Metadata for Data dIscoverability aNd Study rEplicability in obseRVAtional Studies (MINERVA): Development and Pilot of a Metadata List and Catalogue in Europe

PURPOSE

Metadata for data dIscoverability aNd study rEplicability in obseRVAtional studies (MINERVA), a European Medicines Agency-funded project (EUPAS39322), defined a set of metadata to describe real-world data sources (RWDSs) and piloted metadata collection in a prototype catalogue to assist investigators from data source discoverability through study conduct.

METHODS

A list of metadata was created from a review of existing metadata catalogues and recommendations, structured interviews, a stakeholder survey, and a technical workshop. The prototype was designed to comply with the FAIR principles (findable, accessible, interoperable, reusable), using MOLGENIS software. Metadata collection was piloted by 15 data access partners (DAPs) from across Europe.

RESULTS

A total of 442 metadata variables were defined in six domains: institutions (organizations connected to a data source); data banks (data collections sustained by an organization); data sources (collections of linkable data banks covering a common underlying population); studies; networks (of institutions); and common data models (CDMs). A total of 26 institutions were recorded in the prototype. Each DAP populated the metadata of one data source and its selected data banks. The number of data banks varied by data source; the most common data banks were hospital administrative records and pharmacy dispensation records (10 data sources each). Quantitative metadata were successfully extracted from three data sources conforming to different CDMs and entered into the prototype.

CONCLUSIONS

A metadata list was finalized, a prototype was successfully populated, and a good practice guide was developed. Setting up and maintaining a metadata catalogue on RWDSs will require substantial effort to support discoverability of data sources and reproducibility of studies in Europe.

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.

A feasibility assessment of real-world data capabilities for monitoring vaccine safety and effectiveness in China: Human papillomavirus vaccination in the Yinzhou district as a use case

BACKGROUND

Real-world data (RWD) are increasingly used to generate real-world evidence (RWE) of vaccine safety and effectiveness for regulatory purposes. Assessing feasibility of using RWD sources prior to implementing observational studies is recommended. As a use case, we described the process and findings of a feasibility assessment to identify reliable and relevant data sources for monitoring the safety and effectiveness of the AS04-HPV-16/18 human papillomavirus (HPV) vaccine in China.

METHODS

Iterative multi-step process: (1) targeted literature review and data source mapping; (2) expert opinion from national RWD experts; (3) survey to evaluate the identified data source operational infrastructure; and (4) continuous appraisal of published studies using the identified data source.

RESULTS

The Yinzhou Regional Health Information Platform (YRHIP) was identified as a data source of main interest, based on its large population coverage, high cervical cancer screening rates, and availability of adult electronic immunization records. Field meetings with national RWD experts confirmed its suitability for post-authorization vaccine studies. Survey results showed that exposure data and relevant safety and effectiveness endpoints were recorded and linkable at the individual level across the platform. Iterative appraisal of emerging evidence from the literature corroborated these findings.

CONCLUSIONS

This feasibility assessment indicates that the YRHIP has the capacity to capture demographic, exposure, outcome and other data required to generate RWE on HPV vaccine safety and effectiveness in China. Studies using the YRHIP to monitor the AS04-HPV-16/18 vaccine in routine use building on this feasibility assessment are ongoing.

Tactical Considerations for Designing Real-World Studies: Fit-for-Purpose Designs That Bridge Research and Practice

Real-world evidence (RWE) is being used to provide information on diverse groups of patients who may be highly impacted by disease but are not typically studied in traditional randomized clinical trials (RCT) and to obtain insights from everyday care settings and real-world adherence to inform clinical practice. RWE is derived from so-called real-world data (RWD), ie, information generated by clinicians in the course of everyday patient care, and is sometimes coupled with systematic input from patients in the form of patient-reported outcomes or from wearable biosensors. Studies using RWD are conducted to evaluate how well medical interventions, services, and diagnostics perform under conditions of real-world use, and may include long-term follow-up. Here, we describe the main types of studies used to generate RWE and offer pointers for clinicians interested in study design and execution. Our tactical guidance addresses (1) opportunistic study designs, (2) considerations about representativeness of study participants, (3) expectations for transparency about data provenance, handling and quality assessments, and (4) considerations for strengthening studies using record linkage and/or randomization in pragmatic clinical trials. We also discuss likely sources of bias and suggest mitigation strategies. We see a future where clinical records - patient-generated data and other RWD - are brought together and harnessed by robust study design with efficient data capture and strong data curation. Traditional RCT will remain the mainstay of drug development, but RWE will play a growing role in clinical, regulatory, and payer decision-making. The most meaningful RWE will come from collaboration with astute clinicians with deep practice experience and questioning minds working closely with patients and researchers experienced in the development of RWE.

Strategies to Address Current Challenges in Real-World Evidence Generation in Japan

The generation of real-world evidence (RWE), which describes patient characteristics or treatment patterns using real-world data (RWD), is rapidly growing more popular as a tool for decision-making in Japan. The aim of this review was to summarize challenges to RWE generation in Japan related to pharmacoepidemiology, and to propose strategies to address some of these challenges. We first focused on data-related issues, including the lack of transparency of RWD sources, linkage across different care settings, definitions of clinical outcomes, and the overall assessment framework of RWD when used for research purposes. Next the study reviewed methodology-related challenges. As lack of design transparency impairs study reproducibility, transparent reporting of study design is critical for stakeholders. For this review, we considered different sources of biases and time-varying confounding, along with potential study design and methodological solutions. Additionally, the implementation of robust assessment of definition uncertainty, misclassification, and unmeasured confounders would enhance RWE credibility in light of RWD source-related limitations, and is being strongly considered by task forces in Japan. Overall, the development of guidance for best practices on data source selection, design transparency, and analytical methods to address different sources of biases and robustness in the process of RWE generation will enhance credibility for stakeholders and local decision-makers.

The impact of commercial health datasets on medical research and health-care algorithms

As the health-care industry emerges into a new era of digital health driven by cloud data storage, distributed computing, and machine learning, health-care data have become a premium commodity with value for private and public entities. Current frameworks of health data collection and distribution, whether from industry, academia, or government institutions, are imperfect and do not allow researchers to leverage the full potential of downstream analytical efforts. In this Health Policy paper, we review the current landscape of commercial health data vendors, with special emphasis on the sources of their data, challenges associated with data reproducibility and generalisability, and ethical considerations for data vending. We argue for sustainable approaches to curating open-source health data to enable global populations to be included in the biomedical research community. However, to fully implement these approaches, key stakeholders should come together to make health-care datasets increasingly accessible, inclusive, and representative, while balancing the privacy and rights of individuals whose data are being collected.

An overview of methodological flaws of real-world studies investigating drug safety in the post-marketing setting

INTRODUCTION

The evaluation of the post-marketing safety profile of drugs is a continuous monitoring process for approved and marketed medicines and it is crucial for detecting new adverse drug reactions. As such, real-world studies are essential to complement pre-marketing evidence with information concerning drug risk-benefit profile and use in wider patient populations and they have a great potential to support post-marketing drug safety evaluations.

AREAS COVERED

A detailed description of the main limitations of real-world data sources (i.e. claims databases, electronic healthcare records, drug/disease registers and spontaneous reporting system databases) and of the main methodological challenges of real-world studies in generating real-world evidence is provided.

EXPERT OPINION

Real-world evidence biases can be ascribed to both the methodological approach and the specific limitations of the different real-world data sources used to carry out the study. As such, it is crucial to characterize the quality of real-world data, by establishing guidelines and best practices for the assessment of data fitness for purpose. On the other hand, it is important that real-world studies are conducted using a rigorous methodology, aimed at minimizing the risk of bias.

