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ORIGINAL ARTICLE
Year : 2021  |  Volume : 21  |  Issue : 1  |  Page : 9-12

District health information system 2 routine immunisation dashboard: A tool for improving routine immunisation data quality in Katsina State, Nigeria


1 State Primary Health Care Agency, Katsina State, Nigeria
2 AFENET NSTOP, Katsina State Field Office, Katsina State, Nigeria
3 Department of Community Medicine, Ahmadu Bello University Zaria, Kaduna, Nigeria
4 AFENET-NSTOP, Nigeria Country Field Office, Abuja, Nigeria
5 World Health Organization, Katsina State Field Office, Katsina State, Nigeria

Correspondence Address:
Dr. J R Yahaya
Katsina State Field office, Africa Field Epidemiology Network, Katsina State
Nigeria
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/njhs.njhs_4_21

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Background: District Health Information System (DHIS) is a web-based electronic data capturing platform built on a framework of Health Management Information System (HMIS). In 2014, Nigeria adopted DHIS as the only government-approved electronic reporting platform for all HMIS data. In Katsina State, poor data quality has been identified to be a measure setback despite the robust data quality monitoring tools contained in the DHIS package and this had adversely affected the use of data for informed decision-making. Materials and Methods: Retrospective and prospective studies were conducted on routine immunisation (RI) data uploaded on the DHIS of Katsina State. These studies were carried out to determine the root causes of data quality issues in the state and to conduct field spot checks using predesigned Data Quality and Use Supportive Supervision (DQUSS) checklists. RI data uploaded on the DHIS2 for the period of January 2018 to December 2018 were downloaded and analysed for varying data quality issues. These data served as baseline data for prospective follow-up. The data quality issues were segregated by local government areas (LGAs) for purposive supervision visits. Data quality monitoring tools on the DHIS2 RI dashboard were used for monitoring these data quality issues. The LGAs were monitored overtime for the period of January 2019 to September 2019 through predefined indicators on the DHIS2 RI dashboard. Results: Training gap (odds ratio of 0.85 at 95% confidence interval) was identified to be the modal cause of poor data quality in the study area. A continuum of improved data quality was observed over time post conduct of DQUSS. Conclusion: It was concluded that persistence of RI data quality issues was attributed to inadequate quality supportive supervision in the state.


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