Qingwei Luo1, Xue Qin Yu2, Stephen Wade3, Michael Caruana4, Francesca Pesola5, Karen Canfell6, Dianne L O'Connell7. 1. Cancer Research Division, Cancer Council NSW, Sydney, NSW, Australia; The University of Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia. Electronic address: qingweil@nswcc.org.au. 2. Cancer Research Division, Cancer Council NSW, Sydney, NSW, Australia; The University of Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia. Electronic address: xueqiny@nswcc.org.au. 3. Cancer Research Division, Cancer Council NSW, Sydney, NSW, Australia. Electronic address: Stephen.Wade@nswcc.org.au. 4. Cancer Research Division, Cancer Council NSW, Sydney, NSW, Australia. Electronic address: Michael.Caruana@nswcc.org.au. 5. Faculty of Life Sciences & Medicine, School of Cancer & Pharmaceutical Sciences, Innovation Hub, Guys Cancer Centre, Guys Hospital, King's College London, London, UK. Electronic address: francesca.pesola@kcl.ac.uk. 6. Cancer Research Division, Cancer Council NSW, Sydney, NSW, Australia; The University of Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia. Electronic address: karen.canfell@nswcc.org.au. 7. Cancer Research Division, Cancer Council NSW, Sydney, NSW, Australia; The University of Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia; School of Medicine and Public Health, University of Newcastle, Newcastle, NSW, Australia. Electronic address: dianneo@nswcc.org.au.
Abstract
OBJECTIVES: The aim was to develop and validate a statistical model which uses past trends for lung cancer mortality and historical and current data on tobacco consumption to project lung cancer mortality rates into the future for Australia. METHODS: We used generalized linear models (GLMs) with Poisson distribution including either age, birth cohort or period, and/or various measures of population tobacco exposure (considering cross-sectional smoking prevalence, cigarettes smoked and tar exposure per capita). Sex-specific models were fitted to data for 1956-2015 and age-standardized lung cancer mortality rates were projected forward to 2040. Possible lags of 20-30 years between tobacco exposure and lung cancer mortality were examined. The best model was selected using analysis of deviance. To validate the selected model, we temporarily re-fitted it to data for 1956-1990 and compared the projected rates to 2015 with the observed rates for 1991-2015. RESULTS: The best fitting model used information on age, birth cohort and tar exposure per capita; close concordance with the observed data was achieved in the validation. The forward projections for lung cancer mortality using this model indicate that male and female age-standardized rates will decline over the period 2011-2015 to 2036-2040 from 27.2 to 15.1 per 100,000, and 15.8 to 11.8 per 100,000, respectively. However, due to population growth and ageing the number of deaths will increase by 7.9% for males and 57.9% for females; from 41,040 (24,831 males, 16,209 females) in 2011-2015 to 52,403 (26,805 males, 25,598 females) in 2036-2040. CONCLUSION: In the context of the mature tobacco epidemic with past peaks in tobacco consumption for both males and females, lung cancer mortality rates are expected to continually decline over the next 25 years. However, the number of lung cancer deaths will continue to be substantial, and to increase, in Australia's ageing population.
OBJECTIVES: The aim was to develop and validate a statistical model which uses past trends for lung cancer mortality and historical and current data on tobacco consumption to project lung cancer mortality rates into the future for Australia. METHODS: We used generalized linear models (GLMs) with Poisson distribution including either age, birth cohort or period, and/or various measures of population tobacco exposure (considering cross-sectional smoking prevalence, cigarettes smoked and tar exposure per capita). Sex-specific models were fitted to data for 1956-2015 and age-standardized lung cancer mortality rates were projected forward to 2040. Possible lags of 20-30 years between tobacco exposure and lung cancer mortality were examined. The best model was selected using analysis of deviance. To validate the selected model, we temporarily re-fitted it to data for 1956-1990 and compared the projected rates to 2015 with the observed rates for 1991-2015. RESULTS: The best fitting model used information on age, birth cohort and tar exposure per capita; close concordance with the observed data was achieved in the validation. The forward projections for lung cancer mortality using this model indicate that male and female age-standardized rates will decline over the period 2011-2015 to 2036-2040 from 27.2 to 15.1 per 100,000, and 15.8 to 11.8 per 100,000, respectively. However, due to population growth and ageing the number of deaths will increase by 7.9% for males and 57.9% for females; from 41,040 (24,831 males, 16,209 females) in 2011-2015 to 52,403 (26,805 males, 25,598 females) in 2036-2040. CONCLUSION: In the context of the mature tobacco epidemic with past peaks in tobacco consumption for both males and females, lung cancer mortality rates are expected to continually decline over the next 25 years. However, the number of lung cancer deaths will continue to be substantial, and to increase, in Australia's ageing population.
Keywords:
Australia; Cohort effect; Generalized linear models; Health service planning; Lung cancer mortality; Population-based; Statistical projections; Tobacco consumption; Tobacco epidemic
Authors: Qingwei Luo; Dianne L O'Connell; Xue Qin Yu; Clare Kahn; Michael Caruana; Francesca Pesola; Peter Sasieni; Paul B Grogan; Sanchia Aranda; Citadel J Cabasag; Isabelle Soerjomataram; Julia Steinberg; Karen Canfell Journal: Lancet Public Health Date: 2022-06
Authors: Qingwei Luo; Julia Steinberg; Xue Qin Yu; Michael Caruana; Karen Canfell; Dianne L O’Connell Journal: Asian Pac J Cancer Prev Date: 2021-02-01
Authors: Qingwei Luo; Julia Steinberg; Xue Qin Yu; Marianne Weber; Michael Caruana; Sarsha Yap; Paul B Grogan; Emily Banks; Dianne L O'Connell; Karen Canfell Journal: J Epidemiol Community Health Date: 2022-06-24 Impact factor: 6.286