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©American Psychological Association, 2019. This paper is not the copy of record and may not exactly replicate the authoritative document published in the APA journal. Please do not copy or cite without author's permission. The final article is available, upon publication, at: https://doi.org/10.1037/abn0000397. Acoustic analysis of vocal expression offers a potentially inexpensive, unobtrusive, and highly sensitive biobehavioral measure of serious mental illness (SMI)-related issues. Despite literature documenting its use for understanding SMI, prior studies have largely ignored that vocal expression is highly dynamic within individuals over time. We employed ambulatory vocal assessment from SMI outpatients to understand links between vocal expression, SMI symptoms, and affective states. Vocal samples were analyzed using a validated acoustic analysis protocol. Overall, vocal expression was not directly related to SMI symptoms but changed as a function of state and state by symptom interactions. The results suggest that (a) vocal expression fails to modulate across changing affective states in individuals with active SMI symptoms, (b) this lack of modulation may be commonly associated with many SMI symptoms, and (c) vocal analysis can accommodate temporal dynamics.

In our target article, we tested the replicability of 4 popular psychopathology network estimation methods that aim to reveal causal relationships among symptoms of mental illness. We started with the focal data set from the 2 foundational psychopathology network papers (i.e., the National Comorbidity Survey-Replication) and identified the National Survey of Mental Health and Wellbeing as a close methodological match for comparison. We compared the psychopathology networks estimated in each data set-as well as in 10 sets of random split-halves within each data set-with the goal of quantifying the replicability of the network parameters as they are interpreted in the extant psychopathology network literature. We concluded that current psychopathology network methods have limited replicability both within and between samples and thus have limited utility. Here we respond to the 2 commentaries on our target article, concluding that the findings of Steinley, Hoffman, Brusco, and Sher (2017)-along with other recent developments in the literature-provide further conclusive evidence that psychopathology networks have poor replicability and utility. (PsycINFO Database Record

The death of a loved one can trigger a range of emotional, behavioral, motivational and cognitive reactions collectively labeled as "grief." There has been a recent resurgence of empirical interest in grief reactions, and there is now a general consensus that 7%-10% of bereaved individuals experience intense and chronic reactions termed Prolonged Grief (PG). However, there is ongoing debate about how best to conceptualize the construct. Recent studies have advanced this debate by applying a network, or causal systems approach. Whereas traditional views of psychopathology posit symptoms of disorders reflect the independent actions of latent entities, the network approach argues that the symptoms themselves interact to give rise to the disorder. A network conceptualization offers novel approaches to studying the mechanisms that contribute to PG. To date, however, research has focused only on spousal loss and only used a single archival data set. Therefore, in this paper we apply network analysis to examine relationships among PG symptoms in samples of individuals bereaved by loss of a spouse (Study 1, N = 193) and a parent (Study 2, N = 180). Participants completed the PG-13 and a measure of depression. A comparison test suggested the networks produced from each sample were not reliably different. The strongest link in both networks was between yearning and emotional pain. Meaninglessness was relatively central, whereas avoidance was peripheral in both networks. Findings are discussed with reference to theoretical models and the potential benefits a network approach may hold for understanding relationships between symptoms of PG. (PsycINFO Database Record

Two nationally representative epidemiological samples (National Longitudinal Alcohol Epidemiological Survey [NLAES], National Epidemiological Survey of Alcohol and Related Conditions [NESARC]) have been used to track changes in the prevalence of alcohol use disorders (AUDs) between 1992 and 2002 in the United States. Strikingly, estimates from these two datasets suggest that the lifetime prevalence of AUD increased by approximately 67% (from 18.2% to 30.3%) during this timeframe. The purpose of the current paper is to explore potential reasons for these discrepant estimates. Analyses indicated that a vast majority of change in lifetime AUD occurred with respect to alcohol abuse and not alcohol dependence. Most of this increase in abuse was attributable to self-reported changes in hazardous use that did not track with other archival measures of outcomes related to hazardous use in the population. Key methodological differences regarding the frequency requirements for prior-to-past-year alcohol abuse appeared to explain most of the discrepancy in lifetime AUD estimates. These findings, in conjunction with the relative lack of differences in the 12-month prevalence of AUDs, suggest that the discrepant lifetime estimates are likely due to methodological differences in the two surveys. These findings have important implications for substance use and other psychiatric surveillance and epidemiology where meaningful cross-temporal comparisons are desired.