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224
D. Lay ton
5.1 The European Network
ofCentres
forPharmacoepidemiology
andPharmacovigilance
(ENCePP) Guide
onMethodological Standards
The ENCePP Guide is a document that provides
guidance on methodological standards for conducting studies in pharmacoepidemiology and
pharmacovigilance. For each topic covered, direct
links to internationally agreed recommendations,
key points from important guidelines, published
articles and textbooks are provided. Where relevant, gaps in existing guidance are addressed with
what ENCePP considers good practice. This guide
is regarded fundamentally as the best practice
standard for the conduct of pharmacoepidemiological observational studies and their application
as PASS and is now in its 11th revision. It is maintained by the ENCePP Research Standards and
Guidance working group under EMA’s coordination and updated annually by structured review to
maintain its dynamic nature [30].
5.2 The European Network
ofCentres
forPharmacoepidemiology
andPharmacovigilance
(ENCePP) Checklist forStudy
Protocols
The Checklist for Study Protocols aims to stimulate researchers to consider important epidemiological principles when designing a
pharmacoepidemiological study and writing a
study protocol; promote transparency regarding
methodologies used in pharmacoepidemiological
studies and increase awareness about developments in science and methodology in the eld of
pharmacoepidemiology [38].
The Checklist is intended to promote the quality
of studies and not their uniformity and is aligned
with scientic and regulatory developments relevant to pharmacoepidemiology. GVP module VIII
on PASS recommends that the Checklist is included
as an annex to study protocols [12].
5.3 The Real-World Evidence
Transparency Initiative
This is a collaboration between the International
Professional Society for Health economics and
outcomes research (ISPOR), the International
Society for Pharmacoepidemiology (ISPE), the
Duke-Margolis Center for Health Policy in the US
and the National Pharmaceutical Council in the
US with the objective of establishing a culture of
transparency for study analysis and reporting of
RWE studies [39]. ISPOR (www.ispor.org) was
established in 1995 with a mission to promote
health economics and outcomes research excellence to improve decision-making for health globally. ISPE (www.pharmacoepi.org) is also an
international organisation dedicated to advancing
the health of the public by providing a global
forum for the open exchange of scientic information and for the development of policy, education
and advocacy for the eld of pharmacoepidemiology, including such areas as pharmacovigilance,
drug utilisation research, comparative effectiveness review and therapeutic risk management.
Both societies are multi-stakeholder organisations
of a wide variety of healthcare stakeholders,
including researchers and academicians, assessors
and regulators, payers and policy-makers, the life
sciences industry, healthcare providers and patient
engagement organisations. As part of the RWE
Transparency initiative, they advocate for a culture
of transparency to build trust in secondary data
study analysis and reporting of hypotheses in regulatory decision-grade studies of comparative effectiveness or safety.
The Initiative has published several position
papers, including a list of seven recommendations of good practices covering study registration, replicability and stakeholder involvement
[40]. Other remarkable publications that support
these recommendations include the HARPER
Protocol Template the purpose of which is to create a shared understanding of intended scientic
decisions through a common text, tabular and
visual structure. The template provides a set of
core recommendations for clear and reproducible
RWE study protocols and is intended to be used
as a backbone throughout the research process
9 Pharmacoepidemiologic Studies
225
from developing a valid study protocol, to registration, through implementation and reporting on
those implementation decisions [41]; and structured frameworks that provide processes for the
design of studies and feasibility assessments of
t-for-purpose data [42–44].
6 Summary andConclusions
This chapter has provided a glimpse of how pharmacoepidemiology is an important part of pharmacovigilance, navigating basic principles of
concepts and study design through to current best
practice for standardisation and harmonisation.
Case studies have been presented that explore
considerations in the application of three common epidemiological techniques in support of
addressing emergent safety issues. Observational
pharmacoepidemiological studies are now routinely used to generate real-world evidence on
the safety of medicines. The expansion of use of
RWD has been profound with regulatory acceptability of such studies being enhanced through
multiple initiatives that drive and assure quality
suitable for decision-making.
7 Case Studies
The following case studies have been selected to
allow the reader to explore how observational
study designs have been applied in regulatory
decision-making. They are presented in a question-and-answer format, exploring why a particular design was chosen, a particular issue
relevant to that design and what a limitation
might be.
