Pharmacoepidemiology and Drug Safety

BackgroundNon-adherence to lipid-lowering therapy (LLT) affects up to half of patients and contributes substantially to preventable cardiovascular morbidity and mortality. Existing measures, such as the proportion of days covered, provide cross-sectional summaries but fail to capture the dynamic patterns of adherence over time. Although group-based trajectory modelling identifies distinct longitudinal adherence patterns, no approach currently predicts trajectory membership prospectively while incorporating patient-reported barriers. We developed BRIDGE, a barrier-informed Bayesian model to predict adherence trajectories and identify their underlying drivers. MethodsBRIDGE incorporates patient-reported barriers as structured prior information within a Bayesian framework for adherence-trajectory prediction. The model was designed not only to estimate which patients are likely to follow different adherence trajectories, but also to generate clinically interpretable probability estimates that help explain why those trajectories may arise and what modifiable factors may be most relevant for intervention. ResultsBRIDGE achieved a macro AUROC of 0.809 (95% CI 0.806 to 0.813), comparable to random forest (0.815 (95% CI 0.812 to 0.819)) and XGBoost (0.821 (95% CI 0.818 to 0.824)), two widely used machine-learning benchmarks for structured clinical prediction. Calibration was superior to random forest (Brier score 0.530 vs 0.545; ), and performance was stable across six independent training runs (AUROC SD = 0.003). Incorporating barrier-informed priors improved accuracy by 3.5% and calibration by 5.5% compared to flat priors, showing that incorporation of patient-reported barriers added value beyond electronic medical record data alone. Four clinically distinct adherence trajectories were identified: gradual decline associated with treatment deprioritisation amid polypharmacy (10.4%), early discontinuation linked to asymptomatic risk dismissal (40.5%), rapid decline associated with intolerance (28.8%), and persistent adherence (20.2%). Counterfactual analysis identified trajectory-specific intervention levers. ConclusionsBRIDGE provides accurate and well-calibrated prediction of adherence trajectories while offering clinically actionable insights into their underlying drivers. By integrating patient-reported barriers with routine clinical data, the model supports targeted, mechanism-informed interventions at the point of prescribing to improve adherence to cardioprotective therapies. FundingMRFF CVD Mission Grant 2017451 Evidence before this studyWe searched PubMed and Scopus from database inception to December 2025 using the terms "medication adherence", "trajectory", "prediction model", "Bayesian", "lipid-lowering therapy", and "barriers", with no language restrictions. Group-based trajectory modelling has consistently identified three to five adherence patterns across cardiovascular cohorts; however, these applications have been descriptive rather than predictive. Machine-learning models for adherence prediction achieve moderate discrimination but treat adherence as a binary or continuous outcome, thereby overlooking the clinically meaningful heterogeneity captured by trajectory approaches. One prior study applied a Bayesian dynamic linear model to examine adherence-outcome associations, but it did not predict adherence trajectories or incorporate patient-reported barriers. To our knowledge, no published model integrates patient-reported barriers into trajectory prediction. Added value of this studyBRIDGE is, to our knowledge, the first model to incorporate patient-reported adherence barriers as hierarchical domain-informed priors within a Bayesian framework for trajectory prediction. Using 108 predictors derived from routine electronic medical records, the model achieves discrimination comparable to state-of-the-art machine-learning approaches while additionally providing uncertainty quantification, barrier-level interpretability, and counterfactual insights to inform intervention strategies. The identified trajectories differed not only in adherence level but also in switching behaviour, drug-class evolution, and medication burden, suggesting distinct underlying mechanisms of non-adherence that may require tailored clinical responses. Implications of all the available evidenceEach adherence trajectory implies a distinct intervention target: asymptomatic risk communication for early discontinuers (40.5% of patients), proactive tolerability management for rapid decliners, medication simplification for patients with gradual decline associated with polypharmacy, and maintenance support for persistent adherers. By integrating routinely collected clinical data with patient-reported barriers, BRIDGE can be deployed within existing primary care EMR infrastructure to generate actionable, trajectory and patient--specific recommendations at the point of prescribing, helping to bridge the gap between adherence measurement and targeted adherence management.

