Racial Disparities in COVID-19 Experiences Among Older Adults With Disabling Conditions

OBJECTIVES

This paper examines the health, work, and financial experiences of older adults with disabling conditions during the COVID-19 pandemic. It also explores the role of county- and state-level conditions in these experiences.

METHODS

Using data from the 2020 Health and Retirement Study, we estimated regression models to assess differences in outcomes between those with and without disabling conditions and by race/ethnicity. We used multilevel modeling to assess whether and how county or state factors might be associated with the differences in these effects.

RESULTS

Older adults with disabilities were more likely to report experiencing financial hardships, delaying health care, and experiencing effects on work than those without disabilities; these differences are heighted between race and ethnicity. Older adults with disabilities were more likely to live in counties with greater social vulnerability.

DISCUSSION

This work underscores the importance of developing a robust, disability-inclusive public health response that protects older adults.

The effect of Medicaid expansion on the take-up of disability benefits by race and ethnicity

Public disability programs provide financial support to 12 million working-age individuals per year, though not all eligible individuals take up these programs. Mixed evidence exists regarding the impact of Medicaid eligibility expansion on program take-up, and even less is known about the relationship between Medicaid expansion and racial and ethnic disparities in take-up. Using 2009-2020 Current Population Survey data, we compare changes in Supplemental Security Income (SSI) and Social Security Disability Insurance (SSDI) take-up among respondents with disabilities living in Medicaid expansion states to respondents with disabilities living in non-expansion states, before and after Medicaid expansion. We further explore heterogeneity by race/ethnicity. We find that Medicaid expansion reduced SSI take-up by 10% overall, particularly among White and Hispanic respondents (10% and 21%, respectively). Medicaid expansion increased SSDI take-up by 8% overall, particularly among White and Black respondents (9% and 11%, respectively). Moreover, we find that Medicaid expansion reduced the probability that respondents with disabilities had employer-sponsored health insurance by approximately 8%, suggesting that expansion may have reduced job-lock among the SSDI-eligible, contributing to the observed increase in SSDI take-up.

Volunteering and Changes in Cardiovascular Biomarkers: Longitudinal Evidence From the Health and Retirement Study

Background and Objectives

Growing body of research shows that volunteering is beneficial for those served, the volunteers, and the larger communities. However, major challenges remain that hinder the practical implications for volunteer activity as a public health intervention, including potential selection effects, lack of longitudinal studies that adjust for baseline characteristics, and a paucity of studies that consider multiple physical health outcomes in a single model.

Research Design and Methods

Data from 2006 to 2016 waves of the Health and Retirement Study (2006-2016) were used (N = 18,847). Outcome-wide analyses were utilized to evaluate if changes in volunteering between 2006/2008 (t0) and 2010/2012 (t1) were associated with 7 cardiovascular disease biomarkers 4 years later (2014/2016, t2). These models were adjusted for demographic factors, socioeconomic status, health behaviors, chronic conditions, baseline biomarkers, and volunteering. Additionally, selection into volunteering and attrition were taken into account.

Results

Compared with nonvolunteers, volunteering more than 200 hr a year was associated with a lower risk for clinically high diastolic blood pressure. In addition, increased volunteering effort (change from 1 to 99 hr at t0 to >100 hr at t1) was associated with a lower likelihood of clinically high systolic and diastolic blood pressure levels. Sustained high volunteering (>100 hr at both t0 and t1) was associated with lower diastolic blood pressure.

Discussion and Implications

The current study adds to the evidence on the health benefits of volunteering for adults 50 and older by inferring a potential causal link between high-intensity volunteering and reduced blood pressure.

Cardiometabolic Risk Trajectory Among Older Americans: Findings From the Health and Retirement Study

BACKGROUND

Cardiometabolic risk (CMR) is a key indicator of physiological decline with age, but age-related declines in a nationally representative older US population have not been previously examined.

METHODS

We examined the trajectory of CMR over 8 years of aging, from 2006/2008 to 2014/2016, among 3528 people older than age 50 in the Health and Retirement Study. We used growth curve models to examine change in total CMR as well as in individual cardiometabolic biomarkers to understand how baseline differences and rates of change vary across sociodemographic characteristics, by smoking status, and medication use.

