TOWARDS EVIDENCE-BASED AND DATA-DRIVEN RECOMMENDATIONS PROMOTING INDEPENDENCE IN LATER LIFE: GAIT SPEED, FALLS, AND ACTIVITIES OF DAILY LIVING IN OLDER ADULTS

Background: Falls in older adults are a significant public health challenge. Fall prevention as well as intervention after a fall both are critical to reduce the negative consequences and improve quality of life in older age. Purpose: 1) Quantify the association between gait speed and fall risk in a cross-sectional analysis for older adults with and without cognitive impairment. 2) Determine if there is an association between change in gait speed and fall risk in a longitudinal analysis including older adults with and without cognitive impairment. 3) Quantify the association between falls and difficulty with activities of daily living (ADLs) and instrumental activities of daily living (IADLs) and determine the trajectory of difficulty with ADLs/IADLs pre- and post-fall for older. Methods: The study population for this research was the Ginkgo Evaluation of Memory Study, a randomized controlled trial, conducted from 2000-2008, including 3069 older adults from four locations in the United States. The longitudinal study design, number of measures, and rigorous ascertainment of MCI and dementia provided an excellent data set for this research, which included a cross-sectional analysis of gait speed and falls, a longitudinal analysis of change in gait speed and falls, and falls and difficulty with ADLs/IADLs using Cox proportional hazards models, and latent class trajectory modeling to determine trajectories of difficulty with ADLs/IADLs pre- and post- fall. Results: 1) The results of this study provide evidence of a significant association between faster gait speed and lower fall risk for older adults. 2) A decrease in gait speed of more than 0.15 m/s (mean speed 0.93 m/s) over 12 months is associated with increased risk of falls for older. 3) Falls are associated with an increased risk of difficulty with ADLs/IADLs, which persists and worsens over time for some older adults. Conclusion: Gait speed and change in gait speed could be used as screening tools for fall risk in older adults with and without mild cognitive impairment. Understanding the characteristics of older adults more likely to have difficulty with ADLs and IADLs post-fall can be utilized to target interventions to decrease fall-related negative outcomes

THE INFLUENCE OF PHYSICAL HEALTH, EMOTIONAL HEALTH, AND SOCIOECONOMIC FACTORS ON THE MUSCULOSKELETAL PAIN EXPERIENCE IN PATIENTS ATTENDING A PRO BONO PHYSICAL THERAPY CLINIC

Non-communicable, chronic diseases are highly prevalent in the United States, reducing the quality of life for those affected and contributing to the majority of the nation’s healthcare expenditure. These conditions include, among others, cardiovascular disease, diabetes, and musculoskeletal disease. Musculoskeletal disease is particularly of interest for the field of physical therapy as the vast majority of patients seeking care in the outpatient setting present with musculoskeletal pain complaints, resulting in limitations in function, participation, and quality of life for the patient. The factors influencing health outcomes are diverse and include a person’s physical environment, social and economic factors, access to quality clinical care, and health behaviors. Thus, managing chronic disease requires intervention at the level of the patient, provider, healthcare organization, community, and the local, state, and federal governments. Implementing multilevel intervention and advocacy can reduce the impact of chronic disease and allow people to more meaningfully engage in their lives. The purpose of this dissertation was to first describe a population attending a pro bono physical therapy clinic for musculoskeletal pain complaints in the southeastern United States in regards to measures of physical health, emotional health, socioeconomic status, and pain presentation. These measures were then assessed to discover their usefulness in identifying chronic disease as well as their ability to identify clinically-important patient subgroups that may require a more tailored treatment approach. By understanding the patient population more completely, future directions for addressing patient needs through clinical intervention, clinical programming, and advocacy endeavors can be implemented to produce more positive health outcomes. Theoretical foundation for the management of chronic disease was informed by the Innovative Care for Chronic Conditions framework (World Health Organization, 2002). The County Health Ratings Model (University of Wisconsin Population Health Institute, 2019) and the Tool for Health & Resilience in Vulnerable Environments (Prevention Institute, 2004) were used as guides in determining the important factors influencing health outcomes and routes of intervention to improve health equity. Models of the pathophysiology of metabolic syndrome (Eckel et al, 2005), a precursor to cardiovascular disease and diabetes, and their impact on musculoskeletal disease (Collins et al, 2018) were also considered to identify clinical measures in the physical therapy setting that can better inform the clinician of the patient’s condition. A clinically-based, standardized intake process was created and implemented at a pro bono physical therapy clinic to capture measures of physical health, emotional health, health behaviors, and social and economic variables. The measures chosen fall within the scope of physical therapy practice and were selected to bolster the treating clinician’s clinical decision making to provide patient-centered care. A retrospective chart review was performed over a two-year period (December 2017 to December 2019) to collect these data from the initial patient evaluation. Descriptive statistics were used to define the population attending the clinic and their potential healthcare needs. Regression analysis was then performed to determine which measures best inform the clinician regarding metabolic disease status in this population and whether those at risk of metabolic disease presented differently from those without. Finally, a latent class analysis was performed to identify unique patient subgroups within those presenting to the clinic and the distinguishing features of these subgroups

