Pharmacoepidemiology and Drug Safety

BackgroundIn pharmacoepidemiological studies, days of treatment (DoT) duration associated with individual electronic drug utilization records (DUR) are usually missing. Researcher-defined duration (RDD) calculation approaches, as opposed to data-driven approaches, can be used to estimate DoT based on the specific choices and assumptions made by investigators. These are usually underreported or even undocumented. We aimed to develop a framework for the standardization of terminology, formulas, implementation, and reporting of possible RDD approaches. MethodsA systematic classification of RDD calculation approaches was developed via expert consensus. Universal concepts used to operationalise RDDs were identified and described using standard terminologies. An open-source R function, CreateDoT, was created to implement the formulas universal concepts as input parameter. A step-by-step workflow was developed to facilitate implementation and reporting. ResultsRDD approaches were classified in two main classes: I) daily dose (DD)-based calculation approaches (n=3 formulas), and II) fixed-duration approaches (n=2). Seven universal concepts were identified to describe the five corresponding generalized formulas for DoT calculation. Input parameters of the CreateDoT function can be retrieved from source data through its mapping to universal concepts, or inputted by the investigator based on the chosen calculation approach. The input file structure itself represents a standard reporting template for documenting investigators assumptions and methodological choices adopted for DoT calculation. ConclusionsThe CreateDoT framework can facilitate the documentation and reporting of RDD approaches for DoT calculation, increasing transparency and reproducibility of pharmacoepidemiological studies regardless of the data model used, and facilitates sensitivity analyses to evaluate the impact of alternative assumptions in DoT calculation.

BackgroundPost-marketing surveillance is essential for complementing the safety profiles of medicinal products, especially for populations generally excluded from clinical trials such as pregnant individuals. However, the absence of a standardised pregnancy indicator in the electronic transmissions of adverse event reports hampers their correct identification in pharmacovigilance databases and complicates the study of safety concerns related to pregnancy exposures. Three recently developed rule-based algorithms with the common aim to systematically retrieve pregnancy-related reports differ in scope and are tailored to different databases (A. FAERS, B. EudraVigilance, C. VigiBase). AimTo compare the design and outputs of the three pregnancy algorithms. MethodsThis study was a collaboration among the authors of the three pregnancy algorithms. We harmonised their rules, implemented them in an R package to enable execution in both VigiBase and FAERS, and analysed key characteristics of reports flagged by each algorithm. ResultsThe pregnancy algorithms A, B, and C flagged 235653, 279515, and 446957 reports respectively in VigiBase, and 265015, 260734, 350479 in FAERS. Reports exclusively retrieved by each algorithm (994, 3248, and 142324 in VigiBase, and 1528, 1100, and 59643 in FAERS) were mostly explained by Algorithm A having no age restriction, Algorithm B excluding normal pregnancy and ineffective contraception, and Algorithm C excluding paternal exposure. ConclusionsDifferences in flagging were largely related to varying scopes. Understanding commonalities and differences is crucial for empowering professionals working with pregnancy-related pharmacovigilance to select and use the most appropriate algorithm for their specific needs. Key pointsO_LIThree independently developed algorithms were designed to retrieve pregnancy-related adverse event reports and support research into pregnancy-specific safety concerns. C_LIO_LIBy applying these algorithms to VigiBase and FAERS, we highlighted overlaps and differences in the reports they flag, reflecting heterogeneous scope and implementation. C_LIO_LIAwareness of these distinctions is essential to select and apply the most suitable algorithm for their specific needs. C_LI

BackgroundLong-acting injectable buprenorphine (LAI-BUP) is safe and effective, however is dramatically underutilized in comparison to oral formulations. Little is known regarding how buprenorphine prescribers view LAI-BUP, and which medication attributes they prioritize when selecting treatment for opioid use disorder (OUD). MethodsA secondary analysis of a national, cross-sectional online survey of U.S. physicians who prescribe buprenorphine for OUD was conducted. Respondents reported OUD caseload, LAI-BUP use, and the importance of medication attributes relevant to treatment selection (e.g., efficacy, safety, ease of administration, ease of prescribing, and administrative requirements). Providers were categorized as no LAI-BUP use or, among LAI-BUP prescribers, Low vs High use based on a median split. Group comparisons used chi-square (or Fishers exact) tests for categorical variables and Jonckheere-Terpstra tests for ordinal responses. ResultsAmong 125 respondents, 39 (31.2%) reported no patients receiving LAI-BUP. The remaining 86 (68.8%) were LAI-BUP prescribers, split evenly into Low and High (ns=43; 34.4%) groups using a median cut of 23.2%. LAI-BUP use did not differ meaningfully by specialty, region, or practice setting. Greater LAI-BUP use was reported by providers with larger OUD panels. Ratings of key medication attributes were uniformly high. ConclusionsLAI-BUP remains underused, with uptake highest among clinicians managing larger OUD caseloads. Measured attitudes toward medication attributes did not explain these differences. Future work should assess clinic workflow, staffing, reimbursement, and REMS burden, testing targeted implementation strategies using mixed-methods trials. Identifying what shifts clinicians from no use to low and high use may guide scalable implementation interventions.

