Andrew Bate

Disproportionality methods for pharmacovigilance in longitudinal observational databases

Data mining disproportionality methods (PRR, ROR, EBGM, IC, etc.) are commonly used to identify drug safety signals in spontaneous report system (SRS) databases. Newer data sources such as longitudinal observational databases (LOD) provide time-stamped patient-level information and overcome some of the SRS limitations such as an absence of the denominator, total number of patients who consume a drug, and limited temporal information. Application of the disproportionality methods to LODs has not been widely explored. The scale of the LOD data provides an interesting computational challenge. Larger health claims databases contain information on more than 50 million patients and each patient has records for up to 10 years. In this article we systematically explore the application of commonly used disproportionality methods to simulated and real LOD data.

Exposure to benzodiazepines (anxiolytics, hypnotics and related drugs) in seven European electronic healthcare databases: a cross-national descriptive study from the PROTECT-EU Project

Studies on drug utilization usually do not allow direct cross‐national comparisons because of differences in the respective applied methods. This study aimed to compare time trends in BZDs prescribing by applying a common protocol and analyses plan in seven European electronic healthcare databases.

Incidence Rates and Trends of Hip/Femur Fractures in Five European Countries: Comparison Using E-Healthcare Records Databases

Hip fractures represent a major public health challenge worldwide. Multinational studies using a common methodology are scarce. We aimed to estimate the incidence rates (IRs) and trends of hip/femur fractures over the period 2003–2009 in five European countries. The study was performed using seven electronic health-care records databases (DBs) from Denmark, The Netherlands, Germany, Spain, and the United Kingdom, based on the same protocol. Yearly IRs of hip/femur fractures were calculated for the general population and for those aged ≥50xa0years. Trends over time were evaluated using linear regression analysis for both crude and standardized IRs. Sex- and age-standardized IRs for the UK, Netherlands, and Spanish DBs varied from 9 to 11 per 10,000 person-years for the general population and from 22 to 26 for those ≥50xa0years old; the German DB showed slightly higher IRs (about 13 and 30, respectively), whereas the Danish DB yielded IRs twofold higher (19 and 52, respectively). IRs increased exponentially with age in both sexes. The ratio of females to males was ≥2 for patients aged ≥70–79xa0years in most DBs. Statistically significant trends over time were only shown for the UK DB (CPRD) (+0.7xa0% per year, Pxa0<xa00.01) and the Danish DB (−1.4xa0% per year, Pxa0<xa00.01). IRs of hip/femur fractures varied greatly across European countries. With the exception of Denmark, no decreasing trend was observed over the study period.

An evaluation of the THIN database in the OMOP Common Data Model for active drug safety surveillance.

BackgroundThere has been increased interest in using multiple observational databases to understand the safety profile of medical products during the postmarketing period. However, it is challenging to perform analyses across these heterogeneous data sources. The Observational Medical Outcome Partnership (OMOP) provides a Common Data Model (CDM) for organizing and standardizing databases. OMOP’s work with the CDM has primarily focused on US databases. As a participant in the OMOP Extended Consortium, we implemented the OMOP CDM on the UK Electronic Healthcare Record database—The Health Improvement Network (THIN).ObjectiveThe aim of the study was to evaluate the implementation of the THIN database in the OMOP CDM and explore its use for active drug safety surveillance.MethodsFollowing the OMOP CDM specification, the raw THIN database was mapped into a CDM THIN database. Ten Drugs of Interest (DOI) and nine Health Outcomes of Interest (HOI), defined and focused by the OMOP, were created using the CDM THIN database. Quantitative comparison of raw THIN to CDM THIN was performed by execution and analysis of OMOP standardized reports and additional analyses. The practical value of CDM THIN for drug safety and pharmacoepidemiological research was assessed by implementing three analysis methods: Proportional Reporting Ratio (PRR), Univariate Self-Case Control Series (USCCS) and High-Dimensional Propensity Score (HDPS). A published study using raw THIN data was selected to examine the external validity of CDM THIN.ResultsOverall demographic characteristics were the same in both databases. Mapping medical and drug codes into the OMOP terminology dictionary was incomplete: 25xa0% medical codes and 55xa0% drug codes in raw THIN were not listed in the OMOP terminology dictionary, representing 6xa0% condition occurrence counts, 4xa0% procedure occurrence counts and 7xa0% drug exposure counts in raw THIN. Seven DOIs had <0.3xa0% and three DOIs had 1xa0% of unmapped drug exposure counts; each HOI had at least one definition with no or minimal (≤0.2xa0%) issues with unmapped condition occurrence counts, except for the upper gastrointestinal (UGI) ulcer hospitalization cohort. The application of PRR, USCCS and HDPS found, respectively, a sensitivity of 67, 78 and 50xa0%, and a specificity of 68, 59 and 76xa0%, suggesting that safety issues defined as known by the OMOP could be identified in CDM THIN, with imperfect performance. Similar PRR scores were produced using both CDM THIN and raw THIN, while the execution time was twice as fast on CDM THIN. There was close replication of demographic distribution, death rate and prescription pattern and trend in the published study population and the cohort of CDM THIN.ConclusionsThis research demonstrated that information loss due to incomplete mapping of medical and drug codes as well as data structure in the current CDM THIN limits its use for all possible epidemiological evaluation studies. Current HOIs and DOIs predefined by the OMOP were constructed with minimal loss of information and can be used for active surveillance methodological research. The OMOP CDM THIN can be a valuable tool for multiple aspects of pharmacoepidemiological research when the unique features of UK Electronic Health Records are incorporated in the OMOP library.

