Pharmacoepidemiology and Drug Safety

Purpose Lactation status is not recorded as a discrete data element in U.S. administrative claims databases, limiting research on lactation in health databases. We developed a claims-based phenotype to identify periods of probable lactation and used it to describe medications dispensed to lactating women enrolled in a national health plan. Methods This retrospective cohort study used data from the 2012-2014 Merative MarketScan Commercial Claims Database. Deliveries were identified by diagnosis-related group classification system. Lactation windows ran from the delivery date to the date of the last surrogate lactation indicator, such as breast pump and supply codes, the encounter code for supervision of a lactating mother, and mastitis. New prescriptions filled within a window were characterized by therapeutic group, prescriber type, lactation risk category, and availability of published lactation data. Results Of 566,284 women with a single delivery, 84,679 were determined to be lactating using the developed algorithm based on maternal phenotype. The median length of assignable lactation window was 17 days (IQR 5-47). Windows most often closed on a breast pump claim (53.2%) or a postpartum visit code (28.2%). Among 158,471 new prescriptions filled during lactation, central nervous system agents (54.5%), anti-infectives (17.5%), and hormonal products accounted for > 80%. Combined oral contraceptives represented 8.2% of hormonal prescriptions during lactation against 29.0% among women of unknown status. Obstetricians wrote approximately one third of prescriptions filled during lactation windows. Conclusions Lactation can be identified in administrative claims using codes already present in the data, making national description of dispensing during lactation possible.

Purpose: Accurately capturing a patient's health journey is essential for ensuring the validity of scientific and public health studies. Electronic health records (EHR) and administrative claims are commonly used in healthcare research, albeit separately. Previous research has demonstrated that EHR and claims capture different aspects of patient history and care, potentially introducing bias when used alone. In this study, we leveraged the largest public US EHR resource, the National Clinical Cohort Collaborative (N3C), alongside Centers for Medicare & Medicaid Services (CMS) claims data to compare their information coverage and agreement. Methods: We integrated N3C EHR and CMS data to create one cohesive dataset and quantified the proportion of key variables present in EHR alone, CMS alone, and the overlap present in both data sources. Overlap in information coverage was quantified pre- and post-landmark diagnosis of COVID-19. To evaluate the impact of data coverage differences, we conducted a case study evaluating the effect of COVID-19 vaccination on ischemic stroke using both data sources alone and in combination. Results: CMS data helped identify many individuals that were duplicated among EHR identifiers. Examination of the overlap showed that CMS data more completely captured conditions, while EHR data more completely captured medications after COVID-19 diagnosis. Some variables exhibited marked differences over time after landmark COVID-19 diagnosis. Using target trial emulation and self-controlled case series methods, we found that data capture variations altered effect estimates: EHR data alone yielded lower risk of ischemic stroke after vaccination in the target trial emulation (odds ratio 0.47, 95% CI 0.28-0.79), with a smaller, non-significant reduction in the self-controlled case series (incidence rate ratio 0.81, 95% CI 0.64-1.02), whereas analyses using CMS data alone or both sources combined yielded estimates close to the null in both designs. Conclusions: Results indicated that when using either a national database combining multiple EHR sources, or CMS claims data alone, patients' medical histories are less completely captured compared to when these data sources are combined. Case study results highlighted the value of combining data sources for not only yielding fuller patient health histories and valid scientific results, but for identifying and explaining biases that may emerge in research studies that rely on a single data source.

Purpose Prescribing to lactating women is constrained by limited evidence. Published compatibility classifications provide clinical guidance and a discrete research comparator. How often prescribing diverges from that guidance, and what characteristics are associated with divergence, is unknown at national scale. Methods Retrospective analysis of prescriptions filled during lactation, identified in the 2012-2014 MarketScan Commercial Claims Database. Prescriptions were classified as concordant (Hale's lactation risk category L1-L3) or discordant (L4-L5). Multivariable logistic regression identified factors associated with concordance. Results Of 96,484 new prescriptions, 95,710 (99.2%) were concordant and 774 (0.8%) were discordant. Discordant prescriptions were filled later after delivery (median 59 vs 33 days) and had a day supply three times longer (30 vs 10 days). In multivariable analysis (N=28,732), concordant prescriptions were associated with maternal age under 35 (adjusted odds ratio (aOR)=1.34, 95% confidence interval (CI): 1.02-1.75) and chronic-condition medications (aOR=10.94, 95% CI: 6.47-18.50), and were less likely within 14 days of delivery (aOR=0.53, 95% CI: 0.38-0.73) and with each additional day of supply (aOR=0.96, 95% CI: 0.96-0.96). Compared with family practice, concordance did not differ for obstetric, internal medicine, or emergency medicine prescriptions, but prescriptions from other provider types had lower odds of concordance (aOR=0.57, 95% CI: 0.41-0.79). Conclusions Prescribing during lactation was overwhelmingly concordant with published compatibility guidance; the rare divergence was patterned by prescription and prescriber characteristics. Because the categories are an expert classification rather than consensus guidance, divergence should not be read as inappropriate prescribing.

