Disproportionality Analysis for Pharmacovigilance Signal Detection in Small Databases or Subsets: Recommendations for Limiting False-Positive Associations.

INTRODUCTION: Uncovering safety signals through the collection and assessment of individual case reports remains a core pharmacovigilance activity. Despite the widespread use of disproportionality analysis in signal detection, recommendations are lacking on the minimum size of databases or subsets of databases required to yield robust results. OBJECTIVE: This study aims to investigate the relationship between database size and robustness of disproportionality analysis, with regards to limiting spurious associations. METHODS: Three types of subsets were created from the global database VigiBase: random subsets (500 replicates each of 11 fixed subset sizes between 250 and 100,000 reports), country-specific subsets (all 131 countries available in the original VigiBase extract) and subsets based on the Anatomical Therapeutic Chemical classification. For each subset, a spuriousness rate was computed as the ratio between the number of drug-event combinations highlighted by disproportionality analysis in a permuted version of the subset and the corresponding number in the original subset. In the permuted data, all true reporting associations between drugs and adverse events were broken. Subsets with fewer than five original associations were excluded. Additionally, the set of disproportionately over-reported drug-event combinations in three specific countries at three different time points were clinically assessed for labelledness. These time points corresponded to database sizes of less than 10,000, 5000 and 1000 reports, respectively. All disproportionality analysis was based on the Information Component (IC), implemented as IC025 > 0. RESULTS: Spuriousness rates were below 0.15 for all 110 included countries regardless of subset size, with only seven countries (6%) exceeding the empirical threshold of 0.10 observed for large subsets. All 21 excluded countries had < 500 reports. For random subsets containing 3000-5000 or more reports, the higher end of observed spuriousness rates was close to 0.10. In the clinical assessment, the proportion of labelled or otherwise known drug-event combinations was very high (87-100%) across all countries and time points studied. CONCLUSIONS: To mitigate the risk of highlighting spurious associations with disproportionality analysis, a minimum size of 500 reports is recommended for national databases. For databases or subsets that are not country-specific, our recommendation is 5000 reports. This study does not consider sensitivity, which is expected to be poor in smaller databases.

Recommendations for the Use of Social Media in Pharmacovigilance: Lessons from IMI WEB-RADR.

Over a period of 3 years, the European Union's Innovative Medicines Initiative WEB-RADR project has explored the value of social media (i.e., information exchanged through the internet, typically via online social networks) for identifying adverse events as well as for safety signal detection. Many patients and clinicians have taken to social media to discuss their positive and negative experiences of medications, creating a source of publicly available information that has the potential to provide insights into medicinal product safety concerns. The WEB-RADR project has developed a collaborative English language workspace for visualising and analysing social media data for a number of medicinal products. Further, novel text and data mining methods for social media analysis have been developed and evaluated. From this original research, several recommendations are presented with supporting rationale and consideration of the limitations. Recommendations for further research that extend beyond the scope of the current project are also presented.

Reported adverse drug reactions in women and men: Aggregated evidence from globally collected individual case reports during half a century.

BACKGROUND: Adverse drug reactions (ADRs) are an important cause of morbidity and mortality. Reports on differences in reporting patterns between women and men exist nationally. The goal of the present study was to assess the global evidence on spontaneous post-marketing ADR reporting differences between reports for women and men. METHODS: We analysed data collected within VigiBase, the WHO global database of individual case safety reports, between 1967-2 January 2018. VigiBase contains more than 18 million reports from the 131 member countries of the WHO Programme for International Drug Monitoring. FINDINGS: Of the reports with information on sex, 9,056,566 (60.1%) concerned female and 6,012,804 (39.9%) male children and adults. More female ADR reports were submitted in all regions of the world and by all types of reporters. A higher proportion of female reports was seen in all age groups from the age group 12-17 years and older. The largest difference was observed in the age group of 18-44 years and could not be explained by hormonal contraceptive use. The proportion of serious and fatal reports was higher for male reports. INTERPRETATION: Global post marketing surveillance data on spontaneous reports indicate that women, from puberty and onwards and especially in their reproductive years, report more ADRs than men. However, there is a higher proportion of serious and fatal ADRs among male reports. Our results suggest important underlying sex-related differences in ADRs. These findings highlight the importance of considering sex throughout the entire life-cycle of drug development and surveillance and understanding the underlying reasons for reporting ADRs.

