American Journal of Preventive Medicine

BackgroundCannabinoid hyperemesis syndrome (CHS) is characterized by episodes of severe nausea, vomiting, and abdominal pain among those with heavy cannabis use. We estimated differences between those reporting CHS symptoms and other daily and less frequent cannabis users on drug use, psychiatric problems, other health problems, antisocial behavior, and personality. MethodsThe National Firearms, Alcohol, Cannabis, and Suicide survey was administered to 7034 US adults in 2025. Survey items assessed substance use, common psychiatric symptoms, personality traits, and symptoms of CHS. ResultsThose with CHS symptoms reported the highest rates and greatest variety of drug use compared to others who used cannabis. Those with CHS symptoms reported higher rates of other drug use than those who used cannabis daily without CHS symptoms across a variety of drug classes, including opioids, hallucinogens, and sedatives, higher rates of drug overdoses, and greater use of all drug classes than those with less-than-daily cannabis use. Those with CHS symptoms also reported more depression, anxiety, sleep problems, chronic pain, antisocial behavior, intimate partner violence, and disinhibited personality traits than those who used daily (mean d = 0.58) and less frequently (mean d = 0.69) and those with no cannabis use in the past 12 months (mean d = 0.99). ConclusionsThose with CHS symptoms exhibit a variety of psychological and behavioral problems including higher rates of other drug use, psychiatric symptoms, antisocial behavior, and dysfunctional personality traits. Results highlight the importance of understanding and addressing the broader psychosocial challenges faced by people experiencing CHS symptoms. Highlights O_LICHS symptoms are linked to greater polysubstance use and overdose risk C_LIO_LICHS symptoms are associated with depression, anxiety, sleep, and pain problems C_LIO_LICHS tied to antisocial behavior and intimate partner violence C_LIO_LICHS shows disinhibited personality traits and low well-being C_LIO_LINational survey identifies high-risk psychosocial CHS profile C_LI

ObjectiveAs cannabis use has increased in the United States, so has cannabinoid hyperemesis syndrome (CHS), a disorder characterized by severe nausea, vomiting, and abdominal pain among heavy cannabis users. We previously showed that CHS symptoms are associated with several behavioral and psychological characteristics linked to psychosocial impairment. We examined links between CHS symptoms and suicidal thoughts, behaviors, and proximal suicide risk factors. MethodsWe used data from the National Firearms, Alcohol, Cannabis, and Suicide survey, a nationally representative survey of 7,034 US adults. Items assessed symptoms of CHS and suicidal thoughts and behaviors. Comparisons focused on: those with daily cannabis use and CHS symptoms (n = 191), those with daily cannabis use without CHS symptoms (n = 882), those with past year cannabis use but not daily use (n = 1288), and those without past year cannabis use (n = 4673). ResultsThose with CHS symptoms reported the highest prevalence of suicidal thoughts and behaviors with most lifetime rates being significantly higher than those with daily cannabis use without CHS symptoms. Those with CHS symptoms also reported higher mean-levels of thoughts and feelings associated with suicide (i.e., perceived burdensomeness, thwarted belongingness, defeat, entrapment) than all the other groups. ConclusionsThose with CHS symptoms reported especially high rates of suicidal thoughts, behaviors, and attempts even when compared to others with daily cannabis use. People with CHS symptoms appear to be at high risk of suicide, possibly related to distress from their gastrointestinal symptoms and psychiatric, substance use, and medical comorbidities.

