Abstract
We employed a regionalization algorithm called regionalization with dynamically constrained agglomerative clustering and partitioning (REDCAP) to analyze the Body Mass Index (BMI) of school students in Texas and its association with social economic status (SES). The study includes 741 school districts in Texas. BMI data were extracted from the Physical Fitness Assessment Initiative program managed by Texas Education Agency. SES was described using six variables that cover two aspects, Household SES and Neighborhood SES. The study period was the 2012–2013 academic year. We used three REDCAP algorithms to delineate regions considering both spatial contiguity and attribute homogeneity. The result shows that Full-order-CLK algorithm of REDCAP is most effective in producing regions to delineate the patterns of Texas students’ BMI and its relationship with SES. Moreover, two- and six -class regions are the optimal numbers for such a regionalization. Our study reveals the regional patterns of school youth obesity in Texas when connecting to SES—particularly in regions with high rate of obesity and low SES, such as the regions in South and West Texas, and the three school districts in San Antonio. The findings can provide guidance for regionalized policy and practice to fight against school youth obesity in Texas.
Similar content being viewed by others
References
Adu-Prah, S., & Oyana, T. J. (2015). Regionalization of youth and adolescent weight metrics for the continental united states using contiguity-constrained clustering and partitioning. Cartographica: The International Journal for Geographic Information and Geovisualization, 50(2), 61–70.
Benassi, F., Bocci, C., & Petrucci, A. (2010). Spatial data mining for clustering: From the literature review to an application using RedCap. Working paper 2010/2011, Università degli Studi di Firenze.
Chalkias, C., Papadopoulos, A. G., Kalogeropoulos, K., Tambalis, K., Psarra, G., & Sidossis, L. (2013). Geographical heterogeneity of the relationship between childhood obesity and socio-environmental status: Empirical evidence from athens, greece. Applied Geography, 37, 34–43.
Chen, T., Modin, B., Ji, C., & Hjern, A. (2011). Regional, socioeconomic and urban-rural disparities in child and adolescent obesity in china: A multilevel analysis. Acta Paediatrica, 100(12), 1583–1589.
Coops, N. C., Wulder, M. A., & Iwanicka, D. (2009). An environmental domain classification of canada using earth observation data for biodiversity assessment. Ecological Informatics, 4(1), 8–22.
Egger, G., & Swinburn, B. (1997). An “ecological” approach to the obesity pandemic. BMJ (Clinical Research Ed.), 315(7106), 477–480.
Escarce, J., Morales, L., & Rumbaut, R. (2006). The health status and health behaviors of Hispanics. In M. Tienda & F. Mitchell (Eds.), Hispanics and the future of America (pp. 362– 409). Washington, DC: National Academies Press.
Freedman, D. S., Mei, Z., Srinivasan, S. R., Berenson, G. S., & Dietz, W. H. (2007). Cardiovascular risk factors and excess adiposity among overweight children and adolescents: The bogalusa heart study. The Journal of Pediatrics, 150(1), 12–17.
Guo, D. (2008). Regionalization with dynamically constrained agglomerative clustering and partitioning (REDCAP). International Journal of Geographical Information Science, 22(7), 801–823.
Index, B. M. (2008). About BMI for adults. Atlanta: Department of Health and Human Services,Centers for Disease Control and Prevention.
Jin, H., & Lu, Y. (2017a). Academic performance of texas public schools and its relationship with students’ physical fitness and socioeconomic status. International Journal of Applied Geospatial Research (IJAGR), 8(3), 37–52.
Jin, H., & Lu, Y. (2017b). The relationship between obesity and socioeconomic status among texas school children and its spatial variation. Applied Geography, 79, 143–152.
Kohonen, T. (1998). The self-organizing map. Neurocomputing, 21(1), 1–6.
Kupfer, J. A., Gao, P., & Guo, D. (2012). Regionalization of forest pattern metrics for the continental united states using contiguity constrained clustering and partitioning. Ecological Informatics, 9, 11–18.
Lenihan, P. (2008). Regionalization in local public health departments: The northern illinois public health consortium. Public Health Reports, 123(4), 1–13.
Low, S., Chin, M. C., & Deurenberg-Yap, M. (2009). Review on epidemic of obesity. Annals Academy of Medicine Singapore, 38(1), 57.
Martikainen, P. T., & Marmot, M. G. (1999). Socioeconomic differences in weight gain and determinants and consequences of coronary risk factors. The American Journal of Clinical Nutrition, 69(4), 719–726.
McMurray, R. G., Harrell, J. S., Deng, S., Bradley, C. B., Cox, L. M., & Bangdiwala, S. I. (2000). The influence of physical activity, socioeconomic status, and ethnicity on the weight status of adolescents. Obesity Research, 8(2), 130–139.
