Geographical variation of overweight, obesity and related risk factors: Findings from the European Health Examination Survey in Luxembourg, 2013-2015
Bibliographic record
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".