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Malaria Data Quality and Use in Selected Centers of Excellence in Madagascar:
Results from a Cross-Sectional Baseline Survey
Background
	ƒ In Madagascar, higher-quality data are needed for informed decision
making.
	ƒ Reporting completeness (65.3%), timeliness (45.5%), and data
analysis and use for decision making remain relatively low
(MEASURE Evaluation, 2016).
	ƒ Through support from the U.S. President’s Malaria Initiative (PMI),
Madagascar’s National Malaria Control Program engaged with
MEASURE Evaluation to implement Centers of Excellence (COEs)
to improve data quality and streamline data use.
	ƒ COEs are health centers selected to serve as regional drivers by
generating high-quality data to guide program implementation. The
COEs will share experience with other health facilities and promote
best practices in malaria surveillance, monitoring, and evaluation,
which will benefit the overall integrated health management
information system.
	ƒ The process for implementing the COEs starts with a baseline
assessment, and those results will serve for comparison during and
after the intervention (implementation of the COEs).
Maurice Ye,1
Jean-Marie Ngbichi,1
Thierry Franchard,2
Solo H. Rajaobary,2
Brune Ramiranirina,2
Andriamananjara N. Mauricette,2
Laurent Kapesa,3
Jocelyn Razafindrakoto,3
Yazoumé Yé1
1
MEASURE Evaluation, ICF, Madagascar and USA; 2
Ministry of Public Health, National Malaria Control Program, Antananarivo, Madagascar; 3
President’s Malaria Initiative, Madagascar
Results
References
World Health Organization (WHO). (2010). Malaria programme reviews: A manual for reviewing the performance of malaria
control and elimination programmes. Geneva, Switzerland: WHO.
National Malaria Control Program (NMCP). (2017). National malaria strategic plan 2013-2017 and 2018-2022. Madagascar:
NMCP, Monitoring and Evaluation Unit.
Institut National de la Statistique (INSTAT)/Madagascar, Programme National de lutte contre le Paludisme (PNLP)/Madagascar,
Institut Pasteur de Madagascar (IPM)/Madagascar, & ICF International. (2016). Enquête sur les indicateurs du paludisme 2016.
Calverton, MD, USA: INSTAT, PNLP, IPM and ICF International.
MEASURE Evaluation. (2019). Strengthening: what worked in the Democratic Republic of the Congo. Chapel Hill, NC, USA:
MEASURE Evaluation, University of North Carolina.
Ly, M., N’Gbichi, JM., Lippeveld, T., & Yé Y. (2016). Rapport d’évaluation de la performance du Système d’Information
Sanitaire de Routine (SISR) et de la Surveillance Intégrée de la Maladie et la Riposte (SIMR). Chapel Hill, NC, USA: MEASURE
Evaluation, University of North Carolina.
Data accuracy was assessed by comparing discrepancies between data reported to data recounted and re-aggregated from health
facilities’ registers.
Timeliness of reporting was assessed by comparing the number of reports submitted on time to the total number of reports
expected.
Completeness of reporting was assessed by comparing the number of reports submitted to the total number of reports expected.
Acknowledgments—This publication 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. MEASURE Evaluation is implemented by the Carolina Population Center at the University of North
Carolina at Chapel Hill, in partnership with ICF International; John Snow, Inc.; Management Sciences for Health; Palladium; and
Tulane University. Views expressed are not necessarily those of PMI, USAID, or the United States Government.
The authors would like to thank the following: USAID/PMI Madagascar Team, for their technical and financial support;
Madagascar’s NMCP surveillance, monitoring, and evaluation unit, for their contributions to the data quality assessment process;
Democratic Republic of the Congo’s MEASURE Evaluation Team, for their support during the 2017 exchange field trip.
