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    The impact of payer status on hospital admissions: evidence from an academic medical center

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    © The Author(s). 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate
credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were
made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless
indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your
intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly
from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative
Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made
available in this article, unless otherwise stated in a credit line to the data.
    Date Issued
    2021-09-07
    Publisher Version
    10.1186/s12913-021-06886-3
    Author(s)
    Zhao, Yanying
    Paschalidis, Ioannis C.
    Hu, Jianqiang
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    Permanent Link
    https://hdl.handle.net/2144/44401
    Version
    Published version
    Citation (published version)
    Y. Zhao, I.C. Paschalidis, J. Hu. 2021. "The impact of payer status on hospital admissions: evidence from an academic medical center." BMC Health Serv Res, Volume 21, Issue 1: 930. https://doi.org/10.1186/s12913-021-06886-3
    Abstract
    BACKGROUND: There are plenty of studies investigating the disparity of payer status in accessing to care. However, most studies are either disease-specific or cohort-specific. Quantifying the disparity from the level of facility through a large controlled study are rare. This study aims to examine how the payer status affects patient hospitalization from the perspective of a facility. METHODS: We extracted all patients with visiting record in a medical center between 5/1/2009-4/30/2014, and then linked the outpatient and inpatient records three year before target admission time to patients. We conduct a retrospective observational study using a conditional logistic regression methodology. To control the illness of patients with different diseases in training the model, we construct a three-dimension variable with data stratification technology. The model is validated on a dataset distinct from the one used for training. RESULTS: Patients covered by private insurance or uninsured are less likely to be hospitalized than patients insured by government. For uninsured patients, inequity in access to hospitalization is observed. The value of standardized coefficients indicates that government-sponsored insurance has the greatest impact on improving patients' hospitalization. CONCLUSION: Attention is needed on improving the access to care for uninsured patients. Also, basic preventive care services should be enhanced, especially for people insured by government. The findings can serve as a baseline from which to measure the anticipated effect of measures to reduce disparity of payer status in hospitalization.
    Rights
    © The Author(s). 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
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    • ENG: Electrical and Computer Engineering: Scholarly Papers [376]
    • BU Open Access Articles [4757]


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