Moving towards the detection of frailty with biomarkers: A population health study.

Aging adults experience increased health vulnerability and compromised abilities to cope with stressors, which are the clinical manifestations of frailty. Frailty is complex, and efforts to identify biomarkers to detect frailty and pre-frailty in the clinical setting are rarely reproduced across cohorts. We developed a predictive model incorporating biological and clinical frailty measures to identify robust biomarkers across data sets. Data were from two large cohorts of older adults: "Invecchiare in Chianti (Aging in Chianti, InCHIANTI Study") (n = 1453) from two small towns in Tuscany, Italy, and replicated in the Atherosclerosis Risk in Communities Study (ARIC) (n = 6508) from four U.S. communities. A complex systems approach to biomarker selection with a tree-boosting machine learning (ML) technique for supervised learning analysis was used to examine biomarker population differences across both datasets. Our approach compared predictors with robust, pre-frail, and frail participants and examined the ability to detect frailty status by race. Unique biomarker features identified in the InCHIANTI study allowed us to predict frailty with a model accuracy of 0.72 (95% confidence interval (CI) 0.66-0.80). Replication models in ARIC maintained a model accuracy of 0.64 (95% CI 0.66-0.72). Frail and pre-frail Black participant models maintained a lower model accuracy. The predictive panel of biomarkers identified in this study may improve the ability to detect frailty as a complex aging syndrome in the clinical setting. We propose several concrete next steps to keep research moving toward detecting frailty with biomarker-based detection methods.

Cultivating Relationships as a Community-Based Recruitment Strategy in Transdisciplinary Aging Research: Lessons From an Academic-Community Partnership.

Participation of Black American older adults in community-engaged research remains challenging in health sciences. The objectives of this study were to describe the specific efforts, successes, and challenges in recruiting Black American older adults in research led by the Health and Wellness in Aging Across the Lifespan core, part of the Virginia Commonwealth University Institute for Inclusion, Inquiry, and Innovation (iCubed). We conducted a cross-case analysis of 6 community-engaged research projects using the community-engaged research continuum model. Successful recruitment strategies comprised a multifaceted approach to community-based collaboration, including a wellness program with a long standing relationship with the community, engaging key stakeholders and a community advisory board, and building a community-based coalition of stakeholders. Posting flyers and modest monetary compensation remain standard recruitment strategies. The cross-case analysis offered critical lessons on the community's nature and level of engagement in research. Relationship building based on trust and respect is essential to solving complex aging issues in the community.

Low-Income Older Adults' Vulnerability to Anticholinergic Medication-Associated Frailty.

BACKGROUND: A growing body of research supports the negative impact of anticholinergic drug burden on physical frailty. However, prior research has been limited to homogeneous white European populations, and few studies have evaluated how anticholinergic burden tools compare in their measurement function and reliability with minority community-dwelling adult populations. This study investigated the association between anticholinergic drug exposure and frailty by conducting a sensitivity analysis using multiple anticholinergic burden tools in a diverse cohort. METHODS: A comprehensive psychometric approach was used to assess the performance of five clinical Anticholinergic Burden Tools: Anticholinergic Cognitive Burden Scale (ACB), Anticholinergic Drug Scale (ADS), average daily dose, total standardized daily doses (TSDD), and Cumulative Anticholinergic Burden scale (CAB). Spearman correlation matrix and intraclass correlation coefficients (ICC) were used to determine the association among the variables. Ordinal logistic regression is used to evaluate the anticholinergic burden measured by each scale to determine the prediction of frailty. Model performance is determined by the area under the curve (AUC). RESULTS: The cohort included 80 individuals (mean age 69 years; 55.7% female, 71% African American). All anticholinergic burden tools were highly correlated (p < 0.001), ICC3 0.66 (p < 0.001, 95% confidence interval (CI) 0.53-0.73). Among individuals prescribed anticholinergics, 33% were robust, 44% were prefrail, and 23% were frail. All five tools predicted prefrail and frail status (p < 0.05) with low model misclassification rates for frail individuals (AUC range 0.78-0.85). CONCLUSION: Anticholinergic burden tools evaluated in this cohort of low-income African American older adults were highly correlated and predicted prefrail and frail status. Findings indicate that clinicians can select the appropriate instrument for the clinic setting and research question while maintaining confidence that all five tools will produce reliable results. Future anticholinergic research is needed to unravel the association between interventions such as deprescribing on incident frailty in longitudinal data.

