The link between local environment and obesity: A multilevel analysis in the Lisbon Metropolitan Area, Portugal
Introduction
Obesity has reached epidemic proportions in almost all developed countries (Backett-Milburn, Wills, Gregory, & Lawton, 2006) and in many urban areas in developing countries. Recent estimates suggest that one billion adults in the world are overweight (Kjellstrom, 2007).
Portugal has a significant obesity problem, which is rapidly worsening. In 1996, 10.3% of males and 11.4% of females had a BMI of over 30 kg/m2 (OECD, 2006); by 2006, this had increased to 16% for males and 16.9% for females (ACS, 2008), representing an additional 55.3% and 43.9% for males and females respectively over a period of ten years. Thus, by 2006, 1 in 2 adults were overweight or obese (ACS, 2008). Targets have been set by the National Health Plan (ACS, 2008) to reduce obesity and excess weight in the population by the year 2010. Obesity is a national public health problem: it has damaging effects on the social, economic and health status of individuals, exacerbating their risks of disease and premature death and increasing health care costs.
Several authors have claimed that excess body weight results from multifactorial causes, including polygenic, metabolic, psychosocial and environmental influences, and behavioural aspects (Borders et al., 2006, Ellaway et al., 1997, Poortinga, 2006a, Poston and Foreyt, 1999). Genes play a role, but the gene pool in industrialised countries has not changed significantly in the few decades in which obesity has rocketed. Therefore, environmental (obesogenic) factors must also contribute significantly to weight gain. Some authors (Cohen et al., 2006, Ellaway et al., 1997, Kim et al., 2006, Poortinga, 2006a, Poston and Foreyt, 1999, Stafford et al., 2007, Van Lenthe and Mackenbach, 2002) have indicated the potential obesogenicity of certain environmental factors (i.e. the extent to which they could promote caloric intake and/or discourage the expenditure of energy in routine physical activity).
However, the precise cause of the epidemic remains unknown, and no possible solutions are yet in sight. It is important, therefore, to identify exactly what is a “toxic environment” (Poston & Foreyt, 1999) in order to focus upon changing it. Swinburn, Egger, & Raza (1999: 564) argue that “there is an urgent need for a conceptual and practical framework to dissect the rather nebulous concept of the environment into concrete elements which are amenable to measurement and interventions”.
Increasing obesity amongst the Portuguese population is associated with trends in: excessive energy intake (3747 calories on average per person, in 2003, representing an increase of 32.6% in the last thirty years, that is 1.6% per year) (OECD, 2006); and low rates of leisure-time physical activity (overall age-adjusted prevalence for a sedentary lifestyle was 62.8% for males and 75.4% for females in 1999) (Demarest et al., 2007). By 2004, 73% of Portuguese people reported that they seldom or never did physical exercise for recreation or as a leisure-time activity, and 54% reported identical levels of inactivity at work (Eurobaromater, 2004). These behavioural risks for obesity, in isolation or together with genetic susceptibilities or vulnerabilities, may vary from individual to individual (in association with socioeconomic status) and according to place of residence (opportunities for healthy behaviours: physical recreation and eating) (Ellaway et al., 1997, Stafford et al., 2007).
The purpose of this paper is to examine the contribution of the local environment and personal attributes to the risk of weight gain in LMA neighbourhoods and evaluate their importance for the understanding and treatment of obesity. This information will be relevant for the design and planning of health promotion interventions, in so far as it will facilitate the identification of areas requiring analysis, major issues and priorities for tackling the obesity epidemic in the Lisbon Metropolitan Area (LMA).
To achieve this, the authors intend to: (1) develop indicators to measure social environment and availability of local resources; (2) link individuals to their neighbourhoods through the characterization of each individual personally and according to the specific features of their place of residence; (3) establish associations between sustainable communities, their citizens' body mass index (BMI) and risk factors for obesity (i.e. behaviours such as physical activity and healthy eating habits).
The next section describes the conceptual framework for obesity, including data and methods used, followed by the main achieved results. Finally, in the last part, possible explanations are suggested, some conclusions are drawn and key proposals are made for changes that could lead to alternative health outcomes.
Section snippets
A conceptual framework for obesity, and hypotheses
How might area characteristics be associated with the risk of increased BMI? Does place act or interact with individual characteristics? How does this happen?
Multilevel models
We present in Table 3 the models that showed the better fit according to DIC, for the four models developed. The values of variance between neighbourhoods and DIC for the null models are presented to highlight the reduction of variance between neighbourhoods after taking into account the included variables and of DIC.
Age, gender, occupation, education and income, marital status, lifestyles and overall health /initial health status (self-rated) were included, although there were some differences
Discussion
This paper considers the features of the local social and physical environment that may affect body mass index (BMI) by encouraging behaviours such as physical activity and eating patterns.
Our results from Lisbon are in concordance with previous empirical work elsewhere (Poortinga, 2006a, Kim et al., 2006) and generally with theoretical reviews on weight gain and its pathways – diet and physical activity – (Stafford et al., 2007). The literature suggests that levels of physical activity
Acknowledgements
The authors would like to thank Sally Macintyre, Anne Ellaway and Min Hua Jen for their useful insights and advice, Cláudia Costa for helping to collate and manage geographical data, Karen Bennett for the language review, and two anonymous referees for their very helpful comments. This research was supported by grant POCTI/GEO/45730/2002 from the Portuguese Foundation for Science and Technology (FCT) “Healthy Urban Planning”.
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