Background: Studies have suggested that economically vulnerable populations may have had increases in suicide rates during the COVID-19 pandemic. Understanding factors that contribute to these increases may help to prevent such suicides.

Objective: This study seeks to assess whether suicides among workers were associated with unemployment rates during the COVID-19 pandemic.

Methods: Data about suicides were obtained from the National Violent Death Reporting System for working-age adults in 2020 and 2021. Workers were matched to monthly unemployment rates at the occupational and industry level using Bureau of Labor Statistics data. Poisson Regression analysis was used to examine the relationship between these monthly unemployment rates (along with one, two, and three-month lags) and monthly mortality rates.

Results: This study found that there was a small, statistically significant association between unemployment rates and suicide rates. These associations tended to be the strongest with a one or two-month lag. Workers in construction and extraction occupations had a significant RR with a three-month lag. Workers in the wholesale and retail trade industry consistently had a significant relationship for the actual unemployment and the one and two-month lags.

Conclusion: These findings suggest that although there were declines in suicide rates during the first year of the COVID-19 pandemic before increases in 2021, some of the risk during the pandemic may have been related to unemployment. Further investigation is needed to assess whether this relationship is likely causal. Efforts should be made to intervene on the mental health consequences of unemployment and precarious employment.

Background: The COVID-19 pandemic exacerbated psychological stress for many racialized groups, including Asian Americans. While coping is generally known to moderate racial discrimination and other stressors, it is unknown which specific coping behaviors are effective for this population.

Objective: Investigate the association between coping behaviors and psychological distress in Asian American adults during the COVID-19 pandemic.

Methods: Survey-weighted data (N=3,151) from the 2021 Asian American and Native Hawaiian/Pacific Islander COVID-19 Needs Assessment Project were analyzed. Multivariate logistic regression was performed with coping behaviors as predictor variables and psychological distress (positive or negative PHQ-4 screen) as the outcome variable, adjusting for sociodemographic factors as covariates.

Results: The most common coping behaviors were talking with friends and family (69.7%), screen time (57.9%), and exercise (45.2%). Coping behaviors associated with increased psychological distress included tobacco use (aOR 3.22, 95% CI: 1.71-6.06), marijuana/CBD use (aOR 2.31, 95% CI: 1.36-3.95), and other (aOR 2.46, 95% CI: 1.06-5.71). Coping behaviors associated with decreased psychological distress were talking with friends and family (aOR 0.60, 95% CI: 0.46-0.79), hobbies (aOR 0.75, 95% CI: 0.58-0.99), and exercise (aOR 0.66, 95% CI: 0.51-0.85). Reporting no coping was also associated with lower psychological distress. Female gender, age <24 years, annual income <$25,000, and some college education were associated with higher psychological distress.

Conclusion: In this cohort of Asian Americans, health-promoting and health-compromising coping behaviors and demographic characteristics were associated with psychological distress consistent with previous literature. Future research will examine relationships between coping behaviors and subjective well-being and benefit finding.

Background/Objective(s):
The COVID-19 pandemic profoundly reshaped the healthcare landscape, affecting healthcare accessibility and quality of services, workforce demands, and exacerbated a growing mental health crisis. This study sought to: 1) Evaluate changes in the prevalence of depression and/or anxiety, before and after the COVID-19 pandemic; and 2) estimate the association between mental health disorders and perceived quality of healthcare.

Methods:
Data from the 2018 and 2022 HINTS datasets included 9,756 adult respondents who self-reported a healthcare professional diagnosis of depression or anxiety disorder. Participants also rated the quality of healthcare services they received in the previous 12 months on a 5-item Likert scale. Chi-square tests assessed the relationship between depression/anxiety and perceived healthcare quality. Logistic regression identified predictors of depression/anxiety. Receiver Operating Characteristic curve analysis evaluated the model’s performance.

Results:
Depression/anxiety prevalence increased significantly from 23.6% in 2018 to 26.3% in 2022 (χ²=8.175; p=0.004). Although median perceived healthcare quality remained "Very Good," reports of "Fair" or "Poor" quality increased from 5.9% to 8.6%, with significant associations between depression/anxiety and healthcare quality (p<0.001). Adults with depression/anxiety were significantly more likely (AOR:2.65; 95%CI:[2.598 - 2.702]) to report lower perceived healthcare quality versus adults without depression/anxiety even while controlling for the pandemic period, age, race, income, and BMI.

Conclusion:
The observed increase in depression and anxiety following the COVID-19 pandemic was significantly associated with decreased perceptions of healthcare quality. These findings underscore the critical need for enhanced patient-centered training among healthcare providers and targeted interventions aimed at improving healthcare experiences for adults with depression/anxiety.

Background:
The COVID-19 pandemic disrupted essential services, increasing challenges in healthcare access, housing, food, clean water, medication, and transportation. Such material hardships may adversely affect mental health.

Objective:
This study demonstrates the use of Latent Class Analysis (LCA) to identify distinct subgroups based on COVID-19–related challenges and examine their association with demographic factors and depression.

Methods:
Data was collected from 1,094 participants during the pandemic using a mix of online and in-person surveys. LCA was applied to models with 2–5 classes; with model selection based on Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) favoring a three-class model. Demographic differences were evaluated using chi-square tests. Logistic regression was used to assess the association between class membership and depression, as measured by the Patient Health Questionnaire (PHQ-8), while controlling for demographic factors.

Results:
Three latent classes were identified: Class 1 (52.8%) with low challenge endorsement; Class 2 (24.0%) with moderate endorsement; and Class 3 (23.1%) with high endorsement. Significant demographic differences (p < 0.001) showed that higher challenge classes had a greater proportion of older individuals, lower income, non-US nativity, and lower English proficiency. Compared with Class 1, respondents in Class 2 had higher odds of depression (OR = 2.37, 95% CI: 1.19–4.35) and those in Class 3 had even higher odds (OR = 3.11, 95% CI: 1.56–6.20).

Conclusion:
Increased exposure to COVID-19–related challenges is linked to a greater odds of depression. Identifying vulnerable subgroups through LCA can inform equity-driven public health interventions aimed at reducing mental health disparities.