Original Articles
18 June 2020
Vol. 15 No. 1 (2020)
Geographic clustering and region-specific determinants of obesity in the Netherlands
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
3363
Views
1396
Downloads
181
HTML
Authors
As a leading cause of morbidity and premature mortality, obesity has become a major global public health problem. It is therefore important to investigate the spatial variation of obesity prevalence and its associations with environmental and behavioral factors (e.g., food environment, physical activity), to optimize the targeting of scarce public health resources. In this study, the geographic clustering of obesity in the Netherlands was explored by analyzing the local spatial autocorrelation of municipal-level prevalence rates of adulthood obesity (aged ‰¥19 years) in 2016. The potential influential factors that may be associated with obesity prevalence were first selected from five categories of healthrelated factors through binary and Least Absolute Shrinkage and Selection Operator (LASSO) regressions. Geographically Weighted Regression (GWR) was then used to investigate the spatial variations of the associations between those selected factors and obesity prevalence. The results revealed marked geographic variations in obesity prevalence, with four clusters of high prevalence in the north, south, east, and west, and three clusters of low prevalence in the north and south of the Netherlands. Lack of sports participation, risk of anxiety, falling short of physical activity guidelines, and the number of restaurants around homes were found to be associated with obesity prevalence across municipalities. Our findings show that effective, region-specific strategies are needed to tackle the increasing obesity in the Netherlands.
Altmetrics
Downloads
Download data is not yet available.
Citations
Abarca-Gómez, L., Abdeen, Z.A., Hamid, Z.A., Abu-Rmeileh, N.M., Acosta-Cazares, B., Acuin, C., Adams, R.J., Aekplakorn, W., Afsana, K., & Aguilar-Salinas, C.A. (2017). Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128· 9 million children, adolescents, and adults. The Lancet, 390, 2627-2642 DOI: https://doi.org/10.1016/S0140-6736(17)32129-3
Anselin, L. (1995). Local indicators of spatial association—LISA. Geographical analysis, 27, 93-115 DOI: https://doi.org/10.1111/j.1538-4632.1995.tb00338.x
Arredondo, A., Torres, C., Orozco, E., Pacheco, S., Huang, F.Y., Zambrano, E., & Bolanos-Jimenez, F. (2019). Socio-economic indicators, dietary patterns, and physical activity as determinants of maternal obesity in middle-income countries: Evidences from a cohort study in Mexico. International Journal of Health Planning and Management, 34, E713-E725 DOI: https://doi.org/10.1002/hpm.2684
Arroyo-Johnson, C., & Mincey, K.D. (2016). Obesity epidemiology worldwide. Gastroenterology Clinics, 45, 571-579 DOI: https://doi.org/10.1016/j.gtc.2016.07.012
Bunt, S., Mérelle, S., Steenhuis, I., & Kroeze, W. (2017). Predictors of need for help with weight loss among overweight and obese men and women in the Netherlands: a cross-sectional study. BMC health services research, 17, 819 DOI: https://doi.org/10.1186/s12913-017-2759-1
Chen, D.R., & Truong, K. (2012). Using multilevel modeling and geographically weighted regression to identify spatial variations in the relationship between place-level disadvantages and obesity in Taiwan. Applied Geography, 32, 737-745 DOI: https://doi.org/10.1016/j.apgeog.2011.07.018
Chen, Y., Rennie, D.C., Karunanayake, C.P., Janzen, B., Hagel, L., Pickett, W., Dyck, R., Lawson, J., Dosman, J.A., Pahwa, P., & Saskatchewan Rural Hlth Study, G. (2015). Income adequacy and education associated with the prevalence of obesity in rural Saskatchewan, Canada. BMC public health, 15 DOI: https://doi.org/10.1186/s12889-015-2006-9
Cook, W.K., Tseng, W., Tam, C., John, I., & Lui, C. (2017). Ethnic-group socioeconomic status as an indicator of community-level disadvantage: A study of overweight/obesity in Asian American adolescents. Social Science & Medicine, 184, 15-22 DOI: https://doi.org/10.1016/j.socscimed.2017.04.027
Cui, J., Sun, X.F., Li, X.J., Ke, M., Sun, J.P., Yasmeen, N., Khan, J.M., Xin, H.L., Xue, S.Y., & Baloch, Z. (2018). Association Between Different Indicators of Obesity and Depression in Adults in Qingdao, China: A Cross-Sectional Study. Frontiers in Endocrinology, 9 DOI: https://doi.org/10.3389/fendo.2018.00549
Elmokhallalati, Y., FarajAllah, H., & Albarqouni, L. (2019). Socio-demographic and economic determinants of overweight and obesity in preschool children in Palestine: analysis of data from the Palestinian Multiple Indicator Cluster Survey. Lancet, 393, 22-22 DOI: https://doi.org/10.1016/S0140-6736(19)30608-7
FAO, I., UNICEF, WFP and WHO (2017). The State of Food Security and Nutrition in the World 2017.
