Literature DB >> 34623641

Impact of including second and later cancers in cause-specific survival estimates using population-based registry data.

Gonçalo Forjaz1,2, Nadia Howlader1, Steve Scoppa3, Christopher J Johnson4, Angela B Mariotto1.   

Abstract

BACKGROUND: Second or later primary cancers account for approximately 20% of incident cases in the United States. Currently, cause-specific survival (CSS) analyses exclude these cancers because the cause of death (COD) classification algorithm was available only for first cancers. The authors added rules for later cancers to the Surveillance, Epidemiology, and End Results cause-specific death classification algorithm and evaluated CSS to include individuals with prior tumors.
METHODS: The authors constructed 2 cohorts: 1) the first ever primary cohort, including patients whose first cancer was diagnosed during 2000 through 2016) and 2) the earliest matching primary cohort, including patients with any cancer who matched the selection criteria irrespective of whether it was the first or a later cancer diagnosed during 2000 through 2016. The cohorts' CSS estimates were compared using follow-up through December 31, 2017. The new rules were used in the second cohort for patients whose first cancers during 2000 through 2016 were their second or later cancers.
RESULTS: Overall, there were no statistically significant differences in CSS estimates between the 2 cohorts. Estimates were similar by age, stage, race, and time since diagnosis, except for patients with leukemia and those aged 65 to 74 years (3.4 percentage point absolute difference).
CONCLUSIONS: The absolute difference in CSS estimates for the first cancer ever cohort versus earliest of any cancers cohort in the study period was small for most cancer types. As the number of newly diagnosed patients with prior cancers increases, the algorithm will make CSS more inclusive and enable estimating survival for a group of patients with cancer for whom life tables are not available or life tables are available but do not capture other-cause mortality appropriately.
© 2021 American Cancer Society.

Entities:  

Keywords:  Surveillance, Epidemiology and End Results (SEER) program; algorithms; cause of death; frailty; life tables; multiple primary; neoplasms; survival analysis

Mesh:

Year:  2021        PMID: 34623641      PMCID: PMC8776580          DOI: 10.1002/cncr.33940

Source DB:  PubMed          Journal:  Cancer        ISSN: 0008-543X            Impact factor:   6.921


  34 in total

1.  Multiple cancer prevalence: a growing challenge in long-term survivorship.

Authors:  Angela B Mariotto; Julia H Rowland; Lynn A G Ries; Steve Scoppa; Eric J Feuer
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2007-03       Impact factor: 4.254

2.  Second cancers among 40,576 testicular cancer patients: focus on long-term survivors.

Authors:  Lois B Travis; Sophie D Fosså; Sara J Schonfeld; Mary L McMaster; Charles F Lynch; Hans Storm; Per Hall; Eric Holowaty; Aage Andersen; Eero Pukkala; Michael Andersson; Magnus Kaijser; Mary Gospodarowicz; Timo Joensuu; Randi J Cohen; John D Boice; Graça M Dores; Ethel S Gilbert
Journal:  J Natl Cancer Inst       Date:  2005-09-21       Impact factor: 13.506

3.  Disparities by Race, Age, and Sex in the Improvement of Survival for Major Cancers: Results From the National Cancer Institute Surveillance, Epidemiology, and End Results (SEER) Program in the United States, 1990 to 2010.

Authors:  Chenjie Zeng; Wanqing Wen; Alicia K Morgans; William Pao; Xiao-Ou Shu; Wei Zheng
Journal:  JAMA Oncol       Date:  2015-04       Impact factor: 31.777

4.  Measuring cancer survival in populations: relative survival vs cancer-specific survival.

Authors:  Diana Sarfati; Tony Blakely; Neil Pearce
Journal:  Int J Epidemiol       Date:  2010-02-08       Impact factor: 7.196

5.  Early death rate in acute promyelocytic leukemia remains high despite all-trans retinoic acid.

Authors:  Jae H Park; Baozhen Qiao; Katherine S Panageas; Maria J Schymura; Joseph G Jurcic; Todd L Rosenblat; Jessica K Altman; Dan Douer; Jacob M Rowe; Martin S Tallman
Journal:  Blood       Date:  2011-06-08       Impact factor: 22.113

6.  The impact of follow-up type and missed deaths on population-based cancer survival studies for Hispanics and Asians.

Authors:  Paulo S Pinheiro; Cyllene R Morris; Lihua Liu; Timothy J Bungum; Sean F Altekruse
Journal:  J Natl Cancer Inst Monogr       Date:  2014-11

7.  Measuring the effect of including multiple cancers in survival analyses using data from the Canadian Cancer Registry.

Authors:  Larry F Ellison
Journal:  Cancer Epidemiol       Date:  2010-07-17       Impact factor: 2.984

8.  Standard cancer patient population for age standardising survival ratios.

Authors:  Isabella Corazziari; Mike Quinn; Riccardo Capocaccia
Journal:  Eur J Cancer       Date:  2004-10       Impact factor: 9.162

9.  Multiple tumours in survival estimates.

Authors:  Stefano Rosso; Roberta De Angelis; Laura Ciccolallo; Eugenio Carrani; Isabelle Soerjomataram; Enrico Grande; Giulia Zigon; Hermann Brenner
Journal:  Eur J Cancer       Date:  2009-01-02       Impact factor: 9.162

Review 10.  Evaluation of data quality in the cancer registry: principles and methods. Part I: comparability, validity and timeliness.

Authors:  Freddie Bray; D Max Parkin
Journal:  Eur J Cancer       Date:  2008-12-29       Impact factor: 9.162

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