IDEAS home Printed from https://ideas.repec.org/a/vrs/offsta/v36y2020i1p49-62n3.html
   My bibliography  Save this article

The Joinpoint-Jump and Joinpoint-Comparability Ratio Model for Trend Analysis with Applications to Coding Changes in Health Statistics

Author

Listed:
  • Chen Huann-Sheng

    (Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, MD, U.S.A.)

  • Zeichner Sarah

    (Division of Geological and Planetary Sciences, California Institute of Technology, 1200 E California Blvd, Pasadena, CA 91125, U.S.A.)

  • Anderson Robert N.

    (Division of Vital Statistics, National Center for Health Statistics, Centers for Disease Control and Prevention, Hyattsville, MD, U.S.A.)

  • Espey David K.

    (Division of Population Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Albuquerque, NM, U.S.A.)

  • Kim Hyune-Ju

    (Department of Mathematics, Syracuse University, Syracuse, NY, U.S.A.)

  • Feuer Eric J.

    (Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, MD, U.S.A.)

Abstract

Analysis of trends in health data collected over time can be affected by instantaneous changes in coding that cause sudden increases/decreases, or “jumps,” in data. Despite these sudden changes, the underlying continuous trends can present valuable information related to the changing risk profile of the population, the introduction of screening, new diagnostic technologies, or other causes. The joinpoint model is a well-established methodology for modeling trends over time using connected linear segments, usually on a logarithmic scale. Joinpoint models that ignore data jumps due to coding changes may produce biased estimates of trends. In this article, we introduce methods to incorporate a sudden discontinuous jump in an otherwise continuous joinpoint model. The size of the jump is either estimated directly (the Joinpoint-Jump model) or estimated using supplementary data (the Joinpoint-Comparability Ratio model). Examples using ICD-9/ICD-10 cause of death coding changes, and coding changes in the staging of cancer illustrate the use of these models.

Suggested Citation

  • Chen Huann-Sheng & Zeichner Sarah & Anderson Robert N. & Espey David K. & Kim Hyune-Ju & Feuer Eric J., 2020. "The Joinpoint-Jump and Joinpoint-Comparability Ratio Model for Trend Analysis with Applications to Coding Changes in Health Statistics," Journal of Official Statistics, Sciendo, vol. 36(1), pages 49-62, March.
    • Handle: RePEc:vrs:offsta:v:36:y:2020:i:1:p:49-62:n:3
    DOI: 10.2478/jos-2020-0003
    as

    Download full text from publisher

    File URL: https://doi.org/10.2478/jos-2020-0003
    Download Restriction: no
    File URL: https://libkey.io/10.2478/jos-2020-0003?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
    ---><---

    References listed on IDEAS

    as
    1. P. M. Lerman, 1980. "Fitting Segmented Regression Models by Grid Search," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(1), pages 77-84, March.
    Full references (including those not matched with items on IDEAS)

