Geographical variation of overweight, obesity and related risk factors: Findings from the European Health Examination Survey in Luxembourg, 2013-2015

被引:26
作者
Samouda, Hanen [1 ]
Ruiz-Castell, Maria [1 ]
Bocquet, Valery [2 ]
Kuemmerle, Andrea [3 ]
Chioti, Anna [1 ]
Dadoun, Frederic [4 ]
Kandala, Ngianga-Bakwin [5 ]
Stranges, Saverio [6 ]
机构
[1] Luxembourg Inst Hlth, Populat Hlth Dept, Epidemiol & Publ Hlth Res Unit, Strassen, Luxembourg
[2] Luxembourg Inst Hlth, Competence Ctr Methodol & Stat, Strassen, Luxembourg
[3] Swiss Trop & Publ Hlth Inst, Basel, Switzerland
[4] Ctr Hosp Luxembourg, Endocrinol & Diabetol Dept, Luxembourg, Luxembourg
[5] Northumbria Univ, Fac Engn & Environm, Dept Math Phys & Elect Engn, Newcastle Upon Tyne, Tyne & Wear, England
[6] Western Univ, Schulich Sch Med & Dent, Dept Epidemiol & Biostat, London, ON, Canada
关键词
BODY-MASS INDEX; PHYSICAL-ACTIVITY; SOCIOECONOMIC INEQUALITY; WEIGHT; ASSOCIATIONS; PREVALENCE; EXPOSURE; PROGRAM; FITNESS; BURDEN;
D O I
10.1371/journal.pone.0197021
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
The analyses of geographic variations in the prevalence of major chronic conditions, such as overweight and obesity, are an important public health tool to identify "hot spots" and inform allocation of funding for policy and health promotion campaigns, yet rarely performed. Here we aimed at exploring, for the first time in Luxembourg, potential geographic patterns in overweight/obesity prevalence in the country, adjusted for several demographic, socioeconomic, behavioural and health status characteristics. Data came from 720 men and 764 women, 25-64 years old, who participated in the European Health Examination Survey in Luxembourg (2013-2015). To investigate the geographical variation, geo-additive semiparametric mixed model and Bayesian modelisations based on Markov Chain Monte Carlo techniques for inference were performed. Large disparities in the prevalence of overweight and obesity were found between municipalities, with the highest rates of obesity found in 3 municipalities located in the South-West of the country. Bayesian approach also underlined a nonlinear effect of age on overweight and obesity in both genders (significant in men) and highlighted the following risk factors: 1. country of birth for overweight in men born in a non-European country (Posterior Odds Ratio (POR): 3.24 [1.61-8.69]) and women born in Portugal (POR: 2.44 [1.25-4.43]), 2. low educational level (secondary or below) for overweight (POR: 1.66 (1.06-2.72)] and obesity (POR:2.09 [1.05-3.65]) in men, 3. single marital status for obesity in women (POR: 2.20 [1.24-3.91]), 4.fair (men: POR: 3.19 [1.58-6.79], women: POR: 2.24 [1.33-3.73]) to very bad health perception (men: POR: 15.01 [2.16-98.09]) for obesity, 5. sleeping more than 6 hours for obesity in unemployed men (POR: 3.66 [2.02-8.03]). Protective factors highlighted were: 1. single marital status against overweight (POR: [0.60 (0.38-0.96)]) and obesity (POR: 0.39 [0.16-0.84]) in men, 2. the fact to be widowed against overweight in women (POR: [0.30 (0.07-0.86)], as well as a non European country of birth (POR: 0.49 [0.19-0.98]), tertiary level of education (POR: 0.34 [0.18-0.64]), moderate alcohol consumption (POR: 0.54 [0.36-0.90]) and aerobic physical activity practice (POR: 0.44 [0.27-0.77]) against obesity in women. A double burden of environmental exposure due to historic mining and industrial activities and past economic vulnaribility in the South-West of the country may have participated to the higher prevalence of obesity found in this region. Other demographic, socioeconomic, behavioural and health status covariates could have been involved as well.
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