Elena A. Erosheva

The Physical and Mental Health of Lesbian, Gay Male, and Bisexual (LGB) Older Adults: The Role of Key Health Indicators and Risk and Protective Factors

PURPOSE Based on resilience theory, this paper investigates the influence of key health indicators and risk and protective factors on health outcomes (including general health, disability, and depression) among lesbian, gay male, and bisexual (LGB) older adults. DESIGN AND METHODS A cross-sectional survey was conducted with LGB older adults, aged 50 and older (N = 2,439). Logistic regressions were conducted to examine the contributions of key health indicators (access to health care and health behaviors), risk factors (lifetime victimization, internalized stigma, and sexual identity concealment), and protective factors (social support and social network size) to health outcomes, when controlling for background characteristics. RESULTS The findings revealed that lifetime victimization, financial barriers to health care, obesity, and limited physical activity independently and significantly accounted for poor general health, disability, and depression among LGB older adults. Internalized stigma was also a significant predictor of disability and depression. Social support and social network size served as protective factors, decreasing the odds of poor general health, disability, and depression. Some distinct differences by gender and sexual orientation were also observed. IMPLICATIONS High levels of poor general health, disability, and depression among LGB older adults are of major concern. These findings highlight the important role of key risk and protective factors, which significantly influences health outcomes among LGB older adults. Tailored interventions must be developed to address the distinct health issues facing this historically disadvantaged population.

Describing disability through individual-level mixture models for multivariate binary data

Data on functional disability are of widespread policy interest in the United States, especially with respect to planning for Medicare and Social Security for a growing population of elderly adults. We consider an extract of functional disability data from the National Long Term Care Survey (NLTCS) and attempt to develop disability profiles using variations of the Grade of Membership (GoM) model. We first describe GoM as an individual-level mixture model that allows individuals to have partial membership in several mixture components simultaneously. We then prove the equivalence between individual-level and population-level mixture models, and use this property to develop a Markov Chain Monte Carlo algorithm for Bayesian estimation of the model. We use our approach to analyze functional disability data from the NLTCS.

Block-LDA: Jointly modeling entity-annotated text and entity-entity links.

Identifying latent groups of entities from observed interactions between pairs of entities is a frequently encountered problem in areas like analysis of protein interactions and social networks. We present a model that combines aspects of mixed membership stochastic block models and topic models to improve entity-entity link modeling by jointly modeling links and text about the entities that are linked. We apply the model to two datasets: a protein-protein interaction (PPI) dataset supplemented with a corpus of abstracts of scientific publications annotated with the proteins in the PPI dataset and an Enron email corpus. The model is evaluated by inspecting induced topics to understand the nature of the data and by quantitative methods such as functional category prediction of proteins and perplexity which exhibit improvements when joint modeling is used over baselines that use only link or text information.

Bayesian Mixed Membership Models for Soft Clustering and Classification

The paper describes and applies a fully Bayesian approach to soft clustering and classification using mixed membership models. Our model structure has assumptions on four levels: population, subject, latent variable, and sampling scheme. Population level assumptions describe the general structure of the population that is common to all subjects. Subject level assumptions specify the distribution of observable responses given individual membership scores. Membership scores are usually unknown and hence we can also view them as latent variables, treating them as either fixed or random in the model. Finally, the last level of assumptions specifies the number of distinct observed characteristics and the number of replications for each characteristic. We illustrate the flexibility and utility of the general model through two applications using data from: (i) the National Long Term Care Survey where we explore types of disability; (ii) abstracts and bibliographies from articles published in The Proceedings of the National Academy of Sciences. In the first application we use a Monte Carlo Markov chain implementation for sampling from the posterior distribution. In the second application, because of the size and complexity of the data base, we use a variational approximation to the posterior. We also include a guide to other applications of mixed membership modeling.

Use of Spoken and Written Japanese Did Not Protect Japanese-American Men From Cognitive Decline in Late Life

OBJECTIVES Spoken bilingualism may be associated with cognitive reserve. Mastering a complicated written language may be associated with additional reserve. We sought to determine if midlife use of spoken and written Japanese was associated with lower rates of late life cognitive decline. METHODS Participants were second-generation Japanese-American men from the Hawaiian island of Oahu, born 1900-1919, free of dementia in 1991, and categorized based on midlife self-reported use of spoken and written Japanese (total n included in primary analysis = 2,520). Cognitive functioning was measured with the Cognitive Abilities Screening Instrument scored using item response theory. We used mixed effects models, controlling for age, income, education, smoking status, apolipoprotein E e4 alleles, and number of study visits. RESULTS Rates of cognitive decline were not related to use of spoken or written Japanese. This finding was consistent across numerous sensitivity analyses. DISCUSSION We did not find evidence to support the hypothesis that multilingualism is associated with cognitive reserve.

Self-rated health among foreign- and U.S.-born Asian Americans: a test of comparability.

