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Exposure–Lag Response Of Smoking Prevalence On Lung Cancer Incidence Using A Distributed Lag Non-Linear Model

Lookup NU author(s): Dr Daniel Smith, Dr Michaela Goodson



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


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.

Publication metadata

Author(s): Smith DR, Behzadnia A, Imawana RA, Solim MN, Goodson ML

Publication type: Article

Publication status: Published

Journal: Scientific Reports

Year: 2021

Volume: 11

Issue: 1

Online publication date: 14/07/2021

Acceptance date: 11/05/2021

Date deposited: 24/03/2022

ISSN (electronic): 2045-2322

Publisher: Nature Publishing Group


DOI: 10.1038/s41598-021-91644-y

PubMed id: 34262067


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