A mixed-methods exploration of nurse loneliness and burnout during COVID-19.

AIM: This study investigated the phenomenon of nurse loneliness as a potential contributor to burnout. BACKGROUND: Nurse wellbeing is critical for safe and efficient healthcare delivery. However, evidence indicates nurses' wellbeing is at risk. The levels of burnout, the most commonly measured symptom of suboptimal wellbeing, are rising and may relate to a largely unexplored phenomenon: loneliness. METHODS: A mixed-methods approach was used to investigate burnout and loneliness in direct-care nurses in four diverse hospitals in the midwestern and southeastern United States. Burnout and loneliness were measured, prevalence was estimated, and correlation was examined. Interpretive descriptive inquiry and analysis was used to develop a richer understanding of nurse loneliness in the context of burnout. While this study did not explicitly explore the impact of the global pandemic, data was collected in late 2021 and early 2022, during the Delta variant wave. RESULTS: In the study population (n = 117), rates of burnout are high and positively correlate with loneliness. Qualitative interviews (n = 11) revealed that nurses feel unseen, emotionally detached from their work, and dehumanized. However, social connection with peers is protective and nurses still report a strong sense of devotion to the profession and solidarity with peers. CONCLUSIONS: This study offers insight into nurse loneliness, highlighting the importance of social connectedness to improve nurse wellbeing.

Genetic risk factor clustering within and across neurodegenerative diseases.

Overlapping symptoms and co-pathologies are common in closely related neurodegenerative diseases (NDDs). Investigating genetic risk variants across these NDDs can give further insight into disease manifestations. In this study we have leveraged genome-wide single nucleotide polymorphisms and genome-wide association study summary statistics to cluster patients based on their genetic status across identified risk variants for five NDDs (Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, Lewy body dementia and frontotemporal dementia). The multi-disease and disease-specific clustering results presented here provide evidence that NDDs have more overlapping genetic aetiology than previously expected and how neurodegeneration should be viewed as a spectrum of symptomology. These clustering analyses also show potential subsets of patients with these diseases that are significantly depleted for any known common genetic risk factors suggesting environmental or other factors at work. Establishing that NDDs with overlapping pathologies share genetic risk loci, future research into how these variants might have different effects on downstream protein expression, pathology and NDD manifestation in general is important for refining and treating NDDs.

Evaluating the harmonisation potential of diverse cohort datasets.

Data discovery, the ability to find datasets relevant to an analysis, increases scientific opportunity, improves rigour and accelerates activity. Rapid growth in the depth, breadth, quantity and availability of data provides unprecedented opportunities and challenges for data discovery. A potential tool for increasing the efficiency of data discovery, particularly across multiple datasets is data harmonisation.A set of 124 variables, identified as being of broad interest to neurodegeneration, were harmonised using the C-Surv data model. Harmonisation strategies used were simple calibration, algorithmic transformation and standardisation to the Z-distribution. Widely used data conventions, optimised for inclusiveness rather than aetiological precision, were used as harmonisation rules. The harmonisation scheme was applied to data from four diverse population cohorts.Of the 120 variables that were found in the datasets, correspondence between the harmonised data schema and cohort-specific data models was complete or close for 111 (93%). For the remainder, harmonisation was possible with a marginal a loss of granularity.Although harmonisation is not an exact science, sufficient comparability across datasets was achieved to enable data discovery with relatively little loss of informativeness. This provides a basis for further work extending harmonisation to a larger variable list, applying the harmonisation to further datasets, and incentivising the development of data discovery tools.

Challenges and opportunities faced by nurse scientists in integrating data and technology in research to promote health equity.

Advances in technologies including omics, apps, imaging, sensors, and big data are increasingly being integrated into research by nurse scientists, but the impact on improving health equity is still unclear. In this article, nursing research faculty from one institution discuss challenges and opportunities experienced when integrating various technologies into their research aimed at promoting health equity. Using exemplars from faculty experiences, a three-pronged approach to keeping patients and communities and the goal of health equity central in research while incorporating advancing technologies is described. This approach includes establishing long-term engagement with populations underrepresented in research, adopting strategies to increase diversity in study participant recruitment, and training and collaboration among a diverse workforce of educators, clinicians, and researchers. Training nurse scientists in integrating data and technology for advancing the science on health equity will shift the culture of how we understand, collaborate, and grow with the communities in which we train and practice as nurse scientists.

