Pharmacoepidemiology and Drug Safety

BackgroundClinical Practice Research Datalink (CPRD) GOLD is an invaluable resource for clinical research. However, some exposures are difficult to capture, including continuous subcutaneous insulin infusion pump systems ("insulin pumps"). We present a strategy we developed to classify insulin pump users and to estimate the duration of pump use in CPRD GOLD. This was done to study adverse skin events in new adult pump users. MethodsInsulin pump users were defined as patients who had a specific insulin pump code (prescription for an insulin pump cartridge or clinical code for continuous insulin infusion) in their record. Duration of use was defined as the continuous use of any insulin formulation commonly used in pump systems before and after the insulin pump specific code. Each patients pump start and end dates were calculated programmatically and then confirmed by manual review of the patients CPRD record. ResultsThere were 1032 patients with an insulin pump specific code recorded in CPRD GOLD through December 2018, of which 302 met the inclusion criteria for our safety study. Due to high variability in the patterns of insulin use, programmatic determination of pump start and end dates was insufficient. The start and/or end dates of >50% of patients required adjustment upon manual review. ConclusionsInsulin pump users in CPRD GOLD could be easily identified using this strategy, but we may have missed additional insulin pump users without specific pump codes. The duration of pump use, however, was difficult to capture. This strategy, though time intensive, is a useful tool for the study of insulin pumps. FundingThis study was funded by AbbVie. Conflicts of Interest StatementBoston Collaborative Drug Surveillance Program (BCDSP) received funding from AbbVie for this study. Susan Jick and Rebecca Persson are employees of BCDSP. Todd Sponholtz and Brenda Baak were interns at BCDSP. Authors retain full and scientific control over the content of this manuscript. DisclosuresThis manuscript has not been peer-reviewed. We provide this information as a reference for other users of Clinical Practice Research Datalink GOLD. This study is based in part on data from the Clinical Practice Research Datalink obtained under license from the UK Medicines and Healthcare products Regulatory Agency. The data is provided by patients and collected by the NHS as part of their care and support. The interpretation and conclusions contained in this study are those of the authors alone. This study was approved by the Independent Scientific Advisory Committee (ISAC) for Medicines and Healthcare products Regulatory Agency (protocol no: 19_216R).

ObjectiveAngiotensin-II Receptor Blockers (ARBs) are commonly prescribed; however, their adverse events may prompt new drug prescription(s), known as prescribing cascades. We aimed to identify potential ARB-induced prescribing cascades using high-throughput sequence symmetry analysis. MethodsUsing claims data from a national sample of Medicare beneficiaries (2011-2020), we identified new ARB users aged [≥]66 years with continuous enrollment [≥]360 days before and [≥]180 days after ARB initiation. We screened for initiation of 446 other (non-antihypertensive) marker drug classes within {+/-}90 days of ARB initiation, generating sequence ratios (SRs) reflecting proportions of ARB users starting the marker class after versus before ARB initiation. Adjusted SRs (aSRs) accounted for prescribing trends over time, and for significant aSRs, we calculated the naturalistic number needed to harm (NNTH); significant signals were reviewed by clinical experts for plausibility. ResultsWe identified 320,663 ARB initiators (mean {+/-} SD age 76.0 {+/-} 7.2 years; 62.5% female; 91.5% with hypertension). Of the 446 marker classes evaluated, 17 signals were significant, and three (18%) were classified as potential prescribing cascades after clinical review. The strongest signals ranked by the lowest NNTH included benzodiazepine derivatives (NNTH 2130, 95% CI 1437-4525), adrenergics in combination with anticholinergics, including triple combinations with corticosteroids (NNTH 2656, 95% CI 1585-10074), and other antianemic preparations (NNTH 9416, 95% CI 6606-23784). The strongest signals ranked by highest aSR included other antianemic preparations (aSR 1.7, 95% CI 1.19-2.41), benzodiazepine derivatives (aSR 1.18, 95% CI 1.08-1.3), and adrenergics in combination with anticholinergics, including triple combinations with corticosteroids (aSR 1.12, 95% CI 1.03-1.22). ConclusionThe identified prescribing cascade signals reflected known and possibly under-recognized ARB adverse events in this Medicare cohort. These hypothesis-generating findings require further investigation to determine the extent and impact of these prescribing cascades on patient outcomes.