RESULTS

Total CMR did not change among respondents who survived over 8 years. Despite significant differences in CMR across demographic and education groups at baseline, the pace of change with age did not differ by these characteristics. Among individual biomarkers, risk levels of diastolic blood pressure, resting heart rate, and total cholesterol decreased over 8 years while glycosylated hemoglobin, waist circumference, and pulse pressure increased over that time. Both the statistical significance levels and the magnitudes of the reduction over time with age in diastolic blood pressure, resting heart rate, and total cholesterol in models adjusted for age, race/ethnicity, gender, smoking, and education were reduced after controlling for blood pressure and cholesterol medication.

CONCLUSIONS

The relatively constant total CMR level over 8 years occurred because some indicators improved with age while some deteriorated in this period. Medication use contributed to the improvement in blood pressure, resting heart rate, and total cholesterol.

Linking Free Text Documentation of Functioning and Disability to the ICF With Natural Language Processing

Background: Invaluable information on patient functioning and the complex interactions that define it is recorded in free text portions of the Electronic Health Record (EHR). Leveraging this information to improve clinical decision-making and conduct research requires natural language processing (NLP) technologies to identify and organize the information recorded in clinical documentation.

Methods: We used natural language processing methods to analyze information about patient functioning recorded in two collections of clinical documents pertaining to claims for federal disability benefits from the U.S. Social Security Administration (SSA). We grounded our analysis in the International Classification of Functioning, Disability, and Health (ICF), and used the Activities and Participation domain of the ICF to classify information about functioning in three key areas: mobility, self-care, and domestic life. After annotating functional status information in our datasets through expert clinical review, we trained machine learning-based NLP models to automatically assign ICF categories to mentions of functional activity.

Results: We found that rich and diverse information on patient functioning was documented in the free text records. Annotation of 289 documents for Mobility information yielded 2,455 mentions of Mobility activities and 3,176 specific actions corresponding to 13 ICF-based categories. Annotation of 329 documents for Self-Care and Domestic Life information yielded 3,990 activity mentions and 4,665 specific actions corresponding to 16 ICF-based categories. NLP systems for automated ICF coding achieved over 80% macro-averaged F-measure on both datasets, indicating strong performance across all ICF categories used.

Conclusions: Natural language processing can help to navigate the tradeoff between flexible and expressive clinical documentation of functioning and standardizable data for comparability and learning. The ICF has practical limitations for classifying functional status information in clinical documentation but presents a valuable framework for organizing the information recorded in health records about patient functioning. This study advances the development of robust, ICF-based NLP technologies to analyze information on patient functioning and has significant implications for NLP-powered analysis of functional status information in disability benefits management, clinical care, and research.

Automated Coding of Under-Studied Medical Concept Domains: Linking Physical Activity Reports to the International Classification of Functioning, Disability, and Health

Linking clinical narratives to standardized vocabularies and coding systems is a key component of unlocking the information in medical text for analysis. However, many domains of medical concepts, such as functional outcomes and social determinants of health, lack well-developed terminologies that can support effective coding of medical text. We present a framework for developing natural language processing (NLP) technologies for automated coding of medical information in under-studied domains, and demonstrate its applicability through a case study on physical mobility function. Mobility function is a component of many health measures, from post-acute care and surgical outcomes to chronic frailty and disability, and is represented as one domain of human activity in the International Classification of Functioning, Disability, and Health (ICF). However, mobility and other types of functional activity remain under-studied in the medical informatics literature, and neither the ICF nor commonly-used medical terminologies capture functional status terminology in practice. We investigated two data-driven paradigms, classification and candidate selection, to link narrative observations of mobility status to standardized ICF codes, using a dataset of clinical narratives from physical therapy encounters. Recent advances in language modeling and word embedding were used as features for established machine learning models and a novel deep learning approach, achieving a macro-averaged F-1 score of 84% on linking mobility activity reports to ICF codes. Both classification and candidate selection approaches present distinct strengths for automated coding in under-studied domains, and we highlight that the combination of (i) a small annotated data set; (ii) expert definitions of codes of interest; and (iii) a representative text corpus is sufficient to produce high-performing automated coding systems. This research has implications for continued development of language technologies to analyze functional status information, and the ongoing growth of NLP tools for a variety of specialized applications in clinical care and research.

Within-Couple Personality Concordance Over Time: The Importance of Personality Synchrony for Perceived Spousal Support

OBJECTIVES

Within-couple similarities in personality traits tend to be positively associated with relationship well-being. However, research in this area is typically based on cross-sectional designs, thereby limiting examination of longitudinal personality concordance. Given that life experiences shape within-person change in personality, and that partners within a couple often experience similar life events, investigation of within-couple personality synchrony and associations with marital outcomes is warranted.

