SCOR has recently released a new report entitled SCOR Underwriting Cancer Project: Optimizing Individual Cancer-Rating Assessments Based on Updated Algorithms. Authored by Dr. Eric Raymond, Oncologist, Associate Medical Director, and Thibault Antoine, Head of Critical Illness R&D Center, the report covers topics of epidemiology and underwriting of cancers, model methodology and materials, results and corrected conditional survival ratings.
As stated in the report: “Cancer represents a major cause of death worldwide and is associated with a high level of morbidity. Three risk factors for cancer are predominant in terms of prevalence: smoking, aging and the obesity prevalent in Western countries, but other factors can be frequently associated with cancer. The incidence of most cancers has been increasing over the last 50 years, while progresses in the detection and management of cancers have led to significant increases in both prevalence and survival.”
Because the number of newly diagnosed cancer patients has been increasing, the need to predict the evolution of critical illness prevalence at various stages becomes more important. Higher numbers of long term survivors — patients fit and alive after more than five years of diagnosis — challenge insurance companies for better coverage, which has led to innovative products and underwriting approaches.
A peculiar feature associated with the analyses of survival results in oncology and other areas of medicine is that the longer an individual survives, the higher her/his likelihood of expected survival is at any point in time. Poor prognosis patients have already died, so the expected survival for long-term survivors continuously increases over time.
The recent SCOR report discusses a newly developed mathematical model to estimate excess mortality rates of cancer patients. Both highly heterogeneous tumors (breast cancer) and lower heterogeneous tumors (colon cancer) were chosen to test the proposed algorithm. Breast and colon cancers are among the most frequent tumor types with both good potential long-term survival and highly variable individual outcomes.
As knowledge in oncology grows, prognostic factors will continue to be fine-tuned, helping to better adjust survival ratings to the individual patient risk and predicted outcomes. The new model functions for the entire patient population and aims to individualize survival ratings in the context of all available patient characteristics. The methodology is flexible, allowing implementation of current data and additional parameters as new scientific and medical data become available.
For more details about the SCOR Underwriting Cancer Project including the results of the scoring model, please see the full report on SCOR.com.