    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. Fan, Xudong & Wang, Xiaowei & Zhang, Xijin & ASCE Xiong (Bill) Yu, P.E.F., 2022. "Machine learning based water pipe failure prediction: The effects of engineering, geology, climate and socio-economic factors," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    2. Adham Alsharkawi & Mohammad Al-Fetyani & Maha Dawas & Heba Saadeh & Musa Alyaman, 2021. "Poverty Classification Using Machine Learning: The Case of Jordan," Sustainability, MDPI, vol. 13(3), pages 1-16, January.
    3. Ben Q. Liu & Dale L. Goodhue, 2012. "Two Worlds of Trust for Potential E-Commerce Users: Humans as Cognitive Misers," Information Systems Research, INFORMS, vol. 23(4), pages 1246-1262, December.
    4. Tan, Xiujie & Xiao, Ziwei & Liu, Yishuang & Taghizadeh-Hesary, Farhad & Wang, Banban & Dong, Hanmin, 2022. "The effect of green credit policy on energy efficiency: Evidence from China," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    5. Muñoz, J.F. & Arcos, A. & Álvarez, E. & Rueda, M., 2014. "New ratio and difference estimators of the finite population distribution function," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 102(C), pages 51-61.
    6. Suwon Song & Chun Gun Park, 2019. "Alternative Algorithm for Automatically Driving Best-Fit Building Energy Baseline Models Using a Data—Driven Grid Search," Sustainability, MDPI, vol. 11(24), pages 1-11, December.
    7. Yu, Binbing & Barrett, Michael J. & Kim, Hyune-Ju & Feuer, Eric J., 2007. "Estimating joinpoints in continuous time scale for multiple change-point models," Computational Statistics & Data Analysis, Elsevier, vol. 51(5), pages 2420-2427, February.
    8. Tahira Kootbodien & Nisha Naicker & Kerry S. Wilson & Raj Ramesar & Leslie London, 2020. "Trends in Suicide Mortality in South Africa, 1997 to 2016," IJERPH, MDPI, vol. 17(6), pages 1-16, March.
    9. Jonathan Readshaw & Stefano Giani, 2020. "Using Company Specific Headlines and Convolutional Neural Networks to Predict Stock Fluctuations," Papers 2006.12426, arXiv.org.
    10. Erjia Ge & Yee Leung, 2013. "Detection of crossover time scales in multifractal detrended fluctuation analysis," Journal of Geographical Systems, Springer, vol. 15(2), pages 115-147, April.
    11. Bucarey, Víctor & Labbé, Martine & Morales, Juan M. & Pineda, Salvador, 2021. "An exact dynamic programming approach to segmented isotonic regression," Omega, Elsevier, vol. 105(C).
    12. Lutz Bornmann & Rüdiger Mutz, 2015. "Growth rates of modern science: A bibliometric analysis based on the number of publications and cited references," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(11), pages 2215-2222, November.
    13. Tan, Xiujie & Dong, Hanmin & Liu, Yishuang & Su, Xin & Li, Zixian, 2022. "Green bonds and corporate performance: A potential way to achieve green recovery," Renewable Energy, Elsevier, vol. 200(C), pages 59-68.
    14. Siu-Tong Au & Rong Duan & Siamak Hesar & Wei Jiang, 2010. "A framework of irregularity enlightenment for data pre-processing in data mining," Annals of Operations Research, Springer, vol. 174(1), pages 47-66, February.
    15. Matúš Maciak & Ivan Mizera, 2016. "Regularization techniques in joinpoint regression," Statistical Papers, Springer, vol. 57(4), pages 939-955, December.
    16. Rodrigo R. Soares & Rudi Rocha & Michel Szklo, 2021. "American Delusion: Life Expectancy and Welfare in the US from an International Perspective," Working Papers 13, Instituto de Estudos para Políticas de Saúde.
    17. Ghosh, Pulak & Huang, Lan & Yu, Binbing & Tiwari, Ram C., 2009. "Semiparametric Bayesian approaches to joinpoint regression for population-based cancer survival data," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4073-4082, October.
    18. Walter Mazzucco & Rosanna Cusimano & Sergio Mazzola & Giuseppa Rudisi & Maurizio Zarcone & Claudia Marotta & Giorgio Graziano & Paolo D’Angelo & Francesco Vitale, 2018. "Childhood and Adolescence Cancers in the Palermo Province (Southern Italy): Ten Years (2003–2012) of Epidemiological Surveillance," IJERPH, MDPI, vol. 15(7), pages 1-14, June.
    19. de Oliveira Nogueira, Tiago & Palacio, Gilderlânio Barbosa Alves & Braga, Fabrício Damasceno & Maia, Pedro Paulo Nunes & de Moura, Elineudo Pinho & de Andrade, Carla Freitas & Rocha, Paulo Alexandre C, 2022. "Imbalance classification in a scaled-down wind turbine using radial basis function kernel and support vector machines," Energy, Elsevier, vol. 238(PC).
    20. Aziza Usmanova & Ahmed Aziz & Dilshodjon Rakhmonov & Walid Osamy, 2022. "Utilities of Artificial Intelligence in Poverty Prediction: A Review," Sustainability, MDPI, vol. 14(21), pages 1-39, October.

    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:vrs:offsta:v:36:y:2020:i:1:p:49-62:n:3. 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: Peter Golla (email available below). General contact details of provider: https://www.sciendo.com .

    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.