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Background and ObjectivesThe use of over-the-counter laxatives is common in the general population. The microbiome-gut-brain axis hypothesis suggests that the use of laxatives could be associated with dementia. We aimed to examine the association between the regular use of laxatives and the incidence of dementia in UK Biobank participants.MethodsThis prospective cohort study was based on UK Biobank participants aged 40–69 years without a history of dementia. Regular use of laxatives was defined as self-reported use in most days of the week for the last 4 weeks at baseline (2006–2010). The outcomes were all-cause dementia, Alzheimer disease (AD), and vascular dementia (VD), identified from linked hospital admissions or death registers (up to 2019). Sociodemographic characteristics, lifestyle factors, medical conditions, family history, and regular medication use were adjusted for in the multivariable Cox regression analyses.ResultsAmong the 502,229 participants with a mean age of 56.5 (SD 8.1) years at baseline, 273,251 (54.4%) were female, and 18,235 (3.6%) reported regular use of laxatives. Over a mean follow-up of 9.8 years, 218 (1.3%) participants with regular use of laxatives and 1,969 (0.4%) with no regular use developed all-cause dementia. Multivariable analyses showed that regular use of laxatives was associated with increased risk of all-cause dementia (hazard ratio [HR] 1.51; 95% CI 1.30–1.75) and VD (HR 1.65; 95% CI 1.21–2.27), with no significant association observed for AD (HR 1.05; 95% CI 0.79–1.40). The risk of both all-cause dementia and VD increased with the number of regularly used laxative types (ptrend 0.001 and 0.04, respectively). Among the participants who clearly reported that they were using just 1 type of laxative (n = 5,800), only those using osmotic laxatives showed a statistically significantly higher risk of all-cause dementia (HR 1.64; 95% CI 1.20–2.24) and VD (HR 1.97; 95% CI 1.04–3.75). These results remained robust in various subgroup and sensitivity analyses.DiscussionRegular use of laxatives was associated with a higher risk of all-cause dementia, particularly in those who used multiple laxative types or osmotic laxative.

The US Food and Drug Administration established the Sentinel System to monitor the safety of medical products. A component of this system includes parameterizable analytic tools to identify mother-infant pairs and evaluate infant outcomes to enable the routine monitoring of the utilization and safety of drugs used in pregnancy. We assessed the feasibility of using the data and tools in the Sentinel System by assessing a known association between topiramate use during pregnancy and oral clefts in the infant.We identified mother-infant pairs using the mother-infant linkage table from six data partners contributing to the Sentinel Distributed Database from January 1, 2000, to September 30, 2015. We compared mother-infant pairs with first-trimester exposure to topiramate to mother-infant pairs that were topiramate-unexposed or lamotrigine-exposed and used a validated algorithm to identify oral clefts in the infant. We estimated adjusted risk ratios through propensity score stratification.There were 2,007 topiramate-exposed and 1,066,086 unexposed mother-infant pairs in the main comparison. In the active-comparator analysis, there were 1,996 topiramate-exposed and 2,859 lamotrigine-exposed mother-infant pairs. After propensity score stratification, the odds ratio for oral clefts was 2.92 (95% CI: 1.43, 5.93) comparing the topiramate-exposed to unexposed groups and 2.72 (95% CI: 0.75, 9.93) comparing the topiramate-exposed to lamotrigine-exposed groups.We found an increased risk of oral clefts after topiramate exposure in the first trimester in the Sentinel database. These results are similar to prior published observational study results and demonstrate the ability of Sentinel's data and analytic tools to assess medical product safety in cohorts of mother-infant pairs in a timely manner. This article is protected by copyright. All rights reserved.

ObjectiveThis article describes the implementation of a privacy-preserving record linkage (PPRL) solution across PCORnet®, the National Patient-Centered Clinical Research Network.Material and MethodsUsing a PPRL solution from Datavant, we quantified the degree of patient overlap across the network and report a de-duplicated analysis of the demographic and clinical characteristics of the PCORnet population.ResultsThere were ∼170M patient records across the responding Network Partners, with ∼138M (81%) of those corresponding to a unique patient. 82.1% of patients were found in a single partner and 14.7% were in 2. The percentage overlap between Partners ranged between 0% and 80% with a median of 0%. Linking patients’ electronic health records with claims increased disease prevalence in every clinical characteristic, ranging between 63% and 173%.DiscussionThe overlap between Partners was variable and depended on timeframe. However, patient data linkage changed the prevalence profile of the PCORnet patient population.ConclusionsThis project was one of the largest linkage efforts of its kind and demonstrates the potential value of record linkage. Linkage between Partners may be most useful in cases where there is geographic proximity between Partners, an expectation that potential linkage Partners will be able to fill gaps in data, or a longer study timeframe.

Many patients with Crohn's disease (CD) lose response or become intolerant to antitumor necrosis factor (TNF) therapy and subsequently switch out of class. We compared the effectiveness and safety of ustekinumab to vedolizumab in a large, geographically diverse US population of TNF-experienced patients with CD.We conducted a retrospective cohort study using longitudinal claims data from a large US insurer (Anthem, Inc.). We identified patients with CD initiating vedolizumab or ustekinumab with anti-TNF treatment in the prior 6 months. Our primary outcome was treatment persistence for52 weeks. Secondary outcomes included (i) all-cause hospitalization, (ii) hospitalization for CD with surgery, (iii) hospitalization for CD without surgery, and (iv) hospitalization for infection. Propensity score fine stratification was used to control for demographic and baseline clinical characteristics and prior treatments.Among 885 new users of ustekinumab and 490 new users of vedolizumab, we observed no difference in treatment persistence (adjusted risk ratio 1.09 [95% confidence interval 0.95-1.25]). Ustekinumab was associated with a lower rate of all-cause hospitalization (adjusted hazard ratio 0.73 [0.59-0.91]), nonsurgical CD hospitalization (adjusted hazard ratio 0.58 [0.40-0.83]), and hospitalization for infection (adjusted hazard ratio 0.56 [0.34-0.92]).This real-world comparative effectiveness study of anti-TNF-experienced patients with CD initiating vedolizumab or ustekinumab showed similar treatment persistence rates beyond 52 weeks, although secondary outcomes such as all-cause hospitalizations, nonsurgical CD hospitalizations, and hospitalizations for infection favored ustekinumab initiation. We, therefore, advocate for individualized decision making in this medically refractory population, considering patient preference and other factors such as cost and route of administration.