ObjectivesTo examine associations between cardiometabolic conditions and health-related quality of life (HRQoL) and to evaluate whether condition-associated HRQoL changed from 2001 to 2022. MethodsWe analyzed nationally representative data from U.S. adults aged [≥]18 years in the Medical Expenditure Panel Survey, 2001-2022. Survey years without BMI data (2017, 2019, 2021) were excluded. EQ-5D utilities were mapped from SF-12 scores using a validated algorithm. For each survey year, survey-weighted multivariable regression models estimated associations of sociodemographic characteristics, BMI, and cardiometabolic conditions (diabetes, heart disease, high blood pressure, high cholesterol, obesity, stroke) with HRQoL measured by EQ-5D. Temporal changes in condition-associated HRQoL decrements were assessed using meta-regression across years. Associations in recent survey years were summarized using pooled estimates from 2015, 2016, 2018, and 2022. ResultsOverall HRQoL improved from 2001 to 2022 across age groups, with the largest improvement among older adults. In pooled analyses, stroke was associated with the largest adjusted HRQoL decrement (-0.0714), followed by heart disease (-0.0503), diabetes (-0.0427), high blood pressure (-0.0328), obesity (-0.0305), and high cholesterol (-0.0236). Additional adjustment for BMI attenuated condition-associated decrements, most notably for obesity (-0.0305 to -0.0183), diabetes (-0.0427 to -0.0414), and high blood pressure (-0.0328 to -0.0316). Over time, diabetes- and heart disease-associated decrements attenuated linearly (diabetes: - 0.0489 in 2001 to -0.0406 in 2022; heart disease: -0.0591 to -0.0493). High blood pressure (-0.0337 in 2001, -0.0415 in 2012, -0.0306 in 2022) and obesity (-0.0305 in 2001, -0.0283 in 2012, -0.0367 in 2022) showed nonlinear patterns. ConclusionsCondition-associated HRQoL decrements varied over time, and recent-year utility estimates are recommended for population health research. HRQoL decrements for diabetes and heart disease attenuated, consistent with improvements in treatment and survival. High blood pressure-associated were lowest around 2012, and obesity-associated became more negative after 2012, consistent with worsening blood pressure control and obesity severity.

ObjectivesEstimate the HIV testing, diagnoses, and test positivity rates among Medicaid beneficiaries in 2016-2021 and assess the impact of the COVID-19 pandemic on these outcomes. DesignProspective observational study of Medicaid enrollment, inpatient, and outpatient claims data from 27 states, 2016-2021. MethodsWe assessed Medicaid claims from adult beneficiaries with full benefits whose first continuous enrollment was [≥]6 months without dual enrollment in other insurance, and without previous HIV diagnosis. We estimated the rates of annual testing, HIV diagnosis, and proportion of positive HIV tests among the tested using Poisson regression models. Bayesian structural time series modelling was performed to examine the pandemics impact on study outcomes with 3/16/2020-12/31/2021 as the pandemic period. We estimated rates overall and by age, sex, race/ethnicity, and states level of COVID-19-related restriction policies. ResultsWe included 20,508,785 beneficiaries. Male beneficiaries, especially 18-34-year-olds, had lower annual testing uptake and higher test positivity rates than female beneficiaries. Black beneficiaries had higher annual testing rates than White and Hispanic beneficiaries. While the pandemic acutely disrupted the increasing pre-pandemic testing trend, the rates recovered to the expected level had the pandemic not happened, except among 18-34-year-old male beneficiaries, whose pandemic rates were, on average, 18.1% lower (95% confidence interval:-22.3,-13.8) than projected rates. HIV diagnosis and test positivity rates were not affected by the pandemic. ConclusionThe pandemic significantly impacted the testing uptake among young male beneficiaries, highlighting the need for innovative strategies to improve HIV testing uptake in this demographic, restoring it to pre-pandemic levels or better.

OBJECTIVEOpioid misuse exacts a tremendous toll on society. Mindfulness-Oriented Recovery Enhancement (MORE) is an efficacious treatment for opioid misuse. Yet, the cost-effectiveness of this intervention remains unknown. METHODSCost-effectiveness and cost-benefit analyses of a randomized clinical trial with enrollment of 250 adults with chronic pain prescribed long-term opioid therapy who were misusing opioids. Participants were randomized to MORE (training in mindfulness, reappraisal, and savoring positive experiences) or supportive group psychotherapy across 8 weekly 2-hour groups. Incremental cost-effectiveness ratios (ICER) and benefit-to-cost ratios (BCRs) were computed using the primary outcome of opioid misuse at 9-month follow-up, as assessed by a composite measure based on self-report, clinical interview, and urine screen. RESULTS250 randomized patients (64.0% female) had an average age of 51.8 years (SD=11.9), were mostly taking oxycodone or hydrocodone (69%), and had mean morphine equivalent opioid dose of 101.0 (IQR=74) mg. At 9-mo. follow-up, the difference in the probability of having a positive Drug Misuse Index (DMI) rating was 0.24 (0.54 for MORE participants vs. 0.78 for controls). The ICER of MORE relative to supportive psychotherapy was $116.3 per averted case of opioid misuse, $8.9 per life-year, and $8.0 per quality-adjusted life-year. MORE is cost-saving vs. supportive psychotherapy after adjusting for healthcare costs. Excluding all benefits associated with averting fatal overdoses results in a BCR of 84.2. CONCLUSIONSGiven MOREs cost-effectiveness, private and public payers should consider disseminating this evidence-based therapy broadly across the nation to reduce mortality and morbidity associated with the ongoing opioid crisis. HIGHLIGHTSO_LIMindfulness-Oriented Recovery Enhancement (MORE) substantially reduced opioid misuse among adults with chronic pain on long-term opioid therapy. C_LIO_LIMORE was highly cost-effective vs. supportive psychotherapy, costing $116 per averted opioid misuse case, and MORE was cost saving when accounting for healthcare costs associated with opioid misuse. C_LIO_LIFindings suggest wide dissemination of this evidence-based treatment could yield major healthcare and other economic benefits in addressing the opioid crisis. C_LI

