Discussion

The findings of our study highlight the substantial impact of smoking on health and productivity in the Australian population. Among Australians currently aged 20–69 years who are followed up to age 70 years, smoking was predicted to result in an excess of over 400 000 deaths, with a loss of >3 million years of life over the productive working age of current Australian smokers, and a 6% loss of PALYs, equating to $A388 billion lost in GDP.

Productivity losses accrued from a combination of premature mortality, morbidity-associated work absences (absenteeism) and reductions in productive capacity while at work (presenteeism). In our analyses, males and females who smoke were estimated to experience an almost threefold increase in the risk of death compared with people who do not smoke. This result is comparable to a study in 2015 on an Australian cohort population by Banks et al, which estimated smoking increases mortality around twofold to fourfold in current smokers.18 Our study estimated that current rates of smoking would cause >3 million years of life lost among 2.5 million Australian smokers aged 20–69 years when followed up until age 70 years. The 1997 Australian National Tobacco Campaign was estimated to have led to 190 000 people (of all ages) quitting smoking, and a gain of 323 000 years of life with follow-up until 85 years.19 Hence, each quitter gained 1.7 years of life until age 85 years. This estimate is in accord with our estimate of 1.2 years of life lost per smoker followed up for an overall shorter period of time from 20 to 69 years to 70 years.

In our study, the years of life lost was lower among females, due to a lower prevalence of smoking. As expected, years of life lost to smoking was also higher among younger age groups, because of higher smoking prevalence (particularly among men) and follow-up time within the modelled period.

Smoking is well known to decrease life expectancy. A study capturing 50 years of observation of male British doctors by Doll et al suggested that male smokers died on average 10 years earlier compared with non-smokers.20 A study in Chinese adults estimated smokers at age 35 years lost around 3 years of life when compared with people who never smoked,21 while in a Norwegian population it was estimated that 1.4–2.7 years of life were lost in heavy smokers aged 40–70 years.22 A recent study modelling average life expectancy in the Australian population by Mannan et al found that reducing the prevalence of smoking among Australian smokers to 10% would increase the life expectancy by 0.4–2 years for males and 0.7–2 years for females.23

Our study estimated that smoking would cause a loss of over 2.4 million PALYs among Australians currently aged 20–69 years who smoke, if followed up until age 70 years. This equated to 1.0 PALY lost per smoker. This compares with the loss of 1.4 PALYs per working age person with diabetes over a similar time horizon.8 The differences are attributable to a higher prevalence of diabetes than smoking and a greater reduction in productivity conferred by diabetes than smoking. Of course, this does not mean greater priority should be given to prevention of diabetes, which is more difficult to achieve given its multiplicity of risk factors, chief among which is genetic.

The loss of productivity, measured in terms of PALYs, among the working population has economic implications. Our study is the first to examine this cost in terms of PALYs, but previous studies have estimated the cost of productivity loss due to smoking via other means. In a study on the Australian population by Collins and Lapsley, it was estimated that smoking caused a loss of $A4.9 billion due to presenteeism (0.5% of GDP) and $A779 million due to absenteeism (0.08% of GDP) in the single financial year of 2004/2005.6 In 2000, Lightwood et al reported that the total economic costs of smoking, including productivity losses, amounted to 2.1%–3.4% of GDP in Australia.24 A study in Thailand reported that the economic burden of smoking was 0.8% of country GDP, while the revenue from tobacco industry only contributed to 0.5% of the total GDP.25 The results of our study are not directly comparable to those of other studies because of the differences in evaluation time horizons, which varied from 1 to 50 years in our study (depending on the age of the smokers), and which for other studies was limited to a single year. We had also adopted a simple ‘top-down’ approach to allocating total GDP to EFT worker. Nonetheless, our conclusion is the same as that of the other studies; that smoking imposes a large economic burden on productivity.

It is therefore clear that prevention of smoking is important from an economic standpoint. The high indirect costs of smoking suggest that it would be better for policy makers to consider the amount of money spent on prevention strategies as an ‘investment’ rather than as an ‘expenditure’.