BackgroundRoutinely collected prescribing and medicine-related data in Scotland are comprehensive and of high quality. However, they are generated across multiple healthcare settings and stored in complex source systems that are not optimised for longitudinal or outcomes-focused research. To maximise the research value of these data, there is a need for curated, analysis-ready resources that provide consistent representations of medicines exposure and enable linkage to clinical outcomes. The Medicines in Acute and Chronic care Scotland (MACCS) provides standardised, curated medicines data to support longitudinal analyses of medicine-related exposure across NHS healthcare systems. MethodsMACCS resource is a national individual-level medicines dataset for adults (18 years of age and older), derived from routinely collected prescribing and medicine-related data held by Public Health Scotland (PHS). It integrates data from the Hospital Electronic Prescribing and Medicines Administration (HEPMA), Prescribing Information System (PIS), and Homecare Medicines (HCM) datasets, which are linked at the individual level to eleven other national clinical records; including Scottish Morbidity Records (SMR00/01/02/04/06), laboratory data and mortality records; using the unique NHS Scotland person identifier. Data are curated, harmonised and pre-linked within the National Safe Haven and accessed by approved researchers through secure Trusted Research Environments. ResultsMACCS contains individual-level information on adults receiving NHS Scotland care, including patient demographics (such as age, sex and geographical indicators) and detailed records of medicines prescribing in community pharmacies as well as those administered in hospitals and through homecare services. Medicines-related data captures exposure dates and, where available, details on formulation, strength and dose. In addition, MACCS includes cancer registry data, renal registry data, laboratory test results, microbiology surveillance and mortality records. The earliest dates of data availability vary by source dataset. ConclusionMACCS provides a sustainable, longitudinal medicines research resource that simplifies access to complex national prescribing data and enables robust linkage to health outcomes. By supporting population-scale analyses across care settings, MACCS enhances the capacity for high-quality research to inform clinical practice, health policy, and medicines optimisation in Scotland. Key FeaturesO_LIThe Medicines in Acute and Chronic Care in Scotland (MACCS) data resource was established in 2025 to integrate medicine-related data with other electronic data from Scottish healthcare systems, creating a national, linked, routinely updated data resource at population level. C_LIO_LIMACCS provides pre-linked data from multiple routinely collected national datasets within NHS Scotland including, but not limited to, prescribing records, hospital episodes, laboratory results, and death records, within a single secure environment. C_LIO_LIMACCS includes patient demographics, data on medicines prescribing and administration/supply, key biochemistry and haematology test results (e.g., kidney and liver function tests), data on hospital admissions and surgical procedures, and date and cause of death. C_LIO_LIThe data resource provides longitudinal follow-up of the adult population ([≥]18 years of age) receiving medicines through NHS Scotland since 2010, covering approximately 4.6 million individuals, and supports pharmacoepidemiological studies, drug utilisation research, pharmacovigilance projects, as well as health services research. C_LIO_LIApproved researchers can apply through a streamlined process to access the linked MACCS data resource through established NHS Scotland governance processes, with data accessed within a Trusted Research Environment. C_LI