TrialCalibre1 is an agentic system that connects clinical evidence, study design, and executable analyses. It supports reference-trial replication, direct target-trial emulation, and BenchExCal, which combines benchmarking, extension, and cal ibration sensitivity analysis. We evaluated an ASCET-based benchmark and an extension comparing clopidogrel with aspirin among adults aged 81-90 years us ing synthetic PCORnet-style claims. Two reviewers assessed benchmark extrac tion, protocol decisions, and implementation. After discussion, 53/62 and 50/62 assessable SQL checks were rated met; both reviewers rated 12/13 assessable R checks met. Protocol and proxy disagreements remained. A separate ten-question routing development set yielded 10/10 route matches and 7/7 applicable reference trial matches. The agent benchmark and extension included 6,069 and 1,602 pa tients. The extension risk ratio was 0.848 (95% CI 0.569-1.184), with a calibrated sensitivity estimate of 1.274 (0.439-3.700). The human implementation provided descriptive comparisons, not same-data validation. Dose proxies, unpopulated co variates, residual imbalance, implementation deviations, and ignored cross-cohort covariance limited interpretation. This case demonstrates a connected workflow whose protocols, code, and results can be reviewed.

Signal detection is a core task in pharmacovigilance, and disproportionality analysis (DA) remains the dominant approach for spontaneous reporting systems (SRS). However, SRS data are sparse and heavily zero-inflated, reflecting rare adverse drug reactions (ADRs) and substantial under-reporting, which can distort standard count-model assumptions. Using Medicines and Healthcare products Regulatory Agency (MHRA) interactive Drug Analysis Profiles (iDAPs) for selective serotonin reuptake inhibitors (SSRIs), we derived system organ class (SOC) level adverse event counts. Goodness-of-fit testing indicated that SOC counts were variably consistent with Poisson or negative binomial (NB) distributions, motivating the use of zero-inflated Poisson and zero-inflated NB models. Model adequacy was assessed using Pearson-residual dispersion (absolute fit) and Akaikes Information Criterion (relative fit). Zero-inflated models provided improved fit for 10 out of all 27 SOCs (Endocrine disorders, Immune system disorders, Metabolism and nutrition disorders, Infections and infestations, Renal and urinary disorders, Hepatobiliary disorders, Ear and labyrinth disorders, Pregnancy, puerperium and perinatal conditions, Congenital, familial and genetic disorders, Neoplasms benign, malignant and unspecified (including cysts and polyps)) and yielded signals broadly concordant with DA, including for metabolism-related events. These results support zero-inflated modelling as a complementary signal detection method for iDAPs-derived count data, particularly where internal model-based validation and explicit handling of excess zeros are desired.

Research replication is essential to build trust in evidence produced from real-world data. However, methods for conducting and reporting these studies are lacking, particularly related to data quality and fitness assessments. We replicated a single-center study from Children's Hospital of Atlanta in a multi-institutional learning network (PEDSnet) to evaluate the long-term effects of hydroxyurea in children with severe sickle cell disease (SS/S{beta}0 genotype). An AS-IS arm applied the original study's criteria with no major data quality adjustments, while a Data Fitness Enhanced (DFE) arm used systematic data fitness assessment to inform adjustments to cohort inclusion criteria and variable definitions; both arms then replicated the original study's primary analyses. Data quality checks in the DFE arm refined cohort criteria and improved hydroxyurea capture, drug era computation, and hematology specialist mapping. The DFE cohort produced average treatment effects with higher face validity and greater concordance with the original study (e.g., change in ED visits: -0.44 (CI -0.60, -0.26) versus -0.36 (CI -0.57, -0.16) in the original study) than the AS-IS cohort (-0.08 (CI -0.26, 0.09)), which yielded several implausible results. These findings show that superficially plausible cohort characteristics do not guarantee valid results without transparent, systematic data fitness assessment.

