Causality assessment is an algorithm proposed by WHO to identify a causal relationship between vaccines and adverse events following immunization (AEFIs), mostly for serious adverse events. It can be considered consistent, inconsistent, indeterminate or unclassifiable. This study describes AEFIs reported in Puglia from 2013 to 2016 and analyzes the differences between the causality assessments performed on AEFI case-report information and the causality assessments performed after the examination of clinical documentation. 292 AEFI were reported: 191 (65.4%) non serious, 59 (20.2%) serious and 42 (14.4%) undefined. Causality assessment performed on the AEFI case-report information classified 59.2% (n=29/49) of serious AEFIs as consistent while assessment performed after clinical review only classified 30.6% (n=15/49) of serious AEFI as consistent (X2=65.0; p=0,000). In the first approach, inconsistent serious AEFIs were 18.6% (n=11/49) and then became 45.8% (n=27/49) after examination of clinical documentation. Indeterminate serious AEFIs were 6.8% (n=4) at first, and then 3.4% (n=2). Unclassifiables did not change.

Causality assessment is an algorithm proposed by WHO to identify a causal relationship between vaccines and adverse events following immunization (AEFIs), mostly for serious adverse events. It can be considered consistent, inconsistent, indeterminate or unclassifiable. This study describes AEFIs reported in Puglia from 2013 to 2016 and analyzes the differences between the causality assessments performed on AEFI case-report information and the causality assessments performed after the examination of clinical documentation. 292 AEFI were reported: 191 (65.4%) non serious, 59 (20.2%) serious and 42 (14.4%) undefined. Causality assessment performed on the AEFI case-report information classified 59.2% (n=29/49) of serious AEFIs as consistent while assessment performed after clinical review only classified 30.6% (n=15/49) of serious AEFI as consistent (X2=65.0; p=0,000). In the first approach, inconsistent serious AEFIs were 18.6% (n=11/49) and then became 45.8% (n=27/49) after examination of clinical documentation. Indeterminate serious AEFIs were 6.8% (n=4) at first, and then 3.4% (n=2). Unclassifiables did not change.

Systematic use of causality assessment in AEFI surveillance: A 2013-2016 pilot study in Puglia

Stefanizzi P.
Investigation
;
Tafuri S.
Supervision
;
Quarto M.
Supervision
2017-01-01

Abstract

Causality assessment is an algorithm proposed by WHO to identify a causal relationship between vaccines and adverse events following immunization (AEFIs), mostly for serious adverse events. It can be considered consistent, inconsistent, indeterminate or unclassifiable. This study describes AEFIs reported in Puglia from 2013 to 2016 and analyzes the differences between the causality assessments performed on AEFI case-report information and the causality assessments performed after the examination of clinical documentation. 292 AEFI were reported: 191 (65.4%) non serious, 59 (20.2%) serious and 42 (14.4%) undefined. Causality assessment performed on the AEFI case-report information classified 59.2% (n=29/49) of serious AEFIs as consistent while assessment performed after clinical review only classified 30.6% (n=15/49) of serious AEFI as consistent (X2=65.0; p=0,000). In the first approach, inconsistent serious AEFIs were 18.6% (n=11/49) and then became 45.8% (n=27/49) after examination of clinical documentation. Indeterminate serious AEFIs were 6.8% (n=4) at first, and then 3.4% (n=2). Unclassifiables did not change.
2017
Causality assessment is an algorithm proposed by WHO to identify a causal relationship between vaccines and adverse events following immunization (AEFIs), mostly for serious adverse events. It can be considered consistent, inconsistent, indeterminate or unclassifiable. This study describes AEFIs reported in Puglia from 2013 to 2016 and analyzes the differences between the causality assessments performed on AEFI case-report information and the causality assessments performed after the examination of clinical documentation. 292 AEFI were reported: 191 (65.4%) non serious, 59 (20.2%) serious and 42 (14.4%) undefined. Causality assessment performed on the AEFI case-report information classified 59.2% (n=29/49) of serious AEFIs as consistent while assessment performed after clinical review only classified 30.6% (n=15/49) of serious AEFI as consistent (X2=65.0; p=0,000). In the first approach, inconsistent serious AEFIs were 18.6% (n=11/49) and then became 45.8% (n=27/49) after examination of clinical documentation. Indeterminate serious AEFIs were 6.8% (n=4) at first, and then 3.4% (n=2). Unclassifiables did not change.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11586/242271
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