Background: The increasing availability of routinely collected health data offers new opportunities for population-level research, yet access to comprehensive, linked, and standardised datasets remains limited. We describe EST-Health-30, a large-scale, population-representative health data resource from Estonia. Methods: EST-Health-30 comprises a random 30% sample of the Estonian population (~500,000 individuals), with longitudinal data from 2012 to 2024 and annual updates planned through 2026. Individual-level records are linked across five nationwide databases, including electronic health records, health insurance claims, prescription data, cancer registry, and cause of death records. A privacy-preserving hashing approach ensures consistent cohort inclusion over time while maintaining pseudonymisation. All data are harmonised to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (version 5.4) using international standard vocabularies. Data quality was assessed using established OMOP-based validation frameworks. Results: The dataset contains rich multimodal information on diagnoses, procedures, laboratory measurements, prescriptions, free-text clinical notes, healthcare utilisation, and costs, with high population coverage and longitudinal depth. Data quality assessment showed high completeness and consistency, with 99.2% of applicable checks passing. The age-sex distribution closely reflects the national population, supporting representativeness, though coverage is marginally below the target 30% (29.2%), primarily attributable to recent immigrants without health system contact. The dataset enables construction of detailed clinical cohorts, analysis of disease trajectories, and evaluation of healthcare utilisation and outcomes across the life course. Conclusions: EST-Health-30 is a comprehensive, standardised, and population-representative real-world data resource that supports epidemiological, clinical, and methodological research. Its alignment with the OMOP CDM facilitates reproducible analytics and participation in international federated research networks, while secure access infrastructure ensures compliance with data protection regulations.

Drug overdose deaths in the United States reached record levels during the fentanyl era before recently declining. A plausible hypothesis is that a sudden drop in fentanyl purity beginning in 2023 caused the downturn in overdose mortality. We evaluated this hypothesis by replicating a published analysis with regional overdose data, using models that account for time trends and autocorrelation, and negative control indicators to test for spurious correlation. When fentanyl purity was rising, the national purity series did not track overdose increases in most regions and showed only a modest association in the West. When both purity and mortality later declined, the observed associations were also seen with unrelated macroeconomic indicators that shared the same time pattern. National fentanyl purity alone does not provide a sufficient explanation for recent overdose declines.

Objective The aim of this study was to develop and validate an automated, scalable framework to harmonise fragmented UK primary care prescription records into a research-ready dataset by mapping four diverse medical ontologies to a unified, historically comprehensive reference standard. Materials and Methods We used raw prescription records for consented participants in the UK Biobank, in which participants are uniquely characterized by multiple data modalities. Primary care data were preprocessed by selecting one drug code if multiple were recorded, cleaning codes to match reference presentations, expanding code granularity based on drug descriptions, and updating outdated codes to a single reference version. Harmonisation entailed mapping British National Formulary (BNF) and Read2 codes to dm+d, the universal NHS standard vocabulary for uniquely identifying and prescribing medicines. Harmonised dm+d records were then homogenised to a single concept granularity, the Virtual Medicinal Product (VMP). We validated our methods by creating medication profiles mapping contemporary drug prescribing patterns in 312 physical and mental health conditions. Results We preprocessed 57,659,844 records (100%) from 221,868 participants (100%). Of those, 48,950 records were dropped due to lack of drug code. 7,357,572 records (13%) used multiple ontologies. Most (76%) records were encoded in BNF and most had the code granularity expanded via the drug description (N=28,034,282; 49%). 41,244,315 records (72%) were harmonised to dm+d and 99.98% of these were converted to VMP as a homogeneous dataset. Across 312 diseases, we identified 23,352 disease-drug associations with 237 medications (represented as BNF subparagraphs) that survived statistical correction of which most resembled drug - indication pairs. Conclusion Our methodology converts highly fragmented and raw prescription records with inconsistent data quality into a streamlined, enriched dataset at a single reference, version, and granularity of information. Harmonised prescription records can be easily utilised by researchers to perform large-scale analyses in research.

Background Group A streptococcus (GAS) infections have been associated with neuropsychiatric disorders in epidemiologic studies and animal models, but data in US health care populations are limited. GAS is also associated with autoimmune sequelae, including acute rheumatic fever (ARF)/Sydenham chorea (SC), poststreptococcal reactive arthritis (PSRA), poststreptococcal glomerulonephritis (PSGN), and guttate psoriasis (GP). Epstein-Barr virus (EBV) has been linked to systemic lupus erythematosus (SLE) and multiple sclerosis (MS) and the complexity of these associations parallels that of GAS-associated conditions, providing a useful comparison. Objectives 1) Assess the association between a positive GAS test and incident neuropsychiatric diagnoses within 1 year in a large US health care database. 2) Assess the validity of the same database in detecting well-established disease associations while avoiding false associations. Design, Setting, Participants Retrospective cohort study using TriNetX data from US health care organizations. Patients with positive or negative tests were propensity score-matched (GAS cohort n=178,301; EBV cohort n=64,854). Patients with documented neuropsychiatric diagnoses prior to testing were excluded. To approximate a primary care population, inclusion required at least one well-visit. Exposures Positive vs negative GAS test; positive vs negative EBV test (separate cohorts). Main Outcomes and Validations Main outcome: incident neuropsychiatric diagnoses within 1 year of GAS testing. Positive control outcomes: ARF/SC, PSRA, PSGN, and GP (for GAS cohort); SLE and MS (for EBV cohort). Negative control outcomes: conditions without known association with GAS. Results After matching, a positive GAS test was associated with attention-deficit/hyperactivity disorder (ADHD) (RR: 1.09; 95% CI: 1.03-1.15). Among established poststreptococcal conditions, only GP was associated with prior GAS (RR: 1.75; 95% CI: 1.06-2.89). Case counts were insufficient to evaluate ARF/SC, PSRA, and PSGN. Negative control outcomes showed no association. In the EBV cohort, no association was observed with SLE, and MS showed a decreased risk. Conclusions and Relevance A positive GAS test was associated with ADHD but not with other neuropsychiatric disorders. The database detected poststreptococcal GP but did not identify most established postinfectious autoimmune associations, likely reflecting rarity, heterogeneity, and diagnostic complexity. These findings begin to describe the range of real-world health care databases to evaluate postinfectious neuropsychiatric risk.