Objective To evaluate the accuracy and cost of RegCheck, an automated large language model (LLM)-based workflow, for identifying clinical trial outcomes and detecting outcome misreporting by comparing its outputs with manual assessments from the COMPare Trials project. Design Validation study. Setting Sixty-two clinical trials originally assessed in the COMPare Trials project, sampled from five high impact general medical journals. Participants Published clinical trial reports and their corresponding prespecified registrations and/or protocols. Main outcome measures Four prespecified research questions were examined. RQ1 assessed outcome extraction recall relative to COMPare. RQ2 assessed accuracy of outcome classification as primary, secondary, or non-prespecified. RQ3 assessed accuracy of misreporting detection relative to COMPare, with additional manual adjudication of discrepancies between RegCheck and COMPare. RQ4 assessed the average per-paper cost of running the automated workflow. Results Across the validation papers, RegCheck achieved 91.2% outcome extraction recall relative to COMPare, and 83.6% outcome classification accuracy. For detection of outcome misreporting, RegCheck's overall accuracy was 85.6%. However, after resolving discrepancies with the original human judgements (which frequently favoured RegCheck's judgement), revised accuracy for outcome misreporting detection was 94.8%. The mean cost of running the workflow was 5.94 USD per paper. Conclusions RegCheck achieved high overall performance with a rigorous manual benchmark for identifying prespecified and reported outcomes in clinical trials, and detecting outcome misreporting, while operating at very low marginal cost. Adjudication of discrepant judgements suggested that RegCheck frequently identified valid issues not captured in the reference standard. Automated outcome checking may offer a scalable way to support editors, peer reviewers, and authors in detecting outcome switching and improving trial reporting.

Introduction: Primary care electronic health records (EHRs) are an important resource for health research, but death recording in primary care EHRs may be incomplete or differ from Office for National Statistics (ONS) death registrations. We used OpenSAFELY-TPP to assess the completeness and concordance of death recording and provide recommendations for using date-of-death information. Methods: Using linked primary care EHRs and ONS death registrations, we assessed whether ONS-registered deaths from 2020-2025 had a primary care date of death and, where present, calculated the difference between dates. Where a primary care date was absent, we assessed death-related SNOMED CT codes. Analyses were conducted overall and by year, population subgroup, and general practice. We also examined GP registration end dates and primary-care-recorded deaths without a corresponding ONS record, including monthly patterns near the data extraction date. Results: Among 1,409,275 ONS-registered deaths, 88% had the same date recorded in primary care and 8% had no corresponding primary care date. Among 1,324,995 deaths recorded in primary care, <2% had no corresponding ONS record. Deaths recorded in only one source were more frequent among people aged <45 years and those from Mixed, Asian or Asian British, Black or Black British, and Chinese or Other ethnic groups. ONS-only dates were also more frequent in London and the West Midlands. Primary-care-only deaths increased close to date of data extraction, reaching 7% in February 2026. Same-day GP deregistration increased from 9% in 2020 to 85% in 2025. Conclusions: Death recording in primary care EHRs showed high completeness and concordance with ONS registrations, but discrepancies remained for recent deaths and some population groups. Data recency and population-specific completeness should therefore be considered when using linked mortality data for research.

Objective: Small for gestational age (SGA) and large for gestational age (LGA) are critical endpoints in perinatal pharmacoepidemiology, yet gestational age is frequently missing in administrative databases. This study validated algorithms for identifying SGA and LGA in the Korean National Health Information Database (NHID). Methods: We linked the NHID with national vaccination and infant health screening registries (2018-2021) to create a validation cohort of 94,159 pregnancies with reference standard gestational age and birth weight; infants with birth weights below the 10th or above the 90th percentile were considered SGA or LGA, respectively. We evaluated four algorithms: ICD-10 diagnosis codes from infant claims (Method A), maternal claims (Method B), either infant or maternal claims (Method C), and birth weight combined with estimated gestational age (Method D). Results: ICD-10-based algorithms consistently underestimated prevalence, showing high specificity but low sensitivity (<17%). In contrast, Method D demonstrated superior performance: for SGA, sensitivity was 89.3%, specificity 96.8%, and positive predictive value (PPV) 71.6%; for LGA, sensitivity was 77.5%, specificity 98.3%, and PPV 84.7%. Conclusion: SGA and LGA can be identified with reasonable accuracy in the NHID using birth weight and estimated gestational age, whereas diagnosis codes alone can underestimate prevalence.

