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Regionalization of school youth obesity and overweight in Texas by considering both body mass index and socioeconomic status

  • He Jin
  • Yongmei Lu
Article
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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.

Keywords

Heterogeneity Obesity REDCAP Regionalization Spatial contiguity 

Notes

Compliance with ethical standards

Conflict of interest statement

The authors declare that they have no conflict of interest.

References

  1. 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.CrossRefGoogle Scholar
  2. 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.Google Scholar
  3. 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.CrossRefGoogle Scholar
  4. 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.CrossRefGoogle Scholar
  5. 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.CrossRefGoogle Scholar
  6. Egger, G., & Swinburn, B. (1997). An “ecological” approach to the obesity pandemic. BMJ (Clinical Research Ed.), 315(7106), 477–480.CrossRefGoogle Scholar
  7. 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.Google Scholar
  8. 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.CrossRefGoogle Scholar
  9. Guo, D. (2008). Regionalization with dynamically constrained agglomerative clustering and partitioning (REDCAP). International Journal of Geographical Information Science, 22(7), 801–823.CrossRefGoogle Scholar
  10. Index, B. M. (2008). About BMI for adults. Atlanta: Department of Health and Human Services,Centers for Disease Control and Prevention.Google Scholar
  11. 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.CrossRefGoogle Scholar
  12. 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.CrossRefGoogle Scholar
  13. Kohonen, T. (1998). The self-organizing map. Neurocomputing, 21(1), 1–6.CrossRefGoogle Scholar
  14. 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.CrossRefGoogle Scholar
  15. Lenihan, P. (2008). Regionalization in local public health departments: The northern illinois public health consortium. Public Health Reports, 123(4), 1–13.Google Scholar
  16. Low, S., Chin, M. C., & Deurenberg-Yap, M. (2009). Review on epidemic of obesity. Annals Academy of Medicine Singapore, 38(1), 57.Google Scholar
  17. 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.Google Scholar
  18. 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.CrossRefGoogle Scholar
  19. 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.CrossRefGoogle Scholar
  20. 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.CrossRefGoogle Scholar
  21. 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.CrossRefGoogle Scholar
  22. 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.CrossRefGoogle Scholar
  23. 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.CrossRefGoogle Scholar
  24. Parker, L., Burns, A. C., & Nyberg, K. (2010). Childhood obesity prevention in texas: Workshop summary. New York: National Academies Press.Google Scholar
  25. 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.CrossRefGoogle Scholar
  26. Robert, L. K. (2013). Constructing geographic areas for homicide research: A case study of New Orleans, LouisianaGoogle Scholar
  27. 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.Google Scholar
  28. 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.CrossRefGoogle Scholar
  29. 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.CrossRefGoogle Scholar
  30. 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.CrossRefGoogle Scholar
  31. 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.CrossRefGoogle Scholar
  32. 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.CrossRefGoogle Scholar
  33. 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.CrossRefGoogle Scholar
  34. 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.CrossRefGoogle Scholar
  35. 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.CrossRefGoogle Scholar
  36. Texas Education Agency (2014). Physical fitness assessment initiative. Retrieved from http://tea.texas.gov/Texas_Schools/Safe_and_Healthy_Schools/Physical_Fitness_Assessment_Initiative/
  37. 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.CrossRefGoogle Scholar
  38. 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.CrossRefGoogle Scholar
  39. 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.

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of GeographyTexas State UniversitySan MarcosUSA

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