Building resilience for peace and food security. Rome: FAO
Farhadian, M., Moghimbeigi, A., & Aliabadi, M. (2013). Mapping the Obesity in Iran by Bayesian Spatial Model. Iranian Journal of Public Health, 42, 581-587
Fotheringham, A.S., Charlton, M.E., & Brunsdon, C. (1998). Geographically weighted regression: a natural evolution of the expansion method for spatial data analysis. Environment and Planning A, 30, 1905-1927 DOI: https://doi.org/10.1068/a301905
Fraser, L.K., Clarke, G.P., Cade, J.E., & Edwards, K.L. (2012). Fast Food and Obesity A Spatial Analysis in a Large United Kingdom Population of Children Aged 13-15. American journal of preventive medicine, 42, E77-E85
Fu, W.J., Jiang, P.K., Zhou, G.M., & Zhao, K.L. (2014). Using Moran's I and GIS to study the spatial pattern of forest litter carbon density in a subtropical region of southeastern China. Biogeosciences, 11, 2401-2409 DOI: https://doi.org/10.5194/bg-11-2401-2014
Fu, W.J.J. (1998). Penalized regressions: The bridge versus the lasso. Journal of Computational and Graphical Statistics, 7, 397-416
Hajizadeh, M., Campbell, M.K., & Sarma, S. (2016). A Spatial Econometric Analysis of Adult Obesity: Evidence from Canada. Applied Spatial Analysis and Policy, 9, 329-363 DOI: https://doi.org/10.1007/s12061-015-9151-5
Huang, R., Moudon, A.V., Cook, A.J., & Drewnowski, A. (2015). The spatial clustering of obesity: does the built environment matter? Journal of Human Nutrition and Dietetics, 28, 604-612 DOI: https://doi.org/10.1111/jhn.12279
Jia, P., Xue, H., Cheng, X., Wang, Y.G., & Wang, Y.F. (2019a). Association of neighborhood built environments with childhood obesity: Evidence from a 9-year longitudinal, nationally representative survey in the US. Environment International, 128, 158-164 DOI: https://doi.org/10.1016/j.envint.2019.03.067
Jia, P., Xue, H., Yin, L., Stein, A., Wang, M.Q., & Wang, Y.F. (2019b). Spatial Technologies in Obesity Research: Current Applications and Future Promise. Trends in Endocrinology and Metabolism, 30, 211-223 DOI: https://doi.org/10.1016/j.tem.2018.12.003
Michimi, A., & Wimberly, M.C. (2010). Spatial Patterns of Obesity and Associated Risk Factors in the Conterminous U.S. American journal of preventive medicine, 39, E1-E12 DOI: https://doi.org/10.1016/j.amepre.2010.04.008
Moran, P.A. (1950). Notes on continuous stochastic phenomena. Biometrika, 37, 17-23 DOI: https://doi.org/10.1093/biomet/37.1-2.17
National Institute for Public Health and the Environment (2016). Gezondheid per buurt, wijk en gemeente. In
Ogden, C.L., Fakhouri, T.H., Carroll, M.D., Hales, C.M., Fryar, C.D., Li, X.F., & Freedman, D.S. (2017). Prevalence of Obesity Among Adults, by Household Income and Education - United States, 2011-2014. Mmwr-Morbidity and Mortality Weekly Report, 66, 1369-1373 DOI: https://doi.org/10.15585/mmwr.mm6650a1
Pouliou, T., & Elliott, S.J. (2009). An exploratory spatial analysis of overweight and obesity in Canada. Preventive Medicine, 48, 362-367 DOI: https://doi.org/10.1016/j.ypmed.2009.01.017
Schokker, D.F., Visscher, T.L.S., Nooyens, A.C.J., van Baak, M.A., & Seidell, J.C. (2007). Prevalence of overweight and obesity in the Netherlands. Obesity reviews, 8, 101-107 DOI: https://doi.org/10.1111/j.1467-789X.2006.00273.x
Statistics Netherlands (CBS) (2018). Toelichting wijk en buurtkaart 2016-2017-2018. The Netherlands
Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B, 58, 267-288
Traversy, G., & Chaput, J.P. (2015). Alcohol Consumption and Obesity: An Update. Current Obesity Reports, 4, 122-130 DOI: https://doi.org/10.1007/s13679-014-0129-4
