Faculty Bibliography

Cisgender women are underserved by current HIV prevention efforts, and substantial gender disparities persist in pre-exposure prophylaxis (PrEP) use. Recent diagnoses with a bacterial sexually transmitted infection (STI) are objective, readily available indicators of PrEP-eligibility that could be used to improve PrEP prescribing for cisgender women. To better understand missed opportunities for prescribing, we examined the prevalence and correlates of Chlamydia trachomatis (CT) and Neisseria gonorrhoeae (NG) diagnoses among cisgender women seeking care at a New York City obstetrics and gynecology (Ob/Gyn) clinic, along with the number of PrEP prescriptions provided post-STI diagnosis. A cross-sectional, retrospective review of electronic health record data was conducted for all HIV-negative cisgender women tested for CT and/or NG at the clinic between September 1, 2021 and September 19, 2022. Counts and prevalence for CT and NG infection were calculated, and multivariable log-binomial regression was used to examine associated factors. Among 7593 cisgender women receiving CT/NG testing during the study period, 186 had ≥ 1 CT infection (prevalence: 2.45%) and 18 had ≥ 1 NG infection (prevalence: 0.24%). In a multivariable model, CT/NG infection was significantly associated with age, having Spanish as a primary language, and a marital status of divorced, widowed, or separated. No cisgender women who received CT or NG diagnoses were prescribed PrEP during the study period. These findings highlight how opportunities to prescribe PrEP to cisgender women continue to be missed, even with readily available indicators for PrEP eligibility. More effective strategies are needed to promote PrEP prescribing among diverse populations of cisgender women, particularly in Ob/Gyn settings.

Mobile health (mHealth) tools can be used to deliver nonpharmacologic therapies to patients with migraine. However, mHealth studies often report poor treatment adherence. Neuroscience Education Therapy (NET), behavioral economics, and Digital Navigators have the potential to increase treatment adherence and thereby improve remote migraine self-management. We conducted a 6-month prospective pilot randomized controlled trial testing if a multi-component package of behavioral interventions increased treatment adherence among patients using one of two different mHealth migraine self-management programs (low-intensity program consisting only of a headache diary versus high-intensity program consisting of a headache diary and behavioral exercises). Our outcomes were the number of diary entries and behavioral exercises completed/week captured via back-end analytics of the mHealth application. We also compared our adherence data at 90-days (a secondary endpoint to assess the durability of the effect) with adherence data from similar published studies without the adherence-enhancing package. We enrolled 26 participants (n = 15 low intensity group, n = 11 high-intensity group). During the 6-week intervention period, we had a median of 7 headache diary entries/week in both groups and a median of 6 days/week of behavioral exercises in the high-intensity group. The rate of adherence with the adherence-enhancing package included was 2.9-8x higher compared to the median rates of the behavioral exercises to historical controls. With use of NET, behavioral economics, and digital navigators, participants achieved higher levels of adherence to both self-management programs compared to prior remote migraine self-management studies. Therefore, these tools may be beneficial to improving adherence to migraine self-management programs.

Previous studies have developed proteomic aging clocks to estimate biological age and predict mortality and age-related diseases. However, these earlier clocks were based on cross-sectional data, capturing only the cumulative aging burden at a single time point but were unable to reflect the dynamic trajectory of biological aging over time. We constructed a longitudinal proteomic aging index (LPAI) using data from 4684 plasma proteins measured by the SomaScan 5K Array across three visits in the Atherosclerosis Risk in Communities (ARIC) study (ages 67-90 at last visit). Our two-step approach applied functional principal component analysis (FPCA) to capture protein-level change patterns over time, followed by elastic net penalized Cox regression for protein selection. LPAI was constructed in a randomly selected training set of ARIC participants (N = 2954), tested among the remaining ARIC participants (N = 1267), and validated externally in Multi-Ethnic Study of Atherosclerosis (MESA) participants (N = 3726, ages 53-94 at last exam). Using Cox proportional hazards model, higher LPAI was associated with increased all-cause mortality (HR = 2.50, 95% CI: [2.15, 2.92] per SD), CVD mortality (HR = 1.79, 95% CI: [1.34, 2.39] per SD), and cancer mortality (HR = 1.96, 95% CI: [1.45, 2.64] per SD) risk in ARIC, with statistically significant and directionally consistent associations also observed in MESA. Additionally, higher LPAI was associated with increased multimorbidity and frailty. This study demonstrates the feasibility of developing biological aging measures from longitudinal proteomics data and supports LPAI as a biomarker for aging-related health risks.

