Background With the older adult population in the USA increasing, so is the population of those with Alzheimer's disease and related dementias (ADRD). Older adults are vulnerable to the effects of potentially inappropriate medications as established by the Beers Criteria; however, some medications continue to be prescribed against recommendations. Objectives Our objectives were to describe potentially inappropriate medication (PIM) use linked to cognitive impairment or decline (referred to as Cog-PIM) in older adults with and without ADRD and to investigate whether the odds of Cog-PIM report differ by ADRD status in ambulatory care (i.e., outpatient care) in the USA. Methods A cross-sectional analysis was performed using a nationally representative sample of non-perioperative, officebased ambulatory care visits by adults aged ≥ 65 years in 2016 (n = 218,182,131). Data were collected from the National Ambulatory Medical Care Survey. Cog-PIMs were identified as defined in the 2015 Beers Criteria recommendations for medications that may be potentially inappropriate in older adults with cognitive impairment or dementia. ADRD status was determined by clinician report using free text, the ADRD flag, or the presence of a diagnosis code indicating dementia. Multivariable logistic regressions were used to estimate the odds of Cog-PIM use overall and by medication class. Results In 2016, 2.1% (n = 4,651,563) of outpatient visits were made by older adults with ADRD, 33.2% of which reported at least one Cog-PIM. Anticholinergic Cog-PIMs were noted in 20.5% of ADRD visits compared with 8.1% of non-ADRD visits. Antipsychotic PIMs were noted in 15.5% of ADRD visits compared with 0.8% of non-ADRD visits. Benzodiazepine and non-benzodiazepine receptor agonist hypnotic (Z drug) Cog-PIMs were reported in 10.9% of ADRD visits and 10.7% of non-ADRD visits. ADRD status was a significant predictor of Cog-PIM report overall (adjusted odds ratio [aOR] 2.74 [95% confidence interval {CI} 1.20-6.27]) and for anticholinergics and antipsychotics specifically (aOR 3.35 [95% CI 1.24-9.03] and aOR 22.80 [95% CI 5.80-89.50], respectively). Conclusion This study demonstrated a high prevalence of Cog-PIM use and increased odds of Cog-PIM use in older adults with ADRD. Future work should investigate opportunities in the ambulatory care setting for safer prescribing and deescalation of Cog-PIMs.

Objectives: During the last quarter of 2020ddespite improved distribution of personal protective equipment (PPE) and knowledge of COVID-19 managementdnursing homes experienced the greatest increases in cases and deaths since the pandemic's beginning. We sought to update COVID-19 estimates of cases, hospitalization, and mortality and to evaluate the association of potentially modifiable facilitylevel infection control factors on odds and magnitude of COVID-19 cases, hospitalizations, and deaths in nursing homes during the third surge of the pandemic. Design: Cross-sectional analysis. Setting and Participants: Facility-level data from 13,156 US nursing home facilities. Methods: Two series of multivariable logistic regression and generalized linear models to examine the association of infection control factors (personal protective equipment and staffing) on incidence and magnitude, respectively, of confirmed COVID-19 cases, hospitalizations, and deaths in nursing home residents reported in the last quarter of 2020. Results: Nursing homes experienced steep increases in COVID-19 cases, hospitalizations, and deaths during the final quarter of 2020. Four-fifths (80.51%; n ¼ 10,592) of facilities reported at least 1 COVID-19 case, 49.44% (n ¼ 6504) reported at least 1 hospitalization, and 49.76% (n ¼ 6546) reported at least 1 death during this third surge. N95 mask shortages were associated with increased odds of at least 1 COVID-19 case [odds ratio (OR) 1.21, 95% confidence interval (CI) 1.05-1.40] and hospitalization (OR 1.26, 95% CI 1.13-1.40), as well as larger numbers of hospitalizations (OR 1.11,. Nursing aide shortages were associated with lower odds of at least 1 COVID-19 death (OR 1.22, 95% CI 1.12-1.34) and higher hospitalizations (OR 1.09, 95% CI 1.01-1.17). The number of nursing hours per resident per day was largely insignificant across all outcomes. Of note, smaller (<50-bed) and midsized (50-to 150-bed) facilities had lower odds yet higher magnitude of all COVID outcomes. Bed occupancy rates >75% increased odds of experiencing a COVID-19 case (OR 1.48, 95% CI 1.35-1.62) or death (OR 1.25, 95% CI 1.17-1.34). Conclusions and Implications: Adequate staffing and PPEdalong with reduced occupancy and smaller facilitiesdmitigate incidence and magnitude of COVID-19 cases and sequelae. Addressing shortcomings in these factors is critical to the prevention of infections and adverse health consequences of a next surge among vulnerable nursing home residents.

