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

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...

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Autores principales:	Daniel Robert Smith, Alireza Behzadnia, Rabbiaatul Addawiyah Imawana, Muzammil Nahaboo Solim, Michaela Louise Goodson
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ae7ea298dfbc476c93597d07201f9db2
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spelling	oai:doaj.org-article:ae7ea298dfbc476c93597d07201f9db22021-12-02T15:33:01ZExposure–lag response of smoking prevalence on lung cancer incidence using a distributed lag non-linear model10.1038/s41598-021-91644-y2045-2322https://doaj.org/article/ae7ea298dfbc476c93597d07201f9db22021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-91644-yhttps://doaj.org/toc/2045-2322Abstract 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.Daniel Robert SmithAlireza BehzadniaRabbiaatul Addawiyah ImawanaMuzammil Nahaboo SolimMichaela Louise GoodsonNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Daniel Robert Smith Alireza Behzadnia Rabbiaatul Addawiyah Imawana Muzammil Nahaboo Solim Michaela Louise Goodson Exposure–lag response of smoking prevalence on lung cancer incidence using a distributed lag non-linear model
description	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.
format	article
author	Daniel Robert Smith Alireza Behzadnia Rabbiaatul Addawiyah Imawana Muzammil Nahaboo Solim Michaela Louise Goodson
author_facet	Daniel Robert Smith Alireza Behzadnia Rabbiaatul Addawiyah Imawana Muzammil Nahaboo Solim Michaela Louise Goodson
author_sort	Daniel Robert Smith
title	Exposure–lag response of smoking prevalence on lung cancer incidence using a distributed lag non-linear model
title_short	Exposure–lag response of smoking prevalence on lung cancer incidence using a distributed lag non-linear model
title_full	Exposure–lag response of smoking prevalence on lung cancer incidence using a distributed lag non-linear model
title_fullStr	Exposure–lag response of smoking prevalence on lung cancer incidence using a distributed lag non-linear model
title_full_unstemmed	Exposure–lag response of smoking prevalence on lung cancer incidence using a distributed lag non-linear model
title_sort	exposure–lag response of smoking prevalence on lung cancer incidence using a distributed lag non-linear model
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/ae7ea298dfbc476c93597d07201f9db2
work_keys_str_mv	AT danielrobertsmith exposurelagresponseofsmokingprevalenceonlungcancerincidenceusingadistributedlagnonlinearmodel AT alirezabehzadnia exposurelagresponseofsmokingprevalenceonlungcancerincidenceusingadistributedlagnonlinearmodel AT rabbiaatuladdawiyahimawana exposurelagresponseofsmokingprevalenceonlungcancerincidenceusingadistributedlagnonlinearmodel AT muzammilnahaboosolim exposurelagresponseofsmokingprevalenceonlungcancerincidenceusingadistributedlagnonlinearmodel AT michaelalouisegoodson exposurelagresponseofsmokingprevalenceonlungcancerincidenceusingadistributedlagnonlinearmodel
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Exposure–lag response of smoking prevalence on lung cancer incidence using a distributed lag non-linear model

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