Automated Analysis of Fluency Behaviors in Aphasia.

PURPOSE: This study explored the use of an automated language analysis tool, FLUCALC, for measuring fluency in aphasia. The purpose was to determine whether CLAN's FLUCALC command could produce efficient, objective outcome measures for salient aspects of fluency in aphasia. METHOD: The FLUCALC command was used on CHAT transcripts of Cinderella stories from people with aphasia (PWA; n = 281) and controls (n = 257) in the AphasiaBank database. RESULTS: PWA produced significantly fewer total words, fewer words per minute, more pausing, more repetitions, more revisions, and more phonological fragments than controls, with only one exception: The Wernicke's group was similar to the control group in percentage of filled pauses. Individuals with Broca's aphasia had significantly longer inter-utterance pauses and fewer total words than all other aphasia groups. Both the Broca's and conduction aphasia groups had higher percentages of phrase repetitions than the NABW (NotAphasicByWAB) group. The conduction aphasia group also had a higher percentage of phrase revisions than the NABW and the anomic aphasia groups. Principal components analysis revealed two principal components that accounted for around 60% of the variance and related to quantity of output, rate of speech, and quality of output. The Gaussian mixture models showed that the participants clustered in three groups, which corresponded predominantly to the controls, the nonfluent aphasia group, and the remaining aphasia groups (all classically fluent aphasia types). CONCLUSIONS: FLUCALC is an efficient way to measure objective fluency behaviors in language samples in aphasia. Automated analyses of objective fluency behaviors on large samples of adults with and without aphasia can produce measures that can be used by researchers and clinicians to better understand and track salient aspects of fluency in aphasia. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.25979863.

Enhancing the Classification of Aphasia: A Statistical Analysis Using Connected Speech.

Background: Large shared databases and automated language analyses allow for the application of new data analysis techniques that can shed new light on the connected speech of people with aphasia (PWA). Aims: To identify coherent clusters of PWA based on language output using unsupervised statistical algorithms and to identify features that are most strongly associated with those clusters. Methods & Procedures: Clustering and classification methods were applied to language production data from 168 PWA. Language samples were from a standard discourse protocol tapping four genres: free speech personal narratives, picture descriptions, Cinderella storytelling, procedural discourse. Outcomes & Results: Seven distinct clusters of PWA were identified by the K-means algorithm. Using the random forests algorithm, a classification tree was proposed and validated, showing 91% agreement with the cluster assignments. This representative tree used only two variables to divide the data into distinct groups: total words from free speech tasks and total closed class words from the Cinderella storytelling task. Conclusion: Connected speech data can be used to distinguish PWA into coherent groups, providing insight into traditional aphasia classifications, factors that may guide discourse research and clinical work.

A Comparison of Manual Versus Automated Quantitative Production Analysis of Connected Speech.

Purpose Analysis of connected speech in the field of adult neurogenic communication disorders is essential for research and clinical purposes, yet time and expertise are often cited as limiting factors. The purpose of this project was to create and evaluate an automated program to score and compute the measures from the Quantitative Production Analysis (QPA), an objective and systematic approach for measuring morphological and structural features of connected speech. Method The QPA was used to analyze transcripts of Cinderella stories from 109 individuals with acute-subacute left hemisphere stroke. Regression slopes and residuals were used to compare the results of manual scoring and automated scoring using the newly developed C-QPA command in CLAN, a set of programs for automatic analysis of language samples. Results The C-QPA command produced two spreadsheet outputs: an analysis spreadsheet with scores for each utterance in the language sample, and a summary spreadsheet with 18 score totals from the analysis spreadsheet and an additional 15 measures derived from those totals. Linear regression analysis revealed that 32 of the 33 measures had good agreement; auxiliary complexity index was the one score that did not have good agreement. Conclusions The C-QPA command can be used to perform automated analyses of language transcripts, saving time and training and providing reliable and valid quantification of connected speech. Transcribing in CHAT, the CLAN editor, also streamlined the process of transcript preparation for QPA and allowed for precise linking of media files to language transcripts for temporal analyses.

Optimism versus pessimism as predictors of physical health: A comprehensive reanalysis of dispositional optimism research.

