Local Food Environments, Suburban Development, and BMI: A Mixed Methods Study

Int J Environ Res Public Health. 2018 Jul 2;15(7):1392. doi: 10.3390/ijerph15071392.

Abstract

More than half the world's population now live in urban settlements. Worldwide, cities are expanding at their fringe to accommodate population growth. Low-density residential development, urban sprawl, and car dependency are common, contributing to physical inactivity and obesity. However, urban design and planning can modify urban form and enhance health by improving access to healthy food, public transport, and services. This study used a sequential mixed methods approach to investigate associations between food outlet access and body mass index (BMI) across urban-growth and established areas of Melbourne, Australia, and identify factors that influence local food environments. Population survey data for 3141 adults were analyzed to examine associations, and 27 interviews with government, non-government, and private sector stakeholders were conducted to contextualize results. Fast food density was positively associated with BMI in established areas and negatively associated in urban-growth areas. Interrelated challenges of car dependency, poor public transport, and low-density development hampered healthy food access. This study showed how patterns of suburban development influence local food environments and health outcomes in an urbanized city context and provides insights for other rapidly growing cities. More nuanced understandings of the differential effect of food environments within cities have potential to guide intra-city planning for improving health and reducing inequities.

Keywords: cities; food environment; mixed methods; obesity; urban health; urban planning policy.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Australia / epidemiology
  • Body Mass Index*
  • Fast Foods / statistics & numerical data
  • Female
  • Food Supply / statistics & numerical data*
  • Humans
  • Male
  • Middle Aged
  • Obesity / epidemiology*
  • Suburban Population / statistics & numerical data*
  • Urbanization*