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8 Causality Assessment inPharmacovigilance
203
a non-smoker and non-alcoholic. Diet: Mixed
type. Clinician referred Mr. K to Directly
Observed Therapy (DOT) centre where he was
put on treatment with a combination of Tab.
Isoniazid 300 mg + rifampicin 450 mg + pyrazinamide 1500mg+ethambutol 375mg PO once
daily in empty stomach, Tab. Pyridoxine 40mg
PO once daily. Other medications include Tab.
Paracetamol 650 mg PO 1-1-1 for 2 weeks to
ameliorate fever and malaise, Tab. Pantoprazole
40mg PO 1-0-0 (1h before anti-TB drugs), protein powder 2 teaspoon in milk twice daily. Mr. K
was asked to follow as per given schedule at
DOTcentre.
Later, 14days after treatment, Mr. K visited
hospital again with complaints of abdominal
pain, yellowish discolouration of eyes and skin.
Treating doctor examined the patient and ordered
for liver tests. The liver function tests results
were as follows. AST: 140 IU/L (baseline:
45 IU/L); ALT: 160 IU/L (baseline: 40 IU/L);
ALP: 400IU/L (baseline: 160IU/L). Considering
theelevated liver enzymes and symptoms in the
patient,clinician suggested to hold anti-TB drugs
temporarily.
Task: Interpret the available information and
perform the causality assessment using WHO
probability scale.
1. Can elevated liver enzymes (hepatotoxicity)
in this case be reported as an adverse event?
The elevated liver enzymes (hepatotoxicity) could be reported as an adverse event as
it manifested in Mr. K while the patient was
on medications.
2. Is the adverse event attributable to any single
agent?
No. As Mr. K was receiving anti-tubercular medications as combination therapy and
multiple antitubercular agents are reported to
cause the same reaction, at this stage, the
adverse event cannot be attributable to a single agent.
3. Is the data complete/adequate/inadequate to
assess this reaction?
At this stage, the data appears to be neither complete nor inadequate. As the basic
information on patient’s medical history,
event, and all medications are available for
the assessment, it is considered as adequate,
but additional information is warranted for
complete case causality assessment.
4. What is the causalitycategory of the adverse
reaction?
As assessed using the WHO probability
scale, with the current available information, the causality category of the reported
adversereaction (hepatotoxicity) is categorised as possible as this case full three criteria that are required for the event to be
categorised under the said causality category. In this case, the causality could not be
categorised as either certain or probable
due to non-availability of information on
the rechallenge and the outcome of
dechallenge.
What happened next?
During the same hospital visit, clinician
provided supportive care for 4 days to the
patient and asked Mr. K to wait for treatment
till liver enzymes are normal. After a week,
Mr. K’s liver enzymes were observed to be
normal. Clinician wanted to identify the
offending drug and hence suggested for
rechallenge with anti-tubercular drugs.
5. Will the causality category change at this
stage of assessment?
Now, the causality category is expected
to change with the available additional information on outcome of dechallenge. As Mr.
K’s elevated liver enzymes are normal after
the cessation of suspected drugs, there is a
positive dechallenge, and thus the causality
category could now be assigned as probable.
6. Can rechallenge be conducted in this case
and why?
In this case, as Mr. K must be treated for
his tuberculosis as otherwise it may lead to
unnecessary complications leading evento
death. Rechallenge is important to identify
the culprit drug in causing the hepatotoxicity so that the treatment can be reinitiated
with anti-tubercular agents along with secondary medications—avoiding the culprit
drug.
7. If we perform rechallenge, will there be a
change in the causalitycategory?
Further change in the causality category
depends on the outcome of rechallenge. If
204
M. Ramesh and A. Harugeri
there is a positive rechallenge, the causality
would change from probable to certain or
else the causality remains as probable.
What happened next?
A day after liver enzymes became normal,
Mr. K was re-administered with rifampicin
for 7 days. Post 7 days of treatment, liver
tests were performed, and it was observed to
be normal. Later, Isoniazid was added to the
patient’s regimen, and after three days, liver
enzymes were elevated.
