Patient Participation and the Use of Ehealth Tools for Pharmacoviligance

The purpose of this review was to estimate the extent of under-reporting of adverse drug reactions (ADRs) to spontaneous reporting systems and to investigate whether there are differences between different types of ADRs. A systematic literature search was carried out to identify studies providing a numerical estimate of under-reporting. Studies were included regardless of the methodology used or the setting, e.g. hospital versus general practice. Estimates of under-reporting were either extracted directly from the published study or calculated from the study data. These were expressed as the percentage of ADRs detected from intensive data collection that were not reported to the relevant local, regional or national spontaneous reporting systems. The median under-reporting rate was calculated across all studies and within subcategories of studies using different methods or settings. In total, 37 studies using a wide variety of surveillance methods were identified from 12 countries. These generated 43 numerical estimates of under-reporting. The median under-reporting rate across the 37 studies was 94% (interquartile range 82-98%). There was no significant difference in the median under-reporting rates calculated for general practice and hospital-based studies. Five of the ten general practice studies provided evidence of a higher median under-reporting rate for all ADRs compared with more serious or severe ADRs (95% and 80%, respectively). In comparison, for five of the eight hospital-based studies the median under-reporting rate for more serious or severe ADRs remained high (95%). The median under-reporting rate was lower for 19 studies investigating specific serious/severe ADR-drug combinations but was still high at 85%. This systematic review provides evidence of significant and widespread under-reporting of ADRs to spontaneous reporting systems including serious or severe ADRs. Further work is required to assess the impact of under-reporting on public health decisions and the effects of initiatives to improve reporting such as internet reporting, pharmacist/nurse reporting and direct patient reporting as well as improved education and training of healthcare professionals.

A voluntary reporting system of adverse drug reactions (ADRs) is fundamental to drug safety surveillance but under-reporting is its major limitation. This bibliographic review sought to assess the influence of personal and professional characteristics on ADR reporting and to identify knowledge and attitudes associated with ADR reporting. A systematic review was conducted using the MEDLINE and EMBASE databases. We included papers that were published in English, French and Spanish, and covered a study population made up of health professionals. In each case, the following data were extracted: study population; workplace; study type; sample size; type of questionnaire; type of scale for measuring knowledge; response rate; personal and professional factors; and knowledge and attitudes (based on Inman's 'seven deadly sins') associated with reporting. Based on a search of computerized databases, we identified a total of 657 papers in MEDLINE and 973 in EMBASE. In all, the review covered 45 papers that fulfilled the inclusion criteria. Medical specialty was the professional characteristic most closely associated with under-reporting in 76% of studies involving physicians. Other factors associated with under-reporting were ignorance (only severe ADRs need to be reported) in 95%; diffidence (fear of appearing ridiculous for reporting merely suspected ADRs) in 72%; lethargy (an amalgam of procrastination, lack of interest or time to find a report card, and other excuses) in 77%; indifference (the one case that an individual doctor might see could not contribute to medical knowledge) and insecurity (it is nearly impossible to determine whether or not a drug is responsible for a particular adverse reaction) in 67%; and complacency (only safe drugs are allowed on the market) in 47% of studies. While personal and professional factors display a weak influence, the knowledge and attitudes of health professionals appear to be strongly related with reporting in a high proportion of studies. This result may have important implications in terms of public health, if knowledge and attitudes are viewed as potentially modifiable factors.

Background Traditional adverse event (AE) reporting systems have been slow in adapting to online AE reporting from patients, relying instead on gatekeepers, such as clinicians and drug safety groups, to verify each potential event. In the meantime, increasing numbers of patients have turned to social media to share their experiences with drugs, medical devices, and vaccines. Objective The aim of the study was to evaluate the level of concordance between Twitter posts mentioning AE-like reactions and spontaneous reports received by a regulatory agency. Methods We collected public English-language Twitter posts mentioning 23 medical products from 1 November 2012 through 31 May 2013. Data were filtered using a semi-automated process to identify posts with resemblance to AEs (Proto-AEs). A dictionary was developed to translate Internet vernacular to a standardized regulatory ontology for analysis (MedDRA®). Aggregated frequency of identified product-event pairs was then compared with data from the public FDA Adverse Event Reporting System (FAERS) by System Organ Class (SOC). Results Of the 6.9 million Twitter posts collected, 4,401 Proto-AEs were identified out of 60,000 examined. Automated, dictionary-based symptom classification had 72 % recall and 86 % precision. Similar overall distribution profiles were observed, with Spearman rank correlation rho of 0.75 (p < 0.0001) between Proto-AEs reported in Twitter and FAERS by SOC. Conclusion Patients reporting AEs on Twitter showed a range of sophistication when describing their experience. Despite the public availability of these data, their appropriate role in pharmacovigilance has not been established. Additional work is needed to improve data acquisition and automation.