A pharmacoepidemiological nested case-control study of risk factors for venous thromboembolism with the focus on diabetes, cancer, socioeconomic group, medications, and comorbidities.

OBJECTIVES: A pharmacoepidemiological study to assess VTE risk factors in a diabetes-rich population. METHODS: The study comprised 299,590 individuals. We observed 3450 VTEs and matched them with 15,875 controls using a nested case-control approach and collected data on comorbidities and prescriptions. By multivariable conditional logistic regression, we calculated ORs with 95%CIs for comorbidities and medications to evaluate their associations with VTE. RESULTS: Diabetes (aOR 2.16; 95%CI 1.99-2.34), inflammatory bowel disease (1.84; 1.27-2.66), and severe psychiatric disorders (1.72; 1.43-2.05) had the strongest associations among the non-cancer comorbidities. Pancreatic (12.32; 7.11-21.36), stomach (8.57; 4.07-18.03), lung and bronchus (6.26; 4.16-9.43), and ovarian (6.72; 2.95-15.10) cancers were ranked as high-risk for VTE. Corticosteroids, gabapentinoids, psychotropic drugs, risedronic acid, and pramipexole were most strongly associated (aOR exceeding 1.5) with VTE. Insulin (3.86; 3.33-4.47) and sulphonylureas (2.62; 2.18-3.16) had stronger associations than metformin (1.65; 1.49-1.83). Statins and lercanidipine (0.78; 0.62-0.98) were associated with a lowered risk of VTE. CONCLUSIONS: In this cohort, with 50% diabetes prevalence, pancreatic, stomach, lung and bronchus, and ovarian cancers were strongly associated with VTE. Corticosteroids, gabapentinoids, and psychotropic medications had the strongest associations with VTE among medications. This may be valuable for generating hypotheses for the further research. Lercanidipine may be a novel protective medication against VTE.

Transparency, reproducibility, and replicability of pharmacoepidemiology studies in a distributed network environment.

PURPOSE: Our objective is to describe how the U.S. Food and Drug Administration (FDA)'s Sentinel System implements best practices to ensure trust in drug safety studies using real-world data from disparate sources. METHODS: We present a stepwise schematic for Sentinel's data harmonization, data quality check, query design and implementation, and reporting practices, and describe approaches to enhancing the transparency, reproducibility, and replicability of studies at each step. CONCLUSIONS: Each Sentinel data partner converts its source data into the Sentinel Common Data Model. The transformed data undergoes rigorous quality checks before it can be used for Sentinel queries. The Sentinel Common Data Model framework, data transformation codes for several data sources, and data quality assurance packages are publicly available. Designed to run against the Sentinel Common Data Model, Sentinel's querying system comprises a suite of pre-tested, parametrizable computer programs that allow users to perform sophisticated descriptive and inferential analysis without having to exchange individual-level data across sites. Detailed documentation of capabilities of the programs as well as the codes and information required to execute them are publicly available on the Sentinel website. Sentinel also provides public trainings and online resources to facilitate use of its data model and querying system. Its study specifications conform to established reporting frameworks aimed at facilitating reproducibility and replicability of real-world data studies. Reports from Sentinel queries and associated design and analytic specifications are available for download on the Sentinel website. Sentinel is an example of how real-world data can be used to generate regulatory-grade evidence at scale using a transparent, reproducible, and replicable process.

Application of a data continuity prediction algorithm to an electronic health record-based pharmacoepidemiology study.

BACKGROUND AND OBJECTIVES: Use of algorithms to identify patients with high data-continuity in electronic health records (EHRs) may increase study validity. Practical experience with this approach remains limited. METHODS: We developed and validated four algorithms to identify patients with high data continuity in an EHR-based data source. Selected algorithms were then applied to a pharmacoepidemiologic study comparing rates of COVID-19 hospitalization in patients exposed to insulin versus noninsulin antidiabetic drugs. RESULTS: A model using a short list of five EHR-derived variables performed as well as more complex models to distinguish high- from low-data continuity patients. Higher data continuity was associated with more accurate ascertainment of key variables. In the pharmacoepidemiologic study, patients with higher data continuity had higher observed rates of the COVID-19 outcome and a large unadjusted association between insulin use and the outcome, but no association after propensity score adjustment. DISCUSSION: We found that a simple, portable algorithm to predict data continuity gave comparable performance to more complex methods. Use of the algorithm significantly impacted the results of an empirical study, with evidence of more valid results at higher levels of data continuity.