This updated systematic review and network meta-analysis evaluated the efficacy and safety of obesity management medications (OMMs) in terms of reducing body weight and obesity related complications. Medline and Embase were searched up to 21 November 2025 for randomized controlled trials comparing OMMs versus placebo or active comparators in adults. The primary endpoint was percentage total body weight loss (TBWL%) at the end of the study. Secondary endpoints were TBWL% at 1, 2 and 3 years, anthropometric, metabolic, mental health and quality of life outcomes, cardiovascular morbidity and mortality, remission of obesity related complications, serious adverse events and all cause mortality. Sixty six RCTs (66 comparisons) were identified: orlistat (22), semaglutide (18), liraglutide (11), tirzepatide (8), naltrexone/bupropion (5) and phentermine/topiramate (2), enrolling 63,909 patients (34,861 and 29,048 with active compound and placebo, respectively). All OMMs showed significantly greater TBWL% versus placebo; tirzepatide and semaglutide exceeded 10% TBWL and showed the most favourable glycaemic effects. Semaglutide reduced major adverse cardiovascular events and all cause mortality. In dedicated complication specific trials, semaglutide and tirzepatide showed benefit on heart failure related outcomes; tirzepatide was associated with improved obstructive sleep apnoea syndrome and semaglutide with knee osteoarthritis pain remission. Tirzepatide and semaglutide were associated with improvements in metabolic dysfunction-associated steatohepatitis remission, and semaglutide with improvement in liver fibrosis. No OMMs were associated with an increased risk of serious adverse events. These updated results reinforce the need to individualize OMMs selection according to weight loss efficacy, complication profile and safety.

Background: Opioid-related mortality in Texas has escalated dramatically, increasingly driven by illicitly manufactured fentanyl. To address local surges in mortality, the Galveston County Health District deployed the Galveston County Opioid Defense Effort (GCODE) in July 2023, leveraging digitally integrated surveillance data from emergency medical services (EMS) and the Medical Examiner to provide targeted naloxone distribution in identified overdose hot spots. Methods: Using a segmented interrupted time series (ITS) design and Poisson regression with robust standard errors, we evaluated the population-level impact of GCODE on opioid-involved mortality through the end of 2025. Data were sourced from the Galveston Area Ambulance Authority (GAAA) and vital statistics (ICD-10 codes). We assessed mortality trajectory changes, the observed fatality ratio among EMS-detected opioid events (the Survival Gap), and demographic and geographic covariates. Results: The Poisson ITS model included 519 weekly observations (N = 14,827 tract-weeks across 101 census tracts). Pre-intervention, opioid mortality increased by 0.16% weekly (IRR = 1.0016; 95% CI: 1.000-1.003; p = 0.011). Following GCODE deployment, the mortality trajectory reversed to a sustained 0.55% weekly decrease (IRR = 0.9945; 95% CI: 0.990-0.999; p = 0.021). The observed fatality ratio among EMS-detected events declined from 7.59% (preintervention mean; SD = 0.111) to 1.71% (post-intervention; SD = 0.042; Chi^2 = 19.824; p = 0.0001). Opioid decedents were significantly younger than the general mortality population (OR = 0.945 per year of age; p < 0.001), and were descriptively more likely to lack documented race/ethnicity data (41.23% vs. 8.27% Unknown; p < 0.001), limiting equity analysis. Conclusions: The findings are consistent with GCODE having meaningfully reduced opioid mortality by substantially lowering event-level lethality. These results suggest that targeted, digitally coordinated harm reduction can decouple overdose incidence from fatal outcomes, with implications for harm reduction program design in structurally constrained environments.

