Objective: This study is a follow-up to an examination of the racial/ethnic composition of public health students (1996) and faculty (1997) at schools of public health that was conducted 20 years ago. We examined data on the race/ethnicity of students, graduates, and faculty among Association of Schools and Programs of Public Health (ASPPH)–member institutions during 2016-2017 and how these data have changed in the past 20 years. Methods: We obtained data on the race/ethnicity of students (in 1996 and 2016), graduates (in 1996 and 2016), and faculty (in 1997 and 2017) at ASPPH-member institutions from the ASPPH Data Center. We tabulated frequencies, percentages, and 20-year percentage-point changes by race/ethnicity. We examined data for all current ASPPH-member institutions and for comparable subcohorts of 1996 and 1997 member institutions that are current ASPPH members. Results: In graduate student enrollment, the 20-year increase in each nonwhite racial/ethnic subgroup was ≤5 percentage points. Among tenured faculty, the 20-year increase was greatest among Asians (8 percentage points) but was <3 percentage points for black, Hispanic, and Native American faculty. Conclusions: The increasing racial/ethnic diversity among students, graduates, and faculty in schools and programs of public health contributes to parallel increases in racial/ethnic diversity in the public health workforce. Schools and programs of public health should recruit clusters of racial/ethnic minority students using holistic application review processes, provide enrolled students with racially/ethnically diverse role models and mentors, and dedicate staffing to ensure a student-centered approach. In addition, those who mentor racially/ethnically diverse students and junior faculty should be rewarded.

Background: Upon reintegration into society, formerly incarcerated individuals (FIIs) experience chronic financial stress due to prolonged unemployment, strained social relationships, and financial obligations. This study examined whether marriage and perceived social status can mitigate financial stress, which is deleterious to the well-being of FIIs. We also assessed whether sociodemographic factors influenced financial stress across marital status. We used cross-sectional data from 588 FIIs, collected in the 2023 Survey of Racism and Public Health. The financial stress outcome (Cronbach’s $$\mathrm{\alpha }$$α = 0.86) comprised of five constructs: psychological distress, financial anxiety, job insecurity, life satisfaction, and financial well-being. Independent variables included marital and social status, age, race/ethnicity, gender identity, educational attainment, employment status, and number of dependents. Multivariable models tested whether financial stress levels differed by marital and perceived social status (individual and interaction effects). Stratified multivariable models assessed whether social status and sociodemographic associations varied by marital status. Results: We found that being married/living with a partner (M/LWP, b = -5.2) or having higher social status (b = -2.4) were protective against financial stress. Additionally, the social status effect was more protective among divorced, separated, or widowed participants (b = -2.5) compared to never married (NM, b = -2.2) and M/LWP (b = -1.7) participants. Lower financial stress correlated with Black race and older age, with the age effect being more pronounced among M/LWP participants (b = -9.7) compared to NM participants (b = -7.3). Higher financial stress was associated with woman gender identity (overall sample b = 2.9, NM sample b = 5.1), higher education (M/LWP sample b = 4.4), and having two or more dependents (overall sample b = 2.3, M/LWP sample b = 3.4). Conclusions: We provide novel insights into the interrelationship between marriage, perceived social status, and financial stress among FIIs. Our findings indicate the need for policies and programs which may target the family unit, and not only the individual, to help alleviate the financial burden of FIIs. Finally, programs that offer legal aid to assist in expungement or sealing of criminal records or those offering opportunities for community volunteer work in exchange for vouchers specific to legal debt among FIIs could serve to reduce financial stress and improve social standing.

Background: Little attention has been devoted to framing multiple continuous social variables as a “mixture” for social epidemiologic analysis. We propose using the Bayesian kernel machine regression analytic framework that yields univariate, bivariate, and overall exposure mixture effects. Methods: Using data from the 2023 Survey of Racism and Public Health, we conducted a Bayesian kernel machine regression analysis to study several individual, social, and structural factors as an exposure mixture and their relationships with psychological distress among individuals with at least one police arrest. Factors included racial and economic polarization, neighborhood deprivation, perceived discrimination, police perception, subjective social status, and substance use. We complemented this analysis with a series of unadjusted and adjusted models for each exposure mixture variable. Results: We found that more self-reported discrimination experiences in the past year (posterior inclusion probability = 1.00) and greater substance use (posterior inclusion probability = 1.00) correlated with higher psychological distress. These associations were consistent with the findings from the unadjusted and adjusted linear regression analyses: past year perceived discrimination (unadjusted b = 2.58, 95% confidence interval [CI]: 1.86, 3.30; adjusted b = 2.20, 95% CI: 1.45, 2.94) and substance use (unadjusted b = 2.92, 95% CI: 2.21, 3.62; adjusted b = 2.59, 95% CI: 1.87, 3.31). Conclusion: With the rise of big data and the expansion of variables in long-standing cohort and census studies, novel applications of methods from adjacent disciplines are a step forward in identifying exposure mixture associations in social epidemiology and addressing the health needs of socially vulnerable populations.

