Impact of hospital geographic remoteness on overall survival after colorectal cancer resection using state‐wide administrative data. Issue 7 (4th June 2020) - Udayasiri, Dilshan K.; MacCallum, Caroline; Da Silva, Nigel; Skandarajah, Anita; Hayes, Ian P.
Abstract: Background: This study aimed to use administrative data (AD) linked to the Victorian death index (VDI) to report on overall long‐term survival following colorectal cancer (CRC) surgery, comparing regional to metropolitan hospitals. Methods: A retrospective cohort study using prospectively gathered AD linked to VDI. The primary outcome was overall survival (OS). Outcomes were adjusted for potential confounders via multivariable Cox proportional hazard regression analysis. Results: Total of 17 533 patients: 12 879 metropolitan patients, 3835 inner regional patients and 719 outer regional patients. Multivariable Cox regression, adjusted for the effects of age, ASA score, Charlson score, position of tumour, mode of access, admission type, lymph node metastases, distant metastases, return to theatre, length of stay, HDU admission and discharge destination showed no difference in OS comparing CRC resection patients from inner or outer regional hospitals to metropolitan ((HR 1.02, 95% CI 0.95–1.09, P = 0.59) and (HR 0.97, 95% CI 0.85–1.11, P = 0.68) respectively). Conclusion: This is the largest and most detailed study concerning OS after CRC resection involving Victorian public hospitals. There was no difference in OS following CRC resection when inner or outer regional hospitals were compared to metropolitan hospitals in Victoria. The study demonstrated the utility of AD with validated algorithms, linked to death data for reporting CRC survival outcomes. Abstract : This study reports the overall survival (OS) comparing regional to metropolitan hospitals in patients having resections for colorectal cancers. It uses validated administrative data linked to death data to report on OS over 10 years in the state of Victoria. Results are adjusted for potential confounders using a multivariable Cox regression analysis.