Trajectories of long-term exposure to anticholinergic and sedative drugs: A latent class growth analysis

Introduction: A variety of drugs, which are frequently prescribed to older people, have anticholinergic and sedative effects whereby they may impair cognitive and physical function. Although substantial inter-individual variation in anticholinergic and sedative exposure has been documented, little is known about subpopulations with distinct trajectories of exposure. Methods: Data from the Longitudinal Aging Study Amsterdam (LASA), an ongoing Dutch population-based cohort study, collected over 20 years (1992-2012) at seven occasions, were analyzed. On each occasion, cumulative anticholinergic and sedative exposure was quantified with the Drug Burden Index, a linear additive pharmacological dose-response model. The most likely number of trajectories were empirically derived with Latent Class Growth Analysis using "Goodness of fit" statistics. Trajectories were then compared on physical and cognitive function. Results: A total of 763 participants completed all follow-ups (61% women; mean age 83, ±6). "Goodness of fit" statistics (Bayesian In-formation Criterion = 22916, Bootstrapped Likelihood Ratio Test of 3 vs. 2 classes = 514.12

Riesgo de Caídas de los Ancianos Residentes en la Comunidad: Revisión Sistemática de la Literatura

OBJECTIVE: To identify the risk factors for falls of the community-dwelling elderly in order to update the Taxonomy II of NANDA International. METHOD: A systematic literature review based on research using the following platforms: EBSCOHost®, CINAHL and MEDLINE, from December 2010 to December 2014. The descriptors used were (Fall* OR Accidental Fall) AND (Community Dwelling OR Community Health Services OR Primary health care) AND (Risk OR Risk Assessment OR Fall Risk Factors) AND (Fall* OR Accidental Fall) AND (Community Dwelling OR older) AND Nurs* AND Fall Risk Factors. RESULTS: The sample comprised 62 studies and 50 risk factors have been identified. Of these risk factors, only 38 are already listed in the classification. CONCLUSIONS: Two new categories of risk factors are proposed: psychological and socio-economical. New fall risk factors for the community-dwelling elderly have been identified, which can contribute to the updating of this nursing diagnosis of the Taxonomy II of NANDA International.info:eu-repo/semantics/publishedVersio

