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.

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.

Depression and Anxiety on Twitter During the COVID-19 Stay-At-Home Period in 7 Major U.S. Cities.

Introduction: Although surveys are a well-established instrument to capture the population prevalence of mental health at a moment in time, public Twitter is a continuously available data source that can provide a broader window into population mental health. We characterized the relationship between COVID-19 case counts, stay-at-home orders because of COVID-19, and anxiety and depression in 7 major U.S. cities utilizing Twitter data. Methods: We collected 18 million Tweets from January to September 2019 (baseline) and 2020 from 7 U.S. cities with large populations and varied COVID-19 response protocols: Atlanta, Chicago, Houston, Los Angeles, Miami, New York, and Phoenix. We applied machine learning‒based language prediction models for depression and anxiety validated in previous work with Twitter data. As an alternative public big data source, we explored Google Trends data using search query frequencies. A qualitative evaluation of trends is presented. Results: Twitter depression and anxiety scores were consistently elevated above their 2019 baselines across all the 7 locations. Twitter depression scores increased during the early phase of the pandemic, with a peak in early summer and a subsequent decline in late summer. The pattern of depression trends was aligned with national COVID-19 case trends rather than with trends in individual states. Anxiety was consistently and steadily elevated throughout the pandemic. Google search trends data showed noisy and inconsistent results. Conclusions: Our study shows the feasibility of using Twitter to capture trends of depression and anxiety during the COVID-19 public health crisis and suggests that social media data can supplement survey data to monitor long-term mental health trends.

COVID-related social determinants of substance use disorder among diverse U.S. racial ethnic groups.

OBJECTIVE: Black, Asian, and Hispanic/Latino people are disproportionately impacted by the COVID-19 pandemic and were more likely to experience coronavirus-related racial discrimination. This study examined the association between pandemic-related stressors, including employment and housing disruptions, coronavirus-related victimization distress, and perceptions of pandemic-associated increase in societal racial biases, and substance use disorder (SUD) risk among Asian, Black, Hispanic/Latino, and non-Hispanic White adults in the U.S. METHODS: Data were collected as part of a larger national survey on substance use during the pandemic. Eligible participants for the current study were 1336 adults self-identified as Asian (8.53%), Black (10.55%), Hispanic/Latino (10.93%), and non-Hispanic White (69.99%). Measures included demographic and COVID-19-related employment, housing, and health items, the coronavirus victimization distress scale (CVD), the coronavirus racial bias scale (CRB), and measures of substance use risk. RESULTS: Across racial/ethnic groups, employment disruption distress and housing disruption due to the pandemic were associated with SUD risk. Binary logistic regression analyses controlling for demographic variables indicated CVD was associated with higher odds of tobacco use risk (AOR = 1.36, 95% CI [1.01, 1.81]) and polysubstance use risk (AOR = 1.87, 95% CI [1.14, 3.06]), yet CRB was unrelated to any SUDs. Logistic regressions for each racial/ethnic group found different patterns of relationships between stressors and risk for SUDs. CONCLUSIONS: Results highlight the significance of examining how the current pandemic has exacerbated racial/ethnic systemic inequalities through COVID-19 related victimization. The data also suggest that across all racial/ethnic groups employment and housing disruptions and perceptions of pandemic instigated increases in societal racial bias are risk factors for SUD. The study calls for further empirical research on substance use prevention and intervention practice sensitive to specific needs of diverse populations during the current and future health crises.

COVID-related social determinants of substance use disorder among diverse U.S. racial ethnic groups

Objective Black, Asian, and Latinx people are disproportionately impacted by the COVID-19 pandemic and were more likely to experience coronavirus-related racial discrimination. This study examined the association between pandemic-related stressors, including employment and housing disruptions, coronavirus-related victimization distress, and perceptions of pandemic-associated increase in societal racial biases, and substance use disorder (SUD) risk among Asian, Black, Latinx, and non-Hispanic White adults in the U.S. Methods Data were collected as part of a larger national survey on substance use during the pandemic. Eligible participants for the current study were 1336 adults self-identified as Asian (8.53%), Black (10.55%), Latinx (10.93%), and non-Hispanic White (69.99%). Measures included demographic and COVID-19-related employment, housing, and health items, the coronavirus victimization distress scale (CVD), the coronavirus racial bias scale (CRB), and measures of substance use risk. Results Across race/ethnicity, employment disruption distress and housing disruption due to the pandemic were associated with SUD risk. Binary logistic regression analyses controlling for demographic variables indicated CVD was associated with higher odds of tobacco use risk (AOR = 1.36, 95% CI [1.01, 1.81]) and polysubstance use risk (AOR = 1.87, 95% CI [1.14, 3.06]), yet CRB was unrelated to any SUDs. Logistic regressions for each racial/ethnic group found different patterns of relationships between stressors and risk for SUDs. Conclusions Results highlight the significance of examining how the current pandemic has exacerbated racial/ethnic systemic inequalities through COVID-19 related victimization. The data also suggest that across all racial/ethnic groups employment and housing disruptions and perceptions of pandemic instigated increases in societal racial bias are risk factors for SUD. The study calls for further empirical research on substance use prevention and intervention practice sensitive to specific needs of diverse populations during the current and future health crises.

Daily diary study of loneliness, alcohol, and drug use during the COVID-19 Pandemic.