8. What is the status of dechallenge/rechallenge
in Mr. K’s case?
At this stage, both dechallenge and rechallenge were performed, and the outcome of
them was positive as there was recovery from
the adverse reaction following cessation of
suspected drugs (positive dechallenge) and
also there was recurrence of same event (elevation of liver enzymes) after the rechallenge
with the anti-tubercular agents.
9. Can the adverse event be attributed to any
single agent?
At this stage, as Mr. K developed the
similar event (elevated liver enzymes) following re- administration of isoniazid, the
event now is more likely to be attributable
to single agent (isoniazid). The involvement
of rifampicin in causing the hepatotoxicity
could be ruled out as Mr. K did not experience a similar event following the re-administration of rifampicin for 7days. However,
in similar line, the possibilities of the likelihood involvement of other agents should
also be ruled out prior to arriving any conclusion. Though paracetamol may not be
the culprit drug considering the dose that
was used and there are more likely drugs,
the contribution of paracetamol in increasing the risk of hepatotoxicity associated
with anti-tubercular agents as wellneeds to
be considered.
10. Will causality category of the reaction
change now?
Yes. In Mr. K’s case, the likelihood
involvement of isoniazid causing hepatotoxicity is certain as it currently fulls all the
required criteria for the said causality category as per the WHO probability scale.
12.2 Case Study 2: Causality
Assessment Using Naranjo’s
Algorithm
A 50-year-old Asian male weighing 60 kg was
admitted to the hospital with fever, loose stools,
cough, breathlessness, and upper abdomen tenderness over the preceding week. Three years
ago, he was diagnosed to have had human immunodeciency virus (HIV) infection with pulmonary tuberculosis. Following the completion of
6-month treatment for his tuberculosis, he was
initiated on anti-retroviral treatment with zidovudine 300 mg BD, lamivudine 150 mg BD, and
nevirapine 200mg BD.Also, he was on prophylaxis for pneumocystis carinii pneumonia (PCP)
with co-trimoxazole 80mgOD since 2years. At
the time of initiating the anti-retroviral treatment,
his CD4+ count was 290 cells/mm3. After
18months, his antiretroviral therapy was changed
to nevirapine 200 mg BD, lamivudine 150 mg
BD, and stavudine 30mgBD as he has had anaemia (Hb 10g/dL). He was prescribed multivitamins for anaemia. After 2months of treatment
with nevirapine, lamivudine, and stavudine,
patient’s Hb was 13.5g/dL.
After 6months of treatment with nevirapine,
lamivudine, and stavudine, he was hospitalised
with the complaints of nausea, vomiting,
anorexia, and pain in epigastric region. His
serum amylase and lipase levels were 211U/L
(normal reference range—up to 100 U/L) and
1267 U/L (normal reference range—114 to
286 U/L), respectively. He was diagnosed to
have had pancreatitis. Stavudine was discontinued, and he was treated symptomatically, and
subsequently he was discharged after 7days of
hospitalisation. At the time of discharge, serum
amylase and lipase levels were 195 U/L and
1021 U/L, respectively. After 8 weeks of discharge from the hospital, the amylase and lipase
levels decreased to 140U/L and 440U/L, respectively. After 20weeks of discharge from the hos-
8 Causality Assessment inPharmacovigilance
205
pital, considering his CD4+ count (23 cells/
mm3), he was initiated on the antiretroviral treatment with emtricitabine 200mgOD, tenofovir
300mgOD, and the xed dose combination of
lopinavir–ritonavir (200mg+50mg) BD.
1. What are the suspected adverse reactions in
this patient?
Anaemia and pancreatitis are adverse
events identied in this patient. It is essential
to note that both these conditions were not
present in the patient prior to starting the
drug therapy and manifested only after 18
and 6 months of treatment with the suspected drugs, respectively. It is also important to note that certain ADRs may take
longer duration to manifest, while others
may occur within a few minutes to days.
