Assessing the well-being of residents in nursing facilities

Purpose Well-adapted and validated well-being (WB) instruments for the nursing home population are scarce. To our knowledge, the Laurens Well-Being Inventory for Gerontopsychiatry (LWIG) is a practical and reliable well-being assessment tool that has never been validated for German nursing home populations. Therefore, the purpose of our study was to (1) translate and cross-culturally adapt the LWIG to a German context and (2) test the reliability and validity of the German LWIG in a group of older nursing home residents using the Rasch model. Methods This study has a cross-sectional, descriptive study design. Cross-cultural adaption of the LWIG-GER from English to German was performed according to a standardized method. The data obtained from 104 long-term nursing home residents (57 women, 47 men) aged 60–99 years (mean 79.5, standard deviation ±9.11) were analyzed for psychometric testing (exploratory and confirmatory factor analysis, item fit, McDonald’s ω, convergent validity, and known-group validity, Rasch). Results The final LWIG-GER consists of 19 items with three subscales, including “psychological WB”, “social WB”, and “physical WB”. The LWIG-GER showed good overall reliability with McDonald’s ω of 0.83; the LWIG-GER dimensions’ scores were significantly correlated with depression, functional performance, activities, fear of falling, and education. Conclusions Our findings suggest that the German language version of the LWIG is a reliable and valid tool for measuring WB in nursing home residents. Furthermore, we propose that the LWIG-GER questionnaire can broaden and deepen our understanding of residents’ perception of quality of care and their environment.

135. Influence of Prescribers on Antibiotic Use among Skilled Nursing Care Residents in 29 U.S. Nursing Homes

Background In nursing homes, federal mandates call for more judicious use of antibiotics and antipsychotics. Previous research indicates that practice patterns of nursing home practitioners, rather than resident’s signs and symptoms or overall medical conditions, drive antibiotic use. We hypothesized that nursing home practitioners who prescribe antibiotics more frequently than their peers may display a similar practice pattern for antipsychotics. Here, we examine similarities in prescribing patterns for antibiotics and antipsychotics among practitioners at 29 U.S. nursing homes. Methods Prescription data came from 2016 invoices from a pharmacy common to all 29 nursing homes. We defined practitioners as individuals who prescribed ≥1% of systemic medications at a nursing home and excluded practitioners without no prescriptions for anti-hypertensive drugs assuming they were not treating a general nursing home population (i.e. treating hospice or dementia patients). Using anti-hypertensive starts for standardization, we calculated the expected number of starts for both antibiotics and antipsychotics. Using funnel plots with Poisson 99% control limits for the observed-to-expected ratio, we identified practitioners whose use of either class of drugs exceeded these control limits. Practitioners were classified as high, average, or low prescribers for each class of drugs. Results We analyzed 129 practitioners who wrote for 113669 systemic medications. For antibiotics, 27 (20%) and 19 (15%) of practitioners were low and high prescribers, respectively. For antipsychotics, 53 (41%) and 14 (11%) were low and high prescribers, respectively (Figure 1). Among the low antibiotic prescribers, 59% (16/27) were also low antipsychotic prescribers. Among the high antibiotic prescribers, 21% (4/19) were also high antipsychotic prescribers (Figure 2). Figure 1. (a) Funnel plot for antibiotics (b) Funnel plot for antipsychotics Figure 2. Type of prescriber Conclusion Practitioners who were low prescribers for antibiotics were also likely to be low prescribers for antipsychotics, suggesting judicious use for both classes of medications. Further understanding of the behaviors of these individuals, as well as those who are high prescribers for both classes, has implications for improving antibiotic stewardship practices in nursing homes. Disclosures Robin L. Jump, MD, PhD, Pfizer (Individual(s) Involved: Self): Consultant

Regional Characteristics of the Second Wave of SARS-CoV-2 Infections and COVID-19 Deaths in Germany

(1) Background: In the absence of individual level information, the aim of this study was to identify the regional key features explaining SARS-CoV-2 infections and COVID-19 deaths during the upswing of the second wave in Germany. (2) Methods: We used COVID-19 diagnoses and deaths from 1 October to 15 December 2020, on the county-level, differentiating five two-week time periods. For each period, we calculated the age-standardized COVID-19 incidence and death rates on the county level. We trained gradient boosting models to predict the incidence and death rates by 155 indicators and identified the top 20 associations using Shap values. (3) Results: Counties with low socioeconomic status (SES) had higher infection and death rates, as had those with high international migration, a high proportion of foreigners, and a large nursing home population. The importance of these characteristics changed over time. During the period of intense exponential increase in infections, the proportion of the population that voted for the Alternative for Germany (AfD) party in the last federal election was among the top characteristics correlated with high incidence and death rates. (4) Machine learning approaches can reveal regional characteristics that are associated with high rates of infection and mortality.

Factor Analysis of the Short-Form Cohen-Mansfield Agitation Inventory (CMAI) and the Measurement Invariance by Gender

Background and PurposeThe Cohen-Mansfield Agitation Inventory (CMAI) is a widely used measure of agitation. The purpose of this study was to test the internal consistency, reliability, and validity of short-form CMAI in a sample of nursing home residents with cognitive impairment and examine if it is invariant across gender.MethodsThis study utilized baseline data from a randomized trial including 553 residents from 55 nursing homes. Data was analyzed using structural equation modeling.ResultsConfirmatory factory analysis supported the three-factor structure including aggressive (α = .794), physically nonaggressive (α = .617), and verbally agitated (α = .718) behaviors. Invariance testing confirmed that the shortened measure is invariant across gender.ConclusionsFindings provide validity evidence of short-form CMAI to assess agitation and gender differences in agitation in nursing home population.

