Inferring lung cancer risk factor patterns through joint Bayesian spatio-temporal analysis

Susanna M Cramb; Peter D Baade; Nicole M White; Louise M Ryan; Kerrie L Mengersen

doi:10.1016/j.canep.2015.03.001

Inferring lung cancer risk factor patterns through joint Bayesian spatio-temporal analysis

Cancer Epidemiol. 2015 Jun;39(3):430-9. doi: 10.1016/j.canep.2015.03.001. Epub 2015 Mar 21.

Authors

Susanna M Cramb¹, Peter D Baade², Nicole M White³, Louise M Ryan⁴, Kerrie L Mengersen³

Affiliations

¹ Cancer Council Queensland, Brisbane, Australia; Mathematical Sciences School, Queensland University of Technology, Brisbane, Australia. Electronic address: susannacramb@cancerqld.org.au.
² Cancer Council Queensland, Brisbane, Australia; School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia; Griffith Health Institute, Griffith University, Gold Coast, Australia.
³ Mathematical Sciences School, Queensland University of Technology, Brisbane, Australia; Cooperative Research Centre for Spatial Information, Australia.
⁴ School of Mathematical Sciences, University of Technology, Sydney, Australia.

PMID: 25805551
DOI: 10.1016/j.canep.2015.03.001

Abstract

Background: 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.

Keywords: Bayesian methods; Lung cancer; Risk factor; Shared component model; Spatio-temporal analysis; Tobacco smoking.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Adolescent
Adult
Aged
Aged, 80 and over
Australia / epidemiology
Bayes Theorem*
Child
Child, Preschool
Databases, Factual
Female
Humans
Infant
Infant, Newborn
Lung Neoplasms / epidemiology*
Lung Neoplasms / etiology
Male
Middle Aged
Risk Factors
Spatio-Temporal Analysis*
Young Adult