In pharmacoepidemiologic studies of COVID-19, there were concerns about bias from residual confounding. We applied high-dimensional propensity scores (HDPS) to a case study investigating the role of inhaled corticosteroids (ICS) in COVID-19 to adjust for unmeasured confounding. We selected patients with chronic obstructive pulmonary disease on 01 March 2020 from Clinical Practice Research Datalink (CPRD) Aurum, comparing ICS/LABA/(+-LAMA) and LABA/LAMA users. ICS effects on the outcomes COVID-19 hospitalisation and death were assessed through weighted and unweighted Cox proportional hazards models. HDPS were estimated from primary care clinical records, prescriptions and hospitalisations. SNOMED-CT codes and dictionary of medicines and devices codes from CPRD Aurum were mapped to International Classification of Disease 10th revision codes and British National Formulary paragraphs respectively. We estimated propensity scores (PS) combining prespecified and HDPS covariates, selecting the top 100, 250, 500, 750 and 1000 covariates ranked by confounding potential. When excluding triple therapy users, the conventional PS-weighted estimates showed weak evidence of increased risk of COVID-19 hospitalisation among ICS users (HR 1.19 (95% CI 0.92-1.54)). Results varied slightly based on the number of covariates included in HDPS (HR using 100 HDPS covariates 1.01 (95% CI 0.76-1.33), HR using 250 HDPS covariates 1.24 (95% CI 0.83-1.87)). For COVID-19 death, conventional PS-weighted models showed weak evidence of harm of ICS when excluding triple therapy users (HR 1.24 (95% CI 0.87-1.75)). HDPS-weighting moved estimates toward the null, suggesting no effect of ICS (HR using 250 HDPS covariates excluding triple therapy 1.08 (95% CI 0.73- 1.59)). HDPS may have provided better confounding control for COVID-19 deaths and may be able to partially compensate for suboptimal comparison groups. HDPS results can be sensitive to the number of covariates included, highlighting the importance of sensitivity analyses. Key pointsO_LIResidual confounding, including residual confounding by indication, is a major concern in pharmacoepidemiologic studies of COVID-19 outcomes. C_LIO_LIWe apply high-dimensional propensity scores (HDPS) to adjust for residual confounding in a case study of inhaled corticosteroids (ICS) on COVID-19 hospitalisation and death in CPRD Aurum. C_LIO_LIConventional PS-weighted analyses suggested harmful effects of ICS on COVID-19 hospitalisation and, to a lesser extent, deaths. C_LIO_LIHDPS weighted analyses of COVID-19 hospitalisations were sensitive to the number of covariates included, with results moving towards the null for smaller number of covariates and away from the null when including more covariates, while for deaths, estimates moved towards the null consistently. C_LIO_LIHDPS demonstrated promise in addressing confounding even when comparison groups are suboptimal, but its performance depends on the careful selection and ranking of covariates. C_LI Plain Language SummaryA key challenge when researching the effects of medications using electronic health records is accounting for the fact that people who receive different medications often differ in important ways. Such differences, called confounding, is typically accounted for using statistical methods which require researchers to pre-specify all important confounders. A newer method, called high-dimensional propensity scores (HDPS), uses a data-driven approach to select what confounders to account for instead. These methods have not yet been applied to studies of inhaled corticosteroids and COVID-19 outcomes, an area where studies have found conflicting findings. We used electronic health records from the UK to compare the risk of COVID-19 hospitalisation and death among patients with chronic obstructive pulmonary disease taking two different treatments (ICS/LABA and LABA/LAMA) using both conventional and HDPS methods. Our findings showed that HDPS can reduce important differences between patients (confounding), but that the results can be sensitive to the number of covariates included. This demonstrates the value of HDPS and the need for researchers to run their analysis using several different assumptions.