Ascertainment of acute liver injury in two European primary care databases

PurposeThe purpose of this study was to ascertain acute liver injury (ALI) in primary care databases using different computer algorithms. The aim of this investigation was to study and compare the incidence of ALI in different primary care databases and using different definitions of ALI.MethodsThe Clinical Practice Research Datalink (CPRD) in UK and the Spanish “Base de datos para la Investigación Farmacoepidemiológica en Atención Primaria” (BIFAP) were used. Both are primary care databases from which we selected individuals of all ages registered between January 2004 and December 2009. We developed two case definitions of idiopathic ALI using computer algorithms: (i) restrictive definition (definite cases) and (ii) broad definition (definite and probable cases). Patients presenting prior liver conditions were excluded. Manual review of potential cases was performed to confirm diagnosis, in a sample in CPRD (21xa0%) and all potential cases in BIFAP. Incidence rates of ALI by age, sex and calendar year were calculated.ResultsIn BIFAP, all cases considered definite after manual review had been detected with the computer algorithm as potential cases, and none came from the non-cases group. The restrictive definition of ALI had a low sensitivity but a very high specificity (95xa0% in BIFAP) and showed higher rates of agreement between computer search and manual review compared to the broad definition. Higher incidence rates of definite ALI in 2008 were observed in BIFAP (3.01 (95xa0% confidence interval (CI) 2.13–4.25) per 100,000 person-years than CPRD (1.35 (95xa0% CI 1.03–1.78)).ConclusionsThis study shows that it is feasible to identify ALI cases if restrictive selection criteria are used and the possibility to review additional information to rule out differential diagnoses. Our results confirm that idiopathic ALI is a very rare disease in the general population. Finally, the construction of a standard definition with predefined criteria facilitates the timely comparison across databases.

A Comparative Assessment of Observational Medical Outcomes Partnership and Mini-Sentinel Common Data Models and Analytics: Implications for Active Drug Safety Surveillance