For information, contact:
Maurice.Ye2@icf.com
https://www.measureevaluation.org/
Study Design
	ƒ Cross-sectional facility survey conducted in 12 health centers in 2018
	ƒ Pre- and post-intervention in two arms:
	- Eight health centers in moderate malaria transmission areas
(intervention arm)
	- Four health centers in moderate malaria transmission areas
(comparison arm)
Components of the Assessment
	ƒ Availability of tools: data collection and reporting
	ƒ Data quality: accuracy, completeness, and timeliness
	ƒ Data analysis and interpretation and use
	ƒ Data use: data discussion meetings at health center level, data use for
decision making
	ƒ Availability of commodities: rapid diagnostic tests (RDTs),
artemisinin-based combination therapies (ACTs)
Analysis
	ƒ Data quality compared between intervention and comparison groups
	ƒ Kruskal-Wallis test estimated the difference between the two groups,
p-values significant if p0.05
	ƒ Data analyzed using Stata 14
Figure 1. Study sites
COEs study intervention
and control districts
Figure 2. Sampling procedure
3 health districts
2 future COE
districts
1 control
districts
ANKAZOBE
Health center II
Health center II
Health center II
Health center II
Health center II
Health center II
Health center II
Health center II
ANTSIRABE II
Table 1. Availability of data collection and reporting tools at health facilities
Arms % of facilities
Intervention (n=8) 79.7 (89/112)
Comparison (n=4) 81.0 (45/56)
p-value (p=0.97)
Availability of Tools
Table 2. Completeness and timeliness of reporting from health facility to district
(last 3 months)
Arms
Completeness
% of facility reports
Timeliness
Intervention (n=8) 95.3 (23/24) 29.2 (7/24)
Control (n=4) 95.0 (11/12) 41.7 (5/12)
p-value* (p=0.240) (p=0.364)
* p0.05, both arms are similar for baseline performance
Table 3. Data accuracy in health facilities (last 3 months)
Variables measured
Intervention (n=8)
% of facility reports
Comparison (n=4)
% of facility reports
Outpatient visit registry and monthly report 41.7 (10/24) 66.7 (8/12)
Number of fevers in registry and monthly report 62.5 (15/24) 58.3 (7/12)
Number of fevers tested with RDTs in registry and
monthly report
45.8 (11/24) 41.7 (5/12)
Number of patients tested positive with RDTs in
registry and monthly report
62.5 (15/24) 50.0 (6/12)
Number of positive RDTs treated with ACTs in
registry and monthly report
62.5 (15/24) 50.0 (6/12)
Mean 55.0% 53.3%
p-value* (p=0.236)
Discussion and Conclusion
	ƒ Data quality remains an issue to address regarding accuracy, analysis, and use for decision
making.
	ƒ Both intervention and control arms have similar performance for completeness and
timeliness of reporting (Table 3).
	ƒ The assessment provided baseline information on comparable groups of health facilities
to measure improvement after COE implementation in Madagascar.
	ƒ The implementation of COEs is expected to be a driver for change in other health centers
and neighboring districts.
	ƒ The fact that only 3 of 114 health districts in Madagascar were sampled for the data
quality assessment could constitute a limitation in terms of generalizing findings.
Table 4. Availability of RDTs and RDTs at health facilities
Arms % of facilities with RDTs % of facilities with ACTs
Intervention (n=8) 96.0 (23/24) 95.0 (23/24)
Control (n=4) 90.0 (11/12) 90.0 (11/12)
p-value (p=0.61) (p=1)
Availability of Commodities
Data Analysis and Use
Table 5. Data analysis and use at health facilities
Arms
Data analysis
% of facilities
Data use for decision making
% of facilities
Intervention (n=8) 25.0 25.5
Control (n=4) 28.0 27.0
p-value (p=0.926) (p=0.144)

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Malaria Data Quality and Use in Selected Centers of Excellence in Madagascar: Results from a Cross-Sectional Baseline Survey

  • 1. Malaria Data Quality and Use in Selected Centers of Excellence in Madagascar: Results from a Cross-Sectional Baseline Survey Background ƒ In Madagascar, higher-quality data are needed for informed decision making. ƒ Reporting completeness (65.3%), timeliness (45.5%), and data analysis and use for decision making remain relatively low (MEASURE Evaluation, 2016). ƒ Through support from the U.S. President’s Malaria Initiative (PMI), Madagascar’s National Malaria Control Program engaged with MEASURE Evaluation to implement Centers of Excellence (COEs) to improve data quality and streamline data use. ƒ COEs are health centers selected to serve as regional drivers by generating high-quality data to guide program implementation. The COEs will share experience with other health facilities and promote best practices in malaria surveillance, monitoring, and evaluation, which will benefit the overall integrated health management information system. ƒ The process for implementing the COEs starts with a baseline assessment, and those results will serve for comparison during and after the intervention (implementation of the COEs). Maurice Ye,1 Jean-Marie Ngbichi,1 Thierry Franchard,2 Solo H. Rajaobary,2 Brune Ramiranirina,2 Andriamananjara N. Mauricette,2 Laurent Kapesa,3 Jocelyn Razafindrakoto,3 Yazoumé Yé1 1 MEASURE Evaluation, ICF, Madagascar and USA; 2 Ministry of Public Health, National Malaria Control Program, Antananarivo, Madagascar; 3 President’s Malaria Initiative, Madagascar Results References World Health Organization (WHO). (2010). Malaria programme reviews: A manual for reviewing the performance of malaria control and elimination programmes. Geneva, Switzerland: WHO. National Malaria Control Program (NMCP). (2017). National malaria strategic plan 2013-2017 and 