Core concepts in pharmacoepidemiology: Key biases arising in pharmacoepidemiologic studies

Pharmacoepidemiology has an increasingly important role in informing and improving clinical practice, drug regulation, and health policy. Therefore, unrecognized biases in pharmacoepidemiologic studies can have major implications when study findings are translated to the real world. We propose a simple taxonomy for researchers to use as a starting point when thinking through some of the most pervasive biases in pharmacoepidemiology. We organize this discussion according to biases best assessed with respect to the study population (including confounding by indication, channeling bias, healthy user bias, and protopathic bias), the study design (including prevalent user bias and immortal time bias), and the data source (including misclassification bias and missing data/loss to follow up). This tutorial defines, provides a curated list of recommended references, and illustrates through relevant case examples these key biases to consider when planning, conducting, or evaluating pharmacoepidemiologic studies.

Validation study on definition of cause of death in Japanese claims data

Identifying the cause of death is important for the study of end-of-life patients using claims data in Japan. However, the validity of how cause of death is identified using claims data remains unknown. Therefore, this study aimed to verify the validity of the method used to identify the cause of death based on Japanese claims data. Our study population included patients who died at two institutions between January 1, 2018 and December 31, 2019. Claims data consisted of medical data and Diagnosis Procedure Combination (DPC) data, and five definitions developed from disease classification in each dataset were compared with death certificates. Nine causes of death, including cancer, were included in the study. The definition with the highest positive predictive values (PPVs) and sensitivities in this study was the combination of "main disease" in both medical and DPC data. For cancer, these definitions had PPVs and sensitivities of > 90%. For heart disease, these definitions had PPVs of > 50% and sensitivities of > 70%. For cerebrovascular disease, these definitions had PPVs of > 80% and sensitivities of> 70%. For other causes of death, PPVs and sensitivities were < 50% for most definitions. Based on these results, we recommend definitions with a combination of "main disease" in both medical and DPC data for cancer and cerebrovascular disease. However, a clear argument cannot be made for other causes of death because of the small sample size. Therefore, the results of this study can be used with confidence for cancer and cerebrovascular disease but should be used with caution for other causes of death.

c4c: Paediatric pharmacovigilance: Methodological considerations in research and development of medicines for children - A c4c expert group white paper

Children frequently respond differently to therapies compared to adults. Differences also exist between paediatric age groups for pharmacokinetics and pharmacodynamics in both efficacy and safety. Paediatric pharmacovigilance requires an understanding of the unique aspects of children with regard to, for example, drug response, growth and development, clinical presentation of adverse drug reactions (ADRs), how they can be detected and population-specific factors (e.g., more frequent use of off-label/unlicensed drugs). In recognition of these challenges, a group of experts has been formed in the context of the conect4children (c4c) project to support paediatric drug development. This expert group collaborated to develop methodological considerations for paediatric drug safety and pharmacovigilance throughout the life-cycle of medicinal products which are described in this article. These considerations include practical points to consider for the development of the paediatric section of the risk management plan (RMP), safety in paediatric protocol development, safety data collection and analysis. Furthermore, they describe the specific details of post-marketing pharmacovigilance in children using, for example, spontaneous reports, electronic health care records, registries and record-linkage, as well as the use of paediatric pharmacoepidemiology studies for risk characterisation. Next the details of the assessment of benefit-risk and challenges related to medicinal product formulation in the context of a Paediatric Investigation Plan (PIP) are presented. Finally, practical issues in paediatric signal detection and evaluation are included. This paper provides practical points to consider for paediatric pharmacovigilance throughout the life-cycle of medicinal products for RMPs, protocol development, safety data collection and analysis and PIPs.

Community pharmacists' views and experiences toward over-the-counter medicines misuse and abuse in Saudi Arabia: A qualitative study

Background: Community pharmacists are uniquely positioned to identify and address the issue of misuse and abuse of over-the-counter (OTC) medicines. To date, no study has explored the Saudi community pharmacists' views and experiences regarding aspects of OTC medicines' misuse and abuse. Objective: To explore the views and experiences of the Saudi community pharmacists towards OTC medicines misuse and abuse. Furthermore, we aimed to identify frequently misused and abused medicines, the reasons and contributing factors, the role of pharmacists, and potential risk-mitigating strategies. Methods: Semi-structured interviews were conducted with a convenient sample of sixteen community pharmacists recruited from community pharmacies across the AL-Baha region, Saudi Arabia. Interviews were conducted using a pilot-tested interview guide in the Arabic language. All interviews were audio-recorded, transcribed verbatim, translated from Arabic into English, and then thematically analysed. Results: Analysis of interviews generated five main themes, including the commonly misused and abused OTC medicines, reasons and factors contributing to misuse and abuse of OTC medicines, pharmacists' interventions to manage misuse and abuse, challenges and barriers to pharmacists' interventions in misuse and abuse issues; and potential strategies to reduce the risk of OTC medicines misuse and abuse and improve pharmacists' practice. Sedative antihistamines, cough products containing dextromethorphan, codeine-based analgesics, and non-codeine-based analgesics were commonly misused and abused OTC medicines. Managing ongoing medical conditions was the main reason for misusing OTC analgesics while recreational use and inducing sleep were the common reasons for abuse. Several factors contributing to misuse and abuse were reported, including unprofessional advice sought from other people, lack of awareness about medicines, and commercial advertisement of OTC products. Community pharmacists identified misuse and abuse among customers by judging their behaviours and attitudes and using structured questioning techniques. Counselling customers on the appropriate use of medicines, providing safe alternatives, and refusing to sell products were among the commonly used actions of pharmacists to address misuse/abuse. Pharmacists proposed several strategies to reduce the risk of OTC medicines misuse/abuse but believed that rescheduling OTC medicines with abuse potential to prescription-only medicine was the best option. Conclusion: Community pharmacists believed that the misuse and abuse of OTC medicines amongst pharmacy customers was common. A multidimensional strategy consisting of upskilling community pharmacists, a comprehensive review of OTC medicines sale regulations, and patient education to limit the risks of OTC medicines misuse/abuse is required.

Data Sources for Drug Utilization Research in Brazil-DUR-BRA Study

Background: In Brazil, studies that map electronic healthcare databases in order to assess their suitability for use in pharmacoepidemiologic research are lacking. We aimed to identify, catalogue, and characterize Brazilian data sources for Drug Utilization Research (DUR).

Methods: The present study is part of the project entitled, “Publicly Available Data Sources for Drug Utilization Research in Latin American (LatAm) Countries.” A network of Brazilian health experts was assembled to map secondary administrative data from healthcare organizations that might provide information related to medication use. A multi-phase approach including internet search of institutional government websites, traditional bibliographic databases, and experts’ input was used for mapping the data sources. The reviewers searched, screened and selected the data sources independently; disagreements were resolved by consensus. Data sources were grouped into the following categories: 1) automated databases; 2) Electronic Medical Records (EMR); 3) national surveys or datasets; 4) adverse event reporting systems; and 5) others. Each data source was characterized by accessibility, geographic granularity, setting, type of data (aggregate or individual-level), and years of coverage. We also searched for publications related to each data source.

Results: A total of 62 data sources were identified and screened; 38 met the eligibility criteria for inclusion and were fully characterized. We grouped 23 (60%) as automated databases, four (11%) as adverse event reporting systems, four (11%) as EMRs, three (8%) as national surveys or datasets, and four (11%) as other types. Eighteen (47%) were classified as publicly and conveniently accessible online; providing information at national level. Most of them offered more than 5 years of comprehensive data coverage, and presented data at both the individual and aggregated levels. No information about population coverage was found. Drug coding is not uniform; each data source has its own coding system, depending on the purpose of the data. At least one scientific publication was found for each publicly available data source.

Conclusions: There are several types of data sources for DUR in Brazil, but a uniform system for drug classification and data quality evaluation does not exist. The extent of population covered by year is unknown. Our comprehensive and structured inventory reveals a need for full characterization of these data sources.

Use of diagnostic likelihood ratio of outcome to evaluate misclassification bias in the planning of database studies

Background

The diagnostic likelihood ratio (DLR) and its utility are well-known in the field of medical diagnostic testing. However, its use has been limited in the context of an outcome validation study. We considered that wider recognition of the utility of DLR would enhance the practices surrounding database studies. This is particularly timely and important since the use of healthcare-related databases for pharmacoepidemiology research has greatly expanded in recent years. In this paper, we aimed to advance the use of DLR, focusing on the planning of a new database study.

