Health Net

Abstract The climate crisis is increasingly contributing to a mental health crisis. Policy and practice inadequately account for the mental health costs of climate change and the mental health benefits of climate action. The climate and mental health field is expanding rapidly but unevenly. Connecting Climate Minds (CCM), a global initiative, aimed to develop a guiding vision to ensure investment in and implementation of future research aligns with lived experience needs and appropriately informs policies. CCM convened 1184 contributors with diverse expertise across 126 countries, through 21 online and in-person dialogues. Their insights produced a Global Research and Action Agenda for climate change and mental health. This paper outlines the 53 research priorities structured into four high-level themes (impacts, risks and vulnerable groups; pathways and mechanisms; mental health benefits and risks of climate action; mental health interventions). It also outlines how to implement research and translate evidence to action.

Excessive alcohol drinking results in an increased risk for chronic diseases, and the rates of alcohol consumption have increased among U.S. adults in the past decade. The U.S. Surgeon General called for updating consumer labels to include this risk. This paper aims to understand the regional distribution of excessive drinking and how these patterns may be explained according to the American Nations model of the first U.S. settlement streams. We present data from the 2024 County Health Rankings program to demonstrate the distribution of excessive drinking, showing that excessive alcohol drinking patterns are region-specific and predicted by the American Nations model. This paper introduces the American Nations model in promoting alcohol consumption reduction messages.

Multifaceted disparities exist in the natural history of aortic valvular disease (AVD) between men and women. Despite a similar prevalence of aortic stenosis (AS) in both sexes, women face marked disadvantages in time to diagnosis, referral to specialised care, intervention frequency, and survival. Importantly, the pathophysiology of and myocardial compensation for AS in women exhibit distinct features that warrant intentional sex-tailored investigations and haemodynamic stratifications to ensure diagnostic accuracy and optimal timing of intervention. Furthermore, women with AS represent a patient population with unique biological, physiological, and anatomical considerations that render lifetime disease management particularly challenging. Recognising and rigorously investigating these sex-specific differences across the disease course will be essential to advancing evidence-based and patient-tailored clinical practice. Ultimately, implementing a personalised, lifetime approach to AS in women will help bridge the gap in their care and improve long-term outcomes.

The blood-brain barrier (BBB) is an important physiological barrier that protects the brain by preventing entry of harmful substances into the central nervous system (CNS). While this barrier is effective at blocking most drugs from disrupting the tightly controlled environment of the CNS, certain classes of non-neurologic drugs can cross the BBB, potentially causing neurological side effects. Developing safer alternatives to these drugs requires a reliable method for predicting the BBB non-penetrance, whilst maintaining a high level of drug-like properties to the molecules. Existing regression methods for BBB penetration are inaccurate for this task, tending to overestimate logBB values due to imbalances in available datasets. To address this, we have developed a LightGBM-based model with a custom multi-task loss function that learns from both regression and classification data. Improving performance across classification and regression metrics, our model outperforms existing models in identifying non-penetrant molecules and achieves regression accuracy approximately equal to experimental uncertainty. Based on this, we introduce the BBB Exclusion (BBBX) Score, a scoring method which stratifies compounds by predicted BBB penetrance and confidence. To interpret model outputs, SHAP analysis was used to identify features contributing most to model outputs. We use these features to define threshold values associated with increased likelihood of BBB Exclusion whilst maintaining high oral bioavailability and drug-like properties required for developing a safe, non-CNS drug.

