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Using SEER Data to Quantify Effects of Low Income Neighborhoods on Cause Specific Survival of Skin Melanoma


Abstract

Background: This study used receiver operating characteristic (ROC) curves to screen Surveillance, Epidemiology and End Results (SEER) skin melanoma data to identify and quantify the effects of socioeconomic factors on cause specific survival. Methods: 'SEER cause-specific death classification' used as the outcome variable. The area under the ROC curve was to select best pretreatment predictors for further multivariate analysis with socioeconomic factors. Race and other socioeconomic factors including rural-urban residence, county level % college graduate and county level family income were used as predictors. Univariate and multivariate analyses were performed to identify and quantify the independent socioeconomic predictors. Results: This study included 49,999 parients. The mean follow up time (SD) was 59.4 (17.1) months. SEER staging (ROC area of 0.08) was the most predictive foctor. Race, lower county family income, rural residence, and lower county education attainment were significant univariates, but rural residence was not significant under multivariate analysis. Living in poor neighborhoods was associated with a 2-4% disadvantage in actuarial cause specific survival. Conclusions: Racial and socioeconomic factors have a significant impact on the survival of melanoma patients. This generates the hypothesis that ensuring access to cancer care may eliminate these outcome disparities.

Keywords

References

  1. Baine M, Sahak F, Lin C, et al (2011). Marital status and survival in pancreatic cancer patients: a SEER based analysis. PLoS One, 6, 21052. https://doi.org/10.1371/journal.pone.0021052
  2. Bhatia S (2011). Disparities in cancer outcomes: lessons learned from children with cancer. Pediatr Blood Cancer, 56, 994-1002. https://doi.org/10.1002/pbc.23078
  3. Brewer JD, Shanafelt TD, Otley CC, et al (2012). Chronic lymphocytic leukemia is associated with decreased survival of patients with malignant melanoma and Merkel cell carcinoma in a SEER population-based study. J Clin Oneal, 30, 843-9. https://doi.org/10.1200/JCO.2011.34.9605
  4. Cheung MC, Zhuge Y, Yang R, et al (2010). Incidence and outcomes of extremity soft-tissue sarcomas in children. J Surg Res, 163, 282-9. https://doi.org/10.1016/j.jss.2010.04.033
  5. Cheung R (2012). Poor treatment outcome of neuroblastoma and other peripheral nerve cell tumors may be related to under usage of radiotherapy and socio-economic disparity: a us SEER data analysis. Asian Pac J Cancer Prev, 13, 4587-91. https://doi.org/10.7314/APJCP.2012.13.9.4587
  6. Cheung R, Altschuler MD, D'Amico AV, et al (2001a). ROC- optimization may improve risk stratification of prostate cancer patients. Urology, 57, 286-90. https://doi.org/10.1016/S0090-4295(00)00911-0
  7. Cheung R, Altschuler MD, D'Amico AV, et al (2001b). Using the receiver operator characteristic curve to select pretreatment and pathologic predictors for early and late post-prostatectomy PSA failure. Urology, 58, 400-5. https://doi.org/10.1016/S0090-4295(01)01209-2
  8. Gimotty PA, Elder DE, Fraker DL, et al (2007). Identification of high-risk patients among those diagnosed with thin cutaneous melanomas. J Clin Oneal, 25, 1129-34. https://doi.org/10.1200/JCO.2006.08.1463
  9. Hanley JA, McNeil BJ (1982). The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143, 29-36. https://doi.org/10.1148/radiology.143.1.7063747
  10. Lee M, Cronin KA, Gail MH, Feuer EJ (2012). Predicting the absolute risk of dying from colorectal cancer and from other causes using population-based cancer registry data. Stat Med, 31, 489-500. https://doi.org/10.1002/sim.4454
  11. Ludwig J, Duncan G J, Gennetian L A, et al (2012). Neighborhood effects on the long-term well-being of low-income adults. Science, 387, 1505-10.
  12. McDowell HP, Foot AB, Ellershaw C, et al (2010). Outcomes in paediatric metastatic rhabdomyosarcoma: results of The International Society of Paediatric Oncology (SIOP) study MMT-98. Eur J Cancer, 46, 1588-95. https://doi.org/10.1016/j.ejca.2010.02.051
  13. Ognjanovic S, Linabery AM, Charbonneau B, Ross JA (2009). Trends in childhood rhabdomyosarcoma incidence and survival in the United States, 1975-2005. Cancer, 115, 4218-26. https://doi.org/10.1002/cncr.24465
  14. Pappo A S, Krailo M, Chen Z, Rodriguez-Galindo C, Reaman G (2010). Infrequent tumor initiative of the children's oncology group: initial lessons learned and their impact on future plans. J Clin Oneal, 28, 5011-6. https://doi.org/10.1200/JCO.2010.31.2603
  15. Perez EA, Kas s i ra N, Cheung MC, et al (2011) . Rhabdomyosarcoma in children: a SEER population based study. J Surg Res, 170, 243-251. https://doi.org/10.1016/j.jss.2011.03.001
  16. Pradhan TS, Stevens EE, Ablavsky M, et al (2011). Figure O staging for carcinosarcoma: can the revised staging system predict overall survival? Gynecol Oneal, 123, 221-4. https://doi.org/10.1016/j.ygyno.2011.08.007
  17. Shaikh WR, Weinstock MA, Halpern AC, et al (2012). The characterization and potential impact of melanoma cases with unknown thickness in the United States' Surveillance, Epidemiology, and End Results Program, 1989-2008. Cancer Epidemiol, 37, 64-70.
  18. Siegel R, Desantis C, Virgo K, et al (2012). Cancer treatment and survivorship statistics, 2012. CA Cancer J Clin, 62, 220-41. https://doi.org/10.3322/caac.21149
  19. Singal V, Singal AK, Kuo YF (2012). Racial disparities in treatment for pancreatic cancer and impact on survival: a population-based analysis. J Cancer Res Clin Oneal, 715-22.
  20. Sultan I, Qaddoumi I, Yaser S, Rodriguez-Galindo C, Ferrari A (2009). Comparing adult and pediatric rhabdomyosarcoma in the surveillance, epidemiology and end results program, 1973 to 2005: an analysis of 2,600 patients. J Clin Oneal, 27, 3391-7. https://doi.org/10.1200/JCO.2008.19.7483
  21. Weir HK, Marrett LD, Cokkinides V, et al (2011). Melanoma in adolescents and young adults (ages 15-39 years): United States, 1999-2006. J Am AcadDermatol, 65, 38-49.

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