BackgroundReal-world data are valuable for detecting adverse drug events, and Sequence Symmetry Analysis (SSA) is a simple yet effective method frequently used for this purpose. However, heterogeneous implementations across studies limit reproducibility and scalability. To address this, we developed an open-source R package that standardises SSA analytics using data mapped to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). MethodsWe developed CohortSymmetry, an R package that implements SSA for OMOP CDM data. The package was validated through unit testing and evaluated empirically by estimating adjusted sequence ratios (ASRs) with 95% confidence intervals (CIs) for 23 positive and 10 negative controls across six European databases, including CPRD GOLD (UK) and THIN(R) (Belgium, Italy, Romania, Spain, UK). Sensitivity and specificity were defined as the proportions of positive and negative controls correctly identified by SSA. Sensitivity analyses varied key parameters, including the washout period. ResultsCohortSymmetry passed high-coverage unit tests. Of 33 eligible controls, four showed results consistent with expectations across all databases; for example, the amiodarone-levothyroxine pair had a lower 95% CI bound >1 in each. Sensitivity was moderate, whereas specificity was high in the primary analyses. Parameter variation influenced outcomes; a 365-day prior observation requirement reduced specificity in CPRD GOLD from 75% to 38%. ConclusionsCohortSymmetry enables reproducible SSA using OMOP CDM data. Differences across databases likely reflect heterogeneity in data capture and prescribing patterns. Limitations include residual data variability and SSAs susceptibility to time-varying confounding, underscoring the need for tailored analytic design in pharmacovigilance studies. Key MessagesO_LIWe developed CohortSymmetry, an open-source R package that standardises SSA analytics using OMOP CDM-mapped data and verified the correctness of functions via unit testing and application to real-world datasets. C_LIO_LICohortSymmetry passed high-coverage tests, and among 33 selected controls, four showed results consistent with expectations across all databases; varying analytical parameters affected results. C_LIO_LIThe package provides a reproducible and scalable framework for multi-database SSA studies, supporting robust pharmacovigilance, but careful specification of parameters is required to account for the characteristics of the medical domain under investigation. C_LI

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.

ImportanceDrug shortages leave affected patients in a vulnerable position. ObjectiveTo describe incidence and prevalence of use for medicines with suggested shortages in at least one European country, as announced by the European Medicines Agency, and to characterise the users of these drugs including the indication of use, duration of use, and dosage. DesignWe performed a descriptive cohort study from 2010 and up to 2024 in a network of databases which have mapped their data to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). SettingSettings included primary care, secondary care, claims and various disease registries. ParticipantsWe included all patients with at least 365 days of history on the database. ExposuresAll medicines with a suggested shortage in at least one European country for more than 365 days (n=18). We also assessed their key alternatives (n=39). Main outcomes and measuresWe estimated annual incidence rates and period prevalence. A drop in incidence or prevalence of >33% after the shortage was announced was considered confirmation of a shortage. ResultsAmong 52 databases from Europe and the United States, we observed shortages according to decreased incidence of use for 8 drugs and shortages according to prevalence of use for 9 drugs. The drugs varenicline and amoxicillin alone or plus clavulanate were in shortage in the most number of countries. Conclusion and relevanceWe compiled and analysed data of annual incidence and prevalence of use plus information on patient characteristics, indication, and dose for 57 medicines among 52 databases in Europe and the United States between 2010 and 2024. We detected shortages and observed a change in the users characteristics for several drugs. We have described timely real-world scenarios of drug shortages and those unobserved in various health care settings and countries which helps to better understand how drug shortages play out in real life.

