénicos; resaltándose en todos ellos su utilidad para el desarrollo de programas de intervención y detección de áreas de intervención (29,30) . De igual manera, nuestro estudio identificó mayores prevalencias de sobrepeso y obesidad en las zonas urbanas de los distritos en comparación con las rurales.…”

Relationship between land cover type and Body Mass Index in Geneva

“…Moreover, further investigations need to rely on an adapted sampling design adding constraints e.g. weighting depending on population density on top of the stratified random sampling strategy implemented [10]. And other parameters known to influence BMI like the age and the sex of the residents will have to be included in the analysis.…”

“…Correlations were then computed between the BMI statistics listed above and land cover percentages. GIRECs with less than 5 sampled individuals were not taken into account in the analysis, as proposed by Guessous et al [10].…”

“…Between 2001 and 2009, every year a representative stratified sample of 500 men and 500 women from the Geneva States population (∼ 500 000 inhabitants) is 978-1-4799-6652-3/15/$31.00 c 2015 IEEE recruited and studied [10]. The sample used here is composed of 6663 adults, and describes the age, the postal address and the BMI of each individual.…”

“…As income level is thought to be an obesity risk factor to be considered when conducting BMI spatial dependence analyses [10] (here corr coeff r=-0.08), BMI was adjusted for areas income level by means of median regression. The area used here is a statistical subsector named "GIREC" characterized with income level obtained from the 2009 Geneva Census (Office Cantonal de la Statistique, www.ge.ch/statistique).…”