Exposure-lag response of smoking prevalence on lung cancer incidence using a distributed lag non-linear model

The prevalence of smokers is a major driver of lung cancer incidence in a population, though the exposure-lag effects are ill-defined. Here we present a multi-country ecological modelling study using a 30-year smoking prevalence history to quantify the exposure-lag response. To model the temporal dependency between smoking prevalence and lung cancer incidence, we used a distributed lag non-linear model (DLNM), controlling for gender, age group, country, outcome year, and population at risk, and presented the effects as the incidence rate ratio (IRR) and cumulative incidence rate ratio (IRRcum). The exposure-response varied by lag period, whilst the lag-response varied according to the magnitude and direction of changes in smoking prevalence in the population. For the cumulative lag-response, increments above and below the reference level was associated with an increased and decreased IRRcum respectively, with the magnitude of the effect varying across the lag period. Though caution should be exercised in interpretation of the IRR and IRRcum estimates reported herein, we hope our work constitutes a preliminary step towards providing policy makers with meaningful indicators to inform national screening programme developments. To that end, we have implemented our statistical model a shiny app and provide an example of its use.

Automated summary

The study utilizes multi-country data to analyze the impact of smoking prevalence on lung cancer incidence, showing that the response to smoking prevalence changes varies across different lag periods. Additionally, changes in cumulative lag-response are significantly associated with IRRcum compared to the reference level.