Amalia Benke, MPH, Health Scientist, Global Immunization Division
Amalia Benke, MPH, Health Scientist, Global Immunization Division

Frontline health workers have incredibly tough jobs. Almost always they have competing priorities, with only a limited number of resources at their disposal. These are the doctors, nurses and support staff who work at the point of care. These are the people who deliver our babies, help keep us healthy, and heal us when we are sick.

Because health workers have so many competing priorities, certain less visible tasks –like recording and reporting of health data—can slip through the cracks. When people don’t know where they fit into the bigger picture, time-consuming and tedious tasks like recording vaccine doses in a child health register can seem trivial. They might not realize how the data they collect and report are used to monitor, evaluate, and improve health services and why the accuracy of the data is so important.

The health of a population is supported by strong health systems. Reliable, high quality data are needed in order to allocate resources and make strategic decisions about program planning and policy development. . Without high quality data, kids can miss out on essential, basic preventive care like vaccines. Just over 50% of children in Uganda are fully vaccinated, leaving many of them and their communities vulnerable to preventable but deadly diseases.

Uganda vax coveragePoor quality data can lead to a multitude of problems for health programs. For example, if the number of vaccines given in a certain month at a health facility is under-reported to the district office responsible for vaccine forecasting and purchasing, that health facility is likely to experience stock outs of essential, life-saving vaccines. In addition, if data are recorded poorly at the health facility level, it’s impossible to have high-quality data at all other levels of the health information system.

It is often said that the first step in improving something is to admit that there is a problem. But how do you know if you have a problem with data quality? In 2013, Uganda’s Ministry of Health (MOH) conducted a Data Quality Self-Assessment to better understand the causes of poor immunization data quality in the country. The self-assessment provided several recommendations on how to improve the quality of immunization data. To help address these challenges, CDC partnered with WHO-Uganda, UNICEF, the Uganda EPI program (UNEPI), the MOH and other stakeholders to develop the Data Improvement Teams (DIT) strategy.

What makes a good Data Improvement Teams mentor?

  • A good mentor must be able to provide SMART (Specific, Measurable, Achievable, Realistic and Time-bound) recommendations that are immediately actionable, and
  • She or he must be able to communicate feedback to health workers and district staff in a constructive way that fosters mutual trust and respect.

Through the DIT strategy, Makerere University School of Public Health students and district staff involved with disease detection, immunization, and biostatistics are trained by CDC and partners. The objective of the interactive, hands-on training is to give DIT trainees the capacity and tools needed to improve immunization data quality through mentorship and oversight. Then they are deployed for five days to assess immunization data quality at health facilities and the district health office using a simple action-oriented approach to providing mentorship to local staff and recommendations on improving data quality.

The purpose of doing an assessment at each health facility and district health office is to demonstrate to frontline health workers and district staff where they fit into the larger health puzzle and why their recording and reporting are essential contributions to program planning and policy development.

DIT districts mapIn November 2014, the DIT strategy first launched in Soroti and Hoima Regions (see map), and 13 teams were trained and sent to work at the district level. To date, DITs have visited and provided mentorship to health workers at more than 1500 health facilities in Uganda and staff at all 56 districts. Depending on the challenges identified, mentorship can focus on topics ranging from data recording, reporting, analysis, interpretation and use.

Accurate recording of data is the first step to improving immunization data quality and enables the EPI program to make programmatic decisions based on reliable data. Data should be used at all levels of the system to make evidence-based decisions and take action, from the lowest to highest levels of the system. When data only get reported upward, opportunities can be missed to immediately improve access to and utilization of health services at the health facility level. For example, if health facility staff analyze the health facility data using monitoring charts and update them regularly, they are better able to determine how many children have missed scheduled vaccinations and can determine how to reach unvaccinated children.

The DIT strategy aims to not only identify challenges related to data quality at the health facility and district level, but also to determine the root causes of data quality issues. What we’re finding is that data quality issues stem from a variety of different causes – from lack of knowledge on how to correctly record immunization data, to not having enough standard data collection tools available, to not having enough resources to physically send the monthly report to the district level (sometimes health facilities are very far away from the district office). There’s no magic bullet to improve data quality, and it’s important to understand the root causes in order to develop targeted, specific and immediately actionable recommendations. Emphasizing the importance of every person’s contribution is also critical; being part of such a large health system, it’s easy to feel lost and unimportant.

The need for high-quality data has never been greater. At the end of the day, improved data quality means that mothers, children, and families will have better access to improved health services. With the DIT strategy only partially implemented, it’s difficult to quantify any immediate results. However, from on-the-ground experience, I can say that we are making progress – with all credit due to the men and women who fully dedicate themselves to improving public health.

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3 comments on “Data Matters”

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    Well said Amalia! Great job! And congratulations! You shared my sentiments and underlined my gripes. Truly: if we as EPI managers are given recognition to deserve the luxury of a few more hands to deal with mundane things such as admin and peronnel administration, we’ll be able to perform miracles through mentorship: that takes most time, energy and tenacity! How about writing on staffing norms next time? Warm regards, Johann (EPI Manager: South Africa)

    Amalia,
    Very good article stressing not only the importance of data, but how we can improve our practices in documenting and data quality.
    I would also ask that the discussion include First Responders. The EMT’s and Paramedics who are truly on the ground and on the front lines. We are notoriously poor at documentation and data gathering and our quality could use some help too!
    I know the direction of your article, and work, is immunization based, but in many countries First responders are capable and utilized in the delivery of vaccines. In my experience, delivery of the skill is much easier than the documentation! In countries where the health care network is thin or threatened, consider the use of EMT’s and Paramedics for the delivery of routine, basic health needs, teach them about quality data and accurate documentation and help everyone achieve the goals of improving the health of local populations!
    Just my $.02!
    Nick

    This is an excellent article. Without good quality of data, it is very tough to make important decision for effective intervention. DQSA is one of the tolls for improving data quality and it also helps to train health workers at different level.

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Page last reviewed: May 11, 2021
Page last updated: May 11, 2021
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