Assessment of the Utility of Social Media for Broad-Ranging Statistical Signal Detection in Pharmacovigilance: Results from the WEB-RADR Project.

INTRODUCTION AND OBJECTIVE: Social media has been proposed as a possibly useful data source for pharmacovigilance signal detection. This study primarily aimed to evaluate the performance of established statistical signal detection algorithms in Twitter/Facebook for a broad range of drugs and adverse events. METHODS: Performance was assessed using a reference set by Harpaz et al., consisting of 62 US Food and Drug Administration labelling changes, and an internal WEB-RADR reference set consisting of 200 validated safety signals. In total, 75 drugs were studied. Twitter/Facebook posts were retrieved for the period March 2012 to March 2015, and drugs/events were extracted from the posts. We retrieved 4.3 million and 2.0 million posts for the WEB-RADR and Harpaz drugs, respectively. Individual case reports were extracted from VigiBase for the same period. Disproportionality algorithms based on the Information Component or the Proportional Reporting Ratio and crude post/report counting were applied in Twitter/Facebook and VigiBase. Receiver operating characteristic curves were generated, and the relative timing of alerting was analysed. RESULTS: Across all algorithms, the area under the receiver operating characteristic curve for Twitter/Facebook varied between 0.47 and 0.53 for the WEB-RADR reference set and between 0.48 and 0.53 for the Harpaz reference set. For VigiBase, the ranges were 0.64-0.69 and 0.55-0.67, respectively. In Twitter/Facebook, at best, 31 (16%) and four (6%) positive controls were detected prior to their index dates in the WEB-RADR and Harpaz references, respectively. In VigiBase, the corresponding numbers were 66 (33%) and 17 (27%). CONCLUSIONS: Our results clearly suggest that broad-ranging statistical signal detection in Twitter and Facebook, using currently available methods for adverse event recognition, performs poorly and cannot be recommended at the expense of other pharmacovigilance activities.

Characteristics, Quality and Contribution to Signal Detection of Spontaneous Reports of Adverse Drug Reactions Via the WEB-RADR Mobile Application: A Descriptive Cross-Sectional Study.

INTRODUCTION: Spontaneous reporting of suspected adverse drug reactions is key for efficient post-marketing safety surveillance. To increase usability and accessibility of reporting tools, the Web-Recognising Adverse Drug Reactions (WEB-RADR) consortium developed a smartphone application (app) based on a simplified reporting form. OBJECTIVE: The objective of this study was to evaluate the characteristics, quality and contribution to signals of reports submitted via the WEB-RADR app. METHODS: The app was launched in the UK, the Netherlands and Croatia between July 2015 and May 2016. Spontaneous reports submitted until September 2016 with a single reporter were included. For each country, app reports and reports received through conventional means in the same time period were compared to identify characteristic features. A random subset of reports was assessed for clinical quality and completeness. The contribution to signal detection was assessed by a descriptive analysis. RESULTS: Higher proportions of app reports were submitted by patients in the UK (28 vs. 18%) and Croatia (32 vs. 7%); both p < 0.01. In the Netherlands, the difference was small (60 vs. 57%; p = 0.5). The proportion of female patients and the median patient ages in app reports submitted by patients were similar to the reference. The proportion of reports of at least moderate quality was high in both samples (app: 78-85%, reference: 78-98%), for all countries. App reports contributed to detecting eight potential safety signals at the national level, four of which were eventually signalled. CONCLUSION: The WEB-RADR app offers a new route of spontaneous reporting that shows promise in attracting reports from patients and that could become an important tool in the future. Patient demographics are similar to conventional routes, report quality is sufficient despite a simplified reporting form, and app reports show potential in contributing to signal detection.

Does patient reporting lead to earlier detection of drug safety signals? A retrospective comparison of time to reporting between patients and healthcare professionals in a global database.

