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Registry-based stroke research in Taiwan: past and future
Cheng-Yang Hsieh, Darren Philbert Wu, Sheng-Feng Sung
Epidemiol Health. 2018;40:e2018004.   Published online February 4, 2018
DOI: https://doi.org/10.4178/epih.e2018004
  • 21,791 View
  • 377 Download
  • 18 Web of Science
  • 16 Crossref
AbstractAbstract PDF
Abstract
Stroke registries are observational databases focusing on the clinical information and outcomes of stroke patients. They play an important role in the cycle of quality improvement. Registry data are collected from real-world experiences of stroke care and are suitable for measuring quality of care. By exposing inadequacies in performance measures of stroke care, research from stroke registries has changed how we manage stroke patients in Taiwan. With the success of various quality improvement campaigns, mortality from stroke and recurrence of stroke have decreased in the past decade. After the implementation of a nationwide stroke registry, researchers have been creatively expanding how they use and collect registry data for research. Through the use of the nationwide stroke registry as a common data model, researchers from many hospitals have built their own stroke registries with extended data elements to meet the needs of research. In collaboration with information technology professionals, stroke registry systems have changed from web-based, manual submission systems to automated fill-in systems in some hospitals. Furthermore, record linkage between stroke registries and administrative claims databases or other existing databases has widened the utility of registry data in research. Using stroke registry data as the reference standard, researchers have validated several algorithms for ascertaining the diagnosis of stroke and its risk factors from claims data, and have also developed a claims-based index to estimate stroke severity. By making better use of registry data, we believe that we will provide better care to patients with stroke.
Summary

Citations

Citations to this article as recorded by  
  • Predicting ischemic stroke patients’ prognosis changes using machine learning in a nationwide stroke registry
    Ching-Heng Lin, Yi-An Chen, Jiann-Shing Jeng, Yu Sun, Cheng-Yu Wei, Po-Yen Yeh, Wei-Lun Chang, Yang C. Fann, Kai-Cheng Hsu, Jiunn-Tay Lee
    Medical & Biological Engineering & Computing.2024;[Epub]     CrossRef
  • Data source profile reporting by studies that use routinely collected health data to explore the effects of drug treatment
    Wen Wang, Mei Liu, Qiao He, Mingqi Wang, Jiayue Xu, Ling Li, Guowei Li, Lin He, Kang Zou, Xin Sun
    BMC Medical Research Methodology.2023;[Epub]     CrossRef
  • Registry Studies of Stroke in Japan
    Ryu Matsuo
    Journal of Atherosclerosis and Thrombosis.2023; 30(9): 1095.     CrossRef
  • TREAT‐AIS: A Multicenter National Registry
    Sung‐Chun Tang, Yi‐Chen Hsieh, Chun‐Jen Lin, Yu‐Wei Chen, Kuan‐Hung Lin, Pi‐Shan Sung, Meng‐Tsang Hsieh, Chih‐Wei Tang, Hai‐Jui Chu, Kun‐Chang Tsai, Chao‐Liang Chou, Cheng‐Yu Wei, Shang‐Yih Yen, Po‐Lin Chen, Hsu‐Ling Yeh, Lung Chan, Sheng‐Feng Sung, Hon‐M
    Stroke: Vascular and Interventional Neurology.2023;[Epub]     CrossRef
  • Risk of Incident Epilepsy After a Middle Cerebral Artery Territory Infarction
    Cheng-Yang Hsieh, Chien-Chou Su, Edward Chia-Cheng Lai, Yu-Shiue Chen, Tzu-Hsin Huang, Yea-Huei Kao Yang, Chih-Hung Chen, Sheng-Feng Sung, Chin-Wei Huang
    Frontiers in Neurology.2022;[Epub]     CrossRef
  • Clinical registries data quality attributes to support registry-based randomised controlled trials: A scoping review
    Khic-Houy Prang, Bill Karanatsios, Ebony Verbunt, Hui-Li Wong, Justin Yeung, Margaret Kelaher, Peter Gibbs
    Contemporary Clinical Trials.2022; 119: 106843.     CrossRef
  • Hypoperfusion Index Ratio as a Surrogate of Collateral Scoring on CT Angiogram in Large Vessel Stroke
    Chun-Min Wang, Yu-Ming Chang, Pi-Shan Sung, Chih-Hung Chen
    Journal of Clinical Medicine.2021; 10(6): 1296.     CrossRef
  • The Riga East University Hospital Stroke Registry—An Analysis of 4915 Consecutive Patients with Acute Stroke
    Guntis Karelis, Madara Micule, Evija Klavina, Iveta Haritoncenko, Ilga Kikule, Biruta Tilgale, Inese Polaka
    Medicina.2021; 57(6): 632.     CrossRef
  • 10th Anniversary of the Asia Pacific Stroke Organization: State of Stroke Care and Stroke Research in the Asia-Pacific
    Kay-Sin Tan, Byung-Woo Yoon, Ruey-Tay Lin, Man Mohan Mehndiratta, Nijasri C. Suwanwela, Narayanaswamy Venketasubramanian
    Cerebrovascular Diseases Extra.2021; 12(1): 14.     CrossRef
  • Smoking Paradox in Stroke Survivors?
    Hao-Kuang Wang, Chih-Yuan Huang, Yuan-Ting Sun, Jie-Yuan Li, Chih-Hung Chen, Yu Sun, Chung-Hsiang Liu, Ching-Huang Lin, Wei-Lun Chang, Jiunn-Tay Lee, Sheng-Feng Sung, Po-Yen Yeh, Ta-Chang Lai, I-Ju Tsai, Mei-Chen Lin, Cheng-Li Lin, Chi-Pang Wen, Chung Y.
    Stroke.2020; 51(4): 1248.     CrossRef
  • Two Decades of Research Using Taiwan’s National Health Insurance Claims Data: Bibliometric and Text Mining Analysis on PubMed
    Sheng-Feng Sung, Cheng-Yang Hsieh, Ya-Han Hu
    Journal of Medical Internet Research.2020; 22(6): e18457.     CrossRef
  • Home-Time as a Surrogate Measure for Functional Outcome After Stroke: A Validation Study


