"Insights into ALD & AUD diagnosis and prognosis: Exploring AI and multi-modal data streams".

The rapid evolution of artificial intelligence (AI) and the widespread embrace of digital technologies have ushered in a new era of clinical research and practice in hepatology. Although its potential is far from realization, these significant strides have generated new opportunities to address existing gaps in the delivery of care for patients with liver disease. In this review, we discuss how AI and opportunities for multimodal data integration can improve the diagnosis, prognosis and management of alcohol-associated liver disease (ALD). An emphasis is made on how these approaches will also benefit the detection and management of alcohol use disorder (AUD). Our discussion encompasses challenges and limitations, concluding with a glimpse into the promising future of these advancements.

Differential temporal utility of passively sensed smartphone features for depression and anxiety symptom prediction: a longitudinal cohort study.

While studies show links between smartphone data and affective symptoms, we lack clarity on the temporal scale, specificity (e.g., to depression vs. anxiety), and person-specific (vs. group-level) nature of these associations. We conducted a large-scale (n = 1013) smartphone-based passive sensing study to identify within- and between-person digital markers of depression and anxiety symptoms over time. Participants (74.6% female; M age = 40.9) downloaded the LifeSense app, which facilitated continuous passive data collection (e.g., GPS, app and device use, communication) across 16 weeks. Hierarchical linear regression models tested the within- and between-person associations of 2-week windows of passively sensed data with depression (PHQ-8) or generalized anxiety (GAD-7). We used a shifting window to understand the time scale at which sensed features relate to mental health symptoms, predicting symptoms 2 weeks in the future (distal prediction), 1 week in the future (medial prediction), and 0 weeks in the future (proximal prediction). Spending more time at home relative to one's average was an early signal of PHQ-8 severity (distal ß = 0.219, p = 0.012) and continued to relate to PHQ-8 at medial (ß = 0.198, p = 0.022) and proximal (ß = 0.183, p = 0.045) windows. In contrast, circadian movement was proximally related to (ß = -0.131, p = 0.035) but did not predict (distal ß = 0.034, p = 0.577; medial ß = -0.089, p = 0.138) PHQ-8. Distinct communication features (i.e., call/text or app-based messaging) related to PHQ-8 and GAD-7. Findings have implications for identifying novel treatment targets, personalizing digital mental health interventions, and enhancing traditional patient-provider interactions. Certain features (e.g., circadian movement) may represent correlates but not true prospective indicators of affective symptoms. Conversely, other features like home duration may be such early signals of intra-individual symptom change, indicating the potential utility of prophylactic intervention (e.g., behavioral activation) in response to person-specific increases in these signals.

Key language markers of depression on social media depend on race.

Depression has robust natural language correlates and can increasingly be measured in language using predictive models. However, despite evidence that language use varies as a function of individual demographic features (e.g., age, gender), previous work has not systematically examined whether and how depression's association with language varies by race. We examine how race moderates the relationship between language features (i.e., first-person pronouns and negative emotions) from social media posts and self-reported depression, in a matched sample of Black and White English speakers in the United States. Our findings reveal moderating effects of race: While depression severity predicts I-usage in White individuals, it does not in Black individuals. White individuals use more belongingness and self-deprecation-related negative emotions. Machine learning models trained on similar amounts of data to predict depression severity performed poorly when tested on Black individuals, even when they were trained exclusively using the language of Black individuals. In contrast, analogous models tested on White individuals performed relatively well. Our study reveals surprising race-based differences in the expression of depression in natural language and highlights the need to understand these effects better, especially before language-based models for detecting psychological phenomena are integrated into clinical practice.

A linguistic analysis of dehumanization toward substance use across three decades of news articles.

