Faculty Bibliography

AIMS/OBJECTIVE:To characterize decreases in overdose death rates in the United States (US) between 2023 and 2024 by race/ethnicity, and substance involvement. DESIGN/METHODS:Population-based study of national death records accessed via the Centers for Disease Control and Prevention (CDC) Wide-ranging ONline Data for Epidemiologic Research (WONDER) platform using an underlying cause of death approach. SETTING/METHODS:US. PARTICIPANTS/CASES/METHODS:All individuals who died from drug overdose between January 1999 and December 2024. MEASUREMENTS/METHODS:Annual overdose deaths per 100 000 population. Year of occurrence of overdose death, substance involvement, race/ethnicity of decedents. FINDINGS/RESULTS:After many years of increases, the US overdose death rate dropped 24.4% between 2023 and 2024. Decreases reflected declining illicit fentanyl-involved deaths (with and without stimulant involvement). The fourth wave of the US overdose crisis-defined by deaths involving fentanyl together with stimulants-declined for the first time in 2024. Despite overall decreases, deaths involving stimulants without fentanyl and deaths involving xylazine continued to represent a growing fraction of overdose fatalities. Non-Hispanic Black and African Americans had the largest decrease in death rates in 2023-2024, falling by 29.3% but remaining elevated at 36.0 per 100 000, 1.51 times higher than the national average of 23.7 per 100 000. Non-Hispanic American Indian and Alaska Native individuals had the highest overdose death rates rate in 2024, at 50.8 per 100 000, 2.15 times the national average rate, and experienced a below-average relative decrease of 20.1%. CONCLUSIONS:All four previously defined waves of the US overdose crisis appear to be in decline, as deaths involving illicit fentanyl, with and without stimulants, dropped sharply between 2023 and 2024. Concurrently, the fraction of overdose deaths involving stimulants without fentanyl and those involving xylazine continued to increase. While racial disparities in drug overdose death rates narrowed slightly during this period, large gaps remain, with the highest overdose death rates among American Indian, Alaska Native, and Black individuals.

IMPORTANCE/UNASSIGNED:Current pregnancy surveillance efforts in the US face substantial challenges in providing timely and accurate data on prenatal care use. Electronic health record (EHR) networks have the potential to enhance existing surveillance systems by providing near real-time, clinically documented data. OBJECTIVE/UNASSIGNED:To assess whether EHR network data could be used to define valid and reliable surveillance metrics of prenatal care use. DESIGN, SETTING, AND PARTICIPANTS/UNASSIGNED:This longitudinal cohort study included US adults (age ≥18 years) who received prenatal care and delivered a live birth from January 1, 2023, to December 31, 2024, at a facility that used the Epic Cosmos EHR network. EXPOSURE/UNASSIGNED:Live birth at a facility that used the selected EHR network. MAIN OUTCOMES AND MEASURES/UNASSIGNED:Prenatal care use was calculated as the proportions of patients who initiated care by the 13th week of pregnancy (early care) and who received adequate or better prenatal care (adequate care). Raking weights were applied to adjust the EHR sample to match the marginal distributions for US residents with live births by age, race and ethnicity, insurance, pregnancy risk factors, and geographic region. Electronic health records-based metrics were externally validated against published natality data estimates from National Center for Health Statistics (NCHS) using the two 1-sided test of equivalence. Patterns by demographics, state, and year were examined. RESULTS/UNASSIGNED:In total, 1 963 496 patients (mean [SD] age, 29.5 [5.7] years; 100% women) had a live birth and evidence of prenatal care at a facility using the selected EHR network during the study period. Compared with all US birthing people (n = 7 224 951), patients who gave birth at a facility using the selected EHR network had lower Medicaid coverage (40.5% vs 21.1%) and a higher prevalence of pregnancy risk factors (eg, prior preterm birth: 4.0% vs 8.8%). After weighting to the national population, EHR-based estimates of early care were consistently lower than those from NCHS data (68.0% [95% CI, 67.9%-68.2%] vs 76.1% [95% CI, 76.1%-76.1%]). However, adequacy estimates were equivalent to NCHS-based estimates (76.0% [95% CI, 75.9%-76.2%] vs 75.2% [95% CI, 75.1%-75.2%]; P < .001 at 0.01 equivalence bound), aligned with expected demographic patterns, and were stable across place and time. CONCLUSIONS AND RELEVANCE/UNASSIGNED:In this cohort study, EHR network data reliably informed surveillance of prenatal care adequacy after adjusting for nonrepresentativeness of the patient population. These findings suggest that near real-time availability of EHR data has the potential to improve the timeliness of population-level pregnancy surveillance to better inform policy, public health, and clinical efforts aimed at enhancing prenatal care access and use among individuals receiving inadequate care.

