Skip to main content
Top
Published in: Cancer Causes & Control 8/2016

01-08-2016 | Original paper

Temporal changes in loss of life expectancy due to cancer in Australia: a flexible parametric approach

Authors: Peter D. Baade, Danny R. Youlden, Therese M. Andersson, Philippa H. Youl, Euan T. Walpole, Michael G. Kimlin, Joanne F. Aitken, Robert J. Biggar

Published in: Cancer Causes & Control | Issue 8/2016

Login to get access

Abstract

Purpose

To evaluate changes in cancer mortality burden over time by assessing temporal trends in life expectation for Australian residents diagnosed with cancer.

Methods

The study cohort consisted of all people diagnosed with cancer in the period 1990–2000 and aged 15–89 years (n = 1,275,978), with mortality follow-up to 31 December 2010. Flexible parametric survival models incorporating background age–sex–year-specific population mortality rates were applied to generate the observed survival curves for all cancers combined and selected major cancer types. Predicted values of loss of life expectancy (LOLE) in years were generated and then averaged across calendar year and age group (15–49, 50–69 and 70–89 years) or spread of disease (localized, regional, distant, unknown).

Results

The greatest LOLE burden was for lung cancer (14.3 years per diagnosis) and lowest for melanoma (2.5 years). There was a significant decrease in LOLE over time (−0.13 LOLE per year) for all cancers combined. Decreases were also observed for female breast cancer (−0.21), prostate cancer (−0.17), colorectal cancer (−0.08), melanoma (−0.07) and stomach cancer (−0.02), with slight increases for lung cancer (+0.04). When restricted to the sub-cohort from New South Wales with spread of disease information, these decreases in LOLE were primarily among cancers categorized as localized or regional spread at diagnosis.