AIMS: To explore if there is a difference between patients and healthcare professionals (HCPs) in time to reporting drug-adverse drug reaction (ADR) associations that led to drug safety signals. METHODS: This was a retrospective comparison of time to reporting selected drug-ADR associations which led to drug safety signals between patients and HCPs. ADR reports were selected from the World Health Organization Global database of individual case safety reports, VigiBase. Reports were selected based on drug-ADR associations of actual drug safety signals. Primary outcome was the difference in time to reporting between patients and HCPs. The date of the first report for each individual signal was used as time zero. The difference in time between the date of the reports and time zero was calculated. Statistical differences in timing were analysed on the corresponding survival curves using a Mann-Whitney U test. RESULTS: In total, 2822 reports were included, of which 52.7% were patient reports, with a median of 25% for all included signals. For all signals, median time to signal detection was 10.4 years. Overall, HCPs reported earlier than patients: median 7.0 vs. 8.3 years (P < 0.001). CONCLUSIONS: Patients contributed a large proportion of reports on drug-ADR pairs that eventually became signals. HCPs reported 1.3 year earlier than patients. These findings strengthen the evidence on the value of patient reporting in signal detection and highlight an opportunity to encourage patients to report suspected ADRs even earlier in the future.

vigiRank for statistical signal detection in pharmacovigilance: First results from prospective real-world use.

PURPOSE: vigiRank is a data-driven predictive model for emerging safety signals. In addition to disproportionate reporting patterns, it also accounts for the completeness, recency, and geographic spread of individual case reporting, as well as the availability of case narratives. Previous retrospective analysis suggested that vigiRank performed better than disproportionality analysis alone. The purpose of the present analysis was to evaluate its prospective performance. METHODS: The evaluation of vigiRank was based on real-world signal detection in VigiBase. In May 2014, vigiRank scores were computed for pairs of new drugs and WHO Adverse Reaction Terminology critical terms with at most 30 reports from at least 2 countries. Initial manual assessments were performed in order of descending score, selecting a subset of drug-adverse drug reaction pairs for in-depth expert assessment. The primary performance metric was the proportion of initial assessments that were decided signals during in-depth assessment. As comparator, the historical performance for disproportionality- guided signal detection in VigiBase was computed from a corresponding cohort of drug-adverse drug reaction pairs assessed between 2009 and 2013. During this period, the requirement for initial manual assessment was a positive lower endpoint of the 95% credibility interval of the Information Component measure of disproportionality, observed for the first time. RESULTS: 194 initial assessments suggested by vigiRank's ordering eventually resulted in 6 (3.1%) signals. Disproportionality analysis yielded 19 signals from 1592 initial assessments (1.2%; P < .05). CONCLUSIONS: Combining multiple strength-of-evidence aspects as in vigiRank significantly outperformed disproportionality analysis alone in real-world pharmacovigilance signal detection, for VigiBase.

Current Safety Concerns with Human Papillomavirus Vaccine: A Cluster Analysis of Reports in VigiBase®.

INTRODUCTION: A number of safety signals-complex regional pain syndrome (CRPS), postural orthostatic tachycardia syndrome (POTS), and chronic fatigue syndrome (CFS)-have emerged with human papillomavirus (HPV) vaccines, which share a similar pattern of symptomatology. Previous signal evaluations and epidemiological studies have largely relied on traditional methodologies and signals have been considered individually. OBJECTIVE: The aim of this study was to explore global reporting patterns for HPV vaccine for subgroups of reports with similar adverse event (AE) profiles. METHODS: All individual case safety reports (reports) for HPV vaccines in VigiBase® until 1 January 2015 were identified. A statistical cluster analysis algorithm was used to identify natural groupings based on AE profiles in a data-driven exploratory analysis. Clinical assessment of the clusters was performed to identify clusters relevant to current safety concerns. RESULTS: Overall, 54 clusters containing at least five reports were identified. The four largest clusters included 71 % of the analysed HPV reports and described AEs included in the product label. Four smaller clusters were identified to include case reports relevant to ongoing safety concerns (total of 694 cases). In all four of these clusters, the most commonly reported AE terms were headache and dizziness and fatigue or syncope; three of these four AE terms were reported in >50 % of the reports included in the clusters. These clusters had a higher proportion of serious cases compared with HPV reports overall (44-89 % in the clusters compared with 24 %). Furthermore, only a minority of reports included in these clusters included AE terms of diagnoses to explain these symptoms. Using proportional reporting ratios, the combination of headache and dizziness with either fatigue or syncope was found to be more commonly reported in HPV vaccine reports compared with non-HPV vaccine reports for females aged 9-25 years. This disproportionality remained when results were stratified by age and when those countries reporting the signals of CRPS (Japan) and POTS (Denmark) were excluded. CONCLUSIONS: Cluster analysis reveals additional reports of AEs following HPV vaccination that are serious in nature and describe symptoms that overlap those reported in cases from the recent safety signals (POTS, CRPS, and CFS), but which do not report explicit diagnoses. While the causal association between HPV vaccination and these AEs remains uncertain, more extensive analyses of spontaneous reports can better identify the relevant case series for thorough signal evaluation.

