Citations

ABSTRACT Purpose This study assesses the association between colorectal cancer (CRC) screening and a validated, housing-based measure of individual-level socioeconomic status (SES, called HOUSES hereafter) within rural communities and determines whether HOUSES-integrated geospatial analysis can be used to tailor interventions. Methods We used CRC screening data from a subset of Mayo Clinic Midwest patients living in cities without ready access to routine care in the Mayo Clinic Health System in 2019 to represent rural communities. At the individual level, we assessed the association between CRC screening rates and the HOUSES index, adjusting for age, sex, race/ethnicity, comorbidity, distance from home address to clinic, and area deprivation index, using a multilevel mixed-effects logistic regression model. Additionally, we conducted geospatial analysis to examine the correlation between hotspots of 1) lower CRC screening rates and 2) lower SES of the subject population (HOUSES quartile 1). Findings Among 34,489 individuals (median age 64.0 years, 52.4% female), those with the lowest SES (HOUSES Q1) had 37% lower odds of being CRC screening adherent than those with the highest SES (HOUSES Q4) (adj. OR [95% CI]: 0.63 [0.58-0.69]). In the 14 identified HOUSES Q1 hotspots, there was a significant correlation in counts of HOUSES Q1 and low CRC screening (correlation coefficient=0.81). Conclusion Lower SES was significantly associated with lower CRC screening among rural populations. HOUSES-enabled geospatial analysis identified geographic hotspots with lower CRC screening rates for targeted interventions to address disparities in CRC screening in rural communities. HOUSES may be a useful digital tool for cancer preventive care and research.

Importance Thirty-day hospital readmission is used to guide postdischarge care; however, a single binary 30-day estimate does not distinguish when risk occurs from when intervention is most actionable, with implications for allocation of postdischarge resources. Objective To evaluate time-structured hospital readmission risk within 30 days after discharge and implications for clinical utility. Design, Setting, and Participants Retrospective prognostic study of adults with hospital discharges from 2017 to 2024 (from 19 hospitals within a US health system for the development cohort) and 2008 to 2019 (from Medical Information Mart for Intensive Care IV for the external cohort) with 30-day follow-up. Analyses were conducted from November 2025 through June 2026. Exposure Postdischarge prediction window, including landmark intervals of 0 to 7, 8 to 14, 15 to 21, and 22 to 30 days and cumulative horizons through days 7, 14, 21, and 30 after discharge. Main Outcomes and Measures First all-cause hospital readmission before death during each landmark interval and by each cumulative horizon. Risk estimates were derived using clinical information available at hospital discharge. Performance was assessed using discrimination, calibration, decision curves, and capacity-constrained analyses. Results Among 617 841 discharges in the development cohort, the median (IQR) patient age was 65 (50-76) years, and 315 220 discharges (51.0%) occurred among female patients. The 30-day readmission rate was 16.3% (95% CI, 16.2%-16.5%), with 68 797 readmissions (68.2%) occurring within 14 days. Cumulative-horizon analyses characterized readmission risk through days 7, 14, 21, and 30, whereas landmark interval-specific analyses showed lower positive predictive values (PPVs) beyond day 7. At 5% intervention capacity, landmark PPVs were 58.3% (95% CI, 57.4%-59.1%) for days 0 to 7, 16.0% (95% CI, 15.5%-16.6%) for days 8 to 14, 14.5% (95% CI, 13.9%-15.0%) for days 15 to 21, and 12.9% (95% CI, 12.4% to 13.5%) for days 22 to 30; numbers needed to prevent 1 readmission were 8.6, 31.3, 34.6, and 38.6, respectively. Conclusions and Relevance In this prognostic study of adult hospital discharges, hospital readmission risk varied over time and was not captured by a single binary 30-day estimate. These findings suggest that time-structured readmission modeling can characterize when readmission risk occurs, but additional work is needed before such models can reliably guide interventions during specific postdischarge intervals.

Abstract Artificial intelligence is spreading through public health agencies and healthcare systems faster than workforces are trained to govern it. We describe RHAMI (Responsible Health AI Readiness and Maturity Index), the first human-centered automated composite index for health organizations to score AI readiness and maturity as one measurable system of science, economics and ethics, and the physician certification-to-Master’s pathway built to teach what it measures—both designed jointly by frontline public health professionals, clinicians, ethicists, engineers, leaders, and policy-makers in low- and middle-income countries and high-income countries.

Abstract Introduction: There is growing focus on community-engaged research (CEnR) in the clinical and translational sciences, supportive policies, environments, and processes within academic medical centers. The purpose of this evaluation was to gather academic and community member perspectives on key actionable strategies for supporting CEnR in an academic medical center. Materials and Methods: We used a sequential mixed method design by first conducting qualitative in-depth interviews with academic leaders, investigators, and staff (total, n = 54) and focus group discussions with community members ( n = 33). Themes from these qualitative data informed the adaptation of an institutional survey that gathered perspectives from a broader group of academic and community members affiliated with the institution ( n = 85). Both qualitative and quantitative data were integrated to inform actionable strategies. Results: Survey findings revealed that the institution was perceived as committed to health equity and CEnR, but institutional respondents expressed more concerns than community members about instrumental support of the work. Interviews and focus groups highlighted the opportunity to better align research efforts with community needs through more robust CEnR infrastructure and through interface with the clinical practice to promote patient access. Academic and community members emphasized the need for authentic, sustained engagement beyond federal mandates, including financial investment, capacity-building, and fostering trust. Leaders acknowledged structural challenges, siloed efforts, and the necessity for stronger coordination to increase integration of community engagement into the institutional mission. Conclusion: Findings suggest that aligning academic leaders, researchers, staff perspectives, and community members is essential to support and promote CEnR at an institutional level.

