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BMJ Open 6:e010145 doi:10.1136/bmjopen-2015-010145
  • Nutrition and metabolism
    • Research

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

  1. Idris Guessous3,4,5,6,7
  1. 1Laboratory of Geographic Information Systems (LASIG), School of Architecture, Civil and Environmental Engineering (ENAC), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
  2. 2MicroGIS Foundation for Spatial Analysis (MFSA), Saint-Sulpice, Switzerland
  3. 3Group of Geographic Information Research and Analysis in Public Health (GIRAPH)
  4. 4Department of Internal Medicine, Lausanne University Hospital (CHUV), Lausanne, Switzerland
  5. 5Division of Chronic Diseases, Institute of Social and Preventive Medicine (IUMSP), Lausanne University Hospital (CHUV), Lausanne, Switzerland
  6. 6Faculty of Medicine, Unit of Population Epidemiology, Division of Primary Care Medicine, Department of Community Medicine, Primary Care and Emergency Medicine, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
  7. 7Department of Epidemiology, Emory University, Atlanta, Georgia, USA
  1. Correspondence to Dr Stéphane Joost; stephane.joost{at}epfl.ch
  • Received 30 September 2015
  • Revised 18 November 2015
  • Accepted 4 December 2015
  • Published 5 January 2016

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 (m2). 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.

This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/

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