Serious psychological distress, unmet mental health treatment needs, and help-seeking among Asian American adults in New York City: A population-based study.

Short-term origin-destination (OD) demand prediction is critical in managing the multimodal transportation system. The joint short-term OD demand prediction for multimodal systems faces three challenges: (1) data availability: real-time OD demand is not available for prediction; (2) sparsity and high-dimensionality of OD demand: the OD demand is spatiotemporal sparse and usually high dimension; (3) impact of different transportation modes: the future OD demand for one mode is affected by others, and extensive studies primarily focus on a single transportation mode, overlooking the influence between different modes. To tackle these challenges, we propose a multitask learning and Partial-Differential-based model to predict the short-term Multimodal Transport Systems OD demand (PD-MTSOD), which includes (1) an OD demand learner to estimate real-time OD demand, (2) data aggregation with hypergraph attention to capture spatiotemporal features, and (3) OD demand decomposition into self-generated increment, other-modes-generated increment, and real-time OD demand, and use partial-differential-based methods to model intermodal correlations. Extensive tests on Beijing and New York city's multimodal systems show that PD-MTSOD surpasses baseline models. In addition, we prove the benefits of joint considering multiple transportation and explore the correlations of different transportation modes. This paper offers a reliable method for understanding multimodal transportation systems.

Relationship Between Fibrinogen-to-Albumin Ratio and Pre-Eclampsia: A Retrospective Cohort Study.

Gender identity invalidation and suicide risk among trans and nonbinary individuals.

Uncovering streams, dreams, and dilemmas: A case study analyzing trade-offs and justice in the urban stream daylighting of Tibbetts Brook in New York City, USA

Background: Tuberculous pleuritis (TBP) presents a diagnostic challenge due to its paucibacillary nature. The gold standard for diagnosing TBP requires the identification of Mycobacterium tuberculosis in tissue culture and evidence of granulomatous inflammation in pleural specimens. We aimed to determine the diagnostic yield and accuracy of medical thoracoscopy, as well as its utility in early diagnosis and treatment in a low-prevalence region. Methods: This is a retrospective study of patients with suspected TBP between 2020 and 2024 in a large hospital system in New York City. A subgroup of patients underwent medical thoracoscopy with pleural biopsy for histopathological and microbiological evaluation, and their results were compared to those of a cohort initiated on RIPE treatment without pleural tissue analysis. Results: Of 129 patients included, medical thoracoscopy was performed in 46 cases, with 31 (67.4%) diagnosed with TBP, 4 (8.7%) with nontuberculous pleuritis, and 11 (23.9%) with chronic idiopathic pleuritis. The diagnostic yield for tuberculosis was 67.4% in a low-prevalent region, with an overall accuracy of 89.1%. Out of 83 patients who were initiated on RIPE therapy based on clinical suspicion of TBP, 18 (21.7%) had no microbiological evidence of tuberculosis. The time to RIPE initiation was shorter in the medical thoracoscopy group (3.0 [IQR, 1.0–5.0] days vs. 5.0 [IQR, 3.0–13.0] days, P = 0.006). Conclusions: Medical thoracoscopy offers high accuracy and a diagnostic yield in a low-prevalent region, facilitating early diagnosis and leading to more timely and appropriate treatment initiation. Diagnosis of TBP in areas of low prevalence may help reduce drug resistance and inappropriate treatment regimens.

Safety and efficacy of performing medical thoracoscopy on a "dry space" using an optical trocar.

Evidence of environmental gentrification in New York City

Participation in the house-ball community as a social determinant of HIV care and prevention outcomes among transgender women of color.

Long-term trends reflecting regulatory impacts on VOCs sources in the New York City metropolitan area

ObjectivesThe COVID-19 pandemic complicated hospice and palliative care (HPC) experiences of patients and family caregivers. We sought to understand HPC professionals' perceptions of patients' and family caregivers' HPC experiences during the COVID-19 pandemic in New York City and to make recommendations for improving HPC delivery during future public health crises.MethodsThis was a qualitative descriptive study among a purposive, multidisciplinary sample of HPC team members at two large New York City metro hospice care organizations and one outpatient palliative care practice. Following individual interviews, we analyzed demographic data using descriptive statistics and interview data using inductive thematic analysis.ResultsParticipants (n = 30) included nurses, physicians, social workers, chaplains, community health workers, and administrators and averaged 17 years in their profession and 10 years in HPC. Three themes characterized their perspectives on patient and family caregiver HPC experiences. Grappling with Tensions captured strains arising from a time of high patient/family needs and low HPC resources that related to care resources/delivery, patient-caregiver-HPC communication, and hospice policy/guidelines. Managing Dilemmas involved negotiating difficult choices around prioritizing patients for care by diagnosis/need, balancing necessary care and exposure risks, and prioritizing patient visitors (family/professionals). Experiencing Amplifications described intensification of existing HPC challenges including distressing deaths, misconceptions of hospice, family caregiver burden/distress, racial/ethnic disparities, and staff workload and turnover.ConclusionsTo strengthen HPC delivery during crises that disrupt in-person HPC, we recommend enhancing continuity of care, expanding telehealth within hybrid care models, providing structured training and support for family caregivers, improving equitable access to high-quality HPC, and addressing workforce issues.

Patients have largely been excluded from discussions on the use of their health data in developing medical artificial intelligence (AI), despite being directly affected by its integration into care. This study assessed ophthalmology patients' perspectives on AI to inform patient-aligned development and implementation. We conducted a cross-sectional survey across ophthalmology clinics in a large academic hospital system in New York City. Consecutive patients were approached in waiting rooms by a research coordinator to maximise sociodemographic diversity and minimise bias from digital literacy or access. The survey, developed by experts in AI, ethics, ophthalmology and survey methodology, was administered via paper and Qualtrics. It addressed attitudes towards AI in clinical scenarios, willingness to share various types of personal data for AI model development and understanding of AI in ophthalmology. Among 403 respondents, 67% reported a low or no understanding of AI, and 71% expressed interest in learning more. Patients prioritised physician involvement and transparency. Comfort decreased with task complexity: highest for screening, lower for diagnosis and lowest for treatment/surgery. For model development, patients were more comfortable sharing de-identified optical coherence technology or lab data than facial images or genetic data. 90% felt consent was always necessary when using personal data to train AI models. These findings highlight the need for patient education and robust data consent protocols. Implementing an opt-out system for retrospective data use may enhance trust while supporting innovation. Integrating patient perspectives into AI governance can foster trust and transparency in ophthalmology and beyond.

This collaborative autoethnography explores how we—Ellin, a recent PhD graduate, and Jing, a current doctoral student—both from the same English Education program at a graduate school in New York City, navigate vulnerability as both an inherent condition and a methodological stance within American academia. Through the lens of Dialogical Self Theory (DST), we examine how our multiple I-positions engage in continuous dialogue, revealing vulnerability not as weakness but as a pathway to understanding and authentic scholarship. Our narratives demonstrate how the destabilization of our established identities creates productive discomfort that challenges dominant academic paradigms while fostering deeper understanding of belonging, representation, and knowledge production. By embracing vulnerability as both lived experience and research approach, we contribute to expanding conceptualizations of who can produce knowledge and how our transnational perspectives enrich academic discourse.

Abstract Objectives Crime prevention strategies often rely on the small set of micro-places where crime is most concentrated, the so-called hotspots, yet it has remained unclear how close existing hotspot detection methods come to the maximum coverage theoretically possible. This study introduces GraphVenn, the first algorithm that identifies the globally optimal placement of N fixed-radius hotspots directly from the empirical crime distribution, without relying on heuristic or approximate approaches. Methods GraphVenn was evaluated on three years of crime data from Malmö, Boston, and New York City (in total 1.75 million crimes) and compared against kernel density estimation (KDE), greedy PAI maximization (PAI-Max), and GraphTrace. Both the globally optimal and the greedy (fast approximation) modes of GraphVenn were evaluated across different spatial resolutions, demonstrating scalability to large urban datasets. Results In optimal mode, GraphVenn identified the absolute maximum coverage of incidents achievable under fixed-radius constraints. The greedy variant reached within 0.1–−1.9% of this optimum while reducing runtimes by up to two orders of magnitude. By contrast, existing methods consistently fell short, e.g., in New York City the optimal GraphVenn captured 51,522 crimes within its top-100 hotspots compared to 35,098 with KDE and 28,241 with GraphTrace, while PAI-Max was excluded due to its runtimes. In practical terms, the baselines therefore missed between 16,000 and 23,000 crime incidents that could have been covered. Conclusions Globally optimal detection of fixed-radius hotspots that maximize the distinct crime count is now computationally feasible at city scale. GraphVenn offers (i) a practical tool for researchers, law enforcement, and crime analysts to identify the most effective fixed-radius hotspot locations with confidence that no better configuration exists, and (ii) a benchmark for evaluating approximate methods against the true maximum crime count. Open-source code is provided to support replication and further research.

Abstract Racial differences in fracture risk are not fully explained by differences in areal bone density (aBMD) as measured by dual energy x-ray absorptiometry (DXA). Other aspects of bone quality, such as bone material properties, are not captured by DXA, but may contribute to fracture risk. Few studies have investigated racial/ethnic differences in bone material properties, and none have included Hispanic Americans. We assessed differences in bone material strength index (BMSi) measured by impact micro indentation (IMI) by race/ethnicity. Caribbean Hispanic (CH), Non-Hispanic Black (NHB), and Non-Hispanic White (NHW) participants from the Washington Heights Inwood Community Aging Project (N=148), a population-based study of aging in New York City, were included. While there were racial differences in aBMD, BMSi did not differ by race/ethnicity (CH vs NHB vs NHW: 79.3±6.3 vs. 78.3±15 vs. 77.7±5.2) before (p=0.22) or after (p=0.47) adjustment for age, sex, BMI, fracture status, diabetes, alcohol consumption, smoking, and osteoporosis treatment. There was no significant interaction between race and diabetes status in relation to BMSi. Stratifying by sex, results were similar. In summary, BMSi was similar among CH, NHB, and NHW participants, suggesting that differences in bone material strength, as captured by BMSi, do not account for established racial/ethnic differences in fracture risk in these groups.

The goal of the School-Based Health Center (SBHC) Reproductive Health Project (RHP) was to increase access to effective methods of contraception for New York City (NYC) public school students. This paper evaluates the impact of the SBHC RHP by examining differences in contraceptive use between sexually active female high school students with and without project access. Data from the 2011-2019 NYC Youth Risk Behavior Surveys were pooled and flagged for SBHC RHP access. Complete case descriptive analyses compared contraceptive use at last sex, reported by weighted percentages. Multinomial logistic regression was conducted to identify the association between access to the SBHC RHP and use of more effective contraception. We found that the percentage of any contraceptive use at last sex did not differ between the intervention and comparison groups; however, there were significant differences in the category of method used. Compared to those without access, more students at schools with the SBHC RHP reported use of moderately effective hormonal contraception (28.6% vs 16.3%) or long-acting reversible contraception (LARC; 4.4% vs 1.8%). SBHC RHP access significantly increased the probability of primary use of moderately effective hormonal contraception (pill/patch/ring/Depo-Provera shot) and LARC at last sex by 12.9 and 1.9 percentage points, respectively. While these more effective methods are generally less accessible to teens, the provision of these methods within an SBHC setting can increase access and use.

Advance care planning (ACP) facilitates discussions about future healthcare decisions to ensure alignment with individuals' values, goals, and medical preferences. Despite its benefits, ACP engagement and completion of advance directives (AD) remain limited due to low awareness, misconceptions, and structural barriers. The Tisch Cancer Institute Community Outreach and Engagement Department at Mount Sinai in New York City (NYC) identified this knowledge gap as a key community need. In response, a community-based educational module was created to enhance ACP and AD knowledge. Curriculum development was informed by a literature review and collaboration with health educators, social workers, and palliative care specialists. The intervention included a 30-minute slideshow, a two-page glossary and resource handout, and a healthcare proxy AD form. Workshop impact, acceptability, and feedback were assessed using a validated survey with eight Likert-scale items and two open-ended questions. Descriptive statistics summarized quantitative responses, and thematic analysis was applied to open-ended feedback. Community adult members across NYC participated in eleven workshops held at diverse community sites from June 2024 to June 2025, engaging 141 individuals with a feedback survey response rate of approximately 40% (N = 56). Findings demonstrated acceptability and feasibility. Participants reported increased understanding of ACP concepts and improved confidence in completing ADs. Qualitative comments emphasized clarity, relevance, and practicality of materials. Our community-based educational workshops effectively enhanced ACP knowledge and empowered participants in healthcare decision-making. Future directions include expanding workshop reach, translating materials, and conducting longitudinal follow-up to evaluate sustained engagement and impact on AD completion rates over time.