Introduction: Substances and the people who use them have been dehumanized for decades. As a result, lawmakers and healthcare providers have implemented policies that subjected millions to criminalization, incarceration, and inadequate resources to support health and wellbeing. While there have been recent shifts in public opinion on issues such as legalization, in the case of marijuana in the U.S., or addiction as a disease, dehumanization and stigma are still leading barriers for individuals seeking treatment. Integral to the narrative of "substance users" as thoughtless zombies or violent criminals is their portrayal in popular media, such as films and news. Methods: This study attempts to quantify the dehumanization of people who use substances (PWUS) across time using a large corpus of over 3 million news articles. We apply a computational linguistic framework for measuring dehumanization across three decades of New York Times articles. Results: We show that (1) levels of dehumanization remain high and (2) while marijuana has become less dehumanized over time, attitudes toward other substances such as heroin and cocaine remain stable. Discussion: This work highlights the importance of a holistic view of substance use that places all substances within the context of addiction as a disease, prioritizes the humanization of PWUS, and centers around harm reduction.

Smartphone sensor data estimate alcohol craving in a cohort of patients with alcohol-associated liver disease and alcohol use disorder.

BACKGROUND: Sensors within smartphones, such as accelerometer and location, can describe longitudinal markers of behavior as represented through devices in a method called digital phenotyping. This study aimed to assess the feasibility of digital phenotyping for patients with alcohol-associated liver disease and alcohol use disorder, determine correlations between smartphone data and alcohol craving, and establish power assessment for future studies to prognosticate clinical outcomes. METHODS: A total of 24 individuals with alcohol-associated liver disease and alcohol use disorder were instructed to download the AWARE application to collect continuous sensor data and complete daily ecological momentary assessments on alcohol craving and mood for up to 30 days. Data from sensor streams were processed into features like accelerometer magnitude, number of calls, and location entropy, which were used for statistical analysis. We used repeated measures correlation for longitudinal data to evaluate associations between sensors and ecological momentary assessments and standard Pearson correlation to evaluate within-individual relationships between sensors and craving. RESULTS: Alcohol craving significantly correlated with mood obtained from ecological momentary assessments. Across all sensors, features associated with craving were also significantly correlated with all moods (eg, loneliness and stress) except boredom. Individual-level analysis revealed significant relationships between craving and features of location entropy and average accelerometer magnitude. CONCLUSIONS: Smartphone sensors may serve as markers for alcohol craving and mood in alcohol-associated liver disease and alcohol use disorder. Findings suggest that location-based and accelerometer-based features may be associated with alcohol craving. However, data missingness and low participant retention remain challenges. Future studies are needed for further digital phenotyping of relapse risk and progression of liver disease.

Relationship between self-stigma about alcohol dependence and severity of alcohol drinking and craving.

BACKGROUND: The correlates and consequences of stigma surrounding alcohol use are complex. Alcohol use disorder (AUD) is typically accompanied by self-stigma, due to numerous factors, such as shame, guilt and negative stereotypes. Few studies have empirically examined the possible association between self-stigma and alcohol-related outcomes. OBJECTIVE: To investigate the relationship between self-stigma about alcohol dependence and the severity of alcohol consumption and craving. METHODS: In a sample of 64 participants, the majority of whom had a diagnosis of AUD (51), bivariate correlations were first conducted between Self-Stigma and Alcohol Dependence Scale (SSAD-Apply subscale) scores and Alcohol Use Disorders Identification Test (AUDIT) scores, Alcohol Timeline Follow-Back, Obsessive-Compulsive Drinking Scale (OCDS) scores and Penn Alcohol Cravings Scale scores. Based on the results, regression analyses were conducted with SSAD scores as the predictor and AUDIT and OCDS scores as the outcomes. FINDINGS: SSAD scores positively correlated with AUDIT scores, average drinks per drinking day, number of heavy drinking days and OCDS scores (p<0.001, p=0.014, p=0.011 and p<0.001, respectively). SSAD scores were also found to be a significant predictor of AUDIT and OCDS scores (p<0.001 and p<0.001, respectively), even after controlling for demographics. CONCLUSIONS: Higher levels of self-stigma were associated with more severe AUD, greater alcohol consumption, and more obsessive thoughts and compulsive behaviours related to alcohol. CLINICAL IMPLICATIONS: Our results suggest that potential interventions to reduce self-stigma may lead to improved quality of life and treatment outcomes for individuals with AUD.

Specific associations of passively sensed smartphone data with future symptoms of avoidance, fear, and physiological distress in social anxiety.