Real-world evidence used for regulatory, payer, and clinical decision-making requires principled epidemiology in design and analysis, applying methods to minimize confounding given the lack of randomization. One technique to deal with potential confounding is propensity score (PS) analysis, which allows for the adjustment for measured preexposure covariates. Since its first publication in 2009, the high-dimensional propensity score (hdPS) method has emerged as an approach that extends traditional PS covariate selection to include large numbers of covariates that may reduce confounding bias in the analysis of healthcare databases. hdPS is an automated, data-driven analytic approach for covariate selection that empirically identifies preexposure variables and proxies to include in the PS model. This article provides an overview of the hdPS approach and recommendations on the planning, implementation, and reporting of hdPS used for causal treatment-effect estimations in longitudinal healthcare databases. We supply a checklist with key considerations as a supportive decision tool to aid investigators in the implementation and transparent reporting of hdPS techniques, and to aid decision-makers unfamiliar with hdPS in the understanding and interpretation of studies employing this approach. This article is endorsed by the International Society for Pharmacoepidemiology.

Clinical trials have suggested that glucagon-like peptide-1 receptor agonists (GLP-1RAs) may be associated with a higher risk of biliary-related diseases in patients with type 2 diabetes. Limited real-world studies have examined the comparative biliary safety of GLP-1RAs versus other antihyperglycemic drugs. We aimed to estimate the comparative risk of biliary-related diseases between GLP-1RAs and sodium glucose cotransporter 2 inhibitors (SGLT2is), which are indicated for patients with similar diabetes severity in Taiwan.Retrospective cohort study.Taiwan National Health Insurance Database during 2011 to 2018.Patients with type 2 diabetes who initiated GLP-1RAs or SGLT2is.GLP-1RAs versus SGLT2is.We used an on-treatment approach to examine the effect of continuous use and an intention-to-treat approach to assess the effect of initiation of GLP-1RAs versus SGLT2is. We used Coxregression models to estimate the hazard ratios (HRs) and 95% confidenceintervals (CIs) for the composite hospitalized biliary-related diseases, including acute cholecystitis or cholecystectomy, choledocholithiasis, and acute cholangitis, after matching each GLP-1RA initiator to up to 10 SGLT2iinitiators using propensity scores (PSs). Among 78,253 PS-matched patients, GLP-1RA use was associated with a numerically higher risk of biliary-related diseases versus SGLT2i use in the on-treatment analysis, with an HR of 1.20 (95% CI, 0.93-1.56) for the composite outcome, an HR of 1.22 (95% CI, 0.92-1.62) for acute cholecystitis or cholecystectomy, an HR of 1.20 (95% CI, 0.69-2.07) for choledocholithiasis, and an HR of 1.14 (95% CI,0.82-2.42) for acute cholangitis. The HRs were more pronounced in theintention-to-treat analysis (1.27 [95% CI, 1.05-1.53] for the composite outcome, 1.29 [95% CI, 1.04-1.58] foracute cholecystitis or cholecystectomy, 1.74 [95% CI, 1.23-2.46] for choledocholithiasis, and 1.31 [95% CI, 0.89-1.94] for acute cholangitis). The increased risk of the composite outcome associated with GLP-1RAs was more evident in patients aged 〉60 years, women, and 120 days after treatment initiation. Liraglutide, but not dulaglutide, was associated with an elevated risk.GLP-1RAs might be associated with an elevated risk of biliary-related diseases compared to SGLT2is in Asian patients with type 2 diabetes.

Pharmacoepidemiologic studies are increasingly conducted within linked databases, often to obtain richer confounder data. However, the potential for selection bias is frequently overlooked when linked data is available only for a subset of patients. We highlight the importance of accounting for potential selection bias by evaluating the association between antipsychotics and type 2 diabetes in youths within a claims database linked to a smaller laboratory database. We used inverse probability of treatment weights (IPTW) to control for confounding. In analyses restricted to the linked cohorts, we applied inverse probability of selection weights (IPSW) to create a population representative of the full cohort. We used pooled logistic regression weighted by IPTW only or IPTW and IPSW to estimate treatment effects. Metabolic conditions were more prevalent in linked cohorts compared with the full cohort. Within the full cohort, the confounding-adjusted hazard ratio was 2.26 (95% CI: 2.07, 2.49) comparing initiation of antipsychotics with initiation of control medications. Within the linked cohorts, a different magnitude of association was obtained without adjustment for selection, whereas applying IPSW resulted in point estimates similar to the full cohort’s (e.g., an adjusted hazard ratio of 1.63 became 2.12). Linked database studies may generate biased estimates without proper adjustment for potential selection bias.

Health plan claims may provide complete longitudinal data for timely, real-world population-level COVID-19 assessment. However, these data often lack laboratory results, the standard for COVID-19 diagnosis.We assessed the validity of ICD-10-CM diagnosis codes for identifying patients hospitalized with COVID-19 in U.S. claims databases, compared to linked laboratory results, among six Food and Drug Administration Sentinel System data partners (two large national insurers, four integrated delivery systems) from February 20-October 17, 2020. We identified patients hospitalized with COVID-19 according to five ICD-10-CM diagnosis code-based algorithms, which included combinations of codes U07.1, B97.29, general coronavirus codes, and diagnosis codes for severe symptoms. We calculated the positive predictive value (PPV) and sensitivity of each algorithm relative to laboratory test results. We stratified results by data source type and across three time periods: February 20-March 31 (Time A), April 1-30 (Time B), May 1-October 17 (Time C).The five algorithms identified between 34 806 and 47 293 patients across the study periods; 23% with known laboratory results contributed to PPV calculations. PPVs were high and similar across algorithms. PPV of U07.1 alone was stable around 93% for integrated delivery systems, but declined over time from 93% to 70% among national insurers. Overall PPV of U07.1 across all data partners was 94.1% (95% CI, 92.3%-95.5%) in Time A and 81.2% (95% CI, 80.1%-82.2%) in Time C. Sensitivity was consistent across algorithms and over time, at 94.9% (95% CI, 94.2%-95.5%).Our results support the use of code U07.1 to identify hospitalized COVID-19 patients in U.S. claims data.

