Timeliness of Lung Cancer Care and Area-Level Determinants in Victoria: A Bayesian Spatiotemporal Analysis.

Abstract

Background: The reports have stated that the timeliness of lung cancer care varies significantly across different regions. According to the Victorian Lung Cancer Registry report, the timeliness of lung cancer care in Victoria has changed over time. Therefore, we aimed to quantify the extent of these spatial inequalities over time and to identify area-level determinants contributing to these changes.

Methods: The study analyzed lung cancer cases reported to the Victorian Lung Cancer Registry between 2011 and 2022. Bayesian spatiotemporal conditional autoregressive models were fitted, incorporating spatial random effects, temporal random effects, and spatiotemporal interactions. The best performing model was selected using the deviance information criterion. For the final best fit model, the adjusted RRs and their 95% credible intervals were reported.

Results: More than half (51.24%) of patients with lung cancer experienced treatment delays, whereas approximately one third (30.98%) encountered diagnostic delays. Moderate spatiotemporal variations were observed in both delayed diagnosis and treatment. In the final best fit model for treatment delay, an increase in the percentage of smokers was significantly associated with a higher risk of treatment delay (RR = 2.13; 95% credible interval, 1.13-4.20).

Conclusions: Identifying high-risk areas provides useful information for policymakers, helping in the reduction of delays in lung cancer diagnosis and treatment.

Impact: This study has revealed spatiotemporal inequalities in diagnostic and treatment delays, providing valuable insights for identifying areas that should be prioritized to ensure timely care for lung cancer.