Background: Prior literature links passively sensed information about a person's location, movement, and communication with social anxiety. These findings hold promise for identifying novel treatment targets, informing clinical care, and personalizing digital mental health interventions. However, social anxiety symptoms are heterogeneous; to identify more precise targets and tailor treatments, there is a need for personal sensing studies aimed at understanding differential predictors of the distinct subdomains of social anxiety. Our objective was to conduct a large-scale smartphone-based sensing study of fear, avoidance, and physiological symptoms in the context of trait social anxiety over time. Methods: Participants (n = 1013; 74.6 % female; M age = 40.9) downloaded the LifeSense app, which collected continuous passive data (e.g., GPS, communication, app and device use) over 16 weeks. We tested a series of multilevel linear regression models to understand within- and between-person associations of 2-week windows of passively sensed smartphone data with fear, avoidance, and physiological distress on the self-reported Social Phobia Inventory (SPIN). A shifting sensor lag was applied to examine how smartphone features related to SPIN subdomains 2 weeks in the future (distal prediction), 1 week in the future (medial prediction), and 0 weeks in the future (proximal prediction). Results: A decrease in time visiting novel places was a strong between-person predictor of social avoidance over time (distal ß = -0.886, p = .002; medial ß = -0.647, p = .029; proximal ß = -0.818, p = .007). Reductions in call- and text-based communications were associated with social avoidance at both the between- (distal ß = -0.882, p = .002; medial ß = -0.932, p = .001; proximal ß = -0.918, p = .001) and within- (distal ß = -0.191, p = .046; medial ß = -0.213, p = .028) person levels, as well as between-person fear of social situations (distal ß = -0.860, p < .001; medial ß = -0.892, p < .001; proximal ß = -0.886, p < .001) over time. There were fewer significant associations of sensed data with physiological distress. Across the three subscales, smartphone data explained 9-12 % of the variance in social anxiety. Conclusion: Findings have implications for understanding how social anxiety manifests in daily life, and for personalizing treatments. For example, a signal that someone is likely to begin avoiding social situations may suggest a need for alternative types of exposure-based interventions compared to a signal that someone is likely to begin experiencing increased physiological distress. Our results suggest that as a prophylactic means of targeting social avoidance, it may be helpful to deploy interventions involving social exposures in response to decreases in time spent visiting novel places.

Extended impact of the COVID-19 pandemic: Trajectories of mental health and substance use among U.S. adults, September 2020-August 2021.

Background: Americans reported significant increases in mental health and substance use problems after the COVID-19 pandemic outbreak. This can be a product of the pandemic disruptions in everyday life, with some populations being more impacted than others. Objectives: To assess the ongoing impact of the COVID-19 pandemic on mental health and substance use in U.S. adults from September 2020 to August 2021. Methods: Participants included 1056 adults (68.5% women) who participated in a national longitudinal online survey assessing the perceived impact of COVID-19 on daily life, stress, depression and anxiety symptoms, and alcohol and cannabis use at 3-time points from September 2020 to August 2021. Results: Individuals with lower self-reported social status reported the highest perceived impact. Participants' perceived impact of the COVID-19 pandemic on daily life, stress, anxiety, and alcohol use risk significantly decreased over time but remained high. However, there was no change in depressive symptoms and cannabis use. Higher levels of perceived impact of the pandemic significantly predicted both more baseline mental health concerns and lower decreases over time. Lower self-report social status predicted more baseline mental health concerns and smaller decreases in those concerns. Black adults reported significantly higher cannabis use rates than non-Hispanic White adults. Conclusion: The impact of COVID-19 on daily life continued to be a risk factor for mental health during the second wave of the pandemic. In addition to infection prevention, public health policies should focus on pandemic-related social factors such as economic concerns and caretaking that continue to affect mental health.

Ethanol exacerbates pulmonary complications after burn injury in mice, regardless of frequency of ethanol exposures.

