Future smoking-attributable and all-cause mortality: its sensitivity to indirect estimation techniques

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1 Short abstract Future attributable and all-cause mortality: its sensitivity to indirect estimation techniques Lenny Stoeldraijer a and Fanny Janssen b,c a Statistics Netherlands, The Hague, the Netherlands b Population Research Centre, Faculty of Spatial Sciences, University of Groningen, the Netherlands c Unit of PharmacoEpidemiology & PharmacoEconomics, Department of Pharmacy, University of Groningen, the Netherlands Smoking has been the most important non-linear determinant of mortality in low-mortality countries. With changes in smoking behaviour, projections of attributable and of all-cause mortality including smoking become more important, especially for health care programs and insurance. However, these projections might be sensible to the indirect techniques to estimate smokingattributable mortality being used. We estimate future attributable and all-cause mortality and analyse its sensitivity to different indirect techniques for estimating attributable mortality. Future attributable mortality is obtained by applying different indirect estimation methods to projected lung cancer mortality, e.g. Peto-Lopez and, Preston-Glei-Wilmoth methods. Lung cancer mortality rates are extrapolated using age-period-cohort analysis. smoking related mortality is projected using the Lee-Carter model. Smoking-attributable mortality will further decline for males and first increase but then decline for females. The different indirect estimation techniques have an effect on attributable mortality levels and its age structure. Furthermore, they will lead to higher differences in projected smokingattributable and all-cause mortality for women because of their shorter history in smoking.

2 Extended abstract Future attributable and all-cause mortality: its sensitivity to indirect estimation techniques Lenny Stoeldraijer a and Fanny Janssen b,c a Statistics Netherlands, The Hague, the Netherlands b Population Research Centre, Faculty of Spatial Sciences, University of Groningen, the Netherlands c Unit of PharmacoEpidemiology & PharmacoEconomics, Department of Pharmacy, University of Groningen, the Netherlands Introduction In recent decades, smoking has been the most important non-linear determinant of mortality in lowmortality countries (Lopez et al. 1994). In many countries male lung cancer mortality, the most profound cause-of-death attributable to smoking, decreased over the past two decades, while female lung cancer mortality is still increasing and, in some countries, even accelerating in recent past as a result of smoking behaviour. Lately, there has been wide interest in the projection of attributable mortality and, more specific, lung cancer mortality (e.g. Bray et al. 2000, Kaneko et al. 2003). The projections provide an estimate of the future burden of smoking and are a valuable tool in evaluating the effectiveness of tobacco control programs. Also, ample attention for including smoking in the projection of all-cause mortality exists. For example, Wang and Preston (2009) incorporate cohort smoking indices into a projection model, while Janssen et al. (2012) excluded the nonlinear related mortality trends from all-cause mortality to obtain a more robust long-term trend to be used as the projection basis. In absence of data on smoking prevalence, indirect estimation techniques have been developed to estimate attributable mortality in a given population. These techniques use the observed lung cancer mortality as an indicator for the accumulated damage from smoking. One of the most important indirect estimation techniques is Peto et al. (1992). Examples of more recent methods are Preston et al. (2010), Rostron (2010), Rostron and Wilmoth (2011), and Fenelon and Preston (2011). The different indirect estimation techniques are likely to have an impact on the level of smokingattributable mortality as well as on future all-cause mortality when its projection includes smokingattribute mortality. Most previous studies have only focused on estimating attributable mortality and differences in outcome between techniques (e.g. Rostron 2010, Fenelon and Preston 2011). However, so far there have been no studies which examine the effect of the indirect estimation technique on the projection of all-cause mortality and measures such as the life expectancy. Furthermore, little is known about the effect of using background lung-cancer mortality among non-smokers from the nation itself instead of from the United States.

3 The objective of this paper is to estimate future all-cause and attributable mortality and analyse its sensitivity to different indirect techniques to estimate attributable mortality. We will eventually do so for a number of countries, but for now we focus on results for the Netherlands. Data & Methods Future attributable mortality Future attributable mortality is calculated by extrapolating lung cancer mortality and by applying the different indirect estimation techniques (see below) to the extrapolated values. For extrapolating the lung cancer mortality the classical age-period-cohort (APC) model is used. Predictions of male lung cancer mortality rates are made by projecting the common linear drift of period and cohort in the APC model. The non-linear components are taken zero. This means that the observed decline is projected to continue into the future. Female lung cancer mortality rates are extrapolated using the common linear drift and the non-linear cohort component from the APC model. When the rates reach the future levels of men, the same period projection is applied. The projection is based on experiences in Denmark and England & Wales and generalities from the smoking epidemic model (Janssen et al. forthcoming). Lung cancer mortality deaths from 1950 to 2009 by age (40-44, 45-49,, 80+) and sex are obtained from WHOSIS. Future all-cause mortality The projection of all-cause mortality is done in a few steps. First, non-related mortality is obtained by subtracting related mortality estimated by means of the indirect estimation methods from all-cause mortality. Then, non-related mortality is projected using the Lee- Carter methodology (Lee and Carter 1992). The future related mortality is estimated using the projected lung cancer mortality and the same indirect estimation method from the first step. Allcause mortality is then the sum of non-related and related mortality. Age- and sex specific total deaths and population numbers for are obtained from the Human Morality Database. Techniques applied to estimate attributable mortality One of the first - and most commonly used - indirect estimation techniques to estimate smokingattributable mortality is the Peto-Lopez method (Peto et al. 1992). They combined the level of excess lung cancer mortality due to smoking with relative risks observed in the Cancer Prevention Studies (CPS-II) of the American Cancer Society to estimate the fraction of deaths that is attributable to smoking. Janssen and Kunst (2007) adapted and simplified the indirect Peto-Lopez method by combining all causes of death in the calculation of the relative risks instead of by cause of death as in the original Peto-Lopez method. Furthermore, they applied a second-degree polynomial to smooth the relative risks (Bonneux et al. 2003). The relative risks were adjusted downwards by reducing the excess risk by 30% instead of halving (Ezzati and Lopez 2003). Rostron and Wilmoth (2011) modified the method of Peto-Lopez to estimate attributable mortality for more-specific age groups and they introduced an adjustment factor to increase mortality from CPS-II to levels that approximate those of the U.S. population at the time of the study. Preston, Glei and Wilmoth (2010) developed an alternative to the method of Peto-Lopez. In this alternative indirect estimation method, lung cancer mortality is also used as the indicator of smoking damage, but the method does not rely on the relative risks form CPS-II or any other study. Instead, they assume that lung cancer is a good proxy for the impact of smoking on mortality from other

