IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0197021.html
   My bibliography  Save this article

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

Author

Listed:
  • Hanen Samouda
  • Maria Ruiz-Castell
  • Valery Bocquet
  • Andrea Kuemmerle
  • Anna Chioti
  • Frédéric Dadoun
  • Ngianga-Bakwin Kandala
  • Saverio Stranges

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.

Suggested Citation

  • Hanen Samouda & Maria Ruiz-Castell & Valery Bocquet & Andrea Kuemmerle & Anna Chioti & Frédéric Dadoun & Ngianga-Bakwin Kandala & Saverio Stranges, 2018. "Geographical variation of overweight, obesity and related risk factors: Findings from the European Health Examination Survey in Luxembourg, 2013-2015," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-23, June.
    • Handle: RePEc:plo:pone00:0197021
    DOI: 10.1371/journal.pone.0197021
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0197021
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0197021&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0197021?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Ngianga-Bakwin Kandala & Saverio Stranges, 2014. "Geographic Variation of Overweight and Obesity among Women in Nigeria: A Case for Nutritional Transition in Sub-Saharan Africa," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-11, June.
    2. Amina Aitsi-Selmi & Tarani Chandola & Sharon Friel & Reza Nouraei & Martin J Shipley & Michael G Marmot, 2012. "Interaction between Education and Household Wealth on the Risk of Obesity in Women in Egypt," PLOS ONE, Public Library of Science, vol. 7(6), pages 1-8, June.
    3. Zhang, Qi & Wang, Youfa, 2004. "Socioeconomic inequality of obesity in the United States: do gender, age, and ethnicity matter?," Social Science & Medicine, Elsevier, vol. 58(6), pages 1171-1180, March.
    4. Sobal, Jeffrey & Rauschenbach, Barbara S. & Frongillo Jr., Edward A., 1992. "Marital status, fatness and obesity," Social Science & Medicine, Elsevier, vol. 35(7), pages 915-923, October.
    5. Amina Aitsi-Selmi & Ruth Bell & Martin J Shipley & Michael G Marmot, 2014. "Education Modifies the Association of Wealth with Obesity in Women in Middle-Income but Not Low-Income Countries: An Interaction Study Using Seven National Datasets, 2005-2010," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-10, March.
    6. Marion Devaux & Franco Sassi & Jody Church & Michele Cecchini & Francesca Borgonovi, 2011. "Exploring the Relationship Between Education and Obesity," OECD Journal: Economic Studies, OECD Publishing, vol. 2011(1), pages 1-40.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Woojin Chung & Roeul Kim, 2020. "A Reversal of the Association between Education Level and Obesity Risk during Ageing: A Gender-Specific Longitudinal Study in South Korea," IJERPH, MDPI, vol. 17(18), pages 1-19, September.
    2. Gulay Avsar & Roger Ham & W. Kathy Tannous, 2017. "Factors Influencing the Incidence of Obesity in Australia: A Generalized Ordered Probit Model," IJERPH, MDPI, vol. 14(2), pages 1-13, February.
    3. Cornelie Nienaber-Rousseau & Olusola F. Sotunde & Patricia O. Ukegbu & P. Hermanus Myburgh & Hattie H. Wright & Lize Havemann-Nel & Sarah J. Moss & Iolanthé M. Kruger & H. Salomé Kruger, 2017. "Socio-Demographic and Lifestyle Factors Predict 5-Year Changes in Adiposity among a Group of Black South African Adults," IJERPH, MDPI, vol. 14(9), pages 1-16, September.
    4. Chen, Duan-Rung & Wen, Tzai-Hung, 2010. "Socio-spatial patterns of neighborhood effects on adult obesity in Taiwan: A multi-level model," Social Science & Medicine, Elsevier, vol. 70(6), pages 823-833, March.
    5. Brunello, Giorgio & D'Hombres, Beatrice, 2007. "Does body weight affect wages?: Evidence from Europe," Economics & Human Biology, Elsevier, vol. 5(1), pages 1-19, March.