Cheminformatics-aided pharmacovigilance: application to Stevens-Johnson Syndrome.

OBJECTIVE: Quantitative Structure-Activity Relationship (QSAR) models can predict adverse drug reactions (ADRs), and thus provide early warnings of potential hazards. Timely identification of potential safety concerns could protect patients and aid early diagnosis of ADRs among the exposed. Our objective was to determine whether global spontaneous reporting patterns might allow chemical substructures associated with Stevens-Johnson Syndrome (SJS) to be identified and utilized for ADR prediction by QSAR models. MATERIALS AND METHODS: Using a reference set of 364 drugs having positive or negative reporting correlations with SJS in the VigiBase global repository of individual case safety reports (Uppsala Monitoring Center, Uppsala, Sweden), chemical descriptors were computed from drug molecular structures. Random Forest and Support Vector Machines methods were used to develop QSAR models, which were validated by external 5-fold cross validation. Models were employed for virtual screening of DrugBank to predict SJS actives and inactives, which were corroborated using knowledge bases like VigiBase, ChemoText, and MicroMedex (Truven Health Analytics Inc, Ann Arbor, Michigan). RESULTS: We developed QSAR models that could accurately predict if drugs were associated with SJS (area under the curve of 75%-81%). Our 10 most active and inactive predictions were substantiated by SJS reports (or lack thereof) in the literature. DISCUSSION: Interpretation of QSAR models in terms of significant chemical descriptors suggested novel SJS structural alerts. CONCLUSIONS: We have demonstrated that QSAR models can accurately identify SJS active and inactive drugs. Requiring chemical structures only, QSAR models provide effective computational means to flag potentially harmful drugs for subsequent targeted surveillance and pharmacoepidemiologic investigations.

Quantitative benefit-risk assessment of methylprednisolone in multiple sclerosis relapses.

BACKGROUND: High-dose short-term methylprednisolone is the recommended treatment in the management of multiple sclerosis relapses, although it has been suggested that lower doses may be equally effective. Also, glucocorticoids are associated with multiple and often dose-dependent adverse effects. This quantitative benefit-risk assessment compares high- and low-dose methylprednisolone (at least 2000 mg and less than 1000 mg, respectively, during at most 31 days) and a no treatment alternative, with the aim of determining which regimen, if any, is preferable in multiple sclerosis relapses. METHODS: An overall framework of probabilistic decision analysis was applied, combining data from different sources. Effectiveness as well as risk of non-serious adverse effects were estimated from published clinical trials. However, as these trials recorded very few serious adverse effects, risk intervals for the latter were derived from individual case reports together with a range of plausible distributions. Probabilistic modelling driven by logically implied or clinically well motivated qualitative relations was used to derive utility distributions. RESULTS: Low-dose methylprednisolone was not a supported option in this assessment; there was, however, only limited data available for this treatment alternative. High-dose methylprednisolone and the no treatment alternative interchanged as most preferred, contingent on the risk distributions applied for serious adverse effects, the assumed level of risk aversiveness in the patient population, and the relapse severity. CONCLUSIONS: The data presently available do not support a change of current treatment recommendations. There are strong incentives for further clinical research to reduce the uncertainty surrounding the effectiveness and the risks associated with methylprednisolone in multiple sclerosis relapses; this would enable better informed and more precise treatment recommendations in the future.

Improved statistical signal detection in pharmacovigilance by combining multiple strength-of-evidence aspects in vigiRank.