BACKGROUND Extensive research has documented medication abortion safety. Large-scale insurance claims data offer an important complement to clinical studies by assessing real-world outcomes in routine practice. In claims data, researchers must infer mifepristone use, clinical outcomes, and event severity from diagnosis, treatment, and utilization codes. Understanding how these analytic choices influence safety and effectiveness estimates is important for interpreting evidence used to inform care delivery and medication policy. METHODS We conducted a retrospective cohort study of mifepristone use with national multipayer claims data, both medical and pharmacy, from 2017 through 2023. We identified both a broad cohort and a more specific medication-abortion cohort after excluding evidence of other indications. We classified potential safety events, effectiveness outcomes, and health care utilization within 45 days of mifepristone use. Serious adverse events required claims-observable consequences consistent with FDA seriousness criteria. RESULTS The broad cohort included 869,409 mifepristone uses among 698,780 unique patients for medication abortion or other indications. After restriction to uses with additional evidence of medication abortion, 775,931 uses among 626,443 unique patients remained. In the medication-abortion cohort, 3,306 uses were followed by at least one serious adverse event, resulting in an absolute 45-day risk of 0.43%. Serious bleeding or hemorrhage occurred after 0.24% of uses, serious infection or sepsis after 0.05%, serious hypersensitivity or anaphylaxis after 0.01%, and serious ectopic-pregnancy outcomes after 0.05%. CONCLUSIONS Serious adverse events after mifepristone use were rare in this national study. Applying established, clinically grounded criteria markedly reduced the number of events classified as serious.

Background: Multiple performance measures are used to assess the management of common cardiovascular conditions, such as coronary artery disease (CAD), heart failure with reduced ejection fraction (HFrEF), and atrial fibrillation (AF). These measures are sometimes used to assess individual clinicians, despite lack of evidence of reliability and accuracy. We used national Veteran Affairs (VA) data to test medication-based performance measures for assessment of clinician performance in outpatient cardiology care. Methods: This retrospective study examined VA cardiology clinician/Veteran dyads from 2017-2023 to evaluate 10 medication-based performance measures for CAD, HFrEF, or AF with elevated stroke risk. Achievement of each performance measure was defined as an active prescription of an indicated medication for each eligible patient encounter, summarized at the clinician level. Reliability was calculated using the beta-binomial method. A Bayesian causal model was used to assess the impact of case complexity on performance measure achievement. Finally, we performed peer review of a subset of cases (n=952) to assess the accuracy of three representative performance measures. Results: Of 456,071 unique cardiology encounters, 312,331 included CAD, 224,599 included HFrEF, and 178,678 included AF. These patients were treated by 1,043 cardiology clinicians. Median performance measure achievement ranged from 0.23 (SGLT2i for HFrEF) to 0.99 (beta blocker for HFrEF). Measure reliability varied from 0.63 to 0.90. Case complexity did not significantly affect performance measure achievement. Using peer review as the gold standard, accuracy was poor to moderate (balanced accuracy 0.51-0.73), and measures underestimated performance relative to peer review. Conclusions: Ten medication-based performance measures for CAD, HFrEF, and AF can be efficiently measured using electronic health record data, and most have sufficient reliability to support clinician benchmarking and the identification of potential care gaps. However, suboptimal accuracy compared with a peer review indicates that these measures may be insufficient as a standalone assessment of clinician quality.

