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Determinants of Inappropriate Admissions in County Hospitals in Rural China: A Cross-Sectional Study

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  • Yan Zhang

    (School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
    Research Centre for Rural Health Service, Key Research Institute of Humanities & Social Sciences of Hubei Provincial Department of Education, Wuhan 430030, China)

  • Liang Zhang

    (School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
    Research Centre for Rural Health Service, Key Research Institute of Humanities & Social Sciences of Hubei Provincial Department of Education, Wuhan 430030, China)

  • Haomiao Li

    (School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
    Research Centre for Rural Health Service, Key Research Institute of Humanities & Social Sciences of Hubei Provincial Department of Education, Wuhan 430030, China)

  • Yingchun Chen

    (School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
    Research Centre for Rural Health Service, Key Research Institute of Humanities & Social Sciences of Hubei Provincial Department of Education, Wuhan 430030, China)

Abstract

Inappropriate admissions have contributed to the rapid increase in hospitalisations in rural China. This study characterised the degree and determinants of inappropriate admissions in county hospitals. We used expert consultation to develop an appropriateness evaluation protocol that included nine requirements for services and 21 indicators of disease severity. A total of 2230 medical records from 2014 were collected from five county hospitals by stratified cluster sampling and evaluated for appropriateness using the protocol in 2016. The determinants of inappropriate admissions were analysed by two-level logistic regression. The overall inappropriate admission rate was 15.2%. Patients aged <20 years (19.3%), patients in the paediatrics department (22.9%), patients with lower disease severity (22.3%), and patients without complications (17.0%) were more likely to have been inappropriately admitted than other groups. Age, treating department, disease severity, causes of hospitalisation, complications, and length of stay were determinants of inappropriate admission. Policymakers must act to reduce the high prevalence of inappropriate admissions in county hospitals in rural China, by guiding patients to seek primary care and changing the motivating mechanism of these hospitals.

Suggested Citation

  • Yan Zhang & Liang Zhang & Haomiao Li & Yingchun Chen, 2018. "Determinants of Inappropriate Admissions in County Hospitals in Rural China: A Cross-Sectional Study," IJERPH, MDPI, vol. 15(6), pages 1-10, May.
    • Handle: RePEc:gam:jijerp:v:15:y:2018:i:6:p:1050-:d:148438
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    1. N. T. Longford, 1997. "Letter to the Editors: Improved Approximations for Multilevel Models with Binary Responses," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 160(3), pages 593-593, September.
    2. McClain, J.O. & Riedel, D.C., 1973. "Screening for utilization review: on the use of explicit criteria and non-physicians in case selection," American Journal of Public Health, American Public Health Association, vol. 63(3), pages 247-251.
    3. McDonagh, Marian S. & Smith, David H. & Goddard, Maria, 2000. "Measuring appropriate use of acute beds: A systematic review of methods and results," Health Policy, Elsevier, vol. 53(3), pages 157-184, October.
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    Cited by:

    1. Qing Ye & Yan Zhang & Hong-xia Gao & Ying-chun Chen & Hao-miao Li & Hui Zhang & Xiao-mei Hu & Shi-han Lei & Di Jiang, 2019. "Distribution of the Indicator of the Appropriate Admission of Patients with Circulatory System Diseases to County Hospitals in Rural China: A Cross-Sectional Study," IJERPH, MDPI, vol. 16(9), pages 1-13, May.

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