PEPFAR's annual planning process is done either at the country (COP) or regional level (ROP).
PEPFAR's programs are implemented through implementing partners who apply for funding based on PEPFAR's published Requests for Applications.
Since 2010, PEPFAR COPs have grouped implementing partners according to an organizational type. We have retroactively applied these classifications to earlier years in the database as well.
Also called "Strategic Areas", these are general areas of HIV programming. Each program area has several corresponding budget codes.
Specific areas of HIV programming. Budget Codes are the lowest level of spending data available.
Expenditure Program Areas track general areas of PEPFAR expenditure.
Expenditure Sub-Program Areas track more specific PEPFAR expenditures.
Object classes provide highly specific ways that implementing partners are spending PEPFAR funds on programming.
Cross-cutting attributions are areas of PEPFAR programming that contribute across several program areas. They contain limited indicative information related to aspects such as human resources, health infrastructure, or key populations programming. However, they represent only a small proportion of the total funds that PEPFAR allocates through the COP process. Additionally, they have changed significantly over the years. As such, analysis and interpretation of these data should be approached carefully. Learn more
Beneficiary Expenditure data identify how PEPFAR programming is targeted at reaching different populations.
Sub-Beneficiary Expenditure data highlight more specific populations targeted for HIV prevention and treatment interventions.
PEPFAR sets targets using the Monitoring, Evaluation, and Reporting (MER) System - documentation for which can be found on PEPFAR's website at https://www.pepfar.gov/reports/guidance/. As with most data on this website, the targets here have been extracted from the COP documents. Targets are for the fiscal year following each COP year, such that selecting 2016 will access targets for FY2017. This feature is currently experimental and should be used for exploratory purposes only at present.
Years of mechanism: 2007 2008 2009
ACTIVITY HAS BEEN MODIFIED IN THE FOLLOWING WAYS:
The Data Warehouse (DW) activity will work closely with the newly-awarded John Snow Inc. (JSI) Enhance-
SI project, with JSI focusing more on improving data extraction and outbound reports. A new cube viewer
has been developed during FY 2008 and this will be further enhanced to assist users to create custom
queries. With the hope that the PEPFAR partner GIS data will be improved as a result of current USG
initiatives, the DW team will roll out the online mapping system that was developed this year. Further
refinement will be made to pre-populating performance results onto reporting and planning forms. More
resources will be put into development testing. Finally, the DW will work with the large treatment partners to
allow electronic transfer of data that these partners already collect with existing computerized systems.
For the Data Quality Assessments (DQA) project, in FY 2008 Khulisa initiated a revision of the data quality
audit tools with the intention of moving them from a "deficit-oriented" assessment, to a "strength-oriented"
assessment. This initiative was taken to further support the PEPFAR goal of making the DQA more
collaborative and useful for capacity building. The new tools were still being piloted at the end of FY 2008.
Khulisa is also exploring the possibility of aligning the South Africa DQA tool with an adapted version of the
Global Fund/PEPFAR DQA tool.
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SUMMARY:
The South Africa PEPFAR program works with over 100 prime partners, who in turn work with over 300 sub
-partners and 350 service delivery sites, to implement HIV and AIDS activities across South Africa. This
immense level of effort poses a significant challenge to the USG in efficiently monitoring and evaluating
programs (mainly because there is no single source from which to obtain PEPFAR data) and in building
monitoring and evaluation (M&E) capacity among partners. Khulisa helps to address these challenges
through a web-based data warehouse (DW) and through on-going independent Data Quality Assessments
(DQA) of PEPFAR partners' data management systems. Both the DW and DQA activities prioritize M&E
capacity building among PEPFAR/South Africa partners.
This project addresses the emphasis areas of Health Management Information Systems, monitoring,
evaluation and reporting, as well as USG database and reporting systems. The main target populations are
the USG and PEPFAR prime partners, sub-partners, and sites in all nine provinces.
BACKGROUND:
Khulisa Management Services is a South African-based consulting firm offering quality management and
technical services to development projects throughout Africa. With PEPFAR funding, Khulisa has
implemented both the DW and DQA activities since FY 2005. With FY 2006 funding, Khulisa conducted
DQAs of 26 PEPFAR partners and provided data quality training for USG staff and partners. This exercise
provided invaluable feedback on risks to data quality regarding reported PEPFAR data, and also sought to
build M&E capacity and improve data management systems (DMS) among PEPFAR partners. These DQAs
were more collaborative than traditional audits, allowing partners to receive advice on how to improve
practices. The proposed DQAs will continue to build partners' understanding and capacity in M&E systems,
as well as improve the overall quality of data they report.
Since October 2004, Khulisa has provided web-based data warehousing services to PEPFAR through a sub
-grant through John Snow Inc. (JSI) funded through the MEASURE Evaluation project. The DW has
transformed a paper-based COP planning and reporting system to a more efficient web-based system with
data integrity. The DW has undergone continuous revisions to address the changing needs of PEPFAR.
The proposed project activities will further support the DW and develop a sustainable and replicable system.