Creating structural community cohesion: Addressing racial equity in older adult homelessness.

Older adults and racial minorities are overrepresented in homeless populations. Shelter and housing options for homeless older adults who have complex health and social needs are necessary, but not readily available. Older homeless adults that require, but do not receive, health-sensitive, age-sensitive, and racial equity housing, remain vulnerable to poor outcomes and premature mortality. Accordingly, this study examines the development of a coalition to better address older adult homelessness within a racial equity framework. A community coalition was established to better address older adult homelessness within the lens of age-sensitivity and racial equity, due to a disconnect between healthcare and senior housing placement programs, creating unaddressed multifaceted health issues/complications. The community coalition development is described, including the coalition process, activities, and outcomes. Local rehoused older adults are also interviewed and described to better understand their central life circumstances.

Understanding the preference for receiving mental health and substance use support in African Americans 50 and older.

OBJECTIVE: This study aims to determine whether current tobacco and/or alcohol use is associated with setting preferences for seeking support for substance use (SU) and mental health (MH) services to African Americans ages 50 and older. METHODS: Data from 368 African American individuals (aged 50+) who participated in a community-based needs assessment survey were used. Preferences included community-based (e.g., health centers) and traditional settings (e.g., doctor's office). SU was measured as a categorical variable detailing past-month use of conventional cigarettes and alcohol graded by risk levels. Logistic regression models tested the associations between SU and setting preference before and after adjusting for the influence of self-reported MH diagnoses. RESULTS: Prior to adjustment for the influence of MH outcomes, high-risk use of tobacco and alcohol in the past month was associated with a lower odds of preferring MH/SU support in traditional settings (OR = 0.23, 95% CI = 0.06-0.85) compared to participants engaged in no-/low- risk substance use. This association was no longer significant after accounting for the influence of mental health symptoms and covariates. DISCUSSION: These results provide preliminary evidence that mental health outcomes mediate the association between substance use and setting preference for seeking MH/SU support in traditional settings. TRANSLATIONAL SIGNIFICANCE: This exploratory study encourages additional investigation of the association between substance use, setting preferences, and the likelihood of seeking treatment in community health centers using larger sample sizes. Additional opportunities to offer mental health/substance use support to African American older adults within clinical settings should be explored.

Non-Latinx Healthcare Provider's Knowledge and Awareness of Latinx Geriatric Clinical Health Needs.

OBJECTIVES: Due to the exponential growth in the Latinx older adult population, culturally responsive services are needed, especially since most healthcare providers are non-Latinx with limited Spanish or bilingual skills. One place to start is by drawing a formative assessment of the healthcare providers' knowledge and awareness of the healthcare needs of Latinx older adults. METHODS: Focus groups were conducted to explore the healthcare providers' knowledge and awareness of cultural and structural barriers and facilitators to accessing health care services for Latinx older adults. RESULTS: Results note that healthcare providers perceived the healthcare needs for Latinx older adults to be underutilized for healthcare services, preventive interventions for healthy diet/lifestyle, and healthcare knowledge. Providers reported Latinx family over-involvement, religiosity, immigration, and language/lack of interpreters as barriers to seeking timely healthcare. Finally, healthcare providers said that family support, the location of healthcare services, and community-based partnerships were all facilitators for seeking healthcare. CONCLUSIONS: Findings suggest providers' conflicting perspectives toward the Latinx communities. CLINICAL IMPLICATIONS: Healthcare services can consider implementing trainings for non-Latinx providers to recognize conflicting perspectives and reduce implicit bias toward the Latinx communities.

Cognitive and physical impairment in spinal cord injury: A scoping review and call for new understanding.

Study Design: Scoping review.Objective: To examine potential underlying mechanisms of cognitive and physical impairment in patients with spinal cord injury and identify current research gaps.Methods: A scoping review of the literature was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) extension for scoping reviews to identify primary studies that explored mechanisms of cognitive and/or physical impairment after spinal cord injury. The databases searched were PubMed/MEDLINE, EMBASE (OVID), Cumulative Index to Nursing and Allied Health Literature (CINAHL; EBSCO), Web of Science, Scopus, and PsycInfo. These databases were searched from inception through December 20, 2021.Results: Accumulating research suggests that neuroinflammation and neurodegeneration after a traumatic event may be possible mechanisms for cognitive impairment among patients with SCI. In addition, lack of physical activity due to impaired mobility is associated with an increased risk of cognitive impairment.Conclusion: While the results establish a foundation for understanding how cognitive impairment, mental health, and physical function independently affect patients with SCI, further research is warranted to understand how these factors systemically impact the patient and discover refined targets for future rehabilitation therapies. Studies should also explore potential predisposing factors for the relationship between cognitive and physical impairment among patients with SCI.