Background Anticholinergic medications have been consistently linked to cognitive impairment or decline, resulting in their classification as potentially inappropriate medications for older adults according to the Beers Criteria. Despite their potential for adverse effects, these medications continue to be used for individuals regardless of their cognitive status and concurrent medications. Here, we investigated risk factors for anticholinergic medication use in populations with and without Alzheimer’s disease or related dementias (ADRD). For patients with ADRD, we also investigated whether anticholinergic use may be interacting with memory‐enhancing medication therapy. Methods We conducted a cross‐sectional study using nationally representative data from the 2016 National Ambulatory Medical Care Survey. We included non‐perioperative office‐based visits for patients ≥ 65 years old. Visit characteristics were compared between those with and without ADRD using descriptive statistics; adjusted logistic regression was used to identify predictors of anticholinergic use. Results Of 218,182,131 outpatient visits, 2.2% were for patients with ADRD (N=4,651,563). 20.5% of visits for patients with ADRD reported an anticholinergic, compared to 8.1% of visits for patients without ADRD. The most common anticholinergics reported for ADRD visits were cyclobenzaprine (25.3%), benztropine (12.8%), and meclizine (12.2%). Significant predictors of anticholinergic report (aOR [95% CI]) were ADRD status (3.1 [1.2‐8.3]), female sex (2.1 [1.4‐3.1]), new problem as primary visit reason (1.8 [1.2‐2.7]), former tobacco use (1.7 [1.1‐2.7]), and polypharmacy (11.6 [6.9‐19.7]), respectively). Of the 1,633,358 visits for patients with ADRD that reported use of a cholinesterase inhibitor, 29.3% also reported an anticholinergic, which combination may negate any cognitive benefits the cholinesterase inhibitor may have been able to impart. Conclusion In a nationally representative sample, anticholinergic use was reported in approximately one‐fifth of visits for patients with ADRD. After adjusting for potential confounders, ADRD was an independent risk factor for report of an anticholinergic. Of those visits for ADRD patients whose cognitive impairment was being treated with a cholinesterase inhibitor, almost one‐third reported a drug‐drug interaction with an anticholinergic that may have detrimental results on cognition. Future work should investigate points of intervention to reduce anticholinergic use and inappropriate drug interactions in this population.

ObjectivesTo evaluate the use of a risk stratification tool and explore the contributing factors to variation in practice by clinical pharmacists.MethodsThe quantitative phase was a prospective evaluation of adherence to the risk stratification tool. Patients were selected by convenience sampling from medical wards across two hospital sites. Researchers applied the risk stratification tool to each patient, documented the code, accessed health records in subsequent days, and recorded the code assigned by the pharmacist. These codes were compared. The kappa (κ) coefficient test was performed using SPSS software as a statistical measure of agreement. The qualitative phase was designed using focus groups with clinical pharmacists. One focus group was conducted at each of the two study sites. Participants were grouped to ensure a mix of experience levels. To augment the discussion, participants completed a short survey. Focus groups were recorded and a thematic analysis undertaken.ResultsThe final cohort for quantitative analysis was 73. Researchers and pharmacists allocated the same code to 19 (26%) patients. The highest match rate was observed between researchers and rotational pharmacists. The κ coefficient was 0.039 (slight agreement) with p value=0.52 (not significant). Ten pharmacists participated in the focus groups: three from site 1 and seven from site 2. All participants reported using the principles of the risk stratification tool every day, but they rarely accessed the tool. Pharmacists reported using the tool as a workload management and communication system.ConclusionsVariation in application of the risk stratification tool exists among pharmacists. Focus group participants described multiple scenarios where non-patient factors were considered in assigning a priority code for the patient. A schedule of regular review of the criteria; training and peer review; tool validation; and research identifying the relationship between structured professional judgement and risk stratification tools is recommended.