IDEAS home Printed from https://ideas.repec.org/a/eee/insuma/v78y2018icp316-324.html
   My bibliography  Save this article

Using Taiwan National Health Insurance Database to model cancer incidence and mortality rates

Author

Listed:
  • Yue, Jack C.
  • Wang, Hsin-Chung
  • Leong, Yin-Yee
  • Su, Wei-Ping

Abstract

The increasing cancer incidence and decreasing mortality rates in Taiwan worsened the loss ratio of cancer insurance products and created a financial crisis for insurers. In general, the loss ratio of long-term health products seems to increase with the policy year. In the present study, we used the data from Taiwan National Health Insurance Research Database to evaluate the challenge of designing cancer products. We found that the Lee–Carter and APC models have the smallest estimation errors, and the CBD and Gompertz models are good alternatives to explore the trend of cancer incidence and mortality rates, especially for the elderly people. The loss ratio of Taiwan’s cancer products is to grow and this can be deemed as a form of longevity risk. The longevity risk of health products is necessary to face in the future, similar to the annuity products.

Suggested Citation

  • Yue, Jack C. & Wang, Hsin-Chung & Leong, Yin-Yee & Su, Wei-Ping, 2018. "Using Taiwan National Health Insurance Database to model cancer incidence and mortality rates," Insurance: Mathematics and Economics, Elsevier, vol. 78(C), pages 316-324.
    • Handle: RePEc:eee:insuma:v:78:y:2018:i:c:p:316-324
    DOI: 10.1016/j.insmatheco.2017.09.016
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167668717304304
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.insmatheco.2017.09.016?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Andrew Cairns & David Blake & Kevin Dowd & Guy Coughlan & David Epstein & Alen Ong & Igor Balevich, 2009. "A Quantitative Comparison of Stochastic Mortality Models Using Data From England and Wales and the United States," North American Actuarial Journal, Taylor & Francis Journals, vol. 13(1), pages 1-35.
    2. Konstantin Arbeev & Anatoli Yashin & Svetlana Ukraintseva & Lyubov S. Arbeeva, 2005. "Mathematical Models for Human Cancer Incidence Rates," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 12(10), pages 237-272.
    3. Wang, Hsin Chung & Yue, Jack C. & Tsai, Yi-Hsuan, 2016. "Marital Status As A Risk Factor In Life Insurance: An Empirical Study In Taiwan," ASTIN Bulletin, Cambridge University Press, vol. 46(2), pages 487-505, May.
    4. Plat, Richard, 2009. "On stochastic mortality modeling," Insurance: Mathematics and Economics, Elsevier, vol. 45(3), pages 393-404, December.
    5. Ching-Syang Jack Yue & Hong-Chih Huang, 2011. "A Study of Incidence Experience for Taiwan Life Insurance," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 36(4), pages 718-733, October.
    6. Cesare, Mariachiara Di & Murphy, Mike, 2009. "Forecasting Mortality, Different Approaches for Different Cause of Deaths? The Cases of Lung Cancer; Influenza, Pneumonia, and Bronchitis; and Motor Vehicle Accidents," British Actuarial Journal, Cambridge University Press, vol. 15(S1), pages 185-211, January.
    7. Debon, A. & Montes, F. & Mateu, J. & Porcu, E. & Bevilacqua, M., 2008. "Modelling residuals dependence in dynamic life tables: A geostatistical approach," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 3128-3147, February.
    8. Renshaw, A.E. & Haberman, S., 2006. "A cohort-based extension to the Lee-Carter model for mortality reduction factors," Insurance: Mathematics and Economics, Elsevier, vol. 38(3), pages 556-570, June.
    9. Carter, Lawrence R. & Lee, Ronald D., 1992. "Modeling and forecasting US sex differentials in mortality," International Journal of Forecasting, Elsevier, vol. 8(3), pages 393-411, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chu-Chang Ku & Peter J Dodd, 2019. "Forecasting the impact of population ageing on tuberculosis incidence," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-13, September.
    2. Blake, David & Cairns, Andrew J.G., 2021. "Longevity risk and capital markets: The 2019-20 update," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 395-439.
    3. Jack C Yue & Hsin-Chung Wang & Ting-Chung Chang, 2024. "Application of type II diabetes incidence and mortality rates for insurance," PLOS ONE, Public Library of Science, vol. 19(9), pages 1-16, September.
    4. Jack C. Yue & Ming-Huei Tu & Yin-Yee Leong, 2024. "A spatial analysis of the health and longevity of Taiwanese people," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 49(2), pages 384-399, April.