BackgroundLimited recent data exist on prescribing patterns and patient characteristics for glucagon-like peptide 1 receptor agonists (GLP-1 RAs), an important drug class used as anti-diabetic medication (ADM) for patients with type 2 diabetes mellitus (T2D) and/or anti-obesity medication (AOM) in patients with overweight or obesity. For brevity, we use the term GLP-1 RA to refer to both GLP-1 RA and dual GLP-1 RA/GIP medications. ObjectiveTo describe recent trends in prescribing and dispensing of GLP-1-based medications in the US. MethodsUsing a subset of real-world electronic health record (EHR) data from Truveta, a growing collective of health systems that provide more than 18% of all daily clinical care in the US, we identified people who were prescribed a GLP-1-based medication between January 01, 2019 and December 31, 2025. We describe prescribing volumes and patient characteristics over time, by medication, and by FDA-labeled use. Among the subset of patients for whom post-prescription dispensing data is available, we describe the proportion and characteristics of patients who were and were not dispensed a GLP-1 RA following their prescription. Results2,185,238 patients were prescribed a GLP-1 RA between January 2019 and December 2025, with 11,194,909 total prescriptions during this period. Among first-time prescriptions for which use could be established, 69.1% were ADMs and 30.9% were AOMs. Overall prescribing rates (GLP-1 RA prescriptions per total prescriptions) increased slightly from September to December 2025 (+5.02%); however, first-time prescribing rates declined over the same period (-6.62%). As of December 2025, GLP-1 RA prescriptions account for more than 7% of all prescriptions.

BackgroundPeople with comorbidities are under-represented in clinical trials. Empirical estimates of treatment effect modification by comorbidity are lacking leading to uncertainty in treatment recommendations. We aimed to produce estimates of treatment effect modification by comorbidity using individual participant data (IPD). Methods and ResultsUsing 126 industry-sponsored phase 3/4 trials across 23 index conditions, we performed a two-stage IPD meta-analysis to estimate modification of treatment effect by comorbidity. We estimated the effect of comorbidity measured in 3 ways: (i) the number of comorbidities (in addition to the index condition), (ii) presence or absence of the six commonest comorbid diseases for each index condition, and (iii) using continuous markers of underlying conditions (e.g., estimated glomerular function). Comorbidities were under-represented in trial participants and few had >2 comorbidities. We found no evidence of modification of treatment efficacy by comorbidity, for any of the 3 measures of comorbidity. This was the case for 20 conditions for which the outcome variable was continuous (e.g., change in glycosylated haemoglobin in diabetes) and for three conditions in which the outcomes were discrete events (e.g., number of headaches in migraine). Although all were null, estimates of treatment effect modification were more precise in some cases (e.g., Sodium-glucose co-transporter inhibitors for type 2 diabetes - interaction term for comorbidity count 0.004, 95% CI - 0.01 to 0.02) while for others credible intervals were wide (e.g., corticosteroids for asthma - interaction term -0.22, 95% CI -1.07 to 0.54). ConclusionFor trials included in this analysis, there was no empirical evidence of treatment effect modification by comorbidity. Our findings support the assumption that estimates of treatment efficacy are constant, at least across modest levels of comorbidity.

O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=180 SRC="FIGDIR/small/25332171v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@8d02e8org.highwire.dtl.DTLVardef@1b9e538org.highwire.dtl.DTLVardef@177d5f5org.highwire.dtl.DTLVardef@7fe54c_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOVisual AbstractC_FLOATNO C_FIG BackgroundSemaglutide is a glucagon-like peptide 1 receptor agonist that has been used to treat type 2 diabetes and for weight management since its approval in 2018 and 2021, respectively. Our research aimed to examine the geographic distribution of semaglutide prescriptions across the United States Medicaid program and determine the accessibility of this efficacious but costly to those on Medicaid. MethodsMedicaid data on state drug utilization was collected to identify the number of semaglutide prescriptions distributed in each state per quarter from 2018 to 2022 and standardized them per 10,000 Medicaid enrollees. States whose rates fell outside the 95% confidence interval were considered significant outliers. Preferred drug lists for 2019-2022 were retrieved from state Medicaid website archives to explore coverage policies. ResultsNational semaglutide prescribing in Medicaid rose steadily from 2018 to 2022. Notably, states in the Midwest, West, and Northeast regions showed the largest increases. After adjusted for enrollment, Indiana led usage of semaglutide from 2018 to 2020 at 185.6 prescriptions/10,000 enrollees. A notable surge in semaglutide prescriptions was observed in West Virginia in 2021. Conversely, states within the South and lower Midwest regions recorded the lowest prescription rates, with Arkansas (0.1 prescriptions/10,000 enrollees) ranking the lowest in 2020. Prescription volume did not correlate with obesity prevalence, per enrollee Medicaid spending, or the percentage of non-White population. ConclusionSemaglutide use within Medicaid climbed rapidly, but unevenly. Marked state-level variations highlight potential barriers to access despite the drugs growing clinical importance.