In 2022, over 6 million individuals in the United States were affected by opioid use disorder (OUD), and more than 81,000 died from opioid-related overdoses. This umbrella review synthesized evidence on the associations between a broad range of social determinants of health (SDoH) and OUD/overdose. An umbrella review of meta-analyses and reviews on SDoH and OUD/overdose was conducted using PubMED, PsycINFO, Embase, and Web of Science. A total of 29 reviews were deemed appropriate for inclusion. Adverse childhood experiences and unemployment were associated with OUD outcomes. In the United States, immigrant status was associated with a lower risk of OUD, though this risk increased in subsequent generations. Individuals who experienced intimate partner violence and sexual minority individuals showed high prevalence of OUD. For overdose, associations were found in unemployment, low income, high poverty, and justice system involvement. Significant increases in odds of overdose were noted among individuals experiencing homelessness, lacking private insurance, engaging in sex work for pay, or witnessing an overdose. This study identified SDoH associated with OUD outcomes and overdose risk, with findings having implications from a public health perspective for clinical practice, public health policy, and research priorities. There is a need for meta-analyses to clarify the magnitude of SDoH influences on OUD/overdose, with the goal of informing targeted interventions to reduce opioid-related morbidity and mortality. The most comprehensive umbrella review to date on intersections among SDoH, OUD outcomes, and overdose risk.

Abstract Background Accurate optical diagnosis of colorectal polyps is essential for decision-making during colonoscopy, yet it remains a key skills gap among trainees and practicing endoscopists. To address this gap, we developed a cognitive simulation platform using high-fidelity polyp images to systematically develop endoscopists’ diagnostic and management decision-making skills through deliberate practice. Aims To evaluate endoscopists’ learning curves for optical diagnosis and polyp management decision-making and to determine the number of cases required to achieve predefined performance benchmarks. Methods Residents, fellows, and practicing endoscopists were recruited through training programs and national societies. Participants reviewed endoscopic images (in white light, narrow band imaging [NBI], and near focus), classified polyps using Paris, laterally spreading tumor [LST], NBI International Colorectal Endoscopic [NICE], and Japanese NBI Expert Team [JNET] classifications, predicted histology, and selected appropriate resection techniques. The primary outcome was learning rate, measured by a composite score of classification, histology prediction, and management decision-making. Learning curves were modeled using a deterministic signal processing model that allowed prediction of the number of cases needed to reach predefined accuracy benchmarks of 50%, 80%, and 90% for composite score, histology, and management. Results A total of 248 participants completed 15,051 cases; 75 (30.2%) completed ≥50 cases and 46 (18.5%) completed all 135. Participants were attending physicians (n = 95, 38.3%), fellows (n = 133, 53.6%), residents (n = 6, 5.6%), and 2.4% unreported. Correct response rates and learning curves are shown in Table 1 and Figure 1, respectively. The mean number (±SD) of cases to reach 50%, 80%, and 90% accuracy for composite score were 16.2(±12.2), 40.0(±7.9), and 81.9(±11.9), respectively. Similar mean numbers of cases were observed to achieve 50%, 80%, and 90% accuracy for histology (16.3±12.5, 37.3±7.2, and 71.2±11.3) and management (16.3±11.8, 37.3±6.9, and 71.3±9.7). Conclusions This is the first report of a cognitive simulation-based training platform that effectively improves endoscopists’ colorectal polyp optical diagnosis and management skills. Rapid early learning with flattening of learning curves as participants approach high accuracy supports the validity of these findings. Funding Agencies American Society for Gastrointestinal Endoscopy; Physician Services Incorporated

An older adult affordable housing community and a Federally Qualified Health Center partnered to improve the health of low-income older adults. The Unite Care Model included establishing onsite primary care, wellness support, and social needs assistance. The RE-AIM framework guided our mixed-methods evaluation. We sought to determine whether the model was associated with change in blood pressure, social or physical environment and understand model reach and implementation. Of the 251 residents, 13.1% had a clinic visit, and of respondents 70% attended at least one wellness event, and 35% received social needs assistance (reach). Clinic utilization was not associated with improved blood pressure or environment change (effectiveness). Residents reported barriers to clinic utilization including an established provider, uncertainty in transferring care, lack of urgent visits, and insurance barriers. Administrators echoed policy, staffing, and patient barriers to utilization (implementation). Expanding appointment options, increasing staffing, and overcoming insurance barriers may increase effectiveness.