Added Value of Electronic Immunization Registries in Low- and Middle-Income Countries: Observational Case Study in Tanzania

"As EIRs [electronic immunisation registries] are introduced and scaled in low- and middle-income countries, implementers and researchers should continue to share real-world examples and build an evidence base for how EIRs can add value to immunization programs, particularly for innovative uses."

Electronic immunisation registries (EIRs) aim to improve the immunisation delivery system to reach every child by supporting more effective, efficient, and data-driven care. The Government of Tanzania introduced the Tanzania Immunization Registry (TImR) in 2017, and the system has since been rolled out in 3,736 facilities in 15 regions. The aims of this study are to conceptualise the ways EIRs can add value to immunisation programmes (beyond measuring vaccine coverage) and to assess the potential value added, using EIR data from Tanzania as a case study.

This study comprised 2 sequential phases. First, a list of ways EIRs can potentially add value to immunisation programmes was developed through semistructured web-based interviews with 7 stakeholders from November 2019 to January 2020. Immunisation barriers and potential EIR-based solutions include:

• Lack of understanding about what drives immunisation demand
  • EIR data can identify un- or underimmunised children and explore drivers of their vaccination status.
  • EIR data can be used to analyse at what point children drop out of the continuum of care.
  • EIRs can have embedded decision support to guide health workers in delivering tailored messages or services to increase acceptance and uptake.
• Overly complex processes
  • EIRs can be designed to streamline data capture and reduce the burden of data entry.
  • EIRs can be designed to meet decision-making needs for end users.
• Issues with skill level and availability of human resources
  • Access to data through EIRs can empower and motivate users and strengthen agency.
  • If EIRs are designed with individual health worker log-ins, EIRs can track human resources based on active health worker profiles.
  • EIR data can identify error rates of individual health workers and link them to additional training or supportive supervision.
  • EIRs can have embedded training resources or capacity assessments.
  • EIR data can be used to forecast service delivery needs by facility or district to optimise the distribution of human resources and session times.
• Geographic and social barriers to access
  • EIR data can identify un- or underimmunised children to explore whether they are concentrated in certain geographic areas and if they have shared demographic characteristics to inform targeted outreach.
  • EIRs can track an individual's vaccinations across public and private sector facilities.
• Microplanning challenges
  • EIRs can capture more accurate, timely, and complete denominators to inform microplanning.
  • EIR data can be used to understand population movement or health-seeking behaviours to inform microplanning (e.g., how common it is for children to move between multiple facilities).
• Inadequate introduction of new vaccines: EIR data on current vaccine delivery can be used to forecast the necessary stock and human resources to introduce new vaccines.
• Inadequate governance structures and capacities
  • The process of designing and introducing an EIR can help clarify and document governance structures related to immunisation data.
  • EIR data can provide more accurate denominator estimates to inform costing and budgeting for the EPI.
• A lack of resilience in leadership
  • EIRs can encourage continuous quality improvement (QI) by highlighting trends, outliers, or patterns that may require adaptive management.
  • EIRs provide more timely, detailed data compared with traditional paper-based reporting, which enables timely, responsive action from leaders.
  • EIRs can provide a platform for remote, web-based supportive supervision.
• Gaps in information systems
  • EIRs can show which facilities are entering data or not and factors associated with reporting.
  • EIRs can be designed to mimic health worker workflows to streamline data collection and reporting practices.
• Poor quality of stock data from health facilities
  • EIR service delivery data can be triangulated to see how consistent it is with vaccine stock data and to forecast stock needs.
  • EIR service delivery data can be used to inform decisions about vial size.
• Poor quality of service delivery
  • EIRs can identify service delivery patterns to optimise health worker allocation and session timing to match demand.
  • EIRs that capture check-in time and vaccination time can calculate patient wait times.
  • EIRs can identify missed opportunities for vaccination (MOVs).
  • EIRs can include stock reorder alerts to reduce stockout frequency.
• Vaccine safety and effectiveness: EIR data triangulated with patient-level data on adverse events following immunisation (AEFIs) or surveillance data can answer questions about the effectiveness of vaccines given at different times.

Second, the added value was evaluated using descriptive and regression analyses of TImR data (958,870 visits for 559,542 patients from 2,359 health facilities) for a prioritised subset of programme needs - that is, for the 4 areas prioritised through stakeholder interviews:

1. Denominators and population movements, including patient movement between facilities or geographic areas for care: EIRs can simplify tracking patients who seek care at multiple facilities, decreasing the likelihood of missed or redundant doses. In the case of Tanzania, analyses revealed that few patients sought care outside their assigned facility (44,733/810,568, 5.52% of applicable visits); however, this figure varied by: region; facility urbanicity, type, ownership, patient volume, and duration of TImR system use; density of facilities in the immediate area; and patient age. According to the researchers, these insights can help inform resource allocation.
2. MOVs, including their frequency and any associated characteristics: Analyses showed that MOVs were highest among children aged under 12 months (215,576/831,018, 25.94% of visits included an MOV and were applicable visits); however, there were few significant differences based on other individual or facility characteristics. This information can be used, for example, to identify providers and facilities with higher rates of MOVs for supportive supervision or refresher training or identify areas with high rates of vaccine hesitancy for outreach campaigns. In addition, EIRs can provide insight into the mechanisms behind MOVs, such as vaccine-specific hesitancy.
3. Continuum of care, including which children drop out and when in the vaccination schedule: Nearly half (133,337/294,464, 45.28%) of the children aged 12 to 35 months were fully vaccinated or had received all doses except measles-containing vaccine-1 of the 14-dose under-12-month schedule, and facility and patient characteristics associated with dropout varied by vaccine. As reported here, understanding which vaccine doses and child and facility characteristics are associated with failure to complete a vaccine sequence or the full vaccine schedule can help inform service provision, training, and QI measures at the facility, regional, and national levels.
4. Continuous QI, including trends or outliers in data quality or service delivery: The analysis showed that most quality issues (e.g., MOVs) were concentrated in less than 10% of facilities, indicating the potential for EIRs to target QI efforts - thereby improving the quality of care and increasing improvement in intervention effectiveness.

"These analyses were designed to show the potential of EIRs to allow for a more nuanced, rapid, and cost-effective evaluation of vaccine program data to facilitate data use for decision-making....Designed well, EIRs can democratize immunization data. However, they require the necessary support to function effectively."

In conclusion: "EIRs have the potential to add value to immunization stakeholders at all levels of the health system. Individual-level electronic data can enable new analyses to understand service delivery or care-seeking patterns, potential risk factors for underimmunization, and where challenges occur. However, to achieve this potential, country programs need to leverage and strengthen the capacity to collect, analyze, interpret, and act on the data."