IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v17y2020i6p1944-d333189.html
   My bibliography  Save this article

Stroke to Dementia Associated with Environmental Risks—A Semi-Markov Model

Author

Listed:
  • Kung-Jeng Wang

    (Department of Industrial Management, National Taiwan University of Science and Technology, Taipei 106, Taiwan)

  • Chia-Min Lee

    (Department of Industrial Management, National Taiwan University of Science and Technology, Taipei 106, Taiwan)

  • Gwo-Chi Hu

    (Department of Rehabilitation Medicine, Mackay Memorial Hospital, Number 92, Section 2, Zhongshan North Road, Zhongshan District, Taipei City 10449, Taiwan)

  • Kung-Min Wang

    (Department of Surgery, Shin-Kong Wu Ho-Su Memorial Hospital, Shilin District, Taipei 111, Taiwan)

Abstract

Background: Most stroke cases lead to serious mental and physical disabilities, such as dementia and sensory impairment. Chronic diseases are contributory risk factors for stroke. However, few studies considered the transition behaviors of stroke to dementia associated with chronic diseases and environmental risks. Objective: This study aims to develop a prognosis model to address the issue of stroke transitioning to dementia associated with environmental risks. Design: This cohort study used the data from the National Health Insurance Research Database in Taiwan. Setting: Healthcare data were obtained from more than 25 million enrollees and covered over 99% of Taiwan’s entire population. Participants: In this study, 10,627 stroke patients diagnosed from 2000 to 2010 in Taiwan were surveyed. Methods: A Cox regression model and corresponding semi-Markov process were constructed to evaluate the influence of risk factors on stroke, corresponding dementia, and their transition behaviors. Main Outcome Measure: Relative risk and sojourn time were the main outcome measure. Results: Multivariate analysis showed that certain environmental risks, medication, and rehabilitation factors highly influenced the transition of stroke from a chronic disease to dementia. This study also highlighted the high-risk populations of stroke patients against the environmental risk factors; the males below 65 years old were the most sensitive population. Conclusion: Experiments showed that the proposed semi-Markovian model outperformed other benchmark diagnosis algorithms (i.e., linear regression, decision tree, random forest, and support vector machine), with a high R 2 of 90%. The proposed model also facilitated an accurate prognosis on the transition time of stroke from chronic diseases to dementias against environmental risks and rehabilitation factors.

Suggested Citation

  • Kung-Jeng Wang & Chia-Min Lee & Gwo-Chi Hu & Kung-Min Wang, 2020. "Stroke to Dementia Associated with Environmental Risks—A Semi-Markov Model," IJERPH, MDPI, vol. 17(6), pages 1-13, March.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:6:p:1944-:d:333189
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/17/6/1944/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/17/6/1944/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Król, Agnieszka & Saint-Pierre, Philippe, 2015. "SemiMarkov: An R Package for Parametric Estimation in Multi-State Semi-Markov Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 66(i06).
    2. Qi Cao & Erik Buskens & Talitha Feenstra & Tiny Jaarsma & Hans Hillege & Douwe Postmus, 2016. "Continuous-Time Semi-Markov Models in Health Economic Decision Making," Medical Decision Making, , vol. 36(1), pages 59-71, January.
    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. Chien-Lung Chan & Chi-Chang Chang, 2022. "Big Data, Decision Models, and Public Health," IJERPH, MDPI, vol. 19(14), pages 1-9, July.
    2. Kung-Min Wang & Kun-Huang Chen & Chrestella Ayu Hernanda & Shih-Hsien Tseng & Kung-Jeng Wang, 2022. "How Is the Lung Cancer Incidence Rate Associated with Environmental Risks? Machine-Learning-Based Modeling and Benchmarking," IJERPH, MDPI, vol. 19(14), pages 1-19, July.

    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. Qi Cao & Erik Buskens & Hans L. Hillege & Tiny Jaarsma & Maarten Postma & Douwe Postmus, 2019. "Stratified treatment recommendation or one-size-fits-all? A health economic insight based on graphical exploration," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 20(3), pages 475-482, April.
    2. Guglielmo D'Amico & Stefania Scocchera & Loriano Storchi, 2021. "Randentropy: a software to measure inequality in random systems," Papers 2103.09107, arXiv.org.
    3. Fuino, Michel & Wagner, Joël, 2018. "Long-term care models and dependence probability tables by acuity level: New empirical evidence from Switzerland," Insurance: Mathematics and Economics, Elsevier, vol. 81(C), pages 51-70.
    4. Hongwen Guo & Mo Zhang & Paul Deane & Randy E. Bennett, 2019. "Writing Process Differences in Subgroups Reflected in Keystroke Logs," Journal of Educational and Behavioral Statistics, , vol. 44(5), pages 571-596, October.
    5. Guibert, Quentin & Planchet, Frédéric, 2018. "Non-parametric inference of transition probabilities based on Aalen–Johansen integral estimators for acyclic multi-state models: application to LTC insurance," Insurance: Mathematics and Economics, Elsevier, vol. 82(C), pages 21-36.

    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:gam:jijerp:v:17:y:2020:i:6:p:1944-:d:333189. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.