ACTIVITIES AND EXPECTED RESULTS:
ACTIVITY 1 - Data Quality Assessments (DQAs)
The DQAs are designed as a three-phased approach, using standardized tools based on USAID and other
internationally accepted standards. At each phase, the risks to data quality are identified prompting a
dialogue between the assessor and the partner about how to improve systems, resolve problems, and
resolve data quality risks. The findings of each phase, with associated recommendations, are reported in
detail to both the USG and the partner. In addition, the USG receives a summary report for each phase. A
plan for technical assistance is developed between the partner and the USG.
Phase 1: Phase 1 assessments are conducted with a new group of partners as identified by the USG Task
Force. In this phase, the partner's Data Management System (DMS) and associated processes and
procedures are examined through a self-evaluation, followed by a review of the DMS by the Khulisa
assessor. The main objective is to prepare the partner for Phase 2 and familiarize them with the DQA
process.
Phase 2: Phase 2 involves validation and verification of reported data. The assessor uses two selected
indicators (from source) and tracks it through the partner's DMS to evaluate the reported data for validity,
reliability, timeliness, precision and integrity. In the process of conducting Phase 2 the assessor derives
scores for several dimensions of data quality; these scores are interpreted in the context of data quality
risks. Any identified risks are reported to the partner and the USG with recommendations for corrective
action. Partners with high risk scores are issued compliance notes indicating data management and quality
practices that could be improved in specific ways. The compliance notes also provide recommendations for
resolving practices that contribute to compromised data quality of reported results.
Phase 3: Phase 3 is the follow-up visit which is only done with those partners who received a compliance
Activity Narrative: note based on a high risk score in Phase 2. The assessor re-examines the data quality issues found during
Phase 2 and assesses whether the corrective action taken by the partners reduces the risks that were
outlined. When the assessor and partner achieve consensus on the corrective action, the compliance note
is considered closed. This final visit also serves as an additional opportunity for the partner to receive
technical assistance from the assessor on data quality practices.
ACTIVITY 2: Data Warehouse (DW)
The DW project is an ambitious and unique activity, and has proven to be a useful tool for PEPFAR
reporting and planning. During the last two years, Khulisa built a web-based DW that is password-protected,
through which implementing partners can electronically submit both narrative and quantitative information
on progress towards their expected results as well as their plan for the forthcoming fiscal year. The DW also
allows the USG Task Force to verify submitted data, make adjustments for partner double counts, and to
maintain an audit trail by tracking changes made to data.
Over the last three years, substantial progress has been made in developing a PEPFAR reporting system.
Multiple tests were performed on the system, which brought about numerous adjustments to improve
efficiency and effectiveness. Feedback has been positive so far and USG staff and partners have now
become more "fluent" in using the system. Last year, in addition to the reporting side of the DW, a planning
side has been added to electronically capture information for the COP, enabling the USG to better manage
the large amount of COP data through version control. An online "track changes" function has been added
this year to assist activity managers with their final COP edits.
Currently, the DW captures progress reports (quarterly, annual and semi-annual) and COP data; provides
tools for managing budgets and targets through online, editable grids; provides a tool for the removal of
double counting; tracks data changes through audit trails; and extracts indicator data, sub-partner and site
information.
In FY 2008, Khulisa will continue to maintain and host the DW, with a focus on expanding features for better
use and analysis of program data at the partner level. Specifically, the project will:
-- Continue improving the currently active functions for even greater ease of use by partners and USG staff.
-- Further extend the extraction and reporting capacity for indicator data, sub-partner and site information,
status information and trend data. The extensions will focus on online graphical representation of data
including maps. Manually-produced maps are already a significant aspect of data use and their availability
online, in real time will improve the usage of this data.
-- Further improve partner-level data usage and data quality through site-level data capture for partners
(other than the treatment partners who currently do so). Site-level capture will be started for partners who
request it, starting with Orphans and Vulnerable Children partners and likely followed by Counseling and
Testing partners.
EXPECTED RESULTS:
These two activities will allow the USG Task Force to make better, data-driven programming and planning
decisions at the macro level, as well as assist partners develop and utilize more effective M&E systems.
The sustainable impact of the system will be the partner's ability to make better programming and planning
decisions for their own programs based on accurate and reliable data.
This activity will assist the entire PEPFAR program achieve its goals through effective M&E of partner
achievements in meeting South Africa's portion of the 2-7-10 goals.
New/Continuing Activity: Continuing Activity
Continuing Activity: 13981
Continued Associated Activity Information
Activity Activity ID USG Agency Prime Partner Mechanism Mechanism ID Mechanism Planned Funds
System ID System ID
13981 3345.08 U.S. Agency for Khulisa 6674 4642.08 $1,800,000
International
Development
7945 3345.07 U.S. Agency for Khulisa 4642 4642.07 Data Quality $1,800,000
International Contract
3345 3345.06 U.S. Agency for Khulisa 3170 3170.06 Data Quality $200,000
Table 3.3.17:
