By Daines Mgidange and Joseph Hakili, Change Management Committee Members, BID Initiative Tanzania
Sep 7, 2017
There are two important indicators for measuring the efficacy of immunization programs. Immunization coverage can be defined as the percentage of fully immunized children in relation to the total number of surviving infants in the target population. Dropout rate is the percent difference between those who start the immunization schedule and those who complete it. Children who don’t complete their full vaccine series, or “defaulters” as they’re commonly known, can contribute to low immunization coverage.
Defaulter tracing is among the challenges the BID Initiative aims to address using data use and data quality interventions. In the past, paper registries typically included rows and rows of handwritten entries, making it difficult to search for children, identify any recent vaccines they’d missed, and follow up with their families in a timely manner. In recognition of these data-related challenges, the BID Initiative is working with the governments of Tanzania and Zambia to introduce a range of tools and practices to improve data quality and data use. Barcodes on child health cards, for instance, provide a unique way to identify children and an electronic immunization registry creates and automatically updates vaccine schedules, so health workers know when to expect children in their clinic.
Oloipiri Dispensary is among the facilities that are using Tanzania’s Electronic Immunization Registry (TImR) to record and analyze immunization information. Located in Ngorongoro district in Arusha, Tanzania, the district borders Kenya and is comprised of mostly pastoral communities. Because of its porous border and high rates of migration, many families struggle to make immunization clinics. Children are, therefore, more likely to default on their needed immunizations. Recently, we caught up with Nurse Iddi Ally Mwambuly of Oloipiri Dispensary to understand how better data and greater visibility into that data is helping to identify defaulting children and close Tanzania’s immunization gap.
What information do you have difficulty finding with the paper system?
It hasn’t been easy to search different books for children who have not appeared for a scheduled immunization. We see 80-90 children per month, and must check their nutritional status, vaccinate them, and provide counseling. We also capture their records in four different tools that can’t be easily visualized until the end of the month, when compiling our monthly reports. For example, we have data in our child register books, but can’t easily pinpoint defaulters because it takes time to go through each record, page by page, and to identify missed doses for each child.
Have you seen any improvement in the access and use of information with the introduction of TImR?
TImR captures all relevant data on one device and generates automated reports detailing our facility’s immunization coverage, defaulter lists, dropout rates, vaccine stock status, and other data elements. The easy availability of this data and data visualization features have motivated us to use this information to improve our performance.
What decisions have you made, that have been informed by this data?
One of the decisions that we have started implementing is to regularly and more systematically trace defaulters. We decided to start using the electronic defaulter report that generates lists of children who missed a recent vaccine. A report is easy to access and understand and contains enough information for follow-up. Each defaulter list includes the name of the child, his or her mother and village, and details which vaccine the child has missed. It also lists the mother’s phone number. We access this report every two weeks to monitor the trend.
After identifying defaulters, what do you do?
After initial training, we now go through the immunization registry and data use guides for reference. The process starts with verifying our information so as to identify true defaulters, in case there were children who were vaccinated and their record was not updated in the system. After verification, we make arrangements for follow-up. If we can’t reach someone, we share their names with village and community leaders, who can help to locate patients. Community leaders also have insight into whether a family has moved away.
What changes have you seen, since you started using TImR and other data use interventions to improve your facility’s performance?
Defaulter numbers have been decreasing. Oloipiri has decreased defaulters by 50%. We have traced 46 out of 48 defaulters among children registered in the system. In July, we had nine defaulters. Four of them have been immunized and we are following up with the other five now. At a district level, vaccine defaulters have decreased from 41% between January and April of 2017, to 27% between May and July of 2017. Assistant District Immunization and Vaccine Officer Mr. Thomas Nchimbi attributed the decline to simplified, automated defaulter tracing and TImR.
With system data and the help of village and community leaders to trace defaulters, we have started seeing an improvement in our immunization coverage. Having accessible, more accurate, and more complete data has helped us to use this information to improve our services and reach every child within our service area.