Importance. Observational studies dominate evidence in cardiovascular outcomes research, yet no widely adopted reporting standard requires authors to disclose how much of an observed association is attributable to systematic error. The negative-control calibration framework of the Observational Health Data Sciences and Informatics consortium returns a binary answer: a calibrated P-value either crosses 0.05 or it does not. That answer conflates "is there a signal" with "is the signal credible." Objective. To define the Bias Attribution Fraction (BAF), a continuous estimator in [0, 1] that quantifies the fraction of an observed log relative risk that is attributable to systematic bias, together with its 95% credible interval, and to evaluate whether reporting calibrated P-value plus BAF plus interval width produces more defensible decisions than reporting the calibrated P-value alone. Design. Three-part study: (i) metric definition and interval construction; (ii) Monte Carlo simulation across 1920 conditions by 1000 repetitions = 1,920,000 simulated target estimates (a primary grid plus a sign-flipped companion grid of identical size, pooled for all analyses), with truth known by construction; (iii) two-arm beta-blocker target trial emulation in MIMIC IV, adhering to the TARGET guideline, with targeted maximum likelihood estimation (TMLE) and 12 negative-control outcomes. Setting. MIMIC IV (critical care) and MIMIC IV ECG, 2008 to 2019. Participants. Adults with a first intensive care unit stay and a recorded 12-lead ECG. Exposures. Two clinical questions are evaluated: (1) reaching at least 50% of guideline-recommended target dose of an index beta-blocker within 7 days of admission, sustained to 12 months, versus lower or no exposure; (2) receiving guideline-directed medical therapy (GDMT) with at least two of three foundational drug classes at adequate dose for 12 months versus fewer. Main Outcomes and Measures. One-year all-cause mortality. Primary analytic outputs: (i) the calibrated P-value from a negative-control empirical null distribution with 12 outcomes; (ii) BAF-hat with a Markov chain Monte Carlo 95% credible interval; (iii) a verdict drawn from a truth-calibrated lookup table mapping the BAF-hat by interval-width cell to a probability of being bias-dominated. Results. Across 1,920,000 simulated repetitions, the calibrated P-value alone yielded a misuse rate (declared usable while truth was bias-dominated) of 36.02%. Adding the BAF point estimate reduced misuse to 16.48%, and requiring the whole credible interval of BAF to lie below 0.5, clear of the bias-dominated zone, reduced misuse to 10.16%. Requiring in addition a calibrated probability of bias dominance below 15% reduced misuse to 3.02% at the cost of declaring 22.1% of estimates usable (versus 61.8% for the calibrated P-value alone). Read from the bias-dominated view, meaning that withholding a repetition is a positive call and being truly bias-dominated is a positive truth, the BAF estimator achieved an area under the receiver operating characteristic curve of 0.922, compared with 0.787 for the calibrated P-value. In MIMIC IV, question 1 produced a calibrated P-value of 0.894 and BAF-hat = 0.875 (95% credible interval 0.385 to 0.992); the apparent one-year mortality reduction of 16.9% was entirely attributable to systematic bias and the association was not usable as effect evidence. Question 2 also failed layer 1, with a calibrated P-value of 0.1026, and BAF-hat = 0.460 placed it in the "competitive, no verdict" zone. Conclusions and Relevance. Pairing the calibrated P-value with BAF and its 95% credible interval turns a binary credibility judgement into a three-channel report (signal existence, effect size, and bias fraction). Despite TARGET-adherent design, doubly robust TMLE, and 12 negative controls, both clinical questions in this study still failed to qualify as effect evidence, with question 1 demonstrating complete bias domination and BAF quantifying the systematic error that remained. The reporting standard proposed here can be applied to any observational study that includes negative controls, without requiring additional data.

Background. Mendelian randomization (MR) studies often evaluate exposures such as LDL cholesterol (LDL-C). The widespread use of lipid lowering medications complicates the interpretation and the validity of MR estimates. Methods. We describe two causal estimands in populations with medication use: a lifetime effect, and a lifetime effect under no medication use. In simulations, we compared the common approach of excluding medication users with estimation based on inverse probability (IP) weighting to adjust for medication use. We applied both approaches to a MR analysis of LDL-C and coronary artery disease in the Million Veteran Program (MVP), a large prospective cohort of U.S. veterans with linked electronic health record and genetic data. Results. In simulations, MR analyses that did not adjust for medication use estimated a lifetime effect that reflected a valid estimate of a total effect that included both the harms of higher LDL-C and the benefits of statins. To estimate the effect under no medication use, excluding statin users introduced selection bias. Alternatively, IP weighting could address bias from incident statin users, but could not address bias related to prevalent medication use. Estimates from MVP data varied considerably, reflecting the importance of these analytic choices. Conclusions. Different medication adjustment strategies in MR studies implicitly target different causal estimands and are subject to distinct biases. Transparent analytical choices and careful interpretation are essential for informative MR results in the context of widespread medication use.