Methods

Theoretical frameworks were developed for an outcome validation study and a comparative cohort database study; these two were combined to form the overall relationship. Graphical presentations based on these relationships were used to examine the implications of validation study results on the planning of a database study. Additionally, novel uses of graphical presentations were explored using some examples.

Results

Positive DLR was identified as a pivotal parameter that connects the expected positive-predictive value (PPV) with the disease prevalence in the planned database study, where the positive DLR is equal to sensitivity/(1-specificity). Moreover, positive DLR emerged as a pivotal parameter that links the expected risk ratio with the disease risk of the control group in the planned database study. In one example, graphical presentations based on these relationships provided a transparent and informative summary of multiple validation study results. In another example, the potential use of a graphical presentation was demonstrated in selecting a range of positive DLR values that best represented the relevant validation studies.

Conclusions

Inclusion of the DLR in the results section of a validation study would benefit potential users of the study results. Furthermore, investigators planning a database study can utilize the DLR to their benefit. Wider recognition of the full utility of the DLR in the context of a validation study would contribute meaningfully to the promotion of good practice in planning, conducting, analyzing, and interpreting database studies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-022-01757-1.

Inches, Centimeters, and Yards: Overlooked Definition Choices Inhibit Interpretation of Morphine Equivalence

OBJECTIVE

Morphine-standardized doses are used in clinical practice and research to account for molecular potency. Ninety milligrams of morphine equivalents (MME) per day are considered a "high dose" risk threshold in guidelines, laws, and by payers. Although ubiquitously cited, the "CDC definition" of daily MME lacks a clearly defined denominator. Our objective was to assess denominator-dependency on "high dose" classification across competing definitions.

METHODS

To identify definitional variants, we reviewed literature and electronic prescribing tools, yielding 4 unique definitions. Using Prescription Drug Monitoring Programs data (July to September 2018), we conducted a population-based cohort study of 3,916,461 patients receiving outpatient opioid analgesics in California (CA) and Florida (FL). The binary outcome was whether patients were deemed "high dose" (>90 MME/d) compared across 4 definitions. We calculated I2 for heterogeneity attributable to the definition.

RESULTS

Among 9,436,640 prescriptions, 42% overlapped, which led denominator definitions to impact daily MME values. Across definitions, average daily MME varied 3-fold (range: 17 to 52 [CA] and 23 to 65 mg [FL]). Across definitions, prevalence of "high dose" individuals ranged 5.9% to 14.2% (FL) and 3.5% to 10.3% (CA). Definitional variation alone would impact a hypothetical surveillance study trying to establish how much more "high dose" prescribing was present in FL than CA: from 39% to 84% more. Meta-analyses revealed strong heterogeneity (I2 range: 86% to 99%). In sensitivity analysis, including unit interval 90.0 to 90.9 increased "high dose" population fraction by 15%.

DISCUSSION

While 90 MME may have cautionary mnemonic benefits, without harmonization of calculation, its utility is limited. Comparison between studies using daily MME requires explicit attention to definitional variation.

Methodologic approaches in studies using real-world data (RWD) to measure pediatric safety and effectiveness of vaccines administered to pregnant women: A scoping review

OBJECTIVE

This scoping review mapped studies using real-world data (RWD) to measure pediatric safety and effectiveness of vaccines administered to pregnant women.

INTRODUCTION

In the US, two vaccines are recommended for all pregnant women to prevent illness in the infant: inactivated influenza vaccine (recommended since 2004), and the combined tetanus-diphtheria-acellular pertussis (Tdap) vaccine (recommended since 2013). This scoping review maps the studies conducted to date that address questions about pediatric safety and effectiveness of vaccines administered during pregnancy and provides a knowledge base for evaluating the use of RWD to study this issue.

METHODS

The scoping review was conducted following a published protocol. Methods included an electronic search of PubMed and Embase, screening of titles and abstracts by two reviewers, and double extraction of data for summary and synthesis. Studies that reported on pregnant women and the effectiveness or safety outcomes in their infants were included.

RESULTS

Forty-eight studies met the inclusion criteria of the scoping review protocol using RWD to assess safety or effectiveness of influenza or pertussis vaccinations administered to pregnant women with respect to pregnancy, infant or child outcomes. Detailed information about data sources, linkage of maternal and infant data, and operational definitions for gestational age were largely absent from the majority of studies raising concerns about reproducibility and validity of study findings.

CONCLUSIONS

A body of literature is available from which to plan and design future studies of vaccination in pregnant women using RWD. This is of intense importance as new vaccines, such as those for COVID-19, become available to the general population via approval or authorization without inclusion of pregnant women in the clinical trials.

Methodologic approaches in studies using real-world data to measure pediatric safety and effectiveness of vaccines administered to pregnant women: a scoping review protocol

OBJECTIVE

This scoping review aims to map studies using real-world data (RWD) to measure pediatric safety and effectiveness of vaccines administered to pregnant women.

INTRODUCTION

In the United States, two vaccines are recommended for all pregnant women to prevent illness in the infant: inactivated influenza vaccine (recommended since 2004) and the combined tetanus-diphtheria-acellular pertussis (Tdap) vaccine (recommended since 2013). Because of the ethical constraints in conducting randomized clinical trials to measure the effects on the infant, there is great interest in using electronic health care data or administrative claims data to study the effects of maternal immunization on the infant's health, and it is anticipated that such studies may be submitted to support regulatory decision-making. This scoping review will map the studies conducted to date that address these questions and provide a context for considering the regulatory issues that may arise in the future.

INCLUSION CRITERIA

Studies that report on pregnant women receiving immunization and the effectiveness or safety outcomes in their infants will be included. Study participants may be from any population or country, of any reproductive age, and with any health status. Studies will be included if they use real-world data (from electronic health records, administrative claims, pharmacy benefit records, or registries).

METHODS

An electronic search of PubMed and Embase will identify citations for screening. The search will be limited to studies published in English during the preceding 10 years. Two reviewers will screen citations in a two-step process (titles and abstracts, then full-text articles), and two reviewers will extract data for summary and synthesis.

Organized structure of real-world evidence best practices: moving from fragmented recommendations to comprehensive guidance

Decision-makers have become increasingly interested in incorporating real-world evidence (RWE) into their decision-making process. Due to concerns regarding the reliability and quality of RWE, stakeholders have issued numerous recommendation documents to assist in setting RWE standards. The fragmented nature of these documents poses a challenge to researchers and decision-makers looking for guidance on what is 'high-quality' RWE and how it can be used in decision-making. We offer researchers and decision-makers a structure to organize the landscape of RWE recommendations and identify consensus and gaps in the current recommendations. To provide researchers with a much needed pathway for generating RWE, we discuss how decision-makers can move from fragmented recommendations to comprehensive guidance.

Validation process of a high-resolution database in a paediatric intensive care unit-Describing the perpetual patient's validation

RATIONALE

High data quality is essential to ensure the validity of clinical and research inferences based on it. However, these data quality assessments are often missing even though these data are used in daily practice and research.

AIMS AND OBJECTIVES

Our objective was to evaluate the data quality of our high-resolution electronic database (HRDB) implemented in our paediatric intensive care unit (PICU).

METHODS

We conducted a prospective validation study of a HRDB in a 32-bed paediatric medical, surgical, and cardiac PICU in a tertiary care freestanding maternal-child health centre in Canada. All patients admitted to the PICU with at least one vital sign monitored using a cardiorespiratory monitor connected to the central monitoring station.

RESULTS

Between June 2017 and August 2018, data from 295 patient days were recorded from medical devices and 4645 data points were video recorded and compared to the corresponding data collected in the HRDB. Statistical analysis showed an excellent overall correlation (R² = 1), accuracy (100%), agreement (bias = 0, limits of agreement = 0), completeness (2% missing data), and reliability (ICC = 1) between recorded and collected data within clinically significant pre-defined limits of agreement. Divergent points could all be explained.