The US Food and Drug Administration (FDA) created the Sentinel System in response to a requirement in the FDA Amendments Act of 2007 that the agency establish a system for monitoring risks associated with drug and biologic products using data from disparate sources. The Sentinel System has completed hundreds of analyses, including many that have directly informed regulatory decisions. The Sentinel System also was designed to support a national infrastructure for a learning health system. Sentinel governance and guiding principles were designed to facilitate Sentinel’s role as a national resource. The Sentinel System infrastructure now supports multiple non-FDA projects for stakeholders ranging from regulated industry to other federal agencies, international regulators, and academics. The Sentinel System is a working example of a learning health system that is expanding with the potential to create a global learning health system that can support medical product safety assessments and other research.

To conceptualize a particular target population and estimand for multi-site pharmacoepidemiologic studies within data networks and to analytically examine sample-standardization as a meta-analytic method compared with inverse-variance weighted meta-analyses.The target population of interest is all and only all individuals from the data-contributing sites. Standardization, a general conditioning technique frequently employed for confounding control, was adopted to estimate the network-wide causal treatment effect. Specifically, the proposed sample-standardization yields a meta-analysis estimator, that is, a weighted summation of site-specific results, where the weight for a site is the proportion of its size in the entire network. This sample-standardization estimator was evaluated analytically in comparison to estimators from inverse-variance weighted fixed-effect and random-effects meta-analyses in terms of statistical consistency.A proof is reported to justify the consistency of the sample-standardization estimator with and without treatment effect heterogeneity by site. Both inverse-variance weighted fixed-effect and random-effects meta-analyses were found to generally result in inconsistent estimators in the presence of treatment effect heterogeneity by site for this particular target population and estimand.Sample-standardization is a valid approach to generate causal inference in multi-site studies when the target population comprises all and only all individuals within the network, even in the presence of heterogeneity of treatment effect by site. Multi-site studies should clearly specify the target population and estimand to help select the most appropriate meta-analytic methods.

We previously found that antibiotic use at24 months of age was associated with slightly higher body weight at 5 years of age. In this study, we examine associations of early life antibiotic prescriptions with weight outcomes at 108 to 132 months of age ("10 years").We used electronic health record data from 2009 through 2016 from 10 health systems in PCORnet, a national distributed clinical research network. We examined associations of any (vs no) antibiotics at24 months of age with body mass index z-score (BMI-z) at 10 years adjusted for confounders selected a priori. We further examined dose response (number of antibiotic episodes) and antibiotic spectrum (narrow and broad).Among 56,727 included children, 57% received any antibiotics at24 months; at 10 years, mean (standard deviation) BMI-z was 0.54 (1.14), and 36% had overweight or obesity. Any versus no antibiotic use at24 months was associated with a slightly higher BMI-z at 10 years among children without a complex chronic condition (β 0.03; 95% confidence interval [CI] 0.01, 0.05) or with a complex chronic condition (β 0.09; 95% CI 0.03, 0.15). Any versus no antibiotic use was not associated with odds of overweight or obesity at 10 years among children without (odds ratio 1.02; 95% CI 0.97, 1.07) or with a complex chronic condition (odds ratio 1.07; 95% CI 0.96, 1.19).The small and likely clinically insignificant associations in this study are consistent with our previous 5-year follow-up results, suggesting that, if this relationship is indeed causal, early increases in weight are small but maintained over time.

The inverse probability weighted Cox model is frequently used to estimate the marginal hazard ratio. Its validity requires a crucial condition that the propensity score model be correctly specified. To provide protection against misspecification of the propensity score model, we propose a weighted estimation method rooted in the empirical likelihood theory. The proposed estimator is multiply robust in that it is guaranteed to be consistent when a set of postulated propensity score models contains a correctly specified model. Our simulation studies demonstrate satisfactory finite sample performance of the proposed method in terms of consistency and efficiency. We apply the proposed method to compare the risk of postoperative hospitalization between sleeve gastrectomy and Roux-en-Y gastric bypass using data from a large medical claims and billing database. We further extend the development to multisite studies to enable each site to postulate multiple site-specific propensity score models.

At the time medical products are approved, we rarely know enough about their comparative safety and effectiveness vis-à-vis alternative therapies to advise patients and providers. Postmarket generation of evidence on rare adverse events following medical product exposure increasingly requires analysis of millions of longitudinal patient records that can provide complete capture of data on patient experiences. In the accompanying article by Pradhan et al. (Am J Epidemiology. 2022;191(8):1352–1367), the authors demonstrate how observational database studies are often the most practical approach, provided these databases are carefully chosen to be “fit for purpose.” Distributed data networks with common data models have proliferated in the last 2 decades in pharmacoepidemiology, allowing efficient capture of patient data in a standardized and structured format across disparate real-world data sources. Use of common data models facilitates transparency by allowing standardized programming approaches that can be easily reproduced. The distributed data network architecture, combined with a common data approach, supports not only multisite observational studies but also pragmatic clinical trials. It also helps bridge international boundaries and further increases the sample size and diversity of study populations.

IntroductionThis study aimed to assess data relevancy and data quality of the Innovation in Medical Evidence Development and Surveillance System Distributed Database (IMEDS-DD) for diabetes research and to evaluate comparability of its type 2 diabetes cohort to the general type 2 diabetes population.Research design and methodsA retrospective study was conducted using the IMEDS-DD. Eligible members were adults with a medical encounter between April 1, 2018 and March 31, 2019 (index period). Type 2 diabetes and co-existing conditions were determined using all data available from April 1, 2016 to the most recent encounter within the index period. Type 2 diabetes patient characteristics, comorbidities and hemoglobin A1c(HbA1c) values were summarized and compared with those reported in national benchmarks and literature.ResultsType 2 diabetes prevalence was 12.6% in the IMEDS-DD. Of 4 14 672 patients with type 2 diabetes, 52.8% were male, and the mean age was 65.0 (SD 13.3) years. Common comorbidities included hypertension (84.5%), hyperlipidemia (82.8%), obesity (45.3%), and cardiovascular disease (44.7%). Moderate-to-severe chronic kidney disease was observed in 20.2% patients. The most commonly used antihyperglycemic agents included metformin (35.7%), sulfonylureas (14.8%), and insulin (9.9%). Less than one-half (48.9%) had an HbA1cvalue recorded. These findings demonstrated the notable similarity in patient characteristics between type 2 diabetes populations identified within the IMEDS-DD and other large databases.ConclusionsDespite the limitations related to HbA1cdata, our findings indicate that the IMEDS-DD contains robust information on key data elements to conduct pharmacoepidemiological studies in diabetes, including member demographic and clinical characteristics and health services utilization.