BACKGROUND:Artificial intelligence (AI)-enabled "ambient" documentation may reduce clinician administrative burdens and improve care delivery, but implementation in clinical practice is complex. AIM/OBJECTIVE:To evaluate the implementation of commercially available ambient documentation tools in multi-specialty ambulatory clinical workflows at an academic medical center. SETTING/METHODS:A large urban academic health system in New York City. PARTICIPANTS/METHODS:Ninety-seven ambulatory clinicians across specialties. PROGRAM DESCRIPTION/METHODS:A multidisciplinary team conducted a 6-month proof-of-concept structured evaluation of two commercially available ambient documentation tools through initial vendor evaluations, technical review and integration with the electronic health record (EHR), clinician training and onboarding, implementation and technical support, and structured evaluation based on objective and key results (OKR) metrics. A single-group, pre-post evaluation of the impact of the tools on clinician EHR-based efficiency was conducted on a subset of participating clinicians. PROGRAM EVALUATION/RESULTS:Compared to the 3-month period immediately prior to initiating the ambient trial, clinicians experienced a 0.35-min-per-note and a 2.07-min-per-day reduction in documentation time. "Vendor B" showed higher utilization rates and superior user experience compared to "Vendor A." Implementation challenges included workflow integration, training resource requirements, data interoperability and analytics, and ongoing technical support needs. DISCUSSION/CONCLUSIONS:Ambient documentation shows promise in reducing documentation burden, but its success depends on technical stability and integration, product fit and support for clinicians, and adequate implementation resourcing. A multidisciplinary approach with clear metrics, strong vendor partnership and executive sponsorship, and ongoing technical support enables scalability.