Background: High intensity statins are foundational after acute coronary syndrome (ACS), yet intensive care unit prescribing occurs while renal reserve, perfusion, and interacting therapies are changing. We tested a renal safety checkpoint integrating kidney status, hemodynamic instability, and drug interaction burden to identify when statin intensity may become nonexchangeable. Methods: We emulated an active-comparator target trial across MIMIC-IV, eICU, and MIMIC-III. Critically ill adults with ACS, acute myocardial infarction, or percutaneous coronary intervention who received high- or moderate-intensity statins within 24 hours were included. The primary outcome was 7-day KDIGO stage 2 or 3 acute kidney injury or incident renal replacement therapy. Eligibility, time zero, treatment assignment, and follow-up were aligned. Database-specific propensity scores, overlap weighting, and standardization addressed confounding and treatment overlap. Safety domains, longitudinal analyses, bootstrap resampling, source omission, and endpoint sensitivities assessed robustness. Results: Among 5,178 patients, 761 developed the primary outcome, including 223 who initiated renal replacement therapy. Standardized risks were 17.40% with high-intensity therapy and 15.01% with moderate-intensity therapy (risk difference, 2.39 percentage points [95% confidence interval (CI), -0.23 to 5.05]; risk ratio, 1.16 [95% CI, 0.99 to 1.39]). Risk separation was greatest with high hemodynamic instability (5.78 percentage points [95% CI, 1.56 to 9.74]) and high drug-interaction burden (6.24 percentage points [95% CI, -0.44 to 12.19]). Renal replacement therapy showed a 1.33-point risk difference (95% CI, 0.18 to 2.67). Conclusions: This study moves statin safety assessment beyond fixed dose label or isolated creatinine measurement. The findings support a clinically actionable monitoring strategy in which early statin intensity is reassessed against evolving perfusion, kidney status, and interaction burden. This approach preserves intensive lipid lowering for physiologically suitable patients while identifying a high risk window in which temporary moderation.

Background: Epidermal Growth Factor Receptor (EGFR) tyrosine kinase inhibitors (TKIs) are widely used for EGFR-mutated non-small cell lung cancer (NSCLC) but have distinct toxicity profiles across generations. Aims: To investigate relationships between EGFR-TKI physicochemical, pharmacokinetic and pharmacological properties and drug-specific adverse drug reactions (ADRs) using national and global pharmacovigilance data. Methods: ADRs for erlotinib, afatinib and osimertinib were extracted from the UK MHRA Yellow Card scheme and WHO VigiAccess. Disproportionality analysis used reporting odds ratios (RORs). Signals were defined as ROR >2, lower 95% confidence interval >1 and >3 cases. Drug properties were curated from ChEMBL, emc, SmPC and EMA sources. Results: VigiAccess contained 88,662 individual case safety reports (163,619 ADRs) and Yellow Card contained 752 reports (1,814 ADRs). Four System Organ Class (SOC) signals were identified in VigiAccess and three in Yellow Card, with five additional Yellow Card Preferred Term signals. Eye disorders showed the strongest consistent association with erlotinib (VigiAccess ROR 4.33; Yellow Card ROR 4.18). Osimertinib was associated with pneumonitis (ROR 7.67) and pulmonary embolism (ROR 3.94). Afatinib showed psychiatric disorder signals in both databases (ROR 3.2-3.3) and diarrhoea (ROR 2.79). Neoplasm-related signals likely reflected confounding by indication. Conclusions: EGFR-TKIs demonstrate generation-specific pharmacovigilance profiles. Integration with drug properties supports mechanistic plausibility for several signals, while the atypical afatinib psychiatric association warrants further investigation. Findings are hypothesis-generating and require confirmation in controlled observational or mechanistic studies.

