Joe C. Wen School of Population & Public Health

INTRODUCTION: Treatment indications for oligometastatic/oligoprogressive lung tumors are growing. Safety and lack of detrimental effect on patients' quality of life are critical for novel local therapies. METHODS: We tested that the additive effect of pulsed electric field (PEF) ablation with lower-dose stereotactic body radiation therapy (SBRT) on health-related quality of life (HRQoL) as a secondary endpoint in a prospective clinical trial. FACT-Lung Cancer Subscale (FACT-LCS) and FACT-General domain surveys were collected at screening, 3 months, and 12 months. Functional clinical data included forced vital capacity (FVC), forced expiratory volume in one second (FEV1), and diffusing capacity of the lung for carbon monoxide (DLCO). RESULTS: Six patients with eight tumors were enrolled. Baseline well-being domain scores were: Physical 25.9 (Std Dev 2.3), Social 21.0 (Std Dev 6.9), Emotional 17.3 (Std Dev 4.7), Functional 21.2 (Std Dev 5.8), and LCS 19.4 (Std Dev 5.3). There were no significant changes following combined modality treatment in HRQoL domain scores or pulmonary function metrics after treatment. Lower EWB and SWB were associated with worse FVC and FEV1. CONCLUSION: In this small pilot study, no clinically meaningful declines in pulmonary function or patient-reported quality of life were observed at 3 months following combination therapy.

Introduction: Quantifying the impacts of armed conflict on civilians and infrastructure remains a major challenge, particularly where reporting is limited. Most conflict measurement tools require affected populations to report events and are limited by short time series, under-reporting, and varying methods. These tools do not capture infrastructural rebuilding, which has important health implications. Given this, we demonstrate the utility of nighttime lights (NTL) as a complementary tool for measuring conflict dynamics and infrastructure recovery with an epidemiological application. Methods: We used monthly NASA Black Marble data to analyze NTL patterns in Yemen (2012–2022) and Ukraine (2019–2024) before and after the onset of large-scale military operations. We calculated month-specific NTL ratios relative to pre-event baselines and assessed the alignment of structural breakpoints, identified using BFAST methods, with aerial attack onset. Generalized additive models were used to measure the relationship between NTL and aerial attacks while accounting for the built environment, population, diesel price (Yemen), and spatiotemporal factors. Finally, we applied NTL to an existing model on the association between conflict, measured via air raids, and cholera in Yemen by replacing the original conflict categories with ones defined by NTL and included a variable for NTL recovery. Results: Mean NTL declined by 53.3% in Yemen and 21.0% in Ukraine following conflict escalation, with detected breakpoints aligning with aerial attack onset in 85.7% of Yemeni governorates and 51.9% of Ukrainian oblasts. Generalized additive models showed that attacks were significantly associated with NTL reductions, independent of built environment factors. Incorporating NTL-based conflict measures into a cholera transmission model for Yemen produced results consistent with attack-based models and found that light recovery was associated with reduced disease risk. Discussion: NTL is a viable tool for measuring conflict and can offer insights on dynamics that are not present in standard tools while avoiding many of these tools’ limitations. These data have epidemiological applications and can be a proxy for important events affecting transmission dynamics. While event-based tools have vast utility, NTL can complement them with specific strengths and means of application.

BACKGROUND: Undocumented immigrants face significant barriers maintaining regular health care, which could lead to emergency department (ED) visits for chronic diseases such as type 2 diabetes (T2D). Local health coverage programs like MyHealthLA (MHLA) in Los Angeles County can improve disease management by providing regular access to care. This study examines the relationship between enrollment patterns in MHLA and ED utilization for T2D-related conditions, focusing on how the duration of enrollment impacts the likelihood of ED visits. RESEARCH DESIGN: We analyzed 115,690 ED encounters from 44,333 MHLA patients in the Los Angeles Department of Health Service (LADHS) between 2016 and 2020. There were 5 categories based on enrollment 12 months before the ED encounter: (1) continuously enrolled ≥6 months, (2) newly enrolled for <6 months, (3) consistently unenrolled for ≥6 months, (4) newly unenrolled <6 months, and (5) never enrolled, who visited the ED before ever enrolling in MHLA. RESULTS: Patients continuously enrolled in MHLA were less likely to visit the ED for short-term T2D complications, suggesting that consistent primary care helps manage chronic conditions and reduce ED use. Conversely, patients newly enrolled or unenrolled had higher odds of T2D-related ED visits, indicating that enrollment lapses may worsen disease management. CONCLUSIONS: These findings highlight the importance of continuous access to primary care and the potential benefits of Medicaid expansion for undocumented adults in California. Health systems should prioritize continuity of care to improve chronic disease management and reduce avoidable ED visits in underserved populations.