Background: The 2024-25 influenza season was the most severe in the United States (US) since 2017-18, with co-circulation of both influenza A virus subtypes (H1N1 and H3N2). Influenza vaccine effectiveness (VE) has varied by season, setting, and patient characteristics. Methods: Using electronic healthcare encounter data from eight US states, we evaluated influenza vaccine effectiveness (VE) against influenza-associated hospitalizations and emergency department or urgent care (ED/UC) encounters from October 2024-April 2025 among children aged 6 months-17 years and adults aged 18+ years. Using a test-negative, case-control design, we compared the odds of influenza vaccination between acute respiratory illness (ARI) encounters with a positive (cases) versus negative (controls) test for influenza by molecular assay, adjusting for confounders. Results: Analyses included 108,618 encounters (5,764 hospitalizations and 102,854 ED/UC encounters) among children and 309,483 encounters (76,072 hospitalizations and 233,411 ED/UC encounters) among adults. Among children across care settings, 17.0% (6,097/35,765) of cases versus 29.4% (21,449/72,853) of controls were vaccinated. Among adults, 28.2% (21,832/77,477) of cases versus 44.2% (102,560/232,006) of controls were vaccinated. VE was 51% (95% confidence interval [95% CI]: 41-60%) against influenza-associated hospitalizations and 54% (95% CI: 52-55%) against influenza-associated ED/UC encounters among children. VE was 43% (95% CI: 41-46%) against influenza-associated hospitalizations and 49% (95% CI: 47-50%) against influenza-associated ED/UC encounters among adults. Conclusions: Influenza vaccination provided protection against influenza-associated hospitalizations and ED/UC encounters among children and adults in the US during the severe 2024-25 influenza season. These findings support influenza vaccination as an important tool to reduce influenza-associated disease.

Background: Lung cancer screening can reduce lung cancer mortality through early detection, but uptake of the NHS Targeted Lung Health Check (TLHC) programme remains low. Behaviourally informed invitation messages have been proposed as a low-cost approach to increase attendance, but evidence of their effectiveness in lung cancer screening is mixed. Few intervention studies used evidence-based behaviour change frameworks, and rarely tailored invitation strategies to empirically identified barriers and enablers. Methods: In an online experiment, 3,274 adults aged 55-74 years and with a history of smoking were randomised to see one of four behaviourally informed invitation messages or a control message. Participants then rated their intention to attend a TLHC appointment, and selected barriers and enablers to attending from a pre-defined list, which were classified according to the Theoretical Domains Framework. Invitation messages were mapped to Behaviour Change Techniques using the Theory and Techniques Tool. Message conditions were compared on intention to attend TLHC using bootstrapped ANOVA followed by pairwise comparisons. Exploratory counterfactual mediation analyses examined the role of fear in intention to attend. Results: Behaviourally informed invitation messages did not meaningfully increase intention to attend TLHC compared with the control message. While a GP-endorsed message showed a small potential benefit relative to the other conditions, this finding was not robust after adjustment for multiple comparisons. Participants most frequently reported barriers related to Emotion (particularly fear), Social Influence, and Knowledge, while Beliefs about Consequences emerged as the primary enabler of attendance. Only around half of reported barriers and enablers were addressed by the invitation messages. Exploratory analyses found that fear was associated with lower intention to attend a TLHC appointment, yet none of the behaviourally informed messages appeared to reduce fear compared to the control message. Conclusions: Improving lung cancer screening uptake will likely require invitation messages that directly address emotional concerns, particularly fear, alongside credible recommendations. These findings highlight the importance of systematically aligning invitation message content with empirically identified behavioural influences when designing scalable interventions to improve lung cancer screening uptake.

Background Individuals experiencing or at risk of homelessness face substantial barriers to preventive eye care that are poorly addressed by standard service models. Interdisciplinary optometry-social work collaboration offers a rights-based approach to improving engagement and continuity of care. Methods A convergent mixed-methods study was conducted between February and August 2024 at a multidisciplinary community centre. Clients experiencing or at risk of homelessness received integrated optometry and social work assessment and were prioritised as high, medium, or low based on combined clinical and social risk. Social work follow-up was guided by the Triple Mandate and W-Questions framework. Quantitative data were summarised using mean (SD), median [IQR], or n (%). Qualitative case notes were analysed using content analysis with inductive coding and secondary review for consistency. Results A total of 165 clients had priority categories coded (high: 68; medium: 47; low: 154). Demographic data were available for 132 clients (60% male; mean age 49.5 years [SD 16]); 27% had not completed high school, 89% reported weekly income below AUD 1000, and 28% had vision impairment. Two hundred forty-five case-note entries were consolidated into 146 unique records. SMS (46%) and phone calls (38%) were the most documented contact methods, although only 21% of calls were answered; missed calls (13%) and disconnected numbers (7%) were common. Multi-modal contact was more frequently documented for higher-priority clients. Appointment assistance was the most recorded facilitator (71%), while rights-based supports, including interpreter and transport assistance, were infrequently documented (<=5%). Qualitative analysis identified unstable communication, reliance on informal supports, and service fragmentation as key influences on recall outcomes. Conclusion This study supports an interdisciplinary, rights-based optometry-social work model to address barriers to preventive eye care among people experiencing or at risk of homelessness. Embedding structured handovers and tiered recall processes within community-based services may strengthen continuity and accountability for high-priority clients. Future implementation should evaluate outcomes related to equity of reach, service integration, and sustained engagement in care.

