Causality Assessment in Pharmacovigilance: Practical Challenges for Safety Teams

Determining whether a medicinal product caused an adverse event is one of the most important and challenging tasks in pharmacovigilance. Accurate causality assessment supports patient safety, regulatory compliance, and informed benefit-risk decisions. This article explores the principles, common methods, and practical challenges safety teams face when evaluating suspected adverse drug reactions.

What Is Causality Assessment in Pharmacovigilance?

Causality assessment in pharmacovigilance is the systematic evaluation of the reasonable possibility that a medicinal product caused or contributed to an adverse event. Rather than simply identifying that two events occurred together, its purpose is to determine whether a genuine causal relationship exists. 

This distinction is reflected in pharmacovigilance terminology: an adverse event (AE) is any untoward medical occurrence that follows the use of a medicinal product, regardless of causality, whereas an adverse drug reaction (ADR) implies at least a reasonable possibility that the medicine caused the event. Because many adverse events occur independently of drug exposure, establishing causality is rarely straightforward. Patients may have underlying diseases, receive multiple concomitant medications, or experience events related to the natural progression of their condition. 

For this reason, pharmacovigilance professionals evaluate all available clinical evidence to determine whether a reported adverse event can reasonably be considered a suspected adverse drug reaction.

How Is Causality Different From Correlation?

One of the fundamental principles of pharmacovigilance is recognising that correlation does not necessarily imply causation.

For example, a patient may develop elevated liver enzymes shortly after starting a new medicine. While the timing suggests a possible association, the abnormal laboratory results could also be explained by viral hepatitis, alcohol consumption, another medication, or an underlying liver disorder.

A temporal relationship is therefore only one component of the assessment of causality. Safety reviewers must also consider factors such as biological plausibility, alternative explanations, previous evidence from the scientific literature, and the patient’s overall clinical picture before determining whether the medicinal product is likely responsible.

This balanced approach helps avoid both overestimating and underestimating medicine-related risks.

Why Causality Assessment Matters in Pharmacovigilance

Causality assessment is a core pharmacovigilance activity that supports patient safety throughout a medicinal product’s lifecycle. Every individual case safety report (ICSR) contributes to the overall understanding of a medicine’s safety profile. At the same time, consistent and well-documented assessments help distinguish true adverse drug reactions from coincidental events. Although a single case rarely changes the benefit-risk profile of a product, multiple reports with consistent findings may contribute to signal detection and further evaluation.

Accurate causality assessment also supports key pharmacovigilance activities, including regulatory reporting, benefit-risk assessment, signal management, product labelling updates, risk minimisation measures, Risk Management Plans (RMPs), and aggregate reports such as PSURs and PBRERs. Using a structured and transparent approach helps improve consistency across reviewers and provides a stronger evidence base for regulatory decision-making.

From Individual Case Reports to Signal Detection

Most safety concerns begin with individual reports submitted by healthcare professionals, patients, clinical investigators, or scientific publications.

Each report contributes a small piece of evidence. When multiple cases describe similar adverse reactions with consistent clinical features and limited alternative explanations, they may collectively support the detection and validation of a potential safety signal.

Accurate causality assessment improves the quality and consistency of individual case evaluations, providing a more reliable foundation for signal detection and subsequent benefit-risk assessment. However, the causality assessment of a single case does not confirm or exclude a safety signal on its own.

This is particularly important for rare adverse drug reactions, where regulatory decisions often rely on the careful evaluation of multiple sources of evidence rather than any individual report.

Supporting Regulatory Decision-Making

Marketing authorisation holders should document medically and scientifically justified evaluations in accordance with applicable case-processing, signal-management, and aggregate-reporting procedures. Good Pharmacovigilance Practices (GVP) Module VI emphasises the importance of documenting the rationale supporting each causality assessment to ensure transparency, consistency, and regulatory compliance.

Reliable causality assessments contribute to:

• signal validation and prioritisation;
• benefit-risk assessments;
• labelling updates;
• periodic safety reports;
• responses to regulatory authority questions;
• post-authorisation safety studies;
• risk management activities.

Ultimately, consistent evaluations improve confidence in the overall pharmacovigilance system and support evidence-based regulatory decisions.

The Pharmacovigilance Causality Assessment Process

The pharmacovigilance causality assessment process follows a structured medical evaluation rather than a simple checklist. While organisations may use different internal workflows, most assessments rely on the same core principles.