2018-2022. Madagascar: NMCP, Monitoring and Evaluation Unit. Institut National de la Statistique (INSTAT)/Madagascar, Programme National de lutte contre le Paludisme (PNLP)/Madagascar, Institut Pasteur de Madagascar (IPM)/Madagascar, & ICF International. (2016). Enquête sur les indicateurs du paludisme 2016. Calverton, MD, USA: INSTAT, PNLP, IPM and ICF International. MEASURE Evaluation. (2019). Strengthening: what worked in the Democratic Republic of the Congo. Chapel Hill, NC, USA: MEASURE Evaluation, University of North Carolina. Ly, M., N’Gbichi, JM., Lippeveld, T., & Yé Y. (2016). Rapport d’évaluation de la performance du Système d’Information Sanitaire de Routine (SISR) et de la Surveillance Intégrée de la Maladie et la Riposte (SIMR). Chapel Hill, NC, USA: MEASURE Evaluation, University of North Carolina. Data accuracy was assessed by comparing discrepancies between data reported to data recounted and re-aggregated from health facilities’ registers. Timeliness of reporting was assessed by comparing the number of reports submitted on time to the total number of reports expected. Completeness of reporting was assessed by comparing the number of reports submitted to the total number of reports expected. Acknowledgments—This publication 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. MEASURE Evaluation is implemented by the Carolina Population Center at the University of North Carolina at Chapel Hill, in partnership with ICF International; John Snow, Inc.; Management Sciences for Health; Palladium; and Tulane University. Views expressed are not necessarily those of PMI, USAID, or the United States Government. The authors would like to thank the following: USAID/PMI Madagascar Team, for their technical and financial support; Madagascar’s NMCP surveillance, monitoring, and evaluation unit, for their contributions to the data quality assessment process; Democratic Republic of the Congo’s MEASURE Evaluation Team, for their support during the 2017 exchange field trip. For information, contact: Maurice.Ye2@icf.com https://www.measureevaluation.org/ Study Design ƒ Cross-sectional facility survey conducted in 12 health centers in 2018 ƒ Pre- and post-intervention in two arms: - Eight health centers in moderate malaria transmission areas (intervention arm) - Four health centers in moderate malaria transmission areas (comparison arm) Components of the Assessment ƒ Availability of tools: data collection and reporting ƒ Data quality: accuracy, completeness, and timeliness ƒ Data analysis and interpretation and use ƒ Data use: data discussion meetings at health center level, data use for decision making ƒ Availability of commodities: rapid diagnostic tests (RDTs), artemisinin-based combination therapies (ACTs) Analysis ƒ Data quality compared between intervention and comparison groups ƒ Kruskal-Wallis test estimated the difference between the two groups, p-values significant if p0.05 ƒ Data analyzed using Stata 14 Figure 1. Study sites COEs study intervention and control districts Figure 2. Sampling procedure 3 health districts 2 future COE districts 1 control districts ANKAZOBE Health center II Health center II Health center II Health center II Health center II Health center II Health center II Health center II ANTSIRABE II Table 1. Availability of data collection and reporting tools at health facilities Arms % of facilities Intervention (n=8) 79.7 (89/112) Comparison (n=4) 81.0 (45/56) p-value (p=0.97) Availability of Tools Table 2. Completeness and timeliness of reporting from health facility to district (last 3 months) Arms Completeness % of facility reports Timeliness Intervention (n=8) 95.3 (23/24) 29.2 (7/24) Control (n=4) 95.0 (11/12) 41.7 (5/12) p-value* (p=0.240) (p=0.364) * p0.05, both arms are similar for baseline performance Table 3. Data accuracy in health facilities (last 3 months) Variables measured Intervention (n=8) % of facility reports Comparison (n=4) % of facility reports Outpatient visit registry and monthly report 41.7 (10/24) 66.7 (8/12) Number of fevers in registry and monthly report 62.5 (15/24) 58.3 (7/12) Number of fevers tested with RDTs in registry and monthly report 45.8 (11/24) 41.7 (5/12) Number of patients tested positive with RDTs in registry and monthly report 62.5 (15/24) 50.0 (6/12) Number of positive RDTs treated with ACTs in registry and monthly report 62.5 (15/24) 50.0 (6/12) Mean 55.0% 53.3% p-value* (p=0.236) Discussion and Conclusion ƒ Data quality remains an issue to address regarding accuracy, analysis, and use for decision making. ƒ Both intervention and control arms have similar performance for completeness and timeliness of reporting (Table 3). ƒ The assessment provided baseline information on comparable groups of health facilities to measure improvement after COE implementation in Madagascar. ƒ The implementation of COEs is expected to be a driver for change in other health centers and neighboring districts. ƒ The fact that only 3 of 114 health districts in Madagascar were sampled for the data quality assessment could constitute a limitation in terms of generalizing findings. Table 4. Availability of RDTs and RDTs at health facilities Arms % of facilities with RDTs % of facilities with ACTs Intervention (n=8) 96.0 (23/24) 95.0 (23/24) Control (n=4) 90.0 (11/12) 90.0 (11/12) p-value (p=0.61) (p=1) Availability of Commodities Data Analysis and Use Table 5. Data analysis and use at health facilities Arms Data analysis % of facilities Data use for decision making % of facilities Intervention (n=8) 25.0 25.5 Control (n=4) 28.0 27.0 p-value (p=0.926) (p=0.144)