Innovative use of Australian cancer registry data for early detection of the effects of epidemics and other mass disruptions on cancer incidence

Abstract

Background: Predictive modelling using pre-epidemic data have long been used to guide public health responses to communicable disease outbreaks and other health disruptions. In this study, cancer registry and related health data available 2-3 months from diagnosis were used to predict changes in cancer detection that otherwise would not have been identified until full registry processing was completed about 18-24 months later. A key question was whether these earlier data could be used to predict cancer incidence ahead of full processing by the cancer registry as a guide to more timely health responses. The setting was the Australian State of New South Wales, covering 31 % of the Australian population. The study year was 2020, the year of emergence of the COVID-19 pandemic.

Methods: Cancer detection in 2020 was modelled using data available 2-3 months after diagnosis. This was compared with data from full registry processing available from 2022. Data from pre-pandemic 2018 were used for exploratory model building. Models were tested using pre-pandemic 2019 data. Candidate predictor variables included pathology, surgery and radiation therapy reports, numbers of breast screens, colonoscopies, PSA tests, and melanoma excisions recorded by the universal Medical Benefits Schedule (MBS). Data were analysed for all cancers collectively and 5 leading types.

Results: Compared with full registry processing, modelled data for 2020 had a >95 % accuracy overall, indicating key points of inflexion of cancer detection over the COVID-disrupted 2020 period. These findings highlight the potential of predictive modelling of cancer-related data soon after diagnosis to reveal changes in cancer detection during epidemics and other health disruptions.

Conclusions: Data available 2-3 months from diagnosis in the pandemic year indicated changes in cancer detection that were ultimately confirmed by fully-processed cancer registry data about 24 months later. This indicates the potential utility of using these early data in an early-warning system.

Keywords: COVID-19; Cancer incidence detection; Close to real-time data; Early cancer data; NSW; Time-series modelling.