PurposeIncidence rate estimates are sensitive to a range of factors, including age, sex, and geographical setting (data source). The magnitude of the impact of drug indication on incidence rates remains underexplored. MethodsWe conducted an observational cohort study using 13 healthcare databases to estimate the incidence rates of 73 health outcomes across 8 drug classes with multiple indications. We calculated incidence rates for each drug-outcome pair and performed random-effects meta-analyses to pool results across databases. Then, we conducted variance components analysis to find the proportions of variability attributed to database, age, sex, and indication. We reported the median of the variance components across all 73 health outcomes as a measure of the magnitude of differences across indications, age, sex, and database, per drug class. ResultsAdjusting for database, age, and sex differences, the drug classes with the highest median VC were trimethoprim (0.49), SGLT-2 inhibitors (0.26), and beta blockers (0.10), while the drug class with the lowest VC was GLP-1 agonists (<0.01). Within each drug class, and adjusting for all other factors, age was frequently the strongest contributor to incidence variation (for 5/8 drug classes, the highest class-wide median VC was the age median VC), followed by database, indication, then biological sex. ConclusionThis study showed that for some drug classes, there exists substantial variation in incidence rates estimates across indications even after accounting for heterogeneity due to age, biological sex, and data source. As many drugs have multiple indications in clinical practice, it may be important to consider drug indication when estimating incidence rates in observational studies for the purpose of patient safety evaluations.

BackgroundThe U.S. FDA authorized the monovalent third primary series or booster doses of COVID-19 mRNA vaccines in August 2021 for persons 18 years and older. Monitoring of outcomes following updated authorizations is critical to evaluate vaccine safety and can provide early detection of rare adverse events (AEs) not identified in pre-licensure trials. MethodsWe evaluated the risk of 17 AEs following third doses of COVID-19 mRNA vaccines from August 2021 through early 2022 among adults aged 18-64 years in three commercial databases (Optum, Carelon Research, CVS Health) and adults aged >65 years in Medicare Fee-For-Service. We compared observed AE incidence rates to historical (expected) rates prior to the pandemic, estimated incidence rate ratios (IRRs) for the Medicare database and pooled IRR across the three commercial databases. Analyses were also stratified by prior history of COVID-19 diagnosis. Estimates exceeding a pre-defined threshold were considered statistical signals. ResultsFour AEs met the threshold for statistical signals for BNT162b2 and mRNA-1273 vaccines including Bells Palsy and pulmonary embolism in Medicare, and anaphylaxis and myocarditis/pericarditis in commercial databases. Nine AEs and three AEs signaled among adults with and without prior COVID-19 diagnosis, respectively. ConclusionsThis early monitoring study identified statistical signals for AEs following third doses of COVID-19 mRNA vaccination. Since this method is intended for screening purposes and generates crude results, results do not establish a causal association between the vaccines and AEs. FDAs public health assessment remains consistent that the benefits of COVID-19 vaccination outweigh the risks of vaccination.