CONCLUSIONS

This prospective validation of a representative sample showed an excellent overall data quality.

Establishing a sorting protocol for healthcare databases

BACKGROUND

Health information records in many countries, especially developing countries, are still paper based. Compared to electronic systems, paper-based systems are disadvantageous in terms of data storage and data extraction. Given the importance of health records for epidemiological studies, guidelines for effective data cleaning and sorting are essential. They are, however, largely absent from the literature. The following paper discusses the process by which an algorithm was developed for the cleaning and sorting of a database generated from emergency department records in Lebanon.

DESIGN AND METHODS

Demographic and health related information were extracted from the emergency department records of three hospitals in Beirut. Appropriate categories were selected for data categorization. For health information, disease categories and codes were selected according to the International Classification of Disease 10th Edition.

RESULTS

A total of 16,537 entries were collected. Demographic information was categorized into groups for future epidemiological studies. Analysis of the health information led to the creation of a sorting algorithm which was then used to categorize and code the health data. Several counts were then performed to represent and visualize the data numerically and graphically.

CONCLUSIONS

The article describes the current state of health information records in Lebanon and the associated disadvantages of a paper-based system in terms of storage and data extraction. Furthermore, the article describes the algorithm by which health information was sorted and categorized to allow for future data analysis using paper records.

Approaches for combining primary care electronic health record data from multiple sources: a systematic review of observational studies

OBJECTIVE

To identify observational studies which used data from more than one primary care electronic health record (EHR) database, and summarise key characteristics including: objective and rationale for using multiple data sources; methods used to manage, analyse and (where applicable) combine data; and approaches used to assess and report heterogeneity between data sources.

DESIGN

A systematic review of published studies.

DATA SOURCES

Pubmed and Embase databases were searched using list of named primary care EHR databases; supplementary hand searches of reference list of studies were retained after initial screening.

STUDY SELECTION

Observational studies published between January 2000 and May 2018 were selected, which included at least two different primary care EHR databases.

RESULTS

6054 studies were identified from database and hand searches, and 109 were included in the final review, the majority published between 2014 and 2018. Included studies used 38 different primary care EHR data sources. Forty-seven studies (44%) were descriptive or methodological. Of 62 analytical studies, 22 (36%) presented separate results from each database, with no attempt to combine them; 29 (48%) combined individual patient data in a one-stage meta-analysis and 21 (34%) combined estimates from each database using two-stage meta-analysis. Discussion and exploration of heterogeneity was inconsistent across studies.

CONCLUSIONS

Comparing patterns and trends in different populations, or in different primary care EHR databases from the same populations, is important and a common objective for multi-database studies. When combining results from several databases using meta-analysis, provision of separate results from each database is helpful for interpretation. We found that these were often missing, particularly for studies using one-stage approaches, which also often lacked details of any statistical adjustment for heterogeneity and/or clustering. For two-stage meta-analysis, a clear rationale should be provided for choice of fixed effect and/or random effects or other models.

Outcome misclassification: Impact, usual practice in pharmacoepidemiology database studies and an online aid to correct biased estimates of risk ratio or cumulative incidence

PURPOSE

It is well documented that outcome misclassification can bias a point estimate. We aimed to understand current practice in addressing this bias in pharmacoepidemiology database studies and to develop an open source application (app) from existing methodology to demonstrate the impact and mechanism of this bias on results.

METHODS

Studies of an exposure and a clinical outcome were selected from all Pharmacoepidemiology and Drug Safety publications during 2017 and any reference to outcome misclassification described. An app to correct risk ratio (RR) and cumulative incidence for outcome misclassification was developed from a published methodology and used to demonstrate the impact of correction on point estimates.

RESULTS

Eight (19%) of 43 papers selected reported estimates of outcome ascertainment accuracy with positive predictive value (PPV) the most commonly reported measure (7 of 8 studies). Three studies (7%) corrected for the bias, 1 by exposure strata, and 5 (12%) restricted analyses to confirmed cases. The app (app http://apps.p-95.com/ISPE/) uses values of PPV and sensitivity (or a range of possible values) in each exposure strata and returns corrected point estimates and confidence intervals. The app demonstrates that small differences between comparison groups in PPV or sensitivity can introduce bias even when accuracy estimates are high.

CONCLUSIONS

Outcome misclassification is not usually corrected in pharmacoepidemiology database studies although correction methods using routinely measured indices are available. Error indices are needed for each comparison group to correct RR estimates for these errors. The app should encourage understanding of this bias and increase adjustment.

Data extraction for epidemiological research (DExtER): a novel tool for automated clinical epidemiology studies

The use of primary care electronic health records for research is abundant. The benefits gained from utilising such records lies in their size, longitudinal data collection and data quality. However, the use of such data to undertake high quality epidemiological studies, can lead to significant challenges particularly in dealing with misclassification, variation in coding and the significant effort required to pre-process the data in a meaningful format for statistical analysis. In this paper, we describe a methodology to aid with the extraction and processing of such databases, delivered by a novel software programme; the "Data extraction for epidemiological research" (DExtER). The basis of DExtER relies on principles of extract, transform and load processes. The tool initially provides the ability for the healthcare dataset to be extracted, then transformed in a format whereby data is normalised, converted and reformatted. DExtER has a user interface designed to obtain data extracts specific to each research question and observational study design. There are facilities to input the requirements for; eligible study period, definition of exposed and unexposed groups, outcome measures and important baseline covariates. To date the tool has been utilised and validated in a multitude of settings. There have been over 35 peer-reviewed publications using the tool, and DExtER has been implemented as a validated public health surveillance tool for obtaining accurate statistics on epidemiology of key morbidities. Future direction of this work will be the application of the framework to linked as well as international datasets and the development of standardised methods for conducting electronic pre-processing and extraction from datasets for research purposes.

Evaluating Population Coverage in a Regional Distributed Data Network: Implications for Electronic Health Record-Based Public Health Surveillance

OBJECTIVE

Electronic health records (EHRs) hold promise as a public health surveillance tool, but questions remain about how EHR patients compare with populations in health and demographic surveys. We compared population characteristics from a regional distributed data network (DDN), which securely and confidentially aggregates EHR data from multiple health care organizations in the same geographic region, with population characteristics from health and demographic surveys.

METHODS

Ten health care organizations participating in a Colorado DDN contributed data for coverage estimation. We aggregated demographic and geographic data from 2017 for patients aged ≥18 residing in 7 counties. We used a cross-sectional design to compare DDN population size, by county, with the following survey-estimated populations: the county population, estimated by the American Community Survey (ACS); residents seeking any health care, estimated by the Colorado Health Access Survey; and residents seeking routine (eg, primary) health care, estimated by the Behavioral Risk Factor Surveillance System. We also compared data on the DDN and survey populations by sex, age group, race/ethnicity, and poverty level to assess surveillance system representativeness.

RESULTS

The DDN population included 609 840 people in 7 counties, corresponding to 25% coverage of the general adult population. Population coverage ranged from 15% to 35% across counties. Demographic distributions generated by DDN and surveys were similar for many groups. Overall, the DDN and surveys assessing care-seeking populations had a higher proportion of women and older adults than the ACS population. The DDN included higher proportions of Hispanic people and people living in high-poverty neighborhoods compared with the surveys.

CONCLUSION

The DDN population is not a random sample of the regional adult population; it is influenced by health care use patterns and organizations participating in the DDN. Strengths and limitations of DDNs complement those of survey-based approaches. The regional DDN is a promising public health surveillance tool.

Qualitative subjective assessment of a high-resolution database in a paediatric intensive care unit-Elaborating the perpetual patient's ID card

OBJECTIVE

The main purpose of our study was to subjectively assess the quality of a paediatric intensive care unit (PICU) database according to the Directory of Clinical Databases (DoCDat) criteria.

DESIGN AND SETTING

A survey was conducted between April 1 and June 15, 2018, among the Sainte Justine PICU research group.