To examine the comparative effectiveness of glucagon-like peptide-1 receptor agonists (GLP-1RAs) and sodium-glucose cotransporter-2 (SGLT2) inhibitors for select cardiovascular outcomes and to examine whether the relative risks varied across different patient subgroups in patients with type 2 diabetes.We conducted a nationwide cohort study of patients with type 2 diabetes who initiated GLP-1RAs or SGLT2 inhibitors between 2012 and 2018 in Taiwan. The study outcomes included myocardial infarction and total stroke, further classified into ischaemic or haemorrhagic stroke. We estimated the hazard ratios (HRs) and 95% confidence intervals (CIs) for each outcome, comparing GLP-1RAs with SGLT2 inhibitors using Cox proportional hazards models after 1:1 propensity-score (PS) matching. We also examined if there was effect modification by age, underlying chronic kidney disease, or coexisting cardiovascular disease in prespecified subgroup analyses.Among 26 032 PS-matched patients, GLP-1RA initiators and SGLT2 inhibitor initiators showed similar risks of myocardial infarction (HR 0.99, 95% CI 0.65-1.52), total stroke (HR 0.90, 95% CI 0.69-1.17), ischaemic stroke (HR 0.86, 95% CI 0.65-1.14) and haemorrhagic stroke (HR 0.88, 95% CI 0.63-1.25). However, GLP-1RA treatment was associated with an increased risk of total stroke (HR 1.76, 95% CI 1.06-2.94) and ischaemic stroke (HR 1.88, 95% CI 1.09-3.23) among patients with chronic kidney disease, but not among patients without chronic kidney disease. GLP-1RA therapy seemed to have a lower risk of haemorrhagic stroke among patients with cardiovascular disease (HR 0.64, 95% CI 0.43-0.97), but not in patients without cardiovascular disease.Glucagon-like peptide-1 receptor agonists and SGLT2 inhibitors appeared to have comparable effectiveness with regard to several cardiovascular outcomes overall, but their comparative effectiveness may vary in certain patient subgroups.

PURPOSE Using real-world data to support regulatory decision has become a global movement. However, a robust platform for active surveillance of medical product safety has not been established in Taiwan. METHODS Following the common data model structure of the U.S. Food and Drug Administration's Sentinel System, we built the Taiwan Sentinel Data Model (TSDM) using the National Health Insurance Research Database with longitudinal claims data from 23 million individuals, linked death and cause of death data from a national registry, and linked electronic health record data from a delivery system. We examined the conversion of the TSDM using the Sentinel Data Quality Review and Characterization Programs in a sample of sex- and age-stratified cohort of 3 million individuals. RESULTS The TSDM fulfilled the requirements of data quality assurance. Only about 6% of sex and 0.0007% of birth year were missing, and

A fundamental question in using real-world data for clinical and regulatory decision making is: How certain must we be that the algorithm used to capture an exposure, outcome, cohort-defining characteristic, or confounder is what we intend it to be? We provide a practical framework to help researchers and regulators assess and classify the fit-for-purposefulness of real-world data by study variable for a range of data sources. The three levels of certainty (optimal, sufficient, and probable) must be considered in the context of each study variable, the specific question being studied, the study design, and the decision at hand.

Inverse probability weighted Cox models can be used to estimate marginal hazard ratios under different point treatments in observational studies. To obtain variance estimates, the robust sandwich variance estimator is often recommended to account for the induced correlation among weighted observations. However, this estimator does not incorporate the uncertainty in estimating the weights and tends to overestimate the variance, leading to inefficient inference. Here we propose a new variance estimator that combines the estimation procedures for the hazard ratio and weights using stacked estimating equations, with additional adjustments for the sum of terms that are not independently and identically distributed in a Cox partial likelihood score equation. We prove analytically that the robust sandwich variance estimator is conservative and establish the asymptotic equivalence between the proposed variance estimator and one obtained through linearization by Hajage et al. in 2018. In addition, we extend our proposed variance estimator to accommodate clustered data. We compare the finite sample performance of the proposed method with alternative methods through simulation studies. We illustrate these different variance methods in both independent and clustered data settings, using a bariatric surgery dataset and a multiple readmission dataset, respectively. To facilitate implementation of the proposed method, we have developed an R package ipwCoxCSV.

Supplemental Digital Content is available in the text., We use simulated data to examine the consequences of depletion of susceptibles for hazard ratio (HR) estimators based on a propensity score (PS). First, we show that the depletion of susceptibles attenuates marginal HRs toward the null by amounts that increase with the incidence of the outcome, the variance of susceptibility, and the impact of susceptibility on the outcome. If susceptibility is binary then the Bross bias multiplier, originally intended to quantify bias in a risk ratio from a binary confounder, also quantifies the ratio of the instantaneous marginal HR to the conditional HR as susceptibles are depleted differentially. Second, we show how HR estimates that are conditioned on a PS tend to be between the true conditional and marginal HRs, closer to the conditional HR if treatment status is strongly associated with susceptibility and closer to the marginal HR if treatment status is weakly associated with susceptibility. We show that associations of susceptibility with the PS matter to the marginal HR in the treated (ATT) though not to the marginal HR in the entire cohort (ATE). Third, we show how the PS can be updated periodically to reduce depletion-of-susceptibles bias in conditional estimators. Although marginal estimators can hit their ATE or ATT targets consistently without updating the PS, we show how their targets themselves can be misleading as they are attenuated toward the null. Finally, we discuss implications for the interpretation of HRs and their relevance to underlying scientific and clinical questions. See video Abstract: http://links.lww.com/EDE/B727.

