Literature Collection

BACKGROUND: Major depressive disorder (MDD) and insulin resistance-related conditions are major contributors to global disability. Their co-occurrence complicates clinical outcomes, increasing mortality and symptom severity. AIMS: In this study, we investigated the association of insulin resistance-related conditions and related polygenic scores (PGSs) with MDD clinical profile and treatment outcomes, using primary care records from UK Biobank. METHOD: We identified MDD cases and insulin resistance-related conditions, as well as measures of depression treatment outcomes (e.g. resistance) from the records. Clinical-demographic variables were derived from self-reports, and insulin resistance-related PGSs were calculated using PRS-CS. Univariable analyses were conducted to compare sociodemographic and clinical variables of MDD cases with (IR+) and without (IR-) lifetime insulin resistance-related conditions. Multiple regressions were performed to identify factors, including insulin resistance-related PGSs, potentially associated with treatment outcomes, adjusting for confounders. RESULTS: Among 30 919 MDD cases, 51.95% were IR+. These had more antidepressant prescriptions and classes utilisation and longer treatment duration than patients without insulin resistance-related conditions (P < 0.001). IR+ participants showed distinctive depressive profiles, characterised by concentration issues, loneliness and inadequacy feelings, which varied according to the timing of MDD diagnosis relative to insulin resistance-related conditions. After adjusting for confounders, insulin resistance-related conditions (i.e. cardiovascular diseases, hypertension, non-alcoholic fatty liver disease, obesity/overweight, prediabetes and type 2 diabetes mellitus) were associated with antidepressant non-response/resistance and longer treatment duration, particularly when MDD preceded insulin resistance-related conditions. No significant PGS associations were found with antidepressant treatment outcomes. CONCLUSIONS: Our findings support an integrated treatment approach, prioritising both psychiatric and metabolic health, and public health strategies aimed at early intervention and prevention of insulin resistance in MDD.

Systemic barriers, including language, navigation complexity, and long specialist wait-times, result in the under-utilization of mental health services by newcomers and significant gaps in timely care for anxiety and depression. This paper proposes an integrated, hybrid-federated AI triage and referral architecture that bridges system silos, connecting primary care, settlement and community resources, while maintaining privacy and regulatory compliance. Newcomers navigate a multilingual digital intake where an AI captures cultural context and psychometric test scores, enabling automated triage and directing them to self-help, community resources, or urgency-based specialist referrals, supported by continuous feedback processes. The system is co-designed with newcomer communities to ensure patient-centered, culturally relevant, and equity-driven care.

The public health impact of alcohol-associated liver disease (ALD), a serious consequence of problematic alcohol use, and alcohol use disorder (AUD) is growing, with ALD becoming a major cause of alcohol-associated death overall and the leading indication for liver transplantation in the United States. Comprehensive care for ALD often requires treatment of AUD. Although there is a growing body of evidence showing that AUD treatment is associated with reductions in liver-related morbidity and mortality, only a minority of patients with ALD and AUD receive this care. Integrated and collaborative models that streamline both ALD and AUD care for patients with ALD and AUD are promising approaches to bridge this treatment gap and rely on multidisciplinary and interprofessional teams and partnerships. Here, we review the role of AUD care in ALD treatment, the effects of AUD treatment on liver-related outcomes, the impact of comorbid conditions such as other substance use disorders, obesity, and metabolic syndrome, and the current landscape of integrated and collaborative care for ALD and AUD in various treatment settings. We further review knowledge gaps and unmet needs that remain, including the role of precision medicine, the application of harm reduction approaches, the impact of health disparities, and the need for additional AUD treatment options, as well as further efforts to support implementation and dissemination.

Healthcare is plagued with many problems that Artificial Intelligence (AI) can ameliorate or sometimes amplify. Regardless, AI is changing the way we reason towards solutions, especially at the frontier of public health applications where autonomous and co-pilot AI integrated systems are now rapidly adopted for mainstream use in both clinical and non-clinical settings. In this regard, we present empirical analysis of thematic concerns that affect patients within AI integrated healthcare systems and how the experience of care may be influenced by the degree of AI integration. Furthermore, we present a fairly rigorous mathematical model and adopt prevailing techniques in Machine Learning (ML) to develop models that utilize a patient's general information and responses to a survey to predict the degree of AI integration that will maximize their experience of care. We model the patient's experience of care as a continuous random variable on the open interval ([Formula: see text]) and refer to it as the AI Affinity Score which encapsulates the degree of AI integration that the patient prefers within a chosen healthcare system. We present descriptive statistics of the distribution of the survey responses over key demographic variables viz. Age, Gender, Level of Education as well as a summary of perceived attitudes towards AI integrated healthcare in these categories. We further present the results of statistical tests conducted to determine if the variance across distributions of AI Affinity Scores over the identified groups are statistically significant and further assess the behavior of any independent distribution of AI Affinity Scores using a Bayesian nonparametric model.

OBJECTIVE: To determine whether the presence of integrated behavioral health care (IBH) in a pediatric practice is associated with improved implementation of Safe Environment for Every Kid (SEEK), an evidence-based approach to prevention of child maltreatment. METHODS: Pediatric primary care practices across the United States (n = 44) expressed interest in participating in a longitudinal multisite trial. Half of the practices included IBH. Semi-structured interviews were conducted at different points in time with 49 practice leaders, primary care professionals, behavioral health professionals, and nursing and administrative staff. Quantitative data on implementation stage and phase, proportion of activities completed at each stage, and length of time to complete each stage were collected by the Stages of Implementation Completion measure. RESULTS: Qualitative data revealed several instances in which IBH facilitated the adoption and implementation of SEEK and where SEEK supported IBH. However, apart from a longer duration devoted to program startup among IBH practices, none of the quantitative differences in rate of program startup, better completion of implementation activities, more tasks completed within each stage, and greater competency were statistically significant. CONCLUSION: Integrated behavioral health care in pediatric primary care settings may help to facilitate the implementation of interventions like SEEK designed to address social determinants of health and reduce the risk of child maltreatment. However, the current study did not find evidence, based on quantitative analyses, that IBH significantly affected the uptake of Project SEEK and that more research may be warranted.

The Arizona Department of Health Services joined with the Milbank Memorial Fund to sponsor a forum on January 25 and 26, 2011 in Chandler, AZ, for policy makers in both the mental health and community health center fields. The public forum webpage provides links to a dozen presentations from the event.