Suicide remains one of the leading causes of death among adolescents in the US. From 2018 to 2021, suicide was the second leading cause of death for adolescents aged 10 to 14 years and the third leading cause of death among those aged 15 to 19 years.¹ Suicide is linked to numerous, overlapping factors and paths of marginalization, including mental health conditions, historical and intergenerational trauma, discrimination, neighborhood deprivation, accessibility to lethal means, exposure to violence and abuse, and, more recently, the COVID-19 pandemic.^2-6 Although older and male adolescents have historically had higher suicide rates than younger and female adolescents, respectively,^7,8 recent evidence suggests these gaps may be closing as suicide rates are increasing more rapidly for female adolescents than male adolescents.^9,10 Previous reports identified racial and ethnic differences in adolescent suicide; in recent years, the adolescent suicide rate among Black and Asian and Pacific Islander individuals has increased rapidly, whereas the rate among White individuals has decreased.^11-13 Race and ethnicity are social constructs; hence, racial and ethnic differences in health may be driven by social determinants, environment, and systems of oppression and marginalization that produce disparities in health and longevity.¹⁴

Background: The burdens of anxiety and depression symptoms have significantly increased in the general US population, especially during this COVID-19 epidemiological crisis. The first step in an effective treatment for anxiety and depression disorders is screening. The Patient Health Questionnaire-4 (PHQ-4, a 4-item measure of anxiety/depression) and its subscales (PHQ-2 [a 2-item measure of depression] and Generalized Anxiety Disorder [GAD-2, a 2-item measure of anxiety]) are brief but effective mass screening instruments for anxiety and depression symptoms in general populations. However, little to no study examined the psychometric properties (i.e., reliability and validity) of the PHQ-4 and its subscales (PHQ-2 and GAD-2) in the general US adult population or based on US nativity (i.e., foreign-born vs. the US-born). We evaluated the psychometric properties of the PHQ-4 and its subscales in US adults, as well as the psychometric equivalence of the PHQ-4 scale based on nativity. Methods: We conducted a cross-sectional survey of 5,140 adults aged ≥ 18 years. We examined the factorial validity and dimensionality of the PHQ-4 with confirmatory factor analysis (CFA). A multiple-group confirmatory factor analysis (MCFA) was used to evaluate the comparability of the PHQ-4 across nativity groups. Reliability indices were assessed. Also, the scales’ construct validities were assessed by examining the associations of both the PHQ-4 and its subscales’ scores with the sociodemographic characteristics and the 3-item UCLA Loneliness scale. Results: The internal consistencies were high for the PHQ-4 scale (α = 0.92) and its subscales of PHQ-2 (α = 0.86) and GAD-2 (α = 0.90). The CFA fit indices showed evidence for the two-factor structure of the PHQ-4. The two factors (i.e., anxiety and depression) were significantly correlated (r = 0.92). The MCFA demonstrated measurement invariance of the PHQ-4 across the nativity groups, but the model fits the data better in the foreign-born group. There were significant associations of the PHQ-4 scale and its subscales’ scores with the sociodemographic characteristics and the UCLA Loneliness scale (all p < 0.001). Conclusions: The PHQ-4 and its subscales are reliable and valid measures to screen anxiety and depression symptoms in the general US adult population, especially in foreign-born individuals during the COVID-19 pandemic.

Aim: Public health systems have embraced health informatics and information technology as a potential transformational tool to improve real-time surveillance systems, communication, and sharing of information among various agencies. Global pandemic outbreaks like Zika and Ebola were quickly controlled due to electronic surveillance systems enabling efficient information access and exchange. However, there is the need for a more robust technology to enhance adequate epidemic forecasting, data sharing, and effective communication. The purpose of this review was to examine the use of informatics and information technology tools and its impact on public health delivery. Method: Investigators searched six electronic databases. These were MEDLINE, Cumulative Index to Nursing and Allied Health Literature (CINAHL) Complete, Cochrane Database of Systematic Reviews, COMPENDEX, Scopus, and Academic Search Premier from January 2000 to 31 March 2016. Results: A total of 60 articles met the eligibility criteria for inclusion. These studies were organized into three areas as (1) definition of the term public health informatics; (2) type of public health surveillance systems and implications for public health; and (3) electronic surveillance systems functionality, capability, training, and challenges. Our analysis revealed that due to the growing expectations to provide real-time response and population-centered evidence-based public health in this information-driven age there has been a surge in informatics and information technology adoption. Education and training programs are now available to equip public health students and professionals with skills in public health informatics. However, obstacles including interoperability, data standardization, privacy, and technology transfer persist. Conclusion: Re-engineering the delivery of public health is necessary to meet the demands of the 21^st century and beyond. To meet this expectation, public health must invest in workforce development and capacity through education and training in informatics.