The goal is to determine whether available evidence supports a causal association between the suspected medicinal product and the reported adverse event.

Step 1: Collecting Complete Safety Information

Every assessment begins with gathering all relevant case information.

Reviewers typically evaluate:

• patient demographics;
• medical history;
• concomitant medications;
• indication for treatment;
• dosing information;
• dates of treatment initiation and discontinuation;
• onset of the adverse event;
• laboratory findings;
• diagnostic procedures;
• clinical outcomes.

The quality of a causality assessment depends heavily on the completeness of the available information. Missing dates, incomplete narratives, or absent laboratory results often increase uncertainty and may limit the confidence of the conclusion.

Step 2: Evaluating Temporal Relationships

The timing between drug administration and the onset of adverse events is one of the first aspects considered during medical review.

Questions commonly include:

• Did the reaction occur after treatment started?
• Is the onset biologically plausible?
• Did the event improve after discontinuation?
• Did symptoms worsen following dose escalation?
• Was there sufficient exposure for the reaction to occur?

Although temporal association is essential, it is never sufficient on its own to establish causality.

Step 3: Assessing Alternative Explanations

Safety professionals must carefully evaluate whether factors other than the suspected medicine could explain the reported event.

Potential alternative causes include:

• underlying diseases;
• concomitant medicinal products;
• infections;
• genetic predisposition;
• environmental exposures;
• age-related conditions;
• disease progression.

A thorough differential evaluation strengthens the overall reliability of the assessment and reduces the risk of attributing unrelated events to the medicinal product.

Step 4: Considering Dechallenge and Rechallenge

Whenever available, information about dechallenge and rechallenge provides valuable evidence.

A positive dechallenge occurs when the adverse event improves after discontinuing the suspected medicine.

A positive rechallenge refers to the recurrence of the event following re-administration of the same medicine.

Although positive rechallenge findings may provide strong evidence of causality, they are relatively uncommon in routine pharmacovigilance because intentional re-exposure is often unethical, particularly after serious adverse reactions.

Consequently, many assessments must rely on indirect clinical evidence rather than definitive proof.

Step 5: Reaching a Documented Medical Conclusion

The final stage integrates all available evidence into a documented medical assessment.

Rather than seeking absolute certainty, reviewers determine the likelihood that the medicinal product contributed to the adverse event based on current knowledge.

The assessment should clearly explain:

• supporting evidence;
• conflicting evidence;
• remaining uncertainties;
• rationale for the assigned causality category.

Transparent documentation is particularly valuable during internal quality reviews, regulatory inspections, and future reassessment if additional information becomes available.

Common Causality Assessment Methods in Pharmacovigilance

Various methods of causality assessment have been developed to improve consistency in pharmacovigilance. However, no single approach is considered the gold standard or universally superior. The choice of method depends on factors such as the type of report, regulatory expectations, available clinical information, and organisational procedures. In practice, pharmacovigilance teams use structured assessment methods to support consistency while relying on expert medical judgment to interpret the available evidence and reach scientifically sound conclusions.

WHO-UMC System

The World Health Organisation–Uppsala Monitoring Centre (WHO-UMC) system is one of the most widely used approaches worldwide. Rather than assigning numerical scores, it classifies cases into predefined categories based on the strength of available evidence.

Common causality categories include:

• Certain
• Probable/Likely
• Possible
• Unlikely
• Conditional/Unclassified
• Unassessable/Unclassifiable

The WHO-UMC system considers factors such as temporal relationship, dechallenge and rechallenge information, alternative explanations, and the overall clinical picture. Its flexibility makes it suitable for routine pharmacovigilance activities, particularly when reviewing spontaneous reports and literature cases.

Naranjo Algorithm

The Naranjo Algorithm, sometimes referred to as the Naranjo Scale, is a structured scoring system used to estimate the likelihood that a suspected drug caused an adverse reaction. It evaluates factors such as:

• temporal association;
• previous reports of the reaction;
• improvement after drug withdrawal;
• recurrence after re-exposure;
• alternative causes;
• objective clinical evidence.

Each answer contributes to a numerical score that categorises the reaction as definite, probable, possible, or doubtful.

Although the algorithm improves consistency, it has limitations. Many questions cannot be answered for spontaneous reports because information is often incomplete, making the tool less suitable for routine post-marketing pharmacovigilance than for controlled clinical settings.