AbstractIntroductionAn often key component to ncoordinating surveillance activities across distributed networks is the design and implementation of a common data model (CDM). The purpose of this study was to evaluate two drug safety surveillance CDMs from an ecosystem perspective to better understand how differences in CDMs and analytic tools affect usability and interpretation of results.MethodsHumana claims data from 2007 to 2012 were mapped to Observational Medical Outcomes Partnership (OMOP) and Mini-Sentinel CDMs. Data were described and compared at the patient level by source code and mapped concepts. Study cohort construction and effect estimates were also compared using two different analytical methods—one based on a new user design implementing a high-dimensional propensity score (HDPS) algorithm and the other based on univariate self-controlled case series (SCCS) design—across six established positive drug-outcome pairs to learn how differences in CDMs and analytics influence steps in the database analytic process and results.ResultsClaims data for approximately 7.7xa0million Humana health plan members were transformed into the two CDMs. Three health outcome cohorts and two drug cohorts showed differences in cohort size and constituency between Mini-Sentinel and OMOP CDMs, which was a result of multiple factors. Overall, the implementation of the HDPS procedure on Mini-Sentinel CDM detected more known positive associations than that on OMOP CDM. The SCCS method results were comparable on both CDMs. Differences in the implementation of the HDPS procedure between the two CDMs were identified; analytic model and risk period specification had a significant impact on the performance of the HDPS procedure on OMOP CDM.ConclusionsDifferences were observed between OMOP and Mini-Sentinel CDMs. The analysis of both CDMs at the data model level indicated that such conceptual differences had only a slight but not significant impact on identifying known safety associations. Our results show that differences at the ecosystem level of analyses across the CDMs can lead to strikingly different risk estimations, but this can be primarily attributed to the choices of analytic approach and their implementation in the community-developed analytic tools. The opportunities of using CDMs are clear, but our study shows the need for judicious comparison of analyses across the CDMs. Our work emphasizes the need for ongoing efforts to ensure sustainable transparent platforms to maintain and develop CDMs and associated tools for effective safety surveillance.

Teaching Pharmacovigilance: the WHO-ISoP Core Elements of a Comprehensive Modular Curriculum

The importance of pharmacovigilance (PV) for safe medicines and their safe use has increasingly been recognised during the last few years [1]. PV has been subject of intense research and regulation. In particular, it has earned more and more importance and attention in low-resource countries. This is largely due to the globalisation of trade and the availability of new, highly effective but potentially harmful chemical medicinal products in those parts of the world where traditional treatments, in particular herbal or other complementary remedies, used to prevail. A plethora of publications, guidelines and information about newly observed or further investigated adverse drug reactions (ADRs) from all over the world creates a growing burden for people working with medicines or patients to keep abreast of this development. Largely due to the global availability of information through the Internet, patients are nowadays more and more critical and often concerned about, or even frightened of, potential ADRs of their medicines. This poses an additional demand on the up-todate capacities of their doctors and other healthcare professionals (HCPs). A particular challenge is the multidisciplinary character of PV which requires know-how in topics as different as molecular mechanisms of ADRs, clinical medicine, pharmacoepidemiology, information technology, pharmaceutical manufacturing, legal aspects, public health situations on various levels, and traditions in The views expressed in this article reflect a consensus reached between the personal views of all authors. They do not necessarily reflect the views of the authors’ employers or any institutions the authors are otherwise affiliated to.

Safety surveillance of longitudinal databases : methodological considerations

We read with interest the paper by Schuemie describing an implementation of Bayesian disproportionality analysis (Gamma Poisson Shrinker (GPS)) for longitudinal data and emphasizing the importance of graphical representation. In this commentary, we describe a previously published related approach to safety signal detection in longitudinal medical records,2,3 which we argue has important advantages. We describe some details of the method that can serve as an extension of the approach described by Schuemie. We believe this makes the case for graphical representation coupled with Bayesian analysis in safety surveillance of longitudinal data more convincingly. As with Schuemie’s method, the method for temporal pattern discovery in Norén et al.2,3 uses Bayesian shrinkage to protect against spurious associations, contrasts event rates in different periods to filter out indications for treatment, and proposes a graphical statistical approach to characterize temporal patterns and facilitate clinical interpretation. In addition, it controls for time‐constant confounders through a self‐ controlled design while incorporating information on unexposed patients separately to account for systematic variability in event rates over time. It contrasts four distinct periods relative to treatment initiation to highlight time‐varying confounding by underlying disease. Its computational framework has been evaluated in the UK IMS Disease Analyzer data set of more

Prevalence of antibiotic use : a comparison across various European health care data sources

There is widespread concern about increases in antibiotic use, but comparative data from different European countries on rates of use are lacking. This study was designed to measure and understand the variation in antibiotic utilization across five European countries.

Hip/femur fractures associated with the use of benzodiazepines (anxiolytics, hypnotics and related drugs) : a methodological approach to assess consistencies across databases from the PROTECT-EU project

Results from observational studies may be inconsistent because of variations in methodological and clinical factors that may be intrinsically related to the database (DB) where the study is performed.