Identification and prediction of Parkinson's disease subtypes and progression using machine learning in two cohorts.

The clinical manifestations of Parkinson's disease (PD) are characterized by heterogeneity in age at onset, disease duration, rate of progression, and the constellation of motor versus non-motor features. There is an unmet need for the characterization of distinct disease subtypes as well as improved, individualized predictions of the disease course. We used unsupervised and supervised machine learning methods on comprehensive, longitudinal clinical data from the Parkinson's Disease Progression Marker Initiative (n = 294 cases) to identify patient subtypes and to predict disease progression. The resulting models were validated in an independent, clinically well-characterized cohort from the Parkinson's Disease Biomarker Program (n = 263 cases). Our analysis distinguished three distinct disease subtypes with highly predictable progression rates, corresponding to slow, moderate, and fast disease progression. We achieved highly accurate projections of disease progression 5 years after initial diagnosis with an average area under the curve (AUC) of 0.92 (95% CI: 0.95 ± 0.01) for the slower progressing group (PDvec1), 0.87 ± 0.03 for moderate progressors, and 0.95 ± 0.02 for the fast-progressing group (PDvec3). We identified serum neurofilament light as a significant indicator of fast disease progression among other key biomarkers of interest. We replicated these findings in an independent cohort, released the analytical code, and developed models in an open science manner. Our data-driven study provides insights to deconstruct PD heterogeneity. This approach could have immediate implications for clinical trials by improving the detection of significant clinical outcomes. We anticipate that machine learning models will improve patient counseling, clinical trial design, and ultimately individualized patient care.

Quality of life in older adults with opioid use disorder: A scoping review.

The increasing prevalence of Substance Use Disorder (SUD) and opioid use disorder (OUD) is part of a national health crisis and reflects an unfortunate trend among populations of older adults. Opioid Use Disorder and opioid-related mortalities are also rising among older adults following this trend. Compared to younger populations, the effect of SUD and OUD on quality of life (QOL) in older adults is complex and poorly understood. This scoping review explores how QOL has been evaluated in high-risk subpopulations of older adults with SUD, specifically OUD. The articles reviewed for this paper targeted studies measuring QOL in older adults with OUD. We uncovered a paucity of literature devoted to studying interventions to improve QOL in older adults with OUD. This review supports further research on clinical interventions targeting improving QOL for older adults with OUD.

Frailty Risks of Prescription Analgesics and Sedatives across Frailty Models: the Health and Retirement Study.

INTRODUCTION: Limited evidence for incident frailty risks associated with prescription analgesics and sedatives in older (≥ 65 years) community-living adults prompted a more comprehensive investigation. METHODS: We used data from older Health and Retirement Study respondents and three frailty models (frailty index, functional domain, frailty phenotype with 8803, 10,470, and 6850 non-frail individuals, respectively) and estimated sub-hazard ratios of regular prescription drug use (co-use, analgesic use, and sedative use), by frailty model. We addressed confounding with covariate adjustment and propensity score matching approaches. RESULTS: The baseline prevalence of analgesic and sedative co-use, analgesic use, and sedative use among non-frail respondents was 1.8%, 12.8%, and 4.7% for the frailty index model, 4.2%, 16.2%, and 5.3% for the functional domain model, and 4.3%, 15.4%, and 6.1% for the frailty phenotype model, respectively. Cumulative frailty incidence over 10 years was 39.3%, 36.1%, and 14.2% for frailty index, functional domain, and frailty phenotype models, respectively; covariate-adjusted sub-hazard ratio estimates were 2.00 (1.63-2.45), 1.83 (1.57-2.13), and 1.68 (1.21-2.33) for co-use; 1.72 (1.56-1.89), 1.38 (1.27-1.51), and 1.51 (1.27-1.79) for analgesic use; and 1.46 (1.24-1.72), 1.25 (1.07-1.46), and 1.31 (0.97-1.76) for sedative use. Frailty risk ranking (co-use > analgesic use > sedative use) persisted across all model sensitivity analyses. DISCUSSION: Consistently significant frailty risk estimates of regular prescription analgesic and sedative co-use and of prescription analgesic use support existing clinical, public health, and regulatory guidance on opioid and benzodiazepine co-prescription, on opioid prescription, and on NSAID prescription. Frailty phenotype measurement administration limited power to detect significant frailty risks. Research into specific pharmaceutical exposures and comparison of results across cohorts will be required to contribute to the deprescribing evidence base.

