ABSTRACT
On March 23, 2020, the Moroccan Ministry of Health issued a circular authorizing the off label use of hydroxychloroquine or chloroquine in combination with azythromycin as first-line treatment for all COVID-19 patients. The aim of this study is to detect, assess and manage safety signals from individual cardiovascular adverse events (AE) reported. Methods: During the COVID19 pandemic, pharmacovigilance surveillance was based on spontaneous targeted notification in all health facilities. Newsletters with a suitable reporting form were sent and reporting of AEs was mandatory. The AEs collected were analyzed for causality assessment using the WHO method and the Preventavility method was used to detect any preventable ADR. Safety signals were detected and analyzed using a qualitative pharmacovigilance method. Results: As of December 31, 2020, a total of 527 adverse events have been reported to the NPC related to Hydroxychloroquine or Chloroquine among the Covid 19 patients. We received 09 cases of preventable AEs related to medication error including one drug interaction for 3 cases, dose errors for 5 cases and one case with both errors, these cases have been validated ad managed as safety signals. Discussion and conclusion: The detected signal concerned the QT prolongation, hypokalaemia and arrythmias, due to high dose of azithromycin and to drug interactions with hypokalaemic drugs. The signal was analyzed and validated during the technical committee, and recommendations were established to avoid the occurrence of similar cases. © The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0/)
ABSTRACT
Introduction: During the strengthened Covid-19 vaccine safety surveillance, the Moroccan pharmacovigilance center has collected a higher number of adverse events following immunization (AEFI) reports. More than half of the Individual Case Safety Reports (ICSRs) collected in the National pharmacovigilance database since its creation are linked to Covid-19 vaccines [1]. Objective(s): To assess the impact of the inclusion of the AEFI related to Covid-19 vaccines on the measures of disproportionality in the National pharmacovigilance database. Method(s): Measurement of disproportionality by the calculation of the Reporting odds ratio (ROR) [2] for the ''Drug-Effect'' pairs collected at the National pharmacovigilance database (data from March 16, 2022) with and without inclusion of Covid-19 vaccines. Using ''VigiLyze'' (Uppsala Monitoring Center) we calculated the RORs and exported to ''Excel'' the list of ''Drug-Effect'' pairs for which the lower limit of the 95% confidence interval of the ROR is greater than 1. Then, on the list obtained, we recalculated by Excel the RORs after excluding the Covid-19 vaccines from the database. Result(s): Among the 69,814 cases of adverse effects recorded in the National pharmacovigilance database from March 16, 2022, 36,541 cases (52.6%) concerned Covid-19 vaccines (Table 1). The measurement of the disproportionality showed that 88% of the RORs increased and 5% decreased after inclusion of Covid-19 vaccines in database. The median of the ROR change factor following this inclusion (RORCovid vaccines included/RORCovid vaccines excluded) was 2.1 (1.8-2.1);this can be explained by the higher proportion of Covid-19 vaccines cases (half of the database). Also, the degree of this influence depends on the frequency of AEFI to Covid-19 vaccines. Thus, the ROR had decreased with inclusion of cases of covid-19 vaccines for most common reactions and had been multiplied by a factor more than 2 for the low-reported reactions with Covid-19 vaccines. Conclusion(s): The inclusion of Covid-19 vaccines in the National pharmacovigilance database has influenced the measures of disproportionality, which would influence signal detection. This bias must be taken into account when a type of drug or vaccine is very represented in a database. The proposed solution is to specify database for these types of products or to improve VigiLyze to be able to perform disproportionality measurements from parts of the national database.
ABSTRACT
Introduction: During the strengthened Covid-19 vaccine safety surveillance, the Moroccan pharmacovigilance center has collected a higher number of adverse events following immunization (AEFI) reports. More than half of the Individual Case Safety Reports (ICSRs) collected in the National pharmacovigilance database since its cre-ation are linked to Covid-19 vaccines [1]. Objective: To assess the impact of the inclusion of the AEFI related to Covid-19 vaccines on the measures of disproportionality in the National pharmacovigilance database. Methods: Measurement of disproportionality by the calculation of the Reporting odds ratio (ROR) [2] for the "Drug-Effect" pairs collected at the National pharmacovigilance database (data from March 16, 2022) with and without inclusion of Covid-19 vaccines. Using "VigiLyze" (Uppsala Monitoring Center) we calculated the RORs and exported to "Excel" the list of "Drug-Effect" pairs for which the lower limit of the 95% confidence interval of the ROR is greater than 1. Then, on the list obtained, we recalculated by Excel the RORs after excluding the Covid-19 vaccines from the database. Results: Among the 69,814 cases of adverse effects recorded in the National pharmacovigilance database from March 16, 2022, 36,541 cases (52.6%) concerned Covid-19 vaccines (Table 1). The measurement of the disproportionality showed that 88% of the RORs increased and 5% decreased after inclusion of Covid-19 vaccines in database. The median of the ROR change factor following this inclusion (RORCovid vaccines included/RORCovid vaccines excluded) was 2.1 (1.8-2.1);this can be explained by the higher proportion of Covid-19 vaccines cases (half of the database). Also, the degree of this influence depends on the frequency of AEFI to Covid-19 vaccines. Thus, the ROR had decreased with inclusion of cases of covid-19 vaccines for most common reactions and had been multiplied by a factor more than 2 for the low-reported reactions with Covid-19 vaccines. Conclusion: The inclusion of Covid-19 vaccines in the National pharmacovigilance database has influenced the measures of disproportionality, which would influence signal detection. This bias must be taken into account when a type of drug or vaccine is very represented in a database. The proposed solution is to specify database for these types of products or to improve VigiLyze to be able to perform disproportionality measurements from parts of the national database.