Thesis (Ph.D.)--RAND Graduate School, 2006. / Includes bibliographical references.

The purpose of this study was to retrospectively identify risk factors of elderly nursing home residents that may predict the hospitalization of recently admitted elderly nursing home residents. Two groups of randomly chosen residents were studied. The non-hospitalized group (n = 100) were admitted to a nursing home and remained in the facility for at least 90 days. The hospitalized group (n = 100) were discharged to a hospital within 30 days of admission to the nursing home. The presence of HCFA irregularities increased the odds for hospitalization of an elderly nursing home resident by 1.67. Regular medication orders on discharge increased the odds of hospitalization by 1.25. This study showed that the use of the 33 HCFA indicators are useful in identifying elderly nursing home residents that may be at an increased risk of hospitalization.

Several substantive observations formed the basis for this research. First, the observation of stagnating life expectancy in the United States over the first two decades of the 21st century, representing a dubious form of American exceptionalism. Second, evidence suggesting that novel measures of biological aging might provide allow for early evaluation of population-level health trajectories, based on direct observation of health status in still-living people. Third, the opportunity to apply these measures for study of population-level phenomena, using methods routinely used in the fields of sociology, demography, and economics. This dissertation represents a proof-of-concept work to support the application of biological aging measures to population health surveillance. In Chapter 2, I conduct a systematic literature review of novel measures and approaches to the quantification of population aging published since 2000, and identify 3 major classes of novel population aging measures. Biological-aging measures can be understood as a specific application of Sanderson and Scherbov’s α-ages approach, which indexes “true age” to the distribution of some aging-related characteristic in a reference sample. Relative to other novel measures and approaches, however, biological-aging algorithms hold particular promise in their ability to provide direct measures of pre-clinical, aging-related health risk across the entire adult age range of a population. In chapters 3 and 4, I apply published biological aging algorithms to blood-chemistry and organ-test data collected by the National Health and Nutrition Examination Surveys (NHANES) to test whether the U.S. population has grown biologically older over the past two decades, as some interpretations of life expectancy data would suggest, and to evaluate the extent to which selected social and environmental exposures might explain these trends. Formal age-period-cohort analysis revealed consistent period increases in biological aging from 1999-2018; while population aging slowed after the training cohort was measured in NHANES III (1988-1994), aging trajectories have reverted towards early-1990s levels since the turn of the century. Limited evidence of cohort effects was observed, with findings consistent regardless of age, race, and sex – although racial disparities in biological aging persisted over the entire study period. Kitagawa-Blinder-Oaxaca decomposition analysis of four candidate exposures (i.e., BMI, smoking status, blood lead, and urinary polycyclic aromatic hydrocarbon levels) suggested that changes in the distribution of behavioral and environmental risk factors accounted for a substantial proportion of observed period trends and/or racial disparities in biological aging over the first two decades of the 21st century. Broadly, these results suggest that measures of biological aging can provide earlier and more precise readouts of population health trajectories and their drivers, ultimately informing next-generation public health efforts to promote healthy aging and aging health equity.

The focus of this research project was to evaluate individual stakeholder perceptions of the implementation of Structured Decision Making (SDM) as a tool in risk assessment of elder abuse/neglect case referrals in Riverside County Adult Protective Services (APS). The researchers interviewed ten social workers from Riverside County APS, from line workers up to Deputy Director. Constructivist theory was used to develop a joint construct which indicated that the primary benefit of SDM was consistency. Consistency resulted in safety and proctection of the client, social worker and the agency.

published_or_final_version / Community Medicine / Master / Master of Philosophy

Background: Persistently elevated hospitalization rates in the home healthcare setting indicate the need to prioritize patients with undertreated conditions that can lead to negative outcomes. Urinary incontinence affects approximately 40% of older adults in home healthcare, yet often remains unaddressed. This leaves older adults with urinary incontinence at risk for potentially serious complications that can lead to emergency department visits, hospitalizations, and mortality. Multiple comorbidities, co-occurring symptoms, and disparities in care fuel the complexity of older adults in the home healthcare setting. The overall purpose of this dissertation was to leverage natural language processing to understand symptom clusters and factors associated with acute care utilization among older adults with urinary incontinence in home healthcare to improve comprehensive assessment, treatment, and outcomes. The aims of this dissertation were to: 1) identify relevant comorbidities among community-dwelling older adults with urinary incontinence; 2) develop and test a natural language processing algorithm to extract symptom information from home healthcare free-text clinical notes for older adults with urinary incontinence and analyze differences by race or ethnicity; 3) identify symptom clusters among older adults with urinary incontinence in home healthcare and examine differences by sociodemographic and clinical correlates; and 4) determine factors associated with the risk of emergency department visits or hospitalizations among older adults with urinary incontinence in home healthcare, including the impact of symptom clusters. Methods: This dissertation comprised four studies: 1) a scoping review of the literature to identify comorbidities to broadly characterize community-dwelling older adults with urinary incontinence, 2) a secondary analysis of cross-sectional electronic health record data using natural language processing to extract symptoms from free-text clinical notes and analyze differences by race or ethnicity using Chi-square tests and logistic regression models, 3) a secondary analysis of cross-sectional electronic health record data using hierarchical clustering to analyze the natural language processing-extracted symptom variables and examine differences in sociodemographic and clinical correlates using Chi-square tests, and 4) a retrospective secondary analysis of electronic health record data to identify factors, including symptom clusters, associated with emergency department visits or hospitalizations using Chi-square tests and backward stepwise logistic regression. Results: In the scoping review, we synthesized findings from 10 studies that identified comorbidities among community-dwelling older adults with urinary incontinence across neurologic, cardiovascular, respiratory, endocrine, genitourinary, musculoskeletal, and psychologic systems. In the natural language processing study, we identified eight symptoms of older adults with urinary incontinence (i.e., anxiety, constipation, dizziness, syncope, tachycardia, urinary frequency/urgency, urinary hesitancy/retention, and vision impairment/blurred vision) that were extracted from free-text clinical notes from approximately 29% of home healthcare episodes. Compared to White patients, home healthcare episodes for Asian/Pacific Islander, Hispanic, and Black patients were less likely to have any symptoms documented in clinical notes. In the clustering analysis, we identified five distinct symptom clusters: Cluster 1 (anxiety), Cluster 2 (broadly symptomatic), Cluster 3 (dizziness and anxiety), Cluster 4 (constipation, anxiety, and dizziness), and Cluster 5 (no symptoms) that correlate with sociodemographic and clinical characteristics. Finally, in the retrospective analysis, we found that Clusters 1-4 had higher odds of emergency department visits or hospitalizations, in addition to home healthcare episodes for Black and Hispanic patients, males, patients with an unhealed skin ulcer, and patients with a urinary tract infection 14 days prior to home healthcare admission. Conclusion: Older adults with urinary incontinence in home healthcare have complex physical and psychosocial needs, increasing the risk of negative outcomes. Improving comprehensive assessment and treatment for older adults with urinary incontinence is an urgent priority, given high hospitalization rates in home healthcare. Leveraging natural language processing, this dissertation identified key symptom clusters and factors associated with emergency department visits or hospitalizations, providing valuable insight for multidimensional interventions. Findings provide preliminary evidence to inform improvements in clinical practice, healthcare policies, and future research to enhance the care of older adults with urinary incontinence and reduce negative outcomes in the home healthcare setting.