Journal of Community Health

, Volume 45, Issue 1, pp 41–47 | Cite as

The Association Between Obesity, Socio-Economic Status, and Neighborhood Environment: A Multi-Level Analysis of Spokane Public Schools

  • Ofer AmramEmail author
  • Solmaz Amiri
  • Robert B. Lutz
  • Anna Crowley
  • Pablo Monsivais
Original Paper


Socio economic inequities in obesity have been attributed to individuals’ psychosocial and behavioral characteristics. School environment, where children spend a large part of their day, may play an important role in shaping their health. This study aims to assess whether prevalence of overweight and obesity among elementary school students was associated with the school’s social and built environments. Analyses were based on 28 public elementary schools serving a total of 10,327 children in the city of Spokane, Washington. Schools were classified by percentage of students eligible for free and reduced meals (FRM). Crime rates, density of arterial roads, healthy food access, and walkability were computed in a one-mile walking catchment around schools to characterize their surrounding neighborhood. In the unadjusted multilevel logistic regression analyses, age, sex, percentage of students eligible for FRM, crime, walkability, and arterial road exposure were individually associated with the odds of being overweight or obese. In the adjusted model, the odds of being overweight or obese were higher with age, being male, and percentage of students eligible for FRM. The results call for policies and programs to improve the school environment, students’ health, and safety conditions near schools.


School environment Obesity Socioeconomic status Health equity 


Author Contributions

OA and PM: Conceptualization. OA, PM and SA: Methodology. SA: Software. OA and PM: Validation. RL: Resources. AC: Data Curation. OA, SA and PM: Writing-Original Draft Preparation. PM, RL, SA, AC and OA: Writing-Review & Editing.


This research was supported with funding from the Health Equity Research Center, a strategic research initiative of Washington State University.

Compliance with Ethical Standards

Ethical Approval

The Washington State University Office of Research Assurances determined that this study satisfied the criteria for Exempt Research.

Conflict of interest

Conflict of Interest: The authors declare that they have no conflict of interest.