BackgroundAnalyses of Medicare administrative claims data are faced with methodological challenges, including accounting for the potential effect of insurance status on documented comorbidities. We present an example of how failing to account for informative presence bias related to beneficiary enrollment status in such analyses may lead to flawed results. MethodsIn this retrospective observational study of Medicare beneficiaries undergoing isolated coronary artery bypass grafting (CABG) from 1999-2019, we compare the distribution of documented comorbidities between beneficiaries with Medicare Advantage (MA) and Traditional Medicare (TM) plans. Long-term survival was then compared in both unweighted and overlap weighted analyses with and without the inclusion of documented comorbidities. ResultsAmong 3,015,066 Medicare beneficiaries undergoing CABG from 1999-2019, 2,345,476 underwent isolated CABG and had suitable data for analysis. The annual proportion of MA-enrolled beneficiaries undergoing CABG remained stable from 1999-2007 (1.1-4.5%) and then progressively increased annually, reaching 38.2% in 2019. The incidences of documented comorbidities were substantially lower among MA-enrolled versus TM-enrolled beneficiaries. Among MA-enrolled and TM-enrolled beneficiaries, respectively, the unweighted median survival difference was only 8 [-12,28] days (10.02 [9.96,10.07] vs 10.00 [9.98,10.01] years); the weighted (adjusted for demographics and procedural characteristics, but not beneficiary comorbidities) median survival difference was also minimal at -2 [-28,24] days (10.00 [9.95,10.06] vs 10.01 [9.98,10.04] years). However, the weighted (with adjustments including beneficiary comorbidities) median survival difference demonstrated a substantial survival disadvantage for MA-enrolled beneficiaries compared to their TM-enrolled counterparts: -604 [-626,-575] days (9.78 [9.73,9.83] vs 11.44 [11.41,11.47] years), respectively. Conclusions and RelevanceComorbidities in MA-enrolled beneficiaries may be severely under-reported in Medicare data. Studies failing to account for this are susceptible to informative presence bias with a significant treatment effect. In the absence of policy changes, increasing enrollment in MA plans will continue to decrease the population of Medicare beneficiaries with suitable data for study in comparative analyses.

IntroductionSpontaneous abortion is a common pregnancy outcome, but incomplete recording and missing gestational age in health databases pose challenges for research. Accurate timing of the start of pregnancy is critical information in drug safety studies. ObjectivesTo review the literature on database algorithms to estimate gestational length for spontaneous abortions and clinical studies than can inform such algorithms. To estimate the average gestational age for algorithm-identified spontaneous abortions in Norway using interrupted time series analysis. MethodsWe used an algorithm to identify pregnancies registered in Norway from 2010-2020 and restricted to spontaneous abortions identified from registers of primary and specialist care, and births from the Medical Birth Registry of Norway. For births, we calculated the LMP by subtracting the recorded gestational age from the birth date. We assigned spontaneous abortions gestational ages ranging from 7 to 11 weeks and a corresponding LMP. We identified prescriptions from 70 days before to 97 days after LMP and calculated the number of antidepressant prescriptions per 10,000 pregnancies per day. We applied two-sample interrupted time series analysis with intervention points set at 28 and 55 days after LMP and compared antidepressant prescription trends after 28 gestational days for spontaneous abortions versus births. ResultsDatabase algorithms have used estimates for the gestational age at spontaneous abortion ranging from 8-10 weeks, and clinical studies suggest the mean or median gestational age at spontaneous abortion of around 9-10 weeks. In our interrupted time series analysis including 122,495 spontaneous abortions and 631,929 births, the 7-week assumption showed no post-intervention trend, suggesting underestimation. The 9-week assumption closely matched the trend for births (-0.051 prescriptions/day, 95% CI -0.090 to -0.013 vs. -0.056, 95% CI: -0.067 to - 0.046). The 8, 10, and 11-week assumptions showed less precise alignment. The best alignment occurred with the 64-day assumption (9.1 weeks). ConclusionOur study provides an empirically derived estimate for the average gestational age for algorithm-identified spontaneous abortions which can be applied in future research using the same pregnancy algorithm in Norway. While the 64-day estimate seems most accurate for our dataset, further validation studies are necessary to confirm its applicability in other contexts.

