Module 3: Data quality monitoring and assessment at facility level

This section outlines routine facility-level data quality assessments and monitoring activities for HIV testing services (HTS), focusing on the following activities:

recreation and verification of HTS indicators and their comparison to data reported to the ministry of health (routine data quality assessments; see section 3.1 below)
routine data quality checks and monitoring activities such as cross-validation of key HTS data elements and assessment of the completeness of HTS data across different data sources (see section 3.2 below)
routine site-level reviews of HTS data and performance (see section 3.3 below).

Given current funding constraints and reduced partner support, it is recommended that countries prioritise low-cost activities such as site-level reviews of facility HTS data and cross validation and assessment of the completeness of HTS data. These activities are simple to conduct and can be implemented more frequently at low cost, ensuring sustained data quality and performance monitoring.

3.1 Routine data quality assessment: indicator recreation

Routine data quality assessments are a key approach used by ministries of health, often with partner support, to verify the accuracy and reliability of routinely reported HTS indicators. These assessments follow established joint WHO, UNAIDS, and Global Fund guidance and involve systematically recalculating selected indicators at health facilities and comparing them with values reported through national reporting systems. These steps include forming assessment teams, defining roles and responsibilities, and conducting site assessments to recalculate specific indicators. Where feasible, HTS indicator verification should be integrated into existing data quality assessment activities for HIV treatment or viral load, rather than conducted as a standalone exercise, to maximize efficiency and reduce burden on health systems.

Routine HTS data quality assessments do not need to be nationally representative. Countries may prioritise smaller, more frequent assessments in selected facilities as part of long term data quality improvement efforts, particularly in settings with limited resources. Nationally representative data quality assessments are more resource intensive and are best reserved for situations where there are persistent data quality concerns or when validation of national HTS estimates is required, such as monitor progress towards the first 95% target on people being aware of their HIV status. Nationally representative samples are also required for HIV surveillance system reviews using the standardized WHO checklist to assess completeness, accuracy and consistency. When national HTS estimates are adjusted based on data quality assessment findings, these updates should be reflected in national modelling tools and global reporting systems.

Sampling considerations

Sampling approaches for routine HTS data quality assessments should be tailored to country context and programme priorities. Facilities may be selected purposively based on client or testing volume (involves assessing client/testing volumes across all sites and dividing these sites into three strata, each containing one-third of the total client/testing volume), previous data quality issues, geographical distribution, or level of partner support. Key considerations when designing the sampling frame include ensuring representation of urban and rural facilities, capturing variation in client or testing volume, and including both partner supported and non supported sites where relevant. Stratifying facilities by testing volume can help ensure that high impact sites are adequately represented, while balancing feasibility and available resources. Sample sizes should be appropriate to the goals of the assessment and can be calculated using the sample size estimation tool in Annex C of the 2018 data quality assessment of national and partner HIV treatment and patient monitoring system implementation tool. Countries are encouraged to prioritise sustainable sampling approaches that allow routine implementation over time. Sampling approaches for nationally representative data quality assessments differ from routine HTS approaches, and specific guidance and tools are available in the WHO 2018 Data quality assessment of national and partner HIV treatment and patient monitoring data and systems implementation tool.

Main activities during a routine HTS data quality assessment

A routine HTS data quality assessment typically includes a series of structured activities conducted at facility level. These begin with introductory discussions with facility management and service providers to explain the purpose of the assessment and establish collaboration. Informed consent and confidentiality procedures should be completed before data assessment begins (see Informed consent form and Confidentiality agreement template).

Assessment teams then review facility data systems and reporting procedures (see HTS site level questionnaire), map client and data flows for HTS services (see Client and data flow mapping tool), and identify any real time bottlenecks or weaknesses in documentation and reporting practices. The central activity of the data quality assessment is the recount and recreation of selected HTS indicators using source documents such as registers, client records, electronic databases, or laboratory records. These recounted values are compared with what the facility reported to the ministry of health or partners for the same period (see HTS data quality assessment tally sheet).

Additional data quality assessment activities include cross validation of key data elements between registers and individual HTS records or electronic systems, checks of data completeness (see HTS site level validation tool for cross validation and data completeness activities), and ongoing feedback and mentoring for facility staff throughout the exercise (see Generic templates to display outputs of data quality assessment and assurance activities). Findings are discussed with facility teams, and site specific data quality improvement actions are jointly identified and drafted in a data quality improvement action plan (see Site-level data quality improvement action plan template).

Priority HTS indicators for verification

Routine HTS data quality assessments should prioritise verification of key indicators used for programme monitoring and reporting. The two priority WHO recommended HTS indicators are:

Individuals testing positive for HIV (HTS.3), which measures the percentage of people who received an HIV test and tested positive during the reporting period - first priority.
HTS test volume and positivity (HTS.2), which captures the number of HIV tests performed and the proportion of tests that returned a positive result - second priority.

These indicators should be recalculated using facility level source data and compared with reported values to Ministries of Health and partners if relevant. Differences between recounted and reported numbers should be investigated to understand their causes, documented clearly, and addressed through targeted corrective actions. Routine data quality assessment should also assess data completeness and consistency alongside HTS indicator verification.