OBJECTIVE:Although communicating air pollution risks is critical for protecting public health, particularly in low- and middle-income countries (LMICs), its effectiveness remains underexplored. This study evaluated current risk communication practices in the city of Rio de Janeiro, Brazil, by assessing the associations between short-term exposure to pollutants and respiratory-related hospital admissions; the ability of the Brazilian national índice de qualidade do ar (IQAr, air quality index) to reflect health risks; and the accuracy of pollutant forecasts in comparison with monitored concentrations. METHODS:Exposure and health data for the 2014-2019 period were obtained through a research partnership with local government officials. Poisson generalized linear models were employed to determine whether IQAr values and short-term exposure to air pollutants, including nitrogen dioxide (NO2) and particulate matter (PM), were associated with daily hospital admissions for respiratory disease. Bias-corrected, forecasted daily concentrations of individual air pollutants from the Goddard Earth Observing System Composition Forecast Composition Forecast (GEOS-CF) model were employed to assess the performance of existing forecasting tools for use in risk communication. RESULTS:Significant associations were consistently observed between hospital admissions for respiratory disease and short-term exposures to NO2 and coarse PM, with excess risks of 5.1% (95% CI: 1.3-8.9%) and 5.6% (95% CI: 1.5-9.9%), respectively, per interquartile range increases in lag day 0-1 exposures. Values of IQAr were not significantly associated with respiratory health events, likely due to their failure to capture the health risks associated with NO2. Bias-corrected forecasts from the GEOS-CF model showed strong correlations with observed pollutant concentrations. CONCLUSIONS:These findings indicate that adopting a health-based, multi-pollutant index, combined with improved forecasting tools, could substantially strengthen risk communication in the city of Rio de Janeiro and other LMIC settings.

INTRODUCTION/UNASSIGNED:The growing availability of AI tools is transforming health and science communication by streamlining content creation and promotion. This study investigates the impact of AI-assisted research summaries on user engagement with the NIH-funded RECOVER program's website and evaluates the efficiency and readability of the content. METHODS/UNASSIGNED:We analyzed Google Analytics 4 data from two distinct periods: one with entirely human-generated content and a second with AI-assisted content. We measured changes in page views, active users, and average engagement time, and assessed the review time and readability of the AI-enhanced summaries. RESULTS/UNASSIGNED:There was no significant change in page views or active users between the two periods. However, average engagement time increased by 4.37 seconds (P = .0461), suggesting AI-assisted content may be more compelling. Human review of AI-drafts averaged 19.88 changes, and readability improved, with the mean Flesch-Kincaid grade level decreasing from 12.28 to 11.56. CONCLUSION/UNASSIGNED:This study demonstrates that AI can be a valuable tool for accelerating the creation of accessible and engaging content. Our findings highlight a crucial balance: while AI can save effort and reduce cost in public engagement efforts, human oversight remains essential to ensure the accuracy, clarity, and accessibility of vital health communications.