Malnutrition Prevalence Rates among Dutch Nursing Home Residents: What Has Changed over One Decade? A Comparison of the Years 2009, 2013 and 2018

Objectives To assess changes in prevalence of malnutrition and its associated factors among people living in Dutch nursing homes in 2009, 2013 and 2018. Design Secondary data analysis of the International Prevalence Measurement of Care Quality (LPZ) study. Setting Dutch nursing homes. Participants Residents living at a psychogeriatric or somatic ward in Dutch nursing homes in 2009, 2013 or 2018. Measurements weight and height, unintentional weight loss over the last month and last six months, age, sex, length of stay up to the measurement day, care dependency, and the presence of various diseases (dementia, diabetes mellitus, stroke, diseases of the respiratory system, respiratory diseases and pressure ulcers). Results In total, 14,317 residents were included in this study with a mean age of 82.2, 70.9 female and 66.8% was living on a psychogeriatric ward. Results of this study show relative stability in background characteristics of the nursing home population over the last decade. In the total sample, 16.7% was malnourished and these percentages were 16.6% in 2009, 17.5% in 2013 and 16.3% in 2018. Multiple binary logistic regression analyses revealed having a pressure ulcer, female sex and living on a psychogeriatric department to be positively associated and having diabetes mellitus to be negatively associated with malnutrition throughout the years. These associations were strong and similar across years. Conclusion Even though much attention has been paid to prevent malnutrition in Dutch nursing homes over the last decades, results show a relatively stable malnutrition prevalence rate of around 16%. This leads to the question if nursing staff is able to sufficiently recognize residents with (a risk of) malnutrition, and if they are aware of interventions they could perform to decrease this rate.

Nursing homes during the COVID-19 pandemic: a scoping review of challenges and responses

Introduction COVID-19 has caused unprecedented challenges in nursing homes. In this scoping review, we aimed to describe factors that contributed to the spread and mortality of COVID-19 in nursing homes and provide an overview of responses that were implemented to try to overcome such challenges. Methods The MeSH terms “Nursing homes” and “COVID-19” were searched in MEDLINE Ovid, and English language articles were retrieved that were published between 1 March 2020 and 31 January 2021. Article titles and abstracts were screened by two reviewers, and the results of included articles were grouped by themes. Results The search retrieved 348 articles, of which 76 were included in the thematic review. 8 articles related to COVID-19 disease characteristics (e.g. asymptomatic transmission), 24 to resident-related factors (e.g. comorbidities, nutrition, cognition), 13 to facility characteristics (e.g. physical space, occupancy, for-profit status), 21 to staffing (e.g. staffing levels, staff-to-resident ratio, staff multi-employment), and 10 to external factors (e.g. availability of personal protective equipment, prevailing health and social care policies). In terms of responses, identified themes included widespread testing, isolation and cohorting of residents, staff protection and support, promotion of residents’ well-being, and technological innovations. Conclusion COVID-19 exerted severe challenges on the nursing home population and its staff. Both internal and external factors predisposed nursing homes to an increased propensity of spread. Numerous strategies were employed to attempt to mitigate the negative impacts. Substantial learning occurred that may not only aid future pandemic preparedness but improve quality of care for nursing home residents at all times.

The second wave of SARS-CoV-2 infections and COVID-19 deaths in Germany – driven by values, social status and migration background? A county-scale explainable machine learning approach

ABSTRACTThere is a general consensus that SARS-CoV-2 infections and COVID-19 deaths have hit lower social groups the hardest, however, for Germany individual level information on socioeconomic characteristics of infections and deaths does not exist. The aim of this study was to identify the key features explaining SARS-CoV-2 infections and COVID-19 deaths during the upswing of the second wave in Germany.We considered information on COVID-19 diagnoses and deaths from 1. October to 15. December 2020 on the county-level, differentiating five two-week time periods. We used 155 indicators to characterize counties in nine geographic, social, demographic, and health domains. For each period, we calculated directly age-standardized COVID-19 incidence and death rates on the county level. We trained gradient boosting models to predict the incidence and death rates with the 155 characteristics of the counties for each period. To explore the importance and the direction of the correlation of the regional indicators we used the SHAP procedure. We categorized the top 20 associations identified by the Shapley values into twelve categories depicting the correlation between the feature and the outcome.We found that counties with low SES were important drivers in the second wave, as were those with high international migration and a high proportion of foreigners and a large nursing home population. During the period of intense exponential increase in infections, the proportion of the population that voted for the Alternative for Germany (AfD) party in the last federal election was among the top characteristics correlated with high incidence and death rates.We concluded that risky working conditions with reduced opportunities for social distancing and a high chronic disease burden put populations in low-SES counties at higher risk of SARS-CoV-2 infections and COVID-19 deaths. In addition, noncompliance with Corona measures and spill-over effects from neighbouring counties increased the spread of the virus. To further substantiate this finding, we urgently need more data at the individual level.