Abstract
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.
Highlights
The prevalence of smokers is a major driver of lung cancer incidence in a population, though the “exposure–lag” effects are ill-defined
The lag response was wave-like, with the direction and magnitude varying according to the direction and magnitude of the increment in smoking prevalence compared to the reference level (i.e. 50% smoking prevalence) (Fig. 3)
Increments above and below the reference level of 50% smoking prevalence was associated with an increased and decreased IRRcum respectively, with the magnitude of the effect varying across the lag period (Fig. 5)
Summary
The prevalence of smokers is a major driver of lung cancer incidence in a population, though the “exposure–lag” effects are ill-defined. The World Health Organization (WHO) estimates tobacco related mortality will increase from 100 million in the twentieth century to one billion in twenty-first century if current trends in smoking continue[3] To circumvent this epidemic, the WHO Framework Convention on Tobacco Control (FCTC), the first ever global health treaty, was initiated in 2003, with the overarching goal of implementing effective policies to reduce tobacco consumption[5]. Ecological models that have incorporated smoking data have focused on mortality (rather than incidence), and being projection models, are optimised for predictive accuracy as opposed to estimation of the exposure–lag response[13] The latter calls for explanatory modelling; this will be invaluable to policy makers for estimating the effect of changing the proportion of smokers in a population, facilitating strategic and robust p lanning[14]. We present an ecological modelling study with the overarching aim of estimating the exposure–lag response of smoking prevalence on lung cancer incidence, while controlling for confounding variation
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