ObjectivesTo inform efforts to reduce pediatric antibiotic use, we measured cumulative pediatric prescriptions for antibiotics and non-antibiotics and how this varies across geography and patient subgroups. DesignObservational study. SettingUnited States, 2008-2018. Participants207,814 children under age 5 born in the United States between 2008 and 2013 with private medical insurance coverage. InterventionsNone. Main outcome measuresStudy outcomes included (1) the cumulative number of prescriptions received per child by age 5, (2) the proportion of these prescriptions that were attributable to respiratory infections, (3) the proportion of children who received at least one prescription by age 5, and (4) the fraction of total prescriptions received by the top 20% of prescription recipients. ResultsChildren received a mean of 8.21 (95% confidence interval [CI] (8.19, 8.22)) prescriptions for antibiotics and 9.81 (95% CI 9.80, 9.82) prescriptions for non-antibiotics by age five. Most antibiotic prescriptions (64%, 95% CI 63, 65) and many non-antibiotic prescriptions (25%, 95% CI 24, 26) were associated with outpatient visits for respiratory infections. By age 5, 93.8% (95% CI 93.4, 94.2) of children had received at least one antibiotic prescription while 88.3% (95% CI 87.9, 88.7) had received at least one prescription for a non-antibiotic. The top 20% of antibiotic prescription recipients accounted for 50.6% of all antibiotic prescriptions, and the top 20% of non antibiotic prescription recipients accounted for 64.2% of all non-antibiotic prescriptions. Relative to other regions, the South featured higher prescribing rates and earlier time to first prescription. ConclusionsChildren in the US receive a substantial number of antibiotics and other prescription drugs early in their lives, largely related to respiratory infections.

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

PurposeSimulation studies are used in pharmacoepidemiology for evaluating inferential methods in a controlled setting, whereby a known data-generating mechanism allows evaluation of the performance of different approaches and assumptions. This study aimed to review simulation studies performed in pharmacoepidemiology. MethodsWe conducted a review of all papers published in the journal of Pharmacoepidemiology and Drug Safety (PDS) over the period 2017 to 2024. We extracted data on study characteristics and key simulation choices such as the type of data generating mechanism used, inferential methods tested and simulation size. ResultsAmong 42 simulation studies included, 34 (81%) were informing comparative effectiveness/safety studies. 22 studies (52%) used simulation in the context of a clinical condition, and 36 (86%) used Monte-Carlo simulation. Inputs not derived from empirical data alone (n=22, 52%) or in combination with real-world data sources (n=19, 45%) were most often used for data generation. The complexity of simulations was often relatively low: although 31 studies (74%) generated data based on other covariates, time-dependent covariates (n=3) and effects (n=4) were rarely implemented. Bias was the most often used performance measure (n=26, 62%), although notably 18 studies (43%) did not report uncertainty in the method. ConclusionSimulations contributed a relatively small number of articles (3.2 % of 1320) to PDS over 2017 to 2024. Greater focus on evaluating methods and inferential approaches, using simulation studies that are appropriately complex given clinical realities may be beneficial to the pharmacoepidemiology field.

ImportanceCommercial healthcare claims datasets represent a sample of the US population that is biased along socioeconomic/demographic lines; depending on the target population of interest, results derived from these datasets may not generalize. Rigorous comparisons of claims-derived results to ground-truth data that quantify this bias are lacking. Objectives(1) To quantify the extent and variation of the bias associated with commercial healthcare claims data with respect to different target populations; (2) To evaluate how socioeconomic/demographic factors may explain the magnitude of the bias. DesignThis is a retrospective observational study. Healthcare claims data come from the Merative MarketScan(R) Commercial Database; reference data for comparison come from the State Inpatient Databases (SID) and the US Census. We considered three target populations, aged 18-64 years: (1) all Americans; (2) Americans with health insurance; (3) Americans with commercial health insurance. ParticipantsWe analyzed inpatient discharge records of patients aged 18-64 years, occurring between 01/01/2019 to 12/31/2019 in five states: California, Iowa, Maryland, Massachusetts, and New Jersey. OutcomesWe estimated rates of the 250 most common inpatient procedures, using claims data and using reference data for each target population, and we compared the two estimates. ResultsThe average rate of inpatient discharges per 100 person-years was 5.39 in the claims data (95% CI: [5.37, 5.40]) and 7.003 (95% CI: [7.002, 7.004]) in the reference data for all Americans, corresponding to a 23.1% underestimate from claims. We found large variation in the extent of relative bias across inpatient procedures, including 22.8% of procedures that were underestimated by more than a factor of 2. There was a significant relationship between socioeconomic/demographic factors and the magnitude of bias: procedures that disproportionately occur in disadvantaged neighborhoods were more underestimated in claims data (R2 = 51.6%, p < 0.001). When the target population was restricted to commercially insured Americans, the bias decreased substantially (3.2% of procedures were biased by more than factor of 2), but some variation across procedures remained. Conclusions and relevanceNaive use of healthcare claims data to derive estimates for the underlying US population can be severely biased. The extent of bias is at least partially explained by neighborhood-level socioeconomic factors.

