An exploratory spatial analysis of overweight and obesity in Canada

Pouliou, Theodora; Elliott, Susan J.

doi:10.1016/j.ypmed.2009.01.017

Cited by 53 publications

(48 citation statements)

References 15 publications

(19 reference statements)

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“…The second subfield emerged from the first older subfield on regional health (care) variation. It focusses on the importance of regional factors and geographic spillover effects in the spatial analysis of health and health care utilization and econometrically models spatial interdependencies explicitly (Browning et al, 2003, Filippini et al, 2009, Pouliou and Elliott, 2009, Lauridsen et al 2010a, 2010b, Andersen et al, 2012, Hajizadeh et al, 2012, Barufi et al, 2012, Filippini et al, 2013. This paper contributes to both research fields outlined above.…”

Section: Introductionmentioning

confidence: 99%

Examining the structure of spatial health effects in Germany using Hierarchical Bayes Models

Ziebarth

2014

Regional Science and Urban Economics

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in

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Section: Introductionmentioning

confidence: 99%

Examining the structure of spatial health effects in Germany using Hierarchical Bayes Models

Ziebarth

2014

Regional Science and Urban Economics

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“…Previous research has typically used either geographic analyses to study overweight/obesity (Pouliou & Elliott, 2009; Slater et al, 2009), or hierarchical linear modeling (Wendel-Vos et al, 2004; Li et al, 2005; Nagel et al, 2008) approaches for the study of neighbourhood effects on PA participation. However, since many measures of the urban form are correlated, a third approach to avoid the problem of spatial multicollinearity has been proposed; to develop a walkability index that integrates various measures of the built environment (Frank et al, 2005; Li et al, 2009; Frank et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Neighbourhood walkability, leisure-time and transport-related physical activity in a mixed urban–rural area

Ardern

2014

PeerJ

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Objectives. To develop a walkability index specific to mixed rural/suburban areas, and to explore the relationship between walkability scores and leisure time physical activity.Methods. Respondents were geocoded with 500 m and 1,000 m buffer zones around each address. A walkability index was derived from intersections, residential density, and land-use mix according to built environment measures. Multivariable logistic regression models were used to quantify the association between the index and physical activity levels. Analyses used cross-sectional data from the 2007–2008 Canadian Community Health Survey (n = 1158; ≥18 y).Results. Respondents living in highly walkable 500 m buffer zones (upper quartiles of the walkability index) were more likely to walk or cycle for leisure than those living in low-walkable buffer zones (quartile 1). When a 1,000 m buffer zone was applied, respondents in more walkable neighbourhoods were more likely to walk or cycle for both leisure-time and transport-related purposes.Conclusion. Developing a walkability index can assist in exploring the associations between measures of the built environment and physical activity to prioritize neighborhood change.

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“…In this direction, it is noted also that the nutritional environment has been investigated in order to deepen the understanding of the widespread prevalence of obesity, including the study of local availability, access to health food and environmental conditions (42)(43)(44)(45) . These data support the importance of focusing on places in addition to the individuals who live in those places.…”

Section: Discussionmentioning

confidence: 99%

Spatial distribution of the risk for metabolic complications: an application in south-east Brazil, 2006–2007

Nucci

Zangirolani

Mafra

et al. 2012

Public Health Nutr.

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Objective: To identify spatial variation in the risk for metabolic complications (RMC) by means of a semi-parametric approach for multinomial data. Design: Cross-sectional study. Setting: We visited 730 households selected in the first of a two-stage sample in South health district in Campinas, São Paulo, Brazil, 2006-2007. Subjects: We interviewed 651 individuals and obtained their respective anthropometric measures and geographical coordinates of their house location. They were classified according to a combination of BMI and abdominal circumference as having no risk, increased, high or very high RMC. Results: Gender, age and schooling were associated with RMC. Crude spatial risk for the three levels of RMC in relation to the absence of risk suggested different patterns in each level. Adjusted spatial risk for the RMC showed smaller significant areas, but the pattern remained similar to crude risk. Conclusions: Spatial point analysis with a multinomial approach improves the understanding of differences in RMC found, as we could identify specific areas in which to intervene. The public health significance of these findings may lie in the additional evidence provided that spatial location and its features can influence patterns of RMC. Keywords Obesity Epidemiology Spatial risk Metabolic complicationsThe recent increase in the prevalence of obesity is widely recognized as constituting a major threat to health in most countries as obesity has reached epidemic proportions (4) . In Brazil in 2002-2003, the prevalence was 8?9 % for men and 13?1 % for women (5) . Although developed countries present higher levels of obesity, developing countries also show increasing overweight prevalence, mainly as a consequence of the nutrition transition they are experiencing (6) . Brazil has continental dimensions and inequality is present all over the country. Trends show a shift in the prevalence from the higher to the lower socio-economic level in Brazil (7) and this change has contributed to the coexistence of an overweight person with an underweight person in the same household, called the 'dual burden' of disease (8)(9)(10) .The number of studies on obesity has increased significantly in the last decade, mainly due to the high risk that this condition leads to patients with other chronic diseases, such as diabetes and hypertension (11)(12)(13) . Although several individual-level risk factors for obesity have been identified, population rates of obesity are determined by a complex interplay of biological, social, environmental, behavioural and cultural factors, which collectively have created over decades an adverse environment for maintaining a healthy weight. A comprehensive understanding of how these factors interact is currently lacking (14,15) . Age, sex, socio-economic status, sedentary lifestyle and co-morbidities such as diabetes and hypertension have traditionally been studied as risk factors for overweight or obesity. However, investigations on the risk of obesity and its association with factors other than...

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An exploratory spatial analysis of overweight and obesity in Canada

Cited by 53 publications

References 15 publications

Examining the structure of spatial health effects in Germany using Hierarchical Bayes Models

Examining the structure of spatial health effects in Germany using Hierarchical Bayes Models

Neighbourhood walkability, leisure-time and transport-related physical activity in a mixed urban–rural area

Spatial distribution of the risk for metabolic complications: an application in south-east Brazil, 2006–2007

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