Describing diversity of real world data sources in pharmacoepidemiologic studies: The DIVERSE scoping review.

PURPOSE: Real-world evidence (RWE) is increasingly used for medical regulatory decisions, yet concerns persist regarding its reproducibility and hence validity. This study addresses reproducibility challenges associated with diversity across real-world data sources (RWDS) repurposed for secondary use in pharmacoepidemiologic studies. Our aims were to identify, describe and characterize practices, recommendations and tools for collecting and reporting diversity across RWDSs, and explore how leveraging diversity could improve the quality of evidence. METHODS: In a preliminary phase, keywords for a literature search and selection tool were designed using a set of documents considered to be key by the coauthors. Next, a systematic search was conducted up to December 2021. The resulting documents were screened based on titles and abstracts, then based on full texts using the selection tool. Selected documents were reviewed to extract information on topics related to collecting and reporting RWDS diversity. A content analysis of the topics identified explicit and latent themes. RESULTS: Across the 91 selected documents, 12 topics were identified: 9 dimensions used to describe RWDS (organization accessing the data source, data originator, prompt, inclusion of population, content, data dictionary, time span, healthcare system and culture, and data quality), tools to summarize such dimensions, challenges, and opportunities arising from diversity. Thirty-six themes were identified within the dimensions. Opportunities arising from data diversity included multiple imputation and standardization. CONCLUSIONS: The dimensions identified across a large number of publications lay the foundation for formal guidance on reporting diversity of data sources to facilitate interpretation and enhance replicability and validity of RWE.

Calculating daily dose in the Observational Medical Outcomes Partnership Common Data Model.

PURPOSE: We aimed to develop a standardized method to calculate daily dose (i.e., the amount of drug a patient was exposed to per day) of any drug on a global scale using only drug information of typical observational data in the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) and a single reference table from Observational Health Data Sciences And Informatics (OHDSI). MATERIALS AND METHODS: The OMOP DRUG_STRENGTH reference table contains information on the strength or concentration of drugs, whereas the OMOP DRUG_EXPOSURE table contains information on patients' drug prescriptions or dispensations/claims. Based on DRUG_EXPOSURE data from the primary care databases Clinical Practice Research Datalink GOLD (United Kingdom) and Integrated Primary Care Information (IPCI, The Netherlands) and healthcare claims from PharMetrics® Plus for Academics (USA), we developed four formulas to calculate daily dose given different DRUG_STRENGTH reference table information. We tested the dose formulas by comparing the calculated median daily dose to the World Health Organization (WHO) Defined Daily Dose (DDD) for six different ingredients in those three databases and additional four international databases representing a variety of healthcare settings: MAITT (Estonia, healthcare claims and discharge summaries), IQVIA Disease Analyzer Germany (outpatient data), IQVIA Longitudinal Patient Database Belgium (outpatient data), and IMASIS Parc Salut (Spain, hospital data). Finally, in each database, we assessed the proportion of drug records for which daily dose calculations were possible using the suggested formulas. RESULTS: Applying the dose formulas, we obtained median daily doses that generally matched the WHO DDD definitions. Our dose formulas were applicable to >85% of drug records in all but one of the assessed databases. CONCLUSION: We have established and implemented a standardized daily dose calculation in OMOP CDM providing reliable and reproducible results.

Learning with an evolving medicine label: how artificial intelligence-based medication recommendation systems must adapt to changing medication labels.

INTRODUCTION: Artificial intelligence or machine learning (AI/ML) based systems can help personalize prescribing decisions for individual patients. The recommendations of these clinical decision support systems must relate to the "label" of the medicines involved. The label of a medicine is an approved guide that indicates how to prescribe the drug in a safe and effective manner. AREAS COVERED: The label for a medicine may evolve as new information on drug safety and effectiveness emerges, leading to the addition or removal of warnings, drug-drug interactions, or to permit new indications. However, the speed at which these updates are made to these AI/ML recommendation systems may be delayed and could influence the safety of prescribing decisions. This article explores the need to keep AI/ML tools 'in sync' with any label changes. Additionally, challenges relating to medicine availability and geographical suitability are discussed. EXPERT OPINION: These considerations highlight the important role that pharmacoepidemiologists and drug safety professionals must play within the monitoring and use of these tools. Furthermore, these issues highlight the guiding role that regulators need to have in planning and oversight of these tools.

