Patient- and Areal-Level Risk Factors Associated With Lung Cancer Mortality in Victoria, Australia: A Bayesian Spatial Survival Analysis.

Background: In Australia, lung cancer is the leading cause of cancer-related deaths. In Victoria, the mortality risk is assumed to vary across Local Government Areas (LGAs) due to variations in socioeconomic advantage, remoteness, and healthcare accessibility. Thus, we applied Bayesian spatial survival models to examine the geographic variation in lung cancer survival in Victoria.

Methods: Data on lung cancer cases were extracted from the Victorian Lung Cancer Registry (VLCR). To account for spatial dependence and risk factors of survival in lung cancer patients, we employed a Bayesian spatial survival model. Conditional Autoregressive (CAR) prior was assigned to model the spatial dependence. Deviance Information Criterion (DIC), Watanabe Akaike Information Criterion (WAIC), and Log Pseudo Marginal Likelihood (LPML) were used for model comparison. In the final best-fitted model, the Adjusted Hazard Ratio (AHR) with the 95% Credible Interval (CrI) was reported. The outcome variable was the survival status of lung cancer patients, defined as whether they survived or died during the follow-up period (death was our interest).

Results: Our study revealed substantial variations in lung cancer mortality in Victoria. Poor Eastern Cooperative Oncology Group (ECOG) performance status, diagnosed at a regional hospital, Small Cell Lung Cancer (SCLC), advanced age, and advanced clinical stage were associated with a higher risk of mortality, whereas being female, presented at Multidisciplinary Team (MDT) meeting, and diagnosed at a metropolitan private hospital were significantly associated with a lower risk of mortality.

Conclusion: Identifying geographical disparities in lung cancer survival may help shape healthcare policy to implement more targeted and effective lung cancer care services.