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

Kang²,

Ouyang³

et al. 2014

Section: Methodsmentioning

confidence: 99%

Socio-economic Factors Affect the Outcome of Soft Tissue Sarcoma: an Analysis of SEER Data

Kang²,

Ouyang³

et al. 2014

“…Recently, an important 10-15 years long-term study demonstrated that moving patients from low income neighborhoods to higher ones improved their obesity and diabetes (Ludwig et al, 2011;2012). In this study examined three SEFs: i) whether the patients lived in a rural as opposed to urban counties; ii) whether the patients lived in a county with a family income equal or lower than $50000 per year as opposed to a higher one; and iii) whether the patients lived in a low college education attainment county were examined.…”

Section: Discussionmentioning

confidence: 99%

“…SEER Clinical Outcome Prediction Expert (SCOPE) (Cheung, 2012) was used to mine SEER data and construct accurate and efficient prediction models (Cheung, 2012). The data were obtained from SEER 18 database.…”

Section: Low Income and Rural County Of Residence Increase Mortality mentioning

confidence: 99%

“…This study is a part of a comprehensive effort to survey Surveillance Epidemiology and End Result (SEER) (Cheung, 2012) for socio-economic factors (SEFs) impacting on the cause specific survival (CSS) of bone and joint sarcomas (BJS). SEER cancer registry data have been used to study the biologic and racial prognostic factors for the large number of sub-types of bone and joint sarcoma (Giuffrida et al, 2009;Nathan and Healey, 2012).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

2013

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.

show abstract

“…SEER registry has massive amount 7134 of data available for analysis, however, manipulating this data pipeline could be challenging. SEER Clinical Outcome Prediction Expert (SCOPE) is designed and implemented to mine SEER data and construct accurate and efficient prediction models (Cheung, 2012).…”

Section: Methodsmentioning

confidence: 99%

Assessing the Impact of Socio-economic Variables on Breast Cancer Treatment Outcome Disparity

2013