Other Causality Assessment Approaches

Several additional approaches have been proposed over the years, including the French method and the Kramer algorithm, each offering different frameworks for evaluating the relationship between a medicinal product and an adverse event. While these methods primarily support individual case assessment, broader evidence evaluation may also incorporate the Bradford Hill considerations, particularly when assessing case series and potential safety signals. Factors such as consistency, strength of association, biological plausibility, temporality, dose-response relationship, and coherence help determine whether an observed association is likely to reflect a true causal relationship.

Regardless of the methodology, most organisations rely on structured frameworks to promote consistency while recognising that expert medical judgment remains essential when interpreting complex or incomplete safety data.

Individual Case vs Case-Series Causality Assessment

While the WHO-UMC System and the Naranjo Algorithm are primarily designed to evaluate individual case safety reports (ICSRs), pharmacovigilance also requires assessing evidence across multiple cases. Unlike individual-case assessment, case-series evaluation considers the overall pattern of evidence, including consistency, strength of association, biological plausibility, dose-response relationship, coherence with existing knowledge, and other Bradford Hill considerations. This broader perspective helps determine whether an observed association may represent a true safety risk at the population level. Although closely related, causality assessment, signal detection, and signal validation are distinct processes with different objectives. A high causality assessment for an individual case does not necessarily confirm a population-level risk, just as several poorly documented case reports do not automatically establish causation. Regulatory decisions require evaluating both the quality of individual cases and the consistency of the overall evidence.

Practical Challenges for Safety Teams

Even the most structured assessment framework cannot eliminate uncertainty. In real-world pharmacovigilance, reviewers rarely receive complete, high-quality information. Instead, they must evaluate imperfect data while balancing scientific evidence with clinical judgment.

Incomplete Reports and Evolving Clinical Information

Missing information remains one of the biggest obstacles to reliable causality assessment.

Case reports frequently lack:

• treatment dates;
• laboratory results;
• medical history;
• concomitant medications;
• diagnostic findings;
• patient outcomes.

Without these details, determining whether a medicine contributed to an adverse event becomes considerably more difficult.

In many cases, additional clinical evidence – including imaging results, laboratory confirmation, specialist assessments, or final diagnoses – becomes available only after follow-up. As pharmacovigilance is an ongoing process, causality assessments may need to be revisited and updated as new information emerges. Thorough follow-up and clear documentation are, therefore, essential for improving the quality and reliability of individual case evaluations.

Polypharmacy and Confounding Factors

Many patients – particularly older adults or those with chronic diseases – receive multiple medicines simultaneously.

When several medicinal products are administered together, identifying the most likely cause of an adverse reaction can be extremely challenging. Underlying diseases, drug-drug interactions, lifestyle factors, and concurrent infections may all contribute to the observed event.

Reviewers must therefore evaluate each potential explanation rather than focusing solely on the suspected product.

Literature Cases With Limited Evidence

Scientific publications are an important source of pharmacovigilance data, but they also present unique challenges.

Published case reports often describe unusual or serious reactions while omitting details essential for assessing causality. Information on concomitant medications, medical history, dechallenge, or laboratory findings may be incomplete or unavailable.

For literature monitoring teams, identifying missing information and documenting assessment limitations is therefore just as important as extracting the reported adverse event itself.

Rare Adverse Events

Rare adverse drug reactions present another significant challenge.

Because only a small number of cases may exist worldwide, reviewers often have limited evidence on which to base their conclusions. In these situations, biological plausibility, consistency across reports, and accumulated pharmacovigilance experience become increasingly important.

Why Different Assessors May Reach Different Conclusions

One reality of pharmacovigilance is that two experienced reviewers may evaluate the same case differently.

This does not necessarily indicate that one assessment is incorrect. Instead, it reflects the complexity of medical decision-making when evidence is incomplete or uncertain.

The Role of Expert Medical Judgment

Structured assessment tools provide a valuable framework, but they cannot account for every clinical scenario.

Medical reviewers must interpret:

• competing explanations;
• the quality of available evidence;
• known pharmacological mechanisms;
• previous safety data;
• individual patient characteristics.

Their clinical expertise helps place the available evidence into the appropriate medical context.

Managing Uncertainty in Pharmacovigilance

Unlike diagnostic medicine, pharmacovigilance rarely provides definitive proof of causality.

