Pharmacoepidemiology and Drug Safety最新文献

Purpose: Golimumab (GLM), an anti-tumour necrosis factor alpha (anti-TNFα) agent, is indicated for moderate to severe ulcerative colitis (UC). This post-authorisation safety study evaluated the risk of colectomy due to intractable disease and advanced colonic neoplasia (high-grade dysplasia and/or colorectal cancer) under real-world conditions of GLM use.

Methods: This bidirectional cohort study using Spanish ENEIDA registry data (2013-2022) included adults with UC who initiated GLM, other anti-TNFα agents, or thiopurines (TPs). Crude risk analyses-and, when feasible, multivariable models-in cohort and nested case-control designs were performed. For colectomy, we evaluated exposure to GLM only, other anti-TNFα agents, and both (i.e., overlapping exposure). For ACN, we evaluated exposure to GLM, other anti-TNFα agents, and TPs.

Results: Sixty-four colectomy cases and 10 ACN cases were identified among patients exposed to GLM (N = 474), other anti-TNFα agents (N = 1737), or TPs (N = 1380). Incidence rates per 1000 person-years and 95% confidence intervals were reported for colectomy (GLM-only [4.4, 1.2-11.2] and other anti-TNFα agents only [12.4, 9.1-16.5]) and ACN (GLM [1.5, 0.2-5.4], other anti-TNFα agents [1.3, 0.5-2.8], and TPs [1.0, 0.3-2.6]). In comparisons excluding overlapping exposure, GLM was not associated with an increased risk of colectomy versus other anti-TNFα agents. GLM was also not associated with an increased risk of ACN versus either comparator. Observed events, especially for ACN, were limited for all exposures.

Conclusions: Findings do not indicate an increased risk of colectomy due to intractable disease or ACN with GLM use versus other therapies for similar disease severity in routine UC care (EUPAS15752).

Purpose: To understand the impact of standardizing administrative healthcare data to the Sentinel common data model for cohort selection and descriptive findings.

Methods: Among patients with an outpatient COVID-19 diagnosis (January 2021-December 2022) in HealthVerity using the data in its native and the standardized format, we descriptively compared cohort attrition and sample size, patient characteristics, and healthcare resource utilization during baseline and incidence of selected conditions after COVID-19 diagnosis.

Results: The standardized cohort included fewer patients than the native (164 445 vs. 198 317), but age (median 48 years) and sex (70% female) were the same. The distribution of race was similar; however, the standardized cohort mapped patients with "Other" race to the "Unknown/Missing" race category, which created differences among those categories. Distributions were similar, albeit slightly lower for comorbidities (differences < 1%), and lower for SARS-CoV-2 diagnostic tests (59% vs. 70%). Medical encounter counts were also lower, with substantial differences that were attenuated after limiting encounter counts to one event per day (e.g., mean count of 6.0 vs. 27.7 specialty care visits reduced to 2.9 vs. 3.5). Incidence rates were lower, with the greatest difference for hepatotoxicity (29.6 vs. 37.1 per 1000 person-years).

Conclusions: The data standardization refines the data (e.g., removes duplicate claims and variables or variable categories), which may reduce outliers and errors but yield lower distributions and counts of certain variables than observed in native format data. Therefore, it is critical to understand how standardization impacts the data and subsequently its fitness for use.

Purpose: To describe an international response to the COVID-19 pandemic by estimating the prevalence of medication use for COVID-19 treatment in pregnancy, stratified by hospitalization, trimester of pregnancy, and country.

Methods: We conducted a two-stage individual participant data meta-analysis of proportions from primary data on medications used to treat COVID-19 during pregnancy. A common data model was developed to pool the data from single-country and international registries. Data from pregnant individuals with COVID-19 between February 2020 and October 2022 were included in study platforms across 9 data sources. Patient information was abstracted from medical records.