BackgroundFew studies have examined which patients with migraine might be responders for mind-body interventions. Thus, we examined whether certain baseline mindfulness traits and interest in physical exercise might predict response to treatment.MethodsThis is a planned exploratory analysis of a phase 2 randomized controlled study (N  =  50; 25 per arm) comparing a 6-week physical therapist (PT)-delivered biofeedback-assisted relaxation (BAR) program vs. an Enhanced Usual Care (EUC) migraine self-management program (diary tracking and emailed migraine-related educational materials). We conducted moderation analyses to determine whether the Multidimensional Assessment of Interoceptive Awareness (MAIA), Difficulties in Emotion Regulation Scale (DERS) and Physical Activity Enjoyment Scale (PACES) at baseline influenced the effect of BAR on migraine-related outcomes (Migraine-Specific Quality of Life Role Function Restrictive (MSQv2.1-RFR) and Migraine-Related Disability (MIDAS)) at 6 months.ResultsAmong the n = 40 participants (BAR = 19; EUC = 21), the majority were female (95%), non-Hispanic (77.5%) and white (67.5%). Mean (SD) age was 45.6 (11.2) years. For the MAIA Not-Worrying subscale, BAR produced the greatest improvement in 6-month MSQv2.1-RFR scores among participants with low baseline Not-Worrying scores (those who tended to worry about bodily sensations/discomfort more) (BAR = 77.1 ± 6.6 vs. EUC = 48.9 ± 5.0; p = 0.002, g = 4.75). The benefit diminished at average levels (p = 0.060, g = 2.72) and was absent at high baseline Not-Worrying (p = 0.528, g =  -0.91). For the MAIA Self-Regulation subscale, BAR was most effective among those low in baseline self-regulation (BAR = 71.7 ± 5.3 vs. EUC = 44.3 ± 7.2; p = 0.004, g = 4.27). The DERS total score showed that BAR demonstrated little benefit among participants with better baseline emotion regulation (i.e. lower DERS score; p = 0.907, g = 0.17) but was more effective as baseline emotion regulation difficulties increased, showing a moderate benefit at average levels (BAR = 69.2 ± 4.2 vs. EUC = 55.8 ± 4.0, p = 0.027, g = 3.25) and a large, significant difference at high levels (BAR = 70.6 ± 6.3 vs. EUC = 44.7 ± 5.7; p = 0.004, g = 4.22). The PACES total score indicated that BAR benefits were strongest among those with low (BAR = 76.1 ± 7.0 vs. EUC = 47.1 ± 5.3, p = 0.002, g = 4.60) to average (BAR = 71.2 ± 4.2 vs. EUC = 58.6 ± 4.0, p = 0.035, g = 3.07) enjoyment of physical activity.ConclusionsWe found subgroups of individuals with migraine who may be better responders to PT-delivered BAR, specifically those who tend to worry more about bodily sensations (lower MAIA Not-Worrying score), those with low self-regulation (lower MAIA Self-Regulation score), those with worse emotion regulation (higher DERS score) and those with lower levels of physical activity enjoyment (lower PACES score) at baseline. This may help us determine who may benefit most from BAR.Trial RegistrationClinicalTrials.gov Identifier: NCT06077812.

OBJECTIVE/UNASSIGNED:To examine how exposure to advertising channels is associated with e-cigarette use among youth in the United States. METHODS/UNASSIGNED:This study included 161,700 middle and high school students aged 9-18 years from the 2014-2022 National Youth Tobacco Survey data. Multivariate logistic regressions estimated adjusted odds ratios (aORs) and 95 % confidence intervals (CIs) for the association between exposure to advertising channels and ever e-cigarette use. Annual percent change (APC) for ever e-cigarette use and exposure to each advertising channel were analyzed using Joinpoint regression. Data for 2014-2018 and 2019-2022 were analyzed separately due to the change in survey format and weighting procedure. RESULTS/UNASSIGNED:Ever e-cigarette use prevalence increased (APC = + 2.78: 95 % CI -13.65, 21.38) between 2014 and 2018, then declined (APC = -18.20 %: 95 % CI -27.11, 8.36) between 2019 and 2022. Exposure to retail (2014-2018 aOR = 1.36: 95 % CI 1.29, 1.43; 2019-2022 aOR = 1.46: 95 % CI 1.38, 1.54) and internet advertising (2014-2018 aOR = 1.43: 95 % CI 1.35, 1.51; 2019-2022 aOR = 1.19: 95 % CI 1.13, 1.25) were associated with higher odds of ever e-cigarette use. CONCLUSIONS/UNASSIGNED:Exposure to retail and internet marketing increased the likelihood of ever-use of e-cigarettes among youth in the US. Additional regulations for point-of-sale and internet advertisements should be implemented.