Burn injuries are associated with significant morbidity and mortality, and lungs are the most common organ to fail. Interestingly, patients with alcohol intoxication at the time of burn have worse clinical outcomes, including pulmonary complications. Using a clinically relevant murine model, we have previously reported that episodic ethanol exposure before burn exacerbated lung inflammation. Specifically, intoxicated burned mice had worsened pulmonary responses, including increased leukocyte infiltration and heightened levels of CXCL1 and IL-6. Herein, we examined whether a single binge ethanol exposure before scald burn injury yields similar pulmonary responses. C57BL/6 male mice were given ethanol (1.2 g/kg) 30 min before a 15 % total body surface area burn. These mice were compared to a second cohort given episodic ethanol binge for a total of 6 days (3 days ethanol, 4 days rest, 3 days ethanol) prior to burn injury. 24 h after burn, histopathological examination of lungs were performed. In addition, survival, and levels of infiltrating leukocytes, CXCL1, and IL-6 were quantified. Episodic and single ethanol exposure before burn decreased survival compared to burn only mice and sham vehicle mice, respectively (p < 0.05). However, no difference in survival was observed between burned mice with single and episodic ethanol binge. Examination of H&E-stained lung sections revealed that regardless of ethanol binge frequency, intoxication prior to burn worsened pulmonary inflammation, evidenced by elevated granulocyte accumulation and congestion, relative to burned mice without any ethanol exposure. Levels of infiltrating granulocyte in the lungs were significantly higher in burned mice with both episodic and single ethanol intoxication, compared to burn injury only (p < 0.05). In addition, there was no difference in the granulocyte count between single and ethanol binge mice with burn injury. Neutrophil chemoattractant CXCL1 levels in the lung were similarly increased following single and episodic ethanol exposure prior to burn compared to burn alone (22-fold and 26-fold respectively, p < 0.05). Lastly, we assessed pulmonary IL-6, which revealed that irrespective of frequency, ethanol exposure combined with burn injury raised pro-inflammatory cytokine IL-6 in the lungs relative to burn mice. Again, we did not find any difference in the amount of IL-6 in lungs of burned mice with single and episodic ethanol intoxication. Taken altogether, these data demonstrate that both single and episodic exposure to ethanol prior to burn injury similarly worsens pulmonary inflammation. These results suggest that ethanol-induced exacerbation of the pulmonary responses to burn injury is due to presence of ethanol at the time of injury rather than longer-term effects of ethanol exposure.

Predicting U.S. county opioid poisoning mortality from multi-modal social media and psychological self-report data.

Opioid poisoning mortality is a substantial public health crisis in the United States, with opioids involved in approximately 75% of the nearly 1 million drug related deaths since 1999. Research suggests that the epidemic is driven by both over-prescribing and social and psychological determinants such as economic stability, hopelessness, and isolation. Hindering this research is a lack of measurements of these social and psychological constructs at fine-grained spatial and temporal resolutions. To address this issue, we use a multi-modal data set consisting of natural language from Twitter, psychometric self-reports of depression and well-being, and traditional area-based measures of socio-demographics and health-related risk factors. Unlike previous work using social media data, we do not rely on opioid or substance related keywords to track community poisonings. Instead, we leverage a large, open vocabulary of thousands of words in order to fully characterize communities suffering from opioid poisoning, using a sample of 1.5 billion tweets from 6 million U.S. county mapped Twitter users. Results show that Twitter language predicted opioid poisoning mortality better than factors relating to socio-demographics, access to healthcare, physical pain, and psychological well-being. Additionally, risk factors revealed by the Twitter language analysis included negative emotions, discussions of long work hours, and boredom, whereas protective factors included resilience, travel/leisure, and positive emotions, dovetailing with results from the psychometric self-report data. The results show that natural language from public social media can be used as a surveillance tool for both predicting community opioid poisonings and understanding the dynamic social and psychological nature of the epidemic.

Linguistic Methodologies to Surveil the Leading Causes of Mortality: Scoping Review of Twitter for Public Health Data.