Results: Among 24 937 pregnant individuals, the pooled prevalences of individuals receiving medications to treat COVID-19 were: 34.7% heparin, 9.8% antibiotics, 4.9% corticosteroids, 2.2% antivirals, 0.8% antimalarials, 0.3% convalescent plasma, 0.2% immunosuppressants, and 0.02% monoclonal antibodies. Prevalence of medication use was higher in hospitalized individuals than in non-hospitalized individuals: 58.4% versus 17.9% for heparin, 26.9% versus 5.7% for antibiotics, 17.5% versus 1.3% for corticosteroids, 10.3% versus 0.3% for antivirals, and 4.5% versus 0.1% for antimalarials. The prevalence of corticosteroid use was lower in the first trimester (0.1%) compared with the second (7.2%) and third (4.9%) trimesters of pregnancy. The prevalence of medications differed widely across countries.

Conclusion: Medication to treat COVID-19 was more frequently used in pregnant individuals hospitalized for COVID-19. Corticosteroids were used less in the first trimester of pregnancy. The differences in use between countries could reflect differences in the clinical management and access to medications for this population at risk of severe disease.

Background: Variable selection is essential for propensity score (PS)-weighted estimators. Recent work shows that including instrumental variables (IVs), associated with only treatment but not with the outcome, can impact both the bias and precision of the PS-weighted estimators.

Methods: The outcome-adaptive lasso (OAL) is an innovative model-based method adapting the popular adaptive lasso variable selection to causal inference. It attempts to identify IVs, so one can exclude them from the PS model. Unlike the model-based approach, stable balancing weighting (SBW) estimates inverse probability weights directly while minimizing the variance of the weights and covariate imbalance simultaneously. Based on its variance optimization algorithm, SBW may provide some protection against the impact of IVs. Lastly, we considered stable confounder selection (SCS), which assesses the stability of model-based effect estimates.

Results: The authors present the results of simulation studies to investigate which method performs the best when moderate or strong IVs are used. The simulation studies consider IVs and spurious variables to generate extreme PSs. In simulations, SBW generally outperformed OAL and SCS in terms of reducing mean squared error, notably when the IVs were strong, and many covariates were highly correlated. Our empirical application to the effect of abciximab treatment demonstrates that SBW is a robust method to effectively handle limited overlap.

Conclusions: Our numerical results support the use of SBW in situations where IVs or near-IVs may lead to practical violations of positivity assumptions.

Pharmacoepidemiology should represent and benefit populations equitably, embracing diversity and equity, and ensuring fairness. This article describes equity and fairness in pharmacoepidemiology, depicts key diversity domains, and provides an operational framework and call for action to implement diversity and fairness in pharmacoepidemiologic research. To ensure fairness, studies should address diversity and inclusion while providing equal opportunities and benefits for everyone in the target population. To implement and evaluate fairness in pharmacoepidemiology, we defined the following diversity domains: biological sex, socially constructed gender, age, life stages (e.g., pregnancy, menopause), ethnicity, race, migration, nationality, socioeconomic status, education, health literacy, and health status and capabilities. These are determinants of health, either through biological pathways or through social norms, discrimination, and barriers to healthcare or research participation. They are interlinked, their impact is study- and context-specific, and due to their sensitive and evolving nature, they should be handled with caution. Implementing diversity domains enables researchers to assess the generalizability of findings, identify and address health inequities, account for determinants of health, and ensure the fairness of algorithms, implementations, and recommendations. To successfully implement diversity domains and ensure fair pharmacoepidemiologic research, we recommend researchers to follow the Explore, Tailor, Implement, and Evaluate (ETIE) framework: Explore the role/implication of the diversity domains in the study, tailor their definitions to the study context, implement them appropriately and evaluate the study findings in their context. Increased availability of diversity data is needed, and support from stakeholders is essential. This manuscript was endorsed by the International Society for Pharmacoepidemiology (ISPE).