Objectives:We investigated differences between foreign- and U.S.-born Asian Americans in self-rating their physical and mental health. In particular, we tested whether the foreign-born respondents underreport the extreme categories of the scale as compared with U.S.-born respondents. Methods:We analyzed data from the National Latino and Asian American Study to examine whether immigrants are less likely to use the extreme ends of the 5-category self-rated health scales than their U.S.-born counterparts. We used propensity score matching to derive groups of U.S.- and foreign-born Asian Americans who share similar demographic and health characteristics. We defined propensity scores as predicted probabilities of being U.S. born, given individual background characteristics. The propensity score framework allowed us to make descriptive comparisons of self-rated health responses controlling for background characteristics. We used log-linear symmetry models to examine cross-tabulations of self-rated physical and mental health reports in matched pairs by the 2 (extreme and nonextreme) and 5 (“excellent,” “very good,” “good,” “fair,” and “poor”) categories. Results:Controlling for background characteristics, we found no evidence that foreign-born Asian Americans are less likely to endorse extreme categories in self-rated physical or mental health than U.S.-born Asian Americans, as well as no evidence of imbalances in endorsement of any particular self-rated health category between the 2 groups. Conclusions:Controlling for demographic and health characteristics, we find no systematic differences between foreign- and U.S.-born Asian Americans in reporting self-rated physical and mental health on the 5-category scales from “excellent” to “poor.”

CSF biomarker associations with change in hippocampal volume and precuneus thickness: implications for the Alzheimer’s pathological cascade

Neurofibrillary tangles (NFT) and amyloid plaques are hallmark neuropathological features of Alzheimer’s disease (AD). There is some debate as to which neuropathological feature comes first in the disease process, with early autopsy studies suggesting that NFT develop first, and more recent neuroimaging studies supporting the early role of amyloid beta (Aβ) deposition. Cerebrospinal fluid (CSF) biomarkers of Aβ42 and hyperphosphorylated tau (p-tau) have been shown to serve as in vivo proxy measures of amyloid plaques and NFT, respectively. The aim of this study was to examine the association between CSF biomarkers and rate of atrophy in the precuneus and hippocampus. These regions were selected because the precuneus appears to be affected early and severely by Aβ deposition, and the hippocampus similarly by NFT pathology. We predicted (1) baseline Aβ42 would be related to accelerated rate of cortical thinning in the precuneus and volume loss in the hippocampus, with the latter relationship expected to be weaker, (2) baseline p-tau181p would be related to accelerated rate of hippocampal atrophy and cortical thinning in the precuneus, with the latter relationship expected to be weaker. Using all ADNI cohorts, we fitted separate linear mixed-effects models for changes in hippocampus and precuneus longitudinal outcome measures with baseline CSF biomarkers modeled as predictors. Results partially supported our hypotheses: Both baseline p-tau181p and Aβ42 were associated with hippocampal atrophy over time. Neither p-tau181p nor Aβ42 were significantly related to cortical thinning in the precuneus over time. However, follow-up analyses demonstrated that having abnormal levels of both Aβ42 and p-tau181p was associated with an accelerated rate of atrophy in both the hippocampus and precuneus. Results support early effects of Aβ in the Alzheimer’s disease process, which are less apparent than and perhaps dependent on p-tau effects as the disease progresses. However, amyloid deposition alone may be insufficient for emergence of significant morphometric changes and clinical symptoms.

Care and Feeding of Topic Models: Problems, Diagnostics, and Improvements

@inbook{Boyd-Graber:Mimno:Newman-2014, Publisher = {CRC Press}, Address = {Boca Raton, Florida}, Title = {Care and Feeding of Topic Models: Problems, Diagnostics, and Improvements}, Url = {docs/2014_book_chapter_care_and_feeding.pdf}, Series = {CRC Handbooks of Modern Statistical Methods}, Booktitle = {Handbook of Mixed Membership Models and Their Applications}, Author = {Jordan Boyd-Graber and David Mimno and David Newman}, Year = {2014}, Editor = {Edoardo M. Airoldi and David Blei and Elena A. Erosheva and Stephen E. Fienberg}, }

Reconceptualizing the classification of PNAS articles.

PNAS article classification is rooted in long-standing disciplinary divisions that do not necessarily reflect the structure of modern scientific research. We reevaluate that structure using latent pattern models from statistical machine learning, also known as mixed-membership models, that identify semantic structure in co-occurrence of words in the abstracts and references. Our findings suggest that the latent dimensionality of patterns underlying PNAS research articles in the Biological Sciences is only slightly larger than the number of categories currently in use, but it differs substantially in the content of the categories. Further, the number of articles that are listed under multiple categories is only a small fraction of what it should be. These findings together with the sensitivity analyses suggest ways to reconceptualize the organization of papers published in PNAS.

Bayesian Nonnegative Matrix Factorization with Stochastic Variational Inference

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