BackgroundThe Most Favored Nation (MFN) policy is a mechanism that incorporates foreign prices to determine the maximum allowable net price for any branded drug within US government-funded healthcare. Two proposed rules, the Global Benchmark for Efficient Drug Pricing ("GLOBE") (90 Fed. Reg. 60,244) for Medicare Part B and the Guarding US Medicare Against Rising Drug Costs ("GUARD") (90 Fed. Reg. 60,338) for Medicare Part D, invoke the Center for Medicare and Medicaid Innovation Centers payment and service model demonstration and waiver authority, under Section 1115A of the Social Security Act (42 U.S.C. [§] 1315a), to calculate the US MFN price which is the lowest average price within a basket of specified foreign countries. Unlike voluntary manufacturer agreements, GLOBE and GUARD would mandate participation from all applicable manufacturers. MethodsWe derive MFNs potential impact on Medicare pricing from a proprietary dataset provided by IQVIA which contained net prices for the top 37 oncology products by total US sales from January 1, 2019 through June 30, 2025 ranked by total US sales in the following countries: Australia, Belgium, France, Germany, Ireland, Italy, South Africa, Spain, Switzerland, the UK, and the US. For each drug, we select the lowest GDP-adjusted international price from a basket of those countries within 60% of the US GDP per capita, adjusted for purchasing power parity, and calculate the reduction in US price required to match its MFN price, and hence the corresponding reduction in revenues under MFN. A retrospective Net Present Value (NPV) analysis is then used to address the counterfactual question of whether each drug would have been developed had MFN pricing been in place at the time of its FDA approval. ResultsUnder MFN, the average reduction in US prices across our drug cohort was 67%. Eighty-four percent of the 37 cancer drugs in our cohort evidenced a negative NPV if MFN had been in place at the time of their FDA approval and the commercial market is impacted. When the analysis is restricted to MFNs impact on Medicare, the indications for these lost drugs have a total US population of 2.4 million patients. When the analysis is combined across the Medicare and commercial markets, the loss of lead indications impacts over 15 million US patients. ConclusionsMandatory MFN policies reduce the financial incentives required to develop cancer medicines; our projections show a substantial decline in new cancer drug launches and will likely lead companies to pursue indications for populations outside Medicares authority. If so, MFN will reduce the number of new therapies for the very population the Executive Orders are allegedly designed to aid: the Medicare-aged population who require effective new therapies in areas of high unmet medical need, such as late-stage cancers. This creates the perverse outcome of a policy nominally designed to help Medicare beneficiaries by instead redirecting innovation away from their most urgent therapeutic needs.

Smoking Cessation Efforts for Patients with Asthma and COPD IntroductionSmoking cessation can alter the natural history of both COPD and asthma by reducing the frequency and severity of exacerbations and slowing disease progression. Accordingly, the Global Initiative for Asthma and the Global Initiative for Chronic Obstructive Lung Disease recommend that clinicians address smoking cessation at every visit using counseling and pharmacotherapy. MethodsThe Mount Sinai Health System includes seven hospitals and more than 400 outpatient locations in the New York metropolitan area, all using a unified electronic medical record (Epic). De-identified data from calendar year 2024 were extracted for individuals identified as current smokers via the EMR smoking status tool. Patients with asthma and/or COPD were identified using ICD-10 codes. Tobacco treatment was defined as receipt of counseling or pharmacotherapy, including varenicline, bupropion, or nicotine replacement therapy. ResultsAmong 961,997 patients, 58,566 (6.1%) were identified as current cigarette smokers. Across all health system encounters, 32.6% of smokers with both asthma and COPD were given any treatment, followed by 26.7% of smokers with COPD, 13.0% of smokers with asthma, and 9.9% of cigarette smokers without these conditions. Smokers seen in pulmonary clinics were the most likely to be given treatment (17.4%), followed next by primary care (6.6%).The most commonly used treatment for all cohorts and all treatment settings was nicotine with the exception of the pulmonary clinic where varenicline predominated. DiscussionDespite higher treatment rates among smokers with asthma and COPD, only one-third of those with either condition received cessation treatment over a full year, underscoring the need for sustained system-wide quality improvement efforts.