    6. Aysit Tansel & Ceyhan Ozturk & Erkan Erdil, 2021. "The Impact of Body Mass Index on Growth, Schooling, Productivity, and Savings: A Cross-Country Study," Koç University-TUSIAD Economic Research Forum Working Papers 2118, Koc University-TUSIAD Economic Research Forum.
    7. Helmuth Cremer & Catarina Goulão & Jean-Marie Lozachmeur, 2019. "Soda tax incidence and design under monopoly," CESifo Working Paper Series 7525, CESifo.
    8. Cremer, Helmuth & Goulão, Catarina & Roeder, Kerstin, 2016. "Earmarking and the political support of fat taxes," Journal of Health Economics, Elsevier, vol. 50(C), pages 258-267.
    9. Ver Ploeg, Michele & Mancino, Lisa & Lin, Biing-Hwan & Wang, Chia-Yih, 2007. "The vanishing weight gap: Trends in obesity among adult food stamp participants (US) (1976-2002)," Economics & Human Biology, Elsevier, vol. 5(1), pages 20-36, March.
    10. Ivan Parise & Penelope Abbott & Steven Trankle, 2021. "Drivers to Obesity—A Study of the Association between Time Spent Commuting Daily and Obesity in the Nepean Blue Mountains Area," IJERPH, MDPI, vol. 19(1), pages 1-14, December.
    11. Philip B Mason & Frank M. Howell & Jeremy R. Porter, 2014. "Examining Rural-Urban Obesity Trends among Youth in the U.S.: Testing the Socioeconomic Gradient Hypothesis," International Journal of Business and Social Research, MIR Center for Socio-Economic Research, vol. 4(12), pages 27-42, December.
    12. Chen, Zhuo & Roy, Kakoli, 2009. "Calculating concentration index with repetitive values of indicators of economic welfare," Journal of Health Economics, Elsevier, vol. 28(1), pages 169-175, January.
    13. Melissa Neuman & Ichiro Kawachi & Steven Gortmaker & SV Subramanian, 2014. "National Economic Development and Disparities in Body Mass Index: A Cross-Sectional Study of Data from 38 Countries," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-18, June.
    14. Kazuma Sato, 2021. "Relationship between marital status and body mass index in Japan," Review of Economics of the Household, Springer, vol. 19(3), pages 813-841, September.
    15. Cozac, Marina & Mende, Martin & Scott, Maura L., 2023. "Consumer preferences for fuel snacks at the intersection of caregiving stress and gender," Journal of Business Research, Elsevier, vol. 159(C).
    16. Philip B Mason & Frank M. Howell & Jeremy R. Porter, 2014. "Examining Rural-Urban Obesity Trends among Youth in the U.S.: Testing the Socioeconomic Gradient Hypothesis," International Journal of Business and Social Research, LAR Center Press, vol. 4(12), pages 27-42, December.
    17. Mata, Jutta & Frank, Ronald & Hertwig, Ralph, 2015. "Higher body mass index, less exercise, but healthier eating in married adults: Nine representative surveys across Europe," Social Science & Medicine, Elsevier, vol. 138(C), pages 119-127.
    18. Ver Ploeg, Michele & Mancino, Lisa & Lin, Biing-Hwan, 2007. "Food and Nutrition Assistance Programs and Obesity: 1976-2002," Economic Research Report 55965, United States Department of Agriculture, Economic Research Service.
    19. Smith, Patricia K. & Bogin, Barry & Bishai, David, 2005. "Are time preference and body mass index associated?: Evidence from the National Longitudinal Survey of Youth," Economics & Human Biology, Elsevier, vol. 3(2), pages 259-270, July.
    20. Houle, Brian, 2011. "Obesity disparities among disadvantaged men: National adult male inmate prevalence pooled with non-incarcerated estimates, United States, 2002-2004," Social Science & Medicine, Elsevier, vol. 72(10), pages 1667-1673, May.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0197021. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.