BackgroundSafety data for most medications in pregnancy remain limited, yet pharmacological treatment is often necessary. Evidence on real-world medication use in pregnancy including over-the-counter products and folic acid is scarce, especially in Belgium. MethodsWe conducted a drug utilisation study using self-reported data from BELpREG, a prospective, web-based pregnancy registry established in November 2022. Pregnant individuals aged [≥]18 years receiving healthcare in Belgium can enrol voluntarily at any stage in pregnancy and complete online questionnaires at enrolment and every four weeks until delivery. All participants with follow-up beyond the first trimester were included, and trimester-specific cohorts were constructed based on completion of questionnaires after each trimester. Data were extracted in July 2025. ResultsThis study included 2,096 participants, of whom 1,767 were followed through trimester 2 and 1,136 through trimester 3. Median gestational age at enrolment was 16 weeks. Prevalence estimates of medication use were 80.2% in the six months before conception, 85.8% in trimester 1, 92.0% in trimester 2, and 94.9% in trimester 3. The most common classes were analgesics, vaccines, antihistamines, antianemic preparations, and drugs for acid-related disorders. Paracetamol was most frequently used (35.4% in trimester 1), typically short term (median 3 days), followed by doxylamine-pyridoxine (26.7% in trimester 1). Folic acid supplementation was nearly universal, though only 59.9% met national guideline-concordant criteria. Maternal vaccine uptake was substantial but incomplete, with 67.2% receiving pertussis, 41.5% influenza, and 21.5% COVID-19 vaccination. Exposure to potentially inappropriate or teratogenic medications was rare. ConclusionsMedication use during pregnancy in Belgium was nearly universal, with high use of paracetamol and doxylamine-pyridoxine. Folic acid and vaccine uptake were substantial, but often not guideline-concordant. Key PointsO_LIMedication use during pregnancy in Belgium was nearly universal, with over 85% of participants reporting use in the first trimester and 95% in the third. C_LIO_LIParacetamol (35% in the first trimester) and doxylamine-pyridoxine (27% in the first trimester) were the most frequently used medications. C_LIO_LIFolic acid use was widespread, yet only about 60% of participants followed national timing and duration recommendations. C_LIO_LIMaternal vaccine uptake was substantial, particularly for pertussis (67%), though not universal despite guideline recommendations C_LIO_LIBELpREGs self-reported data capture both prescription and over-the-counter medications, offering a complete picture of real-world use during pregnancy. C_LI Plain Language SummaryThis study looked at how often and when people in Belgium use medications, folic acid supplements, and vaccines during pregnancy. Using data from the BELpREG pregnancy registry, more than 2,000 pregnant participants completed online questionnaires about their health and medication use throughout pregnancy (every four weeks). Almost everyone reported taking at least one medication: 86% during the first trimester and 95% during the third. The most common medicines were paracetamol and doxylamine-pyridoxine. Nearly all participants used folic acid, but only about 60% followed national recommendations for starting timely before pregnancy and continuing through the first trimester. Many received recommended vaccines during pregnancy: about 67% for pertussis, 42% for influenza, and 22% for COVID-19; but uptake was still incomplete. Exposure to potentially inappropriate or teratogenic medications was rare. Because BELpREG collects self-reported data, including both prescribed and over-the-counter products, it provides a comprehensive picture of real-world medication use in pregnant people. Further, these findings help identify gaps between guideline recommendations and actual practice. Social Media QuoteMedication use in pregnancy is nearly universal in Belgium. Paracetamol and doxylamine-pyridoxine top the list. Folic acid and vaccine uptake are high but often not guideline-concordant. BELpREG data reveal unique self-reported real-world patterns. #BELpREG

Objective To describe trends in dispensing of monoclonal antibodies (mAbs) for autoimmune conditions during and around pregnancy. Design Descriptive study. Setting Lombardy, Italy between 2012 and 2024. Population All women of reproductive age (14-49 years) resident in Lombardy. Methods We described trends in mAb dispensations among women of reproductive age and the prevalence of mAb dispensing before, during and after pregnancy. We explored maternal factors associated with discontinuation. Main outcome measures Change in prescribing of mAbs over time in all women of reproductive age, and before, during and after pregnancy in those who became pregnant. Prevalence of discontinuation and switching mAbs around pregnancy. Results We included 3,049,175 women of reproductive age and 859,699 pregnancies. Prevalence of mAb dispensing during pregnancy increased over 60-fold over the study period, from 0.0041% (95%CI:0.00084, 0.012) in 2012 to 0.27% (95%CI:0.23, 0.32) in 2024. Pregnancy affected mAb dispensing, with mean prevalence decreasing from 0.080% (95%CI:0.074, 0.087) before pregnancy to 0.051% (95%CI:0.046, 0.057) by the third trimester. Over half (53.3%) of pre-existing users discontinued before or during pregnancy; discontinuation decreased over time, and varied substantially between mAbs. Switching mAbs during pregnancy was rare (3.3%). We found limited evidence that sociodemographic factors were associated with discontinuation, but that some health factors may be, such as use of assisted reproductive technology (OR=1.92, 95%CI:0.98-3.77). Conclusions Italian population-wide data from 2012-2024 show an increase in mAbs dispensed during pregnancy, and fewer instances of discontinuing these drugs over time. This may reflect recent changes in prescribing guidelines for mAbs in pregnancy.