2. Which are the suspected drugs associated
with the adverse reactions?
Zidovudine and stavudine were implicated for anaemia and pancreatitis, respectively. It is important to narrow down,
considering various factors, to the single
causative agent when two or more drugs are
known to cause the similar ADRs. For
example, co-trimoxazole is also known to
cause pancreatitis but rarely, and also it is
reported to cause pancreatitis at high doses.
In this case, patient was receiving the cotrimoxazole at lower prophylaxis dose for
over 18months and before initiating stavudine. Hence, the likely association of cotrimoxazole causing pancreatitis is weak.
3. Assess the causality of thesuspectedadverse
reactionsusing Naranjo’s algorithm.
Upon the assessment using Naranjo’s
algorithm, the causality category of both zidovudine-induced anaemia and stavudineinduced pancreatitis is found to be probable
owing to the facts that there is a reasonable
time relationship to drug intake and event or
laboratory test abnormality, and furthermore,
these reactions are unlikely to be attributed
to disease, and the response to withdrawal of
the suspected drugs has been clinically
reasonable.
4. What are other learning points in this case?
Asafe drug does not mean it is devoid of
risk. Cliniciansand patients always need to
take aninformed decision considering the
benet–harm ratio of suspected
medications.
When a patient develops more than one
adverse reaction, the process of estimating
the causal association needs to be carried out
separately for each of the reported ADRs
considering all other possible drug-related
and patient-related causes.
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Pharmacoepidemiologic Studies
DeborahLayton
9
Abstract
Over the last 30 years, observational studies
applying epidemiological methods to realworld clinical data from electronic medical
records, registries and health insurance claims
are highly regarded as generating quality realworld evidence that provides complementary
information on safety, particularly postmarketing. This chapter will explore why pharmacoepidemiology is an important part of
pharmacovigilance and describe some common epidemiological study designs—namely
the cross-sectional study design, the cohort
study design and case–control study design—
which can be used to study safety (and effectiveness) of medicines. Considerations in
objectively assessing such methods will be
outlined, including potential confounding and
bias, supported by case studies that explore the
application of three common epidemiological
techniques in support of addressing emergent
safety issues. Finally, important initiatives are
introduced that collectively aim to improve
quality, transparency and reproducibility in
generating real-world evidence from observational pharmacoepidemiologic studies that is
important for regulatory decision-making.
D. Layton (*)
PEPI Consultancy Limited, Southampton, UK
e-mail: drdeborahlayton@outlook.com
Keywords
Pharmacovigilance · Pharmacoepidemiology ·
Pharmacoepidemiologic study methods ·
Post-authorisation safety studies · Measures
of risk and of association · Observational
study designs · Confounding and bias ·
Secondary use data sources · Quality,
transparency and reproducibility · Realworld evidence
Learning Objectives
At the end of the chapter, the reader will be able
to:
• Dene pharmacoepidemiology, pharmacovig-
ilance and real-world evidence.
• Explain the need for observational post-
authorisation safety studies and where these
t within pharmacovigilance.
• Identify three commonly used observational
study designs and the statistics that are used to
report results.
• Explain important limitations of observational
study designs and how these impact our interpretation of study results.
• Describe some key considerations of using
database networks for post-marketing safety
studies.
• Describe the need for quality, transparency
and reproducibility of real-world evidence.
© 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_9
209
210
Key Points
• The Good Pharmacovigilance
Guidelines (GvP) reinforce the mandatory requirement for risk management
planning and underpin the importance
of pharmacoepidemiological observational techniques in supporting regulatory decision-making across the
post- marketing lifecycle of a drug.
• Observational methods can enable the
timely identication of any signicant
safety concerns that arise when a medicinal product is used in the real-world
‘uncontrolled’ setting.
• As the availability of electronic healthcare databases expands, post-authorisation safety studies are being increasingly
conducted to provide real-world evidence, often requested by regulatory
agencies.