BackgroundEndoscopic resection is the standard treatment for rectal neuroendocrine tumors (r-NETs) [&le;]10 mm, yet the optimal technique remains controversial. Modified endoscopic mucosal resection (m-EMR) has emerged as a potential alternative compared to endoscopic submucosal dissection (ESD), but existing evidence is largely retrospective and the results of recent randomized controlled trials (RCTs) are inconclusive. AimsTo compare the efficacy and safety of m-EMR versus ESD for r-NETs [&le;]10 mm. MethodsWe systematically searched CENTRAL, PubMed, Embase, and WanFang from January 1st, 1970 to December 23, 2025 for RCTs comparing m-EMR with ESD in r-NETs [&le;]10 mm. The GRADE framework assessed evidence certainty, while trial sequential analysis (TSA) controlled random errors and evaluated conclusion validity. ResultsSix RCTs involving 440 patients were analyzed. No significant difference between m-EMR and ESD was found in histologic complete resection (RR = 1.00, 95% CI 0.97-1.03; I2 = 0%), en bloc resection rates (P = 0.75) and procedure-related complications (P = 0.94). And m-EMR was associated with a significantly shorter procedure time (P<0.00001) and lower hospitalization cost (P<0.00001). The evidence was of moderate certainty; TSA confirmed its reliability, and both cumulative and sensitivity analyses supported the robustness. ConclusionsModerate-certainty evidence indicates m-EMR achieves oncologic outcomes comparable to ESD while offering clear advantages in procedural efficiency and cost for r-NETs [&le;]10 mm, supporting m-EMR possibly as a preferred endoscopic strategy in clinical practice.

BackgroundThe 340B Drug Pricing Program was established to expand access to care for low-income and uninsured patients by allowing safety-net hospitals and clinics to purchase outpatient drugs at discounted prices. Over time, the program has expanded substantially, raising questions about whether participating hospitals are meeting the programs intended objectives. MethodsUsing 2023 hospital financial data from the RAND Corporation, we conducted cross-sectional descriptive comparisons of 340B and non-340B hospitals nationwide. Key measures included charity care as a percentage of operating expenses, Medicaid admissions as a share of hospital days, uncompensated care, and costs associated with uninsured patients approved for charity care. Subgroup analyses also examined the performance of Disproportionate Share Hospitals (DSH), Critical Access Hospitals (CAH), Rural Referral Centers (RRC), Sole Community Hospitals (SCH), and National Cancer Institute (NCI) designated hospitals. ResultsAmong 3,999 hospitals analyzed, 340B hospitals provided, on average, lower levels of charity care than non-340B hospitals (2.16% vs. 2.82% of operating expenses) and lower costs of charity care for uninsured patients (1.60% vs. 2.26%). However, 340B hospitals served a higher proportion of Medicaid patients (19.69% vs. 17.76%). Substantial variation was observed across 340B subcategories: DSH hospitals reported the highest Medicaid utilization, while CAH hospitals reported the lowest levels of charity care and Medicaid days. ConclusionsParticipation in the 340B program does not uniformly correlate with greater provision of charity care or uncompensated care. These findings suggest a misalignment between program intent and outcomes and support the need for greater transparency, standardized eligibility criteria, and minimum charity care requirements to ensure that 340B savings directly benefit underserved populations.

