Literature DB >> 15280640

Estimating regional variation in cancer survival: a tool for improving cancer care.

Xue Q Yu1, Dianne L O'Connell, Robert W Gibberd, David P Smith, Paul W Dickman, Bruce K Armstrong.   

Abstract

OBJECTIVE: To improve estimation of regional variation in cancer survival and identify cancers to which priority might be given to increase survival.
METHODS: Survival measures were calculated for 25 major cancer types diagnosed in each of 17 health service regions in New South Wales, Australia, from 1991 to 1998. Region-specific risks of excess death due to cancer were estimated adjusting for age, sex, and extent of disease at, and years since, diagnosis. Empirical Bayes (EB) methods were used to shrink the estimates. The additional numbers of patients who would survive beyond five years were estimated by shifting the State average risk to the 20th centile.
RESULTS: Statistically significant regional variation in the shrunken estimates of risk of excess death was found for nine of the 25 cancer types. The lives of 2903 people (6.4%) out of the 45,047 whose deaths within 5 years were attributable to cancer could be extended with the highest number being for lung cancer (791).
CONCLUSIONS: The EB approach gives more precise estimates of region-specific risk of excess death and is preferable to standard methods for identifying cancer sites where gains in survival might be made. The estimated number of lives that could be extended can assist health authorities in prioritising investigation of and attention to causes of regional variation in survival. Copyright 2004 Kluwer Academic Publishers

Entities:  

Mesh:

Year:  2004        PMID: 15280640     DOI: 10.1023/B:CACO.0000036165.13089.e8

Source DB:  PubMed          Journal:  Cancer Causes Control        ISSN: 0957-5243            Impact factor:   2.506


  8 in total

1.  The Victorian Lung Cancer Registry pilot: improving the quality of lung cancer care through the use of a disease quality registry.

Authors:  Rob G Stirling; S M Evans; P McLaughlin; M Senthuren; J Millar; J Gooi; L Irving; P Mitchell; A Haydon; J Ruben; M Conron; T Leong; N Watkins; J J McNeil
Journal:  Lung       Date:  2014-06-08       Impact factor: 2.584

2.  Improved survival for non-Hodgkin lymphoma patients in New South Wales, Australia.

Authors:  Xue Q Yu; Wendy H Chen; Dianne L O'Connell
Journal:  BMC Cancer       Date:  2010-05-24       Impact factor: 4.430

3.  Bayesian accelerated failure time model for space-time dependency in a geographically augmented survival model.

Authors:  Georgiana Onicescu; Andrew Lawson; Jiajia Zhang; Mulugeta Gebregziabher; Kristin Wallace; Jan M Eberth
Journal:  Stat Methods Med Res       Date:  2015-07-28       Impact factor: 3.021

4.  Contributions of prognostic factors to socioeconomic disparities in cancer survival: protocol for analysis of a cohort with linked data.

Authors:  Xue Qin Yu; David Goldsbury; Sarsha Yap; Mei Ling Yap; Dianne L O'Connell
Journal:  BMJ Open       Date:  2019-08-18       Impact factor: 2.692

5.  Socioeconomic disparities in breast cancer survival: relation to stage at diagnosis, treatment and race.

Authors:  Xue Qin Yu
Journal:  BMC Cancer       Date:  2009-10-14       Impact factor: 4.430

6.  Spatial variation in prostate cancer survival in the Northern and Yorkshire region of England using Bayesian relative survival smoothing.

Authors:  L Fairley; D Forman; R West; S Manda
Journal:  Br J Cancer       Date:  2008-11-04       Impact factor: 7.640

7.  Cancer survival in New South Wales, Australia: socioeconomic disparities remain despite overall improvements.

Authors:  Julia F Stanbury; Peter D Baade; Yan Yu; Xue Qin Yu
Journal:  BMC Cancer       Date:  2016-02-01       Impact factor: 4.430

8.  Augmenting disease maps: a Bayesian meta-analysis approach.

Authors:  Farzana Jahan; Earl W Duncan; Susanna M Cramb; Peter D Baade; Kerrie L Mengersen
Journal:  R Soc Open Sci       Date:  2020-08-05       Impact factor: 2.963

  8 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.