School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.
Department of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia.
Cancer Med. 2024 Oct;13(19):e70293. doi: 10.1002/cam4.70293.
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.
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).
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.
Identifying geographical disparities in lung cancer survival may help shape healthcare policy to implement more targeted and effective lung cancer care services.
在澳大利亚，肺癌是导致癌症相关死亡的主要原因。在维多利亚州，由于社会经济优势、偏远程度和医疗保健可及性的差异，预计死亡率在地方政府区域（LGA）之间存在差异。因此，我们应用贝叶斯空间生存模型来研究维多利亚州肺癌生存的地理变化。
从维多利亚肺癌登记处（VLCR）提取肺癌病例数据。为了考虑肺癌患者生存的空间依赖性和风险因素，我们采用了贝叶斯空间生存模型。条件自回归（CAR）先验被分配给模型以模拟空间依赖性。偏差信息准则（DIC）、Watanabe Akaike 信息准则（WAIC）和对数伪边际似然（LPML）用于模型比较。在最终最佳拟合模型中，报告了调整后的风险比（AHR）及其 95%可信区间（CrI）。结局变量是肺癌患者的生存状态，定义为在随访期间是否存活或死亡（死亡是我们关注的）。
我们的研究表明维多利亚州肺癌死亡率存在显著差异。较差的东部合作肿瘤学组（ECOG）表现状态、在地区医院诊断、小细胞肺癌（SCLC）、年龄较大和临床晚期与更高的死亡风险相关，而女性、在多学科团队（MDT）会议上就诊和在大都市私立医院诊断与更低的死亡风险显著相关。
确定肺癌生存的地理差异可能有助于制定医疗保健政策，实施更有针对性和有效的肺癌护理服务。