7.1 Case Study 1: Use ofCross-
Sectional Studies
inRegulatory
Decision-Making
Background A drug utilisation study using a cross-sectional design aimed to
examine whether the impact of European Medicines Agency (EMA)
recommendations to restrict use or stop prescribing uoroquinolones
altogether because of safety concerns as implemented throughout
2018–2019 had an impact on uoroquinolone monthly prescribing
trends, overall and to certain age groups [22].
Q1. Why would a cross-sectional study be
suitable for understanding trends in drug
utilisation patterns?
Q2. The study used electronic healthcare
records from six European countries
between 2016 and 2021. Why would these
types of data sources be appropriate?
Q3. The study found that while changes in
uoroquinolone prescriptions were
observed over time across countries, these
were inconsistent and did not seem to be
temporally related to EMA interventions.
The authors’ conclusion was that the
regulatory action associated with the 2018
referral did not seem to have relevant
effects on uoroquinolone prescribing in
primary care. What might be some
limitations of this study that might have
contributed to this conclusion?
Cross-sectional studies can collect drug use data on study population at
one single time point or multiple points of time. In this example, the
hypothesis was that the trend in prescribing would be decreasing
suggesting prescribers have been compliant with recommendations.
However, the actual results suggested little change after
recommendations were introduced.
Cross-sectional studies often use data from entire populations and also
from sub-populations of interest (e.g. vulnerable patients such as elderly).
In order for the results to be meaningful, the data needs to be representative
of the populations to which one wishes to apply the results to. In this
example, each of the data sources are considered to adequately reect
primary care health provision and collect sufciently granular health-
related information for each individual patient, in their respective country.
Information bias, i.e. missing or inaccurate measurement or recording
of a variable, for example, a disease or characteristic, or information
from a prescription is a key limitation in cross-sectional studies. In this
study, recorded data on prescriptions may not equate to actual use.
Knowing if a drug is stopped prematurely and a new one started and the
reason for these choices may not be recorded in the medical records.
Thus, there may have been under- or over-recording of use by month.
Selection bias is also possible, since the study population did not
include patients prescribed the antibiotic within the secondary care
setting. This means the results may not reect overall total use within
each country.
226
7.2 Case Study 2: Use ofCohort
Study Design
forContextualisation
ofSafety Signals
Background By May 2021, four COVID-19 vaccines had been granted conditional
marketing authorisation by the European Medicines Agency. After millions
of vaccine doses were given in large-scale immunisation campaigns, a safety
signal was noted from spontaneous reporting schemes of thrombotic events
with concurrent thrombocytopenia (TTS) occurring often after the rst dose.
A retrospective cohort study using existing electronic healthcare records
databases aimed to quantify the incidence of thrombosis, thrombocytopenia
or TTS over 28days following the rst dose of an adenovirus-based
COVID-19 vaccine or mRNA-based COVID-19 vaccine and compare them
to historical, pre- pandemic rates estimated for UK general population. Rates
were also estimated following COVID-19 infection [24]
Q1. Why would a cohort study be
suitable for studying the risk
associated with a medicinal product?
Q2. For each event of interest, a
method of indirect standardisation to
the general population was used to
estimate the number of events
expected to have seen postvaccination cohort if their outcome
experience was the same as that of
the general population. Standardised
incidence ratios (SIRs) with 95%
condence intervals (CIs) were
estimated. Why is such adjustment
needed?
Q3. In this study, one of the results
was that more occurrences of TTS
were observed than expected
following the rst dose of the
adenovirus-based vaccine (16
compared to 12 events; SIR 1.38
[0.85–2.26]). This meant rates were
no higher than expected based on
pre-pandemic rates. What might be
an important limitation of this study
that might have contributed to this
observation?
The cohort study design allows one to estimate risk and/or rates of multiple
adverse events in one cohort dened by a particular characteristic (e.g. drug
or vaccine) and compare those ndings to another cohort that does not have
that particular characteristic. This approach is an efcient way to get a
measure of whether the characteristic increases, decreases or has no effect on
multiple risks, and to inform on when those risks occur over time from the
rst exposure
The comparison of adverse event rates may be misleading when there are
differences in the populations being compared with respect to certain
underlying characteristics that can directly (and independently) affect the
overall rate, for example, there may be differences in the age structure.