BACKGROUND: Twitter has become a dominant source of public health data and a widely used method to investigate and understand public health-related issues internationally. By leveraging big data methodologies to mine Twitter for health-related data at the individual and community levels, scientists can use the data as a rapid and less expensive source for both epidemiological surveillance and studies on human behavior. However, limited reviews have focused on novel applications of language analyses that examine human health and behavior and the surveillance of several emerging diseases, chronic conditions, and risky behaviors. OBJECTIVE: The primary focus of this scoping review was to provide a comprehensive overview of relevant studies that have used Twitter as a data source in public health research to analyze users' tweets to identify and understand physical and mental health conditions and remotely monitor the leading causes of mortality related to emerging disease epidemics, chronic diseases, and risk behaviors. METHODS: A literature search strategy following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) extended guidelines for scoping reviews was used to search specific keywords on Twitter and public health on 5 databases: Web of Science, PubMed, CINAHL, PsycINFO, and Google Scholar. We reviewed the literature comprising peer-reviewed empirical research articles that included original research published in English-language journals between 2008 and 2021. Key information on Twitter data being leveraged for analyzing user language to study physical and mental health and public health surveillance was extracted. RESULTS: A total of 38 articles that focused primarily on Twitter as a data source met the inclusion criteria for review. In total, two themes emerged from the literature: (1) language analysis to identify health threats and physical and mental health understandings about people and societies and (2) public health surveillance related to leading causes of mortality, primarily representing 3 categories (ie, respiratory infections, cardiovascular disease, and COVID-19). The findings suggest that Twitter language data can be mined to detect mental health conditions, disease surveillance, and death rates; identify heart-related content; show how health-related information is shared and discussed; and provide access to users' opinions and feelings. CONCLUSIONS: Twitter analysis shows promise in the field of public health communication and surveillance. It may be essential to use Twitter to supplement more conventional public health surveillance approaches. Twitter can potentially fortify researchers' ability to collect data in a timely way and improve the early identification of potential health threats. Twitter can also help identify subtle signals in language for understanding physical and mental health conditions.

Analyzing text message linguistic features: Do people with depression communicate differently with their close and non-close contacts?

BACKGROUND: Relatively little is known about how communication changes as a function of depression severity and interpersonal closeness. We examined the linguistic features of outgoing text messages among individuals with depression and their close- and non-close contacts. METHODS: 419 participants were included in this 16-week-long observational study. Participants regularly completed the PHQ-8 and rated subjective closeness to their contacts. Text messages were processed to count frequencies of word usage in the LIWC 2015 libraries. A linear mixed modeling approach was used to estimate linguistic feature scores of outgoing text messages. RESULTS: Regardless of closeness, people with higher PHQ-8 scores tended to use more differentiation words. When texting with close contacts, individuals with higher PHQ-8 scores used more first-person singular, filler, sexual, anger, and negative emotion words. When texting with non-close contacts these participants used more conjunctions, tentative, and sadness-related words and fewer first-person plural words. CONCLUSION: Word classes used in text messages, when combined with symptom severity and subjective social closeness data, may be indicative of underlying interpersonal processes. These data may hold promise as potential treatment targets to address interpersonal drivers of depression.

Patterns of demoralization and anhedonia during early substance use disorder treatment and associations with treatment attrition.

BACKGROUND: Although depressive symptoms represent a promising therapeutic target to promote recovery from substance use disorders (SUD), heterogeneity in their diagnostic presentation often hinders the ability to effectively tailor treatment. We sought to identify subgroups of individuals varying in depressive symptom phenotypes (i.e., demoralization, anhedonia), and examined whether these subgroups were associated with patient demographics, psychosocial health, and treatment attrition. METHODS: Patients (N = 10,103, 69.2 % male) were drawn from a dataset of individuals who presented for admission to SUD treatment in the US. Participants reported on their demoralization and anhedonia approximately weekly for the first month of treatment, and on their demographics, psychosocial health, and primary substance at intake. Longitudinal latent profile analysis examined patterns of demoralization and anhedonia with treatment attrition as a distal outcome. RESULTS: Four subgroups of individuals emerged: (1) High demoralization and anhedonia, (2) Remitting demoralization and anhedonia, (3) High demoralization, low anhedonia, and (4) Low demoralization and anhedonia. Relative to the Low demoralization and anhedonia subgroup, all the other profiles were more likely to discontinue treatment. Numerous between-profile differences were observed with regard to demographics, psychosocial health, and primary substance. LIMITATIONS: The racial and ethnic background of the sample was skewed towards White individuals; future research is needed to determine the generalizability of our findings to minoritized racial and ethnic groups. CONCLUSIONS: We identified four clinical profiles that varied in the joint course of demoralization and anhedonia. Findings suggest specific subgroups might benefit from additional interventions and treatments that address their unique mental health needs during SUD recovery.