Purpose The CHA2 DS2 -VaSc and HAS-BLED risk scores are commonly used in the studies of oral anticoagulants (OACs). The best ways to map these scores to the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) codes is unclear, as is how they perform in various types of OAC users. We aimed to assess the distributions of CHA2 DS2 -VaSc and HAS-BLED scores and C-statistics for outcome prediction in the ICD-10-CM era using different mapping strategies. Methods We compared the distributions of CHA2 DS2 -VaSc and HAS-BLED scores from various mapping strategies in atrial fibrillation patients before, during, and after ICD-10-CM transition. We estimated the C-statistics predicting the 90-day risk of hospitalized stroke (for CHA2 DS2 -VaSc) or hospitalized bleeding (for HAS-BLED) in patients identified at least 6 months after the ICD-10-CM transition, overall and by anticoagulant type. Results Forward-backward mapping produced higher CHA2 DS2 -VaSc and HAS-BLED scores in the ICD-10-CM era compared to the ICD-9-CM era: the mean difference was 0.074 (95% confidence interval 0.064-0.085) for CHA2 DS2 -VaSc and 0.055 (0.048-0.062) for HAS-BLED. Both scores had higher C-statistics in patients taking no OACs (0.697 [0.677-0.717] for CHA2 DS2 -VaSc; 0.719 [0.702-0.737] for HAS-BLED) or direct OACs (0.695 [0.654-0.735] for CHA2 DS2 -VaSc; 0.700 [0.673-0.728] for HAS-BLED) than those taking warfarin (0.655 [0.613-0.697] for CHA2 DS2 -VaSc; 0.663 [0.6320.695] for HAS-BLED). Conclusions Existing mapping strategies generally preserved the distributions of CHA2 DS2 -VaSc and HAS-BLED scores after ICD-10-CM transition. Both scores performed better in patients on no OACs or direct OACs than patients on warfarin.

Following the release of the framework for the Real-World Evidence (RWE) Program, the US Food and Drug Administration (FDA) is actively evaluating and exploring ways to optimize the utility of real-world data (RWD) and RWE to support regulatory decision making. For rare conditions, conducting traditional randomized clinical trials may not always be feasible, and RWD and RWE have played and will continue to play an important role. We use three case examples-cerliponase alfa, asfotase alfa, and uridine triacetate-to illustrate how RWD from disease registries, medical records with chart review, and literature, respectively, have been used to generate RWE to support regulatory decisions for selected rare diseases. These examples highlight the need for improving data reliability and quality in existing data to expand use of RWD and RWE beyond "hard endpoints" and standardizing data collection for outcome measures in patient registries to expand its utility. We also discuss a recent FDA guidance for using RWE in supporting rare disease drug development, including its recommendations about using natural history studies as external control groups for single-arm interventional trials. The external control group needs to be comparable with the treated group. Selection bias and confounding are major concerns because of lack of randomization and unrecognized baseline differences. Use of valid epidemiological approaches can reduce these biases. Lastly, we discuss future directions to expand the use of RWD and RWE to support orphan drug approvals, including the need for including patient experience data as an important source of RWD.

A wide range of analytic and data sharing options are available in non-experimental multi-database studies designed to assess the real-world benefits and risks of medical products. Researchers often consider six scientific domains when choosing among these options – study design, exposure type, outcome type, covariate summarization technique, covariate adjustment method, and data sharing approach. This article reviews available analytic and data sharing options and discuss key scientific and practical considerations when choosing among these options in multi-database studies of comparative effectiveness and safety of medical products. The scientific considerations must be balanced against what the data-contributing sites are able or willing to share. While pooling of person-level datasets remains the most familiar and analytically flexible approach, newer analytic and data sharing approaches that share less granular summary-level information may be equally valid and preferred in some multi-database studies, especially when sharing of person-level data is challenging or infeasible.

Increasing availability of electronic health databases capturing real-world experiences with medical products has garnered much interest in their use for pharmacoepidemiologic and pharmacovigilance studies. The traditional practice of having numerous groups use single databases to accomplish similar tasks and address common questions about medical products can be made more efficient through well-coordinated multi-database studies, greatly facilitated through distributed data network (DDN) architectures. Access to larger amounts of electronic health data within DDNs has created a growing interest in using data-adaptive machine learning (ML) techniques that can automatically model complex associations in high-dimensional data with minimal human guidance. However, the siloed storage and diverse nature of the databases in DDNs create unique challenges for using ML. In this paper, we discuss opportunities, challenges, and considerations for applying ML in DDNs for pharmacoepidemiologic and pharmacovigilance studies. We first discuss major types of activities performed by DDNs and how ML may be used. Next, we discuss practical data-related factors influencing how DDNs work in practice. We then combine these discussions and jointly consider how opportunities for ML are affected by practical data-related factors for DDNs, leading to several challenges. We present different approaches for addressing these challenges and highlight efforts that real-world DDNs have taken or are currently taking to help mitigate them. Despite these challenges, the time is ripe for the emerging interest to use ML in DDNs, and the utility of these data-adaptive modeling techniques in pharmacoepidemio- logic and pharmacovigilance studies will likely continue to increase in the coming years.

Current algorithms to evaluate gestational age (GA) during pregnancy rely on hospital coding at delivery and are not applicable to non-live births. We developed an algorithm using fertility procedures and fertility tests, without relying on delivery coding, to develop a novel GA algorithm in live-births and stillbirths.Three pregnancy cohorts were identified from 16 health-plans in the Sentinel System: 1) hospital admissions for live-birth, 2) hospital admissions for stillbirth, and 3) medical chart-confirmed stillbirths. Fertility procedures and prenatal tests, recommended within specific GA windows were evaluated for inclusion in our GA algorithm. Our GA algorithm was developed against a validated delivery-based GA algorithm in live-births, implemented within a sample of chart-confirmed stillbirths, and compared to national estimates of GA at stillbirth.Our algorithm, including fertility procedures and 11 prenatal tests, assigned a GA at delivery to 97.9% of live-births and 92.6% of stillbirths. For live-births (n = 4 701 207), it estimated GA within 2 weeks of a reference delivery-based GA algorithm in 82.5% of pregnancies, with a mean difference of 3.7 days. In chart-confirmed stillbirths (n = 49), it estimated GA within 2 weeks of the clinically recorded GA at delivery for 80% of pregnancies, with a mean difference of 11.1 days. Implementation of the algorithm in a cohort of stillbirths (n = 40 484) had an increased percentage of deliveries after 36 weeks compared to national estimates.In a population of primarily commercially-insured pregnant women, fertility procedures and prenatal tests can estimate GA with sufficient sensitivity and accuracy for utility in pregnancy studies.

