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dc.contributor.authorCramb, Susanna M.en_US
dc.contributor.authorBaade, Peteren_US
dc.contributor.authorWhite, Nicole M.en_US
dc.contributor.authorRyan, Louise M.en_US
dc.contributor.authorMengersen, Kerrie L.en_US
dc.date.accessioned2018-09-19T01:30:45Z
dc.date.available2018-09-19T01:30:45Z
dc.date.issued2015en_US
dc.identifier.issn1877-7821en_US
dc.identifier.doi10.1016/j.canep.2015.03.001en_US
dc.identifier.urihttp://hdl.handle.net/10072/140968
dc.description.abstractBackground: Preventing risk factor exposure is vital to reduce the high burden from lung cancer. The leading risk factor for developing lung cancer is tobacco smoking. In Australia, despite apparent success in reducing smoking prevalence, there is limited information on small area patterns and small area temporal trends. We sought to estimate spatio-temporal patterns for lung cancer risk factors using routinely collected population-based cancer data. Methods: The analysis used a Bayesian shared component spatio-temporal model, with male and female lung cancer included separately. The shared component reflected lung cancer risk factors, and was modelled over 477 statistical local areas (SLAs) and 15 years in Queensland, Australia. Analyses were also run adjusting for area-level socioeconomic disadvantage, Indigenous population composition, or remoteness. Results: Strong spatial patterns were observed in the underlying risk factor estimates for both males (median Relative Risk (RR) across SLAs compared to the Queensland average ranged from 0.48 to 2.00) and females (median RR range across SLAs 0.53–1.80), with high risks observed in many remote areas. Strong temporal trends were also observed. Males showed a decrease in the underlying risk across time, while females showed an increase followed by a decrease in the final 2 years. These patterns were largely consistent across each SLA. The high underlying risk estimates observed among disadvantaged, remote and indigenous areas decreased after adjustment, particularly among females. Conclusion: The modelled underlying risks appeared to reflect previous smoking prevalence, with a lag period of around 30 years, consistent with the time taken to develop lung cancer. The consistent temporal trends in lung cancer risk factors across small areas support the hypothesis that past interventions have been equally effective across the state. However, this also means that spatial inequalities have remained unaddressed, highlighting the potential for future interventions, particularly among remote areas.en_US
dc.description.peerreviewedYesen_US
dc.languageEnglishen_US
dc.publisherElsevieren_US
dc.relation.ispartofpagefrom430en_US
dc.relation.ispartofpageto439en_US
dc.relation.ispartofissue3en_US
dc.relation.ispartofjournalCancer Epidemiologyen_US
dc.relation.ispartofvolume39en_US
dc.subject.fieldofresearchPublic Health and Health Services not elsewhere classifieden_US
dc.subject.fieldofresearchcode111799en_US
dc.titleInferring lung cancer risk factor patterns through joint Bayesian spatio-temporal analysisen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Peer Reviewed (HERDC)en_US
dc.type.codeC - Journal Articlesen_US
dcterms.licensehttp://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.description.versionPost-printen_US
gro.rights.copyright© 2015 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.en_US
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