Lung cancer mortality attributable to smoking: a multi-scenario analysis with variable lag periods

Abstract

Purpose: The estimation of smoking-attributable mortality (SAM) is subject to the acceptance of different assumptions that may influence the estimates. We aimed to assess lung cancer mortality attributable to smoking by using both a prevalence-independent method (PIM) and a prevalence-dependent method (PDM) with different lags between exposure (smoking prevalence) and outcome (lung cancer mortality).

Methods: We estimated the population attributable fractions (PAF) and the lung cancer SAM by sex and age group (35-64, 65-84 years), year-by-year from 2011 to 2020, in four scenarios in Spain. In three of these scenarios, a PDM was applied using different lags: no lag, a 15-year lag and a 20-year lag. In the fourth scenario, a PIM was applied.

Results: In the period 2011-2020 in Spain, the SAM was higher when the 20-year lag PDM was considered (173,526 deaths) and lower when no lag PDM or a PIM was applied (161,249 and 157,390 deaths, respectively). In men, the PAFs were similar between the no lag PDM and the PIM (86.7 % and 87.3 %, respectively). However, when a PDM 15-year or 20-year lag was considered, the PAF increased to 91.0 % and 92.3 %, respectively. In women, the lowest PAF was obtained with the PIM (57.3 %), and the highest with the PDM 20-year lag (79.4 %).

Conclusions: SAM estimates differ depending on the methods and lags used. Applying a 15-year or 20-year lag PDM yields higher SAM estimates than when no lag PDM or a PIM is used. Therefore, when feasible, smoking prevalence data that incorporate a lag of 15 or 20 years between exposure and result should be used for accurate estimates