Literature DB >> 10396505

Measuring social class differences in cancer patient survival: is it necessary to control for social class differences in general population mortality? A Finnish population-based study.

P W Dickman1, A Auvinen, E T Voutilainen, T Hakulinen.   

Abstract

STUDY
OBJECTIVES: Estimation of cancer patient survival by social class has been performed using observed, corrected (cause specific), and relative (with expected survival based on the national population) survival rates. Each of these measures are potentially biased and the optimal method is to calculate relative survival rates using social class specific death rates to estimate expected survival. This study determined the degree to which the choice of survival measure affects the estimation of social class differences in cancer patient survival. SETTING AND PARTICIPANTS: All Finnish residents diagnosed with at least one of 10 common malignant neoplasms during the period 1977-1985 were identified from the Finnish Cancer Registry and followed up for deaths to the end of 1992.
DESIGN: Survival rates were calculated by site, sex, and age at 5, 10, and 15 years subsequent to diagnosis for each of three measures of survival; relative survival, corrected (cause specific) survival, and relative survival adjusted for social class differences in general mortality. Regression models were fitted to each set of rates for the first five years of follow up. MAIN
RESULTS: The degree of variation in relative survival resulting from social class decreased, although did not disappear, after controlling for social class differences in general mortality. The results obtained using corrected survival were close to those obtained using relative survival with a social class correction. The differences between the three measures were largest when the proportion of deaths from other causes was large, for example, in cancers with high survival, among older patients, and for longer follow up times.
CONCLUSIONS: Although each of the three measures gave comparable results, it is recommended that relative survival rates are used with expected survival adjusted for social class when studying social class variation in cancer patient survival. If this is not an available option, it is recommended that corrected survival rates are used. Relative survival rates without the social class correction overestimate social class differences and should be used with caution.

Entities:  

Mesh:

Year:  1998        PMID: 10396505      PMCID: PMC1756645          DOI: 10.1136/jech.52.11.727

Source DB:  PubMed          Journal:  J Epidemiol Community Health        ISSN: 0143-005X            Impact factor:   3.710


  50 in total

1.  Socioeconomic distribution of cancer of the lung in New Haven.

Authors:  E M COHART
Journal:  Cancer       Date:  1955 Nov-Dec       Impact factor: 6.860

2.  Socio-economic factors in the prognosis of cancer patients.

Authors:  L Lipworth; T Abelin; R R Connelly
Journal:  J Chronic Dis       Date:  1970-08

3.  A possible artefactual component in specific cause mortality gradients. Social class variations in the clinical accuracy of death certificates.

Authors:  M L Samphier; C Robertson; M J Bloor
Journal:  J Epidemiol Community Health       Date:  1988-06       Impact factor: 3.710

4.  Socioeconomic status and breast cancer survival in the southeastern Netherlands, 1980-1989.

Authors:  C T Schrijvers; J W Coebergh; L H van der Heijden; J P Mackenbach
Journal:  Eur J Cancer       Date:  1995-09       Impact factor: 9.162

5.  Detecting survival effects of socioeconomic status: problems in the use of aggregate measures.

Authors:  H P Greenwald; N L Polissar; E F Borgatta; R McCorkle
Journal:  J Clin Epidemiol       Date:  1994-08       Impact factor: 6.437

6.  Economic differentials in cancer survival: a multivariate analysis.

Authors:  T N Chirikos; N A Reiches; M L Moeschberger
Journal:  J Chronic Dis       Date:  1984

Review 7.  Approaches to studying the effect of socio-economic circumstances on geographic differences in mortality in England and Wales.

Authors:  A J Fox; D R Jones; P O Goldblatt
Journal:  Br Med Bull       Date:  1984-10       Impact factor: 4.291

8.  Social class as a prognostic factor in breast cancer survival.

Authors:  S Karjalainen; E Pukkala
Journal:  Cancer       Date:  1990-08-15       Impact factor: 6.860

9.  Survival of cancer patients by economic status in a free care setting.

Authors:  W Keirn; G Metter
Journal:  Cancer       Date:  1985-04-01       Impact factor: 6.860

10.  Cause-specific mortality: understanding uncertain tips of the disease iceberg.

Authors:  M J Goldacre
Journal:  J Epidemiol Community Health       Date:  1993-12       Impact factor: 3.710

View more
  14 in total

1.  A cancer survival model that takes sociodemographic variations in "normal" mortality into account: comparison with other models.

Authors:  Ø Kravdal
Journal:  J Epidemiol Community Health       Date:  2002-04       Impact factor: 3.710

2.  Inequalities in recovery or methodological artefact? A comparison of models across physical and mental health functioning.

Authors:  Salmela Jatta; Brunton-Smith Ian; Meadows Robert
Journal:  SSM Popul Health       Date:  2022-03-05

3.  Indicators of socioeconomic position (part 2).

Authors:  Bruna Galobardes; Mary Shaw; Debbie A Lawlor; John W Lynch; George Davey Smith
Journal:  J Epidemiol Community Health       Date:  2006-02       Impact factor: 3.710

4.  Partitioning of excess mortality in population-based cancer patient survival studies using flexible parametric survival models.

Authors:  Sandra Eloranta; Paul C Lambert; Therese M L Andersson; Kamila Czene; Per Hall; Magnus Björkholm; Paul W Dickman
Journal:  BMC Med Res Methodol       Date:  2012-06-24       Impact factor: 4.615

5.  The association of cancer survival with four socioeconomic indicators: a longitudinal study of the older population of England and Wales 1981-2000.

Authors:  Andrew Sloggett; Harriet Young; Emily Grundy
Journal:  BMC Cancer       Date:  2007-01-25       Impact factor: 4.430

6.  Ethnicity, deprivation and screening: survival from breast cancer among screening-eligible women in the West Midlands diagnosed from 1989 to 2011.

Authors:  M Morris; L M Woods; N Rogers; E O'Sullivan; O Kearins; B Rachet
Journal:  Br J Cancer       Date:  2015-06-16       Impact factor: 7.640

Review 7.  Critical Points for Interpreting Patients' Survival Rate Using Cancer Registries: A Literature Review.

Authors:  Ayako Okuyama; Akiko Shibata; Hiroshi Nishimoto
Journal:  J Epidemiol       Date:  2017-10-28       Impact factor: 3.211

8.  Cancer survival in England and Wales at the end of the 20th century.

Authors:  B Rachet; L M Woods; E Mitry; M Riga; N Cooper; M J Quinn; J Steward; H Brenner; J Estève; R Sullivan; M P Coleman
Journal:  Br J Cancer       Date:  2008-09-23       Impact factor: 7.640

9.  What might explain deprivation-specific differences in the excess hazard of breast cancer death amongst screen-detected women? Analysis of patients diagnosed in the West Midlands region of England from 1989 to 2011.

Authors:  Melanie Morris; Laura M Woods; Bernard Rachet
Journal:  Oncotarget       Date:  2016-08-02

10.  Educational attainment and differences in relative survival after acute myocardial infarction in Norway: a registry-based population study.

Authors:  Søren Toksvig Klitkou; Knut R Wangen
Journal:  BMJ Open       Date:  2017-08-28       Impact factor: 2.692

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.