Background rates of 41 adverse events of special interest for COVID-19 vaccines in 10 European healthcare databases - an ACCESS cohort study

Willame, C; Dodd, Caitlin; Durán, C; Elbers, RJHJ; Gini, Rosa; Bartolini, Claudia; Paoletti, Olga; Wang, L; Ehrenstein, Véra; Kahlert, Johnny; Haug, U; Schink, Tania; Diez-Domingo, J; Mira-Iglesias, A; Vergara-Hernández, Carlos; Giaquinto, C; Barbieri, E; Stona, L; Huerta, Consuelo; Martín-Pérez, Mar; García-Poza, Patricia; Burgos, A de; Martínez–González, Marina Begoña; Bryant, V; Villalobos, Felipe; Pallejà-Millán, Meritxell; Aragón, María; Carreras, JJ; Souverein, Patrick C.; Thurin, NH; Weibel, Daniel; Klüngel, OH; Sturkenboom, MCJM

doi:10.1016/j.vaccine.2022.11.031

Cited by 43 publications

(43 citation statements)

References 22 publications

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“…Our rates may have been influenced by our inclusion of ED data without associated hospitalisations or may represent coding errors. However, our rates were within the range of pooled estimates published by the ACCESS project (with which our methods aligned), particularly rates derived from both hospitalisation and ED data like ours (pooled incidence rate: 3.39 per 100,000 person-years; 95%CI: 2.16–4.63) [ 30 ]. Additionally, our rates aligned with those published by the Food and Drug Administration (FDA) using claims data including inpatient, ED and/or other outpatient and healthcare provider data [ 37 ].…”

Section: Discussionsupporting

confidence: 70%

“…For example, our rates of all-cause death were lower than in the USA [ [25] , [28] ] but similar to other countries like South Korea, likely a reflection of differences in health systems and population demographics [ 29 ]. Compared with rates published in the 2023 ACCESS report, our rates of convulsions were more than double the age-standardised pooled rates from countries using inpatient and ED data (narrow definition; 142.56 (95%CI: 134.83–150.30); our rates of anaphylaxis were more than 10 times higher than those published by ACCESS (narrow definition; 7.44 (95%CI: 5.54–9.33) [ 30 ]. It is likely that this discrepancy was due to our including all unique events of anaphylaxis and generalised convulsions rather than only incident cases as per the ACCESS protocol.…”

Section: Discussioncontrasting

confidence: 60%

“…While New South Wales is the most populous and has age- and sex-distributions similar to those of the next most populous states, our rates may not be representative of those in the other states and territories [ 51 ]. Finally, our pre-pandemic era background rates may not reflect the trends in background incidence of these AESI during the COVID-19 pandemic due to changes in infectious disease epidemiology, healthcare utilisation and the impacts of COVID-19 disease itself [ [8] , [30] ].…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Background incidence rates of selected adverse events of special interest (AESI) to monitor the safety of COVID-19 vaccines

Pillsbury

Phillips

Deng

et al. 2023

Vaccine

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Section: Discussionsupporting

confidence: 70%

Section: Discussioncontrasting

confidence: 60%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Background incidence rates of selected adverse events of special interest (AESI) to monitor the safety of COVID-19 vaccines

Pillsbury

Phillips

Deng

et al. 2023

Vaccine

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“…As for all outcomes of interest, the identification of VTE in a data source may vary according to the nature of the data captured (e.g., the presence or absence of a diagnosis code and/or related procedures and/or related drug dispensing), and the ontology used to characterize them, when available. The healthcare settings (outpatient or inpatient) may also impact the way outcomes are coded and recorded (1). As a consequence, accurately identifying an outcome, such as VTE, in a data source requires the implementation of a specific algorithm to either refine a diagnosis code or overcome its absence.…”

Section: Purposementioning

confidence: 99%

Design and validation of algorithms to identify Venous Thromboembolism in the French National Healthcare Database