BACKGROUND/OBJECTIVES Pediatric hospital medicine (PHM) is a growing field with a unique scholarly perspective and evolving role in academic leadership. Since the last systematic assessment of PHM research in 2009, predating subspecialty certification in 2016 and fellowship accreditation in 2020, the research infrastructure within PHM has not been reevaluated. We sought to assess the current state of PHM research by assessing the number, scope, and scholarly domains of research faculty, as well as research funding and support. METHODS Using the Pediatrics Research in Inpatient Settings Network, we distributed a cross-sectional survey to PHM group leaders across the United States and Canada. The 16-item instrument, adapted from prior PHM and adult hospital medicine surveys, assessed program demographics, faculty effort allocation, research support, funding, and scholarly domains. Surveys were distributed via REDCap with follow-up reminders. Descriptive statistics were used. RESULTS 49 programs responded (51% response rate), representing 1702 faculty (∼40% of US pediatric hospitalists). Fewer than half of programs (n = 21, 43%) reported faculty with more than 50% research effort. One-hundred and ninety faculty members (11%) had dedicated research time, and 72% of those conducting quality improvement research did so without any protected effort. Most PHM programs (n = 27, 55%) received less than $100 000 in total annual research funding. CONCLUSIONS Despite recent PHM subspecialty designation and pediatric hospitalist desire to conduct research, dedicated time and associated supports are uncommon in PHM groups. Improvements in the PHM research pipeline will require understanding institutional financial constraints and diversifying and strengthening research funding.

ABSTRACT Importance Rural-urban disparities in chronic disease prevalence are well established; however, the extent to which individual-level socioeconomic status (SES) contributes to these disparities remains unclear. Objective To examine the associations of rurality and SES with the prevalence of five most burdensome chronic diseases among adults. Design We conducted a retrospective cross-sectional study of adults across 27 Upper Midwest counties using the Expanded Rochester Epidemiology Project (E-REP) medical record data linkage system to evaluate associations between rurality, SES and chronic disease prevalence. Prevalence of clinically diagnosed asthma, diabetes, hypertension, coronary heart disease, and mood disorders was identified from International Classification of Diseases ICD9/10 codes over a five-year period (2014-2019). Setting Population based Participants Adults over 18 years residing in the 27 E-REP counties, excluding those missing rural-urban residence status. Exposure HOUSES index, an individual-level measure of SES, served as the primary measure, while rurality based on Rural Urban Commuting Area (RUCA) codes 4-10 was the secondary measure. Main Outcome Prevalence of the five clinically diagnosed chronic diseases was identified using ICD9/10 codes from 2014–2019. Mixed effect logistic regression models were used and adjusted for demographics and general medical examination receipt, to assess rural-urban and SES differences for prevalence of each chronic disease. Results Among 455,802 adults with available HOUSES index, 42.8% lived in rural areas, 53.8% were female and 87.4% were non-Hispanic White. In the unadjusted analysis, rural and urban populations showed comparable asthma and CHD prevalence, while mood disorders, hypertension, and diabetes were more common in urban areas. After adjusting for demographic factors and healthcare utilization, rural-urban differences were no longer statistically significant, whereas SES remained strongly associated with all diseases in a dose response manner (e.g., adjusted Odds Ratio for hypertension (ref: HOUSES index Q4): 1.14, 1.27, and 1.42 for HOUSES index Q3, Q2, and Q1, respectively). Conclusions and Relevance Individual-level SES measured by the HOUSES index, was more strongly associated with chronic disease prevalence than rurality, supporting its integration into population health assessment and risk stratification.