Instead, safety professionals evaluate the probability that a medicinal product contributed to an adverse event based on currently available information.

Documenting uncertainty is therefore an important part of the assessment. Clearly explaining why a case was considered “possible” rather than “probable,” for example, provides transparency and supports future reassessment if additional evidence becomes available.

How AI Supports Modern Causality Assessment

Modern AI and machine learning technologies can support case processing and literature monitoring, but cannot replace expert clinical judgment.

Instead, AI helps safety teams process increasing case volumes more efficiently by organising information, identifying relevant evidence, and reducing manual effort.

Tasks AI Can Automate

Modern AI-powered pharmacovigilance platforms can assist by:

• extracting key clinical information from case narratives;
• identifying suspected medicinal products and adverse events;
• generating structured case summaries;
• highlighting missing information;
• identifying relevant literature;
• organising timelines for medical review;
• prioritising reports that require immediate attention.

These capabilities allow reviewers to spend less time searching for information and more time evaluating medical evidence.

Why Final Causality Decisions Remain Human

Despite rapid advances in artificial intelligence, establishing a causal relationship remains a clinical responsibility.

AI cannot fully evaluate medical context, interpret conflicting evidence, or apply professional judgment in complex cases. Final causality assessments require experienced pharmacovigilance professionals who understand disease processes, pharmacology, regulatory expectations, and the limitations of available data.

The most effective approach combines AI-driven efficiency with expert medical oversight, enabling safety teams to work more consistently while maintaining scientific rigour.

Best Practices for Consistent Causality Assessment

Although uncertainty cannot be eliminated, organisations can improve the consistency and quality of causality assessments by adopting standardised processes.

Standardising Internal Workflows

Clear internal procedures help ensure that reviewers evaluate cases using the same principles and documentation standards. Consistent workflows also simplify quality control and regulatory inspections.

Investing in Reviewer Training

Regular training helps safety professionals apply assessment methods consistently, recognise common sources of bias, and remain current with evolving regulatory guidance and scientific knowledge.

Leveraging Literature Monitoring and Quality Control

Comprehensive literature monitoring supports causality assessment by identifying previously reported adverse reactions, similar case reports, and emerging safety signals. Quality review processes further improve consistency by verifying that assessments are well documented and supported by available evidence.

AI-powered literature monitoring platforms can also help reviewers quickly identify relevant publications, summarise clinical evidence, and organise information for medical evaluation. However, the interpretation of that evidence – and the final causality assessment – should always remain the responsibility of qualified pharmacovigilance professionals.

Frequently Asked Questions

What Is the Difference Between Causality and Relatedness?

Causality assessment is essential for evaluating drug safety. Causality refers to the scientific evaluation of whether a medicinal product caused an adverse event. Relatedness is the conclusion reached after that evaluation and describes the likelihood of a causal relationship based on the available evidence.

Can an Adverse Event Be Reportable Without Confirmed Causality?

Yes. Many adverse events must be reported even when a causal relationship has not been established. Pharmacovigilance systems collect suspected adverse events so they can be evaluated individually and alongside other safety data as evidence accumulates.

What Information Is Needed for a Reliable Causality Assessment?

A reliable assessment depends on complete clinical information, including treatment dates, event onset, medical history, concomitant medications, laboratory findings, diagnostic results, and follow-up information – the more complete the case, the more robust the assessment.

Can Causality Assessments Change After Follow-Up?

Yes. Additional information obtained during follow-up, such as laboratory results, specialist evaluations, or patient outcomes, may strengthen, weaken, or change the initial causality assessment.

Is a Positive Dechallenge Enough to Establish Causality?

No. Although improvement after discontinuing a medicine may support a causal relationship, dechallenge findings should always be interpreted alongside other evidence, including temporal association, alternative explanations, and the overall clinical context.

What Is the Difference Between the WHO-UMC System and the Naranjo Algorithm?

The WHO-UMC System relies on expert judgment using predefined causality categories, whereas the Naranjo Algorithm uses a structured questionnaire with weighted scores. Both support causality assessment but should complement, not replace, clinical expertise.

Who Is Responsible for the Final Causality Assessment?

The final causality assessment should be performed by qualified pharmacovigilance or medical professionals. While structured methods and AI tools can support the evaluation, the conclusion requires expert clinical judgment and consideration of all available evidence.