Standardisation is a method for overcoming the effect of confounding by age
and address the imbalance in age structure that exists
A limitation is that residual confounding may exist. In this study, the
standardised rates represent a weighted average of the age-specic rates taken
from the pre-pandemic population and are not actual rates. However, age may
not be the only confounder. Notably, the time period studied covered the
initial phases of vaccination when certain sub- populations were prioritised
such as vulnerable people with higher prevalence of other comorbidities than
the general population. Although there is no clear relationship between age
and risk of TTS according to spontaneous reports, a selection bias towards
older population in this study could mitigate the relative difference towards
the null
D. Lay ton
9 Pharmacoepidemiologic Studies
7.3 Case Study 3: A‘Nested’
Case–Control Study forSafety
Signal Strengthening
Background Selective serotonin re-uptake inhibitors (SSRI) are associated with an
increased risk of bleeding disorders at a number of sites. It is currently unclear
whether they increase the risk of haemorrhagic stroke, with conicting results
reported. This nested case–control aimed to investigate whether SSRI use was
associated with an increased risk of haemorrhagic stroke. This study was
nested in a cohort of antidepressant users using electronic healthcare records
from the United Kingdom General Practice Research Database (CPRD)
between 1992 and 2006 [29]. Risk associated with tricyclic antidepressant
(TCA) exposure was also evaluated
Q1. Why would a case–control
study be suitable for studying the
risk of an adverse event following
exposure to a drug?
Q2. The authors report that this was
a matched case–control study nested
in a cohort of GPRD-registered
patients who had been prescribed an
antidepressant at some time.
Why is matching needed?
Q3. In this study, the primary
analysis showed no evidence of an
association between current SSRI or
TCA use and haemorrhagic stroke
[current use of an SSRI versus no
use: adjusted OR 1.11 (95% CI
0.82, 1.50) and current use of TCA
versus no use: adjusted OR 0.73
(0.52, 1.02)]. What might be an
important limitation of this study
that might have contributed to this
observation?
The case–control design is based on a comparison of the use of a drug among
those with the adverse event (cases) to the use of the drug among people
without the adverse event (controls). In this study, the source population is a
cohort of antidepressant users, hence the term ‘nested’. This design aims to
capture all cases and therefore is suitable for rare events. It facilitates
exploration of multiple risk factors that could be important in understanding
causality
Sampling of the controls is important as they represent the background use of
the drug in the population from which cases arise. The exact same in- and
exclusion criteria should be applied to controls as for cases, including no prior
history of the outcome. Matching is a method that ensures controls are
sampled so that they can be considered a representative subset of the
underlying cohort. In this study, ve controls (people with no diagnosis of
haemorrhagic stroke at any time) were selected per case matched on age
(within 2years), sex, practice and current registration within CPRD on the
date of diagnosis of their matched case (index date)
Although this study was nested in a cohort of antidepressant users to limit the
effects of confounding by indication (since depression is associated with
haemorrhagic stroke), these products are used for indications other than
depression. Subjects with these indications may be different to those treated
for depression, and those characteristics may not have been fully identied for
data capture. Thus residual confounding is possible
227
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doi.org/10.1002/cpt.2883.
Spontaneous Reporting Systems
JanPetracek andMarcelaFialova
10
Abstract
Systems of spontaneous reporting were developed in the 1960s. Nowadays, there are worldwide systems for processing millions of
individual case safety reports per year. Despite
the signicant underreporting bias, the amount
and quality of information collected through
spontaneous reportingare sufcient for justication of the large number of regulatory
actions taken to minimise risks associated
with medicinal products.
Spontaneous reports still represent an irreplaceable source of information for pharmacovigilance. They have been signicantly
strengthened by the new technology of
mobile phone apps, social media, and articial intelligence applications in healthcare.
Essential rules of implied causality and
acceptance of patient reports, paired with the
emphasis on the quality of reports and their
validation at source, have further improved
the usefulness of the collected information
for signal detection, assessment, and regulatory decision-making.
Efforts to integrate data from spontaneous
reports with other types of reports and available data in healthcare systems are ongoing on
a larger scale. Data science techniques are also
being further developed to improve the quality
of data and its interpretation. This may lead to
even quicker risk minimisation, including predicting adverse drug reactions to new medicinal products. Clinicians benet from the lower
thresholds for reporting and quicker sharing of
new knowledge.