OBJECTIVE:To estimate associations of more than minimal prenatal nicotine, alcohol, cannabis, and opioid exposures with gestational age, birth weight, and birth weight for gestational age. METHODS:Data were drawn from the HEALthy Brain and Child Development (HBCD) Study, a multisite, longitudinal study in the United States. Predefined recruitment thresholds for each substance were assessed using maternal self-report, maternal toxicology results, and newborn substance exposure-related diagnoses. Birth outcomes included gestational age at delivery (weeks), birth weight (grams), and birth weight for gestational age (centiles). Mean differences and risk ratios for the associations between substance exposure and birth outcomes were estimated using multilevel mixed-effect linear regression or multilevel mixed-effect Poisson regression. RESULTS:Among 660 mother-infant dyads, 17% (n = 115) of participants met recruitment thresholds for prenatal cannabis, 15% for nicotine (n = 102), 13% for alcohol (n = 86), and 5% for opioids (n = 32). In adjusted models, prenatal cannabis and opioid exposures were each associated with lower birth weight (cannabis: -272.2 [95% CI -444.6 to -99.8] g; opioids: -295.4 [95% CI -574.9 to -15.9] g) and birth weight centiles (cannabis: -8.2 [95% CI -15.3 to -1.1] centiles; opioids: -14.4 [95% CI -25.5, -3.4] centiles), although the results were sensitive to model specifications. Prenatal nicotine and alcohol estimates were in similar directions but not statistically significant. No significant associations between exposures and gestational age at delivery were detected. CONCLUSIONS:In this initial HBCD Study data release, more than minimal exposure to cannabis and opioids was associated with smaller birth size, adding evidence to an inconsistent literature. Future studies from HBCD can more deeply interrogate timing and dose of each substance and expand to childhood outcomes.

Asian American populations are underrepresented within dietary programs and nutrition-specific funding. Existing understanding amongst Asian American communities focuses on those with pre-existing health conditions. There is a gap in published literature providing an overview of the food groups Asian American populations without noncommunicable diseases consume. This scoping review aims to provide a comprehensive mapping of the Asian American diet with a focus on food groups in the United States within existing literature. A scoping review was conducted from December 2023 to December 2024. Research databases (PubMed/Medline, CABI Digital Library, CINAHL, Scopus, Web of Science, EBSCO, and ProQuest Dissertations) were searched to identify articles published since 2000 that described food group consumption of Asian American populations. A total of 1486 articles were independently screened by 3 reviewers, leaving 165 articles for inclusion in this review. An additional 15 gray literature sources were separately reviewed for complementary information. Key findings (population and geographical focus, dataset, food group outcome, study limitations, and recommendations) were extracted and summarized in narrative form. Within the literature, Asian American adults and children generally consume more refined grains and less dairy than the standard United States population, though specific patterns vary: South Asians report higher saturated fat and dairy intake, while Chinese, Filipino, and Southeast Asian groups often fall short of recommended fruit and vegetable consumption. Dietary staples also differ by ethnicity, ranging from white rice and noodles in East/Southeast Asian diets to breads and buns among South Asian and Filipino communities. Recommendations for improving diets in Asian American communities emphasize culturally tailored nutrition education that incorporates traditional diets, promotes whole-grain substitutions, and adopts family-focused approaches. Future research efforts in Asian American diet and nutrition should better understand meal-sharing practices and cooking methods. Identifying broad insights in Asian American dietary consumption lays an essential framework for developing recommendations and interventions to address dietary behaviors contributing to diet-related disease burden among the Asian American population. This scoping review is registered on Open Science Framework (https://doi.org/10.17605/OSF.IO/K3X8S).

Most visits to the emergency department (ED) by persons living with dementia (PLWD) who are then discharged back into the community are preventable. However, care partners and other caregivers of the over 6 million PLWD residing in the United States lack the supports, services and timely access to clinical care to address many common needs in the community. Thus, care partners resort to taking the PLWD to the ED, a sub-optimal environment that can be traumatic to the PLWD, increase iatrogenesis, and ultimately may not resolve the underlying reason for the visit. Longitudinal nurse-led telephonic care (NLTC) provided following an ED visit with community discharge may present an effective and efficient model for health systems to support care partners, decant busy EDs, and provide high-quality, person and family-centered impactful care to support PLWD and their care partners. This paper describes the development of an NLTC program being implemented as part of the Emergency Departments LEading Transformation of Alzheimer's and Dementia Care (ED-LEAD) trial, a factorially designed embedded pragmatic clinical trial in 79 EDs. The NLTC program utilizes components of two previously tested programs, the Aliviado Dementia Care quality improvement program, and the Emergency Medicine Palliative Care Access (EMPallA) nurse-led telephonic palliative and transitional care program to support PLWD and their care partners. Successful implementation of the NLTC program may lead to increased uptake of NLTC programs by health systems, improving quality of care and quality of life for PLWD and their care partners.