Multi-modality machine learning predicting Parkinson's disease.

Personalized medicine promises individualized disease prediction and treatment. The convergence of machine learning (ML) and available multimodal data is key moving forward. We build upon previous work to deliver multimodal predictions of Parkinson's disease (PD) risk and systematically develop a model using GenoML, an automated ML package, to make improved multi-omic predictions of PD, validated in an external cohort. We investigated top features, constructed hypothesis-free disease-relevant networks, and investigated drug-gene interactions. We performed automated ML on multimodal data from the Parkinson's progression marker initiative (PPMI). After selecting the best performing algorithm, all PPMI data was used to tune the selected model. The model was validated in the Parkinson's Disease Biomarker Program (PDBP) dataset. Our initial model showed an area under the curve (AUC) of 89.72% for the diagnosis of PD. The tuned model was then tested for validation on external data (PDBP, AUC 85.03%). Optimizing thresholds for classification increased the diagnosis prediction accuracy and other metrics. Finally, networks were built to identify gene communities specific to PD. Combining data modalities outperforms the single biomarker paradigm. UPSIT and PRS contributed most to the predictive power of the model, but the accuracy of these are supplemented by many smaller effect transcripts and risk SNPs. Our model is best suited to identifying large groups of individuals to monitor within a health registry or biobank to prioritize for further testing. This approach allows complex predictive models to be reproducible and accessible to the community, with the package, code, and results publicly available.

COVID in Context: The Lived Experience of Richmond's Low-Income Older Adults.

Taking a phenomenological approach, this qualitative study describes the lived experiences of low-income older adults during the COVID-19 pandemic. A socio-ecological model was used to organize the five identified themes describing the lived experience: socio-economic context, Black Lives Matter and the politics of race, COVID and polarized views of COVID, interpersonal context (social connections), and individual context (feelings, beliefs, and behaviors). Study findings illustrate the intersectionality of contextual influences on the experience of low-income older adults. Study participants demonstrated remarkable resilience and coping strategies developed in response to the challenges they experienced throughout their lifetime which benefited them when faced with the pandemic, social unrest, and political events that took place in 2020. This study highlights the importance of understanding the larger context of COVID-19 which has significant implications for policy makers and public health leaders.

Socioeconomic Effects on Psychosocial Factors Among Low-Income Older Adults.

OBJECTIVES: Older adults have been disproportionately affected by COVID-19. The primary goal of this study is to determine the socioeconomic effects on psychosocial factors among low-income independent-living older adults, in an urban setting, during the COVID-pandemic. METHODS: Participants were recruited through Virginia Commonwealth University's Richmond Health and Wellness Program. Telephone surveys (n=100) were conducted using the Epidemic - Pandemic Impacts Inventory Geriatric with the Racial/Ethnic Discrimination addendum. Responses were analyzed for income and education effects across seven domains: home life, social activities/isolation, economic, emotional health-wellbeing, physical health, COVID-infection history, and positive change behaviors/experiences. RESULTS: The sample population was between 51 and 87 years of age, 88% were Black, 57% reported incomes of $10,000/year or less, and 60% reported a high-school education or less. There were income effects for social activities/isolation (f = 3.69, p<.05) and positive change (f = 8.40, p<.01), and education effects for COVID History (f = 4.20, p <.04). DISCUSSION: Overall results highlight the social patterns for a diverse sample of low-income urban older adults; education and income are identified as risk factors for social losses, COVID-infection experiences, racial/ethnic discrimination during the COVID-pandemic, and positive change behaviors.

Bringing Transdisciplinary Aging Research From Theory to Practice.

