{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T19:18:54Z","timestamp":1775071134875,"version":"3.50.1"},"reference-count":56,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2021,11,17]],"date-time":"2021-11-17T00:00:00Z","timestamp":1637107200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100000049","name":"National Institute on Aging","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Health Services Research"],"published-print":{"date-parts":[[2022,6]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Objective<\/jats:title><jats:p>Nursing homes (NHs) are serving an increasing proportion of residents with cognitive issues (e.g., dementia) and mental health conditions. This study aims to: (1) implement unsupervised machine learning to cluster NHs based on residents' dementia and mental health conditions; (2) examine NH staffing related to the clusters; and (3) investigate the association of staffing and NH quality (measured by the number of deficiencies and deficiency scores) in each cluster.<\/jats:p><\/jats:sec><jats:sec><jats:title>Data sources<\/jats:title><jats:p>2009\u20132017 Certification and Survey Provider Enhanced Reporting (CASPER) were merged with LTCFocUS.org data on NHs in the United States.<\/jats:p><\/jats:sec><jats:sec><jats:title>Study design<\/jats:title><jats:p>Unsupervised machine learning algorithm (<jats:italic>K<\/jats:italic>\u2010means) clustered NHs based on percent residents with dementia, depression, and serious mental illness (SMI, e.g., schizophrenia, anxiety). Panel fixed\u2010effects regressions on deficiency outcomes with staffing\u2010cluster interactions were conducted to examine the effects of staffing on deficiency outcomes in each cluster.<\/jats:p><\/jats:sec><jats:sec><jats:title>Data extraction methods<\/jats:title><jats:p>We identified 110,463 NH\u2010year observations from 14,671 unique NHs using CASPER data.<\/jats:p><\/jats:sec><jats:sec><jats:title>Principal findings<\/jats:title><jats:p>Three clusters were identified: low dementia and mental illnesses (Postacute Cluster); high dementia and depression, but low SMI (Long\u2010stay Cluster); and high dementia and mental illnesses (Cognitive\u2010mental Cluster). From 2009 to 2017, the number of Postacute Cluster NHs increased from 3074 to 5719, while the number of Long\u2010stay Cluster NHs decreased from 6745 to 3058. NHs in Long\u2010stay\/Cognitive\u2010mental Clusters reported slightly lower nursing staff hours in 2017. Regressions suggested the effect of increasing staffing on reducing deficiencies is statistically similar across NH clusters. For example, 1\u2009hour increase in registered nurse hours per resident day was associated with \u22120.67 (standard error [SE] =\u20090.11), \u22120.88 (SE\u00a0=\u00a00.12), and \u22120.97 (SE\u00a0=\u00a00.15) deficiencies in Postacute Cluster, Long\u2010stay Cluster, and Cognitive\u2010mental Cluster, respectively.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Unsupervised machine learning detected a changing landscape of NH serving residents with dementia and mental illnesses, which requires assuring staffing levels and trainings are suited to residents' needs.<\/jats:p><\/jats:sec>","DOI":"10.1111\/1475-6773.13908","type":"journal-article","created":{"date-parts":[[2021,11,8]],"date-time":"2021-11-08T05:57:43Z","timestamp":1636351063000},"page":"505-514","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Changing landscape of nursing homes serving residents with dementia and mental illnesses"],"prefix":"10.1111","volume":"57","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4033-0659","authenticated-orcid":false,"given":"Huiwen","family":"Xu","sequence":"first","affiliation":[{"name":"Department of Preventive Medicine and Population Health University of Texas Medical Branch  Galveston Texas USA"},{"name":"Sealy Center on Aging University of Texas Medical Branch  Galveston Texas USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Orna","family":"Intrator","sequence":"additional","affiliation":[{"name":"Department of Public Health Sciences University of Rochester School of Medicine and Dentistry  Rochester New York USA"},{"name":"Geriatrics &amp; Extended Care Data Analysis Center (GECDAC) Canandaigua VA Medical Center  Canandaigua New York USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eva","family":"Culakova","sequence":"additional","affiliation":[{"name":"Department of Surgery, Cancer Control University of Rochester School of Medicine and Dentistry  Rochester New York USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4776-4182","authenticated-orcid":false,"given":"John R.","family":"Bowblis","sequence":"additional","affiliation":[{"name":"Department of Economics Farmer School of Business, Miami University  Oxford Ohio USA"},{"name":"Scripps Gerontology Center Miami University  Oxford Ohio USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,11,17]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s40520\u2010014\u20100210\u20106"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1097\/MLR.0000000000001246"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jamda.2019.01.153"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1111\/1475\u20106773.13356"},{"key":"e_1_2_8_6_1","volume-title":"Medicaid Expenditures for Long\u2010Term Services and Supports (LTSS) in FY 2015","author":"Eiken S","year":"2017"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1111\/jgs.16080"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1093\/geront\/gnaa044"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1377\/hlthaff.28.3.689"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jamda.2019.03.006"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1111\/jgs.16017"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1177\/1077558707308754"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.5120\/11430-6785"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1097\/CIN.0000000000000423"},{"key":"e_1_2_8_15_1","unstructured":"ShailajaK SeetharamuluB JabbarM.Machine learning in healthcare. 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