Module 1: Key HIV testing services data quality challenges and improvement approaches

High-quality HIV testing services (HTS) data are essential for understanding testing coverage, identifying gaps in linkage to care, and monitoring programme performance. However, HTS programmes face persistent data quality challenges due to testing across multiple sites and modalities, repeat testing (recommended for individuals at high risk of HIV acquisition) and confirmatory testing, as well as variation in data collection and reporting practices. Additionally, it is crucial for HIV programmes to differentiate between the number of individuals tested and the number of tests conducted. This distinction helps in understanding the true reach and impact of HTS programmes, and supports person centred monitoring and service delivery. In practice, failure to clearly distinguish between people tested and tests performed is a common data quality challenge across HTS programmes, often leading to misinterpretation of coverage, repeat testing patterns, and programme performance.

These challenges frequently result in data duplication, incomplete or delayed reporting, and inconsistencies between facility, subnational, and national data. In the absence of unique identifiers and interoperable systems, it is particularly difficult to distinguish between, repeat testing and individuals testing at multiple sites, increasing the risk of over- or under-counting and weakening confidence in reported HTS indicators. Reflecting these challenges, a WHO-convened technical consultation with strategic information and HTS experts, implementing partners, donors, and country programmes identified data duplication and consistency as priority issues for HTS programmes and as key focus areas for this HTS quality assessment and assurance toolkit to address. 

Key resources

Factors affecting data quality of HTS programmes

Potential solutions and approaches for data quality improvement

To address common HTS data quality challenges, a range of data quality improvement approaches can be implemented at facility, subnational, and national levels:

• Standardising data elements, collection, and reporting to ensure consistent definitions, disaggregations, and reporting formats across testing sites and data sources.
• Improving documentation of retesting, including clear recording of retesting reasons, dates, and outcomes, to distinguish new tests from repeat and confirmatory tests and reduce duplication.
• Strengthening training on data collection and management to support accurate recording, reporting, and handling of HTS data, including the use of paper‑based and digital tools.
• Building capacity for data analysis and enhanced data use, enabling routine review of HTS data to identify errors, correct inconsistencies, and inform service delivery and programme improvement.
• Using unique identifiers to support deduplication, longitudinal tracking of individuals across services, and more accurate counting of people tested.
• Investing in interoperability to enable secure data exchange between community‑ and facility‑based systems, reduce manual data entry, and improve data consistency.
• Using and integrating digital health tools, including electronic medical records, mobile data collection applications, and digital HIV adaptation kits, to support standardised data capture and system integration.

The effective use of digital health tools offers additional benefits, including real-time data collection, automated and streamlined data quality monitoring with built in data validation, and enforced standardisation of data elements, all of which contribute to more timely, complete, and reliable HTS data for decision-making and programme improvement.