Lessons learned from early implementation of the Growing Expertise in E-health Knowledge and Skills (GEEKS) program in Nigeria, 2019 - 2021.

Introduction: the Growing Expertise in E-health Knowledge and Skills (GEEKS) program is an applied apprenticeship program that aims to improve informatics capacity at various levels of the national health system and create a sustainable informatics workforce. Nigeria adapted the GEEKS model in 2019 as a mechanism to strengthen data quality and use of routine immunization (RI) and vaccine-preventable disease (VPD) surveillance data among Expanded Programme on Immunization (EPI) staff. Since the start of the GEEKS-EPI program, there has not been a formal assessment conducted to measure the extent to which GEEKS-EPI has been able to build local informatics workforce capacity and strengthen RI and VPD surveillance (VPDS) data quality and use in Nigeria. Methods: we conducted a qualitative assessment to inform the extent to which GEEKS-EPI has been able to build informatics skillsets to enhance local workforce capacity, foster collaboration across government agencies, and create a sustainable informatics workforce in Nigeria. In-Depth Interviews (IDIs) and Focus Group Discussions (FGDs) were held with GEEKS-EPI supervisors, mentors, and mentees from previous GEEKS-EPI cohorts. Results: while there were challenges reported during early implementation of the GEEKS-EPI program in Nigeria, particularly early on in the COVID-19 pandemic, participants and supervisors reported that the fellowship provided a framework for building a sustainable RI and VPDS informatics workforce through regular mentorship, peer-to-peer exchanges and Subject Matter Expert (SME)-led trainings. Conclusion: lessons learned from early implementation of GEEKS-EPI in Nigeria will help to inform its implementation in other countries, where strengthened national RI and VPDS informatics capacity is the primary objective.

Experience and findings from surveillance peer review in Nigeria, August 2017-May 2019.

Introduction: acute flaccid paralysis (AFP) surveillance is the gold standard of the Global Polio Eradication Initiative (GPEI) for detecting cases of poliomyelitis and tracking poliovirus transmission. Nigeria's AFP surveillance performance indicators are among the highest in countries of the World Health Organization (WHO) African Region. The primary AFP surveillance performance indicators are the rate of non-polio AFP among children and the proportion of timely, adequate specimen collection. The surveillance working group of the National Emergency Operations Centre assessed the quality of AFP surveillance data in some reportedly high-performing states. Methods: we conducted a retrospective review of AFP surveillance performance indicators in Nigeria for 2010-2019. We also reviewed data in reports from four groups of surveillance peer reviews and validation visits (conducted by in-country GPEI partners) during August 2017-May 2019 in 16 states with high primary AFP surveillance indicators; the validation visits reviewed clinical information and the dates of specimen collection and onset of paralysis with caretakers. Results: there were consistently increasing AFP surveillance primary performance indicators during 2010-2016, followed by declines during 2017-2019. From the data for 16 states with peer reviews conducted from August 2017-May 2019, overall concordance of reported and "true" (validated) AFP indicator data in peer review investigations was highly variable. True AFP concordance ranged from 58%-100%, and stool timeliness concordance ranged from 56%-95%. The most common clinical causes of reported AFP cases that were not true AFP were spastic paralysis, malaria, sickle cell disease, and malnutrition. All the states that participated in peer reviews developed surveillance improvement plans based on the gaps identified. Conclusion: Nigeria has highly sensitive AFP surveillance according to reported primary AFP performance indicators. The findings of peer reviews indicate that the AFP surveillance system needs to be strengthened and well-supervised to enhance data quality.