van de Kassteele, J., Zwakhals, L., Breugelmans, O., Ameling, C., & van den Brink, C. (2017). Estimating the prevalence of 26 health-related indicators at neighbourhood level in the Netherlands using structured additive regression. International journal of health geographics, 16 DOI: https://doi.org/10.1186/s12942-017-0097-5
Visscher, T., Kromhout, D., & Seidell, J. (2002). Long-term and recent time trends in the prevalence of obesity among Dutch men and women. International journal of obesity, 26, 1218 DOI: https://doi.org/10.1038/sj.ijo.0802016
Withrow, D., & Alter, D.A. (2011). The economic burden of obesity worldwide: a systematic review of the direct costs of obesity. Obesity reviews, 12, 131-141 DOI: https://doi.org/10.1111/j.1467-789X.2009.00712.x
World Health Organization (2004a). International statistical classification of diseases and related health problems. World Health Organization
World Health Organization (2014b). Global status report on noncommunicable diseases 2014. World Health Organization
Yoon, S.J., Kim, H.J., & Doo, M. (2016). Association between perceived stress, alcohol consumption levels and obesity in Koreans. Asia Pacific Journal of Clinical Nutrition, 25, 316-325
How to Cite
Geographic clustering and region-specific determinants of obesity in the Netherlands. (2020). Geospatial Health, 15(1). https://doi.org/10.4081/gh.2020.839
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.
Most read articles by the same author(s)
- Sherif Amer, Robert Bergquist, Transport geography: Implications for public health , Geospatial Health: Vol. 16 No. 1 (2021)
- Elias Nyandwi, Tom Veldkamp, Frank Badu Osei, Sherif Amer, Spatio-temporal dynamics of schistosomiasis in Rwanda between 2001 and 2012: impact of the national Neglected Tropical Disease control programme , Geospatial Health: Vol. 12 No. 1 (2017)
- Vestine Uwiringiyimana, Antonie Veldkamp, Sherif Amer, Stunting spatial pattern in Rwanda: An examination of the demographic, socio-economic and environmental determinants , Geospatial Health: Vol. 14 No. 2 (2019)
- Yuling Huang, Shujuan Yang, Yuxuan Zou, Jianming Su, Canglang Wu, Bo Zhong, Peng Jia, Spatiotemporal epidemiology of COVID-19 from an epidemic course perspective , Geospatial Health: Vol. 17 No. s1 (2022): Special issue on COVID-19
- Renfei Yang, Fu Ren, Xiangyuan Ma, Hongwei Zhang, Wenxuan Xu, Peng Jia, Explaining the longevity characteristics in China from a geographical perspective: A multi-scale geographically weighted regression analysis , Geospatial Health: Vol. 16 No. 2 (2021)
- Robert Bergquist, Sherif Amer, Good things come in small packages: New trends in acquisition of remotely-sensed data , Geospatial Health: Vol. 14 No. 1 (2019)
- Pengxin Zhang, Shuhan Yang, Shaoqing Dai, Darren How Jin Aik, Shujuan Yang, Peng Jia, Global spreading of Omicron variant of COVID-19 , Geospatial Health: Vol. 17 No. s1 (2022): Special issue on COVID-19
- Ellen-Wien Augustijn-Beckers, Sherif Amer, Rob Lemmens, Robert Bergquist, Does the Geohealth domain require a body of knowledge? , Geospatial Health: Vol. 17 No. 2 (2022)
- Sherif Amer, Ellen-Wien Augustijn, Carmen Anthonj, Nils Tjaden, Justine Blanford, Marc van den Homberg, Laura Rinaldi, Thomas van Rompay, Raúl Zurita Milla, Geospatial Health: achievements, innovations, priorities , Geospatial Health: Vol. 19 No. 2 (2024)
- Adel Al-Huraibi, Sherif Amer, Justine Blanford, Prioritizing the location of vaccination centres during the COVID-19 pandemic by bike in the Netherlands , Geospatial Health: Vol. 20 No. 1 (2025)