POPULATION

Every member of this group whose research activity required the use of the database and/or who was involved in the development/validation of the database.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

All 10 research team members (one Information Technology specialist, one junior medical student, and eight clinician researchers) who used the high-resolution database fulfilled the survey (100% response rate). The median quality level of the Sainte Justine PICU database across all the 10 criteria was 3 (2-4), rated on a 1 (worst) to 4 (best) numeric scale. When compared with previously assessed databases through the DoCDat criteria, we found that the Sainte Justine PICU database performance was similar.

CONCLUSIONS

The PICU high-resolution database appeared of good quality when subjectively assessed by the DoCDat criteria. Further validation procedures are mandatory. We suggest that data quality assessment and validation procedures should be reported when creating a new database.

Ability of Primary Care Health Databases to Assess Medicinal Products Discussed by the European Union Pharmacovigilance Risk Assessment Committee

This study measured the exposure to different categories of medicinal products discussed by the European Union (EU) Pharmacovigilance Risk Assessment Committee from September to November 2018 in four electronic primary care health databases: IQVIA Medical Research Data‐UK, IQVIA Medical Research Data‐France, IQVIA Medical Research Data‐Germany, and Clinical Practice Research Datalink Aurum, in the entire lifespan of each database until August 31, 2018. The assessment of 83 centrally authorized products and 45 nationally authorized products showed that coverage was better for products marketed for longer duration and worse for orphan drugs. The ability to detect associations against hypothetical comparators was better for more common events and for larger effect sizes. Coverage of advanced therapies was worse for those typically administered in a specialized rather than primary care setting. This study shows that to enable better informed regulatory decisions there is a need to access complementary data sources, particularly capturing secondary care prescribing.

Association between oral anticoagulants and osteoporosis: Real-world data mining using a multi-methodological approach

Introduction: Warfarin and direct oral anticoagulants (DOACs) have been widely used in antithrombotic therapy. Although warfarin use has been suspected to be associated with osteoporosis risk, several studies have shown otherwise. Conversely, a few reports have found an association between DOACs and osteoporosis. This study therefore clarifies the association between oral anticoagulants and osteoporosis by analyzing real-world data using different methodologies, algorithms, and databases. Methods: Real-world data from the US Food and Drug Administration Adverse Event Reporting System (FAERS; 2004-2016) and Japanese administrative claims database (2005-2017; JMDC Inc., Tokyo) were used. Reporting odds ratio (ROR) and information component (IC) were calculated through disproportionality analysis (DPA) using reports recorded in the FAERS. Sequence symmetry analysis (SSA) was employed to calculate the adjusted sequence ratio (SR) using the JMDC Claims Database. For the adjusted SR and ROR, a significant signal was detected when the lower limit of the two-sided 95% confidence interval (CI) was more than 1. For the IC, a significant signal was detected when the lower limit of the 95% CI was more than 0. Results: DPA for warfarin found significant signals for osteoporosis in ROR (1.43, 95% CI: 1.32-1.54) and IC (0.50, 95% CI: 0.39-0.61). SSA showed a significant association between warfarin use and osteoporosis or bisphosphonate use. Moreover, a significant association was observed in males and females, albeit only for warfarin. Conclusion: Multi-methodological data mining revealed that warfarin use, not DOACs, is significantly associated with osteoporosis regardless of sex difference.

Risk Factors and the Usual Source of Care on Non-Adherence to Antihypertensive Drugs in Immigrants with Hypertension

BACKGROUND

Immigrants are vulnerable to suboptimal health care utilization including non-adherence of medication use. Thus, we aimed to identify the potential risk factors of non-adherence and evaluate whether utilizing a usual source of care was associated with medication adherence in immigrants.

METHODS

We utilized the Korea National Health Insurance Claims Database between 2012 and 2015. Cases were immigrants who had antihypertensive prescriptions at the time of hypertension diagnosis in 2012. Controls were native-born Koreans with hypertension who were 1:1 matched to immigrants by age, sex, and Charlson comorbidity index. We used the medication possession ratio for three years to assess the adherence to antihypertensive drugs. The likelihood of non-adherence was evaluated between cases and controls by multivariate linear regression models stratified by age, sex, and number of clinic visits. We assessed the potential risk factors of non-adherence in immigrants by multivariate linear regression and logistic regression models, respectively.

RESULTS

In total, 4114 immigrants and 4114 matched native-born Koreans with hypertension were included. The mean MPR was significantly lower in immigrants (56% vs 70%, p<0.0001). Immigrants showed almost two times the level of non-adherence as native-born Koreans (odds ratio [OR], 2.01; 95% confidence interval [CI], 1.83-2.21). Stratified analyses on non-adherence presented the highest non-adherence (2.28 times) in immigrants in the younger group (30-49 years old) and the lowest non-adherence in immigrants in 65 and old group where the risk was 1.69 times higher than native Korean with the same age. The absence of a usual source of care significantly increased medication non-adherence by 1.31 to 1.58 times among immigrants.

CONCLUSION

When the number of visited clinics increased, the degree of non-adherence increased consistently. Therefore, the systematization of registering with primary care (a usual source of care) might be a modifiable health care strategy to improve health care outcomes in immigrants.

National Health Insurance Databases in Indonesia, Vietnam and the Philippines

BACKGROUND

Social health insurance administrative databases were established in Indonesia, Vietnam and the Philippines in 2014, 2017 and 2012, respectively; however, these databases have been scarcely used for research, if at all. This study explored the feasibility and accessibility of using these databases for scientific research, highlighting challenges and barriers in their use.

METHODOLOGY

The databases included in this evaluation comprised the Jaminan Kesehatan Nasional (JKN) from Indonesia, Vietnam Health Insurance Scheme (VHIS) from Vietnam and PhilHealth from the Philippines. These databases were qualitatively assessed based on the data capture, potential linkage to other databases or registries, data access and extraction, privacy and security, and quality and validation procedures.

RESULTS

All databases contain population-based cohort data on the medical costs of reimbursed medical conditions, identified using International Classification of Diseases, Tenth Revision (ICD-10) codes. Linkage to other national databases, ensuring protection of patient privacy data, would improve their usability. Duration to database access and data extraction varies from country to country. The main limitations of all databases include the short span of data records, and the unknown degree of internal validity. Both JKN and PhilHealth databases capture bundled claims, inherently excluding information on prescriptions and out-of-pocket expenditure. Due to the recent establishment of the VHIS database, it may not be suitable for studies that intend to explore trends.

CONCLUSION

The JKN, VHIS and PhilHealth databases offer population-based, financial, utilization, and demographic data, which could provide valuable epidemiological and pharmacoeconomic insights if the findings are interpreted within the limitations of each database.

Detecting Systemic Data Quality Issues in Electronic Health Records

Secondary analysis of electronic health records for clinical research faces significant challenges due to known data quality issues in health data observationally collected for clinical care and the data biases caused by standard healthcare processes. In this manuscript, we contribute methodology for data quality assessment by plotting domain-level (conditions (diagnoses), drugs, and procedures) aggregate statistics and concept-level temporal frequencies (i.e., annual prevalence rates of clinical concepts). We detect common temporal patterns in concept frequencies by normalizing and clustering annual concept frequencies using K-means clustering. We apply these methods to the Columbia University Irving Medical Center Observational Medical Outcomes Partnership database. The resulting domain-aggregate and cluster plots show a variety of patterns. We review the patterns found in the condition domain and investigate the processes that shape them. We find that these patterns suggest data quality issues influenced by system-wide factors that affect individual concept frequencies.

Thyroid replacement therapy, thyroid stimulating hormone concentrations, and long term health outcomes in patients with hypothyroidism: longitudinal study

OBJECTIVE

To explore whether thyroid stimulating hormone (TSH) concentration in patients with a diagnosis of hypothyroidism is associated with increased all cause mortality and a higher risk of cardiovascular disease and fractures.

DESIGN

Retrospective cohort study.

SETTING

The Health Improvement Network (THIN), a database of electronic patient records from UK primary care.