Wildfires have become more frequent and intense worldwide. Wildfire emitted particulate matter (WFPM) can be more toxic than urban background PM due to its greater content of nanoscale size (WFPM 0.1 ) and presence of more polar organic compounds, including polycyclic aromatic hydrocarbons (PAHs). While exposure to WFPM has been linked to cardiovascular and respiratory diseases, its impact on female reproduction remains elusive. Here, we used an in vivo mouse intratracheal exposure model and a 3D ovarian follicle culture system, together with molecular, transcriptomic, and computational approaches, to examine the female reproductive effects of lab-synthesized (LS-WFPM 0.1 ) and real-world Canadian WFPM 0.1 (C-WFPM 0.1 ), collected from the New York City and New Jersey metropolitan area during the June 2023 wildfire events. Intratracheal exposure to environmentally relevant dose of LS-WFPM 0.1 disrupted mouse estrous cycles and elevated serum concentrations of estradiol and testosterone. RT-qPCR and single-follicle RNA-sequencing (RNA-seq) analysis revealed altered steroidogenic genes, transcriptomic changes, and activation of aryl hydrocarbon receptor (AhR) in antral follicles from mice treated with LS-WFPM 0.1 . LS-WFPM 0.1 consistently increased testosterone secretion and stimulated genes related to androgen synthesis and AhR in vitro . Single-follicle and single-oocyte RNA-seq analysis identified differentially expressed genes related to inflammation in somatic cells and mitochondrial respiratory chain in oocytes. Both C-WFPM 0.1 and benzo[a]pyrene, a high-molecular-weight PAH, reproduced these ovarian defects. Mechanistically, AhR inhibition reversed hyperandrogenism induced by WFPM 0.1 . Together, our findings suggest that WFPM 0.1 , an increasingly pervasive environmental exposure, adversely impacts female reproductive functions by disrupting ovarian steroidogenesis and inducing hyperandrogenism through AhR activation, highlighting an urgent unmet need for further mechanistic studies and epidemiological investigations to define the reproductive risks of wildfire smoke exposure in human populations.

The Person-Centered Care Framework for Reproductive Health Equity (PCC) elucidates drivers of health disparities: community determinants, health-seeking behaviors, and quality of care. Limited studies assess person-centered maternal healthcare in underserved populations. Racial and ethnic disparities in maternal health were exacerbated by the COVID-19 pandemic. We applied PCC to evaluate factors influencing maternal healthcare at two public hospitals in NYC before and during the pandemic. We conducted mixed-method, community-engaged research using PCC. A cross-sectional study using EHR data from 5,330 pregnant individuals in 2019 assessed factors related to inadequate maternity care utilization. Qualitative research in 2020 explored perceptions of maternal health equity, barriers, and healthcare quality through 17 in-depth interviews and five focus group discussions with postpartum women, clinicians, and community-organization staff. Among 3,181 women, 90% had public insurance, and 95% were people of color. Using the Adequacy of Prenatal Care Utilization index, 1,648 (51.8%) received no or inadequate prenatal care; 1,267 (40%) lacked postpartum care. Women aged 18-24, Black women, Arabic-speaking women, and those who used tobacco during pregnancy appeared more likely to experience inadequate care. Qualitative data identified community-level determinants, namely health literacy and economic status. Health-seeking barriers included social-distancing, telehealth, and immigration status. Quality of care issues included disruptions in healthcare delivery, patient-provider experience, and continuity of care. Findings indicate disparities in maternal healthcare utilization which are likely downstream effects of broader social inequities. Addressing these disparities requires rights-based, community-informed policies that guarantee equitable, respectful, and accessible maternal care for all.

Accurate estimation of building-specific air change rates is important for reliable urban-scale energy modeling, particularly in densely populated regions where airflow calculations must account for complex boundary conditions associated with urban geometry. This study applied lumped-parameter airflow models to simulate interzone airflow by calculating the internal pressures using simplified building representations. Air change rates were calculated by solving a system of nonlinear equations, with boundary conditions defined by localized wind inputs corrected using aerodynamic parameters extracted from three-dimensional urban geometry. By linking these wind-related boundary conditions with lumped-parameter airflow models, the methodology describes spatial variability in natural infiltration across a broad range of urban densities. Two cities were compared to test the variability in building air change rates using local boundary conditions: New York City, a dense modern city, and Turin, a typical medium-density European city. Moreover, verifying the lumped-parameter model against CONTAM (Version 3.4.0.6) showed accurate results, with a mean absolute percentage error of 1.2% across 120 simulated weather scenarios. Furthermore, comparing energy consumption predictions using building-specific air change rates to those using fixed air change rates showed improved accuracy, resulting in an average error reduction of 27% over the entire heating season for a sample building. This scalable, automated approach enables more accurate assessments of ventilation-driven energy use in compact urban areas.