  1. 1.
    Sokol, R. J. (2000). The chronic disease of childhood obesity: The sleeping giant has awakened. The Journal of Pediatrics,136(6), 711.CrossRefGoogle Scholar
  2. 2.
    Deckelbaum, R. J., & Williams, C. L. (2001). Childhood obesity: The health issue. Obesity Research,9(S11), 239S–243S.CrossRefGoogle Scholar
  3. 3.
    Kimm, S. Y., & Obarzanek, E. (2002). Childhood obesity: A new pandemic of the new millennium. Pediatrics,110(5), 1003–1007.CrossRefGoogle Scholar
  4. 4.
    Lynch, J. W., Kaplan, G. A., & Salonen, J. T. (1997). Why do poor people behave poorly? Variation in adult health behaviours and psychosocial characteristics by stages of the socioeconomic lifecourse. Social Science and Medicine,44(6), 809–819.CrossRefGoogle Scholar
  5. 5.
    Braveman, P., Egerter, S., & Williams, D. R. (2011). The social determinants of health: Coming of age. Annual Review of Public Health,32, 381–398.CrossRefGoogle Scholar
  6. 6.
    Rose, G., & Marmot, M. G. (1981). Social class and coronary heart disease. Heart,45(1), 13–19.CrossRefGoogle Scholar
  7. 7.
    Andrews, M. (2017). Neighborhoods and Health: The Implications of These Relationships. Washington, DC: George Washington University.Google Scholar
  8. 8.
    Barrientos-Gutierrez, T., Moore, K. A. B., Auchincloss, A. H., et al. (2017). Neighborhood physical environment and changes in body mass index: Results from the Multi-Ethnic Study of Atherosclerosis. American Journal of Epidemiology,186(11), 1237–1245.CrossRefGoogle Scholar
  9. 9.
    Leonardi, C., Simonsen, N. R., Yu, Q., Park, C., & Scribner, R. A. (2017). Street connectivity and obesity risk: Evidence from electronic health records. American Journal of Preventive Medicine,52(1), S40–S47.CrossRefGoogle Scholar
  10. 10.
    Christensen, P., Mikkelsen, M. R., Nielsen, T. A. S., & Harder, H. (2011). Children, mobility, and space: Using GPS and mobile phone technologies in ethnographic research. Journal of Mixed Methods Research.,5(3), 227–246.CrossRefGoogle Scholar
  11. 11.
    Leech, J. A., Nelson, W. C., Burnett, R. T., Aaron, S., & Raizenne, M. E. (2002). It’s about time: A comparison of Canadian and American time-activity patterns. Journal of Exposure Science & Environmental Epidemiology,12(6), 427.CrossRefGoogle Scholar
  12. 12.
    Zhang, H. C., & Cowen, D. J. (2009). Mapping academic achievement and public school choice under the no child left behind legislation. Southeastern Geographer,49(1), 24–40.CrossRefGoogle Scholar
  13. 13.
    Black, C., Collins, A., & Snell, M. (2001). Encouraging walking: The case of journey-to-school trips in compact urban areas. Urban Studies,38(7), 1121–1141.CrossRefGoogle Scholar
  14. 14.
    Lucyk, K., & McLaren, L. (2017). Taking stock of the social determinants of health: A scoping review. PLoS ONE,12(5), e0177306–e0177306.CrossRefGoogle Scholar
  15. 15.
    Chaix, B., Gustafsson, S., Jerrett, M., et al. (2006). Children’s exposure to nitrogen dioxide in Sweden: Investigating environmental injustice in an egalitarian country. Journal of Epidemiology and Community Health,60(3), 234–241.CrossRefGoogle Scholar
  16. 16.
    Dellinger, A. M. (2002). Centers for disease control and prevention. Barriers to children walking and biking to school–United States, 1999. MMWR. Morbidity and Mortality Weekly Report,51(32), 701–704.Google Scholar
  17. 17.
    U.S. Census Bureau. American Community Survey. American Fact Finder; 2017.Google Scholar
  18. 18.
    Washington State’s Office of the Superintendent of Public I. Washington State Report Card. 2016.Google Scholar
  19. 19.
    Flegal, K. M., & Cole, T. J. (2013). Construction of LMS parameters for the Centers for Disease Control and Prevention 2000 growth charts. National Health Statistics Reports,2013(63), 1–3.Google Scholar
  20. 20.
    CDC. Percentile Data Files with LMS Values. 2009; Accessed 11, 2018.
  21. 21.
    CDC. (2016). A SAS Program for the 2000 CDC Growth Charts (ages 0 to < 20 years). Retrieved July 16, 2018 from
  22. 22.
    CDC. (2016). Defining Childhood Obesity. Retrieved July 16, 2018 from
  23. 23.
    Greer, S., Schieb, L., Schwartz, G., Onufrak, S., & Park, S. (2014). Association of the neighborhood retail food environment with sodium and potassium intake among US adults. Preventing Chronic Disease, 11, 130340. Scholar
  24. 24.
    Frank, L. D., Schmid, T. L., Sallis, J. F., Chapman, J., & Saelens, B. E. (2005). Linking objectively measured physical activity with objectively measured urban form: Findings from SMARTRAQ. American Journal of Preventive Medicine,28(2 Suppl 2), 117–125.CrossRefGoogle Scholar
  25. 25.
    InfoUsa. InfoUSA. Retrieved July 16, 2018 from
  26. 26.
    Spokane County Data Portal. 2017.Google Scholar
  27. 27.
    Esri. ArcGIS. Redland2017.Google Scholar
  28. 28.
    Google I. Google Maps. 2017.Google Scholar
  29. 29.
    Kingsley, S. L., Eliot, M., Carlson, L., et al. (2014). Proximity of US schools to major roadways: A nationwide assessment. Journal of Exposure Science & Environmental Epidemiology,24(3), 253.CrossRefGoogle Scholar
  30. 30.
    Amram, O., Abernethy, R., Brauer, M., Davies, H., & Allen, R. W. (2011). Proximity of public elementary schools to major roads in Canadian urban areas. International Journal of Health Geographics,10(1), 68.CrossRefGoogle Scholar
  31. 31.
    Meyer, O. L., Castro-Schilo, L., & Aguilar-Gaxiola, S. (2014). Determinants of mental health and self-rated health: A model of socioeconomic status, neighborhood safety, and physical activity. American Journal of Public Health,104(9), 1734–1741.CrossRefGoogle Scholar
  32. 32.
    Molnar, B. E., Gortmaker, S. L., Bull, F. C., & Buka, S. L. (2004). Unsafe to play? Neighborhood disorder and lack of safety predict reduced physical activity among urban children and adolescents. American Journal of Health Promotion,18(5), 378–386.CrossRefGoogle Scholar
  33. 33.
    Wu, Y.-C., & Batterman, S. A. (2006). Proximity of schools in Detroit, Michigan to automobile and truck traffic. Journal of Exposure Science & Environmental Epidemiology,16(5), 457–470.CrossRefGoogle Scholar
  34. 34.
    Houston, D., Ong, P., Wu, J., & Winer, A. (2006). Proximity of licensed child care facilities to near-roadway vehicle pollution. American Journal of Public Health,96(9), 1611–1617.CrossRefGoogle Scholar
  35. 35.
    Brunekreef, B., Janssen, N. A. H., de Hartog, J., Harssema, H., Knape, M., & van Vliet, P. (1997). Air pollution from truck traffic and lung function in children living near motorways. Epidemiology,1997, 298–303.CrossRefGoogle Scholar
  36. 36.
    Gauderman, W. J., Gilliland, G. F., Vora, H., et al. (2002). Association between air pollution and lung function growth in southern California children: Results from a second cohort. American Journal of Respiratory and Critical Care Medicine,166(1), 76–84.CrossRefGoogle Scholar
  37. 37.
    Zhu, X., & Lee, C. (2008). Walkability and safety around elementary schools: Economic and ethnic disparities. American Journal of Preventive Medicine,34(4), 282–290.CrossRefGoogle Scholar
  38. 38.
    Lynch, J., Smith, G. D., Hillemeier, M., Shaw, M., Raghunathan, T., & Kaplan, G. (2001). Income inequality, the psychosocial environment, and health: Comparisons of wealthy nations. The Lancet,358(9277), 194–200.CrossRefGoogle Scholar
  39. 39.
    National Center for Safe Routes to School. Safe Routes. 2018; Accessed 12 Jan 2018.

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Nutrition and Exercise Physiology, Elson S. Floyd College of MedicineWashington State UniversitySpokaneUSA
  2. 2.Spokane Regional Health DistrictSpokaneUSA

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