BackgroundEmerging in January 2020, the SARS-CoV-2 pandemic quickly exposed the limitations of traditional contact tracing and overwhelmed the contact tracing efforts of US health departments. In response, Kaiser Permanente partnered with the Public Health Institute to launch the California Contact Tracing Support Initiative. This innovative, clinically integrated program aimed to link Kaiser Permanente members diagnosed at their facilities directly with contact tracing and supportive clinical care via their network. This approach promised to address key logistical and behavioral challenges hampering traditional public health agencies. This paper evaluates the programs implementation in two California counties. MethodsWe conducted a retrospective, mixed-methods process evaluation of program activities from August 2020 to June 2021, including contact tracing implementation in Fresno and San Bernardino Counties. Our methods included scoping discussions with program stakeholders, development of an epidemiological timeline and program impact model, and document review. We also conducted semi-structured interviews with program stakeholders and staff. Interviews were conducted and audio-recorded via Zoom, transcribed, and analyzed in NVivo using inductive and deductive coding with a Framework Approach. ResultsWe reviewed 474 program documents and interviewed 47 participants. Study findings highlighted difficulties in adapting program scope due to competing partner visions of program mission and collaboration. Unforeseen data demands and complex external data sharing with public health systems further complicated and delayed program implementation. ConclusionEvaluation of this contact tracing program offers key insights into public health interventions during emergencies. While the California Contact Tracing Support Initiatives integrated design showed promise, challenges arose from data systems, inter-organizational dynamics, and planning. Findings emphasize the need for clear operational steps, real-time data monitoring, defined roles, and formalized public-private partnerships in preparedness planning. These are key lessons for future complex public health interventions, especially regarding adapting programs versus maintaining fidelity amidst evolving contexts.

Background and AimsAvoidance of symptom-related situations is common in chronic gastrointestinal (GI) conditions, contributing to greater symptom severity, psychological distress, and reduced quality of life. However, no validated measure exists to comprehensively assess GI-specific avoidance. We developed and validated the GI-specific Avoidance Scale (GIAS), a self-report instrument measuring behavioral and cognitive avoidance specific to GI symptoms. MethodsFollowing literature review and multidisciplinary input, an initial pool of 58 items was generated and refined through expert and patient ratings, yielding 37 items. A sample of 102 adults (mean age 40.8 years) with medically diagnosed GI conditions completed the GIAS and validated measures of avoidance, psychological flexibility, illness shame, GI symptoms, distress, and quality of life. Exploratory factor analysis was used to determine factor structure. Internal consistency, convergent validity, incremental validity, and mediation analyses were conducted. ResultsFactor analysis supported a 20-item, three-factor solution: General Avoidance, Food Avoidance, and Intimacy/Body Exposure Avoidance. Internal consistency was excellent for the total scale ( = .94) and good-to-excellent for subscales ( = .82-.94). GIAS scores correlated positively with illness shame, GI symptoms, and distress, and negatively with psychological flexibility, self-compassion, and quality of life. GIAS showed incremental validity over a general illness avoidance measure (IBAS) in predicting GI symptoms and anxiety. Moreover, mediation models suggested that GI-specific avoidance partially mediates bidirectional associations between GI symptoms and psychological distress. ConclusionsThe GIAS is a novel, psychometrically robust, and multidimensional self-report questionnaire of GI-specific avoidance. It holds potential for clinical assessment, treatment planning, and evaluation of intervention mechanisms in GI populations.

ObjectiveTo examine racial and ethnic inequities in wealth and health among New York City adults. MethodsWe conducted the 2024 NYC Racial Wealth and Health Gap Survey using a stratified quota sample of 2,866 adults across 11 racial and ethnic groups. Wealth was measured through self-reported assets and debts, and health through self-reported status and psychological distress. We calculated descriptive statistics across groups and used quantile regression to test for significant differences in assets and debts compared with White respondents. ResultsWhite and Chinese respondents reported the highest median net worth ($142,000 and $320,000), while Other Black and Puerto Rican respondents reported the lowest ($25 and $160). Lower wealth was associated with poorer health and higher psychological distress. Prevalence of excellent or very good health increased from 36% in the lowest wealth quartile to 59% in the highest, with the steepest wealth-health gradients among Chinese and Multiracial respondents. ConclusionWealth inequities are linked to health disparities across racial and ethnic groups in New York City. Surveillance of local wealth data can guide equity-focused policies addressing economic and racial drivers of health disparities.

