Research article Special Issues

Geographic and Demographic Disparities in Late-stage Breast and Colorectal Cancer Diagnoses Across the US

  • Received: 18 May 2015 Accepted: 13 August 2015 Published: 28 August 2015
  • Problem: In 2009, breast cancer was the most common cancer in women, and colorectal cancer was the third most common cancer in both men and women. Currently, the majority of colorectal and almost 1/3 of breast cancers are diagnosed at an advanced stage in the US, which results in higher morbidity and mortality than would obtain with earlier detection. The incidence of late-stage cancer diagnoses varies considerably across the US, and few analyses have examined the entire US.
    Purpose: Using the newly available US Cancer Statistics database representing 98% of the US population, we perform multilevel analysis of the incidence of late-stage cancer diagnoses and translate the findings via bivariate mapping, answering questions related to both Why and Where demographic and geographic disparities in these diagnoses are observed.
    Methods: To answer questions related to Why disparities are observed, we utilize a three-level, random-intercepts model including person-, local area-, and region- specific levels of influence. To answer questions related to Where disparities are observed, we generate county level robust predictions of late-stage cancer diagnosis rates and map them, contrasting counties ranked in the upper and lower quantiles of all county predicted rates. Bivariate maps are used to spatially translate the geographic variation among US counties in the distribution of both BC and CRC late-stage diagnoses.
    Conclusions: Empirical modeling results show demographic disparities, while the spatial translation of empirical results shows geographic disparities that may be quite useful for state cancer control planning. Late stage BC and CRC diagnosis rates are not spatially random, manifesting as place-specific patterns that compare counties in individual states to counties across all states. Providing a relative comparison that enables assessment of how results in one state compare with others, this paper is to be disseminated to all state cancer control and central cancer registry program officials.

    Citation: Lee R Mobley, Tzy-Mey (May) Kuo. Geographic and Demographic Disparities in Late-stage Breast and Colorectal Cancer Diagnoses Across the US[J]. AIMS Public Health, 2015, 2(3): 583-600. doi: 10.3934/publichealth.2015.3.583

    Related Papers:

  • Problem: In 2009, breast cancer was the most common cancer in women, and colorectal cancer was the third most common cancer in both men and women. Currently, the majority of colorectal and almost 1/3 of breast cancers are diagnosed at an advanced stage in the US, which results in higher morbidity and mortality than would obtain with earlier detection. The incidence of late-stage cancer diagnoses varies considerably across the US, and few analyses have examined the entire US.
    Purpose: Using the newly available US Cancer Statistics database representing 98% of the US population, we perform multilevel analysis of the incidence of late-stage cancer diagnoses and translate the findings via bivariate mapping, answering questions related to both Why and Where demographic and geographic disparities in these diagnoses are observed.
    Methods: To answer questions related to Why disparities are observed, we utilize a three-level, random-intercepts model including person-, local area-, and region- specific levels of influence. To answer questions related to Where disparities are observed, we generate county level robust predictions of late-stage cancer diagnosis rates and map them, contrasting counties ranked in the upper and lower quantiles of all county predicted rates. Bivariate maps are used to spatially translate the geographic variation among US counties in the distribution of both BC and CRC late-stage diagnoses.
    Conclusions: Empirical modeling results show demographic disparities, while the spatial translation of empirical results shows geographic disparities that may be quite useful for state cancer control planning. Late stage BC and CRC diagnosis rates are not spatially random, manifesting as place-specific patterns that compare counties in individual states to counties across all states. Providing a relative comparison that enables assessment of how results in one state compare with others, this paper is to be disseminated to all state cancer control and central cancer registry program officials.


