The Data Quality Breakthrough: How Immunization Programs in Bihar and Uttar Pradesh are Setting New Standards

Concerns about the quality and use of immunization and vaccine-preventable disease (VPD) surveillance data for decision-making have been highlighted on the global agenda for more than two decades. Countries need quality data for immunization program management and decision-making and to meet the ambitious regional and global vaccine coverage and disease elimination goals, such as those that were outlined in the Global Vaccine Action Plan (GVAP). Looking forward, the emphasis on data quality and use will grow and become even stronger as emphasized under the Immunization Agenda 2030 (IA2030), which sets a global immunization strategy to achieve a “world in which everyone, everywhere, at all ages, fully benefits from vaccines for their health and well-being” and which highlights “data-guided” as one of its four core principles. 

In the populous Indian states of Bihar and Uttar Pradesh (UP), the need for reliable information to take action has been programmatically important as they jointly account for nearly nine million live births every year (~30% of India’s annual birth cohort) and their infant mortality rates continue to remain above than the national average rates. (Bihar: 23, Uttar Pradesh: 37, India: 25 per 1000 live births) (SRS 2023). As the two states are committed to improving immunization coverage rates, taking a data-driven approach to health care planning, the stakeholders have been undertaking initiatives that seek to strengthen data availability, quality, and use culture. With the technical support from the William J. Clinton Foundation (WJCF), the government developed and implemented two easy-to-use, decentralized technology-enabled solutions: the Health Management Information System (HMIS)-based Data Validation Tool (DVT) and the Routine Immunization (RI) Coverage Review Tool. These tools have now been successfully scaled across all 75 districts of UP and 38 districts of Bihar, strengthening data management and review processes. By enabling precise monitoring, validation, and use of immunization data, they have contributed to better program implementation quality as well as effective, decentralized decision-making.  

The Challenge: Closing Data Gaps for More Effective Immunization Planning 

The immunization program at the state level in India has continually relied on the Health Management Information System (HMIS) for coverage rate estimates and planning programmatic improvements. However, while HMIS is not an electronic immunization registry and can only help estimate the aggregate number of under-immunized children, the intricacies in the HMIS data recording and reporting process have proven a challenge for the program officials to appropriately navigate the process of taking data-driven decisions to improve antigen specific coverages including Full Immunization Coverage (FIC). Previously executed diagnostic assessments indicated several root causes, including inconsistent use of Monthly Progress Report formats, limited data validation for data quality at the sub-district levels, and an overall limitation in the data review mechanism resulting in poor data utilization. These factors posed systemic challenges that affected both program performance as well as governance and accountability. 

The Solution: Simple Tools driving Smarter Reviews  

To address these challenges, we developed two Excel-based tools.  

1. The HMIS Data validation tool systematically checks administrative data for completeness, correctness, and probable reporting errors across more than 50 immunization and related parameters.  
2. Complementing this, the RI Coverage Review Tool provides detailed data insights about the programmatic performance at district, block, and health facility levels, to enable health officials prioritize low-coverage areas and minimize antigen-specific dropouts. 

Implementation and Scale-Up: Building a Data-Driven Ecosystem

The tools were developed and deployed in 2021, after which their implementation began with a pilot across 100 Aspirational Blocks (ABs) in UP and seven districts (136 blocks) in Bihar, combining a larger coverage of data analysis with the adult learning methods-based capacity building supplement to strengthen the overall analytical and decision-making skills of program officials. We then helped all the 75 District Immunization Officers (DIOs) in UP and 38 DIOs in Bihar get familiar with the use of these tools for running data quality checks, analyzing immunization coverage rates for their respective geographies/health facilities, and preparing or updating microplans.

The institutionalization of the Data Validation process in the current workflow was initiated with the reactivation and regularization of Data Validation Committees (DVCs) and DVC meetings at both district and block levels. These meetings started focusing on the review of identified data quality issues and ensured their timely resolution.

By January 2025, 82% of target implementation districts had a functional District-level Data Validation Committee, and 63% of the target implementation districts had a Block-level Data Validation Committee. These committees now routinely review & flag data quality issues, track their resolutions, and ensure timely follow-ups, leading to a closed-loop self-reinforcing mechanism where data errors are systematically identified, verified, and resolved.