    5. Norkhairunnisa Redzwan & Rozita Ramli, 2022. "A Bibliometric Analysis of Research on Stochastic Mortality Modelling and Forecasting," Risks, MDPI, vol. 10(10), pages 1-17, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Blake, David & El Karoui, Nicole & Loisel, Stéphane & MacMinn, Richard, 2018. "Longevity risk and capital markets: The 2015–16 update," Insurance: Mathematics and Economics, Elsevier, vol. 78(C), pages 157-173.
    2. Blake, David & Cairns, Andrew J.G., 2021. "Longevity risk and capital markets: The 2019-20 update," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 395-439.
    3. David Atance & Ana Debón & Eliseo Navarro, 2020. "A Comparison of Forecasting Mortality Models Using Resampling Methods," Mathematics, MDPI, vol. 8(9), pages 1-21, September.
    4. Graziani, Rebecca & NIGRI, ANDREA, 2023. "An Age–Period–Cohort Model in a Dirichlet Framework: A Coherent Causes of Death Estimation," SocArXiv 856yw, Center for Open Science.
    5. Wang, Chou-Wen & Huang, Hong-Chih & Hong, De-Chuan, 2013. "A feasible natural hedging strategy for insurance companies," Insurance: Mathematics and Economics, Elsevier, vol. 52(3), pages 532-541.
    6. Li, Han & O’Hare, Colin & Zhang, Xibin, 2015. "A semiparametric panel approach to mortality modeling," Insurance: Mathematics and Economics, Elsevier, vol. 61(C), pages 264-270.
    7. Han Lin Shang & Steven Haberman, 2020. "Retiree Mortality Forecasting: A Partial Age-Range or a Full Age-Range Model?," Risks, MDPI, vol. 8(3), pages 1-11, July.
    8. Bravo, Jorge M. & Ayuso, Mercedes & Holzmann, Robert & Palmer, Edward, 2021. "Addressing the life expectancy gap in pension policy," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 200-221.
    9. Susanna Levantesi & Virginia Pizzorusso, 2019. "Application of Machine Learning to Mortality Modeling and Forecasting," Risks, MDPI, vol. 7(1), pages 1-19, February.
    10. Norkhairunnisa Redzwan & Rozita Ramli, 2022. "A Bibliometric Analysis of Research on Stochastic Mortality Modelling and Forecasting," Risks, MDPI, vol. 10(10), pages 1-17, October.
    11. Guibert, Quentin & Lopez, Olivier & Piette, Pierrick, 2019. "Forecasting mortality rate improvements with a high-dimensional VAR," Insurance: Mathematics and Economics, Elsevier, vol. 88(C), pages 255-272.
    12. Li, Hong & Lu, Yang, 2017. "Coherent Forecasting Of Mortality Rates: A Sparse Vector-Autoregression Approach," ASTIN Bulletin, Cambridge University Press, vol. 47(2), pages 563-600, May.
    13. Man Chung Fung & Gareth W. Peters & Pavel V. Shevchenko, 2017. "Cohort effects in mortality modelling: a Bayesian state-space approach," Papers 1703.08282, arXiv.org.
    14. Basellini, Ugofilippo & Camarda, Carlo Giovanni & Booth, Heather, 2023. "Thirty years on: A review of the Lee–Carter method for forecasting mortality," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1033-1049.
    15. Ugofilippo Basellini & Søren Kjærgaard & Carlo Giovanni Camarda, 2020. "An age-at-death distribution approach to forecast cohort mortality," Working Papers axafx5_3agsuwaphvlfk, French Institute for Demographic Studies.
    16. Man Chung Fung & Gareth W. Peters & Pavel V. Shevchenko, 2016. "A unified approach to mortality modelling using state-space framework: characterisation, identification, estimation and forecasting," Papers 1605.09484, arXiv.org.
    17. Apostolos Bozikas & Georgios Pitselis, 2019. "Credible Regression Approaches to Forecast Mortality for Populations with Limited Data," Risks, MDPI, vol. 7(1), pages 1-22, February.
    18. Mercedes Ayuso & Jorge M. Bravo & Robert Holzmann & Edward Palmer, 2021. "Automatic Indexation of the Pension Age to Life Expectancy: When Policy Design Matters," Risks, MDPI, vol. 9(5), pages 1-28, May.
    19. Marcin Bartkowiak, 2018. "Mortality modelling. Model specification and mortality forecast accuracy," Collegium of Economic Analysis Annals, Warsaw School of Economics, Collegium of Economic Analysis, issue 51, pages 13-36.
    20. Bravo, Jorge M. & Ayuso, Mercedes & Holzmann, Robert & Palmer, Edward, 2023. "Intergenerational actuarial fairness when longevity increases: Amending the retirement age," Insurance: Mathematics and Economics, Elsevier, vol. 113(C), pages 161-184.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:insuma:v:78:y:2018:i:c:p:316-324. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/505554 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.