Model Helps Predict Mortality Risk for Breast Cancer Survivors, Payers

Early detection and newer breast cancer treatments have resulted in significant gains in lifespan for survivors. These patients may face other challenges, including when they are deemed cured and obtaining life insurance.

A study presented at the 11^th European Breast Cancer Conference discussed a novel model that could be used to calculate the risk of death among breast cancer survivors.

"In the Netherlands, most applications for life insurance are accepted, but not for cancer survivors. A lot of former breast cancer patients are rejected for life insurance or subjected to higher insurance premiums," said researcher Marissa van Maaren. "Others might not even apply for life insurance because they might think they do not have a chance of being accepted. Former cancer patients in other countries may face similar problems.”

The new model predicts the increased risk of death for patients with breast cancer and survivors for up to 10 years after diagnosis. The model accounts for annual survival rate improvements, which could help patients gain a better understanding of their prognosis and insurers with more information.

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To create the model, the researchers analyzed data for 23,234 patients diagnosed with breast cancer in 2005 and 2006 who were matched with the general population. Of these patients, 10,101 were diagnosed with stage 1 breast cancer, 9868 were diagnosed with stage 2 breast cancer, and 3265 were diagnosed with stage 3 breast cancer.

"We were interested in the excess risk of death for breast cancer survivors, so the risk of death in the patient population minus the risk of death in the general Dutch population. We also wanted to take into account the number of years survived after diagnosis," van Maaren said.

The authors developed 10 models for each stage of breast cancer for each year of survival after diagnosis, stopping after 10 years. The models accounted for type of breast cancer, lymph node metastasis, age at diagnosis, type of surgery, type of treatment, and other prognostic factors.

"All the separate models were integrated into one model that, for every stage of disease, shows the extra risk of death in the first 10 years compared to the general Dutch population, conditional on the number of years survived after diagnosis," van Maaren said.

The authors noted that the model can determine that a certain patient applying for life insurance 2 years after diagnosis may have a 5.8% increased mortality risk within 10 years compared with the general population; however, if the patient applies 5 years after diagnosis, the risk may only be 4.2%, and after 9 years may be 0.6%, according to the study.

"This could result in lower premiums as the years go by, although the exact premium may vary depending on the insurance company," van Maaren said. "It's also important to bear in mind that treatments for breast cancer have improved a lot since 2005 to 2006 when these women were diagnosed."

During validation, the model demonstrated life expectancy accuracy both in the study population and in the general population, according to the authors.

Dutch insurance companies are currently piloting the new model with hopes of implementing it in decision making.

Although it would not completely automate the process, the model could support decisions from clinicians, insurers, and patients, according to the study.

"Clinicians and insurance companies take account of a number of other variables not included in this model, such as the presence of other diseases and conditions,” van Maaren said. “But it could provide clinicians with a basis on which to deem a patient cured of the disease, and insurance companies have a more reliable basis for their life insurance application process that is based on more recent Dutch clinical data.”