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 Corine van Maaren (Corresponding Author), Robert F. Kneepkens, Joke Verbaan, Peter c. Huijgens, Valery Lemmens, Rob H.A. Verhoeven, Sabine Siesling

Research output: Contribution to journalArticleAcademicpeer-review

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.
LanguageEnglish
Article numbere0210887
JournalPLoS ONE
Volume14
Issue number1
DOIs
Publication statusPublished - 24 Jan 2019

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Insurance Carriers
breast neoplasms
Breast Neoplasms
Mortality
Population
Netherlands
Life Insurance
Neoplasms
Adjuvant Radiotherapy
Surgery
neoplasms
Lymph Node Excision
life insurance
surgery
Calibration
Survivors
Registries
Breast
Dissection
Logistic Models

Cite this

@article{50575a4b5dc84229901d4bcb2a0c5a54,
title = "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",
abstract = "ObjectiveMany 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.MethodologyAll 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.ResultsWe 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.ConclusionThe 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.",
author = "{van Maaren}, {Marissa Corine} and Kneepkens, {Robert F.} and Joke Verbaan and Huijgens, {Peter c.} and Valery Lemmens and Verhoeven, {Rob H.A.} and Sabine Siesling",
year = "2019",
month = "1",
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doi = "10.1371/journal.pone.0210887",
language = "English",
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journal = "PLoS ONE",
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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. / van Maaren, Marissa Corine (Corresponding Author); Kneepkens, Robert F.; Verbaan, Joke; Huijgens, Peter c.; Lemmens, Valery; Verhoeven, Rob H.A.; Siesling, Sabine .

In: PLoS ONE, Vol. 14, No. 1, e0210887, 24.01.2019.

Research output: Contribution to journalArticleAcademicpeer-review

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T1 - A conditional model predicting the 10-year annual extra mortality risk compared to the general population

T2 - PLoS ONE

AU - van Maaren, Marissa Corine

AU - Kneepkens, Robert F.

AU - Verbaan, Joke

AU - Huijgens, Peter c.

AU - Lemmens, Valery

AU - Verhoeven, Rob H.A.

AU - Siesling, Sabine

PY - 2019/1/24

Y1 - 2019/1/24

N2 - ObjectiveMany 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.MethodologyAll 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.ResultsWe 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.ConclusionThe 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.

AB - ObjectiveMany 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.MethodologyAll 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.ResultsWe 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.ConclusionThe 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.

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SN - 1932-6203

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