BACKGROUND: Weight changes after menopause contribute to cardiometabolic risk, yet hormonal determinants of long-term weight trajectories remain incompletely understood. Asprosin, a fasting-induced adipokine involved in hepatic gluconeogenesis and appetite regulation, has been associated with metabolic disease, although its prospective role in affecting weight change remains unknown. OBJECTIVES: This study aimed to examine whether plasma asprosin concentrations are directly and prospectively associated with changes in body weight and body composition among postmenopausal women. METHODS: In a case-control study of 4020 postmenopausal women (1987 newly developed/incident diabetes cases and 2033 matched controls) nested within the Women's Health Initiative, we prospectively evaluated participants' baseline plasma concentrations of asprosin in relation to 3-y changes in weight, measures of central obesity, and the risk of major weight gain or loss (≥7% of baseline weight). Associations were examined overall and stratified by baseline body mass index (BMI) or whether the participant developed diabetes during follow-up. Dual-energy X-ray absorptiometry-derived fat and lean mass were available for a subset of participants (n = 178). RESULTS: In the full cohort (n = 4020), baseline asprosin was not associated with 3-y weight change or changes in central adiposity. However, among matched controls with BMI <30 kg/m², participants in the highest asprosin quartile gained 1.61 kg less than those in the lowest quartile [adjusted β: -1.61; 95% confidence interval (CI): -2.69, -0.52; P-trend < 0.01] and had lower odds of major weight gain (adjusted OR: 0.57; 95% CI: 0.37, 0.88; P-trend < 0.01) and higher odds of major weight loss (adjusted odds ratio: 1.83; 95% CI: 1.10, 3.05; P-trend = 0.02). CONCLUSIONS: In this prospective study of postmenopausal women followed for 3 y, baseline asprosin concentrations were associated with weight change in apparently healthy women without diabetes or obesity.

Patient-controlled data-sharing systems are increasingly promoted as a way to empower patients with greater autonomy over their health data. Yet it remains unclear how different stakeholders, especially patients and health system leaders, perceive the benefits and challenges of enabling granular control over the sharing of de-identified medical data for research. To address this gap, we developed a high-fidelity prototype of a patient-controlled, web-based consent platform and conducted a two-phase mixed-methods study: semi-structured interviews with 16 health system leaders and a survey with 523 patient participants. While both groups appreciated the potential of such a platform to enhance transparency and autonomy, their views diverged in meaningful ways. Leaders viewed transparency and granular control through the lens of informed consent and institutional ethics, whereas patients interpreted these factors as safeguards against potential risks and uncertainties. Our findings underscore critical tensions such as individual control and research integrity. We offer design implications for building trustworthy, context-aware systems that support flexible granularity, provide ongoing benefit‑centered transparency, and adapt to diverse literacy and privacy needs.

Abstract Lifestyle and genetic factors are known contributors to breast cancer risk, yet their integration with clinical data into breast cancer risk assessment remains limited. Traditional, self-reported lifestyle measures are subject to recall bias, whereas wearable devices provide objective, continuous measurements of physical activity and sleep behaviors. Using data from the National Institutes of Health All of Us Research Program (n=633,540 participants), we conducted a retrospective matched case-control study to evaluate the association between objectively captured wearable data and breast cancer risk, and to establish a scalable analytical framework for causal and machine learning modeling. Females diagnosed with breast cancer at age ≥50 years with at least five valid weeks of Fitbit data (two or more days per week) within the five years preceding diagnosis (n=154) were each matched to up to 20 cancer-free controls by date of birth (±1 year) and availability of wearable data within the same time temporal window. Numerical variables were analyzed using Wilcoxon signed-rank tests, and categorical variables via chi-square analysis. Cases exhibited lower average daily steps (6766 ± 3040) compared to controls (7248 ± 3266; p=0.011), as well as fewer daily light active and very active minutes (179.8 ± 69.0 and 13.6 ± 13.7 vs. 190.2 ± 69.3 and 16.0 ± 16.9; p = 0.043 and p < 0.001, respectively). Sleep metrics were not significantly different between groups, while family history of breast cancer was more common among cases (p < 0.001). Building on these findings, we propose a multimodal integrative framework that merges wearable, survey, and electronic health record data, with future incorporation of genomic features and causal inference techniques (e.g., propensity score matching and causal forests) to refine individualized risk estimation. Explainable machine learning approaches, including ensemble and time-series models, will enable interpretable and dynamically updated risk predictions. This study demonstrates the feasibility of using real-world wearable data within the All of Us infrastructure and underscores the translational potential of multimodal, causal, and interpretable modeling for precision breast cancer screening and prevention at a population scale. Citation Format: Yoav Weber, Arshia Ilaty, Xuanxi Kuang, Emily Lan Nguyen, Abel Plaza-Florido, Shlomit Radom-Aizik, Argyrios Ziogas, Amir M. Rahmani, Hannah Lui Park. Integrating real-world wearable data into breast cancer risk assessment: Evidence from the All of Us Research Program [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 5031.

