ticipants whose smoking behavior was followed up in 1994 substantially raised the estimated risk of smoking (Taylor et al., 2002). Furthermore, the smoking categories themselves impose a rigid frame on what can be blurry patterns of behavior. For example, CPS-II includes among “lifetime non-smokers” persons who had smoked but who had not reported themselves as smoking daily for at least a year (Leistikow et al., 2008).
Cohort studies have also been used to estimate the number of deaths in a population that are attributable to smoking. This calculation is conventionally made by comparing the actual number of deaths in a particular age-sex group in the population with the number that would have occurred if everyone had had the death rates of lifetime nonsmokers in that category. Based on CPS-II results, Mokdad et al. (2004) used this method to estimate that 435,000 deaths were attributable to smoking in the United States in 2000. There was no control for potentially confounding variables in smoker’s estimated risk. Using a nationally representative sample drawn from the National Health Interview Survey and controlling for many confounding factors, Rogers et al. (2005) estimated that 338,000 U.S. deaths were attributable to smoking in 2001. The wide range of existing estimates illustrates the inherent difficulty of this type of analysis and gives some indication of the uncertainty associated with all such estimates (including those presented here).
While the number of deaths attributable to smoking can be estimated directly from cohort studies, such studies are not available in many populations for which attributable risk estimates are sought. In 1992, Peto, Lopez, and colleagues developed an ingenious method for filling this gap (Peto et al., 1992). The method “borrows” the relative risks of cause-specific mortality for current smokers versus nonsmokers from CPS-II and applies them to the population of interest. Rather than applying them to the distribution of the population by smoking status, they instead used observed death rates from lung cancer as an indicator of the population’s cumulative smoking exposure, which may be a more reliable index of the cumulative damage from smoking than directly measured smoking behavior based on self-report.
Having selected lung cancer death rates as the indicator of the cumulative damage from smoking, Peto et al. then translated observed lung cancer death rates for a given population into an estimate of the smoking impact ratio by referring to the difference between lung cancer death rates for smokers and nonsmokers in CPS-II. This scalar is then used to adjust the cause-specific relative risks for smokers versus nonsmokers from CPS-II in order to derive a population-specific estimate of the risk attributable to smoking for other smoking-related causes of death. Clearly, their approach is heavily dependent on the assumption that CPS-II estimates of lung cancer death rates for smokers and nonsmokers and relative risks for other causes of death can be applied (with some adjustment) to other countries