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Objective: Deprescribing opportunities may differ across health care systems, nursing home settings, and prescribing cultures. The objective of this study was to compare the prevalence of STOPPFrail medications according to frailty status among residents of nursing homes in Australia, China, Japan, and Spain., Design: Secondary cross-sectional analyses of data from 4 cohort studies., Setting and Participants: A total of 1142 residents in 31 nursing homes., Methods: Medication data were extracted from resident records. Frailty was assessed using the FRAIL-NH scale (non-frail 0-2; frail 3-6; most-frail 7-14). Chi-square tests and prevalence ratios (PRs) were used to compare STOPPFrail medication use across cohorts., Results: In total, 84.7% of non-frail, 95.6% of frail, and 90.6% of most-frail residents received ≥1 STOPPFrail medication. Overall, the most prevalent STOPPFrail medications were antihypertensives (53.0% in China to 73.3% in Australia, P < .001), vitamin D (nil in China to 52.7% in Australia, P < .001), lipid-lowering therapies (11.1% in Japan to 38.9% in Australia, P < .001), aspirin (13.5% in Japan to 26.2% in China, P < .001), proton pump inhibitors (2.1% in Japan to 32.0% in Australia, P < .001), and antidiabetic medications (12.3% in Japan to 23.5% in China, P = .010). Overall use of antihypertensives (PR, 1.15; 95% CI, 1.06-1.25), lipid-lowering therapies (PR, 1.78; 95% CI, 1.45-2.18), aspirin (PR, 1.31; 95% CI, 1.04-1.64), and antidiabetic medications (PR, 1.31; 95% CI, 1.00-1.72) were more prevalent among non-frail and frail residents compared with most-frail residents. Antihypertensive use was more prevalent with increasing frailty in China and Japan, but less prevalent with increasing frailty in Australia. Antidiabetic medication use was less prevalent with increasing frailty in China and Spain but was consistent across frailty groups in Australia and Japan., Conclusions and Implications: There were overall and frailty-specific variations in prevalence of different STOPPFrail medications across cohorts. This may reflect differences in prescribing cultures, application of clinical practice guidelines in the nursing home setting, and clinician or resident attitudes toward deprescribing., Competing Interests: Disclosure Australia: J.S.B. has received grant funding or consulting funds from the NHMRC, Medical Research Future Fund, Victorian Government Department of Health and Human Services, Dementia Australia Research Foundation, Yulgilbar Foundation, Aged Care Quality and Safety Commission, Dementia Centre for Research Collaboration, Pharmaceutical Society of Australia, Society of Hospital Pharmacists of Australia, GlaxoSmithKline Supported Studies Programme, Amgen, and several aged care provider organizations unrelated to this work. All grants and consulting funds were paid to the employing institution. R.V. was previously a board member and part of the clinical governance committee of Resthaven Incorporated. R.V. is co-founder and chair of the clinical advisory group for a wearable sensor technology start-up HealthyVibes.ai. China: None. Japan: S.H. belongs to an endowed chair funded by donations from Hakue technology, PROUMED, Japan Bio Products, Towa Pharmaceutical, Yellow Eight and Sugi Holdings. S.H. received research funding from SOMPO Care Incorporated outside of this work. Spain: N.M.V. has received grants and consulting funds from UCB Biopharma and Vegenta., (Copyright © 2024 AMDA – The Society for Post-Acute and Long-Term Care Medicine. Published by Elsevier Inc. All rights reserved.)

Objectives: Excessive daytime sleepiness is an increasingly frequent condition among older adults with comorbidities and living in nursing homes (NHs). This study investigated associations between participants' characteristics and excessive daytime sleepiness (EDS); the ability of the Epworth Sleepiness Scale (ESS) scores, EDS, and EDS severity levels to predict mortality at 12 months of follow-up; and the optimal cut-off for ESS to predict mortality among NH residents., Design: Prospective and cross-sectional analysis in a prospective study., Setting and Participants: Older adults permanently residing in 12 NHs from South Australia., Methods: Baseline characteristics including the ESS were collected and mortality at 12 months was assessed. Logistic regression analyzed associations between participants' characteristics and EDS (ESS >10). Kaplan-Meier cumulative survival estimates followed by log-rank and adjusted Cox proportional hazards models explored associations of ESS scores, EDS, and EDS severity levels with time-to-incident death. Receiver operator curve analysis assessed the best cut-off for ESS to predict mortality risk., Results: A total of 550 participants [mean (SD) age, 87.7 (7.2) years; 968 (50.9%) female]. Malnutrition [adjusted odds ratio (aOR) 2.02, 95% confidence interval (CI) 1.13‒3.61], myocardial infarction (aOR 1.91, 95% CI 1.20‒3.03), heart failure (aOR 2.85, 95% CI 1.68‒4.83), Parkinson's disease (aOR 2.16, 95% CI 1.04‒4.47) and severe dementia (aOR 8.57, 95% CI 5.25‒14.0) were associated with EDS. Kaplan-Meier analyses showed reduced survival among participants with EDS (log-rank test: χ 2  = 25.25, P < .001). EDS predicted increased mortality risk (HR 1.63, 95% CI 1.07-2.51, P = .023). ESS score of 10.5 (>10) was the best cut point predicting mortality risk (area under the curve = 0.62)., Conclusions and Implications: EDS predicts mortality risk and is associated with age-related comorbidities in NH residents. Screening for EDS is a simple strategy to identify NH residents at higher risk of adverse outcomes, triggering an assessment for reversibility or conversations about end-of-life care., (Copyright © 2023 AMDA – The Society for Post-Acute and Long-Term Care Medicine. Published by Elsevier Inc. All rights reserved.)