The influence of physicians on medication adherence

Background: Medication adherence is defined as the extent to which patients take medications according to agreed recommendations from a health care provider. Correspondingly, medication non-adherence is the failure to take medications according to a prescribed medication regimen. Considered one of the greatest challenges to the successful management of people with chronic conditions in the community setting, patients who are non-adherent to medications have higher risks for hospitalization and even death compared to patients who take medications as prescribed. According to a report in 2008, the costs due to non-adherence in the United States (US) was estimated to be between US$100 billion to US$310 billion per year; however, these numbers are based on general assumptions and rigorous estimates are not available in the US or Canada. Despite years of research, major gaps remain in our understanding of the causes of non-adherence. Studies often focus on patient characteristics and patient behaviour. Although some of these factors are influential, they typically only explain a small fraction of the variance in models predicting non-adherence. Prescribing physicians have been identified as having a strong influence on their patient’s adherence to medications; however, their impact has never been comprehensively incorporated into population-based models to help explain the residual variance. Purpose and research approach: The purpose of this research was to examine the impact of physicians on population-based models of medication adherence. Three retrospective cohort studies were conducted using population-based, administrative databases from Saskatchewan, Canada. The study population consisted of new statin users (no statin claims in the previous five year) between 2012 and 2017. Statin medication was the focus in these studies because they are prescribed for chronic treatment only, they had no therapeutic equivalent during the period of study, they are prescribed to a large percentage of the population, and they are associated with reduced morbidity and mortality from atherosclerotic cardiovascular disease. Each study focused on different aspects of the physician’s potential impact on the outcome of optimal medication adherence to statins defined as proportion of days covered (PDC) of at least 80%. Study 1 measured the impact of continuity of care (COC) provided by physician prescribers on optimal adherence; study 2 focused on the impact of demographic characteristics of physicians on optimal adherence; and study 3 measured the overall effect of physicians on the outcome of optimal adherence. Study 1 – The impact of physician continuity of care on medication adherence The first study investigated continuity of care (COC), a factor related to physician practice that is associated with medication adherence and is commonly used as a baseline explanatory variable in population-based studies. COC is typically represented by the usual provider continuity index (UPCI), which is calculated exclusively from the number of outpatient physician visits. However, the number of outpatient visits only represents one aspect of COC. Our aim was to improve the measurement of COC by integrating information on physician services and pharmacy claims (i.e., medication dispensing) data. Our new “integrated COC” definition required patients to have one physician who satisfied all three criteria: a) the most frequently visited general practitioner physician (i.e., usual care provider); b) the statin prescriber; and c) provider of a complete medical examination within the past year. Logistic regression models were constructed with each measure of COC (high UPCI index or integrated COC) on the outcome of optimal statin adherence (PDC ≥80%). Predictive performance of the two models was compared using the DeLong test. In a cohort of 55,144 new statin users, the integrated COC measure had a stronger association with optimal adherence [adjusted odds ratio (aOR) =1.56, 95% confidence interval (CI) 1.50 to 1.63] than UPCI (aOR = 1.23, 95% CI 1.19 to 1.28), and produced greater prediction accuracy of the multivariable model (DeLong test, p<0.0001). The results suggest that physician service and pharmacy claim data should be adopted in COC measures for population-based adherence models because of greater predictive performance in models predicting optimal adherence to statin. Study 2 – Physician demographic factors and medication adherence The second study examined the impact of age or sex concordance (i.e., same age range or same sex) between physicians and patients on optimal adherence to statin medications. We hypothesized that age or sex concordance between physicians and patients would result in higher medication adherence through improved communication and trust compared to non-concordant pairs. Multivariable logistic regression models by generalized estimating equations were applied to examine odds of optimal adherence associated with age and/or sex concordance. Among 51,874 pairs of new statin users and 1,562 prescribers, no influence of age concordance on the odds of optimal adherence could be detected (aOR = 1.02, 95%CI 0.97 to 1.07). The association between sex concordance and optimal statin adherence was stronger but failed to reach statistical significance by a very small margin (aOR=1.05, 95%CI 1.00 to 1.11). It suggested that the potential for an important influence of sex concordance remains and should be investigated in other health care settings. Study 3 – The overall impact of physicians on medication non-adherence The third study aimed to quantify the overall impact of physicians on optimal statin adherence. We identified the prescriber for each new statin user and measured each patient’s adherence one-year after the initial dispensation. The overall physician impact on optimal medication adherence (i.e., PDC >= 80%) was estimated from the intraclass correlation coefficient (ICC) derived from a random intercept model controlled by numerous patient-level variables (e.g., sex, residence, income, etc.). We also examined the impact of unmeasured physician factors or latent effects based on the ICC of a random intercept model controlled by both patient variables and physician-level factors (e.g., country of medical training, remuneration type, statin patient count, etc.). Finally, we estimated the impact of specific physician-level factors [sex, country of medical training, years in practice, remuneration type, number of patients, and number of patients taking a statin (statin patient count)]. Unadjusted odds ratios (uOR) for each factor were generated from logistic regression models; adjusted odds ratios (aORs) were obtained from non-linear mixed-effects logistic regression models adjusted by patient-level variables. Our results were derived from 51,874 new statin users. Addition of the physician effect to a model consisting of multiple patient-level factors only explained an additional 6.4% of the observed variance in adherence between patients, of which physician-level factors had a minimal contribution. The vast majority of the overall physician impact (5.2% out of a possible 6.4%) was attributed to a “latent effect” of the prescriber. The results suggest that the overall impact of prescribers on optimal statin adherence appears to be very limited. Future research Research on the influence of physicians should continue with different types of medications and conditions. Also, specific factors such as COC, type of physician remuneration, sex concordance, and country of medical education require further study to help understand the complex role of physicians and potential new targets for improving medication adherence