IMPORTANCE/UNASSIGNED:Screening by low-dose computed tomography can reduce lung cancer mortality among high-risk individuals, but many lung cancers occur among individuals with a smoking history who are not eligible for screening. OBJECTIVE/UNASSIGNED:To develop and validate the protein-based Integrative Analysis of Lung Cancer Risk and Etiology (INTEGRAL)-Risk model in individuals with a smoking history from the general population. DESIGN, SETTING, AND PARTICIPANTS/UNASSIGNED:Cohorts in the Lung Cancer Cohort Consortium recruited research participants in the US, Europe, Asia, and Australia between 1985 and 2009, who were followed up for lung cancer and other health outcomes until 2021. Fourteen case cohorts of 3695 participants with a smoking history within the Lung Cancer Cohort Consortium, including 2305 randomly sampled participants and 1390 patients diagnosed with lung cancer within 3 years after blood sample collection, were designed. Plasma or serum samples from each participant were assayed using the INTEGRAL protein panel in 2022. The INTEGRAL-Risk model was trained using 7 predefined case cohorts (training set; n = 1951) to estimate absolute risk of being diagnosed with lung cancer based on age, smoking history, and 13 proteins. The validity of the INTEGRAL-Risk model was assessed in 7 independent case cohorts (testing set; n = 1744) at 1, 2, and 3 years after blood collection. EXPOSURE/UNASSIGNED:Absolute risk estimates from the protein-based INTEGRAL-Risk model. MAIN OUTCOMES AND MEASURES/UNASSIGNED:The primary outcome was the validity of the INTEGRAL-Risk model in the testing set with respect to discrimination (area under the curve [AUC]) and calibration (ratio of expected-to-observed cases [E/O]). RESULTS/UNASSIGNED:A total of 3695 participants were included, with 1951 participants (including 807 with lung cancer) in the training set and 1744 participants (including 583 with lung cancer) in the testing set. In the combined 14 training and testing sets, after application of statistical weights, 323 570 participants were represented (185 016 [57%] female; median [IQR] age, 60 [51-67] years). In the independent testing set, discrimination of the INTEGRAL-Risk model was highest at 1 year of follow-up and exceeded that of the questionnaire-based PLCOm2012 (Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial) model (INTEGRAL-Risk AUC of 0.88 [95% CI, 0.85-0.91] vs PLCOm2012 AUC of 0.79 [95% CI, 0.75-0.83]; P value for difference <.001). Using a risk threshold to achieve the same specificity as US Preventive Services Task Force (USPSTF) 2021 criteria, the INTEGRAL-Risk model captured 85% of lung cancer cases compared with 63% by USPSTF 2021 and 70% by PLCOm2012. Discrimination of the INTEGRAL-Risk model decreased with longer prediction horizons, with a 2-year AUC of 0.84 (95% CI, 0.81-0.86) and 3-year AUC of 0.81 (95% CI, 0.79-0.83). The model was well calibrated (E/O over 3 years, 0.87 [95% CI, 0.69-1.14]). CONCLUSIONS AND RELEVANCE/UNASSIGNED:Compared with questionnaire-based approaches, the protein-based INTEGRAL-Risk model improved short-term prediction of lung cancer in people with a smoking history. This model has potential to improve selection of high-risk individuals who are most likely to benefit from lung cancer screening.