Prior research has related dispositional optimism to physical health. Traditionally, dispositional optimism is treated as a bipolar construct, anchored at one end by optimism and the other by pessimism. Optimism and pessimism, however, may not be diametrically opposed, but rather may reflect 2 independent, but related dimensions. This article reports a reanalysis of data from previously published studies on dispositional optimism. The reanalysis was designed to evaluate whether the presence of optimism or the absence of pessimism predicted positive physical health more strongly. Relevant literatures were screened for studies relating dispositional optimism to physical health. Authors of relevant studies were asked to join a consortium, the purpose of which was to reanalyze previously published data sets separating optimism and pessimism into distinguishable components. Ultimately, data were received from 61 separate samples (N = 221,133). Meta-analytic analysis of data in which optimism and pessimism were combined into an overall index (the typical procedure) revealed a significant positive association with an aggregated measure of physical health outcomes (r = .026, p < .001), as did meta-analytic analyses with the absence of pessimism (r = .029, p < .001) and the presence of optimism (r = .011, p < .018) separately. The effect size for pessimism was significantly larger than the effect size for optimism (Z = -2.403, p < .02). Thus, the absence of pessimism was more strongly related to positive health outcomes than was the presence of optimism. Implications of the findings for future research and clinical interventions are discussed. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Association Between the Release of Netflix's 13 Reasons Why and Suicide Rates in the United States: An Interrupted Time Series Analysis.

OBJECTIVE: To estimate the association between the release of the Netflix series 13 Reasons Why and suicide rates in the United States. METHOD: Using segmented quasi-Poisson regression and Holt-Winters forecasting models, we assessed monthly rates of suicide among individuals aged 10 to 64 years grouped into 3 age categories (10-17, 18-29, and 30-64 years) between January 1, 2013, and December 31, 2017, before and after the release of 13 Reasons Why on March 31, 2017. We also assessed the impact of the show's release on a control outcome, homicide deaths. RESULTS: After accounting for seasonal effects and an underlying increasing trend in monthly suicide rates, the overall suicide rate among 10- to 17-year-olds increased significantly in the month immediately following the release of 13 Reasons Why (incidence rate ratio [IRR], 1.29; 95% CI, 1.09-1.53); Holt-Winters forecasting revealed elevated observed suicide rates in the month after release and in two subsequent months, relative to corresponding forecasted rates. Contrary to expectations, these associations were restricted to boys. Among 18- to 29-year-olds and 30- to 64-year-olds, we found no significant change in level or trend of suicide after the show's release, both overall and by sex. The show's release had no apparent impact in the control analyses of homicide deaths within any age group. CONCLUSION: The release of 13 Reasons Why was associated with a significant increase in monthly suicide rates among US youth aged 10 to 17 years. Caution regarding the exposure of children and adolescents to the series is warranted.

Data and methods for studying commercial motor vehicle driver fatigue, highway safety and long-term driver health.

This article summarizes the recommendations on data and methodology issues for studying commercial motor vehicle driver fatigue of a National Academies of Sciences, Engineering, and Medicine study. A framework is provided that identifies the various factors affecting driver fatigue and relating driver fatigue to crash risk and long-term driver health. The relevant factors include characteristics of the driver, vehicle, carrier and environment. Limitations of existing data are considered and potential sources of additional data described. Statistical methods that can be used to improve understanding of the relevant relationships from observational data are also described. The recommendations for enhanced data collection and the use of modern statistical methods for causal inference have the potential to enhance our understanding of the relationship of fatigue to highway safety and to long-term driver health.

Automated Proposition Density Analysis for Discourse in Aphasia.

Purpose: This study evaluates how proposition density can differentiate between persons with aphasia (PWA) and individuals in a control group, as well as among subtypes of aphasia, on the basis of procedural discourse and personal narratives collected from large samples of participants. Method: Participants were 195 PWA and 168 individuals in a control group from the AphasiaBank database. PWA represented 6 aphasia types on the basis of the Western Aphasia Battery-Revised (Kertesz, 2006). Narrative samples were stroke stories for PWA and illness or injury stories for individuals in the control group. Procedural samples were from the peanut-butter-and-jelly-sandwich task. Language samples were transcribed using Codes for the Human Analysis of Transcripts (MacWhinney, 2000) and analyzed using Computerized Language Analysis (MacWhinney, 2000), which automatically computes proposition density (PD) using rules developed for automatic PD measurement by the Computerized Propositional Idea Density Rater program (Brown, Snodgrass, & Covington, 2007; Covington, 2007). Results: Participants in the control group scored significantly higher than PWA on both tasks. PD scores were significantly different among the aphasia types for both tasks. Pairwise comparisons for both discourse tasks revealed that PD scores for the Broca's group were significantly lower than those for all groups except Transcortical Motor. No significant quadratic or linear association between PD and severity was found. Conclusion: Proposition density is differentially sensitive to aphasia type and most clearly differentiates individuals with Broca's aphasia from the other groups.