Artificial intelligence or machine learning (AI/ML) based systems that guide the prescription of medications have the potential to vastly improve patient care, but these tools should only provide recommendations that are in line with the label of a medicine. With a constantly evolving medication label, this is likely to be a challenge, and this also has implications for the off-label use of medicines.

Lessons learned from a multi-data source research collaboration: The mirabegron post-authorization safety study program.

BACKGROUND: Many factors contribute to developing and conducting a successful multi-data source, non-interventional, post-authorization safety study (NI-PASS) for submission to multiple health authorities. Such studies are often large undertakings; evaluating and sharing lessons learned can provide useful insights to others considering similar studies. OBJECTIVES: We discuss challenges and key methodological and organizational factors that led to the delivery of a successful post-marketing requirement (PMR)/PASS program investigating the risk of cardiovascular and cancer events among users of mirabegron, an oral medication for the treatment of overactive bladder. RESULTS: We provide context and share learnings, including sections on research program collaboration, scientific transparency, organizational approach, mitigation of uncertainty around potential delays, validity of study outcomes, selection of data sources and optimizing patient numbers, choice of comparator groups and enhancing precision of estimates of associations, potential confounding and generalizability of study findings, and interpretation of results. CONCLUSIONS: This large PMR/PASS program was a long-term commitment from all parties and benefited from an effective coordinating center and extensive scientific interactions across research partners, scientific advisory board, study sponsor, and health authorities, and delivered useful learnings related to the design and organization of multi-data source NI-PASS.

Updated core competencies in pharmacoepidemiology to inform contemporary curricula and training for academia, government, and industry.

PURPOSE: The first paper to specify the core content of pharmacoepidemiology as a profession was published by an ISPE (International Society for Pharmacoepidemiology) workgroup in 2012 (Jones JK et al. PDS 2012; 21[7]:677-689). Due to the broader and evolving scope of pharmacoepidemiology, ISPE considers it important to proactively identify, update and expand the list of core competencies to inform curricula of education programs; thus, better positioning pharmacoepidemiologists across academic, government (including regulatory), and industry positions. The aim of this project was to update the list of core competencies in pharmacoepidemiology. METHODS: To ensure applicability of findings to multiple areas, a working group was established consisting of ISPE members with positions in academia, industry, government, and other settings. All competencies outlined by Jones et al. were extracted from the initial manuscript and presented to the working group for review. Expert-based judgments were collated and used to identify consensus. It was noted that some competencies could contribute to multiple groups and could be directly or indirectly related to a group. RESULTS: Five core domains were proposed: (1) Epidemiology, (2) Clinical Pharmacology, (3) Regulatory Science, (4) Statistics and data science, and (5) Communication and other professional skills. In total, 55 individual competencies were proposed, of which 25 were new competencies. No competencies from the original work were dropped but aggregation or amendments were made where considered necessary. CONCLUSIONS: While many core competencies in pharmacoepidemiology have remained the same over the past 10 years, there have also been several updates to reflect new and emerging concepts in the field.

Pharmacoepidemiology and Clinical Correlates of Lithium Treatment for Bipolar Disorder in Asia.

BACKGROUND: As clinical practices with lithium salts for patients diagnosed with bipolar disorder (BD) are poorly documented in Asia, we studied the prevalence and clinical correlates of lithium use there to support international comparisons. METHODS: We conducted a cross-sectional study of use and dosing of lithium salts for BD patients across 13 Asian sites and evaluated bivariate relationships of lithium treatment with clinical correlates followed by multivariate logistic regression modeling. RESULTS: In a total of 2139 BD participants (52.3% women) of mean age 42.4 years, lithium salts were prescribed in 27.3% of cases overall, varying among regions from 3.20% to 59.5%. Associated with lithium treatment were male sex, presence of euthymia or mild depression, and a history of seasonal mood change. Other mood stabilizers usually were given with lithium, often at relatively high doses. Lithium use was associated with newly emerging and dose-dependent risk of tremors as well as risk of hypothyroidism. We found no significant differences in rates of clinical remission or of suicidal behavior if treatment included lithium or not. CONCLUSIONS: Study findings clarify current prevalence, dosing, and clinical correlates of lithium treatment for BD in Asia. This information should support clinical decision-making regarding treatment of BD patients and international comparisons of therapeutic practices.