Introduction Large language models are increasingly being used in healthcare. In interventional pain medicine, clinical reasoning is essential for procedural planning. Prior studies show that simplified prompts reduce clinical detail in AI-generated responses. It remains unclear whether this reflects knowledge loss or simply prompt-driven suppression of information. Methods We performed a controlled comparative study using 15 standardized low back pain questions representing common interventional pain questions. Each question was submitted to ChatGPT under three conditions, professional-level prompt (DP), fourth-grade reading-level prompt (D4), and clinician-directed rewriting of the D4 response to a medical level (U4[-&gt;]MD). No follow-up prompting was allowed. Three physicians independently rated responses for accuracy using a 0-2 ordinal scale. Clinical completeness was determined by consensus. Word count and Flesch-Kincaid Grade Level (FKGL) were also measured. Paired t-tests compared conditions. Results Accuracy was highest with professional prompting (1.76). Accuracy declined with the fourth-grade prompt (1.33; p = 0.00086). When simplified responses were rewritten for clinicians, accuracy returned to baseline (1.76; p {approx} 1.00 vs DP). Clinical completeness followed the same pattern showing DP 80.0%, D4 6.7%, U4[-&gt;]MD 73.3%. Fourth-grade responses were shorter and less complex. Upscaled responses were more complex and similar in length to professional responses. Inter-rater reliability was low (Fleiss {kappa} = 0.17), but trends were consistent across conditions. Conclusions Reduced clinical detail under simplified prompts appears to reflect constrained output rather than loss of knowledge. Clinician-directed reframing restores omitted content. LLM performance in interventional pain depends strongly on prompt design and intended audience.

Objective To examine the impact of acute illness on long-term health and describe any differences in these associations between socioeconomic and ethnic groups. Design Longitudinal population study. Setting Linked primary and secondary care data recorded in the Clinical Practice Research Datalink (CPRD). Participants Adults ([&ge;]18 years) residing in England registered with a primary care general practice (GP) between 1st January 2012 and 31st December 2022 who have not opted out of inclusion into CPRD and linked data sources. Socioeconomic deprivation was defined using the Index of Multiple Deprivation (IMD) and ethnicity by UK census 2011 definitions. Main outcome measures The primary outcome was new long-term disease and multimorbidity (two or more long-term diseases). We describe incidence of hospitalisation for acute illness as the exposure. Results We included 18,329,659 people, with 9,339,394 (51.0%) women, 7,430,555 (40.5%) people from the most deprived deciles (IMD 1-4) and 3,009,717 (16.4%) from a minority ethnic group. 6,038,272 (32.9%) people experienced hospitalisation for acute illness. Hospitalisation was associated with increased onset of long-term disease in those alive at the end of follow up (41.1% hospitalised vs 18.7% not hospitalised; adjusted HR 2.48 (2.47 to 2.48)). Compared to non-hospitalised, those who had been hospitalised were more likely to change from being disease free at baseline to having a new long-term disease (12.9% vs. 7.5%), develop multimorbidity (4.7% vs. 1.1%), or transition to multimorbidity if they had pre-existing disease (8.1% vs. 1.8%). Age-standardised hospitalisation rates were highest in the most deprived decile and in people with Black ethnicity. Comparative hospitalisation ratio for IMD 1 compared to IMD 10 ranging from 1.78 in 2018 to 1.96 in 2021 and for Black ethnicity compared to White ranging from 1.03 in 2017 to 1.08 in 2021. Conclusions Acute hospitalisation is a key stage in the development of long-term disease and may be an underutilised opportunity for intervention to change healthy life trajectory and reduce health inequality.

