Urban living and obesity: is it independent of its population and lifestyle characteristics?

The aim of this study was to investigate the poorly understood relationship between the process of urbanization and noncommunicable diseases (NCDs) in Sri Lanka using a multicomponent, quantitative measure of urbanicity. NCD prevalence data were taken from the Sri Lankan Diabetes and Cardiovascular Study, comprising a representative sample of people from seven of the nine provinces in Sri Lanka (n=4,485/ 5,000; response rate=89.7%). We constructed a measure of the urban environment for seven areas using a 7-item scale based on data from study clusters to develop an "urbanicity" scale. The items were population size, population density, and access to markets, transportation, communications/media, economic factors, environment/sanitation, health, education, and housing quality. Linear and logistic regression models were constructed to examine the relationship between urbanicity and chronic disease risk factors. Among men, urbanicity was positively associated with physical inactivity (odds ratio [OR]=3.22; 2.27-4.57), high body mass index (OR=2.45; 95% CI, 1.88-3.20) and diabetes mellitus (OR=2.44; 95% CI, 1.66-3.57). Among women, too, urbanicity was positively associated with physical inactivity (OR=2.29; 95% CI, 1.64-3.21), high body mass index (OR=2.92; 95% CI, 2.41-3.55), and diabetes mellitus (OR=2.10; 95% CI, 1.58 -2.80). There is a clear relationship between urbanicity and common modifiable risk factors for chronic disease in a representative sample of Sri Lankan adults.

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“…There are similarities between obesity prevalence figures presented in previous studies and this paper 30 …”

Section: Strengths and Weaknesses In Relation To Other Studies Discusupporting

confidence: 90%

Quantifying Urbanization as a Risk Factor for Noncommunicable Disease

et al. 2011

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“…A few studies have explored the levels of activity among the general population, diabetic individuals and urban females 3 14 30. The inactivity rates were 11% in the general population in a nationally representative sample3 and 31.8% in an urban community 15…”

Section: Discussionmentioning

confidence: 93%

Physical activity patterns and socio-demographic correlates of physical activity among medical undergraduates in Sri Lanka: an observational study

Medagama

Galgomuwa

Silva

2017

Postgraduate Medical Journal

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“…In addition, the residual associations may be due to factors not taken into account in our analyses. Indeed, beyond measurements issues related to definition of “urbanicity” [63], [64] it has often been shown that living in an urban versus rural area may impact outcomes such as obesity and NCDs, independently of the higher socio-economic level [11], [65]; the same could be true for gender inequities and there could all the more be such a residual association for regional contrasts.…”

Section: Discussionmentioning

confidence: 99%

Gender Obesity Inequities Are Huge but Differ Greatly According to Environment and Socio-Economics in a North African Setting: A National Cross-Sectional Study in Tunisia

et al. 2012

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IntroductionSouthern Mediterranean countries have experienced a marked increase in the prevalence of obesity whose consequences for gender related health inequities have been little studied. We assessed gender obesity inequalities and their environmental and socio-economic modifiers among Tunisian adults.MethodsCross-sectional survey in 2005; national, 3 level random cluster sample of 35–70 years Tunisians (women: n = 2964, men: n = 2379). Overall adiposity was assessed by BMI = weight(kg)/height(m)2 and obesity was BMI≥30, WHtR = waist circumference to height ratio defined abdominal obesity as WHtR≥0.6. Gender obesity inequality measure was women versus men Prevalence Proportion Odds-Ratio (OR); models featuring gender x covariate interaction assessed variation of gender obesity inequalities with area (urban versus rural), age, marital status or socio-economic position (profession, education, household income proxy).ResultsBMI was much higher among women (28.4(0.2)) versus men (25.3(0.1)), P<0.0001) as was obesity (37.0% versus 13.3%, OR = 3.8[3.1–7.4], P<0.0001) and abdominal obesity (42.6% versus 15.6%, 4.0[3.3–4.8], P<0.0001). Gender obesity inequalities (women versus men adjusted OR) were higher in urban (OR = 3.3[1.3–8.7]) than rural (OR = 2.0[0.7–5.5]) areas. These gender obesity inequalities were lower for subjects with secondary education or more (OR = 3.3[1.3–8.6]), than among those with no schooling (OR = 6.9[2.0–23.3]). They were also lower for those with upper/intermediate profession (OR = 1.4[0.5–4.3]) or even employees/workers OR = 2.3[1.0–5.4] than those not professionaly active at all (OR = 3.3[1.3–8.6]). Similar results were observed for addominal obesity.ConclusionThe huge overall gender obesity inequities (women much more corpulent than men) were higher in urban settings, but lower among subjects of higher education and professional activity. Reasons for gender inequalities in obesity and their variation with socio-economic position should be sought so that appropriate policies to reduce these inequalities can be implemented in Tunisia and similar settings.

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Urban living and obesity: is it independent of its population and lifestyle characteristics?

Cited by 30 publications

References 23 publications

Quantifying Urbanization as a Risk Factor for Noncommunicable Disease

Quantifying Urbanization as a Risk Factor for Noncommunicable Disease

Physical activity patterns and socio-demographic correlates of physical activity among medical undergraduates in Sri Lanka: an observational study

Gender Obesity Inequities Are Huge but Differ Greatly According to Environment and Socio-Economics in a North African Setting: A National Cross-Sectional Study in Tunisia

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