BACKGROUND: Research conducted during the COVID-19 Pandemic has identified two co-occurring public health concerns: loneliness and substance use. Findings from research conducted prior to the pandemic are inconclusive as to the links between loneliness and substance use. This study aimed to measure associations of loneliness with three different types of substance use during COVID-19: daily number of alcoholic drinks, cannabis use, and non-cannabis drug use. METHOD: Data were obtained between October 2020 and May 2021 from 2,648 US adults (Mage  = 38.76, 65.4% women) diverse with respect to race and ethnicity using online recruitment. Participants completed baseline surveys and daily assessments for 30 days. A daily loneliness measure was recoded into separate within- and between-person predictor variables. Daily outcome measures included the number of alcoholic drinks consumed and dichotomous cannabis and non-cannabis drug use variables. Generalized linear multilevel models (GLMLM) were used to examine within- and between-person associations between loneliness and substance use. RESULTS: The unconditional means model indicated that 59.0% of the variance in the daily number of alcoholic drinks was due to within-person variability. GLMLM analyses revealed that, overall, people drank more on days when they felt a particularly high or particularly low degree of loneliness (positive quadratic effect). There was a negative and significant within-person association between daily loneliness and the likelihood of cannabis use. There was also a positive and significant within-person association between daily loneliness and the likelihood of non-cannabis drug use. CONCLUSIONS: Associations between loneliness and substance use vary with substance type and whether within- or between-person differences are assessed. These findings are relevant to the persistence of substance use disorders and thus of potential clinical importance. Individuals who do not experience severe loneliness at intake but who show daily increases in loneliness above baseline levels are at heightened risk of alcohol and non-cannabis drug use. Future research could profitably examine just-in-time adaptive interventions that assess fluctuations in loneliness to prevent the development or exacerbation of substance use disorders.

COVID-Related Victimization, Racial Bias and Employment and Housing Disruption Increase Mental Health Risk Among U.S. Asian, Black and Latinx Adults.

Background: The mental health of racial/ethnic minorities in the U.S. has been disproportionately impacted by the COVID-19 pandemic. This study examined the extent to which disruptions in employment and housing, coronavirus-specific forms of victimization and racial bias independently and conjointly contributed to mental health risk among Asian, Black, and Latinx adults in the United States during the pandemic. Methods: This study reports on data from 401 Asian, Black, and Latinx adults (age 18-72) who participated in a larger national online survey conducted from October 2020-June 2021, Measures included financial and health information, housing disruptions and distress in response to employment changes, coronavirus related victimization distress and perceived increases in racial bias, depression and anxiety. Results: Asian participants had significantly higher levels of COVID-related victimization distress and perceived increases in racial bias than Black and Latinx. Young adults (<26 years old) were more vulnerable to depression, anxiety, and coronavirus victimization distress than older respondents. Having at least one COVID-related health risk, distress in response to changes in employment and housing disruptions, pandemic related victimization distress and perceived increases in racial bias were positively and significantly related to depression and anxiety. Structural equation modeling indicated COVID-related increases in racial bias mediated the effect of COVID-19 related victimization distress on depression and anxiety. Conclusions: COVID-19 has created new pathways to mental health disparities among racial/ethnic minorities in the U.S. by exacerbating existing structural and societal inequities linked to race. Findings highlight the necessity of mental health services sensitive to specific challenges in employment and housing and social bias experienced by people of color during the current and future health crises.

Loneliness and Daily Alcohol Consumption During the COVID-19 Pandemic.

AIMS: This pilot study aimed to identify associations of loneliness and daily alcohol consumption among US adults during the Coronavirus Disease-2019 pandemic. METHOD: Participants completed daily assessments for 30 days. RESULTS: Results suggest people who feel lonelier on average drink more alcohol, however, people who feel lonelier than usual drink less. CONCLUSION: Findings highlight the need to disaggregate within- and between-person components of alcohol use.

Bots and Misinformation Spread on Social Media: Implications for COVID-19.

As of March 2021, the SARS-CoV-2 virus has been responsible for over 115 million cases of COVID-19 worldwide, resulting in over 2.5 million deaths. As the virus spread exponentially, so did its media coverage, resulting in a proliferation of conflicting information on social media platforms-a so-called "infodemic." In this viewpoint, we survey past literature investigating the role of automated accounts, or "bots," in spreading such misinformation, drawing connections to the COVID-19 pandemic. We also review strategies used by bots to spread (mis)information and examine the potential origins of bots. We conclude by conducting and presenting a secondary analysis of data sets of known bots in which we find that up to 66% of bots are discussing COVID-19. The proliferation of COVID-19 (mis)information by bots, coupled with human susceptibility to believing and sharing misinformation, may well impact the course of the pandemic.

Understanding Weekly COVID-19 Concerns through Dynamic Content-Specific LDA Topic Modeling.

The novelty and global scale of the COVID-19 pandemic has lead to rapid societal changes in a short span of time. As government policy and health measures shift, public perceptions and concerns also change, an evolution documented within discourse on social media. We propose a dynamic content-specific LDA topic modeling technique that can help to identify different domains of COVID-specific discourse that can be used to track societal shifts in concerns or views. Our experiments show that these model-derived topics are more coherent than standard LDA topics, and also provide new features that are more helpful in prediction of COVID-19 related outcomes including mobility and unemployment rate.