• It is essential that high-quality data are
used and that stakeholders not only
understand the strengths and limitations
of the methods used but can also interpretate the results appropriately in order
to improve clinical decision-making.
This is essential to protect the health and
quality of life of patients.
1 Introduction
Whilst there are many examples of harms associated with medicines since the 1900s, some have
been signicant enough to have remarkable inuence on regulating the safety of medicines and
public health policy, as described in Chaps. 6 and
13. Randomised controlled trials (RCTs) con-
ducted during drug development have traditionally been regarded as the gold standard for
generating evidence on the efcacy of new medicines, with somewhat limited scope for safety.
However, over the last 30 years, observational
studies applying epidemiological methods to
real-world clinical data from electronic medical
records, registries and health insurance claims
are highly regarded as generating quality real-
D. Lay ton
world evidence (RWE) that provides complementary information on safety, particularly
post- marketing. In this chapter, the reader will
explore why pharmacoepidemiology is an important part of pharmacovigilance and gain an understanding of common epidemiological study
designs—namely the cross-sectional study
design, the cohort study design and case–control
study design—which can be used to study safety
(and effectiveness) of medicines. Concepts and
denitions introduced in Chap. 1 will be further
applied in these contexts. Considerations in objectively assessing such methods, including confounding and bias, will be outlined. Notably, it is
out of scope of this chapter to have a detailed deep
dive into technical aspects of epidemiological
methods; other solid introductory texts are available for this. Finally, the reader will be introduced
to important initiatives that collectively aim to
improve quality, transparency and reproducibility
in generating real-world evidence from observational pharmacoepidemiologic studies that are
important for regulatory decision-making.
2 The Origins ofDrug Safety
intheUK andEU
Note: Origin of drug regulation globally is discussed in Chap.6.
Originally thalidomide was introduced in
West Germany in 1956 and marketed in 1958in
the United Kingdom as Distaval®. By 1960, doctors became concerned about possible side
effects, and in November 1961, investigations at
obstetric units in West Germany showed a signicant rise in the number of children born with limb
deformities. During drug development, animal
tests did not include monitoring for congenital
effects during use in pregnancy. By the time thalidomide was withdrawn, over 10,000 babies had
been born deformed [1]. This appalling human
toll led to the establishment of drug regulatory
bodies in a concerted effort to ensure adequate
testing of drugs before marketing and pharmacovigilance systems to identify drug safety hazards
earlier. Pharmacovigilance is dened as the science and activities relating to the detection,
assessment, understanding and prevention of
9 Pharmacoepidemiologic Studies
211
adverse effects or any other medicine-related
problem [2]. The lesson learnt was that no pharmacologically effective drug is without hazard.
In the UK, the Committee on Safety of Drugs
(subsequently the Committee on Safety of
Medicines—CSM and now the Commission on
Human Medicines—CHM) was formed [3]. This
was followed in May 1964 by the launch of a voluntary spontaneous adverse drug reaction (ADR)
reporting system called the Yellow Card Scheme.
Crucially, the scheme depended (and still does to
this day) on the willingness of healthcare professionals to transmit their suspicions of ADRs that
they encountered during their regular work [4].
However, it soon became clear that this system
was not entirely t for purpose. In 1976, practolol
(a selective beta-blocker) was withdrawn following the development of a serious ADR—oculomucocutaneous syndrome in hundreds of people
[5]. Multiple issues were identied—not only
misdiagnosis of early symptoms as conjunctivitis
(a common condition in the elderly) but also failure of clinicians to submit spontaneous reports
and the ability to generate alerts of seemingly
common side effects. In 1980, following several
proposals for a complementary safety monitoring
scheme aimed at generating evidence around
common and uncommon side effects (both serious and non-serious), a small pilot study conducted by the University of Southampton paved
the way for an improved method for early detection of potential hazards of drugs destined for
widespread, long-term use in primary care, not
only for new products but also for established
medicines. This scheme, endorsed by the CSM in
1983, was called Prescription-Event Monitoring
[6]. Its underlying purpose is to extend the safety
database of a new drug to at least 10,000 exposed
individuals. The scheme run by the Drug Safety
Research Unit (DSRU) remains active to the
present day and is available to all primary care
general practitioners (GPs), with some modication [7].
