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Mapping the uninsured using secondary data: an environmental justice application in Dallas

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Abstract

Over the last 5 years, environmental justice (EJ) researchers have been calling for incorporation of health outcomes more directly into spatial studies of socio-demographics and environmental hazards. To date, researchers have not incorporated insurance status (an access to health care variable) in their models although access to care likely has an important association with the probability of health effects due to environmental exposures. As such, insurance status represents an important variable within spatial EJ studies focused on health, and the lack of spatially explicit access to care data is a critical limitation in the field. As a solution, we offer a method of using uninsured appendicitis cases, acquired secondarily from state hospital admissions data, to estimate rates of uninsurance at the zip-code level. We apply the technique to explore relationships between cancer risk from hazardous air pollutants and estimated rates of uninsurance, a previously unexplored phenomenon. Then, we compare the uninsurance findings to those related to poverty to illustrate how uninsurance, as a variable, compares to a more traditional socio-economic predictor used in EJ studies. The relationship between cancer risk from hazardous air pollutants and uninsurance is weaker than the relationship between risk and poverty, but both are statistically significant. As such, we conclude with a discussion of the importance of considering insurance status in spatial studies of EJ focused on health.

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Fig. 1

Notes

  1. Only a few EJ researchers have predicted actual heath outcomes, i.e., asthma hospitalizations, using socio-demographics and air pollution (Grineski 2007; Maantay 2007), although several others have used socio-demographic characteristics to predict estimated health risks from hazardous air pollutants (HAPs) based on toxicology (e.g., Chakraborty 2009).

  2. Appendicitis is most common among older children and young adults, with a peak incidence the second and third decades of life. It is quite uncommon in children under five and the elderly (Prystowsky et al. 2005). Therefore, this method undercounts uninsurance for these groups. In addition, the hospitalization counts are suppressed in very rural areas (Texas Health Care Information Council 2000), so this method underestimate uninsurance in rural communities.

  3. As such, we compared the percent of uninsured cases of appendicitis in Texas to estimates of un-insurance published by the Texas Medical Association. In our appendicitis data set (2003–2005), 21.4% of all cases of appendicitis were uninsured; this compares favorably to 24.5% of all Texans being uninsured in 2005 (Texas Medical Association 2009) given the differences in methodology, time frame, and limitations (e.g., missing the very rural patients in the appendicitis data set).

  4. We ran bi-variate spatial lag regression models using GeoDa software due to spatial autocorrelation (i.e., tendency of variable values to relate to neighboring value, see Chakraborty 2009), as opposed to just presenting basic Pearson correlations. We found positive spatial autocorrelation in the residuals (i.e., the Moran's I values were significant at a p value of 0.001) for both models. Then, the Lagrange Multiplier test suggested spatial lag models (as opposed to spatial error models), and the two models reported in Table 3 were run with weights matrix of 14,000 meters because the spatial autocorrelation was removed at that distance (i.e., Moran’s I values became insignificant at p < .05 level).

References

  • American Academy of Pediatrics. (1999). Pediatric asthma promoting best practices: Guide for managing asthma in children. Rochester, NY: University of Rochester Academic Service Consortium.

    Google Scholar 

  • Apelberg, B. J., Buckley, T. J., & White, R. H. (2005). Socioeconomic and racial disparities in cancer risk from air toxics in Maryland. Environmental Health Perspectives, 113(6), 693–699.

    Article  Google Scholar 

  • Arrieta, M., White, H. L., & Crook, E. D. (2008). Using zip code-level mortality data as a local health status indicator in mobile, Alabama. American Journal of the Medical Sciences, 335(4), 271–274.

    Article  Google Scholar 

  • Brotanek, J. A., Halterman, J., Auinger, P., & Weitzman, M. (2005). Inadequate access to care among children with asthma from Spanish-speaking families. Journal of Health Care for the Poor and Underserved, 16(1), 63–73.

    Article  Google Scholar 

  • Brown, P., Mayer, B., Zavestoski, S., Luebke, T., Mandelbaum, J., & McCormick, S. (2003). The health politics of asthma: Environmental justice and collective illness experience in the United States. Social Science and Medicine, 57, 453–464.

    Article  Google Scholar 

  • Brown, P., Mayer, B., Zavestoski, S., Luebke, T., Mandelbaum, J., & McCormick, S. (2004). Clearing the air and breathing freely: The health politics of air pollution and asthma. International Journal of Health Services, 34(1), 39–63.