There is a growing emphasis to use a transdisciplinary team approach to accelerate innovations in science to solve complex conditions associated with aging. However, the optimal organizational structure and process for how to accomplish transdisciplinary team science is unclear. In this forum, we illustrate our team's experience using transdisciplinary approaches to solve challenging and persistent problems for older adults living in urban communities. We describe our challenges and successes using the National Institutes of Health four-phase model of transdisciplinary team-based research. Using a de-identified survey, the team conducted an internal evaluation to identify features that created challenges including structural incongruities, interprofessional blind spots, group function, and group dynamics. This work resulted in the creation of the team's Transdisciplinary Conceptual Model. This model became essential to understanding the complex interplay between societal factors, community partners, and academic partners. Conducting internal evaluations of transdisciplinary team processes is integral for teams to move beyond the multi- and interdisciplinary niche and to reach true transdisciplinary success. More research is needed to develop measures that assess team transdisciplinary integration. Once the process of transdisciplinary integration can be reliably assessed, the next step would be to determine the impact of transdisciplinary team science initiatives on aging communities.

Factors Affecting Quality of Life in Patients Receiving Autologous Hematopoietic Stem Cell Transplantation.

BACKGROUND: Hematopoietic stem cell transplant (HSCT) is a potentially curative treatment for hematologic malignancies, with 22 000 HSCTs performed annually in the United States. However, decreased quality of life (QoL) is a frequent and concerning state reported by HSCT recipients. OBJECTIVES: We sought to determine if measurements of frailty and cognitive impairment were associated with fatigue and QoL in adult HSCT recipients after autologous HSCT. METHODS: Using a longitudinal study design, 32 participants 18 years or older receiving autologous HSCT were recruited from a bone marrow transplant clinic. Each participant completed 2 visits: pre-HSCT and post-HSCT. At each visit, participants completed assessment tools to measure frailty, cognitive impairment, fatigue, and QoL (assesses physical, social/family, emotional, functional, and transplant-related well-being). RESULTS: Participants with increased fatigue scores reported decreased QoL pre- and post-HSCT. Participants with increased frailty showed decreased functional well-being before HSCT and showed correlations with decreased physical, social, and transplant-related well-being post-HSCT. As expected, fatigued participants also showed increased frailty post-HSCT. Participants showed significant changes in physical well-being and fatigue between pre-HSCT and post-HSCT visits. CONCLUSION: Data analyses from this pilot study show significant correlations between subsets of QoL with fatigue and frailty in autologous HSCT participants pre- and post-HSCT. IMPLICATIONS FOR PRACTICE: Understanding the impact of frailty on fatigue and QoL in HSCT recipients is critical to assist nurses in initiating educational and behavioral interventions to help mitigate the effects of HSCT.

Anticholinergic Medication Burden in Parkinson's Disease Outpatients.

BACKGROUND: Individuals with Parkinson's disease (PD) may be especially vulnerable to future cognitive decline from anticholinergic medications. OBJECTIVE: To characterize anticholinergic medication burden, determine the co-occurrence of anticholinergic and cholinesterase inhibitors, and to assess the correlations among anticholinergic burden scales in PD outpatients. METHODS: We studied 670 PD outpatients enrolled in a clinic registry between 2012 and 2020. Anticholinergic burden was measured with the Anticholinergic Cognitive Burden Scale (ACB), Anticholinergic Drug Scale (ADS), Anticholinergic Risk Scale (ARS), and Drug Burden Index-Anticholinergic component (DBI-Ach). Correlations between scales were assessed with weighted kappa coefficients. RESULTS: Between 31.5 to 46.3% of PD patients were taking medications with anticholinergic properties. Among the scales applied, the ACB produced the highest prevalence of medications with anticholinergic properties (46.3%). Considering only medications with definite anticholinergic activity (scores of 2 or 3 on ACB, ADS, or ARS), the most common anticholinergic drug classes were antiparkinsonian (8.2%), antipsychotic (6.4%), and urological (3.3%) medications. Cholinesterase inhibitors and medications with anticholinergic properties were co-prescribed to 5.4% of the total cohort. The most highly correlated scales were ACB and ADS (κ= 0.71), ACB and ARS (κ= 0.67), and ADS and ARS (κ= 0.55). CONCLUSION: A high proportion of PD patients (20%) were either taking antiparkinsonian, urological, or antipsychotic anticholinergic medications or were co-prescribed anticholinergic medications and cholinesterase inhibitors. By virtue of its detection of a high prevalence of anticholinergic medication usage and its high correlation with other scales, our data support use of the ACB scale to assess anticholinergic burden in PD patients.