IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v16y2019i7p1158-d218688.html
   My bibliography  Save this article

Effects of Early Frequent Nephrology Care on Emergency Department Visits among Patients with End-stage Renal Disease

Author

Listed:
  • Yun-Yi Chen

    (Institute of Health Policy and Management, College of Public Health, National Taiwan University, Taipei 100, Taiwan)

  • Likwang Chen

    (Institute of Population Health Sciences, National Health Research Institutes, Zhunan 350, Taiwan)

  • Jenq-Wen Huang

    (Division of Nephrology, Department of Internal Medicine, National Taiwan University Hospital, Taipei 100, Taiwan)

  • Ju-Yeh Yang

    (Institute of Health Policy and Management, College of Public Health, National Taiwan University, Taipei 100, Taiwan
    Division of Nephrology, Far Eastern Memorial Hospital, New Taipei City 220, Taiwan
    Department of Quality Management Center, Far Eastern Memorial Hospital, New Taipei City 220, Taiwan
    Lee-Ming Institute of Technology, New Taipei City 243, Taiwan)

Abstract

In this retrospective cohort study, we examined the association between predialysis nephrology care status and emergency department (ED) events among patients with end-stage renal disease. Data pertaining to 76,702 patients who began dialysis treatment between 1999 and 2010 were obtained from the National Health Insurance Research Database of Taiwan (NHIRD). The patients were divided into three groups based on the timing of the first nephrology care visit prior to the initiation of maintenance dialysis, and the frequency of nephrologist visits (i.e., early referral/frequent consultation, early referral/infrequent consultation, late referral). At 1-year post-dialysis initiation, a large number of the patients had experienced at least one all-cause ED visit (58%), infection-related ED visit (17%), or potentially avoidable ED visit (7%). Cox proportional hazard models revealed that patients who received early frequent care faced an 8% lower risk of all-cause ED visit (HR: 0.92; 95% CI: 0.90–0.94), a 24% lower risk of infection-related ED visit (HR: 0.76; 95% CI: 0.73–0.79), and a 24% lower risk of avoidable ED visit (HR: 0.76; 95% CI: 0.71–0.81), compared with patients in the late referral group. With regard to the patients undergoing early infrequent consultations, the only marginally significant association was for infection-related ED visits. Recurrent event analysis revealed generally consistent results. Overall, these findings indicate that continuous nephrology care from early in the predialysis period could reduce the risk of ED utilization in the first year of dialysis treatment.

Suggested Citation

  • Yun-Yi Chen & Likwang Chen & Jenq-Wen Huang & Ju-Yeh Yang, 2019. "Effects of Early Frequent Nephrology Care on Emergency Department Visits among Patients with End-stage Renal Disease," IJERPH, MDPI, vol. 16(7), pages 1-17, March.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:7:p:1158-:d:218688
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/16/7/1158/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/16/7/1158/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. D. Y. Lin & L. J. Wei & I. Yang & Z. Ying, 2000. "Semiparametric regression for the mean and rate functions of recurrent events," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(4), pages 711-730.
    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. Na Cai & Wenbin Lu & Hao Helen Zhang, 2012. "Time-Varying Latent Effect Model for Longitudinal Data with Informative Observation Times," Biometrics, The International Biometric Society, vol. 68(4), pages 1093-1102, December.
    2. Julie K. Furberg & Per K. Andersen & Sofie Korn & Morten Overgaard & Henrik Ravn, 2023. "Bivariate pseudo-observations for recurrent event analysis with terminal events," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(2), pages 256-287, April.
    3. Xiaowei Sun & Jieli Ding & Liuquan Sun, 2020. "A semiparametric additive rates model for the weighted composite endpoint of recurrent and terminal events," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(3), pages 471-492, July.
    4. Xiaoyu Wang & Liuquan Sun, 2023. "Joint modeling of generalized scale-change models for recurrent event and failure time data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(1), pages 1-33, January.
    5. Qing Pan & Douglas E. Schaubel, 2009. "Flexible Estimation of Differences in Treatment-Specific Recurrent Event Means in the Presence of a Terminating Event," Biometrics, The International Biometric Society, vol. 65(3), pages 753-761, September.
    6. Miao Han & Liuquan Sun & Yutao Liu & Jun Zhu, 2018. "Joint analysis of recurrent event data with additive–multiplicative hazards model for the terminal event time," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 81(5), pages 523-547, July.
    7. Debashis Ghosh, 2003. "Goodness-of-Fit Methods for Additive-Risk Models in Tumorigenicity Experiments," Biometrics, The International Biometric Society, vol. 59(3), pages 721-726, September.
    8. Xin Chen & Jieli Ding & Liuquan Sun, 2018. "A semiparametric additive rate model for a modulated renewal process," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 24(4), pages 675-698, October.
    9. Tianmeng Lyu & Björn Bornkamp & Guenther Mueller‐Velten & Heinz Schmidli, 2023. "Bayesian inference for a principal stratum estimand on recurrent events truncated by death," Biometrics, The International Biometric Society, vol. 79(4), pages 3792-3802, December.
    10. C.-Y. Huang & J. Qin & M.-C. Wang, 2010. "Semiparametric Analysis for Recurrent Event Data with Time-Dependent Covariates and Informative Censoring," Biometrics, The International Biometric Society, vol. 66(1), pages 39-49, March.
    11. Jianguo Sun & Xingwei Tong & Xin He, 2007. "Regression Analysis of Panel Count Data with Dependent Observation Times," Biometrics, The International Biometric Society, vol. 63(4), pages 1053-1059, December.
    12. D. Y. Lin & L. J. Wei & Z. Ying, 2002. "Model-Checking Techniques Based on Cumulative Residuals," Biometrics, The International Biometric Society, vol. 58(1), pages 1-12, March.
    13. Sankaran, P.G. & Anisha, P., 2012. "Additive hazards models for gap time data with multiple causes," Statistics & Probability Letters, Elsevier, vol. 82(7), pages 1454-1462.
    14. Gang Cheng & Ying Zhang & Liqiang Lu, 2011. "Efficient algorithms for computing the non and semi-parametric maximum likelihood estimates with panel count data," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 23(2), pages 567-579.
    15. Chu-Chih Chen & , Chuan-Pin Lee & Yuan-Horng Yan & Tsun-Jen Cheng & Pranab K. Sen, 2021. "A partial likelihood-based two-dimensional multistate markov model with application to myocardial infarction and stroke recurrence," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(2), pages 282-303, November.
    16. Li, Yang & Zhao, Hui & Sun, Jianguo & Kim, KyungMann, 2014. "Nonparametric tests for panel count data with unequal observation processes," Computational Statistics & Data Analysis, Elsevier, vol. 73(C), pages 103-111.
    17. Kwun Chuen Gary Chan & Mei-Cheng Wang, 2017. "Semiparametric Modeling and Estimation of the Terminal Behavior of Recurrent Marker Processes Before Failure Events," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(517), pages 351-362, January.
    18. Yevkin, Alexander & Krivtsov, Vasiliy, 2020. "A generalized model for recurrent failures prediction," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    19. Zahra Mansourvar & Torben Martinussen & Thomas H. Scheike, 2016. "An Additive–Multiplicative Restricted Mean Residual Life Model," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(2), pages 487-504, June.
    20. Chin-Tsang Chiang & Mei-Cheng Wang, 2009. "Varying-coefficient model for the occurrence rate function of recurrent events," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 61(1), pages 197-213, March.

    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:16:y:2019:i:7:p:1158-:d:218688. 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.