Keywords
Spontaneous reporting system ·
Pharmacovigilance · Signal · Adverse drug
reaction · Adverse event · Individual case
safety report · Post-marketing · Database
system · Causality
Learning Objectives
J. Petracek (*)
Institute of Pharmacovigilance,
Prague, Czech Republic
e-mail: jan.petracek@pharmacovigilance.institute
M. Fialova
Institute of Pharmacovigilance,
Prague, Czech Republic
iVigee, a.s., Prague, Czech Republic
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024
J. Jose et al. (eds.), Principles and Practice of Pharmacovigilance and Drug Safety,
https://doi.org/10.1007/978-3-031-51089-2_10
• Understand the main features of spontaneous
reporting systems, including design, strengths,
and weaknesses.
• Explain the concepts associated with spontaneous individual case safety reports.
• Understandthe origins, present, and future of
spontaneous reporting systems.
231
232
J. Petracek and M. Fialova
• Acquire actionable knowledge that may help
with spontaneous reporting from own clinical
practice.
Key Points
• Spontaneous reporting systems serve as
a pillar of pharmacovigilance worldwide.
• The reporting systems represent a key
service to clinical practice, allowing
both healthcare professionals and consumers to share their experience with
medicinal products and learn from the
collected information.
• Quality and validity of spontaneous
reports greatly inuences the utility of
them.
• Both industry and regulatory authorities
have developed advanced systems for
processing individual case safety reports
from any source, including spontaneous
reports.
• Spontaneous reports from healthcare
professionals as well as patients/public
are important in the success of the spontaneous reporting system.
• New technology is allowing for quicker
and more precise collection of information via both spontaneous and solicited
sources of individual case safety reports.
1 Introduction
Spontaneous reports remain an importantsource
of safety signals following the market approval of
a drug. This chaptersetsout the key attributes of
a spontaneous reporting system, using key examples of such systems, their operation, and their
utility. It also includes the history and function of
spontaneous reporting systems internationally.
The assessment and utility of the data documented in spontaneous reporting systems are
covered. The implications of regulatory decisions
from the National Pharmacovigilance Systems in
clinical practice are discussed.
The ability to communicate and learn from
mistakes is essential for the success of any system. The suboptimal communication between
those who observe adverse drug reactions
(ADRs) and those in charge of benet–harm
assessments and decisions may lead to unnecessary harm caused by medicines.Shortly after the
thalidomide tragedy, the essential pharmacovigilance feedback loop was created, working with
spontaneous reports from healthcare professionals (Fig.10.1).
The essential pharmacovigilance feedback
loop ensures that clinicians can share their
patients’ observations of suspected adverse reactions with regulatory authorities or public institutions in charge of pharmacovigilance. Data
collected at one central database are then available for more advanced analysis, including signal
Data collection Signal detection Risk assessment
Fig. 10.1 Essential pharmacovigilance feedback loop
Communication
No action
Decision
making
Regulatory action
10 Spontaneous Reporting Systems
233
detection and risk assessment. The new knowledge could be shared with clinicians quickly
through ad-hoc communication and label updates.
Regulatory authorities may also withdraw medicinal products with unfavourable benet–harm
proles from the market.
Since their conception in the 1960s, spontaneous reporting systems have developed worldwide, allowing any user to contribute and learn
from the collected data. The system remains to be
a signicant pillar of pharmacovigilance. Its further improvements, thanks to technology, legal
environment, globalisation, and transparency,
offer growing opportunities for its continuous use
in future.
2 The Concept ofSpontaneous
Reporting
Pharmacovigilance’s spontaneous reportingsystems allow clinicians and patients to share their
observations of adverse experiences with a
medicinal product via the reporting of suspected
ADRs. The systems exist globally and locally,
use variable technologies, and are increasingly
linked at regional and international levels to
ensure no information is lost.
Any person can report an adverse experience
with a medicinal product via these systems and
do so without any limitations to the content.
Therefore, spontaneous reports represent the
only completely uncurated source of information
available in pharmacovigilance.
A reporter may have some choice of where to
send her/his report. National regulatory agencies
and pharmaceutical companies must accept these
reports, while in many countries, the national
regulatory authority is the preferred destination
by law. The incoming reports are often acknowledged and processed into pharmacovigilance
databases, including data coding, translations,
assessments, interpretations, and follow-ups. The
spontaneous reporting system covers all medicines used within a whole population for an
unlimited time, encompassing each medicine’s
entire product life cycle.
