Construction and Validation of Risk-adjusted Rates of Emergency Department Visits for Long-stay Nursing Home Residents

Xu, Huiwen; Bowblis, John R.; Li, Yue; Caprio, Thomas V.; Intrator, Orna

doi:10.1097/mlr.0000000000001246

Cited by 14 publications

(67 citation statements)

References 30 publications

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“…Covariates associated with potential shortages included COVID-19 factors, NH and market characteristics, and state policy relating to NHs. 4,9,15,32,33 COVID-19 factors included the cumulative number of residents and staff diagnosed with COVID-19 per 100 beds. We scaled the number of cases to 100 beds to account for differences in facility size.…”

Section: Covariatesmentioning

confidence: 99%

Shortages of Staff in Nursing Homes During the COVID-19 Pandemic: What are the Driving Factors?

Intrator

Bowblis

2020

Journal of the American Medical Directors Association

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(300 words) Objectives During the COVID-19 pandemic, U.S. nursing homes (NHs) have been under pressure to maintain staff levels with limited access to personal protection equipment (PPE). This study examines the prevalence and factors associated with shortages of NH staff during COVID-19 pandemic. Design We obtained self-reported information on staff shortages, resident and staff exposure to COVID-19, and PPE availability from a survey conducted by the Centers for Medicare & Medicaid Services in May 2020. Multivariate logistic regressions of staff shortages with state fixed-effects were conducted to examine the effect of COVID-19 factors in NHs. Setting and participants 11,920 free-standing NHs. Measures The dependent variables were self-reported shortages of licensed nurse staff, nurse aides, clinical staff, and other ancillary staff. We controlled for NH characteristics from the most recent Nursing Home Compare and Certification And Survey Provider Enhanced Reporting, market characteristics from Area Health Resources File, and state Medicaid reimbursement calculated from Truven data. Results Of the 11,920 NHs, 15.9%, 18.4%, 2.5%, and 9.8% reported shortages of licensed nurse staff, nurse aides, clinical staff, and other staff, respectively. Georgia and Minnesota reported the highest rates of shortages in licensed nurse and nurse aides (both > 25%). Multivariate regressions suggest that shortages in licensed nurses and nurse aides were more likely in NHs having any resident with COVID-19 (adjusted odds ratio (AOR) = 1.44, 1.60, respectively) and any staff with COVID-19 (AOR = 1.37, 1.34, respectively). Having one-week supply of PPE was associated with lower probability of staff shortages. NHs with a higher proportion of Medicare residents were less likely to experience shortages. Conclusions /Implications: Abundant staff shortages were reported by NHs and were mainly driven by COVID-19 factors. In the absence of appropriate staff, NHs may be unable to fulfill the requirement of infection control even under the risk of increased monetary penalties.

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Section: Covariatesmentioning

confidence: 99%

Shortages of Staff in Nursing Homes During the COVID-19 Pandemic: What are the Driving Factors?

Intrator

Bowblis

2020

Journal of the American Medical Directors Association

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150

119

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“…For NHs which continue to admit residents with SMI, only a few staff are needed on some dimensions such as physical functioning, but they need staff that are more specialized in behavioral health. Currently, NHs reported very low mental health staffing hours (40% did not have any mental health staff, and the average mental health HPRD was <0.01) 2,10 . Policymakers should support NHs to recruit and retrain the staff with behavioral health training.…”

Section: Discussionmentioning

confidence: 99%

“…NH‐level data were obtained from the 2009 to 2017 Certification and Survey Provider Enhanced Reporting (CASPER) data linked with LTCFocUS.org data. CASPER provides data collected as part of initial and annual recertification inspections of all Medicare and Medicaid certified NHs, with these inspections occurring every 9–15 months 2,28,38 . CASPER captures detailed information of each facility's structure, resident case‐mix, and payer‐mix, staffing, and deficiency citations.…”

Section: Methodsmentioning

confidence: 99%

“…The net result of both of these trends is NHs are now serving greater proportions of residents with dementia and SMI. 2,[6][7][8][9][10] About 60%-90% of NH residents have dementia and/or mental health conditions, 6,[9][10][11] and 64% and 19% of long-stay NH residents reported depression and SMI, respectively. 2 While the high prevalence of dementia and mental health conditions in NHs has been generally recognized, it remains unclear whether NHs have specialized in care for residents with dementia, mental health illness, or both.…”

Section: Introductionmentioning

confidence: 99%

“…2,18 Government-owned NHs (N = 6109) were further removed as their ownership structure might lead to different NH behavior. 2,18 NHs with aberrant staffing levels (e.g., >24 h per resident day [HPRD] or zero staff, staffing hours were beyond four standard deviations of the population average) 7 (N = 8237) were also excluded. The final NH cohort included 110,463 freestanding, privately owned NH-year observations from 14,671 unique NHs.…”

