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Comparison of methods to identify long term care nursing home residence with administrative data

BACKGROUND: To compare different methods for identifying a long term care (LTC) nursing home stay, distinct from stays in skilled nursing facilities (SNFs), to the method currently used by the Center for Medicare and Medicaid Services (CMS). We used national and Texas Medicare claims, Minimum Data S...

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Autores principales: Goodwin, James S., Li, Shuang, Zhou, Jie, Graham, James E., Karmarkar, Amol, Ottenbacher, Kenneth
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5450097/
https://www.ncbi.nlm.nih.gov/pubmed/28558756
http://dx.doi.org/10.1186/s12913-017-2318-9
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author Goodwin, James S.
Li, Shuang
Zhou, Jie
Graham, James E.
Karmarkar, Amol
Ottenbacher, Kenneth
author_facet Goodwin, James S.
Li, Shuang
Zhou, Jie
Graham, James E.
Karmarkar, Amol
Ottenbacher, Kenneth
author_sort Goodwin, James S.
collection PubMed
description BACKGROUND: To compare different methods for identifying a long term care (LTC) nursing home stay, distinct from stays in skilled nursing facilities (SNFs), to the method currently used by the Center for Medicare and Medicaid Services (CMS). We used national and Texas Medicare claims, Minimum Data Set (MDS), and Texas Medicaid data from 2011-2013. METHODS: We used Medicare Part A and B and MDS data either alone or in combination to identify LTC nursing home stays by three methods. One method used Medicare Part A and B data; one method used Medicare Part A and MDS data; and the current CMS method used MDS data alone. We validated each method against Texas 2011 Medicare-Medicaid linked data for those with dual eligibility. RESULTS: Using Medicaid data as a gold standard, all three methods had sensitivities > 92% to identify LTC nursing home stays of more than 100 days in duration. The positive predictive value (PPV) of the method that used both MDS and Medicare Part A data was 84.65% compared to 78.71% for the CMS method and 66.45% for the method using Part A and B Medicare. When the patient population was limited to those who also had a SNF stay, the PPV for identifying LTC nursing home was highest for the method using Medicare plus MDS data (88.1%). CONCLUSIONS: Using both Medicare and MDS data to identify LTC stays will lead to more accurate attribution of CMS nursing home quality indicators. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12913-017-2318-9) contains supplementary material, which is available to authorized users.
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spelling pubmed-54500972017-06-01 Comparison of methods to identify long term care nursing home residence with administrative data Goodwin, James S. Li, Shuang Zhou, Jie Graham, James E. Karmarkar, Amol Ottenbacher, Kenneth BMC Health Serv Res Research Article BACKGROUND: To compare different methods for identifying a long term care (LTC) nursing home stay, distinct from stays in skilled nursing facilities (SNFs), to the method currently used by the Center for Medicare and Medicaid Services (CMS). We used national and Texas Medicare claims, Minimum Data Set (MDS), and Texas Medicaid data from 2011-2013. METHODS: We used Medicare Part A and B and MDS data either alone or in combination to identify LTC nursing home stays by three methods. One method used Medicare Part A and B data; one method used Medicare Part A and MDS data; and the current CMS method used MDS data alone. We validated each method against Texas 2011 Medicare-Medicaid linked data for those with dual eligibility. RESULTS: Using Medicaid data as a gold standard, all three methods had sensitivities > 92% to identify LTC nursing home stays of more than 100 days in duration. The positive predictive value (PPV) of the method that used both MDS and Medicare Part A data was 84.65% compared to 78.71% for the CMS method and 66.45% for the method using Part A and B Medicare. When the patient population was limited to those who also had a SNF stay, the PPV for identifying LTC nursing home was highest for the method using Medicare plus MDS data (88.1%). CONCLUSIONS: Using both Medicare and MDS data to identify LTC stays will lead to more accurate attribution of CMS nursing home quality indicators. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12913-017-2318-9) contains supplementary material, which is available to authorized users. BioMed Central 2017-05-30 /pmc/articles/PMC5450097/ /pubmed/28558756 http://dx.doi.org/10.1186/s12913-017-2318-9 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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.
spellingShingle Research Article
Goodwin, James S.
Li, Shuang
Zhou, Jie
Graham, James E.
Karmarkar, Amol
Ottenbacher, Kenneth
Comparison of methods to identify long term care nursing home residence with administrative data
title Comparison of methods to identify long term care nursing home residence with administrative data
title_full Comparison of methods to identify long term care nursing home residence with administrative data
title_fullStr Comparison of methods to identify long term care nursing home residence with administrative data
title_full_unstemmed Comparison of methods to identify long term care nursing home residence with administrative data
title_short Comparison of methods to identify long term care nursing home residence with administrative data
title_sort comparison of methods to identify long term care nursing home residence with administrative data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5450097/
https://www.ncbi.nlm.nih.gov/pubmed/28558756
http://dx.doi.org/10.1186/s12913-017-2318-9
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