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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
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    References listed on IDEAS

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    1. 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.
    2. 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).
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    Cited by:

    1. 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.
    2. Chien-Lung Chan & Chi-Chang Chang, 2022. "Big Data, Decision Models, and Public Health," IJERPH, MDPI, vol. 19(14), pages 1-9, July.

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