A conditional model predicting the 10-year annual extra mortality risk compared to the general population: a large population-based study in Dutch breast cancer patients

Marissa C van Maaren, Robert F Kneepkens, Joke Verbaan, Peter C Huijgens, Valery E P P Lemmens, Rob H A Verhoeven and Sabine Siesling

PLOS ONE, 2019, vol. 14, issue 1, 1-13

Abstract: Objective: Many cancer survivors are facing difficulties in getting a life insurance; raised premiums and declinatures are common. We generated a prediction model estimating the conditional extra mortality risk of breast cancer patients in the Netherlands. This model can be used by life insurers to accurately estimate the additional risk of an individual patient, conditional on the years survived. Methodology: All women diagnosed with stage I-III breast cancer in 2005–2006, treated with surgery, were selected from the Netherlands Cancer Registry. For all stages separately, multivariable logistic regression was used to estimate annual mortality risks, conditional on the years survived, until 10 years after diagnosis, resulting in 30 models. The conditional extra mortality risk was calculated by subtracting mortality rates of the general Dutch population from the patient mortality rates, matched by age, gender and year. The final model was internally and externally validated, and tested by life insurers. Results: We included 23,234 patients: 10,101 stage I, 9,868 stage II and 3,265 stage III. The final models included age, tumor stage, nodal stage, lateralization, location within the breast, grade, multifocality, hormonal receptor status, HER2 status, type of surgery, axillary lymph node dissection, radiotherapy, (neo)adjuvant systemic therapy and targeted therapy. All models showed good calibration and discrimination. Testing of the model by life insurers showed that insurability using the newly-developed model increased with 13%, ranging from 0%-24% among subgroups. Conclusion: The final model provides accurate conditional extra mortality risks of breast cancer patients, which can be used by life insurers to make more reliable calculations. The model is expected to increase breast cancer patients’ insurability and transparency among life insurers.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0210887

DOI: 10.1371/journal.pone.0210887

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