BACKGROUND: Detection of unknown risks with marketed medicines is key to securing the optimal care of individual patients and to reducing the societal burden from adverse drug reactions. Large collections of individual case reports remain the primary source of information and require effective analytics to guide clinical assessors towards likely drug safety signals. Disproportionality analysis is based solely on aggregate numbers of reports and naively disregards report quality and content. However, these latter features are the very fundament of the ensuing clinical assessment. OBJECTIVE: Our objective was to develop and evaluate a data-driven screening algorithm for emerging drug safety signals that accounts for report quality and content. METHODS: vigiRank is a predictive model for emerging safety signals, here implemented with shrinkage logistic regression to identify predictive variables and estimate their respective contributions. The variables considered for inclusion capture different aspects of strength of evidence, including quality and clinical content of individual reports, as well as trends in time and geographic spread. A reference set of 264 positive controls (historical safety signals from 2003 to 2007) and 5,280 negative controls (pairs of drugs and adverse events not listed in the Summary of Product Characteristics of that drug in 2012) was used for model fitting and evaluation; the latter used fivefold cross-validation to protect against over-fitting. All analyses were performed on a reconstructed version of VigiBase(®) as of 31 December 2004, at around which time most safety signals in our reference set were emerging. RESULTS: The following aspects of strength of evidence were selected for inclusion into vigiRank: the numbers of informative and recent reports, respectively; disproportional reporting; the number of reports with free-text descriptions of the case; and the geographic spread of reporting. vigiRank offered a statistically significant improvement in area under the receiver operating characteristics curve (AUC) over screening based on the Information Component (IC) and raw numbers of reports, respectively (0.775 vs. 0.736 and 0.707, cross-validated). CONCLUSIONS: Accounting for multiple aspects of strength of evidence has clear conceptual and empirical advantages over disproportionality analysis. vigiRank is a first-of-its-kind predictive model to factor in report quality and content in first-pass screening to better meet tomorrow's post-marketing drug safety surveillance needs.

Zoo or savannah? Choice of training ground for evidence-based pharmacovigilance.

Pharmacovigilance seeks to detect and describe adverse drug reactions early. Ideally, we would like to see objective evidence that a chosen signal detection approach can be expected to be effective. The development and evaluation of evidence-based methods require benchmarks for signal detection performance, and recent years have seen unprecedented efforts to build such reference sets. Here, we argue that evaluation should be made against emerging and not established adverse drug reactions, and we present real-world examples that illustrate the relevance of this to pharmacovigilance methods development for both individual case reports and longitudinal health records. The establishment of broader reference sets of emerging safety signals must be made a top priority to achieve more effective pharmacovigilance methods development and evaluation.

Computing limits on medicine risks based on collections of individual case reports.

BACKGROUND: Quantifying a medicine's risks for adverse effects is crucial in assessing its value as a therapeutic agent. Rare adverse effects are often not detected until after the medicine is marketed and used in large and heterogeneous patient populations, and risk quantification is even more difficult. While individual case reports of suspected harm from medicines are instrumental in the detection of previously unknown adverse effects, they are currently not used for risk quantification. The aim of this article is to demonstrate how and when limits on medicine risks can be computed from collections of individual case reports. METHODS: We propose a model where drug exposures in the real world may be followed by adverse episodes, each containing one or several adverse effects. Any adverse episode can be reported at most once, and each report corresponds to a single adverse episode. Based on this model, we derive upper and lower limits for the per-exposure risk of an adverse effect for a given drug. RESULTS: An upper limit for the per-exposure risk of the adverse effect Y for a given drug X is provided by the reporting ratio of X together with Y relative to all reports on X, under two assumptions: (i) the average number of adverse episodes following exposure to X is one or less; and (ii) adverse episodes that follow X and contain Y are more frequently reported than adverse episodes in general that follow X. Further, a lower risk limit is provided by dividing the number of reports on X together with Y by the total number of exposures to X, under the assumption that exposures to X that are followed by Y generate on average at most one report on X together with Y. Using real data, limits for the narcolepsy risk following Pandemrix vaccination and the risk of coeliac disease following antihypertensive treatment were computed and found to conform to reference risk values from epidemiological studies. CONCLUSIONS: Our framework enables quantification of medicine risks in situations where this is otherwise difficult or impossible. It has wide applicability, but should be particularly useful in structured benefit-risk assessments that include rare adverse effects.