To examine whether depression status before metabolic and bariatric surgery (MBS) influenced 5-year weight loss, diabetes, and safety/utilization outcomes in the PCORnet Bariatric Study.Research on the impact of depression on MBS outcomes is inconsistent with few large, long-term studies.Data were extracted from 23 health systems on 36,871 patients who underwent sleeve gastrectomy (SG; n=16,158) or gastric bypass (RYGB; n=20,713) from 2005-2015. Patients with and without a depression diagnosis in the year prior to MBS were evaluated for % total weight loss (%TWL), diabetes outcomes, and postsurgical safety/utilization (reoperations, revisions, endoscopy, hospitalizations, mortality) at 1, 3 and 5 years after MBS.27.1% of SG and 33.0% of RYGB patients had preoperative depression, and they had more medical and psychiatric comorbidities than those without depression. At 5 years of follow-up, those with depression, versus those without depression, had slightly less %TWL after RYGB, but not after SG (between group difference = 0.42%TWL, p=0.04). However, patients with depression had slightly larger HbA1c improvements after RYGB but not after SG (between group difference = -0.19, p=0.04). Baseline depression did not moderate diabetes remission or relapse, reoperations, revision, or mortality across operations; however, baseline depression did moderate the risk of endoscopy and repeat hospitalization across RYGB versus SG.Patients with depression undergoing RYGB and SG had similar weight loss, diabetes, and safety/utilization outcomes to those without depression. The effects of depression were clinically small compared to the choice of operation.

The US Sentinel System and the Canadian Network for Observational Drug Effect Studies (CNODES) are two medical product safety surveillance networks. Using Sentinel's preprogrammed, parameterizable analytic tools, we reproduced two protocol-based studies conducted by CNODES to assess the risks of acute pancreatitis and heart failure (HF) associated with the use of incretin-based drugs, compared with use of ≥ 2 oral hypoglycemic agents. Results from the replication new-user cohort analyses aligned with those from the CNODES nested case-control studies. The adjusted hazard ratios were 0.95 (0.81-1.12; vs. 1.03 (0.87-1.22) in CNODES) for acute pancreatitis and 0.91 (0.84-1.00; vs. 0.82 (0.67-1.00) in CNODES) for HF among patients without HF history. The CNODES's common protocol approach allows studies tailored to specific safety questions, whereas the Sentinel's common data model plus pretested program approach enables more rapid analysis. Despite these differences, it is possible to obtain comparable results using both approaches.

Background Multi-center studies can generate robust and generalizable evidence, but privacy considerations and legal restrictions often make it challenging or impossible to pool individual-level data across data-contributing sites. With binary outcomes, privacy-protecting distributed algorithms to conduct logistic regression analyses have been developed. However, the risk ratio often provides a more transparent interpretation of the exposure-outcome association than the odds ratio. Modified Poisson regression has been proposed to directly estimate adjusted risk ratios and produce confidence intervals with the correct nominal coverage when individual-level data are available. There are currently no distributed regression algorithms to estimate adjusted risk ratios while avoiding pooling of individual-level data in multi-center studies. Methods By leveraging the Newton-Raphson procedure, we adapted the modified Poisson regression method to estimate multivariable-adjusted risk ratios using only summary-level information in multi-center studies. We developed and tested the proposed method using both simulated and real-world data examples. We compared its results with the results from the corresponding pooled individual-level data analysis. Results Our proposed method produced the same adjusted risk ratio estimates and standard errors as the corresponding pooled individual-level data analysis without pooling individual-level data across data-contributing sites. Conclusions We developed and validated a distributed modified Poisson regression algorithm for valid and privacy-protecting estimation of adjusted risk ratios and confidence intervals in multi-center studies. This method allows computation of a more interpretable measure of association for binary outcomes, along with valid construction of confidence intervals, without sharing of individual-level data.

The Observational Patient Evidence for Regulatory Approval Science and Understanding Disease (OPERAND) project examines whether real-world data (RWD) can be used to inform regulatory decision making.OPERAND evaluates whether observational analyses using RWD to emulate index trials can produce effect estimates similar to those of the trials and examines the impact of relaxing the eligibility criteria of the observational analyses to obtain samples that more closely match the real-world populations receiving the treatments. In OPERAND, 2 research teams independently attempt to emulate the ROCKET Atrial Fibrillation and LEAD-2 trials using OptumLabs data. This article describes the design of the project, summarizes the approaches of the 2 research teams, and presents feasibility results for 2 emulations using new-user designs.There were differences in the teams' conceptualizations of the emulation, design decisions for cohort identification, and resulting RWD cohorts. These differences occurred even though both teams were guided by the same index trials and had access to the same source of RWD.Reasonable alternative design and analysis approaches may be taken to answer the same research question, even when attempting to emulate the same index trial. Researcher decision making is an understudied and potentially important source of variability across RWD analyses.

Importance Launch prices of new cancer drugs in the US have substantially increased in recent years despite growing concerns about the quantity and quality of evidence supporting their approval by the US Food and Drug Administration (FDA). Objective To assess the use of and spending on new oral targeted cancer drugs among US residents with employer-sponsored insurance between 2011 and 2018, stratified by the strength of available evidence of benefit. Design, Setting, and Participants In this cross-sectional study, dispensing claims for oral targeted cancer drugs first approved by the FDA between January 1, 2011, and December 31, 2018, were analyzed. The number of patients with drugs dispensed and the total payment for all claims were aggregated by calendar year, and these outcomes were arrayed according to evidence underlying FDA approvals, including pivotal study design (availability of randomized clinical trials) and overall survival (OS) benefit, as documented in drug labels. This study was conducted from July 17, 2019, to July 23, 2021. Main Outcomes and Measures Annual and cumulative numbers of patients who had dispensing events, and annual and cumulative sums of payment for eligible drugs. Results Of 37 348 patients who had at least 1 of the 44 new oral targeted drugs dispensed between 2011 and 2018, 21 324 were men (57.1%); mean (SD) age was 64.1 (13.1) years. Most individuals (36 246 [97.0%]) received drugs for which evidence from randomized clinical trials existed; however, a growing share of patients received drugs without documented OS benefit during the study period: from 12.7% in 2011 to 58.8% in 2018. Cumulative spending on all sample drugs totaled $3.5 billion by the end of 2018, of which 96.8% was spent on drugs that were approved based on a pivotal randomized clinical trial. Cumulative spending on drugs without documented OS benefit ($1.8 billion [51.6%]) surpassed that on drugs with documented OS benefit ($1.7 billion [48.4%]) by the end of 2018. Conclusions and Relevance The findings of this cross-sectional study suggest that drugs used for treatment of cancer without documented OS benefits are adopted in the health system and account for substantial spending.