Multi-database distributed data networks for post-marketing surveillance of drug safety and effectiveness use two main approaches: common data models (CDMs) and common protocols. Networks such as the U.S. Sentinel System, the Observational Health Data Sciences and Informatics (OHDSI) network, and the Data Analysis and Real-World Interrogation Network in Europe (DARWIN-EU) use a CDM approach in which participating databases are translated into a standardized structure so that a single, common analytic program can be used. On the other hand, the common protocol approach involves applying a single common protocol to site-specific data maintained in their native format, with analytic programs tailored to each data source. Some networks, such as the Canadian Network for Observational Drug Effect Studies (CNODES) and the Asian Pharmacoepidemiology Network (AsPEN), use a variety of approaches for multi-database studies. Regardless of the approach, distributed networks support comprehensive pharmacoepidemiologic studies by leveraging large-scale health data. For example, utilization studies can uncover prescribing trends in different jurisdictions and the impact of policy changes on drug use, while safety and effectiveness studies benefit from large, diverse patient populations, leading to increased precision, representativeness, and potential early detection of safety threats. Challenges include varying coding practices and data heterogeneity, which complicate the standardization of evidence and the comparability and generalizability of findings. In this Core Concepts paper, we review the purpose and different types of distributed data networks in pharmacoepidemiology, discuss their advantages and disadvantages, and describe commonly faced challenges and opportunities in conducting research using multi-database networks.

Purpose: Health administrative databases often contain no information on some important confounders, leading to residual confounding in the effect estimate. We aimed to explore the performance of high-dimensional disease risk score (hdDRS) to deal with residual confounding bias for estimating causal effects with survival outcomes.

Methods: We used health administrative data of 49 197 individuals in British Columbia to examine the relationship between tuberculosis infection and time-to-development of cardiovascular disease (CVD). We designed a plasmode simulation exploring the performance of eight hdDRS methods that varied by different approaches to fit the risk score model and also examined results from high-dimensional propensity score (hdPS) and traditional regression adjustment. The log-hazard ratio (log-HR) was the target parameter with a true value of log(3).

Results: In the presence of strong unmeasured confounding, the bias observed was -0.11 for the traditional method and -0.047 for the hdPS method. The bias ranged from -0.051 to -0.058 for hdDRS methods when risk score models were fitted to the full cohort and -0.045 to -0.049 when risk score models were fitted only to unexposed individuals. All methods showed comparable standard errors and nominal bias-eliminated coverage probabilities. With weak unmeasured confounding, hdDRS and hdPS produced approximately unbiased estimates. Our data analysis, after addressing residual confounding, revealed an 8%-11% higher CVD risk associated with tuberculosis infection.

Conclusions: Our findings support the use of selected hdDRS methods to address residual confounding bias when estimating treatment effects with survival outcomes. In particular, the hdDRS method using rate-based risk score modeling on unexposed individuals consistently exhibited the least bias. However, the hdPS method showed comparable performance across most evaluated scenarios. We share reproducible R codes to facilitate researchers' adoption and further evaluation of these methods.

Background: Despite the cardiovascular benefits of sodium-glucose cotransporter 2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP1RA) in patients with type 2 diabetes mellitus (T2DM) and atherosclerotic cardiovascular disease (ASCVD), their utilization remains low globally. This study aimed to evaluate the utilization of SGLT2i and GLP1RA in patients with T2DM and ASCVD, as well as the factors associated with their use in South Korea.

Methods: We conducted a retrospective study using the National Patient Sample claims data from 2015 to 2020. Adults aged 20 years or older with confirmed diagnoses of both T2DM and ASCVD between March 1 and October 31 of each year were included. The utilization of SGLT2i and GLP1RA was assessed based on prescriptions filled within 60 days of the index date. Multivariable logistic regression was used to identify factors associated with their use. Annual trends in utilization were evaluated using the Cochran-Armitage trend test.

