Persistent spatial clusters of high body mass index in a Swiss urban population as revealed by the 5-year GeoCoLaus longitudinal study

• Authors: Stéphane Joost, Ecole Polytechnique Fédérale de Lausanne; Solange Duruz, Ecole Polytechnique Fédérale de Lausanne; Pedro Marques-Vidal, Lausanne University Hospital; Murielle Bochud, Lausanne University Hospital; Silvia Stringhini, Lausanne University Hospital; Fred Paccaud, Lausanne University Hospital; Jean-Michel Gaspoz, University of Geneva; Jean-Marc Theler, University of Geneva; Joël Chételat, MicroGIS Foundation for Spatial Analysis; Gérard Waeber, Lausanne University Hospital; Peter Vollenweider, Lausanne University Hospital; Idris Guessous, Emory University
• Journal Title: BMJ Open
• Volume: Volume 6, Number 1
• Publisher: BMJ Publishing Group: Open Access | 2016-01-01, Pages e010145-e010145
• Type of Work: Article | Final Publisher PDF
• Publisher DOI: http://doi.org/10.1136/bmjopen-2015-010145
• Subjects: Health Sciences, Epidemiology; Health Sciences, Public Health
• Keywords: Science & Technology; Life Sciences & Biomedicine; Medicine, General & Internal; General & Internal Medicine; LEVEL INCOME MEASURES; BUILT ENVIRONMENT; RISK-FACTORS; KING COUNTY; OBESITY; ASSOCIATION; ADULTS; DENSITY; AREA; NEIGHBORHOODS

Abstract

OBJECTIVE: Body mass index (BMI) may cluster in space among adults and be spatially dependent. Whether and how BMI clusters evolve over time in a population is currently unknown. We aimed to determine the spatial dependence of BMI and its 5-year evolution in a Swiss general adult urban population, taking into account the neighbourhood-level and individual-level characteristics. DESIGN: Cohort study. SETTING: Swiss general urban population. PARTICIPANTS: 6481 georeferenced individuals from the CoLaus cohort at baseline (age range 35-74 years, period=2003-2006) and 4460 at follow-up (period=2009-2012). OUTCOME MEASURES: Body weight and height were measured by trained healthcare professionals with participants standing without shoes in light indoor clothing. BMI was calculated as weight (kg) divided by height squared (m(2)). Participants were geocoded using their postal address (geographic coordinates of the place of residence). Getis-Ord Gi statistic was used to measure the spatial dependence of BMI values at baseline and its evolution at follow-up. RESULTS: BMI was not randomly distributed across the city. At baseline and at follow-up, significant clusters of high versus low BMIs were identified and remained stable during the two periods. These clusters were meaningfully attenuated after adjustment for neighbourhood-level income but not individual-level characteristics. Similar results were observed among participants who showed a significant weight gain. CONCLUSIONS: To the best of our knowledge, this is the first study to report longitudinal changes in BMI clusters in adults from a general population. Spatial clusters of high BMI persisted over a 5-year period and were mainly influenced by neighbourhood-level income.