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

Author

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  • 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
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    References listed on IDEAS

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    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.
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