BackgroundRoutinely collected health data are increasingly used to generate real-world evidence for therapeutic decision-making. Yet, stakeholders, including clinicians, pharmaceutical industry representatives, patient advocacy groups, and statisticians, prioritize different aspects of data quality, analysis, and interpretation. Without explicit consideration of these perspectives, analyses risk being fragmented, misaligned with end-user needs, or lacking transparency. MethodsWe developed a stakeholder-inclusive conceptual framework for modeling routine health data, informed by an interdisciplinary workshop and supported by targeted literature examples. The framework maps stakeholder priorities to methodological requirements and identifies analytical strategies that enable integration of diverse perspectives. ResultsClinicians prioritize interpretability and clinical relevance; the pharmaceutical industry emphasizes regulatory compliance and real-world evidence generation; patient groups highlight transparency, inclusion of patient-reported outcomes, and privacy protection; and statisticians focus on bias control and methodological rigor. Our framework illustrates how these priorities can be explicitly incorporated into modeling strategies. Multistate models exemplify a methodological approach that operationalizes these requirements by capturing dynamic disease trajectories, integrating intermediate outcomes, and offering graphical interpretability. Beyond specific methodological choices, clinical research relies fundamentally on statistical expertise. Depending on the research goal, statisticians roles can range from providing statistical consultations for standard analyses to applying or adapting advanced methods for more complex analyses to developing new methods for research questions that require novel approaches due to their specific characteristics. ConclusionsThe stakeholder-inclusive framework provides methodological guidance for designing analyses of routine health data that are clinically meaningful, scientifically rigorous, and socially acceptable. By aligning the research question with the intended perspective from the beginning, it supports more robust and transparent evidence generation, with multistate models serving as a flexible tool to operationalize this integration.

AimBesides registries, healthcare databases can provide useful information for assessing major congenital malformations (MCMs) frequency and investigating their risk factors, particularly medications exposures. This study aimed to assess the validity of MCMs identification based on French national, comprehensive healthcare databases. MethodsUsing information on hospital discharge diagnoses, medical procedures (e.g. surgery) and death causes from the EPI-MERES register nested in the French National Health Data System, 72 specific MCMs grouped in 11 organ groups were assessed among all births occurred after 22 weeks of amenorrhea in France between 2010 and 2023. MCMs prevalence rates were estimated and compared to those from EUROCAT, and associations with prenatal exposure to valproate were assessed. ResultsAmong 10.5 million births, 213,153 live born infants with at least one MCM, i.e. 203.0 cases per 10,000 births, were identified. MCMs prevalence rates among live births were close to those reported in EUROCAT overall (difference: -1.76 per 10,000 births [-1%]), for each organ group (differences ranging from -9.10 [-13%] to +3.44 [+16%] per 10,000 births), and for the 72 specific MCMs (median prevalence difference: 1%). Prenatal exposure to valproate was significantly associated with increased risks of any MCM (adjusted odds ratio (aOR) 2.82, 95% CI [2.33-3.41]) and of 15 specific MCMs including spina bifida (aOR 17.88 [7.88-40.53]). ConclusionThis study supports the validity of MCMs identification based on data of the EPI-MERES register. The EPI-MERES register provides a highly powerful, reactive and operational tool complementing MCMs registries for improving real-life knowledge on drug teratogenicity. Plain language summaryMajor congenital malformations are serious structural abnormalities present at birth that can have lasting consequences on childrens health. Better understanding their risk factors, particularly medication exposures during pregnancy, is crucial. Population-based registries are today the primary source of information on malformations, but healthcare databases could offer a faster and broader alternative. This study tested whether the EPI-MERES register, built upon the French National Health Data System (SNDS), could accurately identify 72 specific malformations across 10.5 million births between 2010 and 2023. Prevalence estimates closely matched those from the European EUROCAT registry, confirming good data accuracy. As expected, valproate (a known teratogen) was associated with an increased risk of various malformations, including spina bifida, EPI-MERES thus constitutes a promising tool for studying medication risks during pregnancy.

BackgroundBiomedical Large Language Models (LLMs) combined with prompt engineering offer domain-specific reasoning, yet their application to individual-level causality assessment remains unexplored. This study evaluated five combinations of biomedical LLMs, prompting strategies, and causality algorithms by comparing their agreement with two human expert evaluators. Research design and methodsA total of 150 Individual Case Safety Reports (ICSRs) were analyzed: 140 reports from Food and Drug Administration Adverse Event Reporting System (FAERS), and 10 myocarditis/pericarditis ICSRs from Vaccine AERS (VAERS). Assessments were conducted using the Naranjo and WHO-UMC algorithms. Biomedical LLMs tested included TinyLlama 1.1B, Medicine LLaMA-3 8B, and MedLLaMA v20, combined with Chain-of-Thought (CoT) or Decomposition prompting. Agreement was measured using Gwets Agreement Coefficient 1 (AC1) and percentage agreement, alongside performance metrics and qualitative error analysis. ResultsThe Medicine LLaMA-3 8B-Naranjo-CoT combination achieved the highest agreement with human assessors for the final classification of causality (64%). Biomedical LLMs demonstrated low inter-rater agreement on critical items of causality assessment such as identification of listed AE, temporal plausibility, alternative causes, and objective evidence of AEs. Frequent model failures included irrelevant responses. ConclusionsBiomedical LLMs showed improved performance over general purpose models previously tested but remain suboptimal for reliable causality assessment of ICSRs.