Our study did not address the issue of smoking cessation. Rather, it sought to provide a conceptual illustration of the productivity losses due to smoking by assuming hypothetically that it did not exist, that is, smoking was not taken up in the first place. It should be acknowledged that this is a hypothetical scenario, and in reality, smoking cessation interventions as well as interventions or policy settings dissuading smoking uptake, would be required to aim for the productivity gains modelled herein, even those projected from more modest reductions in smoking prevalence. Smoking cessation is beneficial to productivity. A recent study in Japan suggested that smoking cessation improved productivity at work, with the productivity and associated costs of former smokers being similar to those who never smoked.26 This finding is supported by the findings of Baker et al, who found no significant difference between former and never smokers in term of productivity loss in China, the US and Europe.27 A 19-year follow-up study among males in Finland by Kiiskinen et al also stated that quitting smoking could avert almost 60% of losses due to the direct and indirect costs of smoking.28

Our study is the first to examine the impact of smoking on productivity in terms of PALYs, a novel and informative measure. Our method uses readily available data to estimate the macroeconomic productivity impact of smoking in a methodologically accessible manner, which could be applied in a variety of other country settings or risk/disease burdens. Further research using PALYs provides the opportunity to compare the effects of different tobacco control measures across various age, sex and employment settings, which can inform the targeting of interventions. In addition, application of this method across countries would provide a greater understanding of the regional and global indirect costs of smoking, and the potential productivity gains from tobacco control. Quantifying burden of disease in terms of PALYs can inform resource allocation and decision making for public and workplace health strategies, and may assist in leveraging employer engagement with tobacco control programmes.

PALYs are like QALYs because they ‘penalise’ time spent alive by people affected by a disease or condition, and do so in the same manner—by proportionally adjusting time according to the relative extent to which productivity (PALYs) or quality of life (QALYs) is affected by that disease or condition. QALYs have limitations that stem from their attempting to quantify the highly subjective nature of quality of life and how much people value it,29–31 but despite these limitations, they remain important measures of burden of disease that help inform healthcare planning. Furthermore, healthcare decision making does not rely on QALYs alone; many other factors need to be taken into consideration. As discussed, we feel that the impact of ill health on productivity should be among these factors, and PALYs offer a convenient method for measuring this. One advantage that PALYs have over QALYs is that the measurement and concept of productivity loss is much more objective than the the measurement and concept of quality of life.

Several limitations of our study warrant mention. First, our analyses did not take into account healthcare costs devoted to managing smoking-related ill health, which were estimated to be $A318 million in the year 2004/2005 (offset for savings accrued through premature mortality).6 Furthermore, potential gains from reductions in passive smoking-related mortality and morbidity, and productivity losses associated with family members caring for those with disabling smoking-related morbidity were also excluded. On the other hand, we did not consider the economic activity associated with production and sale of tobacco products, all of which contribute to GDP, nor government revenue generated from tobacco taxes.

Second, life table modelling is a simple and commonly used tool used in epidemiological and demographical studies, but has established limitations. It was assumed that age-specific mortality did not change over time (this is a well-known limitation called the ‘life table assumption’). However, as the relative impact of smoking is unlikely to change substantially, and the life table assumption was applied to both smokers and non-smokers, this would not have significantly impacted the conclusion that smoking imposes a significant burden on health and productivity. The third limitation stemmed from the assumption that there was no uptake nor cessation of smoking over time within the modelled scenarios. Furthermore, the utility values and productivity indices used in this study were potentially imprecise, as they were not stratified for type of work. The impact of smoking on productivity is likely to differ across different types of jobs, and socioeconomic status. Similarly, assessment of the quality of life differences between smokers and non-smokers (from which QALYs are calculated) can vary by instrument,32 and is also potentially confounded by socioeconomic factors such as educational attainment, household income and occupation.33 We could not account for the duration of smoking among smokers, nor any socioeconomic differences between smokers and non-smokers, and other factors that may confound the association between smoking and utilities and productivity indices.

Fourth, like QALYs, PALYs are imprecise because they attempt to measure entities that are difficult to measure. Nevertheless, even with highly conservative assumptions regarding the effect of smoking on productivity among individuals, the collective impact is large. And perhaps the imperfections of PALYs will help stir debate, as QALYs initially did 40 years ago,34 which in turn will progress the science, economics, art and politics of health-related productivity.

Lastly, in terms of estimating impact on GDP, the present study assumed that all individuals and jobs contributed equally to GDP, which is not the case, and we assumed throughout the simulated follow-up, GDP would be stable, rather than increase. This last assumption would have led to an underestimation of the economic impact of smoking.

The findings of our study provide an important and novel assessment of the burden of smoking on the Australian population. They highlight the importance of preventing smoking, strategies for which, if effective, are very likely to be cost-effective, and possibly even cost-saving, in the long term.35 This issue is even more telling for populations within which the prevalence of smoking is very high, and those low-income and middle-income countries for whom the burden of productivity loss may be considerable, such as Indonesia, a close neighbour to Australia, for which smoking prevalence rates among men is as high as 65%.36 Future studies may also consider the type of jobs in the ‘working’ population when calculating productivity loss, as prevalence rates of smoking, and salaries/GDP per worker may differ, and smoking has been shown to be socioeconomically patterned.37