In this blog, we explore:
- The principles underlying causality assessment
- What can and cannot be done when performing causality assessment
- The different categories of causality assessment, along with brief explanations.
Table of Contents:
- Introduction
- Principles of causality assessment
- Why causality assessment?
- Causality Assessment Categories
- References
- PDF Study Guides
Introduction
In our most recent article, we covered the definitions, significance, and process related to a causality Assessment.
Now, our focus shifts to exploring the methods and criteria employed in the assessment of causality.
The objective of this article is to precisely define the Highlights and standards utilized for evaluating causality.
By accomplishing this, we aim to provide a comprehensive understanding of how to determine the causality relationships between events or factors.
Principles of causality assessment
All causality assessment methods or tools follow 4 cardinal principles of diagnosis of ADR: (i) temporal relationship of drug with the drug reaction, (ii) biological plausibility, (iii) dechallenge, and (iv) rechallenge.
When dechallenge or rechallenge has occurred in the past, it is called positive prechallenge or negative prechallenge.
Highlights:
DRUGVIGIL
What is already known?
Lack of consensus and need of coprehensive approach: To incorporate causal assessment approaches (CAAs) into systematic reviews (SRs) and reviews of reviews (RoRs).
What is new?
Usage of the Bradford Hill viewpoints, or criteria, but implementation and reporting transparency varied significantly. Some approaches focused on specific viewpoints, while others encompassed multiple aspects of causal assessment, resulting in overlapping methodologies.
Why causality assessment?
Pharmacovigilance faces an inherent challenge where most case reports primarily involve suspected adverse drug reactions.
These reactions often lack specificity for the drug, diagnostic tests are generally unavailable, and ethical constraints limit re-challenges.
Consequently, only a few adverse reactions can be classified as definitively ‘certain‘ or ‘unlikely.’
The majority falls within the intermediate range of being ‘possible’ or ‘probable.’ To address this issue, various systems have developed to facilitate a structured and standardized assessment of causality.
However, none of these systems have demonstrated the ability to provide precise and reliable quantitative estimations of the likelihood of a relationship.
Despite this limitation, causality assessment has become a common and essential practice in pharmacovigilance.
Advances and limitations of standardized case causality assessment
This section outlines the scope of the assessment system, detailing what it can and cannot assist in determining.
| What causality assessment can do | What causality assessment cannot do |
| Decrease disagreement b/w accessors | Accurate quantitative measurements of relationship likelihood |
| Classify relationship likelihood | Distinguish valid from invalid case |
| Mark individual case reports | Prove the connection b/w drug and the events |
| Improvement of scientific evaluations | Quantify the contribution of the drug with events |
| Change uncertainity to certainity |
Causality Assessment Categories
Minimum requirement to perform causality assessment:
- Medicinal Products: This includes all medications administered to the patient, along with details such as start date, stop date, doses, and indications.
- Adverse Event: A comprehensive description of the event, including the date of onset, duration to onset, and outcome.
- Dechallenge and Rechallenge Information: Data regarding the removal and reintroduction of the medication, if applicable.
- Patient Medical History: This involves capturing relevant past diseases of significance, such as hepatitis, as well as current co-existing medical conditions (co-morbidities) like tuberculosis or diabetes.
The WHO-UMC Causality Assessment categories:
The various causality categories listed in the table below, the assessment criteria of the various categories shown in a point-wise way.
| Causality term | Assessment criteria |
| Certain | ά Event or laboratory test abnormality, with plausible time relationship to drug intake ά Cannot be explained by disease or other drugs ά Response to withdrawal plausible (pharmacologically, pathologically) ά Event definitive pharmacologically or phenomenologically (i.e. an objective and specific medical disorder or a recognised pharmacological phenomenon) ά Rechallenge satisfactory, if necessary. |
| Probable/likely | ά Event or laboratory test abnormality, with reasonable time relationship to drug intake ά Unlikely to be attributed to disease or other drugs ά Response to withdrawal clinically reasonable ά Rechallenge not required |
Possible | ά Event or laboratory test abnormality, with reasonable time relationship to drug intake ά Could also be explained by disease or other drugs ά Information on drug withdrawal may be lacking or unclear |
| Unlikely | ά Event or laboratory test abnormality, with a time to drug intake that makes a relationship improbable (but not impossible) ά Disease or other drugs provide plausible explanations |
| Conditional/ Unclassified | ά Event or laboratory test abnormality ά More data for proper assessment needed, or ά Additional data under examination |
| Unassessable/ Unclassifable | ά Report suggesting an adverse reaction ά Cannot be judged because information is insufficient or contradictory ά Data cannot be supplemented or verified |
References:
- Bibliography: Pande, S., 2018. Causality or Relatedness Assessment in Adverse Drug Reaction and Its Relevance in Dermatology. Indian J Dermatol 63, 18β21. https://doi.org/10.4103/ijd.IJD_579_17
- Bibliography: Rodrigues, P.P., Ferreira-Santos, D., Silva, A., PolΓ³nia, J., Ribeiro-Vaz, I., 2018. Causality assessment of adverse drug reaction reports using an expert-defined Bayesian network. Artif Intell Med 91, 12β22. https://doi.org/10.1016/j.artmed.2018.07.005
- Bibliography: Shimonovich, M., Pearce, A., Thomson, H., Katikireddi, S.V., 2022. Causal assessment in evidence synthesis: A methodological review of reviews. Res Synth Methods 13, 405β423. https://doi.org/10.1002/jrsm.1569
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