As COVID-19 converges with loneliness and addiction epidemics in the US, both public health and mental health experts forecast dramatic increases in substance use and mental health conditions. This cross-sectional study evaluated relationships of loneliness with depression, anxiety, alcohol use, and drug use during COVID-19, and assessed perceived increases in these symptoms in young adults. Between April 22 and May 11, 2020, 1,008 participants ages 18-35 were recruited through social media to a one-time, online anonymous survey. Symptomatology was assessed using six scales. Perceived changes since COVID-19 were evaluated using 5-point Likert scales. Forty-nine percent of respondents reported loneliness scores above 50; 80% reported significant depressive symptoms; 61% reported moderate to severe anxiety; 30% disclosed harmful levels of drinking. While only 22% of the population reported using drugs, 38% reported severe drug use. Loneliness was associated with higher levels of mental health symptomatology. Participants reported significant increases across mental health and substance use symptoms since COVID-19. While direct impacts of COVID-19 could only be calculated with pre-pandemic assessments of these symptoms, estimates indicate elevated psychosocial symptomatology and suggest that symptoms could have worsened since the pandemic. Findings underscore the importance of prevention and intervention to address these public health problems.

Objective The coronavirus disease 2019 (COVID‐19) pandemic in the United States has exacerbated a number of mental health conditions and problems related to prolonged social isolation. While COVID‐19 has led to greater loneliness and a lack of social connectedness, little is known about who are the most affected and how they are impacted. Therefore, we performed a Latent Class Analysis using items from two scales – the UCLA Loneliness Scale and the Social Connectedness Scale – to characterize different experiences of loneliness and connectedness, examine their relationship with mental health and substance use symptoms, including depression, anxiety, drinking, and drug use. Methods Data were drawn from an anonymous one‐time online survey examining the mental health of 1008 young adults (18–35 years old) during COVID‐19. A latent class analysis (LCA) was conducted to observe and identify classes based on responses to loneliness and connectedness scale items, and to examine the existence of subgroups among this young adult population. Results We identified a 4‐class model of loneliness and connectedness: (1) Lonely and Disconnected – highest probabilities in items of loneliness and disconnectedness, (2) Moderately Lonely and Disconnected – adaptive levels of some isolation and disconnection during COVID‐19, (3) Ambivalent Feelings – displaying negative responses in particular to negatively‐worded items while simultaneously affirming positively worded items, and (4) Connected and Not Lonely – lowest probabilities in items of loneliness and disconnectedness. Conclusion Key findings include (1) the delineation of classes by levels of loneliness and connectedness showcasing differential mental health and substance use symptoms, (2) the utility of item‐level evaluation with LCA in determining specific classes of people in need of outreach and intervention, and (3) the promise of social connection to bolster resilience in young adults.

Increasing rates of overdose and overdose deaths are a significant public health problem. Research has examined co-occurring mental health conditions, including suicidality, as a risk factor for intentional and unintentional overdose among individuals with substance use disorder (SUD). However, this research has been limited to single site studies of self-reported outcomes. The current research evaluated suicidality as a predictor of overdose events in 2541 participants who use substances enrolled across eight multi-site clinical trials completed within the National Drug Abuse Treatment Clinical Trials Network between 2012 to 2021. The trials assessed baseline suicidality with the Concise Health Risk Tracking Self-Report (CHRT-SR). Overdose events were determined by reports of adverse events, cause of death, or hospitalization due to substance overdose, and verified through a rigorous adjudication process. Multivariate logistic regression was performed to assess continuous CHRT-SR score as a predictor of overdose, controlling for covariates. CHRT-SR score was associated with overdose events (p = 0.03) during the trial; the likelihood of overdose increased as continuous CHRT score increased (OR 1.02). Participants with lifetime heroin use were more likely to overdose (OR 3.08). Response to the marked rise in overdose deaths should integrate suicide risk reduction as part of prevention strategies.

Although epidemiology core competencies are established by the Association of Schools and Programs of Public Health for masters-level trainees, no equivalent currently exists for the doctoral level. Thus, the objective of the Doctoral Education in Epidemiology Survey (2019) was to collect information on doctoral-level competencies in general epidemiology (doctoral) degree programs and other pertinent information from accredited programs in the United States and Canada. Participants (doctoral program directors or knowledgeable representatives of the program) from 57 institutions were invited to respond to a 39-item survey (18 core competencies; 9 noncore or emerging topic–related competencies; and 12 program-related items). Participants from 55 institutions (96.5%) responded to the survey, of whom over 85% rated 11 out of 18 core competencies as “very important” or “extremely important.” More than 80% of the programs currently emphasize 2 of 9 noncore competencies (i.e., competency to ( 1) develop and write grant proposals, and ( 2) assess evidence for causality on the basis of different causal inference concepts). “Big data” is the most frequently cited topic currently lacking in doctoral curricula. Information gleaned from previous efforts and this survey should prompt a dialog among relevant stakeholders to establish a cohesive set of core competencies for doctoral training in epidemiology.

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