Conclusions

In Australia, persons diagnosed with cancer have a steadily improving outlook that exceeds that expected by general improvement in population life expectancy. The overall improvement is observed in persons with localized or regional cancers but not in those with advanced cancers, findings which encourage earlier diagnosis.
Appendix
Available only for authorised users
Literature
2.
3.
go back to reference van Kruijsdijk RC, van der Graaf Y, Koffijberg H et al (2016) Cause-specific mortality and years of life lost in patients with different manifestations of vascular disease. Eur J Prev Cardiol 23:160–169 van Kruijsdijk RC, van der Graaf Y, Koffijberg H et al (2016) Cause-specific mortality and years of life lost in patients with different manifestations of vascular disease. Eur J Prev Cardiol 23:160–169
4.
go back to reference Ibayashi H, Pham TM, Fujino Y et al (2011) Estimation of premature mortality from oral cancer in Japan, 1995 and 2005. Cancer Epidemiol 35:342–344CrossRefPubMed Ibayashi H, Pham TM, Fujino Y et al (2011) Estimation of premature mortality from oral cancer in Japan, 1995 and 2005. Cancer Epidemiol 35:342–344CrossRefPubMed
5.
go back to reference Andersson TM, Dickman PW, Eloranta S, Lambe M, Lambert PC (2013) Estimating the loss in expectation of life due to cancer using flexible parametric survival models. Stat Med 32:5286–5300CrossRefPubMed Andersson TM, Dickman PW, Eloranta S, Lambe M, Lambert PC (2013) Estimating the loss in expectation of life due to cancer using flexible parametric survival models. Stat Med 32:5286–5300CrossRefPubMed
6.
go back to reference Baade P, Youlden D, Andersson TM et al (2015) Estimating the change in life expectancy after a diagnosis of cancer among the Australian population. BMJ Open 5:e006740CrossRefPubMedPubMedCentral Baade P, Youlden D, Andersson TM et al (2015) Estimating the change in life expectancy after a diagnosis of cancer among the Australian population. BMJ Open 5:e006740CrossRefPubMedPubMedCentral
7.
go back to reference Royston P, Parmar MK (2002) Flexible parametric proportional-hazards and proportional-odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects. Stat Med 21:2175–2197CrossRefPubMed Royston P, Parmar MK (2002) Flexible parametric proportional-hazards and proportional-odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects. Stat Med 21:2175–2197CrossRefPubMed
8.
go back to reference Hwang JS, Wang JD (1999) Monte Carlo estimation of extrapolation of quality-adjusted survival for follow-up studies. Stat Med 18:1627–1640CrossRefPubMed Hwang JS, Wang JD (1999) Monte Carlo estimation of extrapolation of quality-adjusted survival for follow-up studies. Stat Med 18:1627–1640CrossRefPubMed
9.
go back to reference Hwang JS, Wang JD (2004) Integrating health profile with survival for quality of life assessment. Qual Life Res 13:1–10; discussion 1–4 Hwang JS, Wang JD (2004) Integrating health profile with survival for quality of life assessment. Qual Life Res 13:1–10; discussion 1–4
10.
go back to reference Liu PH, Wang JD, Keating NL (2013) Expected years of life lost for six potentially preventable cancers in the United States. Prev Med 56:309–313CrossRefPubMed Liu PH, Wang JD, Keating NL (2013) Expected years of life lost for six potentially preventable cancers in the United States. Prev Med 56:309–313CrossRefPubMed
12.
go back to reference AIHW (2013) Cancer in Australia: actual incidence data from 1991 to 2009 and mortality data from 1991 to 2010 with projections to 2012. Asia Pac J Clin Oncol 9:199–213CrossRef AIHW (2013) Cancer in Australia: actual incidence data from 1991 to 2009 and mortality data from 1991 to 2010 with projections to 2012. Asia Pac J Clin Oncol 9:199–213CrossRef
13.
go back to reference AIHW, AACR (2012) Cancer in Australia: an overview, 2012. Cancer series 74. Cat. No. CAN 70. Australian Institute of Health and Welfare (AIHW) and Australasian Association of Cancer Registries (AACR), Canberra AIHW, AACR (2012) Cancer in Australia: an overview, 2012. Cancer series 74. Cat. No. CAN 70. Australian Institute of Health and Welfare (AIHW) and Australasian Association of Cancer Registries (AACR), Canberra
15.
go back to reference Baade PD, Youlden DR, Chambers SK (2011) When do I know I am cured? Using conditional estimates to provide better information about cancer survival prospects. Med J Aust 194:73–77PubMed Baade PD, Youlden DR, Chambers SK (2011) When do I know I am cured? Using conditional estimates to provide better information about cancer survival prospects. Med J Aust 194:73–77PubMed
16.
go back to reference Lambert PC, Royston P (2009) Further development of flexible parametric models for survival analysis. Stata J 9:265–290 Lambert PC, Royston P (2009) Further development of flexible parametric models for survival analysis. Stata J 9:265–290
17.
go back to reference Royston P, Lambert PC (2011) Flexible parametric survival analysis using stata: beyond the cox model. Stata Press, College Station Royston P, Lambert PC (2011) Flexible parametric survival analysis using stata: beyond the cox model. Stata Press, College Station
18.
go back to reference Van Ewijk RJ, Schwentner L, Wockel A et al (2013) Trends in patient characteristics, treatment and survival in breast cancer in a non-selected retrospective clinical cohort study of 2600 patients. Arch Gynecol Obstet 287:103–110CrossRefPubMed Van Ewijk RJ, Schwentner L, Wockel A et al (2013) Trends in patient characteristics, treatment and survival in breast cancer in a non-selected retrospective clinical cohort study of 2600 patients. Arch Gynecol Obstet 287:103–110CrossRefPubMed