BackgroundWaiting times for elective procedures increased dramatically during the COVID-19 pandemic. People waiting for orthopaedic procedures often require opioids to manage pain, and longer waiting times may result in a need for more and stronger opioids to treat symptoms. MethodsWith NHS England approval, we used routine clinical data from general practice adult patients in OpenSAFELY-TPP linked with the National Minimum Waiting List Dataset. We identified people on elective waiting lists for inpatient trauma or orthopaedic procedures (May 2021-Apr 2022). We quantified changes in weekly opioid prescribing from 6 months pre-waiting list start date to 1 year after the waiting list end date. We also compared long-term opioid prescribing rates in the 3 months before the waiting list start date and months 4-6 after the waiting list end date. We also stratified analyses by time spent on the waiting list (<=18 weeks, 19-52 weeks, >52 weeks). ResultsAmong 63,850 people on elective trauma or orthopaedic waiting lists (median age = 61 years, 54.6% female), 20.5% waited more than 52 weeks. Weekly opioid prescribing rates per 100 waiting list population were relatively stable over time, with peaks immediately post-treatment, and plateauing again after approximately 3 months. Comparing the 3 months before the waiting list start date to months 4-6 after the waiting list end date, changes in the proportion of people with >=3 opioid prescriptions were -1.6% (95%CI -2.2%, -1.0%) for people on the waiting list <=18 weeks, -1.1% (95%CI -1.7%, -0.5%) among people waiting 19-52 weeks, and -0.5% (95%CI -1.4%, 0.4%) among people waiting >52 weeks. ConclusionDuring the COVID-19 pandemic, one in five people waiting for elective orthopaedic procedures waited more than one year. Nearly one in seven were prescribed opioids long-term prior to their referral date, and only small reductions in long-term opioid prescribing was observed post-procedure, regardless of time spent on the waiting list. However, people on waiting lists experienced much longer wait times during the COVID-19 pandemic which also means greater exposure to opioids while awaiting treatment.

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.

BackgroundHydromorphone is a semi-synthetic opioid agonist and a hydro-genated ketone of morphine. This study observed hydromorphone use in the United States (US) using three databases. Methods: The distribution of hydromorphone in the US (in grams) was provided by US Drug Enforcement Administrations Automated Reports and Consolidated Orders System (ARCOS) by state, zip code, and by business types (pharmacies, hospitals, providers, etc.). Hydromorphone prescriptions claims were also examined using the Medicaid and Medicare Part D programs from 2010 to 2023. Results: Hydromorphone increased by +30.6% by 2013, followed by a decrease of -55.9% by 2023 in ARCOS. Medicaid hydromorphone prescriptions increased +39.6% by 2015 and de-creased -48.9% by 2023. Medicare Part D hydromorphone claims increased +8.5% by 2015 and decreased -31.9% by 2023. There were also pronounced regional disparities in hydro-morphone use identified in ARCOS (158.7 fold), Medicaid (17.5 fold), and Medicare Part D (13.7 fold). Conclusions: Hydromorphone use in the US has decreased substantially from 2010 to 2023. Additionally, these findings highlight regional disparities in hydro-morphone use, which may inform targeted opioid stewardship initiatives and guide policymakers to ensure safe and equitable opioid prescribing practices.