Depression and Anxiety as Risk Factors for Morbidity and Mortality After Organ Transplantation: A Systematic Review and Meta-Analysis.

BACKGROUND: Depression and anxiety are common mental health problems in transplant populations. There is mixed evidence concerning whether they increase morbidity and mortality risks after transplantation. If such associations exist, additional risk reduction strategies may be needed. METHODS: Four bibliographic databases were searched from 1981 through September 2014 for studies prospectively examining whether depression or anxiety (determined with diagnostic evaluations or standardized symptom scales) affected risk for posttransplant mortality, graft loss, acute graft rejection, chronic rejection, cancer, infection, and rehospitalization. RESULTS: Twenty-seven studies (10 heart, total n = 1738; 6 liver, n = 1063; 5 kidney, n = 49515; 4 lung, n = 584; 1 pancreas, n = 80; 1 mixed recipient sample, n = 205) were identified. In each, depression and/or anxiety were typically measured before or early after transplantation. Follow-up for outcomes was a median of 5.8 years (range, 0.50-18.0). Depression increased the relative risk (RR) of mortality by 65% (RR, 1.65; 95% confidence interval [95% CI], 1.34-2.05; 20 studies). Meta-regression indicated that risk was stronger in studies that did (vs did not) control for potential confounders (P = .032). Risk was unaffected by type of transplant or other study characteristics. Depression increased death-censored graft loss risk (RR, 1.65; 95% CI, 1.21-2.26, 3 studies). Depression was not associated with other morbidities (each morbidity was assessed in 1-4 studies). Anxiety did not significantly increase mortality risk (RR, 1.39; 95% CI, 0.85-2.27, 6 studies) or morbidity risks (assessed in single studies). CONCLUSIONS: Depression increases risk for posttransplant mortality. Few studies considered morbidities; the depression-graft loss association suggests that linkages with morbidities deserve greater attention. Depression screening and treatment may be warranted, although whether these activities would reduce posttransplant mortality requires study.

Suicide Trends Among Elementary School-Aged Children in the United States From 1993 to 2012.

IMPORTANCE: Suicide is a leading cause of death among school-aged children younger than 12 years but little is known about the epidemiology of suicide in this age group. OBJECTIVE: To describe trends in suicide among US children younger than 12 years by sociodemographic group and method of death. DESIGN, SETTING, AND PARTICIPANTS: Period trend analysis of national mortality data on suicide in children aged 5 to 11 years in the United States from January 1, 1993, to December 31, 2012. Data were analyzed per 5-year periods, between 1993 to 1997 and 2008 to 2012. MAIN OUTCOMES AND MEASURES: Number of suicide deaths and crude suicide rates. Period trends in rates of suicide were estimated using negative binomial regression incidence rate ratios (IRRs). RESULTS: The overall suicide rate among children aged 5 to 11 years remained stable between 1993 to 1997 and 2008 to 2012 (from 1.18 to 1.09 per 1 million; IRR = 0.96; 95% CI, 0.90-1.03). However, the suicide rate increased significantly in black children (from 1.36 to 2.54 per 1 million; IRR = 1.27; 95% CI, 1.11-1.45) and decreased in white children (from 1.14 to 0.77 per 1 million; IRR = 0.86; 95% CI, 0.79-0.94). The overall firearm suicide rate (IRR = 0.69; 95% CI, 0.57-0.85) and firearm suicide rate among white boys (IRR = 0.72; 95% CI, 0.59-0.88) decreased significantly during the study. The rate of suicide by hanging/suffocation increased significantly in black boys (IRR = 1.35; 95% CI, 1.14-1.61), although the overall change in suicide rates by hanging/suffocation or other suicide methods did not change during the study. CONCLUSIONS AND RELEVANCE: The stable overall suicide rate in school-aged children in the United States during 20 years of study obscured a significant increase in suicide incidence in black children and a significant decrease in suicide incidence among white children. Findings highlight a potential racial disparity that warrants attention. Further studies are needed to monitor these emerging trends and identify risk, protective, and precipitating factors relevant to suicide prevention efforts in children younger than 12 years.