Racial and Ethnic Bias in the Diagnosis of Alcohol Use Disorder in Veterans.

OBJECTIVE: Studies show that racially and ethnically minoritized veterans have a higher prevalence of alcohol use disorder (AUD) than White veterans. The investigators examined whether the relationship between self-reported race and ethnicity and AUD diagnosis remains after adjusting for alcohol consumption, and if so, whether it varies by self-reported alcohol consumption. METHODS: The sample included 700,012 Black, White, and Hispanic veterans enrolled in the Million Veteran Program. Alcohol consumption was defined as an individual's maximum score on the consumption subscale of the Alcohol Use Disorders Identification Test (AUDIT-C), a screen for unhealthy alcohol use. A diagnosis of AUD, the primary outcome, was defined by the presence of relevant ICD-9 or ICD-10 codes in electronic health records. Logistic regression with interactions was used to assess the association between race and ethnicity and AUD as a function of maximum AUDIT-C score. RESULTS: Black and Hispanic veterans were more likely than White veterans to have an AUD diagnosis despite similar levels of alcohol consumption. The difference was greatest between Black and White men; at all but the lowest and highest levels of alcohol consumption, Black men had 23%-109% greater odds of an AUD diagnosis. The findings were unchanged after adjustment for alcohol consumption, alcohol-related disorders, and other potential confounders. CONCLUSIONS: The large discrepancy in the prevalence of AUD across groups despite a similar distribution of alcohol consumption levels suggests that there is racial and ethnic bias, with Black and Hispanic veterans more likely than White veterans to receive an AUD diagnosis. Efforts are needed to reduce bias in the diagnostic process to address racialized differences in AUD diagnosis.

A daily diary study into the effects on mental health of COVID-19 pandemic-related behaviors.

BACKGROUND: Recommendations for promoting mental health during the COVID-19 pandemic include maintaining social contact, through virtual rather than physical contact, moderating substance/alcohol use, and limiting news and media exposure. We seek to understand if these pandemic-related behaviors impact subsequent mental health. METHODS: Daily online survey data were collected on adults during May/June 2020. Measures were of daily physical and virtual (online) contact with others; substance and media use; and indices of psychological striving, struggling and COVID-related worry. Using random-intercept cross-lagged panel analysis, dynamic within-person cross-lagged effects were separated from more static individual differences. RESULTS: In total, 1148 participants completed daily surveys [657 (57.2%) females, 484 (42.1%) males; mean age 40.6 (s.d. 12.4) years]. Daily increases in news consumed increased COVID-related worrying the next day [cross-lagged estimate = 0.034 (95% CI 0.018-0.049), FDR-adjusted p = 0.00005] and vice versa [0.03 (0.012-0.048), FDR-adjusted p = 0.0017]. Increased media consumption also exacerbated subsequent psychological struggling [0.064 (0.03-0.098), FDR-adjusted p = 0.0005]. There were no significant cross-lagged effects of daily changes in social distancing or virtual contact on later mental health. CONCLUSIONS: We delineate a cycle wherein a daily increase in media consumption results in a subsequent increase in COVID-related worries, which in turn increases daily media consumption. Moreover, the adverse impact of news extended to broader measures of psychological struggling. A similar dynamic did not unfold between the daily amount of physical or virtual contact and subsequent mental health. Findings are consistent with current recommendations to moderate news and media consumption in order to promote mental health.

Pandemic distress associated with segregation and social stressors.