Background and aimsPopulation screening for liver disease in high-risk groups is recommended. Community diagnosis of liver disease is a challenge due to the asymptomatic nature of disease until very advanced stages. Moreover, regional variation in testing availability can result in people with clinically significant liver disease being missed. Machine learning (ML) has been proposed as a method to reduce diagnostic error and automate screening. We present a novel machine learning derived algorithm (ID LIVER-ML) designed to predict the risk of clinically significant liver disease in a high-risk community population to identify those needing further investigations or specialist referral. MethodsUsing data from 2039 patients recruited to two UK cohorts, we created a parsimonious model using investigations that would be available in primary care using liver stiffness measurement as reference standard. The performance of ID LIVER-ML was compared against FIB-4 score in a second unseen hold out cohort (n=327). ResultsID LIVER-ML performed well at identifying patients at risk of clinically significant liver fibrosis (sensitivity 0.90, Specificity 0.43, PPV 0.54, NPV 0.86, AUC 0.83) and outperformed conventional risk scoring systems (FIB-4: AUC 0.65; NAFLD Fibrosis Score: AUC 0.66; APRI: AUC 0.53; BARD: AUC 0.58). ConclusionMachine learning derived algorithms can help screen high risk populations in a community setting for liver fibrosis. ClinicalTrials.gov ID: NCT04666402 Impact and ImplicationsThe prevalence of steatotic liver disease is rising globally and is an increasingly significant challenge for healthcare systems. Existing risk stratification scores are not validated in a real-world cohort where patients have risk factors for multiple aetiologies of liver disease. Our work shows that a machine learning model can predict the risk of clinically significant liver disease using routine primary care data, better than existing non-invasive risk stratification tools in a real-world cohort. This highlights a potential role for machine learning in the automation of fibrosis risk assessment in primary care. Highlights- Machine learning derived algorithms can predict the risk of clinically significant liver disease in an at risk community population with a mixed aetiology of liver diseases. - The performance of the ML algorithm (ID LIVER-ML) is not affected by metabolic, alcohol, or mixed aetiologies. - ID LIVER-ML outperforms traditional risk stratification scoring systems such as FIB-4 and NAFLD fibrosis scores. - Compared to the FIB-4 score, the use of Machine Learning can reduce the need for secondary care investigations by 59%.

We developed and validated a self-administered clinical vignette platform powered by a large language model (LLM), deployed through a SurveyCTO web survey, to measure primary health care provider competencies in Vietnam. In a pilot focus group, nine physicians rated LLM-simulated patient interactions as realistic (mean 3.78/5) and user-friendly. In the validation phase, 22 providers completed 132 vignette interactions across ten clinical scenarios in Vietnamese. Essential diagnostic checklist scores (human-coded from translated transcripts) correlated with expert clinician evaluations (Pearsons{rho} = 0.55-0.60). LLM-automated coding of checklist items from translated English transcripts correlated reasonably with human coding ({rho} = 0.53), and coding directly from Vietnamese transcripts performed comparably ({rho} = 0.51), suggesting that a separate translation step may not be necessary. The total cost of 132 chatbot interactions was under USD 2. LLM-driven conversational vignettes represent a low-cost and scalable method for assessing provider competencies in respondents local language, eliminating the need for extensive enumeration staffs while preserving the open-ended format critical to vignette validity, and additionally introducing flexible feature extraction from transcripts using grading rubrics. The platform is open-source and designed for replication in other health system contexts. Author summaryMeasuring the clinical skills of healthcare providers is essential for improving the quality of care, but current survey methods are expensive and require trained enumerators to travel to health facilities in person. We developed a new approach that uses large language models (LLMs) - the technology behind tools like ChatGPT and Claude - to simulate patients in realistic clinical conversations that healthcare providers can complete on their phones or laptops over the Internet in their own language. In Vietnam, we tested this tool with 31 physicians across ten clinical scenarios. Providers found the simulated patient conversations realistic and easy to use. We also tested whether LLMs could automatically score the conversations, which showed reasonable agreement with human scoring, and performed nearly as well when scoring directly from Vietnamese, without requiring a separate translation step. When we compared these results from our tool against holistic expert physician ratings of the same conversations, the scores agreed well, suggesting that automatic transcript grading based on rubrics produces meaningful measures of clinical skill. This tool costs less than two US dollars for over a hundred consultations and required no in-person surveyors, making it potentially transformative for routine, large-scale monitoring of healthcare quality in resource-limited settings. The platform and code are openly available for adaptation.

Alcohol Use Disorder (AUD) is a multifactorial condition with severe individual and societal impacts. Extending our 2024 study, this work examines lifestyle, background, and family history determinants of AUD using an expanded dataset from the All of Us Research Program. The updated analysis includes approximately 2.5 times more participants than the prior study, enabling improved statistical power and evaluation of result stability over time. Using interpretable machine learning models and statistical analyses, we identified annual income, residential stability, recreational drug use, sex/gender, marital status, education, and family history as key contributors to AUD risk. Annual income remained the most influential predictor across both datasets, while other feature rankings showed modest shifts. Family history factors continued to demonstrate non-linear effects, with close relatives AUD status remaining influential despite differences between statistical association and predictive importance. In predicting AUD versus non-AUD status, Random forest models achieved the highest classification accuracy (81%), consistent with 2024 results but with improved precision for identifying AUD cases. Overall, the findings confirm the robustness of previously identified AUD determinants and underscore the need for coordinated, multi-level prevention strategies addressing behavioral, familial, and structural factors contributing to AUD.