Purpose: We evaluated healthcare resource utilization (HCRU) and costs in patients with eosinophilic granulomatosis with polyangiitis (EGPA). Methods: Patients with newly diagnosed EGPA (2017--2021), [&ge;]12 months' pre-diagnosis health plan enrollment, and [&ge;]1 inpatient or [&ge;]2 outpatient claims with an EGPA diagnosis were included. Follow-up was from EGPA diagnosis until disenrollment or database end. HCRU and health insurer payment costs during follow-up were compared with those for matched cohorts of general insured patients without EGPA (comparison A) and without EGPA but with severe uncontrolled asthma (SUA; comparison B). Results: In comparison A, all-cause HCRU was higher in the EGPA cohort (n = 213) versus matched patients (n = 779) for all clinical encounters/pharmacy claim types; annualized, mean total all-cause costs were 16-fold higher ($117,563/patient) versus matched patients ($7,520/patient). In comparison B, all-cause HCRU was higher for the EGPA cohort (n = 182) versus the matched SUA cohort (n = 640) for all clinical encounters/pharmacy claim types, with 5-fold higher mean total all-cause costs ($118,127/patient vs $22,286/patient). In both EGPA cohorts, HCRU and associated costs increased between the baseline and follow-up periods. Conclusions: These findings highlight the need for more effective treatments to reduce the clinical and economic burden of EGPA.

Background: Few studies have examined racioethnic disparities in cardiovascular disease (CVD) in women after breast cancer treatment, who are at higher risk due to cardiotoxic cancer treatment. Methods: Based on the Pathways Heart Study of women with a history of breast cancer, this analysis examines the association between cardiometabolic risk factors (hypertension, diabetes, and dyslipidemia) and CVD events with self-reported race and ethnicity, as well as genetic similarity. Multivariable logistic and Cox proportional hazards regression models were used to test race and ethnicity and genetic similarity with prevalent and incident cardiometabolic risk factors and CVD events. Results: Of the 4,071 patients in this analysis, non-Hispanic Black (NHB), Asian, and Hispanic women were more likely to have prevalent and incident diabetes than non-Hispanic White (NHW) women. Analysis of genetic similarity revealed results consistent with self-reported race and ethnicity. For CVD risk, NHB women were more likely to develop heart failure and cardiomyopathy than NHW women. In contrast, Hispanic women were at lower risk of any incident CVD, serious CVD, arrhythmia, heart failure or cardiomyopathy, and ischemic heart disease, which was consistent with the associations found with Native American ancestry. Conclusions: This is the largest multi-ethnic study of disparities in CVD health in breast cancer survivors, demonstrating corroborating findings between self-reported race and ethnicity and genetic similarity. The results highlight disparities in cardiometabolic risk factors and CVD among breast cancer survivors that warrant more research and clinical attention in these distinct, high-risk populations.

Background Surgery is a widely used treatment option but the impact of surgery on long-term disease across socioeconomic groups is unknown. Methods Longitudinal population study using linked primary and secondary care data describing adults ([&ge;]18 years) in England recorded in the Clinical Practice Research Datalink (CPRD) between 1st January 2012 and 31st December 2021. Socioeconomic deprivation was defined using the Index of Multiple Deprivation (IMD). The exposure was surgery and primary outcome was long-term disease. Data are presented as n (%), median (IQR), and adjusted hazards ratios (HR) with 95% confidence intervals. Findings Of 18,329,659 people, 8,951,145 (48.8%) underwent surgery. 78.6% of index surgeries were elective (n=7,032,475), 21.4% were emergency (n=1,918,670). Amongst surgical patients, 4,741,188 (52.0%) were women, 3,540,136 (39.6%) from the most deprived deciles (IMD 1-4) and 994,595 (11.1%) from a minority ethnic group. Age-standardised rates of surgery were higher in deprived individuals (comparative rate ratio IMD 1 vs. IMD 10 elective: 1.11 (95% CI 1.11-1.11), emergency: 1.54 (1.54-1.54)). Age at first surgery was 42 (27-60) years for elective and 42 (25-65) years for emergency surgery overall, but lower for people from IMD 1-4 (elective: 39 (26-57) years, emergency: 38 (24-60) years). Rates of long-term disease increased following both elective (baseline 19.6%, three years 24.5%) and emergency surgery (baseline 10.3%, three years 12.3%). Risk of new long-term disease following surgery increased with increasing levels of deprivation (IMD 1 vs. IMD 10 elective: HR 1.46 (1.45-1.48), emergency: HR 1.46 (1.44-1.48)). Interpretation Surgical treatment is strongly associated with the onset of long-term disease and factors which limit healthy life expectancy. Surgery occurs at a younger age among socioeconomically deprived groups and may be linked to health inequalities. Similar but more complex patterns of inequality were seen in minority ethnic groups. Funding Barts Charity and UK Academy of Medical Sciences.

