Quality Data is Critical in Fighting HIV, Tuberculosis and Malaria

By fortifying guidance and implementing stringent measures on data, countries and partners can use accurate insights to guide impactful programs to fight HIV, tuberculosis and malaria worldwide. Read more on mitigating data manipulation.

As emphasized in the Global Fund strategy, the fight against HIV, tuberculosis and malaria heavily relies on the availability of quality data to support robust decision making and oversight, both for implementers and the Global Fund Secretariat.

The Global Fund’s Office of the Inspector General (OIG) has published different reports on the quality and use of data in Global Fund grants.

These cases highlight the challenges around in-country data, systems and accuracy, and the associated issues. These issues may contribute to data manipulation if not addressed. Such was the case in Guinea where, manipulation of data in the distribution of malaria nets and falsification of HIV survey data jeopardized the ability of vulnerable beneficiaries of these programs to access services and support.

In-Country Data Challenges

Audit Report ENGLISH or FRENCH

The Global Fund has made significant investments in strengthening in-country data systems, but challenges persist, as revealed by an audit conducted by the OIG. The audit found there was a substantial improvement in programmatic data timeliness from 2017 to 2021 but challenges at the country level relating to fragmented data systems and use of manual tools have hindered progress.

For Procurement and Supply Management (PSM), the audit revealed limited end-to-end data visibility and quality at the country level. Guidance and tools for monitoring programmatic data on the country-level exist, but implementation challenges persist, especially at health facility levels where formalized processes and staff capacity are lacking.

Outside health facilities, limited monitoring, oversight, and supervision at national and regional levels affect data accuracy. In addition, limited triangulation of patient and PSM consumption data hindered error detection.

Manipulation of Malaria Nets Data

Country	GUINEA
	Investigation Report ENGLISH or FRENCH

An investigation by the OIG revealed serious issues with the distribution of long-lasting insecticidal nets in Guinea in 2019, underscoring the need to improve data oversight of such campaigns crucial in the Global Fund’s fight against malaria.

Long-lasting insecticidal net (LLIN) campaigns are the most effective intervention in preventing malaria, and in reducing cases and deaths. Between 2018 and 2020, the Global Fund – which provides 63% of all international financing for malaria programs – invested over US$1.2 billion in LLIN mass campaigns globally and distributed 516 million LLINs.

LLIN mass campaigns are complex and data-heavy undertakings and necessitate effective oversight. Accuracy and completeness of data in determining the quantity of nets needed is critical for an effective LLIN campaign.

The OIG investigation uncovered that in Guinea data relating to its LLIN mass distribution campaign had been fraudulently manipulated, pointing to a lack of clear accountability on data accuracy. OIG investigators found that two critical data sets related to the Guinea LLIN mass distribution campaign were fraudulently manipulated. These data sets included the household counting database, which tracks the total number of beneficiaries in Guinea and the number of LLINs required to cover them, as well as data related to the distribution of LLINs to beneficiaries. There were insufficient controls and no clear accountability for data accuracy in the LLIN distribution campaign. This lack of oversight contributed to the fraudulent manipulation of data.

Falsification of HIV Survey Data

Country	GUINEA
	Investigation Report ENGLISH or FRENCH

In 2015, a non-governmental organization (NGO) in Guinea, was entrusted by a Guinea HIV grant Principal Recipient to conduct an Integrated Biological and Behavioral Surveillance (IBBS) survey. The purpose of this HIV survey was to gather crucial data shaping the design and implementation of effective HIV programs, as well as measuring program results.

Prompted by the Global Fund Secretariat, an investigation by the OIG revealed that the survey data was manipulated. By falsifying both survey participants and responses, along with associated HIV blood test and prevalence data this wrongdoing, extended to the manipulation of costs related to the survey's execution.

The survey, a cornerstone in numerous Global Fund HIV grant portfolios, serves as a compass for informed decision-making in the battle against HIV. The falsification of its data not only misrepresented the program's progress but also had the potential to drastically influence subsequent strategic and financial decisions.

Had these practices gone undetected, the misrepresented data would have been incorporated into the broader narrative of the HIV program's success. This distorted reality could have swayed decisions at both strategic and financial levels, leading to misguided resource allocations and programmatic approaches.

Mitigating Data Manipulation

These cases underscore the importance of vigilance in data collection, management and analysis, supervision and assurance processes in Global Fund programs. By fortifying guidance and implementing stringent measures on data, countries and partners can use accurate insights to guide impactful programs to fight HIV, tuberculosis and malaria worldwide.

To prevent fraudulent data manipulation in health care delivery systems, it is crucial to establish a strong framework for data integrity and monitoring measures including:

• Clearly defined roles, responsibilities, and accountabilities for data collection and verification across all levels.
• Use a triangulation approach across different data sources to identify and investigate discrepancies before decision-making.
• Embed routine independent data quality checks in surveys prior to completion and dissemination of results.
• Routinely perform data quality assurance across all levels, including reconciling underlying records such as registers to reported results and analyzing year-to-year trends and reported results across regions.
• Leverage system/automated controls in District Health Information Software 2 (DHIS2) or equivalent electronic systems with appropriate access controls. This includes internal consistency check of different indicators, controls on revision of reported results and maintain edits or log history

More Resources on Identifying Data Fraud

To empower individuals and organizations with the knowledge and skills needed to detect the signs of data fraud in similar contexts, we offer valuable resources.

Explore our e-lessons page, where you can access essential insights and guidance on identifying red flags.