Section: Introductionmentioning

confidence: 99%

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Changing landscape of nursing homes serving residents with dementia and mental illnesses

Intrator

Culakova

et al. 2021

Health Services Research

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Objective 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. Data sources 2009–2017 Certification and Survey Provider Enhanced Reporting (CASPER) were merged with LTCFocUS.org data on NHs in the United States. Study design Unsupervised machine learning algorithm (K‐means) clustered NHs based on percent residents with dementia, depression, and serious mental illness (SMI, e.g., schizophrenia, anxiety). Panel fixed‐effects regressions on deficiency outcomes with staffing‐cluster interactions were conducted to examine the effects of staffing on deficiency outcomes in each cluster. Data extraction methods We identified 110,463 NH‐year observations from 14,671 unique NHs using CASPER data. Principal findings Three clusters were identified: low dementia and mental illnesses (Postacute Cluster); high dementia and depression, but low SMI (Long‐stay Cluster); and high dementia and mental illnesses (Cognitive‐mental Cluster). From 2009 to 2017, the number of Postacute Cluster NHs increased from 3074 to 5719, while the number of Long‐stay Cluster NHs decreased from 6745 to 3058. NHs in Long‐stay/Cognitive‐mental 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 hour increase in registered nurse hours per resident day was associated with −0.67 (standard error [SE] = 0.11), −0.88 (SE = 0.12), and −0.97 (SE = 0.15) deficiencies in Postacute Cluster, Long‐stay Cluster, and Cognitive‐mental Cluster, respectively. Conclusions 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.

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Immobilität als Grund der Vorstellung in einer Krankenhausnotaufnahme?

Walter

Faust

Wenske

et al. 2022

Notfall Rettungsmed

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Zusammenfassung Hintergrund Die zunehmende Belastung von Krankenhausnotaufnahmen (KHN) durch wenig dringliche Patienten wirft Fragen nach Fehlsteuerungen bzw. Versorgungsdefiziten in anderen Bereichen auf. Methode Retrospektive Datenanalyse einer Zufallsstichprobe aus allen Fällen des Jahres 2019 zweier KHN in einer Metropolregion. Wir untersuchten Patienten, die ambulant versorgt, aber mit einem Krankentransportmittel (KT) wieder aus der Notaufnahme abtransportiert wurden. Ergebnisse 1500 Patienten (767 [51,1 %] weiblich; Alter 56 ± 22,7 Jahre) aus einer Grundgesamtheit von 80.845 Patienten wurden analysiert. 888 (59,2 %) wurden ambulant versorgt. Jeweils 9 (0,5 %), 193 (12,9 %), 684 (45,6 %), 508 (33,9 %) und 48 (3,2 %) waren den Manchester-Triage-System-Kategorien Rot, Orange, Gelb, Grün bzw. Blau zugeordnet. Von 880 ambulanten Patienten wurden 731 (83,2 %) selbstständig und 149 (16,8 %) mit einem KT entlassen. Über die Hälfte der per KT entlassenen Patienten war mit urologischen Problemen, Extremitätenproblemen und nach Stürzen vorgestellt worden. Die liegend transportierten ambulant versorgten Patienten hatten ein höheres Alter (76,2 ± 16,2 vs. 45,1 ± 20,5 Jahre; p < 0,001), einen höheren Charlson Comorbidity Index (5 [3–6] vs. 0 [0–2]; p < 0,001), erhielten seltener eine CT-Diagnostik und häufiger lediglich eine einfache medizinische Maßnahme ohne diagnostischen Aufwand (24,8 % vs. 4,6 %; p < 0,001). Sie wurden zu einem größeren Anteil mehrfach vorgestellt (28,9 % vs. 8,5 %; p < 0,001). Der Anteil an Heimbewohnern war höher (59,5 % vs. 0 %; p < 0,001). Schlussfolgerung Etwa ein Zehntel der insgesamt in den KHN versorgten Patienten blieb ambulant und wurde mit einem KT entlassen. Mehr als die Hälfte wurde aus stationären Pflegeeinrichtungen vorgestellt. Ein Großteil erhielt einfache Leistungen und kaum Diagnostik und hatte eine niedrige Triagedringlichkeit.

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Construction and Validation of Risk-adjusted Rates of Emergency Department Visits for Long-stay Nursing Home Residents

Cited by 14 publications

References 30 publications

Shortages of Staff in Nursing Homes During the COVID-19 Pandemic: What are the Driving Factors?

Shortages of Staff in Nursing Homes During the COVID-19 Pandemic: What are the Driving Factors?

Changing landscape of nursing homes serving residents with dementia and mental illnesses

Immobilität als Grund der Vorstellung in einer Krankenhausnotaufnahme?

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