2.1 Spontaneous Versus Solicited
Reporting
The motivation of the reporter to share information is critical for the correct interpretation of the
report. The circumstances under which the
reporter sends the report determine whether it
would be considered a spontaneous or solicited
report.
Motivation for a solicited report is a request
to the reporter to share a particular type of
information, such as within an organised data
collection scheme. Clinical trials are examples
of the most stringent schemes. Nevertheless,
even post- authorisation uses of medicinal products, such as named patient use, special access
programmes, patient support programmes,
observational studies, and similar, represent
circumstances under which collected reports
are considered solicited.
Solicited reports are those derived from organised
data collection systems, which include clinical tri-
als, registries, post-approval named patient use
programs, other patient support and disease man-
agement programs, surveys of patients or health-
care providers, or information gathering on
efcacy or patient compliance. Adverse event
reports obtained from any of these should not be
considered spontaneous. (ICH E2D) [1]
Motivation for a spontaneous report is not based
on any specic request. It is the reporter’s own
decision to share the information, even though it
might be supported by a general legal obligation
to report in a particular country. Typical motivation for spontaneous reporting is a striking case
about which the reporter wants to learn more or a
case where the reporting healthcare professional
might feel it is a piece of new information they
want to share with others. Patients may feel that
they should share their experiences with the
expectation that regulatory authorities should
know what is happening.
Following the same logic, literature reports
are also considered spontaneous. The authors
publish cases so their colleagues can also learn
from them. Literature case reports are the older
and traditional way of sharing information among
the medical community. Nowadays, a limited
234
J. Petracek and M. Fialova
number of medical journals would accept individual case reports for publication. Sharing them
through a spontaneous reporting system is considered more efcient in most situations, since
the publication of a case report can take many
months. Aggregated data can serve the advanced
analysis better than individual publications.
A spontaneous report is an unsolicited communication by a healthcare professional or consumer to
a company, regulatory authority or other organisation (e.g. WHO, Regional Center, Poison Control
Center) that describes one or more adverse drug
reactions in a patient who was given one or more
medicinal products and that does not derive from a
study or any organised data collection scheme.
(ICH E2D) [1]
Stimulated reporting can occur in certain situations, such as notication by a ‘Direct Healthcare
Professional Communication’ letter, publication
in the press, or questioning healthcare professionals by company representatives. These
reports are spontaneous, as no specic request
has been made to a particular healthcare professional to collect information.
Stimulated reporting may causereporting bias
or false signals. For proper signal detection and
validation, information about the context of such
stimulated reporting is invaluable and should
always be well understood before reaching any
conclusions.
2.2 Individual Case Safety
Reports
The term individual case safety report (ICSR)
encompasses all kinds of reports describing
safety experience(s) with a medicinal product(s)
in a person at a particular time. ICSRs may
include reports ofADR(s),adverse event (AE)s,
or special situations from any source. The ICSR
is the fundamental unit of pharmacovigilance
reporting systems. The International Council for
Harmonisation of Technical Requirements for
Pharmaceuticals for Human Use (ICH) has developed a standard ICSR format (Fig.10.2)to allow
for the electronic exchange of the ICSRs between
all involved stakeholders worldwide.
2.3 Concepts ofSeriousness
andExpectedness
Clinicians are typically very busy people.
Resources available to pharmacovigilance systems are also limited. Therefore, various prioritisation systems have been developed and
implemented for spontaneous reporting systems.
Of those prioritisation concepts, seriousness and
expectedness are the most used for spontaneous
ICSRs.
Serious ICSRs are those that have one of the
following outcomes:
1. Death of the patient
2. Life-threatening
3. Hospitalisation of the patient or extension of
existing hospitalisation
4. Persistent or signicant disability or
incapacity
5. Congenital anomaly
6. Medically serious as considered by the report-
ing physician or a medical reviewer
The serious ICSRs are prioritised for reporting. Globally, a 15-day reporting rule applies to
them, meaning that pharmaceutical companies
and regulatory authorities must process and report
these cases further within 15 calendar days.
The concept of expectedness relates to the
nature and severity of the described reactions
in the case. Every approved medicinal product
has an ofcial label describing its adverse reactions. Those are considered ‘expected’ adverse
drug reactions, as their nature and severity
align with the current knowledge described in
the label. If at least one of the reactions in the
ICSR is unexpected, then the ICSR is considered unexpected too. Unexpected ICSRs may
also be prioritised for reporting and signal
detection approaches.
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