PARTICIPANTS

Adult patients with incident hypothyroidism from 1 January 1995 to 31 December 2017.

EXPOSURE

TSH concentration in patients with hypothyroidism.

MAIN OUTCOME MEASURES

Ischaemic heart disease, heart failure, stroke/transient ischaemic attack, atrial fibrillation, any fractures, fragility fractures, and mortality. Longitudinal TSH measurements from diagnosis to outcomes, study end, or loss to follow-up were collected. An extended Cox proportional hazards model with TSH considered as a time varying covariate was fitted for each outcome.

RESULTS

162 369 patients with hypothyroidism and 863 072 TSH measurements were included in the analysis. Compared with the reference TSH category (2-2.5 mIU/L), risk of ischaemic heart disease and heart failure increased at high TSH concentrations (>10 mIU/L) (hazard ratio 1.18 (95% confidence interval 1.02 to 1.38; P=0.03) and 1.42 (1.21 to 1.67; P<0.001), respectively). A protective effect for heart failure was seen at low TSH concentrations (hazard ratio 0.79 (0.64 to 0.99; P=0.04) for TSH <0.1 mIU/L and 0.76 (0.62 to 0.92; P=0.006) for 0.1-0.4 mIU/L). Increased mortality was observed in both the lowest and highest TSH categories (hazard ratio 1.18 (1.08 to 1.28; P<0.001), 1.29 (1.22 to 1.36; P<0.001), and 2.21 (2.07 to 2.36; P<0.001) for TSH <0.1 mIU/L, 4-10 mIU/L, and >10 mIU/L. An increase in the risk of fragility fractures was observed in patients in the highest TSH category (>10 mIU/L) (hazard ratio 1.15 (1.01 to 1.31; P=0.03)).

CONCLUSIONS

In patients with a diagnosis of hypothyroidism, no evidence was found to suggest a clinically meaningful difference in the pattern of long term health outcomes (all cause mortality, atrial fibrillation, ischaemic heart disease, heart failure, stroke/transient ischaemic attack, fractures) when TSH concentrations were within recommended normal limits. Evidence was found for adverse health outcomes when TSH concentration is outside this range, particularly above the upper reference value.

Prescriptions for Alpha Agonists and Antipsychotics in Children and Youth with Tic Disorders: A Pharmacoepidemiologic Study

Background

Trends in the use of antipsychotics and alpha agonists for the treatment of tic disorders in Canadian children, and how closely these trends align with evidence-based guidelines on the pharmacotherapy of tic disorders, have not been explored.

Methods

IQVIA's Canadian Disease and Therapeutic Index, a survey-based data set, was used to identify prescription patterns by physicians. Respondents recorded all patient visits during a 48-hour period in each quarter of the year, including patient age, gender, drug recommendation and therapeutic indication. Recommendations for alpha agonists and antipsychotics from 2012 to 2016 were analysed for children and adolescents with tic disorders.

Results

Risperidone and clonidine were the most commonly recommended medications for tic disorders over the study period, with 36,868 and 35,500 recommendations in 2016, respectively. Recommendations for clonidine increased over the study period, whereas those for risperidone decreased. Guanfacine (approved in Canada in 2013) was used less frequently than clonidine. Clonidine was more frequently recommended than antipsychotics in children younger than 6, in whom antipsychotic recommendations were uncommon. Aripiprazole was the second most commonly recommended antipsychotic for tic disorders, with 22,892 recommendations in 2016. Of the first-generation antipsychotics, pimozide was most commonly recommended (11,334 recommendations in 2016); haloperidol was infrequently recommended.

Discussion

The trends observed are in line with guideline recommendations reflected in the decreasing use of risperidone, and the growing use of clonidine and guanfacine. The growing use of aripiprazole is likely due to emerging evidence from clinical trials supporting its efficacy for tics. Recommendations for pimozide and haloperidol were limited, likely due to the greater adverse effects associated with these medications.

How Clinical Practice Research Datalink data are used to support pharmacovigilance

Pharmacovigilance can be defined as the science of monitoring medicines and vaccines after license for use, the purpose of which is to quantify and characterise the safety profile of a medicine, identify previously unknown adverse reactions, inform risk-benefit assessment, and support the development of actions that can be taken to reduce risks, optimise benefits and monitor their effectiveness. This review discusses the Clinical Practice Research Datalink (CPRD), which is the source of the largest research database in the UK with longitudinal, representative primary care data linked to data from other healthcare settings. CPRD supports international pharmacovigilance by providing a large, anonymised representative general population database with comprehensive capture of patient risk factors and outcomes to researchers within academic, regulatory and pharmaceutical organisations. The specific advantages of CPRD data are discussed in the context of the 'six Vs of big data' including volume, velocity, variety, veracity, validity and value. Examples of where CPRD data have been used for pharmacovigilance research and how these have fed into guidelines and policy are discussed.

Taiwan's National Health Insurance Research Database: past and future

Taiwan's National Health Insurance Research Database (NHIRD) exemplifies a population-level data source for generating real-world evidence to support clinical decisions and health care policy-making. Like with all claims databases, there have been some validity concerns of studies using the NHIRD, such as the accuracy of diagnosis codes and issues around unmeasured confounders. Endeavors to validate diagnosed codes or to develop methodologic approaches to address unmeasured confounders have largely increased the reliability of NHIRD studies. Recently, Taiwan's Ministry of Health and Welfare (MOHW) established a Health and Welfare Data Center (HWDC), a data repository site that centralizes the NHIRD and about 70 other health-related databases for data management and analyses. To strengthen the protection of data privacy, investigators are required to conduct on-site analysis at an HWDC through remote connection to MOHW servers. Although the tight regulation of this on-site analysis has led to inconvenience for analysts and has increased time and costs required for research, the HWDC has created opportunities for enriched dimensions of study by linking across the NHIRD and other databases. In the near future, researchers will have greater opportunity to distill knowledge from the NHIRD linked to hospital-based electronic medical records databases containing unstructured patient-level information by using artificial intelligence techniques, including machine learning and natural language processes. We believe that NHIRD with multiple data sources could represent a powerful research engine with enriched dimensions and could serve as a guiding light for real-world evidence-based medicine in Taiwan.

Taiwan's National Health Insurance Research Database: past and future

Taiwan’s National Health Insurance Research Database (NHIRD) exemplifies a population-level data source for generating real-world evidence to support clinical decisions and health care policy-making. Like with all claims databases, there have been some validity concerns of studies using the NHIRD, such as the accuracy of diagnosis codes and issues around unmeasured confounders. Endeavors to validate diagnosed codes or to develop methodologic approaches to address unmeasured confounders have largely increased the reliability of NHIRD studies. Recently, Taiwan’s Ministry of Health and Welfare (MOHW) established a Health and Welfare Data Center (HWDC), a data repository site that centralizes the NHIRD and about 70 other health-related databases for data management and analyses. To strengthen the protection of data privacy, investigators are required to conduct on-site analysis at an HWDC through remote connection to MOHW servers. Although the tight regulation of this on-site analysis has led to inconvenience for analysts and has increased time and costs required for research, the HWDC has created opportunities for enriched dimensions of study by linking across the NHIRD and other databases. In the near future, researchers will have greater opportunity to distill knowledge from the NHIRD linked to hospital-based electronic medical records databases containing unstructured patient-level information by using artificial intelligence techniques, including machine learning and natural language processes. We believe that NHIRD with multiple data sources could represent a powerful research engine with enriched dimensions and could serve as a guiding light for real-world evidence-based medicine in Taiwan.

I-O Optimise: a novel multinational real-world research platform in thoracic malignancies

Aim: To describe I-O Optimise, a multinational program providing real-world insights into lung cancer management. Materials & methods: Real-world data source selection for I-O Optimise followed a structured approach focused on population coverage, key variable capture, continuous/consistent data availability, record duration and data latency, and database expertise. Results: As of 31 October 2018, seven real-world data sources were included in I-O Optimise, providing data on characteristics, treatment patterns and clinical outcomes from more than 45,000 patients/year with non-small-cell lung cancer, small-cell lung cancer and mesothelioma across Denmark, Norway, Portugal, Spain, Sweden and the UK. Conclusion: The ongoing I-O Optimise initiative has the potential to provide a broad, robust and dynamic research platform to continually address numerous research objectives in the lung cancer arena.