ImportanceMigraine attacks often occur unpredictably, limiting the ability of individuals to initiate timely preventive or preemptive treatment. Short-term probabilistic forecasting of migraine risk could enable more targeted management strategies. ObjectiveTo externally validate the previously developed Headache Prediction Model (HAPRED-I), evaluate an updated continuously learning model (HAPRED-II), and assess the feasibility and short-term safety of delivering individualized probabilistic migraine forecasts directly to patients. Design, Setting, and ParticipantsProspective 8-week cohort study conducted remotely at two academic medical centers in the United States (Massachusetts General Hospital and Wake Forest Health Sciences) between 2015 and 2019. Adults with recurrent migraine or tension-type headache completed twice-daily electronic diaries. A total of 230 participants contributed 23,335 diary entries across 11,862 participant-days of observation. Main Outcomes and MeasuresOccurrence of a headache attack within 24 hours following each evening diary entry. Model performance was evaluated using discrimination (area under the receiver operating characteristic curve [AUC]) and calibration. ResultsExternal validation of HAPRED-I demonstrated modest discrimination (AUC, 0.59; 95% CI, 0.57-0.61) and poor calibration, with predicted probabilities consistently exceeding observed headache risk. In contrast, the continuously updating HAPRED-II model demonstrated progressive improvement in predictive performance as participant-specific data accumulated. Discrimination increased from an AUC of 0.59 (95% CI, 0.57-0.61) during the first 14 days to 0.66 (95% CI, 0.63-0.70) after the first month, accompanied by improved calibration across predicted risk levels. Over the study period, 6999 individualized forecasts were delivered directly to participants. No evidence suggested that receipt of forecasts was associated with increasing headache frequency or worsening predicted headache risk trajectories. Conclusions and RelevanceA static migraine forecasting model demonstrated limited transportability to new individuals. In contrast, models that continuously update within individuals may improve predictive accuracy over time and enable real-time delivery of personalized migraine risk forecasts. Further work incorporating richer physiologic and contextual predictors will likely be necessary before such systems can reliably guide clinical treatment decisions.

Background: Progression of transthyretin (TTR) amyloid cardiomyopathy (ATTR-CM) can lead to worsening congestion requiring diuretic intensification (DI), heart failure (HF)-related hospitalizations (HFH), and death. Tafamidis was the only approved ATTR-CM therapy in the US from 2019 until the 2024 approval of acoramidis, which achieves near-complete ([&ge;]90%) TTR stabilization. As head-to-head trials are lacking, real-world comparative effectiveness (CE) data are needed to guide treatment selection. Objective: To evaluate real-world CE of acoramidis versus tafamidis in newly treated patients with ATTR-CM. Methods: Retrospective study using Komodo Healthcare Map (R) US claims data tokenized to Claritas. Patients newly initiating acoramidis or tafamidis between 12/11/2024 and 04/30/2025 with [&ge;]1 prescription claim (first defined as index date) and [&ge;]6 months of continuous enrollment preindex date were included and followed until disenrollment, death, treatment switch, or study end date (07/31/2025). Outcomes included DI (initiation or dose-equivalent escalation of oral loop diuretics, parenteral loop diuretic use, or addition of thiazide-like diuretic) and a composite of DI, HFH (inpatient admission with a HF-related ICD-10-CM diagnosis code in any position), and mortality. Propensity score weighting balanced baseline characteristics, disease severity, comorbidity burden, and baseline medication use. Time-to-event outcomes were assessed using weighted Cox proportional hazards models. Results: After weighting, acoramidis (n=170) and tafamidis (weighted sample size=448) patients were comparable at baseline (mean age, 78.6 vs 78.7 years; male, 80.0% vs 80.2%) with mean follow-up of 139 and 143 days, respectively. DI cumulative incidence curves separated early and remained divergent, with acoramidis significantly reducing the hazard of DI events by 43% compared with tafamidis (11.8% vs 20.5%; HR, 0.57; 95% CI, 0.35-0.92; P=0.021). Acoramidis also had a significantly lower risk of composite events, with a 34% reduction in hazard compared with tafamidis (17.6% vs 26.4%; HR, 0.66; 95% CI, 0.44-0.99; P=0.046). Conclusions: In this first real-world CE study of newly treated patients, acoramidis had significantly lower risk of DI events and composite events of DI, HFH, and mortality than tafamidis, potentially supporting improved clinical stability with acoramidis initiation. Additional evaluation with longer follow-up, larger cohorts, and/or prospective clinical outcomes is warranted.