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    [1] Mobley, L., Kuo, T., Watson, L., and Brown, G. 2012. Geographic disparities in late-stage cancer diagnosis: Multilevel factors and spatial interactions, Health& Place, 18: 978-990.
    [2] Kuo, T. , and Mobley, L., and Anselin, L. 2011. Geographic Disparities in Late-Stage Breast Cancer Diagnosis in California, Health& Place, 17: 327-334.
    [3] Henry KA, Sherman R, Farber S, Cockburn M, Goldberg DW, Stroup AM. 2013. The joint effects of census tract poverty and geographic access on late-stage breast cancer diagnosis in 10 US States, Health& Place, 21:110-21.
    [4] Anderson RT, Yang TC, Matthews SA, Camacho F, Kern T, Mackley HB, Kimmick G, Louis C, Lengerich E, Yao N. 2014. Breast cancer screening, area deprivation, and later-stage breast cancer in Appalachia: does geography matter? , Health Serv Res., 49(2):546-67.
    [5] Ward EM, Fedewa SA, Cokkinides V, Virgo K. 2010. The association of insurance and stage at diagnosis among patients aged 55 to 74 years in the national cancer database, Cancer J., 16(6):614-21.
    [6] Congressional Budget Office (CBO). 2005. The price sensitivity of demand for nongroup health insurance. Background Paper. Available May 2015 at http://www.cbo.gov/publication/17110
    [7] Kowalski, A., Congdon, W., & Showalter, M. 2008. State health insurance regulations and the price of high-deductible policies. Forum for Health Economics & Policy, 11(2 ). Available May 2015 at http://www.econ.yale.edu/~ak669/kcs2008.pdf.
    [8] LaPierre, T., Conover, C., Henderson, J., Seward, A., & Taylor, B. 2009. Estimating the impact of state health insurance mandates on premium costs in the individual market. J Insur Regul, 27(3): 3-36.
    [9] New, M. J. 2006. The effect of state regulations on health insurance premiums: A revised analysis. Heritage Foundation Center for Data Analysis Report No. CDA06-04. Available at http://s3.amazonaws.com/thf_media/2006/pdf/cda06-04.pdf
    [10] Parente, S. T., Feldman, R., Abraham, J., and Xu, Y. 2008. Consumer response to a national marketplace for individual insurance. Final Technical Report for DHHS Contract HP-07-024. Available May 2015 at http://aspe.hhs.gov/health/reports/08/consumerresponse/report.html
    [11] Gelman, A., and Hill, J. 2007. Data analysis using regression and multilevel/hierarchical models. Cambridge University Press.
    [12] Mobley, L.R., T. Kuo, and L.S. Andrews. 2008. How Sensitive are Multilevel Regression Findings to Defined Area of Context? A Case Study of Mammography Use in California. Med Care Res Rev, 65: 315-337.
    [13] Aday, L. A., & Andersen, R. 1974. A framework for the study of access to medical care. H Serv Res, 9: 208-220.
    [14] Andersen, R. M. 1995. Revisiting the behavioral model and access to medical care: Does it matter? J H Soci Behav, 3: 1-10.
    [15] Phillips, K. A., Morrison, K. R., Andersen, R., & Aday, L. A. 1998. Understanding the context of healthcare utilization: Assessing environmental and provider-related variables in the behavioral model of utilization. H Serv Res, 33 (3, Pt. 1): 571-596.
    [16] Booth, S. L., Sallis, J. F., Ritenbaugh, C., Hill, J. O., Birch, L. L., Frank, L. D., et al. 2001. Environmental and societal factors affect food choice and physical activity: Rationale, influences, and leverage points. Nutri Rev, 59 (3, Pt, 2): S21-S39.
    [17] Northridge, M., Sclar, E., & Biswas, P. 2003. Sorting out the connections between the built environment and health: A conceptual framework for navigating pathways and planning healthy cities. J Urban H, 80: 556-568.
    [18] Schulz, A. J., Kannan, S., Dvonch, J. T., Israel, B. A., Allen, A., 3rd, James, S. A., et al. 2005. Social and physical environments and disparities in risk for cardiovascular disease: The healthy environments partnership conceptual model. Envir H Perspect, 113: 1817-1825.