BackgroundMultiple stakeholders need to locate results of registered clinical trials but frequently struggle to find them. Summary results of clinical trials are often not published in trial registries, and publications containing trial results are often not explicitly linked to their respective trial registrations. Finding these results is important to researchers, systematic reviewers, research funders, regulators, clinical practitioners, and patients. MethodsWe developed TrialScout, a computer program that uses a large language model to match clinical trials registered on ClinicalTrials.gov with corresponding result publications indexed in PubMed. TrialScouts performance was evaluated through comparison to human-coded matches from previous studies of results reporting rates. Subsequently, TrialScout was applied to a random sample of 9,600 completed or terminated trials. ResultsTrialScout had a sensitivity of 92.5% and a specificity of 81.2% compared to human coders. Manual review of 200 cases where TrialScout disagreed with human researchers showed that a majority (123/200, 61.5%, 95% CI, 54.4-68.3%) of disagreements were due to human errors. When used on 9,600 sampled trials in ClinicalTrials.gov, TrialScout found result publications for 6,110 (63.6%) of trials. DiscussionTrialScout reliably located results of completed clinical trials. The tool offers benefits in terms of speed and efficiency. Estimating TrialScouts accuracy is limited by the lack of a true gold standard. TrialScout can accelerate the process of locating trial results in the scientific literature and can assist in monitoring trial reporting practices.

ObjectiveThe objective of this analysis was to explore temporal and regional trends in breast pump prescription claims in outpatient settings in Germany, and to characterize the types of pumps covered. Study designWe conducted a nationwide secondary analysis of outpatient statutory health insurance billing data for breast pump prescriptions from 2011 to 2024, covering nearly 90% of the German population. Billing data from community pharmacies were scaled to full national coverage using regional extrapolation factors and subsequently linked with national and state-level live birth statistics to adjust for birth rates and population size across federal states. A list of breast pumps covered by German national statutory health insurance funds was queried for information on their characteristics. ResultsPrescription of electric pumps dominate outpatient statutory health insurance breast pump claims in Germany, with national statutory health insurance funds covering {euro}15.3 million for pump rentals. Manual pumps dispensed through community pharmacies accounted for {euro}27 thousand in 2024. Between 2011 and 2024, electric pump claims increased by a factor of 2.57, rising from 235.4 to 605.2 claims per 1000 infants newly enrolled in statutory health insurance (average annual growth rate 8.24%). Claims varied substantially across federal states but increased overall. ConclusionsThis is the first epidemiological analysis of statutory health insurance prescription claims for breast pumps in Germany. We found that electric breast pumps are important medical devices supporting outpatient human milk expression in Germany. Prescription claims appear to be very common and have shown an increase over the past 13 years.

Background and AimsThe glucagon-like peptide-1 receptor agonist (GLP-1 RA) semaglutide has demonstrated efficacy for the secondary prevention of cardiovascular disease among patients with overweight/obesity without diabetes mellitus. However, the comparative effectiveness of GLP-1 RA versus other antiobesity medications (e.g. phentermine-topiramate) not been evaluated. MethodsThis was a retrospective, observational, cohort study using target trial emulation methodology using the Truveta electronic health record database of more than 120 million patients. Adult patients with a body mass index (BMI) >=27 kg/m2, a history of cardiovascular disease (prior ischemic stroke, transient ischemic attack, or myocardial infarction, or known coronary artery disease, heart failure, or peripheral artery disease) without diabetes mellitus were included in the study. The primary endpoint was time to first major adverse cardiovascular or cerebrovascular event (MACCE, defined as stroke or myocardial infarction). ResultsIn total, 35,240 were included in the bupropion-naltrexone versus GLP-1 RA comparison, and 27,051 were included in the phentermine-topiramate versus GLP-1 RA comparison. In the pre-weighting cohort, GLP-1 RA use was associated with decreased hazard of MACCE compared to bupropion-naltrexone (HR 0.50 [95% confidence interval (CI) 0.36-0.69]) and phentermine-topiramate (HR 0.43 [95% CI 0.30-0.60]). In the propensity score-overlap weighted cohort, GLP-1 RA prescription was not associated with a lower hazard of MACCE than bupropion-naltrexone (aHR 0.69 [95% CI 0.47-1.00]) but was associated with a lower hazard compared to phentermine-topiramate (aHR 0.61 [95% CI 0.41-0.91]; adjusted absolute rate difference 0.98 per 1000 person-years). ConclusionsPrescription of a GLP-1 RA was associated with a lower risk of subsequent MACCE than phentermine-topiramate.