Background: Despite persistently high rates of mental illness and suicide, receipt of treatment for mental health conditions remains low. In this context, it is important to quantify the number of inpatient psychiatric beds (IPBs), and to understand differences in the number of IPBs throughout the U.S, as these provide critical evaluation, medication, and stabilization services. Methods and findings: This study used nationally-representative data drawn from the 2011−2023 Centers for Medicare and Medicaid Services’ Healthcare Cost Report Information System (HCRIS). From 2011−2023, while the total number of IPBs—in both psychiatric hospitals (PHs) and short-term acute care hospitals (STACHs)—did not change, the number IPBs within STACHs fell from 11.3 in 2011 to 9.06 in 2023. During this period, 846 counties (in which over 244 million individuals reside) experienced a decline in the rate of IPBs, while another 1,449 counties (in which 59 million individuals reside) never had IPBs. In regression models predicting the number of IPBs in STACHs and PHs, hospitals that received DSH payments (STACHs: IRR:1.93, 95% CI: 1.72, 2.15; PHs: IRR:1.40; 95% CI: 1.06, 1.84), had more full-time employees (STACHs: IRR:1.35, 95% CI: 1.31, 1.38; PHs: IRR:1.77; 95% CI: 1.75, 1.80) and were teaching STACHs (STACHs: IRR:1.78; 95% CI: 1.63, 1.95) had significantly more IPBs. In county-level regression models, counties with a lower percentage of Black residents (β: −21.15; 95% CI: −37.14, −5.16) had a significantly higher rate of IPBs. The absence of a causal design means we cannot assess the reasons behind changes in IPBs across time, and is a limitation of this study. Conclusions: This study provides an overview of the availability of IPBs throughout the U.S, as well as the number of individuals without access to IPBs. Findings indicate a dearth of STACH-based IPBs, particularly in areas with a greater proportion of racial minority residents.

Background: Among the alternative solutions being tested to improve access to genetic services, chatbots (or conversational agents) are being increasingly used for service delivery. Despite the growing number of studies on the accessibility and feasibility of chatbot genetic service delivery, limited attention has been paid to user interactions with chatbots in a real-world health care context. Objective: We examined users’ interaction patterns with a pretest cancer genetics education chatbot as well as the associations between users’ clinical and sociodemographic characteristics, chatbot interaction patterns, and genetic testing decisions. Methods: We analyzed data from the experimental arm of Broadening the Reach, Impact, and Delivery of Genetic Services, a multisite genetic services pragmatic trial in which participants eligible for hereditary cancer genetic testing based on family history were randomized to receive a chatbot intervention or standard care. In the experimental chatbot arm, participants were offered access to core educational content delivered by the chatbot with the option to select up to 9 supplementary informational prompts and ask open-ended questions. We computed descriptive statistics for the following interaction patterns: prompt selections, open-ended questions, completion status, dropout points, and postchat decisions regarding genetic testing. Logistic regression models were used to examine the relationships between clinical and sociodemographic factors and chatbot interaction variables, examining how these factors affected genetic testing decisions. Results: Of the 468 participants who initiated a chat, 391 (83.5%) completed it, with 315 (80.6%) of the completers expressing a willingness to pursue genetic testing. Of the 391 completers, 336 (85.9%) selected at least one informational prompt, 41 (10.5%) asked open-ended questions, and 3 (0.8%) opted for extra examples of risk information. Of the 77 noncompleters, 57 (74%) dropped out before accessing any informational content. Interaction patterns were not associated with clinical and sociodemographic factors except for prompt selection (varied by study site) and completion status (varied by family cancer history type). Participants who selected ≥3 prompts (odds ratio 0.33, 95% CI 0.12-0.91; P=.03) or asked open-ended questions (odds ratio 0.46, 95% CI 0.22-0.96; P=.04) were less likely to opt for genetic testing. Conclusions: Findings highlight the chatbot’s effectiveness in engaging users and its high acceptability, with most participants completing the chat, opting for additional information, and showing a high willingness to pursue genetic testing. Sociodemographic factors were not associated with interaction patterns, potentially indicating the chatbot’s scalability across diverse populations provided they have internet access. Future efforts should address the concerns of users with high information needs and integrate them into chatbot design to better support informed genetic decision-making.