Demonstrable Impact: Transforming Data Quality

The tools’ implementation has yielded striking results across immunization data quality indicators. In UP’s 100 Aspirational Blocks (ABs), data completeness surged from 51% in FY2021–22 to 100% in FY 2024–25. Bihar’s 7 priority districts observed a similar trajectory in data completeness, jumping from 46% in FY2021–22 to 86% in FY 2024–25.

Data accuracy also improved significantly. In the 100 ABs of UP, the proportion of health facilities submitting accurate datapoints increased from 83% in FY 2020–21 to 100% by FY 2024–25. Bihar’s 7 districts also observed a strong progress in data accuracy, improving from 70% to 86% in the same period.

We also note a steep decline in data inconsistencies, indicated by Probable Reporting Errors (PREs) exceeding 10%. In FY 2020–21, focus geographies in both the states were facing significant data inconsistencies (80-100% health facilities with PREs). However, by FY 2024–25, we see the figures dropped to just 27% in 100 ABs of UP and to 47% in 7 districts of Bihar, driven majorly by the tools’ effectiveness in enhancing data reliability.

The results have been transformative.

• Data completeness in UP’s Aspirational Blocks increased from 51% in FY 2021–22 to 100% by FY 2024–25, while Bihar’s priority districts improved from 46% to 86%.
• Data accuracy rose sharply, with UP’s facilities submitting correct reports improving from 83% to 100%, and Bihar’s from 70% to 86% in the same period.
• Probable Reporting Errors (PREs) exceeding 10%, a key indicator of inconsistency, fell from 80–100% in FY 2020–21 to just 27% in UP and 47% in Bihar by FY 2024–25.

Real-World Impact: Swift Outbreak Response with Data-Driven Decisions

The true power of these tools was seen in 2024, during a measles outbreak threat in neighboring state of Madhya Pradesh. Concerned about the risk of measles spreading into UP’s bordering districts as well as historically low-coverage areas, use of these tools guided us for quick action.

Using the RI Coverage Review Tool, our team generated granular facility-level immunization insights within hours, enabling us to swiftly identify immunity gaps across the lowest-tier health facilities (sub-health centers). This enabled the immediate support to strengthen microplanning and field verification in high-risk districts. The very next day, we were able to deploy relevant directives and actions targeting specific villages and health facilities where low coverage was identified.

This rapid response underscored how real-time data can turn potential risk due to VPD outbreaks into opportunities for prevention and strengthening confidence in health systems while saving lives.

Sustainable Institutionalization

The tools, once a proven success, were formally launched by us in June and September 2024 during state-level immunization review meetings in UP and Bihar, respectively. This helped set the stage for a full-scale rollout across all 75 districts of UP and 38 districts of Bihar.

The institutionalization process strategically leveraged existing governance platforms, including Additional Divisional (AD) Health Review meetings, District RI Quarterly Review Meetings, Data Validation Committees (DVCs), and the Data Handlers’ training programs. Standard Operating Procedures (SoPs), guidelines and tools were handed over to functionaries along with handholding and capacity building support to local data champions to ensure a smooth process takeover. Moreover, to sustain the bolstered review framework for effectively utilizing these tools, dedicated funds from within the government’s budget-line have been proposed to be earmarked. We believe that this multi-pronged approach would ensure the tools to become an integrated part of the routine immunization program, generate sustained course-correction for data quality issues, and enhance the quality of program review which will continue to impart strength to our states’ immunization program for years to come.

The Way Forward

The progress achieved so far is encouraging, but maintaining this momentum demands continuous learning and adaptive strategies. The demonstrated success of these tools in the two states exemplify below mentioned learnings:

1. We must ensure the tools remain responsive to evolving user needs, which would help inculcate a sense of ownership among the users, and not just make it problem-centric but also promote bottom-up solutioningin the system.
2. Reinforcing Capacity Building for Data Handlers can ensure the new tools are accompanied with the right know-how to help make effective use of them.
3. Fostering strong collaboration and partnership helps innovate and bring in novel approaches to address root causes, thereby better efficiencies and optimal solutions.

Moving forward, we plan to focus on establishing robust data triangulation processes to alleviate gaps by reviewing data across multiple data systems, including HMIS, Electronic Vaccine Intelligence Network (eVIN), and U-WIN, and synchronize convergent and divergent immunization datapoints, which would help create a cohesive and real-time data ecosystem.

By enhancing data accuracy, streamlining reviews, and empowering local action, these tools should help develop a more resilient and agile immunization system — one capable of responding swiftly to challenges and ensuring every child receives life-saving vaccines.