Shared at the 2017 American Society of Tropical Medicine and Hygiene Annual Meeting

1. The USAID-funded MEASURE Evaluation project supported the Ministry of Health in DRC
to set up centers of excellence (COE) to document best practices and improve data
quality and use at the health facility level. The establishment of COE included selection
of health facilities in targeted health zones, collection of baseline data, and training of
providers in fundamentals of monitoring and evaluation (M&E).
The training curriculum reviewed concepts related to health information systems and
tools for data collection and transmission. It also reviewed routine supervision and
the implementation of data quality assessments. In the original four health facilities
selected in 2015, healthcare workers were equipped with data-management tools
for health information systems, and they received technical support through
supervision visits. .
Centers of Excellence in Monitoring and Evaluation:
An Approach to Improving Data Quality for Effective Decision
Making in the Democratic Republic of the Congo
Johanna.Karemere@icf.com
a. Completeness and timeliness of monthly reports
Johanna Karemere1, Jadhoul Nkongolo1, Hyacinthe Kaseya2, Ramine Bahrambegi1, Olivier Kakesa1
1MEASURE Evaluation, University of North Carolina at Chapel Hill, and ICF 2 National Malaria Control Program in the DRC
Malaria remains a major public health problem in the Democratic Republic of the
Congo (DRC). High-quality data are important to effectively allocate resources,
measure achievement, and ultimately, improve malaria control efforts. Data quality,
however, remains a challenge in health facilities.
To document progress made by the centers of excellence (COE), established in the
pilot phase to improve data quality and use at the health facility level.
The findings indicated an improvement of health information management at the
COE level. However, data use for decision making and the implementation of
decisions drawn from data analysis remained limited. These findings suggested the
need for development of a data demand and use plan in order to increase the
engagement of healthcare workers. Among this approach’s requirements are
increasing M&E skills among healthcare workers, improving Internet connection,
boosting technical support, and ensuring adequate funding. The COE model has now
been scaled-up to 22 health facilities, with a further expansion planned to all health
facilities in the targeted health zones by 2018.
COE are contributing to improvements in data quality and evidence-informed
decision making to reduce malaria burden in the DRC.
b. Data analysis meeting held at COE level
0.0
20.0
40.0
60.0
80.0
100.0
T4 2016 T3 2017
Proportion of monthly
reports transmitted
completly
Proportion of monthly
report transmitted timely
0.0
20.0
40.0
60.0
80.0
100.0
T4 2016 T3 2017
Percentage of COEs
having held monthly
data analysis meeting
98.4
0.0
66.7
100.0 100.0 100.0
0.0
20.0
40.0
60.0
80.0
100.0
120.0
Data accuracy (Proportion of data
validity rules fulfilled in DHIS2)
Proportion of COEs with quartely
data quality assessment carried
out
Proportion of COEs without
management tools stock out
T4 2016
T3 2017
0.0
20.0
40.0
60.0
80.0
100.0
Proportion of health providers
trained in Monitoring and
evaluation (Annual)
Percentage of supervisions
quartely carried out at the
COEs
Proportion of health providers
monthly supervised
T4 2016
T3 2017
0.0
20.0
40.0
60.0
80.0
100.0
T4 2016 T3 2017
Proportion of
decisions drawn
from data
implemented
This research has been supported by the President’s Malaria Initiative (PMI) through the United
States Agency for International Development (USAID) under the terms of MEASURE Evaluation
cooperative agreement AIDOAA-L-14-00004. The opinions expressed are those of the authors
and do not necessarily reflect the views of USAID, or the United States Government.
I. Background
V. Conclusion
II. Objectives
III. Materials and Methods
IV. Findings
c. Data quality and validity
d. Health provider training and supervision
e. Implementation of decisions drawn from data analysis