Prescription Sequence Symmetry Analysis (PSSA) to assess prescribing cascades: a step-by-step guide.

Prescribing cascades occur when patients are prescribed medication to treat the adverse drug reaction of previously prescribed medication. Prescription sequence symmetry analysis (PSSA) can be used to assess the association between two medications in prescription or dispensing databases and thus the potential occurrence of prescribing cascades. In this article, a step-by-step guide is presented for conducting PSSA to assess prescribing cascades. We describe considerations for medication data collection and setting time periods for relevant parameters, including washout window, exposure window, continued exposure interval and blackout period. With two examples, we illustrate the impact of changes in these parameters on the strengths of associations observed. Given the impact seen, we recommend that researchers clearly specify and explain all considerations regarding medication included and time windows set when studying prescribing cascades with PSSA, and conduct subgroup and sensitivity analyses.

Successful implementation of a stakeholder engagement program for pharmacoepidemiologic research.

PURPOSE: Rigorously conducted pharmacoepidemiologic research requires methodologically complex study designs and analysis yet evaluates problems of high importance to patients and clinicians. Despite this, participation in and mechanisms for stakeholder engagement in pharmacoepidemiologic research are not well-described. Here, we describe our approach and lessons learned from engaging stakeholders, of varying familiarity with research methods, in a rigorous multi-year pharmacoepidemiologic research program evaluating the comparative effectiveness of diabetes medications. METHODS: We recruited 5 patient and 4 clinician stakeholders; each was compensated for their time. Stakeholders received initial formal training in observational research and pharmacoepidemiologic methods sufficient to enable contribution to the research project. After onboarding, stakeholder engagement meetings were held virtually, in the evening, 2-3 times annually. Each was approximately 90 min and focused on 1-2 specific questions about the project, with preparatory materials sent in advance. RESULTS: Stakeholder meeting attendance was high (89%-100%), and all stakeholders engaged with the research project, both during and between meetings. Stakeholders reported positive experiences with meetings, satisfaction, and interest in the research project and its findings, and dedication to the success of the project's goals. They affirmed the value of receiving materials to review in advance and the effectiveness of a virtual platform. Their contributions included prioritizing and suggesting research questions, optimizing written evidence briefs for a lay audience, and guidance on broader topics such as research audience and methods of dissemination. CONCLUSIONS: Stakeholder engagement in pharmacoepidemiologic research using complex study designs and analysis is feasible, acceptable, and positively impacts the research project.

The State of Use and Utility of Negative Controls in Pharmacoepidemiologic Studies.

Uses of real-world data in drug safety and effectiveness studies are often challenged by various sources of bias. We undertook a systematic search of the published literature through September 2020 to evaluate the state of use and utility of negative controls to address bias in pharmacoepidemiologic studies. Two reviewers independently evaluated study eligibility and abstracted data. Our search identified 184 eligible studies for inclusion. Cohort studies (115, 63%) and administrative data (114, 62%) were, respectively, the most common study design and data type used. Most studies used negative control outcomes (91, 50%), and for most studies the target source of bias was unmeasured confounding (93, 51%). We identified 4 utility domains of negative controls: 1) bias detection (149, 81%), 2) bias correction (16, 9%), 3) P-value calibration (8, 4%), and 4) performance assessment of different methods used in drug safety studies (31, 17%). The most popular methodologies used were the 95% confidence interval and P-value calibration. In addition, we identified 2 reference sets with structured steps to check the causality assumption of the negative control. While negative controls are powerful tools in bias detection, we found many studies lacked checking the underlying assumptions. This article is part of a Special Collection on Pharmacoepidemiology.

Inventory of real-world data sources in Japan: Annual survey conducted by the Japanese Society for Pharmacoepidemiology Task Force.