Importance. Hypertension is among the most important modifiable risk factors for Alzheimers disease and related dementias (ADRD), but whether different antihypertensive drug classes confer different cognitive risk, particularly through differential modulation of angiotensin II receptor signaling, remains uncertain. Prior pharmacoepidemiologic evidence is based largely on a single Medicare cohort, and external validation across distinct healthcare data environments is needed. Objective. To externally replicate and extend prior findings across two structurally independent healthcare data sources, evaluating whether predominant use of antihypertensive medications that stimulate versus inhibit angiotensin II type 2 and type 4 receptor signaling is associated with risk of incident ADRD. Design, Setting, and Participants. Retrospective new user, active comparator cohort study with propensity score matching, conducted in parallel across two independent data sources: the Vanderbilt University Medical Center Synthetic Derivative (VUMC SD; deidentified academic medical center electronic health record through February 2025) and the Merative MarketScan Medicare Database (2015 to 2023). Adults aged 65 years or older with hypertension and at least 365 cumulative days of antihypertensive exposure were included after a 365-day blanking period. Exposures. Predominant use ([&ge;]80% of cumulative exposure days) of receptor-stimulating antihypertensive medications (angiotensin II receptor blockers, dihydropyridine calcium channel blockers, thiazide diuretics) vs receptor-inhibiting antihypertensive medications (angiotensin-converting enzyme inhibitors, {beta}blockers, nondihydropyridine calcium channel blockers); a third group of nonusers comprised participants without predominant use of either class. Main Outcomes and Measures. Time to first incident ADRD diagnosis was identified using ICD9 and ICD10 codes consistent with the CMS Chronic Conditions Data Warehouse definition. Adjusted hazard ratios (HRs) and 95% CIs were estimated using Cox proportional hazards models with time-dependent covariates. Results. The Medicare cohort included 1,596,034 beneficiaries (mean [SD] age, 75.0 [8.0] years; 55% female), and the VUMC SD cohort included 80,267 patients (mean [SD] age, 72.3 [5.8] years; 57% female). Over approximately 7 years of follow-up, ADRD incidence was lower among predominant users of stimulating vs. inhibiting medications in both cohorts (Medicare: 6.7% vs. 8.2%; VUMC SD: 4.9% vs. 5.7%). After adjustment, predominant use of stimulating medications was associated with lower ADRD risk versus inhibiting medications in both cohorts (Medicare: HR, 0.92; 95% CI, 0.90,0.93; P < .001; VUMC SD: HR, 0.92; 95% CI, 0.85,0.99; P = .03). Established cardiovascular and neuropsychiatric risk factors (atrial fibrillation, heart failure, chronic kidney disease, depression) showed expected associations with ADRD risk in both cohorts, supporting internal validity. Conclusions and Relevance. Across two structurally independent data sources, the predominant use of antihypertensive medications that stimulate angiotensin II type 2 and type 4 receptor signaling was associated with a modestly lower risk of incident ADRD compared with predominantly inhibiting agents. By replicating in two distinct data environments, these findings extend prior single-source pharmacoepidemiologic evidence and support evaluation of receptor targeted antihypertensive strategies for cognitive outcomes in randomized comparative-effectiveness trials.

Objective. To determine the publication rate of abstracts presented at the American Diabetes Association Scientific Sessions and to evaluate the association between statistical significance of study results and subsequent publication. Research Design and Methods. We conducted a retrospective cohort study of abstracts presented at the 2018 American Diabetes Association Scientific Sessions. The primary exposure was study result category (statistically significant vs. non-statistically significant findings), and the primary outcome was publication in an indexed journal within 5 years after conference presentation. Publication status was determined through PubMed/MEDLINE and Scopus searches. Adjusted relative risks (RRs) and 95% CIs were estimated using generalized linear models with Poisson distribution and robust variance. Results. Among 541 included abstracts, 321 (59.3%) were subsequently published in indexed journals. Abstracts reporting statistically significant findings had a higher publication rate than those reporting non-statistically significant findings (61.9% vs. 42.3%; p=0.002). In the adjusted analysis, abstracts with non-statistically significant findings had a lower likelihood of publication compared with those reporting statistically significant findings (adjusted RR 0.71 [95% CI 0.55-0.93]; p=0.013). Conclusions. Approximately four in ten abstracts presented at the ADA Scientific Sessions were not published within 5 years. Abstracts reporting non-statistically significant findings had a lower likelihood of subsequent publication, suggesting persistent publication bias in diabetology research. Future initiatives promoting the interpretation of effect estimates, confidence intervals and clinical relevance, rather than statistical significance alone, may help reduce selective dissemination of evidence

Background and Objective. Missing data frequently occurs in health databases and can bias analyses if not correctly dealt with. Using real-world data, we compared complete-case and multiple-imputation methods for recovering true parameters of a multivariable logistic regression model for the association between maternal glucose levels during pregnancy and child excess weight at preschool age, where missing values were present in as much as 30% of our sample. Methods. This study utilized a cohort of 130,424 children with complete preschool-age body mass index (BMI) measurements from the Calgary and Edmonton health regions of Alberta, Canada. In the complete BMI data, we introduced missingness through deletion following three distinct mechanisms: missing completely at random (MCAR), at random (MAR), and not at random (MNAR). To handle the missing data created, we employed complete-case and multiple-imputation methods. Maternal glucose levels during pregnancy were categorized into five groups and its association with child excess weight at pre-school age was determined based on a logistic regression model using the full observed data (yielding true values), observed data that was not deleted (complete-case estimates), and imputed data (multiple-imputation estimates). The accuracy of complete-case and multiple-imputation estimates were evaluated against the true values. Finally, we conducted a sensitivity analysis for the MNAR mechanism using pattern-mixture models with an additive shift. Results. Under MCAR and MAR, multiple-imputation generally outperformed complete-case, yielding smaller absolute and relative bias. Both methods achieved high significance ([&ge;] 0.96) for most effects. Mean squared errors for multiple-imputation and complete-case were similar missing completely at random, missing at random, and coverage was consistently high ([&ge;] 0.99). Under MNAR, both complete-case and multiple-imputation showed poor performance regarding bias and statistical significance. Sensitivity analysis using pattern-mixture models indicated performance varied by specific effect. Conclusions. Under MCAR and MAR, multiple-imputation introduced higher bias but demonstrated superior overall performance based on mean squared error and restored statistical power. Conversely, both methods failed under MNAR, where pattern-mixture modeling sensitivity analyses revealed highly variable, effect-specific performance due to unverifiable shift assumptions. When faced with missing data, researchers should assess missingness mechanisms, report both complete-case and multiple-imputation estimates under MCAR/MAR while accounting for power-versus-bias tradeoffs, and employ pattern-mixture sensitivity analyses to test robustness when MNAR is plausible.