ImportanceBariatric surgery is the most effective treatment for severe obesity; yet it is unclear whether the long-term safety and comparative effectiveness of these operations differ across racial and ethnic groups.ObjectiveTo compare outcomes of Roux-en-Y gastric bypass (RYGB) and sleeve gastrectomy (SG) across racial and ethnic groups in the National Patient-Centered Clinical Research Network (PCORnet) Bariatric Study.Design, Setting, and ParticipantsThis was a retrospective, observational, comparative effectiveness cohort study that comprised 25 health care systems in the PCORnet Bariatric Study. Patients were adults and adolescents aged 12 to 79 years who underwent a primary (first nonrevisional) RYGB or SG operation between January 1, 2005, and September 30, 2015, at participating health systems. Patient race and ethnicity included Black, Hispanic, White, other, and unrecorded. Data were analyzed from July 1, 2021, to January 17, 2022.ExposureRYGB or SG.OutcomesPercentage total weight loss (%TWL); type 2 diabetes remission, relapse, and change in hemoglobin A1c (HbA1c) level; and postsurgical safety and utilization outcomes (operations, interventions, revisions/conversions, endoscopy, hospitalizations, mortality, 30-day major adverse events) at 1, 3, and 5 years after surgery.ResultsA total of 36 871 patients (mean [SE] age, 45.0 [11.7] years; 29 746 female patients [81%]) were included in the weight analysis. Patients identified with the following race and ethnic categories: 6891 Black (19%), 8756 Hispanic (24%), 19 645 White (53%), 826 other (2%), and 783 unrecorded (2%). Weight loss and mean reductions in HbA1c level were larger for RYGB than SG in all years for Black, Hispanic, and White patients (difference in 5-year weight loss: Black, −7.6%; 95% CI, −8.0 to −7.1; P < .001; Hispanic, −6.2%; 95% CI, −6.6 to −5.9; P < .001; White, −5.9%; 95% CI, −6.3 to −5.7; P < .001; difference in change in year 5 HbA1c level: Black, −0.29; 95% CI, −0.51 to −0.08; P = .009; Hispanic, −0.45; 95% CI, −0.61 to −0.29; P < .001; and White, −0.25; 95% CI, −0.40 to −0.11; P = .001.) The magnitude of these differences was small among racial and ethnic groups (1%-3% of %TWL). Black and Hispanic patients had higher risk of hospitalization when they had RYGB compared with SG (hazard ratio [HR], 1.45; 95% CI, 1.17-1.79; P = .001 and 1.48; 95% CI, 1.22-1.79; P < .001, respectively). Hispanic patients had greater risk of all-cause mortality (HR, 2.41; 95% CI, 1.24-4.70; P = .01) and higher odds of a 30-day major adverse event (odds ratio, 1.92; 95% CI, 1.38-2.68; P < .001) for RYGB compared with SG. There was no interaction between race and ethnicity and operation type for diabetes remission and relapse.Conclusions and RelevanceVariability of the comparative effectiveness of operations for %TWL and HbA1c level across race and ethnicity was clinically small; however, differences in safety and utilization outcomes were clinically and statistically significant for Black and Hispanic patients who had RYGB compared with SG. These findings can inform shared decision-making regarding bariatric operation choice for different racial and ethnic groups of patients.

Current evidence is inconclusive on cognitive benefits or harms of statins among stroke patients, who have high risk of dementia. This observational cohort study investigated the association between statin use and post-stroke dementia using data from the Clinical Practice Research Datalink. Patients without prior dementia who had an incident stroke but received no statins in the preceding year were followed for up to 10 years. We used inverse probability weighted marginal structural models to estimate observational analogues of intention-to-treat (ITT, statin initiation versus no initiation) and per-protocol (PP, sustained statin use versus no use) effects on the risk of dementia. To explore potential impact of unmeasured confounding, we examined the risks of coronary heart disease (CHD, positive control outcome), fracture and peptic ulcer (negative control outcomes). In 18,577 statin initiators and 14,613 non-initiators (mean follow-up of 4.2 years), the adjusted hazard ratio (aHR) for dementia was 0.70 (95% confidence interval [CI] 0.64-0.75) in ITT analysis and 0.55 (95%CI 0.50-0.62) in PP analysis. The corresponding aHRITT and aHRPP were 0.87 (95%CI 0.79-0.95) and 0.70 (95%CI 0.620.80) for CHD, 1.03 (95%CI 0.82-1.29) and 1.09 (95%CI 0.77-1.54) for peptic ulcer, and 0.88 (95%CI 0.80-0.96) and 0.86 (95%CI 0.75-0.98) for fracture. Statin initiation after stroke was associated with lower risk of dementia, with a potentially greater benefit in patients who persisted with statins over time. The observed association of statin use with post-stroke dementia may in part be overestimated due to unmeasured confounding shared with the association between statin use and fracture., NIHR

Objective:We describe the baseline characteristics and complications of individuals with influenza in the US FDA’s Sentinel System by antiviral treatment timing.Design:Retrospective cohort design.Patients:Individuals aged ≥6 months with outpatient diagnoses of influenza in June 2014–July 2017, 3 influenza seasons.Methods:We identified the comorbidities, vaccination history, influenza testing, and outpatient antiviral dispensings of individuals with influenza using administrative claims data from 13 data partners including the Centers for Medicare and Medicaid Services, integrated delivery systems, and commercial health plans. We assessed complications within 30 days: hospitalization, oxygen use, mechanical ventilation, critical care, ECMO, and death.Results:There were 1,090,333 influenza diagnoses in 2014–2015; 1,005,240 in 2016–2017; and 578,548 in 2017–2018. Between 49% and 55% of patients were dispensed outpatient treatment within 5 days. In all periods >80% of treated individuals received treatment on the day of diagnosis. Those treated on days 1–5 after diagnosis had higher prevalences of diabetes, chronic obstructive pulmonary disease, asthma, and obesity compared to those treated on the day of diagnosis or not treated at all. They also had higher rates of hospitalization, oxygen use, and critical care. In 2014–2015, among those aged ≥65 years, the rates of hospitalization were 45 per 1,000 diagnoses among those treated on day 0; 74 per 1,000 among those treated on days 1–5; and 50 per 1,000 among those who were untreated.Conclusions:In a large, national analysis, approximately half of people diagnosed with influenza in the outpatient setting were treated with antiviral medications. Delays in outpatient dispensed treatment were associated with higher prevalence of comorbidities and higher rates of complication.