Pediatric drug safety signal detection of non-chemotherapy drug-induced neutropenia and agranulocytosis using electronic healthcare records

Objectives: This study aimed to develop a procedure to explore the adverse drug reaction signals of drug-induced neutropenia (DIN) or drug-induced agranulocytosis (DIA) in children using an electronic health records (EHRs) database. Methods: A two-stage design was presented. First, the suspected drugs to induce DIN or DIA were selected. Second, the associations were evaluated by a retrospective cohort study. Results: Ten and five drugs were potentially identified to be associated with DIN and DIA, respectively. Finally, five (oseltamivir, chlorpheniramine, vancomycin, meropenem, and ganciclovir) and two (chlorpheniramine, and vancomycin) drugs were found to be associated with DIN and DIA, respectively. Of these, the association between oseltamivir and neutropenia (P = 9.83 × 10^-9; OR, 2.10; 95% CI, 1.62-2.69) was considered as a new signal for both adults and children. Chlorpheniramine-induced neutropenia (P = 3.01 × 10^-8; OR, 1.59; 95% CI, 1.35-1.87) and agranulocytosis (P = 3.16 × 10^-7; OR, 3.76; 95% CI, 2.25-6.26) were considered as new signals in children. Other drugs associated with DIN or DIA were confirmed by previous studies. Conclusion: A method to detect signals for DIN and DIA has been described. Several pediatric drugs were found to be associated with DIN or DIA.

RETRACTED ARTICLE: The Longitudinal Incidence of Human Papillomavirus Vaccination in Spanish Primary Care in the First 6 Years After Approval

BACKGROUND

In Spain, girls and women are vaccinated against human papillomavirus (HPV) in the primary care setting, according to a national vaccination program. Vaccination is voluntary and the cost is covered by the public health system.

OBJECTIVES

The aim of the study was to estimate the incidence and patterns of HPV vaccination amongst girls in Spain.

METHODS

A cohort study was performed using the information recorded in the Spanish Primary Care Database for Pharmacoepidemiological Research (BIFAP) from 7.4 million patients from eight Spanish regions, between 2001 and 2013 (56% of the regional population). Data available in BIFAP include patient age, sex, lifestyle factors, clinical events, specialist referrals, prescriptions, and vaccinations as recorded by the primary care physician (PCP) or administering nurse. The study cohort comprised all girls aged 11-18 years registered in BIFAP between 1 January 2007 and 31 December 2013 who had at least 1 year of clinical record information with their PCP (inclusion criteria). The date the inclusion criteria were met was designated as the start date of the study cohort contribution. In order to estimate the incidence of HPV vaccination, girls forming the study cohort were followed from start date until there was a recorded HPV vaccination, they reached 19 years of age or died, the end of available information, or 31 December 2013. The person-time of all patients forming the study cohort was taken into account in the incidence estimations. The cumulative incidence (CuIn) of vaccination by birth cohort, year and region was estimated using life-tables (proportion of vaccination by intervals in which the denominator is the initial population corrected for losses).

RESULTS

Out of 273,098 girls forming the study population, 81,461 were vaccinated during 2007-2013. Age ranged from 12 to 14 years at first dose in 86.0% of vaccinated girls; 54.1% received a quadrivalent vaccine, 21.9% a bivalent vaccine, and 24.0% an unknown type. Out of the vaccinated population, 87.9% received three doses, 8.2% two and 3.9% one dose, at a maximum of 7 years of follow-up. By calendar year and region, the CuIn reached 70.0-95.8% for birth cohorts between 1993 and 1999, 28.6-99.0% for births cohorts between 1990 and 1992, and exceptionally, 70.6-99.8% for births cohorts between 2000 and 2002 in three regions.

CONCLUSIONS

According to BIFAP primary care data, a high incidence of vaccination among girls aged 13-15 years was observed. Vaccination among younger and older girls was less common although they reached high incidence in those regions that included girls aged 11-18 years in mass programs. Most vaccination patterns adjusted to a three-dose regimen, as recommended.

Use of a Healthcare Claims Database for Post-Marketing Safety Assessments of Eribulin in Japan: A Comparative Assessment with a Prospective Post-Marketing Surveillance Study

BACKGROUND

To understand the extent to which a large-scale healthcare claims database (DB) captures the safety profile of eribulin mesylate (Halaven^®, Eisai Co., Ltd., Japan), we compared patient characteristics, drug use, and adverse events (AEs) between data for patients treated with eribulin retrieved from a DB and data for metastatic breast cancer patients from a conventional prospective post-marketing surveillance (PMS).

METHODS

We descriptively summarized patient characteristics and AEs of 551 and 951 patients retrieved from DB and PMS, respectively, during 2011‒2013. Using 2814 patient data from the DB during 2011‒2016, the drug use and AE incidence over time were assessed.

RESULTS

In both datasets, 99.8% were females, and the mean age was 57.8 ± 10.7 years. The mean number of eribulin administration was 11.1 ± 10.9 and 10.1 ± 7.8 in DB and PMS, respectively. Although, overall, the difference in AE incidence between the two datasets was moderate, gaps were larger for nausea (DB: 73.32% vs. PMS: 15.77%), neutropenia (20.87% vs. 66.67%), stomatitis (37.39% vs. 10.94%), and alopecia (0.36% vs. 12.09%). During 2011‒2016, the observed incidence of anemia or pyrexia significantly decreased (trend test, p = 0.0009 for both).

CONCLUSION

Generally, patient characteristics, drug use, and AE incidence between the DB and PMS were comparable; however, AEs such as neutropenia may require defining based on the laboratory data to achieve more comparable results in DBs. Besides the usefulness of healthcare claims DBs for long-term assessments, they may also serve as a good complementary to PMS in the pharmacovigilance of eribulin.

Who diagnosed and prescribed what? Using provider details to inform observational research

PURPOSE

To describe how often patients with depression initiating antidepressants receive their depression diagnosis and prescriptions from the same provider and, when simultaneously initiating benzodiazepines, how often both prescriptions come from the same provider.

METHODS

Using a US healthcare claims database, we created a cohort of adults (18-64 years) with a depression diagnosis who initiated antidepressants. We examined concordance by provider specialty and provider identifier between (a) the first antidepressant prescription fill and most proximal depression diagnosis, and (b) the initial antidepressant and benzodiazepine prescription fills among simultaneous benzodiazepine and antidepressant initiators.

RESULTS

Among 245 166 antidepressant initiators with a recent depression diagnosis (female = 67%; median age = 39), the specialty of the provider assigning the depression diagnosis matched the antidepressant prescriber's specialty in 94% of cases with known provider details (provider identifier concordance = 93%). Concordance was higher for adults diagnosed by a general practitioner (98%) or psychiatrist (92%) than for those diagnosed by a psychologist (74%). In simultaneous new users of antidepressants and benzodiazepines (n = 19 371), both prescriptions were issued by the same provider specialty and provider identifier 94% and 93% of the time, respectively.

CONCLUSIONS

The vast majority of patients who received antidepressant prescriptions and depression diagnoses appear to have received both diagnosis and antidepressants from the same provider, suggesting that when antidepressants are issued around the time a patient is diagnosed with depression, the antidepressant was likely prescribed for depression. In addition, the great majority of patients who simultaneously initiate benzodiazepines appear to do so under the direction of one provider.

