Introduction in this study, determinants of improved data consistency for routine immunization information at health facilities was measured to identify associated factors. Methods between June and August 2015, 1055 HFs were visited across 44 Local Government Areas in Kano state. We assessed data consistency, frequency of supportive supervision visits, availability of trained staff and attendance to monthly LGA RI review meetings. We compared RI monthly summary forms (MSF) versus national health management information system summary form (NHMIS) and vaccine management form 1a (VM1a) versus HF vaccine utilization summary monthly summary (HFVUM) for consistency. Data consistency at HF was determined at <+10% between number of children reportedly immunized, and doses of vaccine opened using 3 antigens (BCG, Penta and Measles). Levels of discrepancy <10% were considered as good data consistency. Bivariate and multivariate analysis used to determine association. Results data Consistency was observed in 195 (18.5%) HFs between (MSF vs NHMIS) and 90 (8.5%) HFs between (VM1a vs HFVUM). Consistency between MSF vs NHMIS was associated with receiving one or more SS visits in the previous month (p=0.001), data collection tools availability (p=0.001), recent attendance to monthly LGA RI review meeting and availability of trained staff. Data consistency between VM1a form and the HF VU summary was associated with a recent documented SS visit (p=0.05) and availability of trained staff (p=0.05). Conclusion low level of data consistency was observed in Kano. Enhanced SS visits and availability of trained staff are associated with improved data quality.
BACKGROUND: The District Health Information System (DHIS2) is a modular, cloud-based data management system designed for use in integrated health information systems. In Nigeria, it serves as the repository for routine health data, including measles. A first dose of measles is given routinely in most countries, however, for a country to include a second dose of measles in the routine immunization schedule, it must meet certain criteria set by the World Health Organization (WHO). Unfortunately, Nigeria falls into the category of countries that haven’t met the criteria. Despite this, MCV2 data can be seen on the DHIS2 platform. Data from DHIS2 also shows that Gombe State has the highest number of health facilities that reported MCV2 data at least once from 2015 to 2017. The aim of the study was to determine the reasons for the MCV2 reporting on DHIS2 platform for Gombe State. METHOD: We conducted a cross-sectional study among health workers in selected health facilities and LGA RI Officers at the LGA level in Gombe State. Health facility registers were reviewed, and data consistency was ascertained. We reviewed and conducted secondary data analysis of MCV2 data for Gombe State from January 2015 to December 2017. RESULTS: Of the 22 health facilities assessed, 14 health facilities (12 public and 2 private) reported offering MCV2 during the health facility-level interviews. At the LGA level, 5 LGAs out of the 11 LGAs reported during the LGA-level interviews that a second dose of measles is part of the RI schedule in their respective LGAs. For the 6 LGAs that reported not offering a second dose of measles as part of the RI schedule, 3 LGAs identified data entry error as the possible reason for having MCV2 data in the DHSI2 platform while the remaining 3 LGAs reported that the MCV2 data in the DHIS2 platform can be attributed to recording children who didn’t receive a first dose of measles at 9 months but received at 18–23 months as second dose of measles. CONCLUSION: Data entry error and knowledge gap on how to record measles data were identified factors responsible for MCV2 data on the DHIS2 platform. There is a need for targeted interventions towards improving the quality of RI data in Nigeria.
Routine immunization coverage in Nigeria is suboptimal. In the northwestern state of Sokoto, an independent population-based survey for 2016 found immunization coverage with the third dose of Pentavalent vaccine to be 3%, whereas administrative coverage in 2016 was reported to be 69%. One possibility driving this large discrepancy is that administrative coverage is calculated using an under-estimated target population. Official population projections from the 2006 Census are based on state-specific standard population growth rates. Immunization target population estimates from other sources have not been independently validated. We conducted a micro-census in Magarya ward, Wurno Local Government Area of Sokoto state to obtain an accurate count of the total population living in the ward, and to compare these results with other sources of denominator data. We developed a precise micro-plan using satellite imagery, and used the navigation tool EpiSample v1 in the field to guide teams to each building, without duplications or omissions. The particular characteristics of the selected ward underscore the importance of using standardized shape files to draw precise boundaries for enumeration micro-plans. While the use of this methodology did not resolve the discrepancy between independent and administrative vaccination coverage rates, a simplified application can better define the target population for routine immunization services and estimate the number of children still unprotected from vaccine-preventable diseases.
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