    [19] Smedley, B. D., & Syme, S. L. (Eds.). 2000. Promoting health: Strategies from social and behavioral research. Washington, DC: National Academies Press.
    [20] Pickett, K. E., & Pearl, M.. 2000. Multilevel analyses of neighborhood sociodemographic context and health outcomes: A critical review. J Epidemiol Commun H, 5: 111-122.
    [21] Sampson, R., Morenoff, J., & Gannon-Rowley, T. 2002. Assessing “neighborhood effects”: Social processes and new directions for research. Annu Rev Sociol, 28: 443-478.
    [22] Litaker, D., Koroukian, S. M., & Love, T. E.. 2005. Context and healthcare access: Looking beyond the individual. Med Care, 43: 531-540.
    [23] Flowerdew R, Manley D, and Sabel C. 2008. “Neighbourhood effects on health: does it matter where you draw the boundaries?” Soc Sci Med. Mar; 66 (6):1241-55.
    [24] Riva, M., Apparicio, P., Gauvin, L., and Brodeur, J. 2008. “Establishing the soundness of administrative spatial units for operationalising the active living potential of residential environments: an exemplar for designing optimal zones”, Int J H Geogr, 7:43
    [25] Khan, A. A., & Bhardwaj, S. M. (1994). Access to health care: A conceptual framework and its relevance to health care planning. Eval Heal TH Prof, 17: 60-76. doi: 10.1177/016327879401700104
    [26] Wingo, P., Jamison, P., Hiatt, R., Weir, H., Gargiullo, P., Hutton, M., Lee, N., and Hall, I.. 2003. “Building the infrastructure for nationwide cancer surveillance and control-a comparison between The National Program of Cancer Registries(NPCR) and The Surveillance, Epidemiology, and End Results (SEER) Program (United States)”, Cancer Causes Contr , 14: 175-193.
    [27] Centers for Disease Control and Prevention (CDC). 2015. National Program of Cancer Registries, US Cancer Statistics, Available online January 2015: www.cdc.gov/uscs and CDC’s NCHS Research Data Center, available online January 2015 http://www.cdc.gov/rdc
    [28] RTI International, RTI Spatial Impact Factor Database, available May 2015: https://rtispatialdata.rti.org
    [29] National Council of State Regulators (NCSL). 2010. Managed Care State Laws and Regulations, Including Consumer and Provider Protections, Updated: March 2008; Reposted May 2010: http://www.ncsl.org/issues-research/health/managed-care-state-laws.aspx
    [30] Koroukian, S., D. Litaker, A. Dor, and G. Cooper. 2005. “Use of Preventive Services by Medicare Fee-for-Service Beneficiaries Does Spillover From Managed Care Matter?” Med Care, 43(5): 445-452.
    [31] Mobley, L., Subramanian, S., Koschinsky, J., Frech, H.E., Clayton, L., and Anselin, L. 2011. “Managed care and the diffusion of endoscopy in fee-for-service Medicare”, H Ser Res 46 (6): 1905-1927.
    [32] Oakes, J.M.. 2004. The (mis)estimation of neighborhood effects: causal inference for a practicable social epidemiology. Soci Sci Med, 58: 1929-1952.
    [33] Gumpertz, M.L., Pickle, L.W., Miller, B.A., Bell, B.S., “Geographic patterns of advanced breast cancer in Los Angeles: associations with biological and socio- demographic factors (United States)”, Cancer Causes and Contr, 17 (2006): 325-339.
    [34] Mobley, L., and H.E. Frech III (February 2007). “Health Insurance: Designing Products to Reduce Costs.” Chapter 6, In Tremblay, V., and C. Tremblay (Eds.), Indus Firm Studies. Armonk, NY: M.E. Sharpe.
    [35] Whitlock, P., J. S. Lin, E. Liles, T. L. Beil, and R. Fu. 2008. “Screening for Colorectal Cancer: A Targeted, Updated Systematic Review for the U.S. Preventive Services Task Force.” Anna Int Med 149 (9): 638-58.
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