A population-based analysis of attributable hospitalisation costs of invasive fungal diseases in haematological malignancy patients using data linkage of state-wide registry and costing databases: 2009-2015

Background: Invasive fungal diseases (IFD) are associated with significant treatment-related costs in patients with haematological malignancies (HM). Objectives: The objectives of this study were to characterise the gross and attributable hospitalisation costs of a variety of IFD in patients with HM by linking state-wide hospital administrative and costing datasets. Patients/Methods: We linked the Victorian Admitted Episodes Dataset, Victorian Cancer Registry and the Victorian Cost Data Collection from 1 July 2009 to 30 June 2015. IFD cases and uninfected controls were matched 1:1 based on age within ten years, same underlying HM and length of stay prior to IFD diagnosis. The cost difference between surviving cases and controls, indexed to 2019 Australian dollars (AUD) calculated twelve months from IFD diagnosis, was determined using Poisson and negative binomial regression (NBR). Results: From 334 matched pairs, the gross hospitalisation cost of cases was AUD$67 277 compared to AUD$51 158 among uninfected controls, associated with an excess median hospitalisation cost of AUD$16 119 (P <.001) attributable to IFD, approximating to USD$11 362 and €10 154 at purchasing power parity. Median attributable costs were highest for patients with invasive aspergillosis (AUD$55 642; P <.001) and mucormycosis (AUD$51 272; P =.043) followed by invasive candidiasis AUD$24 572 (P <.001). No change in median excess attributable costs was observed over the study period (P =.90) Analyses by NBR revealed a 1.36-fold increase (P <.001) in total hospitalisation costs among cases as compared to controls twelve months from IFD diagnosis. Conclusion: Invasive aspergillosis and mucormycosis have high attributable hospitalisation costs but the overall excess IFD cost of AUD$16 119 is modest, potentially reflecting missed or miscoded fungal episodes arguing for better quality surveillance data at hospital level.