Spontaneous reports of adverse drug effects remain a cornerstone of pharmacovigilance and are collected from a variety of sources, including healthcare providers, national authorities, pharmaceutical companies, medical literature and more recently directly from patients. EudraVigilance is the European Union data processing network and management system for reporting and evaluating suspected adverse drug reactions (ADRs). Other major systems for collections of spontaneous reports are the FDA's Adverse Event Reporting System (FAERS) for adverse event, medication error and product quality complaints resulting in adverse events, and the WHO global database of individual case safety reports, VigiBase, that pools reports of adverse events and suspected ADRs from the members of the WHO programme for international drug monitoring. These systems deal with the electronic exchange of Individual Case Safety Reports (ICSRs), the early detection of possible safety signals and the continuous monitoring and evaluation of potential safety issues in relation to reported ADRs. Spontaneous case reports represent the first line of evidence and the majority of safety signals is still based on them as described in A description of signals during the first 18 months of the EMA pharmacovigilance risk assessment committee (Drug Saf. 2014;37(12):1059-66).
The strength of spontaneous reporting systems is that they cover all types of authorised drugs used in any setting (primary, secondary and specialised healthcare). In addition to this, the reporting systems are built to obtain information specifically on potential adverse drug reactions. The data collection concentrates on variables relevant to this objective and directing reporters towards careful coding and communication of the main aspects of an ADR. Finally, these systems are built to collect and make the information on suspected ADRs rapidly available for analysis, within days. The application of knowledge discovery in databases to post-marketing drug safety: example of the WHO database (Fundam Clin Pharmacol. 2008;22(2):127-40) describes known limitations of spontaneous ADR reporting systems, which can be grouped into four main categories: i) underreporting, embedded in the concept of voluntary reporting whereby known or unknown external factors may influence the reporting rate and data quality, but also influenced from the fact that not all ADRs might be recognised by the reporter as drug induced; ii) limitation in the clinical information reported, not allowing a satisfactory case evaluation and/or the identification of possible risk factors; iii) overreporting following extensive media coverage and public awareness, such that an increased number of cases with similar symptoms are reported (misclassification of diagnosis); iv) lack of collection of control information so that the amount of drug use is not known and there is no direct information on disease incidence. For the above reasons, it is advised that the cases underlying a potential safety signal from spontaneous reports should be verified from a clinical perspective and preferably supported by pharmacological information before further communication. Anecdotes that provide definitive evidence (BMJ. 2006;333(7581):1267-9) describes examples where this is not necessary, where strong and well documented spontaneous reports can be convincing to support the existence of a signal.
Another challenge in spontaneous report databases is the quality of the information provided and adherence to reporting rules; for this reasons comprehensive and multi-faceted quality activities are often an integral part of these systems (see Detailed guide regarding the EudraVigilance data management activities by the European Medicines Agency Rev 1 for an example). One aspect of the data quality activities regards report duplication. Duplicates are separate and unlinked records that refer to one and the same case of a suspected ADR and may mislead clinical assessment or distort statistical screening. They are generally detected by individual case review of all reports or by computerised duplicate detection algorithms. In Performance of probabilistic method to detect duplicate individual case safety reports (Drug Saf. 2014;37(4):249-58) a probabilistic method applied to VigiBase highlighted duplicates that had been missed by a rule-based method and also improved the accuracy of manual review. In the study, however, a demonstration of the performance of de-duplication methods to improve signal detection is lacking. The EMA and FDA have also implemented probabilistic duplicate detection in their databases. A novel feature is an attempt to use narrative text analysed via NLP methods as demonstrated in Using Probabilistic Record Linkage of Structured and Unstructured Data to Identify Duplicate Cases in Spontaneous Adverse Event Reporting Systems (Drug Saf. 2017;40(7):571–82).
Patient reporting is an important source of suspected adverse reactions that can be reported directly through various methods such as online patient reporting forms hosted by national competent authorities or using a phone. Factors affecting patient reporting of adverse drug reactions: a systematic review (Br J Clin Pharmacol. 2017;83(4):875-83) describes the practical difficulties with patient reporting and highlights the patients’ motivation to make their ADRs known to prevent similar suffering in other patients. The value of patient reporting to the pharmacovigilance system: a systematic review (Br J Clin Pharmacol. 2017;83(2):227-46) concludes that patient reporting adds new information and perspective about ADRs in a way otherwise unavailable, and this can contribute to better decision-making processes in regulatory activities. Patient Reporting in the EU: Analysis of EudraVigilance Data (Drug Saf. 2017;40(7):629-45) also concludes that patient reporting complements reporting by health care professionals and that patients were motivated to report especially those ADRs that affected their quality of life.
The study Vaccine side-effects and SARS-CoV-2 infection after vaccination in users of the COVID Symptom Study app in the UK: a prospective observational study (Lancet Infect Dis. 2021;S1473) used an app to examine the proportion and probability of self-reported systemic and local side-effects within 8 days of vaccination in individuals who received one or two doses of the BNT162b2 vaccine or one dose of the ChAdOx1 nCoV-19 vaccine. These data do not represent spontaneous reports from a regulatory perspective, but the authors discuss that such self-reported data can introduce information bias, some participants might be more likely to report symptoms than others and there is the potential for users to drop out of reporting in the app. However, use of an app allowed to recruit a large sample size for the study.
The information collected in spontaneous reports is a reflection of a clinical event that has been attributed to the use of one or more suspected drugs. Although the majority of information provided in the ICSRs is coded, the description of the clinical event, as well as the interpretation of the reporter, contains valuable information for signal detection purposes. Examples are the description of timing and course of the reactions, of the presence or absence of additional risk factors and of the medical history of the patient involved. Since only part of this information is coded and can be used in statistical analysis, it remains important to review the underlying cases at all times for signal detection purposes. Knowledge of the local healthcare system, its corresponding guidelines and the possibilities to follow up for more detailed information are considered important during this review.
The increase in systematic collection of ICSRs in large electronic databases has allowed the application of data mining and statistical techniques for the detection of safety signals (see chapter 9). Validation of statistical signal detection procedures in EudraVigilance post-authorisation data: a retrospective evaluation of the potential for earlier signalling (Drug Saf. 2010;33(6): 475-87) has shown that the statistical methods applied in EudraVigilance can provide significantly early warning in a large proportion of drug safety problems. Nonetheless, this approach should supplement, rather than replace, other pharmacovigilance methods.
The report Characterization of databases (DB) used for signal detection (SD) shows the heterogeneity of spontaneous databases and the lack of comparability of signal detection methods employed.
Chapters IV and V of the Report of the CIOMS Working Group VIII ‘Practical aspects of Signal detection in Pharmacovigilance’ present sources and limitations of spontaneously-reported drug-safety information and databases that support signal detection. Appendix 3 of the report provides a list of international and national spontaneous reporting system databases.
Finally, in EudraVigilance Medicines Safety Database: Publicly Accessible Data for Research and Public Health Protection (Drug Saf. 2018;41(7):665-75), the authors describe how these databases, focusing on EudraVigilance, have been made more easily accessible for external stakeholders. This has allowed to provide better access to information on suspected adverse reactions for healthcare professionals and patients, and opportunities for health research for academic institutions.