Likelihood-Based Random-Effect Meta-Analysis of Binary Events.

Meta-analysis has been used extensively for evaluation of efficacy and safety of medical interventions. Its advantages and utilities are well known. However, recent studies have raised questions about the accuracy of the commonly used moment-based meta-analytic methods in general and for rare binary outcomes in particular. The issue is further complicated for studies with heterogeneous effect sizes. Likelihood-based mixed-effects modeling provides an alternative to moment-based methods such as inverse-variance weighted fixed- and random-effects estimators. In this article, we compare and contrast different mixed-effect modeling strategies in the context of meta-analysis. Their performance in estimation and testing of overall effect and heterogeneity are evaluated when combining results from studies with a binary outcome. Models that allow heterogeneity in both baseline rate and treatment effect across studies have low type I and type II error rates, and their estimates are the least biased among the models considered.

On becoming a Bayesian: early correspondences between J. Cornfield and L. J. Savage.

Jerome Cornfield was arguably the leading proponent for the use of Bayesian methods in biostatistics during the 1960s. Prior to 1963, however, Cornfield had no publications in the area of Bayesian statistics. At a time when frequentist methods were the dominant influence on statistical practice, Cornfield went against the mainstream and embraced Bayes. The goals of this paper are as follows: (i) to explore how and why this transformation came about and (ii) to provide some sense as to who Cornfield was and the context in which he worked.

Treatment of attention-deficit hyperactivity disorder: patterns of evolving care during the first treatment episode.

OBJECTIVE: This study sought to better understand factors associated with different patterns of treatment among children starting treatment for attention-deficit hyperactivity disorder (ADHD). METHODS: Factors associated with service utilization and pharmacy claims data for 2,077 Medicaid-enrolled children aged six to 12 who started treatment for ADHD between October 2006 and December 2007 in a large mid-Atlantic state were investigated by using logistic regressions and Cox proportional hazard models. RESULTS: A total of 45% of children started ADHD treatment with a psychosocial intervention alone, 41% of children started treatment with medication alone, and 14% of children started treatment with a combination of both treatments. By the end of the treatment episode, 42% of children who initiated treatment with psychosocial interventions alone had added medication. Within six months of starting treatment, approximately 40% of children had discontinued treatment. Among those who continued receiving treatment, a majority received medication, either alone or with a psychosocial intervention. Treatment with a psychosocial intervention was significantly more likely to be initiated among nonwhite versus white children and among younger versus older children. Younger versus older children and African-American versus Caucasian children were significantly more likely to drop out of treatment sooner. CONCLUSIONS: During the first episode of treatment for ADHD, the interventions children received frequently changed, suggesting dissatisfaction with initial treatment. Further research is needed to better understand what underlies the patterns of evolving care so that all families seeking care for children with ADHD may receive preferred and effective treatment.

Meta-Analysis of Rare Binary Adverse Event Data.

We examine the use of fixed-effects and random-effects moment-based meta-analytic methods for analysis of binary adverse event data. Special attention is paid to the case of rare adverse events which are commonly encountered in routine practice. We study estimation of model parameters and between-study heterogeneity. In addition, we examine traditional approaches to hypothesis testing of the average treatment effect and detection of the heterogeneity of treatment effect across studies. We derive three new methods, simple (unweighted) average treatment effect estimator, a new heterogeneity estimator, and a parametric bootstrapping test for heterogeneity. We then study the statistical properties of both the traditional and new methods via simulation. We find that in general, moment-based estimators of combined treatment effects and heterogeneity are biased and the degree of bias is proportional to the rarity of the event under study. The new methods eliminate much, but not all of this bias. The various estimators and hypothesis testing methods are then compared and contrasted using an example dataset on treatment of stable coronary artery disease.

Changes in suicide rates by hanging and/or suffocation and firearms among young persons aged 10-24 years in the United States: 1992-2006.

We examined changes in suicide rates among 10-24-year-olds in the United States from 1992 to 2006. The overall suicide rate and the rate by firearms, poisoning, and other methods declined markedly, whereas the hanging/suffocation rate increased significantly from 1992 to 2006. This increase occurred across every major demographic subgroup, but was most dramatic for females.