In 2005, new pharmacovigilance legislation
formally integrated the principles of risk management within the drug safety (and effectiveness) landscape [8]. According to the European
Medicines Agency [9] ‘Medicines in the
European Union (EU) are authorised on the basis
that their benets outweigh their risks for the target population. However, not all potential or
actual adverse reactions are identied at the time
of initial marketing authorisation. The aim of risk
management is to address uncertainties in the
safety prole at different points in the product
lifecycle, and to plan accordingly’.
Guidance on risk management activities to the
pharmaceutical industry was available in Volume
9A of the Rules Governing Medicinal Products in
the EU [10]. In brief, a risk management plan
(RMP) must include information on a medicine’s
safety prole and plans for pharmacovigilance
activities designed to gain greater knowledge.
The RMP also explains how risks will be minimised in patients and how those efforts will be
measured. Every new medicinal product must
have comprehensive RMP in place as part of its
approval and to retain its approved status. In
2012, a revised set of guidelines for the conduct
of pharmacovigilance was developed which
replaced those rules, known as the Good
Pharmacovigilance Guidelines (GvP) [11]. These
reinforced the requirement for risk management
planning through extension of components of the
RMP and further guidance on pharmacovigilance
processes such as inspections, audits and safety
communications as well as product- or population-specic considerations such as management
of ADRs. The importance of observational studies in supporting regulatory decision-making
across the post-marketing lifecycle of a drug was
also recognised formally within the updated
pharmacovigilance legislation. In terms of studying specic safety concerns, Part III: The
Pharmacovigilance Plan describes the inclusion
of additional pharmacovigilance activities which
includes (but not limited to) non-interventional
studies that may be conducted to provide additional characterisation of the (long-term) safety
of a medicinal product.
The GVP Module VIII uses the legal denition of a post-authorisation safety study (PASS)
as dened in Directive 2001/83/EC (DIR) Art
1(15), namely any study relating to an authorised
medicinal product conducted with the aim of
identifying, characterising or quantifying a safety
hazard, conrming the safety prole of the
medicinal product, or of measuring the effective-
212
D. Lay ton
ness of risk management measures (see Box 9.1
for additional conditions of a non-interventional
PASS) [12]. A PASS may be initiated, managed
or nanced by a Marketing Authorisation Holder
(MAH) either voluntary or following an obligation imposed by a competent authority. The most
important consideration of that PASS is that they
are post-marketing studies intended to study a
drug under real-world conditions, where the data
reect such use, outside the context of RCTs. The
term real-world evidence’ (RWE) has been used
for evidence obtained from the analysis of realworld data (RWD) and is often used to support
regulatory decision-making (see Sect. 5).
Box 9.1 The Additional Conditions of a
Non-interventional PASS [12]
• The medicinal product is prescribed in
the usual manner in accordance with the
terms of the marketing authorisation.
• The assignment of the patient to a particular therapeutic strategy is not
decided in advance by a trial protocol
but falls within current practice, and the
prescription of the medicine is clearly
separated from the decision to include
the patient in the study.
• No additional diagnostic or monitoring
procedures are applied to the patients,
and epidemiological methods are used
for the analysis of collected data.
Other types of non-interventional studies also
include those that are prospective (where subjects
are recruited before outcomes of interest are
developed) with de novo primary data collection,
provided the conditions set out above are met
[12]. However, non-interventional studies with
primary data collection may be subject to other
pharmacovigilance processes, such as reporting
of individual cases of suspected ADRs [13].
Clinical trials may be considered as a PASS, but
these are uncommon and will not be discussed
here.