Background: Few studies have comprehensively and contextually examined the relationship of variables associated with opioid use. Our purpose was to fill a critical gap in comprehensive risk models of opioid misuse and use disorder in the United States by identifying the most salient predictors.Methods: A multivariate logistic regression was used on the 2017 and 2018 National Survey on Drug Use and Health, which included all 50 states and the District of Columbia of the United States. The sample included all noninstitutionalized civilian adults aged 18 and older (N = 85,580; weightedN = 248,008,986). The outcome of opioid misuse and/or use disorder was based on reported prescription pain reliever and/or heroin use dependence, abuse, or misuse. Biopsychosocial predictors of opioid misuse and use disorder in addition to sociodemographic characteristics and other substance dependence or abuse were examined in our comprehensive model. Biopsychosocial characteristics included socioecological and health indicators. Criminality was the socioecological indicator. Health indicators included self-reported health, private health insurance, psychological distress, and suicidality. Sociodemographic variables included age, sex/gender, race/ethnicity, sexual identity, education, residence, income, and employment status. Substance dependence or abuse included both licit and illicit substances (i.e., nicotine, alcohol, marijuana, cocaine, inhalants, methamphetamine, tranquilizers, stimulants, sedatives).Results: The comprehensive model found that criminality (adjusted odds ratio [AOR] = 2.58, 95% confidence interval [CI] = 1.98–3.37,p < 0.001), self-reported health (i.e., excellent compared to fair/poor [AOR = 3.71, 95% CI = 2.19–6.29,p < 0.001], good [AOR = 3.43, 95% CI = 2.20–5.34, p < 0.001], and very good [AOR = 2.75, 95% CI = 1.90–3.98,p < 0.001]), no private health insurance (AOR = 2.12, 95% CI = 1.55–2.89,p < 0.001), serious psychological distress (AOR = 2.12, 95% CI = 1.55–2.89,p < 0.001), suicidality (AOR = 1.58, 95% CI = 1.17–2.14,p = 0.004), and other substance dependence or abuse were significant predictors of opioid misuse and/or use disorder. Substances associated were nicotine (AOR = 3.01, 95% CI = 2.30–3.93,p < 0.001), alcohol (AOR = 1.40, 95% CI = 1.02–1.92,p = 0.038), marijuana (AOR = 2.24, 95% CI = 1.40–3.58,p = 0.001), cocaine (AOR = 3.92, 95% CI = 2.14–7.17,p < 0.001), methamphetamine (AOR = 3.32, 95% CI = 1.96–5.64,p < 0.001), tranquilizers (AOR = 16.72, 95% CI = 9.75–28.65,p < 0.001), and stimulants (AOR = 2.45, 95% CI = 1.03–5.87,p = 0.044).Conclusions: Biopsychosocial characteristics such as socioecological and health indicators, as well as other substance dependence or abuse were stronger predictors of opioid misuse and use disorder than sociodemographic characteristics.

Purpose: Psychological distress significantly contributes to the burdens of morbidity and mortality in the United States (U.S.), but our understanding is limited with regards to the risk factors associated with psychological distress. We used nationally representative data to examine (1) the comorbidities of chronic diseases and their risks for psychological distress and (2) the ways in which chronic diseases combine with demographic factors such as sex, race/ethnicity, immigration status, and health insurance coverage to affect the patterning of psychological distress. Methods: We analyzed the 2005–2018 National Health Survey Interview cross-sectional data on U.S. adults aged ≥ 18 years (n = 351,457). We fitted sequential multivariable logistic regression models. Results: There was a dose-response relationship between the number of chronic diseases and psychological distress, with increased number of chronic diseases associated with increased psychological distress risk. Females (vs. males) and those without health insurance (vs. insured) were more likely to experience psychological distress. Immigrants (vs. non-immigrants) and racial/ethnic minorities (vs. White individuals) were less likely to experience psychological distress. There were significant interactions between chronic diseases and insurance coverage, immigration status, and race/ethnicity, but the three-way interactions were not statistically significant with psychological distress: chronic disease status vs. immigration status vs. health insurance coverage, and chronic disease vs. race/ethnicity vs. immigration status. Conclusion: The findings suggest a critical need to consider the complex ways in which chronic diseases and psychosocial factors combine to affect psychological distress and their implications for tailoring mental health screening, initiatives to reduce distress, and prevention strategies for effectively addressing health-related disparities in the general population.

Objective: To assess differences in the prevalence of anxiety/depression symptoms among cancer patients before (2019) and during the COVID‐19 pandemic (2020); and the associations between anxiety/depression and sociodemographic and health behavior factors among cancer patients before and during the pandemic.Methods: We analyzed data from the 2019 (n = 856) and 2020 (n = 626) Health Information National Trends Survey, a nationally representative survey of United States adults aged ≥18 years. Only adults with a cancer diagnosis were used in the analyses. Anxiety/depression was assessed using the Patient Health Questionnaire‐4 (low/none [0–2], mild [3–5], moderate [6–8], and severe [9–12]) and dichotomized as low/none and current anxiety/depression (mild/moderate/severe). Multivariate analysis was performed.Results: The prevalence of anxiety/depression symptoms among cancer patients was 32.7% before the COVID‐19 pandemic and 31.1% during the pandemic. The odds of anxiety/depression among patients with fair/poor health status was higher during the pandemic relative to before (before: odds ratio [OR] = 1.85 vs. during: OR = 3.89). Participants aged 50–64 years (before: OR = 0.29, 95% confidence interval [95% CI] = 0.11–0.76; during: OR = 0.33, 95% CI = 0.11–0.97) and ≥65 years (before: OR = 0.13, 95% CI = 0.05–0.34; during: OR = 0.18, 95% CI = 0.07–0.47) had lower odds of anxiety/depression before and during the pandemic compared to those aged 35–49 years. Hispanics/Latinos had higher odds of anxiety/depression (OR = 2.70, 95% CI = 1.11–6.57) before the pandemic and lower odds of anxiety/depression during the pandemic (OR = 0.2, 95% CI = 0.05–1.01) compared to non‐Hispanic Whites. Those who completed high school (before: OR = 0.08, 95% CI = 0.01–0.42), some college (before: OR = 0.10, 95% CI = 0.02–0.42), ≥college degree had lower odds of anxiety/depression symptoms (before: OR = 0.05, 95% CI = 0.01–0.26; during: OR = 0.06, 95% CI = 0.01–0.61) compared to those with less than a high school education.Conclusion: Our results suggest the need to increase the provision of mental health services to cancer patients at high risk of developing anxiety/depression symptoms, particularly during public health emergencies, to alleviate further health burdens.