The development and evaluation of triage algorithms for early discovery of adverse drug interactions.

BACKGROUND: Around 20 % of all adverse drug reactions (ADRs) are due to drug interactions. Some of these will only be detected in the postmarketing setting. Effective screening in large collections of individual case safety reports (ICSRs) requires automated triages to identify signals of adverse drug interactions. Research so far has focused on statistical measures, but clinical information and pharmacological characteristics are essential in the clinical assessment and may be of great value in first-pass filtering of potential adverse drug interaction signals. OBJECTIVE: The aim of this study was to develop triages for adverse drug interaction surveillance, and to evaluate these prospectively relative to clinical assessment. METHODS: A broad set of variables were considered for inclusion in the triages, including cytochrome P450 (CYP) activity, explicit suspicions of drug interactions as noted by the reporter, dose and treatment overlap, and a measure of interaction disproportionality. Their unique contributions in predicting signals of adverse drug interactions were determined through logistic regression. This was based on the reporting in the WHO global ICSR database, VigiBase™, for a set of known adverse drug interactions and corresponding negative controls. Three triages were developed, each producing an estimated probability that a given drug-drug-ADR triplet constitutes an adverse drug interaction signal. The triages were evaluated against two separate benchmarks derived from expert clinical assessment: adverse drug interactions known in the literature and prospective adverse drug interaction signals. For reference, the triages were compared with disproportionality analysis alone using the same benchmarks. RESULTS: The following were identified as valuable predictors of adverse drug interaction signals: plausible CYP metabolism; notes of suspected interaction by the reporter; and reports of unexpected therapeutic response, altered therapeutic effect with dose information and altered therapeutic effect when only two drugs had been used. The new triages identified reporting patterns corresponding to both prospective signals of adverse drug interactions and already established ones. They perform better than disproportionality analysis alone relative to both benchmarks. CONCLUSIONS: A range of predictors for adverse drug interaction signals have been identified. They substantially improve signal detection capacity compared with disproportionality analysis alone. The value of incorporating clinical and pharmacological information in first-pass screening is clear.

Quantitative benefit-risk assessment using only qualitative information on utilities.

BACKGROUND: Utilities of pertinent clinical outcomes are crucial variables for assessing the benefits and risks of drugs, but numerical data on utilities may be unreliable or altogether missing. We propose a method to incorporate qualitative information into a probabilistic decision analysis framework for quantitative benefit-risk assessment. OBJECTIVE: To investigate whether conclusive results can be obtained when the only source of discriminating information on utilities is widely agreed upon qualitative relations, for example, ''sepsis is worse than transient headache'' or ''alleviation of disease is better without than with complications.'' METHOD: We used the structure and probabilities of 3 published models that were originally evaluated based on the standard metric of quality-adjusted life years (QALYs): terfenadine versus chlorpheniramine for the treatment of allergic rhinitis, MCV4 vaccination against meningococcal disease, and alosetron for irritable bowel syndrome. For each model, we identified clinically straightforward qualitative relations among the outcomes. Using Monte Carlo simulations, the resulting utility distributions were then combined with the previously specified probabilities, and the rate of preference in terms of expected utility was determined for each alternative. RESULTS: Our approach conclusively favored MCV4 vaccination, and it was concordant with the QALY assessments for the MCV4 and terfenadine versus chlorpheniramine case studies. For alosetron, we found a possible unfavorable benefit-risk balance for highly risk-averse patients not identified in the original analysis. CONCLUSION: Incorporation of widely agreed upon qualitative information into quantitative benefit-risk assessment can provide for conclusive results. The methods presented should prove useful in both population and individual-level assessments, especially when numerical utility data are missing or unreliable, and constraints on time or money preclude its collection.

Dose variations associated with formulations of NSAID prescriptions for children: a descriptive analysis of electronic health records in the UK.