Introduction

Methods

Results

Discussion

Background: Aspergillosis is a fungal infection associated with rising hospitalizations and substantial morbidity and mortality. In the United States, data remain fragmented due to the absence of centralized surveillance. This study aimed to evaluate demographic, geographic, and temporal trends in aspergillosis diagnoses across the United States and evaluate changes in those patterns following the emergence of COVID-19. Methods: We conducted a retrospective cohort study using electronic health record data from 142 US healthcare systems (Oracle Health), including adults aged ≥18 years who received care between 2013 and 2023. The cohort included over 76 million patients and 127 million person-years. Aspergillosis prevalence was calculated using post-stratification weights. Adjusted prevalence ratios (aPRs) were estimated via quasi-Poisson and Bayesian spatiotemporal regression. COVID-19-related shifts were evaluated using estimated marginal means. Results: From 2013 to 2023, aspergillosis prevalence increased by 5% annually, peaking in 2022. Rhode Island had the highest state-level aPR; Utah the lowest. Diagnosis was higher among males (aPR 1.37), older adults (≥65 years vs 18-24 years: aPR 4.95), and urban residents (rural aPR 0.86). Following the emergence of COVID-19, prevalence increased disproportionately among Hispanic or Latino patients and several racial minority groups. A nonsignificant upward trend was also observed among rural residents. Conclusions: This study provides a comprehensive national assessment of aspergillosis diagnosis patterns in the United States, revealing rising prevalence and shifts in affected populations following the emergence of COVID-19. These findings may aid earlier clinical recognition, especially among groups not traditionally considered high-risk, and support efforts to expand diagnostic access and improve fungal disease surveillance.

BackgroundPostmenopausal women tend to experience significant changes in body composition, particularly abdominal adipose tissue (AAT) deposition patterns, which are hypothesized to be critical factors influencing future chronic disease risk. The level of protein intake to maintain or achieve a more favorable body composition for health in postmenopausal women is a central, largely unanswered question relating to the appropriateness of current dietary guideline recommendations for sufficient protein intake (set at 0.8 g/kg/day).ObjectiveTo estimate the hypothetical effect of a range of protein intake levels on 3-year mean changes in body composition measures in postmenopausal women.MethodsWe analyzed data from 3789 postmenopausal women aged 50–79 enrolled in the Women’s Health Initiative (WHI) to emulate a 3-year target trial of adhering to increasing levels of protein intake: ≥0.8 g/kg/d, ≥1.0 g/kg/d, ≥1.2 g/kg/d, and ≥1.5 g/kg/d. All participants had repeated Dual X-Ray Absorptiometry (DXA) scans with derived abdominal visceral (VAT) and subcutaneous adipose tissue (SAT). The measured differences in average levels of VAT, SAT, and other body composition measures determined at end of follow-up were estimated with the parametric-g formula.ResultsOver 3 years, hypothetical interventions of increasing levels of dietary protein intake are estimated to have dose-dependent reductions in abdominal VAT, SAT, and overall body fat, and increases in lean soft tissue, with potential benefits observed at ≥1.2 g/kg/day and the greatest estimated benefit at ≥1.5 g/kg/day of dietary protein. Compared to no intervention, if all participants hypothetically adhered to a total daily protein intake of ≥1.5 g/kg/day over 3 years, they would be estimated to have lower levels of VAT (−13.1 cm2, 95% Confidence Interval [CI] −18.9, −7.3), SAT (−25.3 cm2, 95% CI −39.7, −11.0), total body fat % (−1.0%, 95% CI −1.7, −0.3), body weight (−2.5 kg, 95% CI −3.7, −1.2) and greater lean soft tissue % (0.9%, 95% CI 0.3, 1.6) over 3 years.ConclusionThis hypothetical emulated intervention suggests that postmenopausal women who maintain a hypothetical total protein intake of at least 1.2 g/kg/day could experience beneficial changes in abdominal VAT, SAT, and overall body composition over three years, with even greater estimated benefits observed at an intake of 1.5 g/kg/day. These findings suggest that protein intake higher than guideline recommendations may better support healthier body composition and lower chronic disease risk in postmenopausal women.

INTRODUCTION: State and local jurisdictions in the United States (U.S.) are increasingly adopting flavored tobacco sales restrictions (FTSRs) to mitigate tobacco initiation and use. Policy implementation is highly understudied yet can impact policy effectiveness. This review examines existing literature on state and local FTSR policy design and implementation in the U.S. METHODS: We systematically searched for PubMed articles published by 12/31/2024 which were: original research articles in English focused on a U.S. state or local FTSR that reported at least one policy implementation outcome measure. We excluded articles that were systematic reviews or reported on federal or non-FTSR policy. Guided by policy and implementation science frameworks, we developed a data extraction template to report: policy design elements, study characteristics, and implementation measures (i.e., inputs, activities, outcomes). RESULTS: Of 1,595 articles identified, 30 were retained for review. Most evaluated local FTSRs, and eight evaluated a statewide policy. Pre-post test and cross-sectional study designs were the most common. Frequently reported implementation outcomes were acceptability, appropriateness, feasibility, and fidelity/compliance. Studies with pre-post test designs and comparison groups showed significantly increased fidelity/compliance (i.e., reduced availability of restricted products) in FTSR jurisdictions. A majority (56.7%) detailed an implementation input for infrastructure (e.g., resources) or activity (e.g., outreach). Few described enforcement-related mechanisms. CONCLUSIONS: Key gaps exist in policy implementation articles on state and local FTSRs in the U.S., including infrequent reporting of enforcement agencies and penalties which may impact implementation. Additional mixed methods research is needed to compare FTSR implementation across jurisdictions with varying policy designs.