Results: In our study of 57 576 study population, the use of SGLT2i increased from 1.20% in 2015 to 10.51% by 2020. GLP1RA usage increased from 0% to 1.17% over the same period. Older age, chronic kidney disease (OR 0.52, 95% CI 0.41-0.66), and concurrent use of dipeptidyl peptidase 4 inhibitors (DPP4i) (OR 0.09, 95% CI 0.09-0.10) significantly reduced the likelihood of SGLT2i use. In contrast, factors such as comorbid dyslipidemia (OR 1.41, 95% CI 1.25-1.60), heart failure (OR 1.22, 95% CI 1.09-1.37), concurrent use of sulfonylurea (SU) (OR 1.30, 95% CI 1.20-1.40), and prescriptions from cardiologists (OR 1.22, 95% CI 1.07-1.40) were positively associated with higher SGLT2i usage. For GLP1RA, negative influences included older age, concurrent DPP4i use (OR 0.12, 95% CI 0.08-0.16), and non-endocrinologist prescription, whereas female sex (OR 1.35, 95% CI 1.06-1.73), dyslipidemia (OR 1.68, 95% CI 1.10-2.66), and the use of insulin (OR 3.71, 95% CI 2.83-4.85), or SU (OR 3.13, 95% CI 2.44-4.02) use were positive factors.

Conclusions: Despite the known cardiovascular benefits and increasing utilization trends of SGLT2i and GLP1RA, our findings reveal that 88.35% of eligible patients with T2DM and ASCVD remained untreated with these agents as of 2020. This study suggests disparities in the use of these agents based on patients' characteristics and physician specialties. Further efforts to explore and address potential barriers to the use of these agents could enhance their clinical benefits by improving access for high-risk patients.

Purpose: Drug shortages are a growing challenge in health systems across the world. A better understanding of the impacts of shortages on patient drug access and use will guide policies aimed at mitigating shortages. This scoping review aims to summarize observational literature assessing the impact of drug shortages on drug utilization trends.

Methods: We searched Ovid MEDLINE and Ovid EMBASE for studies published between 1946 and September 17, 2024. An extensive grey literature search was conducted through targeted website searches, grey literature databases, and the Google search engine. Observational studies examining the impacts of drug shortages on drug use were included. Study screening and extraction were conducted by two independent reviewers.

Results: We identified 55 published articles and five gray literature reports. Most studies were conducted in North America (n = 42, 70%). Population-level data were most used (n = 34, 57%), and most studies used drug prescription data to assess changes in use (n = 30, 55%). Most studies reported changes in drug use as a percent change, and the magnitude in decreases ranged from 1% to 99%. Many different data sources, methods, and measures were used to study the impact of drug shortages on drug utilization, and a broad range of decreases in drug utilization following the shortages were reported.

Conclusions: It is important to synthesize findings across studies to understand how different drugs and settings are affected by shortages. The findings here will inform future studies aimed at filling this knowledge gap, ultimately yielding real-world evidence that can guide policy decisions to address drug supply challenges.

Purpose: To compare different methods of estimating 95% compatibility intervals (CIs) for the sequence ratio (SR) when performing a sequence symmetry analysis using an active comparator to reduce the risk of time-varying confounding.

Methods: We conducted a simulation study, where we simulated drug-outcome and outcome-drug sequences for a drug of interest and a comparator drug using the binomial distribution and obtained active comparator SRs and 95% CIs. We simulated scenarios with sample sizes between 5 and 50 observed sequences for each SR, which could take values of 0.5, 1.0, or 2.0, yielding 276 scenarios that were replicated 5000 times. For each replication, we calculated 95% CIs using current recommendations based on exact CIs, the Woolf logit, Baptista-Pike mid-p, and Miettinen-Nurminen score estimator and calculated coverage for each scenario.

Results: All interval estimators provided acceptable coverage when sample sizes exceeded 15, except for the current recommendation, the exact Clopper-Pearson interval. The Miettinen-Nurminen score (coverage 0.951) and Baptista-Pike mid-p interval (coverage 0.955) offered more accurate coverage than other methods. The largest divergence from 0.95 was observed for the current recommendations (coverage 0.979).

Conclusions: The Miettinen-Nurminen score estimator provided the most accurate coverage for 95% CIs of active comparator SRs, especially with low sample sizes. Therefore, we recommend using the Miettinen-Nurminen score estimator for active comparator SRs.