METHODS

Using data from 3,988 couples (mean age at baseline = 67.0 years, SD = 9.6), multilevel dyadic growth models estimated within-couple similarity in baseline levels, change, and occasion-to-occasion variability for each of the Big Five personality traits over an 8-year follow-up. Bivariate growth models examined the effect of within-couple similarity on perceived spousal support, accounting for dependency within couples.

RESULTS

Adjusting for baseline age, education, functional ability, and relationship length, analyses revealed within-couple concordance between baseline levels of all 5 personality traits, as well as correlated within-couple fluctuations in neuroticism, extraversion, and openness over time. Similarity in openness, agreeableness, and neuroticism trajectories predicted spousal support. Couples were most similar in openness, showing correlated intercepts, change, and variability, and this longitudinal synchrony was particularly important for perceived spousal support in women.

DISCUSSION

These findings provide evidence for longitudinal personality synchrony over time within older adult couples. Further, concordance in neuroticism, extraversion, and openness predicted perceived spousal support, though there may be some gender differences in personality dynamics and relationship well-being. Effects of similarity were relatively small compared to actor and partner effects of these traits.

Depression in Later Life: The Role of Adult Children's College Education for Older Parents' Mental Health in the United States

OBJECTIVES

Research on the socioeconomic gradient in mental health links disadvantaged family background with subsequent symptoms of depression, demonstrating the "downstream" effect of parental resources on children's mental health. This study takes a different approach by evaluating the "upstream" influence of adult children's educational attainment on parents' depressive symptoms.

METHODS

Using longitudinal data from the U.S. Health and Retirement Study (N = 106,517 person-years), we examine whether children's college attainment influences their parents' mental health in later life and whether this association increases with parental age. We also assess whether the link between children's college completion and parents' depression differs by parents' own education.

RESULTS

Parents with children who completed college have significantly lower levels of depressive symptoms than parents without college-educated children, although the gap between parents narrows with age. In addition, at baseline, parents with less than a high school education were more positively affected by their children's college completion than parents who themselves had a college education, a finding which lends support to theories of resource substitution.

DISCUSSION

Offspring education is an overlooked resource that can contribute to mental health disparities among older adults in a country with unequal access to college educations.

Predictive Bayesian selection of multistep Markov chains, applied to the detection of the hot hand and other statistical dependencies in free throws

Consider the problem of modelling memory effects in discrete-state random walks using higher-order Markov chains. This paper explores cross-validation and information criteria as proxies for a model's predictive accuracy. Our objective is to select, from data, the number of prior states of recent history upon which a trajectory is statistically dependent. Through simulations, I evaluate these criteria in the case where data are drawn from systems with fixed orders of history, noting trends in the relative performance of the criteria. As a real-world illustrative example of these methods, this manuscript evaluates the problem of detecting statistical dependencies in shot outcomes in free throw shooting. Over three National Basketball Association (NBA) seasons analysed, several players exhibited statistical dependencies in free throw hitting probability of various types-hot handedness, cold handedness and error correction. For the 2013-2014 to 2015-2016 NBA seasons, I detected statistical dependencies in 23% of all player-seasons. Focusing on a single player, in two of these three seasons, LeBron James shot a better percentage after an immediate miss than otherwise. Conditioning on the previous outcome makes for a more-predictive model than treating free throw makes as independent. When extended specifically to LeBron James' 2016-2017 season, a model depending on the previous shot (single-step Markovian) does not clearly beat a model with independent outcomes. An error-correcting variable length model of two parameters, where James shoots a higher percentage after a missed free throw than otherwise, is more predictive than either model.

Asymptotic convergence in distribution of the area bounded by prevalence-weighted Kaplan-Meier curves using empirical process modelling

The Kaplan-Meier product-limit estimator is a simple and powerful tool in time to event analysis. An extension exists for populations stratified into cohorts where a population survival curve is generated by weighted averaging of cohort-level survival curves. For making population-level comparisons using this statistic, we analyse the statistics of the area between two such weighted survival curves. We derive the large sample behaviour of this statistic based on an empirical process of product-limit estimators. This estimator was used by an interdisciplinary National Institutes of Health-Social Security Administration team in the identification of medical conditions to prioritize for adjudication in disability benefits processing.