Implementation of data triangulation and dashboard development for COVID-19 vaccine adverse event following immunisation (AEFI) data in Nigeria

• Published in: BMJ global health 2023-01, Vol.8 (1), p.e011006
• Main Authors: Shragai, Talya, Adegoke, Oluwasegun Joel, Ikwe, Hadley, Sorungbe, Temilade, Haruna, Aminu, Williams, Imoiboho, Okonkwo, Rita, Onu, Kenneth, Asekun, Adeyelu, Gberikon, Margaret, Iwara, Emem, Abimiku, Alash'le, Rufai, Ahmed, Okposen, Bassey, Gidudu, Jane, Lam, Eugene, Bolu, Omotayo
• Format: Article
• Language: English
• Subjects: Adverse Drug Reaction Reporting Systems; COVID-19; COVID-19 - prevention & control; COVID-19 vaccines; COVID-19 Vaccines - adverse effects; Data collection; Data management; Decision making; Disease control; Health systems; Humans; Immunization; Immunization - adverse effects; Information systems; Nigeria - epidemiology; Population Surveillance; Practice; Side effects; United States; Vaccination; Vaccines
• ISSN: 2059-7908; 2059-7908
• DOI: 10.1136/bmjgh-2022-011006

Nigeria began administering COVID-19 vaccines on 5 March 2021 and is working towards the WHO’s African regional goal to fully vaccinate 70% of their eligible population by December 2022. Nigeria’s COVID-19 vaccination information system includes a surveillance system for COVID-19 adverse events following immunisation (AEFI), but as of April 2021, AEFI data were being collected and managed by multiple groups and lacked routine analysis and use for action. To fill this gap in COVID-19 vaccine safety monitoring, between April 2021 and June 2022, the US Centers for Disease Control and Prevention, in collaboration with other implementing partners led by the Institute of Human Virology Nigeria, supported the Government of Nigeria to triangulate existing COVID-19 AEFI data. This paper describes the process of implementing published draft guidelines for data triangulation for COVID-19 AEFI data in Nigeria. Here, we focus on the process of implementing data triangulation rather than analysing the results and impacts of triangulation. Work began by mapping the flow of COVID-19 AEFI data, engaging stakeholders and building a data management system to intake and store all shared data. These datasets were used to create an online dashboard with key indicators selected based on existing WHO guidelines and national guidance. The dashboard went through an iterative review before dissemination to stakeholders. This case study highlights a successful example of implementing data triangulation for rapid use of AEFI data for decision-making and emphasises the importance of stakeholder engagement and strong data governance structures to make data triangulation successful.