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.

BackgroundMedication shortages are a considerable and ongoing issue in healthcare, disrupting consumer access. Since 2021, Australias national medicines regulator has issued Serious Scarcity Substitution Instruments (SSSIs), allowing pharmacists to substitute a specific therapeutically equivalent strength and/or formulation of a medicine without prior approval from a prescriber. The impact of SSSIs on utilisation of medicines has not been investigated. ObjectiveDetermine whether SSSIs are effective in addressing medicine shortages and meeting patient need. MethodsThis retrospective cohort study used aggregated pharmacy claims to examine the utilisation of 12 medicines which had an SSSI. We calculated the percentage change in defined daily doses dispensed per 1000 population per day in the 11 months after SSSI implementation, compared with the previous two years. A percentage change of less than 20% was used to indicate success. ResultsFollowing product shortages, utilisation fell for 10 of the 12 medicines examined. For eight of these medicines (amoxicillin, cefalexin, estradiol, fluoxetine, insulin degludec with insulin aspart, isosorbide mononitrate, vigabatrin, and warfarin) decreases in utilisation were minimised to <20%. On average, SSSIs where all permitted substitute products were scarce (e.g. abatacept) were associated with larger decreases in use (between -22% and -68%) than those for which none or only some of the substitutes were in shortage (between -45% and +7%, respectively). ConclusionsWhile product shortages led to decreases in medicines consumption, SSSIs appeared to be successful in limiting decreases. However, SSSIs were less likely to be successful when many of the permitted substitute products were also scarce. Key pointsO_LIThis study is the first to evaluate the effectiveness of Australias Serious Scarcity Substitution Instruments (SSSIs) in mitigating medicine shortages using national dispensing data and interrupted time series analysis. C_LIO_LITwo-thirds of SSSIs successfully limited utilisation declines to less than 20%, with effectiveness strongly linked to the availability of substitute products. C_LIO_LIBy demonstrating variable utilisation outcomes across medicines, this study adds empirical evidence to international debates on substitution policies, suggesting that nationally standardised frameworks like Australias SSSIs may function best when supported by robust supply intelligence. C_LIO_LISSSIs are a valuable policy tool for maintaining continuity of care during shortages, but timely implementation and ensuring substitute supply are critical for optimal impact. C_LI

BackgroundLong-acting injectable buprenorphine (LAIB) has been positioned as a potentially transformative option for opioid use disorder (OUD), in part because patient experiences reported in qualitative studies emphasize reduced daily burden, increased "freedom," reduced stigma, and fewer pressures related to diversion--while also noting barriers such as insufficient information, early adverse experiences, and concerns about coercion. MethodsWe conducted a cross-sectional online survey of adults recruited from the Behavioral Health Research Panel (BHRP). Eligibility included age [&ge;]18, English literacy, and OUD diagnosis or problematic opioid use within the past 5 years. Survey content assessed buprenorphine experience, knowledge and attitudes toward LAIB, attribute preferences, and open-text feedback. Descriptive statistics were generated; analyses were stratified by buprenorphine experience (experienced vs naive). ResultsAmong 105 participants, 82.9% reported prior buprenorphine use, and 17.1% were buprenorphine-naive. Overall, 53.3% preferred a long-acting injection regimen (weekly/monthly/3-monthly) versus 46.7% preferring a daily oral tablet/film. Convenience and adherence-related themes (e.g., not missing doses, fewer visits) drove LAIB preference, while oral-route preference and concerns about side effects and safety were prominent among those favoring oral formulations. ConclusionsIn this national convenience sample, preferences were nearly evenly split between daily oral and long-acting injectable buprenorphine regimens, with a slight overall preference for LAIB. Findings align with the qualitative literature, emphasizing the practical and psychosocial benefits of LAIB, alongside persistent needs for improved education, shared decision-making, and attention to tolerability, safety perceptions, and cost/coverage barriers.