4 causes. Data from developed countries between 1950 and 2006 from the Human Mortality Database is used in the estimation. Rostron (2010) modified the regression model used in the method of Preston-Glei-Wilmoth by including an age-period interaction term. Another modification of the Preston-Glei-Wilmoth method is the method of Fenelon and Preston (2011). They re-estimated the relationship between lung cancer mortality and deaths from other causes using a Poisson regression and annual data between 1990 and 2004 from the Nation Center for Health Statistics. Furthermore, we shall examine the effect of using background lung-cancer mortality among nonsmokers from the nation itself instead of from the United States. This information is used for the calculation of the excess lung cancer mortality due to smoking. Results Projected lung cancer mortality For the Netherlands, lung cancer mortality rates among females reach a maximum between 2013 (age 55) and 2035 (age 80). In 2050 lung cancer mortality rates will be between 05 (age 55) and 39 (age 80) among males and between 04 (age 55) and 38 (age 80) among females. See Appendix 1 for the observed and projected age-specific lung cancer mortality rates. Future attributable mortality Smoking-attributable, as a result, will also show further declines for Dutch males and first increase but then decline for Dutch females. This is likely to be the case in other low-mortality countries as well. When comparing past and future attributable mortality fractions for the simplified Peto- Lopez method (Janssen et al. 2007),, and the modified Preston-Glei-Wilmoth method (Rostron 2010),, for the Netherlands (Figure 1), we can observe that leads to higher mortality effects of smoking among younger males and older females. Thus, not only levels of smokingattributable mortality are likely to be effected by the different indirect estimation techniques, but its age distribution as well. Current male attributable mortality fractions for all ages combined are about 0.25 and will decrease to 0.14 () or 0.16 () in The current fraction for women is around 0.15 in 2010 and will first increase and than decrease to 0.21 () or 0.24 () in Future all-cause mortality If a method overestimates the mortality effects of smoking, the non-related mortality will be lower. In the case where mortality related to smoking is increasing in the base period (such as for women in most countries), the downward slope of the non-related mortality is overestimated. Combined with a decreasing mortality effect of smoking in de projection period, this could result in a very high future life expectancy. For the Netherlands, the comparison of the simplified Peto-Lopez method (Janssen et al. 2007),, and the modified Preston-Glei-Wilmoth method (Rostron 2010),, leads to almost equal projected life expectancy at birth (e0) for men (Table 1). However, leads to smaller declines in non-related mortality than among men. For Dutch women, the historical all-cause and non-related e0 are almost equal for both methods, but the projected values are higher for.

5 Conclusion These initial findings showed that in many low-mortality countries attributable mortality will further decline for males and first increase but then decline for females. The different indirect estimation techniques have an effect on attributable mortality levels and its age structure, but in addition as well on life expectancy at birth and future trends in non-related life expectancy at birth, especially among women. These stronger effects on women can be explained by more difference in the age distribution in female attributable mortality and the overestimation at age 80 and older for some of the techniques. Especially in periods of large changes in smoking the trend in non-related mortality may be incorrectly estimated and will result in an overestimation or underestimation of future all-cause mortality and life expectancy. This is especially the case in female mortality data because of the shorter history in smoking behaviour. Figure 1 Projected attributable mortality fractions for the Netherlands, , based on Dutch mortality data Smoking-attributable mortality fractions, male, age 60 Smoking-attributable mortality fractions, female, age 60 Smoking-attributable mortality fractions, male, age 75 Smoking-attributable mortality fractions, female, age 75 Table 1 All-cause and non-related life expectancy at birth for the Netherlands, resulting from the and method, by sex, Men Women Year All-cause e0 related e0 Total e0 related e0 All-cause e0 related e0 All-cause e0 related e * * * projected values

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7 Appendix 1 Projected age-specific lung cancer mortality rates for the Netherlands, , based on Dutch lung cancer mortality