The global obesity pandemic: shaped by global drivers and local environments

Swinburn, Boyd; Sacks, Gary; Hall, Kevin D.; McPherson, Klim; Finegood, Diane T.; Moodie, Marj; Gortmaker, Steven L.

doi:10.1016/s0140-6736(11)60813-1

Cited by 3,870 publications

(3,267 citation statements)

References 68 publications

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“…The reported interplay of sociodemographic characteristics and lifestyle factors in the development of adulthood obesity supports the notion that high BMI is not randomly distributed within a population (3,10). Therefore, the implementation of intervention programs that target all individuals within a population are limited in their capacity to create change.…”

Section: Introductionsupporting

confidence: 67%

“…This trend is of international concern, with over 13% of men and 21% of women in the world classified as obese according to their body mass index (BMI) (1). Although the financial burden of high BMI raises concerns about the effectiveness of intervention strategies (3–5), increasingly more attention is placed on the role of obesity in the development of other chronic diseases (1). The relationship between obesity, cardiovascular disease (CVD), and type 2 diabetes mellitus is well documented, with high BMI associated with the development of atherosclerosis, hypertension, and insulin resistance (6–9).…”

Section: Introductionmentioning

confidence: 99%

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Using Geographical Convergence of Obesity, Cardiovascular Disease, and Type 2 Diabetes at the Neighborhood Level to Inform Policy and Practice

Smurthwaite

Bagheri

2017

Prev. Chronic Dis.

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IntroductionChronic diseases are increasing across the world. Examination of local geographic variation in chronic disease patterns can enable policy makers to identify inequalities in health outcomes and tailor effective interventions to communities at higher risk. Our study aimed to determine the geographic variation of obesity, cardiovascular disease (CVD), and type 2 diabetes, using general practice clinical data. Further objectives included identifying regions of significantly high and low clusters of these conditions and assessing their association with sociodemographic characteristics.MethodsA cross-sectional approach was used to determine the prevalence of obesity, CVD, and type 2 diabetes in western Adelaide, Australia. The Getis-Ord Gi* method was used to identify significant hot spots of the conditions. Additionally, we used the Pearson correlation test to determine the association between disease clusters and risk factors, including socioeconomic status (SES), smoking history, and alcohol consumption.ResultsThe spatial distribution of obesity, CVD, and type 2 diabetes varied across communities. Hot spots of these conditions converged in 3 locations across western Adelaide. An inverse relationship was observed between area-level prevalence of CVD, obesity, and type 2 diabetes with SES.ConclusionIdentification of significant disease clusters can help policy makers to target prevention strategies at the right people, in the right location. The approach taken in our study can be applied to identify clusters of other chronic diseases across the world, wherever researchers have access to clinical data.

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Section: Introductionsupporting

confidence: 67%

Section: Introductionmentioning

confidence: 99%

Using Geographical Convergence of Obesity, Cardiovascular Disease, and Type 2 Diabetes at the Neighborhood Level to Inform Policy and Practice

Smurthwaite

Bagheri

2017

Prev. Chronic Dis.

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“…From a public health perspective, providing nutritional information at the point of purchase can provide the framework for measured food choice decisions (Geaney et al 2013). However, nutrition information does not always lead to a major change in actual behaviour (Swinburn et al 2011) and often only receives limited attention (Drichoutis et al 2005). …”

Section: Discussionmentioning

confidence: 99%

Workplace foodservice; perception of quality and trust

et al. 2016

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“…Globally, over 600 million adults are obese, with rapidly increasing levels of obesity occurring in Low-to-Middle Income Countries (LMIC) [5,6]. In South Africa, the prevalence of overweight and obesity is 65.1% and 31.3%, for females and males, respectively [7].…”

Section: Introductionmentioning

confidence: 99%

Determinants of body mass index by gender in the Dikgale Health and Demographic Surveillance System site, South Africa

Mashinya

Alberts

Cook

et al. 2018

Global Health Action

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Background: The study was conducted in the Dikgale Health and Demographic Surveillance System (DHDSS) site where we have observed increasing obesity levels, particularly in women, despite evidence of high physical activity (PA) and a relatively low daily energy intake. Objective: This study aimed to assess the socio-demographic, behavioural and biological determinants of body mass index (BMI) in adult residents permanently residing in the DHDSS. Methods: A cross-sectional study was conducted in which socio-demographic, behavioural and biological characteristics from 1143 participants (aged 40–60 years) were collected using a paper questionnaire and standard anthropometric measures. Human immunodeficiency virus (HIV) testing was performed on all participants except those who indicated that they had tested positive. Chi-square and Mann-Whitney tests were used to analyze categorical and continuous variables, respectively, while hierarchical multivariate regression was used to analyze predictors of BMI. Results: The median age of women and men was 51 (46–56) and 50 (45–55) years, respectively. The prevalence of overweight-obesity was 76% in women and 21% in men. A significant negative association of BMI with HIV and smoking and a significant positive association with socio-economic status (SES) was observed in both sexes. In women, BMI was negatively associated with sleep duration (p = 0.015) and age (p = 0.012), but positively associated with sugar-sweetened beverages (SSBs) (p = 0.08). In men, BMI was negatively associated with alcohol use (p = 0.016) and positively associated with being married (p < 0.001). PA was not associated with BMI in either sexes. Full models explained 9.2% and 20% of the variance in BMI in women and men, respectively. Conclusion: BMI in DHDSS adults is not associated with physical inactivity but is associated wealth, marital status, sleep, smoking, alcohol use, and HIV status. Future studies should explore the contribution of nutrition, stunting, psycho-social and genetic factors to overweight and obesity in DHDSS.

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The global obesity pandemic: shaped by global drivers and local environments

Cited by 3,870 publications

References 68 publications

Using Geographical Convergence of Obesity, Cardiovascular Disease, and Type 2 Diabetes at the Neighborhood Level to Inform Policy and Practice

Using Geographical Convergence of Obesity, Cardiovascular Disease, and Type 2 Diabetes at the Neighborhood Level to Inform Policy and Practice

Workplace foodservice; perception of quality and trust

Determinants of body mass index by gender in the Dikgale Health and Demographic Surveillance System site, South Africa

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