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An ecological approach to monitor geographic disparities in cancer outcomes

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  • Jinani Jayasekera
  • Eberechukwu Onukwugha
  • Christopher Cadham
  • Donna Harrington
  • Sarah Tom
  • Francoise Pradel
  • Michael Naslund

Abstract

Background: Area-level indices are widely used to assess the impact of socio-environmental characteristics on cancer outcomes. While area-level measures of socioeconomic status (SES) have been previously used in cancer settings, fewer studies have focused on evaluating the impact of area-level health services supply (HSS) characteristics on cancer outcomes. Moreover, there is significant variation in the methods and constructs used to create area-level indices. Methods: In this study, we introduced a psychometrically-induced, reproducible approach to develop area-level HSS and SES indices. We assessed the utility of these indices in detecting the effects of area-level characteristics on prostate, breast, and lung cancer incidence and stage at diagnosis in the US. The information on county-level SES and HSS characteristics were extracted from US Census, County Business Patterns data and Area Health Resource Files. The Surveillance, Epidemiology, and End Results database was used to identify individuals diagnosed with cancer from 2010 to 2012. SES and HSS indices were developed and linked to 3-year age-adjusted cancer incidence rates. SES and HSS indices empirically summarized the level of employment, education, poverty and income, and the availability of health care facilities and health professionals within counties. Results: SES and HSS models demonstrated good fit (TLI = 0.98 and 0.96, respectively) and internal consistency (alpha = 0.85 and 0.95, respectively). Increasing SES and HSS were associated with increasing prostate and breast cancer and decreasing lung cancer incidence rates. The results varied by stage at diagnosis and race. Conclusion: Composite county-level measures of SES and HSS were effective in ranking counties and detecting gradients in cancer incidence and stage at diagnosis. Thus, these measures provide valuable tools for monitoring geographic disparities in cancer outcomes.

Suggested Citation

  • Jinani Jayasekera & Eberechukwu Onukwugha & Christopher Cadham & Donna Harrington & Sarah Tom & Francoise Pradel & Michael Naslund, 2019. "An ecological approach to monitor geographic disparities in cancer outcomes," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-14, June.
  • Handle: RePEc:plo:pone00:0218712
    DOI: 10.1371/journal.pone.0218712
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    References listed on IDEAS

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    1. Krieger, N., 1992. "Overcoming the absence of socioeconomic data in medical records: Validation and application of a census-based methodology," American Journal of Public Health, American Public Health Association, vol. 82(5), pages 703-710.
    2. Robin Yabroff, K. & Gordis, Leon, 2003. "Does stage at diagnosis influence the observed relationship between socioeconomic status and breast cancer incidence, case-fatality, and mortality?," Social Science & Medicine, Elsevier, vol. 57(12), pages 2265-2279, December.
    3. Crabbe, J.C.F. & Gregorio, D.I. & Samociuk, H. & Swede, H., 2015. "Secular trends, race, and geographic disparity of early-stage breast cancer incidence: 25 years of surveillance in Connecticut," American Journal of Public Health, American Public Health Association, vol. 105, pages 64-70.
    4. Saldana-Ruiz, N. & Clouston, S.A.P. & Rubin, M.S. & Colen, C.G. & Link, B.G., 2013. "Fundamental causes of colorectal cancer mortality in the United States: Understanding the importance of socioeconomic status in creating inequality in mortality," American Journal of Public Health, American Public Health Association, vol. 103(1), pages 99-104.
    5. Oakes, J. Michael & Rossi, Peter H., 2003. "The measurement of SES in health research: current practice and steps toward a new approach," Social Science & Medicine, Elsevier, vol. 56(4), pages 769-784, February.
    6. Rubin, Marcie S. & Clouston, Sean & Link, Bruce G., 2014. "A fundamental cause approach to the study of disparities in lung cancer and pancreatic cancer mortality in the United States," Social Science & Medicine, Elsevier, vol. 100(C), pages 54-61.
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