https://doi.org/10.4081/gh.2020.839
Geographic clustering and region-specific determinants of obesity in the Netherlands
Submitted: 29 November 2019
Accepted: 16 April 2020
Published: 18 June 2020
Accepted: 16 April 2020
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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.
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.
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
Qiu, G., Liu, X., Amiranti, A. Y., Yasini, M., Wu, T., Amer, S., & Jia, P. (2020). Geographic clustering and region-specific determinants of obesity in the Netherlands. Geospatial Health, 15(1). https://doi.org/10.4081/gh.2020.839
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