IntroductionFollowing the start of the World Health Organization (WHO) Programme for International Drug Monitoring (PIDM) by 10 member countries in 1968, it took another 24 years for the first two African countries to join in 1992, by which time the number of member countries in the PIDM had grown to 33. Whilst pharmacovigilance (PV), including the submission of individual case safety reports (ICSR) to VigiBase®, the WHO global ICSR database, is growing in Africa, no data have been published on the growth of ICSR reporting from Africa and how the features of ICSRs from Africa compare with the rest of the world (RoW).ObjectiveThe objective of this paper was to provide an overview of the growth of national PV centres in Africa, the reporting of ICSRs by African countries, and the features of ICSRs from Africa, and to compare ICSRs from Africa with the RoW.MethodsThe search and analysis interface of VigiBase®—VigiLyze®—was used to characterise ICSRs submitted by African countries and the RoW. The distribution of ICSRs by African countries was listed and characterised by anatomic therapeutic chemical (ATC) code, Medical Dictionary for Regulatory Activities (MedDRA®) system organ class (SOC) classification, and patient age and sex. The case-defining features of ICSRs between Africa and the RoW were also compared.ResultsThe number of African countries in the PIDM increased from 2 in 1992 to 35 at the end of September 2015, and African PIDM members have cumulatively submitted 103,499 ICSRs (0.88 % of global ICSRs) to VigiBase®. The main class of products in African ICSRs are nucleoside and nucleotide reverse transcriptase inhibitors (14.04 %), non-nucleoside reverse transcriptase inhibitors (9.09 %), antivirals for the treatment of HIV infections (5.50 %), combinations of sulfonamides and trimethoprim (2.98 %) and angiotensin-converting enzyme (ACE) inhibitors (2.42 %). The main product classes implicated in ICSRs from the RoW are tumour necrosis factor-α (TNFα) inhibitors (5.29 %), topical nonsteroidal anti-inflammatory preparations (2.26 %), selective immunosuppressants (2.08 %), selective serotonin reuptake inhibitors (2.04 %) and HMG CoA reductase inhibitors (1.85 %). The main SOCs reported from Africa versus the RoW include skin and subcutaneous tissue disorders (31.14 % vs. 19.58 %), general disorders and administration site conditions (20.91 % vs. 30.49 %) and nervous system disorders (17.48 % vs. 19.13 %). The 18–44 years age group dominated ICSRs from Africa, while the 45–64 years age group dominated the RoW. Identical proportions of females (57 % Africa and the RoW) and males (37 % Africa and the RoW) were represented.ConclusionsAs at the end of September 2015, 35 of 54 African countries were Full Member countries of the PIDM. Although the number of ICSRs from Africa has increased substantially, ICSRs from Africa still make up <1 % of the global total in VigiBase®. The features of ICSRs from Africa differ to those from the RoW in relation to the classes of products as well as age group of patients affected. Th...
Collaborating across the threshold: the development of inter-professional expertise in child safeguarding. AbstractThis paper reports on an empirical study of the expertise that different professionals develop in working together to safeguard children. The research involved three key professional groups who work with children: nursing, teaching and social work. The methodology used a clinical scenario and critical incident to explore professional perspectives and experiences of collaboration. Data collection was via semi-structured interviews with a sample of 18 practitioners, composed of pre-and post-qualifying practitioners from each professional group. Data analysis was undertaken through an inductive process, with open coding of transcripts followed by the synthesis of themes into a qualitative framework. The findings identified different elements of interprofessional expertise including assessment and decision-making, responsibility, risk and uncertainty, managing relationships, and dealing with conflict and difficulty. Collaborative activity was found to be shaped by the threshold between statutory and non-statutory services and mediated by the relationship between practitioners and parents. The paper concludes by exploring constraints and opportunities for addressing potential gaps in interprofessional expertise in this area.
Introduction and Objective Social media has been suggested as a source for safety information, supplementing existing safety surveillance data sources. This article summarises the activities undertaken, and the associated challenges, to create a benchmark reference dataset that can be used to evaluate the performance of automated methods and systems for adverse event recognition. Methods A retrospective analysis of public English-language Twitter posts (Tweets) was performed. We sampled 57,473 Tweets out of 5,645,336 Tweets created between 1 March, 2012 and 1 March, 2015 that mentioned at least one of six medicinal products of interest (insulin glargine, levetiracetam, methylphenidate, sorafenib, terbinafine, zolpidem). Products, adverse events, indications, product-event combinations, and product-indication combinations were extracted and coded by two independent teams of safety reviewers. Results The benchmark reference dataset consisted of 1056 positive controls ("adverse event Tweets") and 56,417 negative controls ("non-adverse event Tweets"). The 1056 adverse event Tweets contained 1396 product-event combinations referring to personal adverse event experiences, comprising 292 different MedDRA ® Preferred Terms. The 1171 product-event combinations (83.9%) were confined to four MedDRA ® System Organ Classes. The 195 Tweets (18.5%) contained indication information, comprising 25 different Preferred Terms. Conclusions A manually curated benchmark reference dataset based on Twitter data has been created and is made available to the research community to evaluate the performance of automated methods and systems for adverse event recognition in unstructured free-text information.
Introduction Adverse drug reactions related to drug-drug interactions cause harm to patients. There is a body of research on signal detection for drug interactions in collections of individual case reports, but limited use in regular pharmacovigilance. Objective The aim of this study was to evaluate the feasibility of signal detection of drug-drug interactions in collections of individual case reports of suspected adverse drug reactions. Methods This study was conducted in VigiBase, the WHO global database of individual case safety reports. The data lock point was 31 August 2016, which provided 13.6 million reports for analysis after deduplication. Statistical signal detection was performed using a previously developed predictive model for possible drug interactions. The model accounts for an interaction disproportionality measure, expressed suspicion of an interaction by the reporter, potential for interaction through cytochrome P450 activity of drugs, and reported information indicative of unexpected therapeutic response or altered therapeutic effect. Triage filters focused the preliminary signal assessment on combinations relating to serious adverse events with case series of no more than 30 reports from at least two countries, with at least one report during the previous 2 years. Additional filters sought to eliminate already known drug interactions through text mining of standard literature sources. Preliminary signal assessment was performed by a multidisciplinary group of pharmacovigilance professionals from Uppsala Monitoring Centre and collaborating organizations, whereas in-depth signal assessment was performed by experienced pharmacovigilance assessors. Results We performed preliminary signal assessment for 407 unique drug pairs. Of these, 157 drug pairs were considered already known to interact, whereas 232 were closed after preliminary assessment for other reasons. Ten drug pairs were subjected to in-depth signal assessment and an additional eight were decided to be kept under review awaiting additional reports. The triage filters had a major impact in focusing our preliminary signal assessment on just 14% of the statistical signals generated by the predictive model for drug interactions. In-depth assessment led to three signals communicated with the broader pharmacovigilance community, six closed signals and one to be kept under review. Conclusion This study shows that signals of adverse drug interactions can be detected through broad statistical screening of individual case reports. It further shows that signal assessment related to possible drug interactions requires more detailed information on the temporal relationship between different drugs and the adverse event. Future research may consider whether interaction signal detection should be performed not for individual adverse event terms but for pairs of drugs across a spectrum of adverse events.
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