PURPOSE: The Database Task Force of the Japan Society for Pharmacoepidemiology began its annual surveys of databases available for clinico and pharmacoepidemiological studies in 2010. In this report, we summarize the characteristics of the databases available in Japan based on the results of our 2021 survey to illustrate the recent developments in the infrastructure for database research in Japan. METHODS: We included 20 major databases from the academia, government, or industry that were accessible to third parties. We used a web-based questionnaire to ask the database providers about their characteristics, such as their organization, data source(s), numbers of individuals enrolled, age distribution, code(s) used, and average follow-up periods. RESULTS: We received responses from all 20 databases approached: eight hospital-based databases, six insurer-based databases, four pharmacy-based databases, and two in the "other" category. Among them, 17 contained information from medical claims, pharmacy claims, and/or Diagnosis Procedure Combination data. Most insurer databases contained health check-up data that could be attached to the claims component. Some hospital-based databases had data from electronic medical records. Most insurer-based databases collected data from the insurers of working-age employees and therefore had limited coverage of older people. Most databases coded their medication data using the Japanese reimbursement codes, and many provided Anatomical Therapeutic Chemical Classification codes. CONCLUSIONS: The number of databases available for clinico and pharmacoepidemiological research and the proportion of the population they cover are increasing in Japan. The differences in their characteristics mean that the appropriate database must be selected for a particular study purpose.

Overview of global real-world data sources for pediatric pharmacoepidemiologic research.

PURPOSE: Given limited information available on real-world data (RWD) sources with pediatric populations, this study describes features of globally available RWD sources for pediatric pharmacoepidemiologic research. METHODS: An online questionnaire about pediatric RWD sources and their attributes and capabilities was completed by members and affiliates of the International Society for Pharmacoepidemiology and representatives of nominated databases. All responses were verified by database representatives and summarized. RESULTS: Of 93 RWD sources identified, 55 unique pediatric RWD sources were verified, including data from Europe (47%), United States (38%), multiregion (7%), Asia-Pacific (5%), and South America (2%). Most databases had nationwide coverage (82%), contained electronic health/medical records (47%) and/or administrative claims data (42%) and were linkable to other databases (65%). Most (71%) had limited outside access (e.g., by approval or through local collaborators); only 10 (18%) databases were publicly available. Six databases (11%) reported having >20 million pediatric observations. Most (91%) included children of all ages (birth until 18th birthday) and contained outpatient medication data (93%), while half (49%) contained inpatient medication data. Many databases captured vaccine information for children (71%), and one-third had regularly updated data on pediatric height (31%) and weight (33%). Other pediatric data attributes captured include diagnoses and comorbidities (89%), lab results (58%), vital signs (55%), devices (55%), imaging results (42%), narrative patient histories (35%), and genetic/biomarker data (22%). CONCLUSIONS: This study provides an overview with key details about diverse databases that allow researchers to identify fit-for-purpose RWD sources suitable for pediatric pharmacoepidemiologic research.

Core concepts in pharmacoepidemiology: Measurement of medication exposure in routinely collected healthcare data for causal inference studies in pharmacoepidemiology.

BACKGROUND: Observational designs can complement evidence from randomized controlled trials not only in situations when randomization is not feasible, but also by evaluating drug effects in real-world, considering a broader spectrum of users and clinical scenarios. However, use of such real-world scenarios captured in routinely collected clinical or administrative data also comes with specific challenges. Unlike in trials, medication use is not protocol based. Instead, exposure is determined by a multitude of factors involving patients, providers, healthcare access, and other policies. Accurate measurement of medication exposure relies on a similar broad set of factors which, if not understood and appropriately addressed, can lead to exposure misclassification and bias. AIM: To describe core considerations for measurement of medication exposure in routinely collected healthcare data. METHODS: We describe the strengths and weaknesses of the two main types of routinely collected healthcare data (electronic health records and administrative claims) used in pharmacoepidemiologic research. We introduce key elements in those data sources and issues in the curation process that should be considered when developing exposure definitions. We present challenges in exposure measurement such as the appropriate determination of exposure time windows or the delineation of concomitant medication use versus switching of therapy, and related implications for bias. RESULTS: We note that true exposure patterns are typically unknown when using routinely collected healthcare data and that an in-depth understanding of healthcare delivery, patient and provider decision-making, data documentation and governance, as well as pharmacology are needed to ensure unbiased approaches to measuring exposure. CONCLUSIONS: Various assumptions are made with the goal that the chosen exposure definition can approximate true exposure. However, the possibility of exposure misclassification remains, and sensitivity analyses that can test the impact of such assumptions on the robustness of estimated medication effects are necessary to support causal inferences.