Miech, R. A., Kumanyika, S. K., Stettler, N., Link, B. G., Phelan, J. C., & Chang, V. W. (2006). Trends in the association of poverty with overweight among US adolescents, 1971-2004. JAMA, 295(20), 2385–2393.
Mu, L., Wang, F., Chen, V. W., & Wu, X. (2015). A place-oriented, mixed-level regionalization method for constructing geographic areas in health data dissemination and analysis. Annals of the Association of American Geographers, 105(1), 48–66.
O’Dea, J. A., & Dibley, M. J. (2010). Obesity increase among low SES Australian schoolchildren between 2000 and 2006: Time for preventive interventions to target children from low income schools? International Journal of Public Health, 55(3), 185–192.
Ogden, C. L., Carroll, M. D., Kit, B. K., & Flegal, K. M. (2014). Prevalence of childhood and adult obesity in the United States, 2011-2012. JAMA, 311(8), 806–814.
Pakpour, A. H., Yekaninejad, M. S., & Chen, H. (2011). Mothers’ perception of obesity in schoolchildren: A survey and the impact of an educational intervention. Jornal De Pediatria, 87(2), 169–174.
Parker, L., Burns, A. C., & Nyberg, K. (2010). Childhood obesity prevention in texas: Workshop summary. New York: National Academies Press.
Penney, T., Rainham, D., Dummer, T., & Kirk, S. (2014). A spatial analysis of community level overweight and obesity. Journal of Human Nutrition & Dietetics, 27(s2), 65–74.
Robert, L. K. (2013). Constructing geographic areas for homicide research: A case study of New Orleans, Louisiana
Salvador, S., & Chan, P. (2004). Determining the number of clusters/segments in hierarchical clustering/segmentation algorithms. Paper presented at the tools with artificial intelligence, 2004. ICTAI 2004. 16th IEEE International Conference On, pp. 576–584.
Serdula, M. K., Ivery, D., Coates, R. J., Freedman, D. S., Williamson, D. F., & Byers, T. (1993). Do obese children become obese adults? A review of the literature. Preventive Medicine, 22(2), 167–177.
Singh, A. S., Mulder, C., Twisk, J. W., Van Mechelen, W., & Chinapaw, M. J. (2008). Tracking of childhood overweight into adulthood: A systematic review of the literature. Obesity Reviews, 9(5), 474–488.
Skelton, J. A., Cook, S. R., Auinger, P., Klein, J. D., & Barlow, S. E. (2009). Prevalence and trends of severe obesity among US children and adolescents. Academic Pediatrics, 9(5), 322–329.
Skinner, A. C., & Skelton, J. A. (2014). Prevalence and trends in obesity and severe obesity among children in the united states, 1999–2012. JAMA Pediatrics, 168(6), 561–566.
Stamatakis, E., Wardle, J., & Cole, T. J. (2010). Childhood obesity and overweight prevalence trends in england: Evidence for growing socioeconomic disparities. International Journal of Obesity, 34(1), 41–47.
Stoto, M. A. (2008). Regionalization in local public health systems: Variation in rationale, implementation, and impact on public health preparedness. Public Health Reports, 123(4), 441–449.
Sundquist, J., Malmstrom, M., & Johansson, S. E. (1999). Cardiovascular risk factors and the neighbourhood environment: A multilevel analysis. International Journal of Epidemiology, 28(5), 841–845.
Swinburn, B., Egger, G., & Raza, F. (1999). Dissecting obesogenic environments: The development and application of a framework for identifying and prioritizing environmental interventions for obesity. Preventive Medicine, 29(6), 563–570.
Texas Education Agency (2014). Physical fitness assessment initiative. Retrieved from http://tea.texas.gov/Texas_Schools/Safe_and_Healthy_Schools/Physical_Fitness_Assessment_Initiative/
Truong, K. D., & Sturm, R. (2005). Weight gain trends across sociodemographic groups in the united states. American Journal of Public Health, 95(9), 1602–1606.
Wang, F., Guo, D., & McLafferty, S. (2012). Constructing geographic areas for cancer data analysis: A case study on late-stage breast cancer risk in illinois. Applied Geography, 35(1), 1–11.
Wang, Y., & Lim, H. (2012). The global childhood obesity epidemic and the association between socio-economic status and childhood obesity. International Review Of Psychiatry, 24(3), 176–188. https://doi.org/10.3109/09540261.2012.688195.
Author information
Authors and Affiliations
Ethics declarations
Conflict of interest statement
The authors declare that they have no conflict of interest.
Rights and permissions
About this article
Cite this article
Jin, H., Lu, Y. Regionalization of school youth obesity and overweight in Texas by considering both body mass index and socioeconomic status. GeoJournal 84, 55–69 (2019). https://doi.org/10.1007/s10708-018-9849-4
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10708-018-9849-4