BackgroundGLP-1 receptor agonists (GLP-1RAs) and SGLT2 inhibitors (SGLT2Is) have established cardiovascular benefits for patients with type 2 diabetes mellitus (T2DM), with similar class-level effectiveness found in previous studies. However, real-world comparative effectiveness assessments of individual agents remain limited. ObjectivesTo compare the cardiovascular effectiveness of individual GLP-1RAs and SGLT2Is. MethodsWe conducted a multi-national, retrospective, new-user active-comparator cohort study using 10 US and non-US administrative claims and electronic health record databases. The study included 1,245,211 adults with T2DM receiving metformin who initiated second-line therapy with one of six GLP-1RAs (albiglutide, dulaglutide, exenatide, liraglutide, lixisenatide, semaglutide) or one of four SGLT2Is (canagliflozin, dapagliflozin, empagliflozin, ertugliflozin). Empagliflozin (393,499; 31.6%), semaglutide (235,585; 18.9%), dapagliflozin (208,666; 16.8%), and dulaglutide (207,348; 16.8%) were most commonly used. A secondary subgroup analysis included 316,242 patients with established cardiovascular diseases (CVD). Primary outcomes were 3-point major adverse cardiovascular events (MACE: acute myocardial infarction, stroke, sudden cardiac death) and 4-point MACE (adding hospitalization/ER visit with heart failure). Secondary outcomes included the individual components. Hazard ratios (HRs) were estimated for pairwise agent comparisons while on-treatment (per-protocol) and over total follow-up using Cox proportional hazards models, with propensity score adjustments, negative control calibration, and pre-specified study diagnostics to guard against potential confounding. Random-effects meta-analysis produced summary HR estimates across data sources that passed diagnostics. ResultsAcross the study cohort, individual GLP-1RAs and SGLT2Is demonstrated broadly similar cardiovascular effectiveness, both within and across drug classes. For example, semaglutide and empagliflozin showed comparable risks for 3-point MACE (meta-analytic HR 1.05; 95% CI 0.79-1.39) and 4-point MACE (meta-analytic HR 0.95; 95% CI 0.81-1.12), with consistent findings in the CVD subgroup. Study diagnostics confirmed adequate equipoise, covariate balance and statistical power to detect similarity in HRs between 0.8 and 1.2 for commonly used agents. ConclusionsIn this large-scale real-world study, individual GLP-1RAs and SGLT2Is exhibited largely comparable cardiovascular benefits, including in patients with established CVD. These findings align with network meta-analytic estimates from major cardiovascular outcome trials and broadly support current treatment guidelines. Clinical choices should be guided by relevant factors such as safety, adherence, tolerability, cost, and patient preference, where further work is needed.

ObjectivesTo estimate potential launch prices of generic semaglutide following patent expiry from 2026 and to quantify the global obesity and type 2 diabetes (T2DM) burden in countries where generic access may become possible. MethodsWe used World Bank population data and World Obesity and Diabetes Atlas prevalence estimates to calculate obesity and T2DM burden in countries where semaglutide patents expire in 2026 or were not filed. Patent status was identified using MedsPaL and cross-checked with regional databases. We updated established cost-plus pricing methodologies using 2024-2025 Indian API shipment data to estimate production costs for oral and injectable semaglutide, incorporating formulation, packaging, taxation, and profit assumptions. ResultsTen countries with 2026 patent expiry represent 44% of the global population and 48% of the global obesity burden. No patent filings were identified in 150 additional countries. By the end of 2026, generic injectable semaglutide could be distributed in 160 countries where 69% of global T2DM and 84% of clinical obesity occurs. Estimated generic injectable costs ranged from $28-140 per person-year, while oral formulations ranged from $186-380 per person-year. Injection devices contributed disproportionately to total cost. ConclusionPatent expiry could substantially expand access to semaglutide at dramatically lower prices, particularly in high-burden settings. However, device costs, secondary patents, and health system constraints may limit equitable uptake without coordinated policy action. Study ImportanceO_ST_ABSWhat is already known about this subject?C_ST_ABSO_LISemaglutide is highly effective for obesity and cardiometabolic disease but remains unaffordable in many low- and middle-income countries due to high branded prices and patent protections. C_LIO_LIPrevious cost-plus analyses show that generic competition can substantially reduce prices of essential medicines after patent expiry. C_LI What are the new findings in your manuscript?O_LIUsing 2024-2025 API shipment data, we estimate generic injectable semaglutide could be produced for $28-140 per person-year following 2026 patent expiry. C_LIO_LIBy 2026, generic semaglutide could be available in 160 countries comprising 69% of global T2DM and 84% of clinical obesity burden. C_LI How might your results change the direction of research or the focus of clinical practice?O_LIProvides an evidence base for procurement planning and price negotiations ahead of patent expiry. C_LIO_LIHighlights the importance of addressing device costs and secondary patents to ensure equitable global access. C_LI