Use of standard tools and data quality improvement action plans

Standardised tally sheets and validation tools support consistent recounting and comparison of HTS indicators and can be adapted to national definitions and data systems. Findings from the assessment should feed directly into a site level data quality improvement action plan, outlining specific actions, responsible persons, timelines, and follow up mechanisms. Providing clear feedback and engaging facility staff in developing solutions helps build ownership and supports sustainable improvements in data quality.

Considerations for electronic HTS systems

In settings where electronic medical records or electronic HTS databases are used, additional considerations apply. Facilities should conduct a thorough deduplication exercise before HTS indicator recreation to avoid double counting. Indicator calculations generated through electronic systems should be reviewed to ensure they align with national definitions (using the software report or query for the reporting period under review to check alignment with ministry of health definitions), and extracted data should be transferred securely into data quality assessment tools for verification. This can be achieved either by copying and pasting after extraction or by applying direct data transfer from the database to the HTS data quality assessment tally sheet. These steps help ensure that electronic systems support, rather than obscure, accurate HTS reporting.

3.2 Routine data quality checks and monitoring: assessing completeness and consistency of HTS data elements

This section outlines routine data quality checks and monitoring activities that can be conducted at sites to assess the completeness and consistency of HTS data elements across different data sources. By implementing these activities routinely, sites can ensure accurate and reliable data reporting, to support data use and service delivery using a simplified low-cost approach.

Cross validation of key HTS data elements across data sources

Cross-validation of key HTS data elements involves comparing data from primary data sources such as HTS registers against secondary data sources such as individuals level client HTS records or electronic HTS databases or electronic medical records. This process ensures the completeness and accuracy of site-level source documents, which is crucial for maintaining high data quality in HIV testing and reporting. By systematically sampling and comparing data sources, discrepancies can be identified and addressed, leading to improved data quality and more reliable HIV testing data and reporting outcomes. This can be done as a separate exercise or as part of a data quality assessment verifying and recounting HTS indicators as described in section 3.1.

HTS data cross validation implementation steps

1. Sampling	Data quality assessment team/facility staff selects every X client (e.g., every 20th client for high volume sites or every 5th client for low volume sites) to sample 10% of client service records from the HTS register, starting from the beginning of the review period. Key data elements, such as last confirmatory HIV test result and test dates, are abstracted from both the HTS register and individual client HTS record or electronic HTS database depending on which is available as the secondary data source.

2. Data abstraction	Selected data elements (unique identifier; gender; date of birth; date of last HIV test (confirmatory); and HIV test result (confirmatory)) will be abstracted from the HTS register (primary data source) and the secondary data source which is either the individual client HTS record or electronic HTS database. Countries can consider cross validating further data elements based on available resources.

3. Caluclation of discordance	Collected data is used to calculate the percent of discordance between the primary source document (the HTS register) and the secondary data source (the individual client HTS records or electronic HTS database). This helps in identifying discrepancies and areas for improvement in data recording and reporting.

4. Ensuring confidentiality and data security	Data quality/facility teams will have access to client records and registers with personally identifying health information, the teams will apply a standardised practice to data extraction, making sure to cover the names, age, address and phone number of the clients. Personally, identifying information such as name, date of birth and gender will be used to identify the records for this activity, confirming the same client across different data sources. These identifiers will not be removed from the facility and will not be part of the data collected. The identifiers will be destroyed prior to leaving the health facility. Only aggregated data will be captured. All data abstraction will occur in a private area, away from clients, and covered (e.g., closing folder) if clients are present.

Assessing completeness of HTS data

Assessing the completeness of HTS data elements across different data sources is crucial for ensuring high data quality. Completeness is an important dimension of data quality and should be a routine approach to data quality management. By continuously monitoring completeness and taking follow-up actions to address any gaps, inconsistencies can be identified and resolved at site level. Comparing data from various sources for the same client ensures that all necessary information is captured and maintained accurately in one comprehensive client record, thereby enhancing the overall reliability of the data. The completeness of records can be assessed in parallel to the cross validation of data elements across different data sources for the same clients using the sampling approach outlined in the figure above. The data completeness tab of the HTS site validation tool can be adapted for country use to assess the completeness of HTS data across different data sources. As with the cross validation of HTS data, data completeness can be assessed routinely as part of site level data quality monitoring and management or incorporated within a data quality assessment to verify HTS indicators.

3.3 Routine site level review of HTS data

Routine site level reviews of HTS data by facility staff provide a low cost and effective way to verify and correct data before monthly reporting to the ministry of health. These reviews help identify and correct errors at source, support on the spot mentoring for staff involved in data entry and reporting, and strengthen the use of HTS data as part of continuous quality improvement processes. Facility teams should review data completeness and routinely tally key HTS indicators, such as test volume, positivity, and the number of individuals testing positive; by comparing registers with monthly reports and other documentation sources where available.

To ensure a comprehensive view across the HIV care cascade, key HIV treatment and viral load indicators should also be reviewed alongside HTS data. Involving multidisciplinary teams and establishing feedback mechanisms enhances data accuracy, reliability, and shared accountability. The site-level data review tab of the Generic templates to display outputs of data quality assessment and assurance activities includes standardised templates to support facilities in documenting and displaying monthly data review results (see Module 5: Data quality assessment/monitoring results visualisation and dissemination for further guidance). Regular data and performance reviews are recommended for all HTS facilities, with monthly reviews prioritised for facilities experiencing significant or recurring data quality issues identified through previous data quality assessments.