Large-scale epidemics are consistently associated with increased psychological distress and substantial changes in human mobility, yet the relationship between mental health responses and effective population mobility remains overlooked. During the COVID-19 pandemic, non-pharmaceutical interventions (NPIs) such as lockdowns and travel restrictions altered daily movement patterns while simultaneously affecting psychological well-being. Importantly, formal policy stringency alone does not fully capture realized mobility behavior, which also reflects spontaneous adaptation and adherence fatigue over time. In this study, we examine the association between self-reported mental distress and mobility recovery across the United States during the first wave of the COVID-19 pandemic. We combine state-level human mobility data derived from anonymized mobile phone records with large-scale survey data on self-reported anxiety and depression. Our analysis focuses on the U.S. states and territories from April 1 to September 1, 2020. Using fixed-effects regression models, we assess how variations in mental distress relate to deviations from pre-pandemic mobility levels, while controlling for reported COVID-19 mortality and the stringency of NPIs. We find a negative and statistically significant association between mental distress and mobility recovery: higher levels of self-reported anxiety and depression are associated with lower recovery of pre-pandemic mobility. These results indicate that psychological distress is associated with population mobility beyond what is explained by formal restrictions alone. Our findings highlight the relevance of mental health as a factor linked to behavioral responses during public health crises. Incorporating psychological well-being into the evaluation of mobility dynamics may inform more balanced public health strategies in future emergencies. Author summaryDuring the COVID-19 pandemic, governments introduced restrictions on movement, such as stay-at-home orders and travel limits, to slow the spread of the virus. At the same time, many people experienced increased anxiety and depression. In this study, we ask whether changes in mental well-being were linked to how quickly people returned to their usual patterns of movement. Here, we focus on the first wave of the pandemic in the United States and combine mobility data and large-scale digital survey data to study the association between self-reported mental health indicators and effective mobility at the population level. By comparing states over time, we explore whether changes in mental distress were associated with changes in mobility, beyond what can be explained by public restrictions or reported deaths alone. We find that states with higher levels of reported anxiety and depression tended to show slower recovery toward normal mobility levels. This suggests that psychological well-being played an important role in shaping individual and collective responses to the pandemic, with implications for the design of future public health interventions.

ObjectivesNon-communicable diseases (NCDs) account for almost 90% of deaths in Europe, yet comparative estimates of the productivity costs associated with premature NCD mortality across diseases and countries remain limited. This study estimates and compares productivity losses attributable to cardiovascular disease (CVD) and cancer mortality among working-age populations across Europe. Population-based data were used to estimate productivity costs for CVD and cancer deaths across 30 European countries. Sex- and age-specific mortality data for 2021 were obtained from the World Health Organization Mortality Database. Economic data, including wages, unemployment rates, and labour force participation rates, were sourced from Eurostat. Productivity losses were valued using a human capital approach incorporating an age-transition lifecycle simulation model that adjusts for lifetime wage trajectories and labour market dynamics. Costs were discounted at 3.5%. Total productivity losses from cancer and CVD mortality in working-age populations were estimated at {euro}195.7 billion, equivalent to 1.24% of European GDP. Cancer accounted for 62.5% ({euro}122.2 billion) of total productivity losses, while CVD accounted for 37.5% ({euro}73.5 billion). Total CVD-related productivity costs exceeded cancer-related costs in Central and Eastern Europe, whereas cancer productivity costs were higher in Western, Northern, and Southern Europe. Mean productivity costs per death were higher for CVD ({euro}219,848; 95% CI 165,241-270,247) than for cancer ({euro}217,744; 95% CI 166,554-273,144). A larger gender gap was observed for CVD mortality, with a male-to-female cost ratio of 2.5 compared with 1.6 for cancer. Productivity losses associated with premature cancer and CVD mortality represent a substantial economic burden across Europe, with pronounced variation by disease, region, and sex. These findings provide comparative, cross-country estimates of the human capital costs associated with major NCD causes of death.