OBJECTIVE:Early childhood advanced primary care models are promising ways of addressing child and family needs, but there is limited evidence to support short-term sustainability within current Medicaid payment structures. We evaluate claims-based outcomes associated with 3-2-1 IMPACT (IMPACT), an early childhood advanced primary care model, compared with the standard of care. METHODS:Using New York State Medicaid claims, we identified and matched children aged 1 to 35 months receiving care at 3 IMPACT sites and 3 comparison sites within a large public hospital system. Regression models were used to analyze use, expenditure, enrollment, and quality outcomes between groups. RESULTS:There were 6045 children at the treatment sites and 4832 matched children from the comparison sites. IMPACT was associated with a significant increase in 6 or more well-child visits and a decrease in emergency department visit rates. There was also a significant increase in 6 more well-child visits specifically for Black and Hispanic children seeking care at IMPACT sites compared with comparison sites. There were no significant differences in expenditures, other use types, or Medicaid enrollment across groups. CONCLUSION/CONCLUSIONS:An early childhood advanced primary care model that incorporates multiple evidence-based programs can show short-term, positive effects on preventative and acute care use and quality within Medicaid. These results highlight short-term strategies for sustainability while awaiting the long-term, cross-sector benefits expected from models like IMPACT. Future studies addressing additive model component effects and longer-term outcomes across mother-child dyadic and social-emotional outcomes are warranted.

BACKGROUND:Health status is closely linked to both healthcare access and utilization. While previous research has identified associations between health status and DNA methylation-based biomarkers of aging (epigenetic aging), studies exploring these relationships in the context of healthcare access and utilization remain limited. To address this gap, we analyzed cross-sectional associations in a representative sample of 2,343 U.S. adults from the 1999-2000 and 2001-2002 cycles of the National Health and Nutrition Examination Survey (NHANES). Our study examined the relationships of self-rated health status, healthcare access, and healthcare utilization with seven epigenetic aging biomarkers: HannumAge, HorvathAge, SkinBloodAge, PhenoAge, GrimAge2, DNAm Telomere Length (DNAmTL), and DunedinPoAm. RESULTS:After adjusting for chronological age, demographics, lifestyle factors, and health insurance, participants with good-excellent self-rated health had a 1.58-year lower PhenoAge (95% CI - 2.54, - 0.62 P = 0.006) and a 1.16-year lower GrimAge2 (95% CI - 1.80, - 0.53, P = 0.004) than participants with poor-fair health. Participants who reported having a routine place where they received healthcare had a lower GrimAge2 (β = - 1.44-years, 95% CI - 2.66, - 0.22, P = 0.03) than participants without a routine healthcare location. Participants with ≥ 10 healthcare visits in the prior year had a shorter DNAmTL (β = - 0.05-kb, 95% CI - 0.09, - 0.01, P = 0.02) than participants with < 10 visits. After including additional adjustments for estimated leukocyte proportions, participants who were hospitalized overnight in the prior year had a shorter DNAmTL (β = - 0.05-kb, 95% CI - 0.08, - 0.01, P = 0.02) than non-hospitalized individuals. CONCLUSIONS:Our findings reinforce previous reports linking better health status to lower epigenetic aging and provide new evidence of associations of epigenetic aging with measures of healthcare access and utilization. If validated, these findings suggest that epigenetic aging biomarkers may be useful in studying disease processes and assessing health outcomes related to access and utilization.

BACKGROUND/UNASSIGNED:Despite the growing relevance of rural areas in the overdose crisis, research on rural people who inject drugs and their experiences with law enforcement remains limited. This research examines how rural policing and stigma uniquely shape the lives of people who inject drugs. METHODS/UNASSIGNED:Forty-one semi-structured qualitative interviews were conducted with people who inject drugs in southern Illinois. For this analysis, we focused on participants who mentioned police in response to the question, "Have you ever been treated differently because you used drugs?" RESULTS/UNASSIGNED:We identified three interrelated manifestations of stigma in rural people who inject drugs' interactions with police-verbal degradation and discrediting, unwarranted searches, and dehumanization-that align with Earnshaw's (2020) model distinguishing between stigma components (stereotypes, prejudice, discrimination) and health impact pathways. CONCLUSION/UNASSIGNED:This study emphasizes the impact of stigma on people who use drugs, particularly in their interactions with law enforcement.