Lessons from meta-analyses of randomized clinical trials for analysis of distributed networks of observational databases

Networks of constellations of longitudinal observational databases, often electronic medical records or transactional insurance claims or both, are increasingly being used for studying the effects of medicinal products in real-world use. Such databases are frequently configured as distributed networks. That is, patient-level data are kept behind firewalls and not communicated outside of the data vendor other than in aggregate form. Instead, data are standardized across the network, and queries of the network are executed locally by data partners, and summary results provided to a central research partner(s) for amalgamation, aggregation, and summarization. Such networks can be huge covering years of data on upwards of 100 million patients. Examples of such networks include the FDA Sentinel Network, ASPEN, CNODES, and EU-ADR. As this is a new emerging field, we note in this paper the conceptual similarities and differences between the analysis of distributed networks and the now well-established field of meta-analysis of randomized clinical trials (RCTs). We recommend, wherever appropriate, to apply learnings from meta-analysis to help guide the development of distributed network analyses of longitudinal observational databases.

Electronic healthcare databases in Europe: descriptive analysis of characteristics and potential for use in medicines regulation

OBJECTIVE

Electronic healthcare databases (EHDs) are useful tools for drug development and safety evaluation but their heterogeneity of structure, validity and access across Europe complicates the conduct of multidatabase studies. In this paper, we provide insight into available EHDs to support regulatory decisions on medicines.

METHODS

EHDs were identified from publicly available information from the European Network of Centres for Pharmacoepidemiology and Pharmacovigilance resources database, textbooks and web-based searches. Databases were selected using criteria related to accessibility, longitudinal dimension, recording of exposure and outcomes, and generalisability. Extracted information was verified with the database owners.

RESULTS

A total of 34 EHDs were selected after applying key criteria relevant for regulatory purposes. The most represented regions were Northern, Central and Western Europe. The most frequent types of data source were electronic medical records (44.1%) and record linkage systems (29.4%). The median number of patients registered in the 34 data sources was 5 million (range 0.07-15 million) while the median time covered by a database was 18.5 years. Paediatric patients were included in 32 databases (94%). Completeness of information on drug exposure was variable. Published validation studies were found for only 17 databases (50%). Some level of access exists for 25 databases (73.5%), and 23 databases (67.6%) can be linked through a personal identification number to other databases with parent-child linkage possible in 7 (21%) databases. Eight databases (23.5%) were already transformed or were in the process of being transformed into a common data model that could facilitate multidatabase studies.

CONCLUSION

A Few European databases meet minimal regulatory requirements and are readily available to be used in a regulatory context. Accessibility and validity information of the included information needs to be improved. This study confirmed the fragmentation, heterogeneity and lack of transparency existing in many European EHDs.

Reporting to Improve Reproducibility and Facilitate Validity Assessment for Healthcare Database Studies V1.0

Purpose

Defining a study population and creating an analytic dataset from longitudinal healthcare databases involves many decisions. Our objective was to catalogue scientific decisions underpinning study execution that should be reported to facilitate replication and enable assessment of validity of studies conducted in large healthcare databases.

Methods

We reviewed key investigator decisions required to operate a sample of macros and software tools designed to create and analyze analytic cohorts from longitudinal streams of healthcare data. A panel of academic, regulatory, and industry experts in healthcare database analytics discussed and added to this list.

Conclusion

Evidence generated from large healthcare encounter and reimbursement databases is increasingly being sought by decision‐makers. Varied terminology is used around the world for the same concepts. Agreeing on terminology and which parameters from a large catalogue are the most essential to report for replicable research would improve transparency and facilitate assessment of validity. At a minimum, reporting for a database study should provide clarity regarding operational definitions for key temporal anchors and their relation to each other when creating the analytic dataset, accompanied by an attrition table and a design diagram.

A substantial improvement in reproducibility, rigor and confidence in real world evidence generated from healthcare databases could be achieved with greater transparency about operational study parameters used to create analytic datasets from longitudinal healthcare databases.

Conditioning on future exposure to define study cohorts can induce bias: the case of low-dose acetylsalicylic acid and risk of major bleeding

Background

A principle of cohort studies is that cohort membership is defined by current rather than future exposure information. Pharmacoepidemiologic studies using existing databases are vulnerable to violation of this principle. We evaluated the impact of using data on future redemption of prescriptions to determine cohort membership, motivated by a published example seeking to emulate a “per-protocol” association between continuous versus never use of low-dose acetylsalicylic acid (ASA) and major bleeding (e.g., cerebral hemorrhage or gastrointestinal bleeding).

Materials and methods

Danish medical registry data from 2004 to 2011 were used to construct two analytic cohorts. In Cohort 1, we used information about future redemption of low-dose ASA prescriptions to identify cohorts of continuous and never-ASA users. In Cohort 2, we identified ASA initiators and non-initiators using only contemporaneous data and censored follow-up for changes in use over time. We implemented propensity score-matched Poisson regression to evaluate associations between ASA use and major bleeding and estimated adjusted incidence rate differences (IRDs) per 1,000 person-years and ratios (IRRs) overall and stratified by time since initiation.

Results

Among >6 million eligible Danish adults, we identified 403,693 low-dose ASA initiators (Cohort 2), of whom 189,150 were defined as continuous users (Cohort 1). Overall, IRDs and IRRs were similar across cohorts. However, the IRD for major bleeding in the first 90 days was substantially larger in Cohort 1 (IRD=25 per 1,000 person-years) compared with Cohort 2 (IRD=10 per 1,000 person-years).

Conclusion

Using future medication redemption data to define baseline cohorts violates basic epidemiologic principles. Compared with an approach using only contemporaneous data to define cohorts, the approach based on future redemption data generated a substantially higher short-term association between low-dose ASA use and major bleeding on the absolute, but not the relative, scale possibly due to selection and immortal time biases.

A conceptual framework for evaluating data suitability for observational studies

Objective

To contribute a conceptual framework for evaluating data suitability to satisfy the research needs of observational studies.

Materials and Methods

Suitability considerations were derived from a systematic literature review on researchers’ common data needs in observational studies and a scoping review on frequent clinical database design considerations, and were harmonized to construct a suitability conceptual framework using a bottom-up approach. The relationships among the suitability categories are explored from the perspective of 4 facets of data: intrinsic, contextual, representational, and accessible. A web-based national survey of domain experts was conducted to validate the framework.

Results

Data suitability for observational studies hinges on the following key categories: Explicitness of Policy and Data Governance, Relevance, Availability of Descriptive Metadata and Provenance Documentation, Usability, and Quality. We describe 16 measures and 33 sub-measures. The survey uncovered the relevance of all categories, with a 5-point Likert importance score of 3.9 ± 1.0 for Explicitness of Policy and Data Governance, 4.1 ± 1.0 for Relevance, 3.9 ± 0.9 for Availability of Descriptive Metadata and Provenance Documentation, 4.2 ± 1.0 for Usability, and 4.0 ± 0.9 for Quality.

Conclusions

The suitability framework evaluates a clinical data source’s fitness for research use. Its construction reflects both researchers’ points of view and data custodians’ design features. The feedback from domain experts rated Usability, Relevance, and Quality categories as the most important considerations.

Reporting to Improve Reproducibility and Facilitate Validity Assessment for Healthcare Database Studies V1.0

PURPOSE

Defining a study population and creating an analytic dataset from longitudinal healthcare databases involves many decisions. Our objective was to catalogue scientific decisions underpinning study execution that should be reported to facilitate replication and enable assessment of validity of studies conducted in large healthcare databases.

METHODS

We reviewed key investigator decisions required to operate a sample of macros and software tools designed to create and analyze analytic cohorts from longitudinal streams of healthcare data. A panel of academic, regulatory, and industry experts in healthcare database analytics discussed and added to this list.

CONCLUSION

Evidence generated from large healthcare encounter and reimbursement databases is increasingly being sought by decision-makers. Varied terminology is used around the world for the same concepts. Agreeing on terminology and which parameters from a large catalogue are the most essential to report for replicable research would improve transparency and facilitate assessment of validity. At a minimum, reporting for a database study should provide clarity regarding operational definitions for key temporal anchors and their relation to each other when creating the analytic dataset, accompanied by an attrition table and a design diagram. A substantial improvement in reproducibility, rigor and confidence in real world evidence generated from healthcare databases could be achieved with greater transparency about operational study parameters used to create analytic datasets from longitudinal healthcare databases.