PuhrposeTo evaluate the short- and long-term cross-sectional associations between COVID-19 infection and multidimensional sleep health. MethodsData from the COVID-19 Outbreak Public Evaluation (COPE) initiative were used to examine the association between a novel multidimensional sleep health measure (COPE Multidimensional Sleep Health Scale, CMSHS) modeled from the RuSATED instrument and (1) COVID-19 infection and (2) post-acute sequelae of SARS-CoV-2 infection (PASC). ResultsData from 11,326 respondents were used for this study. The cohort was comprised of 51% women, 61% non-Hispanic White, and 17% Hispanic adults. COVID-19 infection was more prevalent among participants who had not received a booster vaccination (55.4% vs. 30.2%, p<0.001); the number of comorbid conditions was higher among those who had been infected (2.2% vs. 1.7%, p<0.001). Participants with COVID-19 infection had significantly lower CMSHS scores indicative of worse sleep health compared with uninfected participants (3.52 {+/-} 1.37 vs. 3.78 {+/-} 1.30; p < 0.001). Participants with PASC had lower CMSHS scores in comparison to those without PASC (2.72 {+/-} 1.30 vs. 3.82 {+/-} 1.28, p<0.001). In adjusted models, a progressive decline in CMSHS scores was observed over 12 months following infection (3.52 {+/-} 0.05 vs. 2.98 {+/-} 0.04; p < 0.001 for <1 month vs. 6-12 months). ConclusionCompared with uninfected individuals, multidimensional sleep health was worse among persons who had a COVID-19 infection. Individuals with PASC had greater and persistent reductions in sleep health for up to 12 months post-infection. Brief summaryO_LISeveral studies have examined the negative effects of COVID-19 on sleep, however the effects of COVID-19 infection on multidimensional sleep health remain poorly understood as do these associations over time. Using a large, population-based cohort, this study evaluates short- and long-term effects of Covid-19 infection on overall sleep health. C_LIO_LIThe study provides evidence that COVID-19 infection is associated with impairments in overall sleep health, with effects persisting up to 12 months post-infection. The findings in this study demonstrate that poor sleep health is an important long-term consequence of COVID-19 infection and emphasizes the need for sleep assessment among patients affected by COVID-19. C_LI

Abstract/SummaryFinancial exploitation of older adults is an increasingly prevalent public health concern, yet few have characterized fraud prevalence longitudinally or evaluated whether financial exploitation vulnerability measures prospectively predict fraud outcomes. Using data from the Health and Retirement Study, we examined fraud prevalence across a 14-year period and tested whether the Perceived Financial Vulnerability Scale (PFVS) predicts subsequent fraud victimization among older adults. Fraud prevalence increased steadily over time, rising from 5.0% in 2008 (347 of N=6,920) to a peak of 10.2% in 2022 (448 of N=4,380). Higher PFVS scores measured in 2018 were associated with greater odds of fraud victimization reported in 2022 (OR=1.62, 95% CI [1.25-2.15], p<.001). Most individuals who later reported fraud fell within the highest group of PFVS scores up to five years earlier. Together, these findings highlight financial exploitation as an emerging aging-related vulnerability and support the PFVS as a brief indicator of future fraud risk.

ObjectivesPeople impacted by the criminal-legal system face significant challenges to securing and sustaining permanent housing. This study was designed to assess housing outcomes of an integrated intervention that offered housing, medical, and behavioral health services to individuals with criminal-legal system involvement. MethodsAfter a baseline needs assessment, participants were linked to services and completed quarterly study visits for up to 12 months. We used descriptive statistics to assess frequency and multivariate logistic regression to assess correlates of being housed at last follow-up. ResultsBetween June 2019 and November 2023, 187 participants were enrolled in Project CHANGE from an area with high incarceration and overdose rates. At baseline, 43% of participants were unstably housed, 37% were homeless, and the remaining resided in a shelter or institution. At the time of last follow-up, 49 participants (26.2%) reported improved housing outcomes, and an additional 121 participants (64.7%) housing situation did not worsen. In multivariate models, individuals who were older (AOR 1.1; 95% CI 1.0-1.1), unstably housed at baseline (AOR 7.2; 95% CI 3.3-16.0), and enrolled in the study for longer (AOR 1.1; 95% CI 1.1-1.3) had higher odds of being housed at last follow-up, whereas those with high severity substance use had lower odds of being housed (AOR 0.3; 95% CI 0.1-0.6.) ConclusionsIn this comprehensive program, integrated housing/health services were time- and cost-intensive to deliver but led to positive housing outcomes. People involved in the criminal-legal system face unique barriers to housing, particularly when compounded by substance use.