Practising prioritisation: exploring variation in applying a clinical pharmacy risk stratification tool

OBJECTIVES

To evaluate the use of a risk stratification tool and explore the contributing factors to variation in practice by clinical pharmacists.

METHODS

The 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.

RESULTS

The 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.

CONCLUSIONS

Variation 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.

Examining transitions of care among nursing home residents with and without antipsychotic medication use

OBJECTIVES

This study examines the association between antipsychotic (AP) medication use and care transitions in the nursing home (NH) population.

METHODS

This cross-sectional study used data from a 5% random sample of Medicare beneficiaries between 2011 and 2015. Propensity score adjusted negative binomial regression was performed and conditional probabilities of having a first transition from the NH to specific locations were calculated.

RESULTS

Among 150,284 eligible beneficiaries, the majority were female (67%), white (84%), and >75 years old (63%). Controlling for resident characteristics, the odds of having any transition was 5% lower among those with AP use [IRR (95% confidence interval (CI))=0.95(0.94-0.96)] relative to those with no AP use. Residents with AP use had higher proportions of transitions to hospital (22.7% vs. 19.5%, p < 0.01), emergency department (19.6% vs. 10.7%, p < 0.01), and different NH (1.5% vs. 0.4%, p < 0.01), and lower proportions of transition to non-healthcare locations compared to those without AP use.

CONCLUSIONS

Findings demonstrate that residents with AP use had higher probabilities of transitions to more costly care settings such as the emergency department and hospital compared to those without AP use. Future longitudinal studies will help to inform clinical interventions aimed at improving the quality of care for this population.

COVID-19 Cases, Hospitalizations, and Deaths in Nursing Homes: Factors Impacting the Second Surge

As of March 2021, over 128,000 nursing home (NH) residents have died due to COVID-19 complications, accounting for one-third of all U.S. COVID-19 deaths. Early studies highlighted factors which heightened residents’ risk—facility size and profit status, CMS Five-Star quality rating, race, and high Medicaid share. Despite improved nationwide social distancing and access to protective equipment, between October-December 2020 nursing home cases, hospitalizations, and deaths peaked to highest levels since the pandemic’s advent. The purpose of this study is to quantify previously unexamined associations between resident, facility, and geographic characteristics and COVID-19 infections, hospitalizations, and fatalities in nursing homes during this second surge. In this cross-sectional study, we constructed a novel dataset with linked facility- and county-level data from the CMS Nursing Home COVID-19 Public File, Nursing Home Compare, Long-Term Care Focus, and The New York Times. Multivariable logistic regression evaluated the odds of COVID-19 infections, hospitalizations, and deaths in nursing homes. Among 13,156 nursing homes, 80.5% reported ≥1 COVID-19 cases; on average, nursing homes reported 4.5 hospitalizations and 3.0 deaths. Facilities with higher acuity patients, chain status, >150 beds, high percentage white residents, low Medicaid share, high surrounding county case rates, and occupancy rates >75% were significantly (p <.001) related to increased odds of all outcomes. N95 mask shortages continued to increase risk of cases and hospitalizations. Five-Star ratings, high influenza vaccination rates, and clinical staff shortages were not significant factors. Findings demonstrate that through 2020, nursing homes continued to face challenges protecting their residents from COVID-19-related morbidity and mortality.

Staffing and Protective Equipment Access Mitigated COVID-19 Penetration and Spread in US Nursing Homes During the Third Surge

Objectives

During the last quarter of 2020—despite improved distribution of personal protective equipment (PPE) and knowledge of COVID-19 management—nursing 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 facility-level 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 = 10,592) of facilities reported at least 1 COVID-19 case, 49.44% (n = 6504) reported at least 1 hospitalization, and 49.76% (n = 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 (1.26, 95% CI 1.13-1.40), as well as larger numbers of hospitalizations (1.11, 95% CI 1.02-1.20). Nursing aide shortages were associated with lower odds of at least 1 COVID-19 death (1.23, 95% CI 1.12-1.34) and higher hospitalizations (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 (1.48, 95% CI 1.35-1.62) or death (1.25, 95% CI 1.17-1.34).

Conclusions and Implications

Adequate staffing and PPE—along with reduced occupancy and smaller facilities—mitigate 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.