Predictors of Adverse Events Following Immunization Reporting amongst Healthcare Workers in Jigawa State, Northern Nigeria, 2022

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DOI: 10.21522/TIJPH.2013.11.01.Art015

Authors : Belinda Vernyuy Uba, Uba Sabo Ado, Idriss Mohammed Bomoi, Babatunde Abiodun Amoo, Adefisoye Adewole, Amos Paul Bassey

Abstract:

An adverse event following immunization (AEFI) is any untoward medical occurrence that follows immunization and does not necessarily have a causal relationship with the usage of the vaccine. Reporting of AEFI is suboptimal amongst healthcare workers (HWs). This study aimed to determine predictors of HW’s reporting of AEFI. A descriptive cross-sectional study was conducted among HWs in selected health facilities (HFs) in Jigawa State using an open data kit self-administered questionnaire to collect data on socio-demographic characteristics, training, knowledge, and their practices on AEFI reporting. Analysis was conducted using Statistical Package for Social Sciences (SPSS) for frequencies, proportions, and associations using bivariate analysis and multivariate analysis using logistic regression to determine predictors of AEFI reporting with statistical significance set at p<0.05 and 95% confidence interval. Of the 400 respondents, 280 (70%) respondents had good knowledge of AEFI, 328(82%) sends routine AEFI reports and of 212 (53%) who recently encountered an AEFI, 174 (82.1%) exhibited some good reporting practices. Female gender (AOR 0.46, p=0.035), full-time employees (AOR 0.227, p=0.019), a recent encounter with an AEFI (AOR 3.087, p=0.007) and being trained on AEFI (AOR 5.54, p=0.011), reporting to elicit training (AOR 2.70, p=0.031) were predictors of routine reporting from health facility and reporting an encountered AEFI respectively. Overall, gender, employment status, being trained, and recent AEFI encounter to elicit training were predictors of AEFI reporting. To improve reporting of AEFI, there is a need to engage and train health workers on AEFI surveillance.

Keywords: Adverse events following immunization, Knowledge, Practice, Predictors, Reporting.

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