19.
go back to reference Roder D, Karapetis CS, Wattchow D et al (2015) Colorectal cancer treatment and survival: the experience of major public hospitals in South Australia over three decades. Asian Pac J Cancer Prev APJCP 16:2431–2440CrossRefPubMed Roder D, Karapetis CS, Wattchow D et al (2015) Colorectal cancer treatment and survival: the experience of major public hospitals in South Australia over three decades. Asian Pac J Cancer Prev APJCP 16:2431–2440CrossRefPubMed
20.
21.
go back to reference Rosa F, Alfieri S, Tortorelli AP, Fiorillo C, Costamagna G, Doglietto GB (2014) Trends in clinical features, postoperative outcomes, and long-term survival for gastric cancer: a western experience with 1278 patients over 30 years. World J Surg Oncol 12:217CrossRefPubMedPubMedCentral Rosa F, Alfieri S, Tortorelli AP, Fiorillo C, Costamagna G, Doglietto GB (2014) Trends in clinical features, postoperative outcomes, and long-term survival for gastric cancer: a western experience with 1278 patients over 30 years. World J Surg Oncol 12:217CrossRefPubMedPubMedCentral
23.
go back to reference Parkin E, O’Reilly DA, Sherlock DJ, Manoharan P, Renehan AG (2014) Excess adiposity and survival in patients with colorectal cancer: a systematic review. Obes Rev 15:434–451CrossRefPubMed Parkin E, O’Reilly DA, Sherlock DJ, Manoharan P, Renehan AG (2014) Excess adiposity and survival in patients with colorectal cancer: a systematic review. Obes Rev 15:434–451CrossRefPubMed
24.
go back to reference Allott EH, Masko EM, Freedland SJ (2013) Obesity and prostate cancer: weighing the evidence. Eur Urol 63:800–809CrossRefPubMed Allott EH, Masko EM, Freedland SJ (2013) Obesity and prostate cancer: weighing the evidence. Eur Urol 63:800–809CrossRefPubMed
25.
go back to reference Goodwin PJ (2010) Commentary on: “effect of obesity on survival in women with breast cancer: systematic review and meta-analysis” (Melinda Protani, Michael Coory, Jennifer H. Martin). Breast Cancer Res Treat 123:637–640CrossRefPubMed Goodwin PJ (2010) Commentary on: “effect of obesity on survival in women with breast cancer: systematic review and meta-analysis” (Melinda Protani, Michael Coory, Jennifer H. Martin). Breast Cancer Res Treat 123:637–640CrossRefPubMed
26.
go back to reference Protani M, Coory M, Martin JH (2010) Effect of obesity on survival of women with breast cancer: systematic review and meta-analysis. Breast Cancer Res Treat 123:627–635CrossRefPubMed Protani M, Coory M, Martin JH (2010) Effect of obesity on survival of women with breast cancer: systematic review and meta-analysis. Breast Cancer Res Treat 123:627–635CrossRefPubMed
27.
go back to reference Allemani C, Weir HK, Carreira H et al (2015) Global surveillance of cancer survival 1995–2009: analysis of individual data for 25,676,887 patients from 279 population-based registries in 67 countries (CONCORD-2). Lancet 385:977–1010CrossRefPubMed Allemani C, Weir HK, Carreira H et al (2015) Global surveillance of cancer survival 1995–2009: analysis of individual data for 25,676,887 patients from 279 population-based registries in 67 countries (CONCORD-2). Lancet 385:977–1010CrossRefPubMed
28.
go back to reference Moyer VA, Force USPST (2014) Screening for lung cancer: U.S. preventive services task force recommendation statement. Ann Intern Med 160:330–338PubMed Moyer VA, Force USPST (2014) Screening for lung cancer: U.S. preventive services task force recommendation statement. Ann Intern Med 160:330–338PubMed
29.
go back to reference Green AC, Baade P, Coory M, Aitken JF, Smithers M (2012) Population-based 20-year survival among people diagnosed with thin melanomas in Queensland, Australia. J Clin Oncol 30:1462–1467CrossRefPubMed Green AC, Baade P, Coory M, Aitken JF, Smithers M (2012) Population-based 20-year survival among people diagnosed with thin melanomas in Queensland, Australia. J Clin Oncol 30:1462–1467CrossRefPubMed
30.
go back to reference Whiteman DC, Baade PD, Olsen CM (2015) More people die from thin melanomas (1 mm) than from thick melanomas (>4 mm) in Queensland, Australia. J Investig Dermatol 135:1190–1193CrossRefPubMed Whiteman DC, Baade PD, Olsen CM (2015) More people die from thin melanomas (1 mm) than from thick melanomas (>4 mm) in Queensland, Australia. J Investig Dermatol 135:1190–1193CrossRefPubMed
Metadata
Title
Temporal changes in loss of life expectancy due to cancer in Australia: a flexible parametric approach
Authors
Peter D. Baade
Danny R. Youlden
Therese M. Andersson
Philippa H. Youl
Euan T. Walpole
Michael G. Kimlin
Joanne F. Aitken
Robert J. Biggar
Publication date
01-08-2016
Publisher
Springer International Publishing
Published in
Cancer Causes & Control / Issue 8/2016
Print ISSN: 0957-5243
Electronic ISSN: 1573-7225
DOI
https://doi.org/10.1007/s10552-016-0762-1

Other articles of this Issue 8/2016

Cancer Causes & Control 8/2016 Go to the issue
Webinar | 19-02-2024 | 17:30 (CET)

Keynote webinar | Spotlight on antibody–drug conjugates in cancer

Antibody–drug conjugates (ADCs) are novel agents that have shown promise across multiple tumor types. Explore the current landscape of ADCs in breast and lung cancer with our experts, and gain insights into the mechanism of action, key clinical trials data, existing challenges, and future directions.

Dr. Véronique Diéras
Prof. Fabrice Barlesi
Developed by: Springer Medicine