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

Abstract

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 semi-parametric 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.

Fig 1

Left: Adjusted total residual spatial effects for men’s overweight risk, at municipalities-level in Luxembourg in 2013–2015. Shown are the posterior odds ratios. Right: Corresponding posterior probabilities at 80% nominal level (EHES, 2013–2015). Red coloured–high risk. Green coloured–low risk. Black coloured–significant positive spatial effect. White coloured- significant negative spatial effect. Grey coloured–no significant effect.

Fig 2

Left: Adjusted total residual spatial effects for men’s obesity risk, at municipalities-level in Luxembourg in 2013–2015. Shown are the posterior odds ratios. Right: Corresponding posterior probabilities at 80% nominal level (EHES, 2013–2015). Red coloured–high risk. Green coloured–low risk. Black coloured–significant positive spatial effect. White coloured- significant negative spatial effect. Grey coloured–no significant effect.

Fig 3

Left: Adjusted total residual spatial effects for women’s overweight risk, at municipalities-level in Luxembourg in 2013–2015. Shown are the posterior odds ratios. Right: Corresponding posterior probabilities at 80% nominal level (EHES, 2013–2015). Red coloured–high risk. Green coloured–low risk. Black coloured–significant positive spatial effect. White coloured- significant negative spatial effect. Grey coloured–no significant effect.

Fig 4

Left: Adjusted total residual spatial effects for women’s obesity risk, at municipalities-level in Luxembourg in 2013–2015. Shown are the posterior odds ratios. Right: Corresponding posterior probabilities at 80% nominal level (EHES, 2013–2015). Red coloured–high risk. Green coloured–low risk. Black coloured–significant positive spatial effect. White coloured- significant negative spatial effect. Grey coloured–no significant effect.