A PASS may have several objectives which
may include the quantication of risks identied
with the RMP as ‘potential’ or ‘identied’ within
all users (often compared to patients taking another
drug), and/or in sub-populations of special interest
for which safety information is limited or missing
(e.g. pregnant women, elderly or young); risks
associated with long-term use; patterns of drug
utilisation that add to knowledge on safety and
effectiveness of a risk minimisation activity (e.g.
such as healthcare professional communications).
3 Epidemiological Techniques
forPost-marketing Studies
The ability to characterise the certainty of
strength of evidence for a safety concern relies
on a robust understanding of scientic methods, including observational epidemiological
methods and where that evidence falls short.
Pharmacoepidemiology has traditionally been
dened concisely as ‘An applied science which
uses epidemiologic techniques to study drug
effects’ [14]. The current concise
denition endorsed in 2023 by the International
Society for Pharmacoepidemiology (ISPE)
is‘Pharmacoepidemiology is a scientic discipline that uses epidemiological methods to
evaluate the use, benets and risks of medical
products and interventions in human populations’[15]. Accordingly, such studies quantify
drug use patterns and adverse drug effects. In
this section, it is hoped that the reader will get
some appreciation of the varied architecture of
the most common observational epidemiological techniques, their application to real-world
drug safety questions and the necessary considerations regarding study validity and bias.
The GVP Module VIII provides a summary of
methods used for PASS.The focus of this section will be restricted to those listed as the following observational study designs:
cross- sectional, case–control and cohort studies [12]. As also stated therein, the classication of a PASS is not constrained by the type of
study design chosen, but by its aims and objec-
9 Pharmacoepidemiologic Studies
213
tives. The most up-to-date advances in quantitative methods and innovative concepts are
beyond the scope of this section, and the reader
should refer to other bespoke texts [16].
However rstly, some fundamental concepts
should be outlined.
3.1.1 Measures ofDisease Frequency
andAssociation
A measure of disease frequency is the epidemiological term that characterises the occurrence of
health outcomes (be it an adverse event, adverse
drug reaction or some other health condition in a
study population). It is descriptive in nature and
is expressed most commonly as risk (incidence),
3.1 Common Terms
rate and prevalence (Table 9.1). Notably, these
measures can be correctly estimated because the
Understanding the nomenclature presented in
publications can be challenging. For each study
design, it is important to know how the results are
presented and their interpretation. This section is
not intended to be a fully comprehensive overview; the reader should again refer to other texts
for that [16].
Table 9.1 Common measures of disease frequency
These estimates should always be reported with 95% condence interval
Measure Denition Example
Risk Also known as ‘incidence’
This measure denes the probability of an unaffected
individual developing a specied health outcome over a
given period of time.
Risk=numerator (number of new caseswith the health
outcome)/denominator (total number of individuals at
risk at the start of study period)
Rate This measure denes how quickly the health outcome
occurs in the population over time.
Rate=numerator (number of new caseswith the health
outcomes)/denominator (total person-time observed for
the individuals during the study where person-time is
the sum of time that each at-risk individual contributes
to the study
population at risk at the start of the study can be
dened; however, there are some situations where
this is not possible. This situation occurs for
case–control studies where the approximation of
a measure of frequency is given as the odds (see
Sect. 3.2.3 for further details on case–control
study design).
The incidence of rst-time use of oral
contraceptive (OC) as recorded in a primary
care database among women aged
15–44years in England in 2022 would be
calculated as follows:
numerator (number of women with their rst
ever (OC) prescription)/denominator (number
of women in database at the start of 2022)
A risk of 0.1 indicates that 1in 10 women
within that age group will start an OC for the
rst time
The incidence rate of myocardial infarction
(MI) in rst-time users of an NSAID in the
rst month after starting treatment, as
recorded in a primary care database would be
calculated as follows:
numerator (number of patients newly
diagnosed with MI)/denominator (the sum of
person-time (months) from each individual
newly prescribed the NSAID within the rst
month)
A rate of 1 per 1000 person-months indicates
that, on average for every 1000 people given
an NSAID and followed for one month, 1 new
case of an MI will develop
(continued)
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