BACKGROUND: NSAIDs, particularly ibuprofen, are commonly prescribed for children but there is limited published research on real-life prescribed doses for this class of drugs. OBJECTIVE: The aim of the study was to investigate if variations in NSAID doses prescribed to children can be explained by patient age, indication, dosage form, type of NSAID or year of prescription. STUDY DESIGN: Recorded daily doses for drugs within the 'Anti-rheumatics, non-steroidal plain' anatomical classification were studied. First prescriptions of a distinct NSAID substance within 13-month time periods in a patient's history were included. To enable grouping and comparison of NSAIDs, doses were analysed as prescribed daily doses (PDDs) relative to the adult defined daily dose, stated as the relative PDD (rPDD) in this study. Multiple regression analysis was performed with the rPDD as the response variable, and age, indication, dosage form, NSAID substance and year of prescription as the explanatory variables. SETTING: Prescriptions from the Intercontinental Medical Statistics (IMS) Health Disease Analyzer database containing electronic health records of general practitioners in the UK issued from 1988 to December 2005. PATIENTS: Data for children aged 2-11 years with NSAID prescriptions including daily dose information. RESULTS: A total of 21 473 first prescriptions for 19 695 patients were studied. The vast majority of prescriptions were for ibuprofen (n = 20 855), which were therefore analysed separately. The other NSAID prescriptions were grouped (n = 618), containing diclofenac, indometacin, mefenamic acid, naproxen and piroxicam ('NSAID group'). The rPDD varied considerably with dosage form in both the ibuprofen and NSAID groups. In particular, tablets/capsules were prescribed at higher doses than liquid dosage forms. In the NSAID group, naproxen was prescribed at noticeably higher doses. The rPDD varied only slightly with age in both groups. Prescriptions indicated for rheumatic disease were associated with lower doses than other indications in the NSAID group. The rPDD was not influenced by year of prescription. CONCLUSIONS: This study shows a correlation between higher prescribed NSAID doses and tablet/capsule formulation, and highlights the need for careful choice of dose formulation when prescribing medicines for children.

Reporting patterns indicative of adverse drug interactions: a systematic evaluation in VigiBase.

BACKGROUND: Adverse drug interaction surveillance in collections of Individual Case Safety Reports (ICSRs) remains underdeveloped. Most efforts to date have focused on disproportionality analysis, but the empirical support for its value is based on isolated examples. Additionally, too little attention has been given to the potential value of the detailed content of ICSRs for improved adverse drug interaction surveillance. OBJECTIVE: The aim of the study was to identify reporting patterns indicative of suspected adverse drug interactions before the drug interactions are generally established. METHODS: A reference set of known adverse drug interactions and drug pairs not known to interact was constructed from information added to Stockley's Drug Interactions Alerts between the first quarter of 2007 and the third quarter of 2009. The reference set was used to systematically study differences in reporting patterns between adverse drug interactions before they are generally established and adverse drug reactions (ADRs) to drug pairs that are not known to interact, in the WHO Global ICSR Database, VigiBase. The scope of the study included pharmacological properties such as common cytochrome P450 metabolism, explicit suspicions of drug interactions as noted by the reporter, clinical details such as dose and treatment overlap, and the lower limit of the 95% credibility interval of a three-way measure of disproportionality, Omega(025) (Ω(025)), based on the total number of reports on two drugs and one ADR together. Analyses were carried out including and excluding concomitant medicines. RESULTS: Five reporting patterns were highlighted as particularly strong indicators of adverse drug interactions before they are known: suspicion of interactions as noted by the reporter in a case narrative, the assignment of the two drugs as interacting or through an ADR term; co-reporting of effect increased with the drug pair; and, finally, an excess total number of reports on the ADR together with the two drugs, as measured by Ω(025). Overall, the inclusion of concomitant medicines led to a larger number of true adverse drug interactions being highlighted, but at a substantial decrease in the strength of most indicators. Notably, the inclusion of concomitant medicines completely eliminated the value of Ω(025) as an indicator of adverse drug interactions, in this systematic evaluation. CONCLUSIONS: Reported suspicion of interactions as noted by the reporter in a case narrative, the assignment of the two drugs as interacting or through an ADR term; co-reporting of effect increased with the drug pair and by the Ω(025) each provide unique information to highlight adverse drug interactions before they become known in the literature. To our knowledge, this is the first systematic analysis demonstrating the value of disproportionality analysis for adverse drug interactions using a comprehensive reference set, and the first study to consider a broader basis including clinical information for systematic drug interaction surveillance.