    Sheng-Feng Sung, Chien-Chou Su, Cheng-Yang Hsieh, Ching-Lan Cheng, Chih-Hung Chen, Huey-Juan Lin, Yu-Wei Chen, Yea-Huei Kao Yang
    Clinical Epidemiology.2020; Volume 12: 617.     CrossRef
  • THE ROLE OF THE HOSPITAL REGISTRY TO ASSESS THE QUALITY OF STROKE DIAGNOSIS
    S.P. Moskovko, D.O. Fiks, A.V. Shayuk, G.V. Datsenko, L.V. Babych
    World of Medicine and Biology.2020; 16(74): 103.     CrossRef
  • Apolipoprotein B Level and the Apolipoprotein B/Apolipoprotein A-I Ratio as a Harbinger of Ischemic Stroke: A Prospective Observation in Taiwan
    Yu-Ching Chou, Po-Chi Chan, Tsan Yang, San-Lin You, Chyi-Huey Bai, Chien-An Sun
    Cerebrovascular Diseases.2020; 49(5): 487.     CrossRef
  • Promising Use of Big Data to Increase the Efficiency and Comprehensiveness of Stroke Outcomes Research
    David Ung, Joosup Kim, Amanda G. Thrift, Dominique A. Cadilhac, Nadine E. Andrew, Vijaya Sundararajan, Moira K. Kapral, Mathew Reeves, Monique F. Kilkenny
    Stroke.2019; 50(5): 1302.     CrossRef
  • STAIR X
    David S. Liebeskind, Colin P. Derdeyn, Lawrence R. Wechsler, Greg Albers, Eric P. Ankerud, Johannes Boltze, Joseph Broderick, Bruce C.V. Campbell, Mitchell S.V. Elkind, Derick En’Wezoh, Anthony J. Furlan, Philip B. Gorelick, James Grotta, David Hess, Anee
    Stroke.2018; 49(9): 2241.     CrossRef
Original Article
Is the Tuberculosis Case Reporting Rate of Medical Care Institutions in Private Sector low?
Jong Seon Han, Won Gi Jhang, Young Hwangbo, Sung Soo Lee, Moran Ki
Korean J Epidemiol. 2008;30(2):230-238.   Published online December 31, 2008
DOI: https://doi.org/10.4178/kje.2008.30.2.230
  • 6,762 View
  • 26 Download
AbstractAbstract PDF
Abstract
PURPOSE
To estimate the reporting rate of tuberculosis in one medium-sized city in Korea.
METHODS
Data claimed by national health insurance corporationand notification data of KTBS (Korea Tuberculosis Surveillance System) were compared through medical record-linkage method. Regarding the cases that were claimed medical care fee as tuberculosis but not notified to KTBS, we reviewed medical charts of the patients and investigated the reasons of failure to notify.
RESULTS
Number of cases claimed health insurance fee as tuberculosis occurrences in Cheonan was 2,331 in 2007, while 956 cases were matched as notified cases to KTBS after electronic record-linkage by personal identifier. Among remaining 1,375 cases that were not matched, real missed cases through medical record review survey were found to be 104. The reasons of failure to notify were because of 'not tuberculosis patients' (500, 36.4%), 'notified in 2006' (421, 30.6%), 'diseases coding error' (341, 24.8%) and 'notified as other diseases' (9, 0.7%). Therefore, the corrected reporting rate was calculated at 93% (95% CI: 91.6% - 94.2%). Notably, reporting rate of clinics (58.1%) was significantly lower than those of hospitals (93.4%) or general hospitals (96.6%).
CONCLUSIONS
All cases of tuberculosis diagnosis, which were claimed and not notified, were verified, the reporting rate was not as low as that of the data known through media. However, to reach the goal of tuberculosis elimination (reporting rate over 95%), more effort into improvement of the reporting system is necessary.
Summary

Epidemiol Health : Epidemiology and Health