Background: Racial/ethnic minorities are disproportionately impacted by the COVID-19 pandemic, as they are more likely to experience structural and interpersonal racial discrimination, and thus social marginalization. Based on this, we tested for associations between pandemic distress outcomes and four exposures: racial segregation, coronavirus-related racial bias, social status, and social support. Methods: Data were collected as part of a larger longitudinal national study on mental health during the pandemic (n = 1,309). We tested if county-level segregation and individual-level social status, social support, and coronavirus racial bias were associated with pandemic distress using cumulative ordinal regression models, both unadjusted and adjusted for covariates (gender, age, education, and income). Results: Both the segregation index (PR = 1.19; 95% CI 1.03, 1.36) and the coronavirus racial bias scale (PR = 1.17; 95% CI 1.06, 1.29) were significantly associated with pandemic distress. Estimates were similar, after adjusting for covariates, for both segregation (aPR = 1.15; 95% CI 1.01, 1.31) and coronavirus racial bias (PR = 1.12; 95% CI 1.02, 1.24). Higher social status (aPR = 0.74; 95% CI 0.64, 0.86) and social support (aPR = 0.81; 95% CI 0.73, 0.90) were associated with lower pandemic distress after adjustment. Conclusion: Segregation and coronavirus racial bias are relevant pandemic stressors, and thus have implications for minority health. Future research exploring potential mechanisms of this relationship, including specific forms of racial discrimination related to pandemic distress and implications for social justice efforts, are recommended.

AI-based analysis of social media language predicts addiction treatment dropout at 90 days.

The reoccurrence of use (relapse) and treatment dropout is frequently observed in substance use disorder (SUD) treatment. In the current paper, we evaluated the predictive capability of an AI-based digital phenotype using the social media language of patients receiving treatment for substance use disorders (N = 269). We found that language phenotypes outperformed a standard intake psychometric assessment scale when predicting patients' 90-day treatment outcomes. We also use a modern deep learning-based AI model, Bidirectional Encoder Representations from Transformers (BERT) to generate risk scores using pre-treatment digital phenotype and intake clinic data to predict dropout probabilities. Nearly all individuals labeled as low-risk remained in treatment while those identified as high-risk dropped out (risk score for dropout AUC = 0.81; p < 0.001). The current study suggests the possibility of utilizing social media digital phenotypes as a new tool for intake risk assessment to identify individuals most at risk of treatment dropout and relapse.

Differences in mental health and alcohol use across profiles of COVID-19 disruptions.

This study aimed to examine differences in mental health and alcohol use outcomes across distinct patterns of work, home, and social life disruptions associated with the COVID-19 pandemic. Data from 2093 adult participants were collected from September 2020 to April 2021 as a part of a larger study examining the impacts of the COVID-19 pandemic on substance use. Participants provided data on COVID-19 pandemic experiences, mental health outcomes, media consumption, and alcohol use at baseline. Alcohol use difficulties, including problems related to the use, desire to use alcohol, failure to cut down on alcohol use, and family/friend concern with alcohol use, were measured at 60-day follow-up. Factor mixture modeling followed by group comparisons, multiple linear regressions, and multiple logistic regressions was conducted. A four-profile model was selected. Results indicated that profile membership predicted differences in mental health and alcohol use outcomes above and beyond demographics. Individuals experiencing the most disruption reported the strongest daily impact of COVID-19 and significantly high levels of depression, anxiety, loneliness, overwhelm, alcohol use at baseline, and alcohol use difficulties measured at 60-day follow-up. The findings highlight the need for integrated mental health and/or alcohol services and social services targeting work, home, and social life during public health emergencies in order to respond effectively and comprehensively to the needs of those requiring different types of support.

Opioid death projections with AI-based forecasts using social media language.

Targeting of location-specific aid for the U.S. opioid epidemic is difficult due to our inability to accurately predict changes in opioid mortality across heterogeneous communities. AI-based language analyses, having recently shown promise in cross-sectional (between-community) well-being assessments, may offer a way to more accurately longitudinally predict community-level overdose mortality. Here, we develop and evaluate, TROP (Transformer for Opiod Prediction), a model for community-specific trend projection that uses community-specific social media language along with past opioid-related mortality data to predict future changes in opioid-related deaths. TOP builds on recent advances in sequence modeling, namely transformer networks, to use changes in yearly language on Twitter and past mortality to project the following year's mortality rates by county. Trained over five years and evaluated over the next two years TROP demonstrated state-of-the-art accuracy in predicting future county-specific opioid trends. A model built using linear auto-regression and traditional socioeconomic data gave 7% error (MAPE) or within 2.93 deaths per 100,000 people on average; our proposed architecture was able to forecast yearly death rates with less than half that error: 3% MAPE and within 1.15 per 100,000 people.