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Productivity Burden of Smoking in Australia: a Life Table Modelling Study Alice J Owen,‍ ‍ 1 Salsabil B Maulida,1,2 Ella Zomer,1 Danny Liew1

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Productivity Burden of Smoking in Australia: a Life Table Modelling Study Alice J Owen,‍ ‍ 1 Salsabil B Maulida,1,2 Ella Zomer,1 Danny Liew1 Research paper Tob Control: first published as 10.1136/tobaccocontrol-2018-054263 on 16 July 2018. Downloaded from Productivity burden of smoking in Australia: a life table modelling study Alice J Owen, 1 Salsabil B Maulida,1,2 Ella Zomer,1 Danny Liew1 ► Additional material is ABStract a proportion of the adverse economic impact of published online only. To view, Objectives This study aimed to examine the impact of tobacco smoking. Indirect costs include second-hand please visit the journal online smoke exposure, costs to employers arising from (http:// dx. doi. org/ 10. 1136/ smoking on productivity in Australia, in terms of years of tobaccocontrol- 2018- 054263). life lost, quality-adjusted life years (QALYs) lost and the absenteeism and lost productivity due to smoking novel measure of productivity-adjusted life years (PALYs) among their workforce, welfare benefits associated 1Centre for Cardiovascular lost. with supporting those with chronic smoking-related Research and Education in Methods Life table modelling using contemporary illness and smoking-attributable fires. Less readily Therapeutics, School of Public Health and Preventive Medicine, Australian data simulated follow-up of current smokers quantifiable societal burdens include the social and Monash Univeristy, Melbourne, aged 20–69 years until age 70 years. Excess mortality, emotional impact of smoking-related mortality and Victoria, Australia health-related quality of life decrements and relative morbidity on family and loved ones. Of the indirect 2 Faculty of Medicine, University reduction in productivity attributable to smoking were costs, productivity losses are substantial, but often of Indonesia, Jakarta, Indonesia sourced from published data. The gross domestic product of lower profile. In Australia in the financial year (GDP) per equivalent full-time (EFT) worker in Australia in 2004/2005, it was estimated that the productivity Correspondence to Dr Alice J Owen, School of 2016 was used to estimate the cost of productivity loss losses associated with smoking was $A8 billion, Public Health and Preventive attributable to smoking at a population level. which far outweighed the $A1.8 billion in direct Medicine, Monash University, Results At present, approximately 2.5 million healthcare costs of smoking.6 Melbourne, VIC 3004, Australia; Australians (17.4%) aged between 20 and 69 years are Price-based tobacco control measures (such as alice. owen@ monash. edu smokers. Assuming follow-up of this population until tobacco taxes) have been shown to be the most Received 18 January 2018 the age of 70 years, more than 3.1 million years of life effective method for reducing tobacco consump- Revised 4 June 2018 would be lost to smoking, as well as 6.0 million QALYs tion.7 However, tobacco consumption also confers Accepted 6 June 2018 and 2.5 million PALYs. This equates to 4.2% of years of economic benefits, including income generated Published Online First life, 9.4% QALYs and 6.0% PALYs lost among Australian as a result of the production and consumption of 16 July 2018 copyright. working-age smokers. At an individual level, this is tobacco and tobacco taxes accrued by governments. equivalent to 1.2 years of life, 2.4 QALYs and 1.0 PALY These counterbalancing financial issues are often lost per smoker. Assuming (conservatively) that each PALY raised when governments are considering tobacco in Australia is equivalent to $A157 000 (GDP per EFT control measures. worker in 2016), the economic impact of lost productivity In order to provide a clearer understanding of would amount to $A388 billion. the macro-economic impact of productivity loss due to smoking, we undertook a study that uses a Conclusions This study highlights the potential health http://tobaccocontrol.bmj.com/ and productivity gains that may be achieved from further novel measure developed by our group, produc- tobacco control measures in Australia via application of tivity-adjusted life years (PALYs),8 to examine the PALYs, which are a novel, and readily estimable, measure productivity burden of smoking in a contemporary of the impact of health and health risk factors on work Australian setting. productivity. METHODS We used life table modelling and decision analysis9 to examine the impact of smoking on years of life, INTRODUCTION quality-adjusted life years (QALYs) and PALYs lived The Global Burden of Disease study demonstrated among Australians of working age. PALYs are a that smoking continues to exert a significant construct similar to QALYs, but with years of life on September 24, 2021 by guest. Protected mortality