    Article  Google Scholar 

  • Brulle, R. J., & Pellow, D. N. (2006). Environmental justice: Human health and environmental inequalities. Annual Review of Public Health, 27, 103–124.

    Article  Google Scholar 

  • Buzzelli, M. (2007). Bordieu does environmental justice? Probing the linkages between population health and air pollution epidemiology. Health and Place, 13(1), 3–13.

    Article  Google Scholar 

  • Call, K. T., Davern, M., & Blewett, L. A. (2007). Estimates of health insurance coverage: Comparing state surveys with the current population survey. Health Affairs, 26(1), 269–278.

    Article  Google Scholar 

  • Chakraborty, J. (2009). Automobiles, air toxics, and adverse health risks: environmental inequities in Tampa bay, Florida. Annals of the Association of American Geographers, 99(4), 674–697.

    Article  Google Scholar 

  • Christie, L. (2009). America’s most polluted cities: Where the air is the most dangerous to breathe. Retrieved 11 March 2010, from http://www.money.cnn.com/2009/04/28/real_estate/most_polluted_cities/index.htm.

  • Downey, L. (2006). Environmental inequality in metropolitan America in 2000. Sociological Spectrum, 26(1), 21–41.

    Article  Google Scholar 

  • Drewnowski, A., Rehm, C. D., & Solet, D. (2007). Disparities in obesity rates: Analysis by ZIP code area. Social Science and Medicine, 65(12), 2458–2463.

    Article  Google Scholar 

  • Environmental Protection Agency. (2009). Technology transfer network 2002 national-scale air toxics assessment (18 Aug 2009). Retrieved 24 Feb 2010, from http://www.epa.gov/nata2002/.

  • EPA. (2007, December 18). Health effects notebook. Retrieved 26 April 2009, from http://www.epa.gov/ttn/atw/hlthef/hapintro.html#gi.

  • EPA. (2009, March 23). Pollutants & sources retrieved 26 April 2009, from http://www.epa.gov/ttn/atw/pollsour.html.

  • EPA. (2010). Environmental justice: Plan EJ 2014. Retrieved 25 Aug 2010, from http://www.epa.gov/environmentaljustice/resources/policy/plan-ej-2014.html.

  • Farhat, S. C. L., Paulo, R. L. P., Shimoda, T. M., Conceicao, G. M. S., Lin, C. A., Braga, A. L. F., et al. (2005). Effect of air pollution on pediatric respiratory emergency room visits and hospital admissions. Brazilian Journal of Medical and Biological Research, 38(2), 227–235.

    Article  Google Scholar 

  • Ferris, T. G., Blumenthal, D., Woodruff, P. G., Clark, S., & Camargo, C. A. (2002). Insurance and quality of care for adults with acute asthma. Journal of General Internal Medicine, 17(12), 905–913.

    Article  Google Scholar 

  • Gould, J. (1986). Quality of life in American neighborhoods: Levels of affluence, toxic waste, and cancer mortality in residential zip code areas. Boulder, CO: Westview.

    Google Scholar 

  • Grineski, S. E. (2007). Incorporating health outcomes into environmental justice research: The case of children’s asthma and air pollution in Phoenix, Arizona. Environmental Hazards, 7(4), 360–371.

    Google Scholar 

  • Grineski, S. E. (2009). Predicting children’s asthma hospitalizations: Rural and urban differences in Texas. Rural Sociology, 74(2), 201–219.

    Article  Google Scholar 

  • Johnson, W. G., Gray, H., & Bannister, W. (2007). Health Insurance in Arizona: Residents of Maricopa County. Tempe, AZ: Arizona State University, Center for Health Information & Research, School of Computing and Informatics.

    Google Scholar 

  • Jones, C. A. (1999). Best practices for pediatric asthma: Improved clinical management for the inner-city patient. Journal of the National Medical Association, 91(8), S16–S25.

    Google Scholar 

  • Linder, S. H., Marko, D., & Sexton, K. (2008). Cumulative cancer risk from air pollution in Houston: Disparities in risk burden and social disadvantage. Environmental Science and Technology, 42(12), 4312–4322.

    Article  Google Scholar 

  • Loh, P., & Sugerman-Brozan, J. (2002). Environmental justice organizing for environmental health: Case study on asthma and diesel exhaust in Roxbury, Massachusetts. Annals of the American Academy of Political and Social Science, 584, 110–124.