Introduction: Smoking cessation after acute coronary syndrome (ACS) is a Class I recommendation, yet prescription pharmacotherapy use remains low and its real-world cardiovascular effectiveness when added to nicotine replacement therapy (NRT) is poorly characterized. Methods: We conducted a retrospective cohort study using the TriNetX US Collaborative Network (67 healthcare organizations). Adults hospitalized with ACS who received NRT within one month, serving as a proxy for active smoking status, were identified. Two co-primary propensity-matched (1:1, 50 covariates, caliper 0.10 SD) comparisons evaluated bupropion + NRT and varenicline + NRT individually versus NRT alone; a supportive analysis evaluated combined pharmacotherapy versus NRT alone. All-cause mortality was the primary endpoint. Secondary outcomes included MACE, heart failure exacerbations, major bleeding, TIA/stroke, emergency rehospitalizations, and cardiac rehabilitation utilization, assessed at 6 months and 1 year via Kaplan-Meier analysis. Hazard ratios (HRs) greater than 1.0 indicate higher hazard in the NRT-only group. Results: After matching, the combined analysis comprised 8,574 pairs, the bupropion analysis 4,654 pairs, and the varenicline analysis 2,126 pairs. At 1 year, the combined pharmacotherapy group had significantly lower all-cause mortality (HR 1.26, 95% CI 1.16-1.37), MACE (HR 1.16, 95% CI 1.12-1.21), heart failure exacerbations (HR 1.16, 95% CI 1.08-1.25), major bleeding (HR 1.18, 95% CI 1.08-1.28), and greater cardiac rehabilitation utilization (HR 0.82, 95% CI 0.74-0.92; all p < 0.001). TIA/stroke did not differ significantly. Six-month results were consistent. Both varenicline and bupropion individually showed lower mortality and MACE. A urinary tract infection falsification endpoint showed no between-group differences, supporting matching validity. The pharmacotherapy group had higher rates of new-onset depression, driven predominantly by bupropion recipients. Conclusions: In this propensity-matched real-world analysis, adding prescription smoking cessation pharmacotherapy to NRT after ACS was associated with lower mortality and fewer adverse cardiovascular events, supporting broader integration into post-ACS care pathways.

Background Regulatory inconsistency across African countries contributes to duplicative scientific assessments, prolonged approval timelines, and delayed access to essential medical products. To inform the operationalisation of the African Medicines Agency (AMA), the African Medicines Regulatory Harmonisation (AMRH) programme implemented Africa's first continental pilot study for the scientific evaluation and listing of human medicinal products. This study evaluates the pilot's procedural performance and examines how continental scientific opinions were translated into national regulatory decisions through reliance mechanisms. Methods and Findings A mixed-methods programme evaluation was conducted using regulatory datasets generated during the pilot study. Quantitative data included assessment timelines, GMP inspection outcomes and national post-listing regulatory actions. Retrospective qualitative thematic analysis was applied to governance documents and National Regulatory Authority (NRA) feedback to identify legal, institutional and procedural determinants influencing uptake. Of 64 expressions of interest, 24 products progressed to full evaluation and 12 received positive continental scientific opinions. Ten met the predefined performance target of [&le;]210 working days. Twenty-four GMP inspections identified no critical deficiencies and aligned with global regulatory benchmarks. National uptake demonstrated active reliance: full reliance (continental opinion as primary basis for national approval) for 7 products (58%); sequential reliance (continental assessment supplemented with targeted national queries) for 3 products (25%); and supplemented national review (separate national assessment undertaken) for 2 products (17%). Products with broader market strategies achieved registration in up to 23 African countries within a median of 77 working days post-listing. Variability in uptake reflected national legal authority, administrative requirements, and applicant submission strategies Conclusions The pilot study demonstrates the feasibility of a continent-wide regulatory assessment mechanism capable of producing trusted scientific outputs and enabling reliance-based national decision-making in Africa. While reliance was widely applied, heterogeneity in national procedures and administrative sequencing affected time to national registration. Findings provide empirical evidence to inform the AMA scale-up, highlighting the need for harmonised reliance pathways, streamlined administrative processes, and coordinated digital regulatory infrastructure.

Non-invasive glucose monitoring (NIGM) has been pursued for decades, yet no device has achieved regulatory approval despite numerous studies reporting high accuracy. This systematic review and meta-analysis of 32 studies (38 cohorts: 20 NIGM, 18 iCGM; N = 1,693) investigated methodological factors underlying this accuracy-regulatory gap. The pooled Mean Absolute Relative Difference (MARD) for NIGM (10.21%; 95% CI: 8.73-11.69%) showed no significant difference from iCGM (11.82%; 95% CI: 10.36-13.29%; p = 0.13), with extreme heterogeneity (I^2 = 95.2%). Meta-regression revealed that study duration was the strongest predictor of NIGM accuracy ({beta} = 3.94, p < 0.001), with MARD degrading from 8.7% in short-term to 15.2% in long-term studies, while iCGM accuracy remained stable. Only 15% of NIGM cohorts validated in the hypoglycemia range, compared to 89% of iCGM studies (p < 0.001). These findings suggest that reported NIGM accuracy is substantially influenced by methodological asymmetries.