BackgroundThe self-management of type 2 diabetes (T2D) typically requires enacting various lifestyle changes, which can challenge people living with T2D. Clinical encounters between people with T2D and their clinicians, however, are often focused on metabolic management, leaving less time available for other self-management topics. The QBSAFE cards help patients articulate aspects of their experience with diabetes and prioritize issues for discussion. MethodsThis report details secondary outcomes of a randomized controlled trial; primary outcomes are reported elsewhere. All data was collected at Fair Haven Community Health Care, a federally qualified primary care clinic. 11 clinicians were randomly assigned to provide either usual care or usual care with QBSAFE cards to 155 of their patients with type 2 diabetes and hemoglobin A1c >8%. All patient encounters were video recorded for analysis. Patients and clinicians were not blinded to arm allocation but were kept unaware of the specific aims of the trial. Encounter video reviewers were blinded to arm allocation, but not to specific aims of the trial. The outcomes of interest for this report were the extent to which the QBSAFE cards were used as intended, their effect on the topics of discussion, and whether they enabled clinicians to notice and respond to each patients situation; comparisons between arms were conducted by a linear mixed model with fixed effect of arm and cluster effect of clinician, analyzed in both intent-to-treat and per-protocol populations. Findings12 patients were excluded post-randomization (A1c <8%). Of 143 eligible patients, 137 encounters (65 in the usual care arm, 72 in QBSAFE) yielded evaluable videos. QBSAFE was used as intended in 61 (85%) QBSAFE arm encounters. Conversations about burden of treatment related to non-pharmacological interventions (17 vs 33, p= 0{middle dot}04) and taking medications (11 vs 33, p= 0{middle dot}0008) and about the patients challenging environment (2 vs 10, p= 0{middle dot}04) were more prevalent in the QBSAFE group. There was no difference in the rate of conversations about metabolic management or of new care plans as a result of conversations between groups. InterpretationWhile there was a difference in the types of conversations observed between the two study arms, this difference was small and only apparent in a few domains. Future work could aim to modify the QBSAFE cards to more effectively stimulate patient-centered discussions and to further prepare clinicians to respond to a variety of issues raised during the clinical visit. FundingThis work was supported by funding from the National Institute of Diabetes and Digestive and Kidney Diseases (R01DK129616).

ObjectiveTo evaluate the diagnostic performance of FeverPAIN and Centor with point-of-care test (POCT) results for Group A Streptococcus (GAS) among children and adults presenting with sore throat in community pharmacies. MethodsCross-sectional analysis of patients aged six years and over with sore throat presenting to community pharmacies across Wales delivering the Sore Throat Test and Treat (STTT) service from November 2018 to September 2024. Patients who scored FeverPAIN [&ge;]2 or Centor [&ge;]3 and were able to undergo POCT were eligible for analysis. We described GAS positivity by age group and assessed diagnostic performance of FeverPAIN at the National Institute for Health and Care Excellence (NICE) antibiotic threshold ([&ge;]4), reporting sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under receiver operating characteristic curve (AUROC) with 95% confidence intervals (CI). We estimated potential overtreatment and undertreatment if antibiotics were supplied based on FeverPAIN alone. ResultsAmong 73,617 eligible patients, 37.0% (n=27,220) tested POCT-positive for GAS. Positivity was highest in children aged 6-10 years (47.0%: 5,339/11,371). FeverPAIN was used in 92.5% (n=68,099) of assessments. At the NICE-recommended threshold for antibiotic treatment (FeverPAIN [&ge;]4), sensitivity was 55.0% (95% CI: 54.4-55.6%) and specificity 77.0% (95% CI: 76.6-77.4%). PPV was 57.6% (95% CI: 57.0-58.2%) and NPV 75.1% (95% CI: 74.7-75.5%). Overall AUROC was 0.70 (95% CI: 0.70-0.71), with the lowest AUROC of 0.69 (95% CI: 0.68-0.70) observed among children aged 6-10 years. Using FeverPAIN alone would undertreat 44% and overtreat 23% of patients based on POCT results. ConclusionsFeverPAIN alone showed limited diagnostic performance for identifying GAS, with more pronounced discordance observed among children. Incorporating POCTs within community pharmacy sore throat pathways may support more targeted antibiotic prescribing. Our findings support a re-evaluation of the role of POCTs within community pharmacy sore throat pathways.

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.