    Article  Google Scholar 

  • Maantay, J. (2007). Asthma and air pollution in the Bronx: Methodological and data considerations in using GIS for environmental justice and health research. Health & Place, 13(1), 32–56.

    Google Scholar 

  • Mansour, M. E., Lanphear, B. P., & DeWitt, T. G. (2000). Barriers to asthma care in urban children: Parent perspectives. Pediatrics, 106(3), 512–519.

    Article  Google Scholar 

  • Mohai, P., Pellow, D. N., & Roberts, J. T. (2009). Environmental justice. Annual Review of Environment and Resources, 34, 405–430.

    Article  Google Scholar 

  • O’Neil, J. E., & O’Neil, D. M. (2009). Who are the uninsured? An analysis of America’s uninsured population, their characteristics and their health. Retrieved 24 April 2010 from www.epionline.org/studies/oneill_06-2009.pdf.

  • Oppong, J. R., Denton, C. J., Moonan, P. K., & Weis, S. E. (2007). Foreign-born status and geographic patterns of tuberculosis genotypes in Tarrant County, Texas. Professional Geographer, 59(4), 478–491.

    Article  Google Scholar 

  • Peng, R. D., Dominici, F., Pastor-Barriuso, R., Zeger, S. L., & Samet, J. M. (2005). Seasonal analyses of air pollution and mortality in 100 US cities. American Journal of Epidemiology, 161(6), 585–594.

    Article  Google Scholar 

  • Pieracci, F., Eachempati, S., Barie, P., & Callahan, M. (2007). Insurance status, but not race, predicts perforation in adult patients with acute appendicitis. Journal of the American College of Surgeons, 205(3), 445–452.

    Article  Google Scholar 

  • Prystowsky, J. B., Pugh, C. M., & Nagle, A. P. (2005). Appendicitis. Current Problems in Surgery, 42(10), 694–742.

    Article  Google Scholar 

  • Right-to-Know Network. (2008). TRI facilities by city. Retrieved 11 March 2010, from http://www.rtknet.org/db/tri.

  • Schweitzer, J., Fairman, N., Schreyer, K., & Waxman, K. (2003). Appendicitis 2002: Relationship between payors and outcome. The American Surgeon, 69, 902–908.

    Google Scholar 

  • Shah, N. S., & Carrasquillo, O. (2006). Twelve-year trends in health insurance coverage among Latinos, by subgroup and immigration status. Health Affairs, 25(6), 1612–1619.

    Article  Google Scholar 

  • Short, P. (2004). Counting and characterizing the uninsured. In C. G. McLaughlin (Ed.), Health policy and the uninsured. Washington, DC: The Urban Institute Press.

    Google Scholar 

  • Smink, D., Fishman, S., Kleinman, L., & Finkelstein, J. (2005). Effects of race, insurance status, and hospital volume on perforated appendicitis in children. Pediatrics, 115(4), 1068–1070.

    Article  Google Scholar 

  • Sommers, B. (2009). Loss of health insurance among non-elderly adults in Medicaid. Journal of General Internal Medicine, 24(1), 1–7.

    Article  Google Scholar 

  • Texas Health Care Information Council. (2000). User manual for texas hospital inpatient discharge public use data file. TX: Austin.

  • Texas Medical Association. (2009). The uninsured in Texas. Retrieved 6 March 2010, from http://www.texmed.org/Template.aspx?id=5517.

  • Todd, J., Armon, C., Griggs, A., Poole, S., & Berman, S. (2006). Increased rates of morbidity, mortality, and charges for hospitalized children with public or no health insurance as compared with children with private insurance in Colorado and the United States. Pediatrics, 118(2), 577–585.

    Article  Google Scholar 

  • United States Department of Health & Human Services. (2009). Prior HHS poverty guidelines and federal register references. Retrieved 24 April 2009, from http://www.aspe.hhs.gov/poverty/figures-fed-reg.shtml.

  • Weil, A. (2007). A health plan to reduce poverty. Future of Children, 17(2), 97–116.

    Google Scholar 

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Authors and Affiliations

  1. Department of Sociology and Anthropology, University of Texas at El Paso, El Paso, TX, USA

    Sara E. Grineski & Yolanda J. McDonald

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  1. Sara E. Grineski
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  2. Yolanda J. McDonald
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Correspondence to Sara E. Grineski.

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Grineski, S.E., McDonald, Y.J. Mapping the uninsured using secondary data: an environmental justice application in Dallas. Popul Environ 32, 376–387 (2011). https://doi.org/10.1007/s11111-010-0129-6

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