Low Income and Rural County of Residence Increase Mortality from Bone and Joint Sarcomas

Abstract

Background: This is a part of a larger effort to characterize the effects on socio-economic factors (SEFs) on cancer outcome. Surveillance, Epidemiology and End Result (SEER) bone and joint sarcoma (BJS) data were used to identify potential disparities in cause specific survival (CSS). Materials and Methods: This study analyzed SEFs in conjunction with biologic and treatment factors. Absolute BJS specific risks were calculated and the areas under the receiver operating characteristic (ROC) curve were computed for predictors. Actuarial survival analysis was performed with Kaplan-Meier method. Kolmogorov-Smirnov's 2-sample test was used to for comparing two survival curves. Cox proportional hazard model was used for multivariate analysis. Results: There were 13501 patients diagnosed BJS from 1973 to 2009. The mean follow up time (SD) was 75.6 (90.1) months. Staging was the highest predictive factor of outcome (ROC area of 0.68). SEER stage, histology, primary site and sex were highly significant pre-treatment predictors of CSS. Under multivariate analysis, patients living in low income neighborhoods and rural areas had a 2% and 5% disadvantage in cause specific survival respectively. Conclusions: This study has found 2-5% decrement of CSS of BJS due to SEFs. These data may be used to generate testable hypothesis for future clinical trials to eliminate BJS outcome disparities.

Rural‐urban disparities in incidence and outcomes of neuroendocrine tumors: A population‐based analysis of 6271 cases

“…14 Similar figures have also been reported for prostate, colorectal, pancreatic, and soft tissue cancers. 13,26,32,33 With a median follow-up of almost 4 years for both groups, when we controlled for other factors potentially associated with inferior outcomes, including demographic, tumor, and socioeconomic characteristics, rural living remained associated with poorer OS for NETs.…”

Is the Number of National Database Research Studies in Musculoskeletal Sarcoma Increasing, and Are These Studies Reliable?

“…Of the 94 studies that shared a major study question with at least one other study, 35% (33 of 94) reported discordant key findings concerning 10 study questions [1, 6, 8, 11‐13, 18, 23, 26, 29, 36, 39‐41, 44, 49, 53, 59, 63, 67, 68, 72, 79, 83, 93, 95, 98‐100, 106, 109‐111] (Table 2), 29% (27 of 94) reported mixed key findings concerning six study questions [3, 7, 15, 19, 22, 25, 27, 28, 30, 35‐37, 43, 45, 47, 50, 56, 75, 76, 82, 88‐91, 103, 104, 107] (Table 3), and 44% (41 of 94) reported concordant key findings concerning 12 study questions [2, 4, 5, 14, 16‐18, 20, 24, 31, 33, 38, 42, 46, 48, 52, 57, 58, 61, 71, 74, 75, 77, 78, 81, 84‐87, 91, 92, 96, 97, 101, 102, 105, 106, 108, 110‐112] (Table 4). Of the 299 studies in total, 20% (59 of 299) reported discordant or mixed findings [1, 3, 6‐8, 11‐13, 15, 18, 19, 22, 23, 25‐30, 35‐37, 39‐41, 43‐45, 47, 49, 50, 53, 56, 59, 63, 67, 68, 72, 75, 76, 79, 82, 83, 88‐91, 93, 95, 98‐100, 103, 104, 106, 107, 109‐111] and 14% (41 of 299) reported concordant findings [2, 4, 5, 14, 16‐18, 20, 24, 31, 33, 38, 42, 46, 48, 52, 57, 58, 61, 71, 74, 75, 77, 78, 81, 84‐87, 91, 92, 96, 97, 101, 102, 105, 106, 108, 110‐112]. Examples of topics with discordant or mixed findings include the effect of radiation on survival in several sarcoma types and the influence of race on treatment and survival.…”

Receiver Operating Characteristic Curve Analysis of SEER Medulloblastoma and Primitive Neuroectodermal Tumor (PNET) Outcome Data: Identification and Optimization of Predictive Models

“…SEER data are used widely as a bench-mark data source for studying MB/PNET cancer outcomes in US and in other countries (Barnholtz-Sloan et al, 2005;Bishop et al, 2012;Curran et al, 2009;Deorah et al, 2006;Gatta et al, 2002;Halperin et al, 2004;Lai, 2008;Smoll, 2012). The extensive ground coverage by the SEER data is ideal for identifying the disparity in oncology outcome and treatment in different geographical and cultural areas (Cheung, 2013a(Cheung, , 2013b(Cheung, , 2013cCheung, 2012;Cheung, 2013;Downing et al, 2010;Gross et al, 2008;Harlan et al, 1995;Lund et al, 2008;Martinez et al, 2010;Martinez Min Rex Cheung Schlichting et al, 2012;Shavers et al, 2003;Wampler et al, 2005;Yao et al, 2012). In addition to the biological staging factors and the treatment factors, this database also contains a large number of county level socio-economic factors data.…”