Thurin¹,

Grelaud²,

Grolleau³

et al. 2023

Preprint

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Purpose This paper aims to introduce the algorithm designed to identify Venous Thromboembolism (VTE) in the French National healthcare database (SNDS) and to estimate its positive predictive value. Methods A case-identifying algorithm was designed using SNDS inpatient and outpatient encounters, including hospital stays with discharge diagnoses, imaging procedures, and drug dispensing. An intra-database validation study was then conducted, drawing 150 cases identified as VTE by the algorithm and requesting 4 vascular specialists to assess them. Patient profiles used to conduct the case adjudication were reconstituted from de-identified pooled and formatted SNDS data with a 6-month look-back period prior to the supposed VTE onset and a 12-month follow-up period after. The positive predictive value (PPV) with its 95% confidence interval [95%CI] was calculated as the number of experts-confirmed VTE divided by the number of algorithm-identified VTE. The PPV and its 95%CI were then recomputed for the same patients once the VTE definition was updated based on expert recommendation. Results On the 150 patients meeting the VTE first definition, the adjudication committee confirmed 92 cases, resulting in a PPV of 61% (95%CI = [54-69]). The final definition including expert suggestions showed a PPV of 92% (95%CI = [86-98]) with a total of 87 algorithm-identified cases, including 80 retrieved from the 92 confirmed by experts Conclusion The identification of VTE in the SNDS is possible with a good PPV.

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“…In May 2020, the ACCESS (The vACCine Covid‐19 monitoring readinESS) project was launched to prepare real‐world monitoring of COVID‐19 vaccines. Within this project, background incidence rates of 41 adverse AESI following administration of COVID‐19 vaccines were calculated based on 10 healthcare databases from 7 European countries 22 …”

Section: Introductionmentioning

confidence: 99%

Observed‐over‐Expected analysis as additional method for pharmacovigilance signal detection in large‐scaled spontaneous adverse event reporting

Boom

Eekeren

Hunsel

2023

Pharmacoepidemiology and Drug

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Background The large‐scale COVID‐19 vaccination campaigns in 2021 and 2022 led to a rapid increase in numbers of received adverse event reports in spontaneous reporting systems. As background incidences of naturally occurring medical events became increasingly relevant for causality assessment of potential associations with the vaccines, a novel approach for signal detection was warranted. Objectives This article illustrates the Observed‐over‐Expected (O/E) analysis as an additional method for signal detection and risk assessment in large‐scaled spontaneous reporting systems. Methods All individual case safety reports (ICSRs) concerning idiopathic peripheral facial paralysis or Bell's palsy following administration of the COVID‐19 vaccines (n = 291) manufactured by Pfizer/BioNTech (Comirnaty), Moderna (Spikevax), AstraZeneca (Vaxzevria) and Janssen (JCOVDEN) received by the National Pharmacovigilance Centre Lareb until 24th March 2022 were included in the O/E analysis, within a risk window of 7 and 14 days following immunisation. Dutch background incidence rates from 2019 and exposure of the Dutch population to the vaccines were obtained from the PHARMO institute and RIVM. The data was stratified in age groups, gender and administered dose in order to differentiate between population subgroups. Results Bell's palsy was reported more frequently than expected in several population subgroups following administration of all four COVID‐19 vaccines, including children and adolescents. Among children, a high O/E ratio was found for boys aged 5–14 years after receiving the Pfizer/BioNTech vaccine. Regarding adolescents and young adults, women aged 15–24 years receiving Pfizer/BioNTech and Moderna, and men aged 15–24 years receiving Janssen developed Bell's palsy more often than expected. Furthermore, O/E ratios were high for individuals aged 25–64, regarding females receiving Pfizer, Moderna and AstraZeneca and males receiving Janssen. As facial paralysis was not labelled as an adverse event for the Janssen vaccine, this analysis contributed to signalling the association and warranting further regulatory action. Conclusions The O/E method is a useful approach for signal detection of potential adverse reactions when handling large numbers of ICSRs. Further research is needed to attest to the causality on a clinical basis.

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Background rates of 41 adverse events of special interest for COVID-19 vaccines in 10 European healthcare databases - an ACCESS cohort study

Cited by 43 publications

References 22 publications

Background incidence rates of selected adverse events of special interest (AESI) to monitor the safety of COVID-19 vaccines

Background incidence rates of selected adverse events of special interest (AESI) to monitor the safety of COVID-19 vaccines

Design and validation of algorithms to identify Venous Thromboembolism in the French National Healthcare Database

Observed‐over‐Expected analysis as additional method for pharmacovigilance signal detection in large‐scaled spontaneous adverse event reporting

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