Abstract Background Glycosylation, a key post-translational modification, plays a crucial role in biological pathways related to atherosclerosis. Purpose To investigate whether specific immunoglobulin G (IgG) N-glycosylation patterns are associated with premature myocardial infarction (MI) and to evaluate whether a glycan-based score improves case identification beyond traditional cardiovascular risk factors. Methods We measured 27 IgG N-glycans using capillary gel electrophoresis in 377 cases of premature first MI (men ≤55 years; women ≤65 years) and 377 controls matched for age (±5 years), sex, and nationality from the Gulf PREVENT study, a case-control study investigating risk factors for premature MI in the United Arab Emirates. We performed conditional logistic regression to evaluate associations between IgG N-glycans and premature MI, adjusting for age, body mass index, LDL-C, HDL-C, hs-CRP, hypertension, diabetes, and smoking. Using LASSO regression, we derived a novel IgG glycan score as a linear combination of selected glycans associated with premature MI. We evaluated model performance using likelihood ratio tests and the area under the curve (AUC) adapted for the case-control design, using 10-fold cross-validation. Results After adjusting for cardiovascular risk factors, glycan peaks (P) 4, 13, 23, and 27 were positively associated with premature MI (false discovery rate [FDR]<0.05). Bisecting N-acetylglucosamine (GlcNAc) was the consistent glycosylation pattern among these four glycans. In contrast, P 22, a core-fucosylated and monogalactosylated glycan, had inverse association (FDR=0.02) with premature MI. An IgG glycan score, composed of Ps 2, 4, 8, 17, 20, and 24, was associated with twofold increase in premature MI risk per standard deviation increment (aOR,1.98 [95%CI,1.56–2.52]; P<0.001) after adjusting for cardiovascular risk factors. Further, the IgG glycan score significantly improved model fit and performance, as indicated by a significant improvement in the likelihood ratio test and AUC (P<0.001). Conclusion We identified novel IgG N-glycosylation patterns that were associated with premature MI independent of cardiovascular risk factors. Glycans enriched with bisecting GlcNAc had increased risk, while core fucosylation and monogalactosylation had decreased risk. A novel IgG glycan score had 2-fold increased risk of premature MI independent of other risk factors and improved model performance. These findings highlight the potential of IgG N-glycans as biomarkers and therapeutic targets for premature MI.

We highlight the complex management considerations in a patient with cerebral amyloid angiopathy who developed venous thromboembolism, explicitly navigating the balance between risks of thromboembolism and bleeding with anticoagulation therapy. This case highlights a man in his 70s who presented to the emergency department with an acute right frontal intracerebral haemorrhage secondary to cerebral amyloid angiopathy with an acute segmental pulmonary embolism managed with an inferior vena cava (IVC) filter. During hospitalisation, the patient developed a large expansile thrombus extending inferior from the IVC filter to the right popliteal vein. This necessitated the initiation of anticoagulation therapy, beginning with unfractionated heparin, transitioning to enoxaparin and ultimately to apixaban. He was treated with 3 months of anticoagulation with no haemorrhagic complications. The patient’s successful outcome underscored the potential for safe and effective anticoagulation management in this complex clinical scenario.

Poor diet is the leading preventable risk factor for death worldwide, associated with over 10 million premature deaths and USD 8 trillion related costs every year. Artificial intelligence or AI is rapidly emerging as the most historically disruptive, innovatively dynamic, rapidly scaled, cost-efficient, and economically productive technology (which is increasingly providing transformative countermeasures to these negative health trends, especially in low- and middle-income countries (LMICs) and underserved communities which bear the greatest burden from them). Yet widespread confusion persists among healthcare systems and policymakers on how to best identify, integrate, and evolve the safe, trusted, effective, affordable, and equitable AI solutions that are right for their communities, especially in public health nutrition. We therefore provide here the first known global, comprehensive, and actionable narrative review of the state of the art of AI-accelerated nutrition assessment and healthy eating for healthcare systems, generated by the first automated end-to-end empirical index for responsible health AI readiness and maturity: the Responsible Health AI readiness and Maturity Index (RHAMI). The index is built and the analysis and review conducted by a multi-national team spanning the Global North and South, consisting of front-line clinicians, ethicists, engineers, executives, administrators, public health practitioners, and policymakers. RHAMI analysis identified the top-performing healthcare systems and their nutrition AI, along with leading use cases including multimodal edge AI nutrition assessments as ambient intelligence, the strategic scaling of practical embedded precision nutrition platforms, and sovereign swarm agentic AI social networks for sustainable healthy diets. This index-based review is meant to facilitate standardized, continuous, automated, and real-time multi-disciplinary and multi-dimensional strategic planning, implementation, and optimization of AI capabilities and functionalities worldwide, aligned with healthcare systems’ strategic objectives, practical constraints, and local cultural values. The ultimate strategic objectives of the RHAMI’s application for AI-accelerated public health nutrition are to improve population health, financial efficiency, and societal equity through the global cooperation of the public and private sectors stretching across the Global North and South.

Abstract Hospital medicine (HM), a well‐established clinical specialty, requires clarity of research priorities to identify target areas for investment in HM research infrastructure and activities. The Society of Hospital Medicine's Research Committee developed a 20‐item survey and used purposeful sampling of US hospitalist leaders to prioritize primary research topic domains and subdomains. Respondents were asked to rank their HM research priorities using a scale (1 [highest] to 8 [lowest]). Of 239 surveys distributed, 81 (34%) responded. Respondents were predominantly White (53%) and from academic institutions (57%). “ Overall, 46% of respondents were male and 33% were female. ” Top‐ranked research domains were innovation in care delivery (median 2, IQR 1,3), value‐based care (median 3.5, IQR 2,6), and health disparities (median 4, IQR 2,5). This survey identified top HM research priorities including systems‐based topics and health disparities, aligning with HM's identity as a specialty focused on improving systems of care and inequities in care.