burden, with worldwide smoking-attrib- lived penalised for time spent with reduced work utable deaths increasing by 20% since 1990.1 In productivity (instead of reduced quality of life) as Australia, following adoption of a series of tobacco a result of ill health.8 Akin to utilities that quan- control measures,2 age-standardised smoking prev- tify quality of life, ‘productivity indices’ represent alence decreased from 30.8% to 16.8% from 1980 the productivity of an individual in proportional to 2012.3 However, given population growth, this terms, ranging from 1.0 (100% productive) to 0 still represents a substantial number of smokers (completely non-productive). Productivity indices © Author(s) (or their and a large burden of tobacco-related disease, may change, for example, with age and/or ill health. employer(s)) 2019. Re-use permitted under CC BY-NC. No with >15 000 Australians projected to succumb to Life tables were constructed using age-specific 4 commercial re-use. See rights premature tobacco-related death each year. and sex-specific rates of mortality for smoking and and permissions. Published The healthcare costs of tobacco-related morbidity non-smoking adults aged 20–69 years, based on by BMJ. and mortality (ie, the costs of treating smoking-re- the 2016 Australian population (see online supple- To cite: Owen AJ, lated illnesses in those who smoke) have been well mentary appendix 1 and table 1). The cohorts Maulida SB, Zomer E, et al. described, with around 15% of healthcare expen- were followed until death or age 70 years. The Tob Control diture attributed to smoking in high-income coun- 20–69 years age range was chosen to reflect the 2019;28:297–304. tries.5 However, these direct costs represent only ages where people are commonly engaged in paid Owen AJ, et al. Tob Control 2019;28:297–304. doi:10.1136/tobaccocontrol-2018-054263 297 Research paper Tob Control: first published as 10.1136/tobaccocontrol-2018-054263 on 16 July 2018. Downloaded from Table 1 Modelled population Males Females Age group (years) n* Smoking prevalence† EFT %‡ n* Smoking prevalence† EFT%‡ 20–24 851 818 0.162 54.1 807 634 0.173 48.7 25–29 885 390 0.255 79.7 873 715 0.142 57.2 30–34 876 875 0.255 79.7 874 000 0.142 57.2 35–39 785 670 0.222 84.3 790 262 0.141 55.3 40–44 819 943 0.222 84.3 835 414 0.141 55.3 45–49 774 379 0.207 78.0 789 310 0.172 56.9 50–54 769 307 0.207 78.0 788 657 0.172 56.9 55–59 714 584 0.183 68.2 736 359 0.129 49.2 60–64 632 862 0.183 52.2 653 546 0.129 33.6 65–69 570 582 0.111 33.6 582 977 0.069 17.7 Total 7 681 410 6 924 240 *Australian population at 2015. †Smoking prevalence data from the Australian National Health Survey 2014–2015.13 ‡Percentage of total EFT workers from Australian workforce participation data.15 EFT, equivalent full time. employment. Analyses were then repeated with the smoking smoking.11 This study found that smokers missed more days cohort assumed to be non-smokers, and years of life, QALYs and at work (absenteeism) (6.7 vs 4.4 days/year) and experienced PALYs lived were recalculated. The differences in these measures more unproductive days (presenteeism) (3.2 vs 1.8 days/year) between the two cohort simulations represented the years of life, compared with non-smokers. As annual working days varies by QALYs and PALYs lost to smoking. age and sex, Australian workforce participation data15 (propor- Within each of the smoking and non-smoking cohorts, we tions in full-time and part-time work) were used to calculate created separate life tables with 1 year cycles for 20 sex-and-age sex-specific weighted-average maximum working days in a subcohorts, with age being stratified into ten 5-year age bands: year among Australians aged 20–69 years. The age-specific copyright. 20–24, 25–29, 30–34, 35–39, 40–44, 45–49, 50–54, 55–59, and sex-specific productivity indices were then calculated by 60–64 and 65–69 years. The starting age in each subcohort was applying productivity penalties of 0.957 for non-smokers and assumed as the mid-point of the age group (eg, 22 years for 0.932 for smokers (calculated from Bunn et al,11 as above) to the age group 20–24 years, 27 years for age group 25–29 years). age-specific workforce participation rates15 (see online supple- For each sex-age cohort, specific mortality rates (by age, sex mentary appendix 2). Assessment of upper and lower bound esti- and smoking status) were applied, as well as smoking-related 10 mates for PALYs were drawn from 95% CIs for smoking-related utilities derived from health-related quality of life measures http://tobaccocontrol.bmj.com/ work absences reported by Weng et al, which found that current and productivity indices calculated from previously reported smokers were absent from work for 1.54–3.95 more days per rates of absenteeism and presenteeism in smoking compared year than non-smokers.16 For these upper and lower estimates, with non-smoking workers.11 presenteeism data were not varied.
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