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and across time (Sterling, Rosenbaum, and Weinkam, 1993). Furthermore, because smokers are self-selected, some of the mortality differential between smokers and nonsmokers may be attributable to confounding with other risk factors. Thus, to avoid overstating the impact of smoking, Peto et al. rather arbitrarily halved the CPS-II relative excess risks for causes other than lung cancer. More recent applications of the method have lowered the reduction to 30 percent (Ezzati and Lopez, 2003). More recently still, researchers have adjusted directly for confounding factors (Ezzati et al., 2005; Danaei et al., 2009). Rostron and Wilmoth (forthcoming) modified the Peto-Lopez approach by using more refined age intervals and adjusting the baseline level of lung cancer mortality.

Staetsky (2009) has applied the Peto-Lopez method to trends in women’s mortality above age 65 between 1973-1975 and 1995-1997. She found that a substantial fraction of the slowdown in women’s mortality improvements in the United States, Denmark, and the Netherlands relative to France and Japan is attributable to smoking.

We have developed an alternative to the Peto-Lopez method for calculating deaths attributable to smoking in high-income countries (Preston, Glei, and Wilmoth, 2010). As they do, we use lung cancer mortality as the basic indicator of the damage caused by smoking in a particular population. However, we do not rely on the relative risks from CPS-II or any other study. Instead, we investigate the macro-level statistical association between lung cancer mortality and mortality from all other causes of death in a data set of 21 countries covering the period 1950 to 2007. This approach is motivated by the expectation that lung cancer mortality is a reliable indicator of the damage from smoking and that such damage has left a sufficiently vivid imprint on other causes of death that it is identifiable in country-level data. A related approach has been applied to subnational time-series data for various cancers (Leistikow and Tsodikov, 2005; Leistikow et al., 2008).

We apply this method to data from 21 high-income countries and estimate the proportion of deaths at ages 50+ that are attributable to smoking. We then estimate the impact of removing these deaths from a population’s mortality profile on life expectancy at age 50 and on international variation therein.


Modeling Strategy2

The model that we use for estimating the impact of smoking on mortality is based on the assumption that lung cancer mortality is a good proxy


The model was introduced by Preston, Glei, and Wilmoth (2010); we repeat the description here for completeness.

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