Purpose To evaluate the effect of diagnostic coding system transition on the identification of common conditions recorded in Taiwan's national claims database. Methods Using the National Health Insurance Research Database, we estimated the 3-month prevalence of recorded diagnosis of 32 conditions based on the ICD-9-CM codes in 2014-2015 and the ICD-10-CM codes in 2016-2017. Two algorithms were assessed for ICD-10-CM: validated ICD-10 codes in the literature and codes translated from ICD-9-CM using an established mapping algorithm. We used segmented regression analysis on time-series data to examine changes in the 3-month prevalence (both level and trend) before and after the ICD-10-CM implementation. Results Significant changes in the level were found in 19 and 11 conditions when using the ICD-10 codes from the literature and mapping algorithm, respectively. The conditions with inconsistent levels by both of the algorithms were valvular heart disease, peripheral vascular disease, mild liver disease, moderate to severe liver disease, metastatic cancer, rheumatoid arthritis and collagen vascular diseases, coagulopathy, blood loss anemia, deficiency anemia, alcohol abuse, and psychosis. Nine conditions had significant changes in the trend when using the ICD-10 codes from the literature or mapping algorithm. Conclusions Less than half of the 32 conditions studied had a smooth transition between the ICD-9-CM and ICD-10-CM coding systems. Researchers should pay attention to the conditions where the coding definitions result in inconsistent time series estimates.

BACKGROUND Privacy-protecting analytic approaches without centralized pooling of individual-level data, such as distributed regression, are particularly important for vulnerable populations, such as children, but these methods have not yet been tested in multi-center pediatric studies. METHODS Using the electronic health data from 34 healthcare institutions in the National Patient-Centered Clinical Research Network (PCORnet), we fit 12 multivariable-adjusted linear regression models to assess the associations of antibiotic use

The inverse probability weighted Cox proportional hazards model can be used to estimate the marginal hazard ratio. In multi-site studies, it may be infeasible to pool individual-level datasets due to privacy and other considerations. We propose three methods for making inference on hazard ratios without the need for pooling individual-level datasets across sites. The first method requires a summary-level eight-column risk-set table to produce the same hazard ratio estimate and robust sandwich variance estimate as those from the corresponding pooled individual-level data analysis (reference analysis). The second and third methods, which are based on two bootstrap re-sampling strategies, require a summary-level four-column risk-set table and bootstrap-based risk-set tables from each site to produce the same hazard ratio and bootstrap variance estimates as those from their reference analyses. All three methods require only one file transfer between the data-contributing sites and the analysis center. We justify these methods theoretically, illustrate their use, and demonstrate their statistical performance using both simulated and real-world data.

PURPOSE Bootstrapping can account for uncertainty in propensity score (PS) estimation and matching processes in 1:1 PS-matched cohort studies. While theory suggests that the classical bootstrap can fail to produce proper coverage, practical impact of this theoretical limitation in settings typical to pharmacoepidemiology is not well studied. METHODS In a plasmode-based simulation study, we compared performance of the standard parametric approach, which ignores uncertainty in PS estimation and matching, with two bootstrapping methods. The first method only accounted for uncertainty introduced during the matching process (the observation resampling approach). The second method accounted for uncertainty introduced during both PS estimation and matching processes (the PS reestimation approach). Variance was estimated based on percentile and empirical standard errors, and treatment effect estimation was based on median and mean of the estimated treatment effects across 1000 bootstrap resamples. Two treatment prevalence scenarios (5% and 29%) across two treatment effect scenarios (hazard ratio of 1.0 and 2.0) were evaluated in 500 simulated cohorts of 10 000 patients each. RESULTS We observed that 95% confidence intervals from the bootstrapping approaches but not the standard approach, resulted in inaccurate coverage rates (98%-100% for the observation resampling approach, 99%-100% for the PS reestimation approach, and 95%-96% for standard approach). Treatment effect estimation based on bootstrapping approaches resulted in lower bias than the standard approach (less than 1.4% vs 4.1%) at 5% treatment prevalence; however, the performance was equivalent at 29% treatment prevalence. CONCLUSION Use of bootstrapping led to variance overestimation and inconsistent coverage, while coverage remained more consistent with parametric estimation.

Background: The incidence of pregnancy-associated cancer (PAC) is expected to increase as more women delay childbearing until later ages. However, information on frequency and incidence of PAC is scarce in the United States. Methods: We identified pregnancies among women aged 10–54 years during 2001–2013 from five U.S. health plans participating in the Cancer Research Network (CRN) and the Medication Exposure in Pregnancy Risk Evaluation Program (MEPREP). We extracted information from the health plans' administrative claims and electronic health record databases, tumor registries, and infants' birth certificate files to estimate the frequency and incidence of PAC, defined as cancer diagnosed during pregnancy and up to 1 year postpartum. Results: We identified 846 PAC events among 775,709 pregnancies from 2001 to 2013. The overall incidence estimate was 109.1 (95% confidence interval [CI] = 101.8–116.7) per 100,000 pregnancies. There was an increase in the incidence between 2002 and 2012 (the period during which complete data were available), from 75.0 (95% CI = 54.9–100.0) per 100,000 pregnancies in 2002 to 138.5 (95% CI = 109.1–173.3) per 100,000 pregnancies in 2012. The most common invasive cancers diagnosed were breast (n = 208, 24.6%), thyroid (n = 168, 19.9%), melanoma (n = 93, 11.0%), hematologic (n = 87, 10.3%), and cervix/uterus (n = 74, 8.7%). Conclusions: Our study provides contemporary incidence estimates of PAC from a population-based cohort of U.S. women. These estimates provide the data needed to help develop clinical and public health policies aimed at diagnosing PAC at an early stage and initiating appropriate therapeutic interventions in a timely manner.