Conference abstract

Assessment of routine immunization data quality in Oyo State, Nigeria, 2016

Pan African Medical Journal - Conference Proceedings. 2018:8(36).28 Dec 2018.
doi: 10.11604/pamj-cp.2018.8.36.618

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Keywords: Routine immunization, data quality, district health information system
Opening ceremony

Assessment of routine immunization data quality in Oyo State, Nigeria, 2016

Clara Oguji1,&, Saheed Gidado1, Adamu Sule1, Adekunle Akerele1, Joel Adegoke1, Olasoji Fasogbon1, Ramatu Obansa1, Babatunde Alabi1, Esther Ayandipo1, Nnamdi Usifoh1, Bidemi Adeoye1, Ndadilnasiya Waziri1

1African Field Epidemiology Network (AFENET), Nigeria

&Corresponding author
Clara Oguji, Africa Field Epidemiology Network, Asokoro, Abuja, Nigeria

Abstract

Introduction: quality routine immunization (RI) data is essential for proper planning and effective decision making. In February 2016, RI module of DHIS 2 was implemented in Oyo State to improve RI data quality and strengthen data management. Prior to its implementation, RI data were reported via administrative methods characterized by good reporting rate but poor quality. In November 2016, we assessed the quality of RI data in the State to determine if there was improvement in RI data quality and identify remaining gaps.

Methods: we used a checklist to assess data quality that had been previously piloted in two states. We selected eight Local Government Areas (LGA) based on good and poor RI performance criteria. Frequencies and proportions were developed using a scatter plot diagram to compare for consistency across selected tools using penta3 and measles vaccine administered.

Results: out of 16 health facilities visited, only 5 (31%) had 100% consistency between the tally sheet and the health facilities immunization monthly summary form, while 8 (50%) had 100% when compared to the NHMIS form. Ten (63%) of health facilities showed 100% consistency with data between DHIS2 platform and NHMIS forms. The Study indicated that the level of inconsistency of Routine immunisation data is higher at the health facility than in the LGA. Gaps identified were poor supportive supervision, minimal capacity gaps and unavailability of updated RI data tools. The quality of reported data remains a concern among stakeholders, there is therefore a need to strengthen data management. Supportive supervision remains critical in improvement of data quality.

Conclusion: this study has led to a data quality improvement study in the State. Assessment of RI data quality is very important and should be considered as routine practice.