Introduction: Clinical guidelines for managing opioid use disorder (OUD) recommend incremental dose titration for methadone and buprenorphine/naloxone to achieve a safe therapeutic maintenance dose. Dose titration recommendations are based on clinical experience, pharmacologic rationale, and expert consensus, with limited real-world evidence, especially in settings with widespread fentanyl use, where traditional titration schedules may be insufficient to address the higher opioid tolerance among people initiating treatment. This study aims to determine the comparative effectiveness of alternative titration schedules on completed induction and time to all-cause mortality. Methods and analysis: We will conduct a population-based retrospective cohort study using linked data from nine provincial health administrative databases. The study population will include adults ([&ge;]18 years) in British Columbia, Canada, who initiated methadone or buprenorphine/naloxone between 01/01/2010 and 30/06/2022. Primary outcomes are completed induction (defined as no dose increase for [&ge;]two weeks without any intervening decrease) and time to all-cause mortality, with overdose-related acute care visits or overdose-related death and treatment discontinuation as secondary outcomes. Using a target trial framework, this study will implement a clone-censor-weight approach to estimate the per-protocol effects of sustained titration schedules capturing adherence to and deviation from clinical guidelines. Sensitivity analyses will assess the robustness of findings through cohort and timeline restrictions as well as alternative exposure and outcome definitions. Discussion: This study will generate real-world evidence on the comparative effectiveness of alternative titration schedules of methadone and buprenorphine/naloxone on completed induction and all-cause mortality. The findings will support evidence-informed updates to OAT guidelines and clinical decision-making in British Columbia and other jurisdictions facing escalating opioid-related harms.

Summary Background We used sequential target trial emulation (TTE) to examine associations between opioid analgesic initiation and adverse maternal and neonatal outcomes while accounting for gestational timing, time at risk, and potential confounding. Methods Using the Early Life Course platform, comprising linked population-based data for births in New South Wales, Australia (2013-2019), we emulated a new trial at each gestational week, comparing women who initiated a prescribed opioid analgesic with eligible non-initiators. Baseline covariates were balanced using inverse probability weighting. Weighted logistic regression estimated odds ratios (ORs) for neonatal outcomes, postpartum haemorrhage, and severe maternal morbidity. Pooled logistic regression estimated hazard ratios (HRs) for other maternal outcomes, combined across gestational weeks using fixed-effects meta-analysis. Sensitivity analyses assessed unmeasured confounding, exposure misclassification, and potential reverse causation. Findings Among 511,357 pregnancies, over 80% of opioid initiators received only one dispensing. Opioid initiation was not associated with increased risks of placental abruption (HR 0.94, 95% CI 0.75-1.16), severe maternal morbidity (OR 1.05, 0.97-1.11), postpartum haemorrhage (OR 0.98, 0.95-1.02), low Apgar score (OR 0.99, 0.87-1.08), small for gestational age (OR 0.90, 0.85-0.95), or neonatal abstinence syndrome (OR 0.98, 0.72-1.30). Risks were elevated for PPROM (HR 1.10, 0.98-1.23), preterm birth (HR 1.26, 1.19-1.33), severe neonatal morbidity (OR 1.08, 1.02-1.17), and perinatal death (OR 1.29, 1.10-1.52). Perinatal death estimates attenuated in analyses addressing potential reverse causation. Interpretation Using a TTE approach that accounted for gestational timing and time at risk, we found no appreciable increase in most maternal or neonatal outcomes following predominantly short-term opioid initiation. Elevated risks of preterm birth, perinatal death, and third-trimester PPROM may reflect causal effects or confounding by pregnancy-related indications. Studies using time-appropriate designs and detailed information on prescribing indications and their timing are needed to clarify these associations.