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

Susanna M Cramb1, Peter D Baade2, Nicole M White3

  • 1Cancer Council Queensland, Brisbane, Australia; Mathematical Sciences School, Queensland University of Technology, Brisbane, Australia.

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Summary

Lung cancer risk factors show distinct spatial patterns in Queensland, Australia, with higher risks in remote areas. Temporal trends indicate decreasing risk for males and a fluctuating pattern for females, highlighting persistent spatial inequalities.

Keywords:
Bayesian methodsLung cancerRisk factorShared component modelSpatio-temporal analysisTobacco smoking

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Area of Science:

  • Epidemiology
  • Public Health
  • Spatial Analysis

Background:

  • Lung cancer poses a significant health burden, with tobacco smoking as the primary risk factor.
  • Understanding small area patterns and temporal trends of lung cancer risk factors is crucial for targeted prevention.
  • Limited information exists on these patterns in Australia despite efforts to reduce smoking prevalence.

Purpose of the Study:

  • To estimate spatio-temporal patterns of lung cancer risk factors in Queensland, Australia.
  • To utilize routinely collected population-based cancer data for risk factor analysis.
  • To investigate the influence of socioeconomic disadvantage, Indigenous population composition, and remoteness on these patterns.

Main Methods:

  • A Bayesian shared component spatio-temporal model was employed, analyzing male and female lung cancer separately.
  • The model covered 477 Statistical Local Areas (SLAs) in Queensland over 15 years.
  • Analyses included adjustments for socioeconomic disadvantage, Indigenous population composition, and remoteness.

Main Results:

  • Significant spatial patterns in risk factors were observed for both males (median Relative Risk (RR) 0.48-2.00) and females (median RR 0.53-1.80), with elevated risks in remote areas.
  • Distinct temporal trends were identified: a decrease over time for males and an initial increase followed by a decrease for females.
  • Risk estimates in disadvantaged, remote, and Indigenous areas were reduced after adjustment, particularly for females.

Conclusions:

  • Modeled risks correlate with past smoking prevalence, showing a ~30-year lag consistent with lung cancer development time.
  • Consistent temporal trends suggest past interventions were uniformly effective across Queensland.
  • Persistent spatial inequalities remain, indicating a need for targeted future interventions, especially in remote areas.