Data Improvement Plan for Nigeria

1.0 Introduction

Nigeria operates a federation comprising of 36 states and a Federal Capital Territory (FCT) Abuja; within these states are 774 LGAs with 9,565 wards, Nigeria shares most of the social and economic problems associated with developing countries. The 36 states and FCT are grouped into six geo-political zones, the South-South, the South-East, the South-West, the North-East, the North-West and the North-Central zones. Nigeria has an estimated population of 204,428,612 for 2018 with a fertility rate of 5.7 and an annual population growth rate of 3.2 %; a birth cohort of 4% infants and surviving infants of 3.8%. The health care system in Nigeria consists of both public and private sectors with the Public health care system made up of a tiered to reflect the three levels of government, Federal (tertiary health care), the State (secondary health care and the Local Government Areas (LGAs - Primary Health Care). Nigeria National Immunisation Coverage Survey, 2016/17 final report shows that:

• only 23% of children age 12-23 months have received all vaccinations recommended in the National immunization schedule by their first birthday with the South West zone is consistently showing high immunization coverage for the basic and complimentary antigens - averaging 56% (FIC), the North-West zone falls behind with FIC of 9%.
• National coverage by antigen (card and recall) are as follows: BCG 53%; Hepatitis B birth dose 40%; Oral polio virus vaccine birth dose (OPV0) 47%; (OPV1) 56%; OPV3 39%; Pentavalent 1 vaccine 49%; Pentavalent 3 vaccine 33%; Yellow Fever 43%; and Measles 42%.
• Data of vaccination coverage trend since 2006 for some key vaccination indicators shows a drop-in coverage of most vaccine antigens and decline in the proportion of children fully immunized. The national immunization coverage rates shows an increase between 2006 to 2010 and then a decline in the 2016/17 for all antigens coverages, however, the Penta1 – Penta 3 drop-out rate was high in 2006 and 2016/17.
• 51% urban as against 25% rural children recived vaccination, the NICS 2016/17 shows that 5 percent of children aged 12 to 23 months are fully immunised in the poorest wealth quintile while 48 percent of children are immunised in the richest wealth index. Pentavalent vaccination increased across the wealth quintiles with only 20 percent of children in the poorest quintile receiving the first pentavalent vaccine contrasted with 82 percent of eligible children in the richest wealth quintile.

Fig. 1 National vaccination coverage NICS 2016/17

Fig. 2 Trends of vaccination coverage NICS 2006, 2010 and 2016/17

Historically there has been a disparity between vaccine coverage figures reported through, UNICEF-WHO estimates, administrative records and coverage figures from household based surveys with administrative records often reporting higher coverage than household based surveys. This still remains a challenge and in the area of RI data management, it has been noted that Health Workers (HWs)s are not properly trained in the use of data tools, analysis of data and using data for action. High attrition rate of HWs also contributed to this as trained HWs are frequently transferred to other sections where the skills acquired would not be useful. The Health Facilities(HFs) frequently experience data tool stock outs due to lack of financing and distribution gaps. In addition, there is also lack of regular feedback from the state to the LGA and from the National to state level, Regular feedback on data analysis from the LGA to Health facility level is also not existent. However, there is no appreciation on the use of data by the facility and LGA staff One of the most important challenge is the ownership of RI data.

RI data at the national level is compiled by partner’s agency (WHO) using the DVD MT tool that generates RI coverage’s from all Health Facilities offering RI and there is no system for tracking defaulters in most health facility’s catchment areas. Nigeria usually conducts one national Data Quality Self-Assessment (DQS) in the 1st quarter of every year. From the DQS findings in the last five years, it is obvious is that the correction factor (CF) of reported RI data had improved in the last 5 years as demonstrated by a gradual increase from CF of 0.743 in 2007 to a CF of 0.95 in 2011. The correction factor of 0.966 in 2011 means that reported coverage for all antigens was approximately 97% correct. However, there is still a need to further improve RI data quality, completeness and the use of data for action especially at the health facility levels so as to improve reporting on coverage and assist decision makers for better planning of routine immunization.

2.0 Diagnosis

For effective management of vaccination programs, accurate coverage information is required to inform planning, logistics and health systems adjustments. Measuring vaccine coverage is therefore not only critical for benchmarking progress made but also for formulating immunization strategies. Traditionally, vaccine coverage has been measured from reports of administrative records and from population-based surveys on vaccination status. Incompleteness and poor timeliness of administrative data limit its usefulness in informing the performance of routine immunization. Furthermore, unreliable denominator (number of children within the vaccination age cohort) estimates further diminish the utility of administrative data in many countries with large inter-census intervals.

In carrying out a system analysis the following are the limitations or bottlenecks to data availability, quality and use in relation to:

2.1 Governance

Not all the key players that use immunization data that are integrated when it comes to immunization data gathering and use as the partners collect data based on their need and use, using different tools.

2.2 Funding

Funding is virtually non existance when it comes to funding for data for immunization as it it is funded by partner agencies and tailored to there specific need and use of data the are collecting

2.3 Roles and Responsibility

Data management is under the realm of the HMIS in the department of planning research and statistics and the M/E officer and the EPI program staff and HF staff are left out especially in the feedback. They have no access to the data in the HMIS/DHIS database

2.4 Denominator

The denominator been used for immunization coverage is the projected total population from the 2006 country census and most times do no match the survey estimates leading to incosistent denominator data especially at the public facilities; with immunization data not generated from the private facilities

2.5. Processes of data collection and verification

There is an established data flow from the facilities upto the National level, with available data collection tools and retrival system but there are inactive data validation teams with some HWs do not use the data collection tools at the facility level as they are not properly trained to enter data directly into the DHIS systm due to non availablity of equipment and the necessary tools.

2.6. Data Collection, Reporting Tools

The Health Facilities(HFs) frequently experience data tool stock outs due to lack of financing and distribution gaps. In addition, there is also lack of regular feedback from the state to the LGA and from the National to state level, Regular feedback on data analysis from the LGA to Health facility level is also not existent. However, there is no appreciation on the use of data by the facility and LGA staff One of the most important challenge is the ownership of RI data.

2.7. Data use

In the area of RI data management, it has been noted that Health Workers (HWs)s are not properly trained in the use of data tools, analysis of data and using data for action. High attrition rate of HWs also contributed to this as trained HWs are frequently transferred to other sections where the skills acquired would not be useful.

3.0 Root cause analysis

The root cause analysis on the lack of use of data for decision making using the ‘but why’ step by step analysis is as shown in the diagram below:

                                Fig.3 Root cause analysis using ‘But Why’ method

4.0 Actionable Recommendations

• Deployment of simple paper based dashboards and SOPs with relevant indicators for each level with feedback mechanism and provision of appropriate equipment and data capturing and analysis tools at all levels
• Build capacity of at HF and LGA HW for use of data analysis tools through training, mentoring and supportive supervision on the use of analysis tools with SOPs.
• Conduct a workshop on the importance and the use of different immunization data collection tools at HF and LGA levels
• Perform assessment to determine current HR gaps and ensure availability of adequate human resources at all levels andesure new staff are equiped with pre-service training needed analytical skills and the capacity for the job requirements for key positions.
• Build standard immunization dashboards with the information that is most relevant for each level, and make these available in the used electronic systems, or through other ways of dissemination.

5.0 Activities