Presented by Yazoume Ye on behalf of Jean-Marie N'Gbich, MEASURE Evaluation/ICF International, as part of a symposium organized by MEASURE Evaluation and MEASURE DHS at the 6th MIM Pan-African Malaria Conference.

1. 6th MIM Panafrican Malaria Conference
Durban, SA October 6-11, 2013
Harnessing mHealth to monitor different epidemics within
one country: Experience from Mali
Jean-Marie NGbichi, MEAUSURE Evaluation / ICF International

2. Background
 Malaria is one of the major causes
of morbidity and mortality in Mali
 Difference transmission zones due
to the variation in climate
 Many internally displaced persons
due to recent political events
 National RHIS don’t fully address
malaria control needs
-
Not all key malaria indicators
Low timeliness, completeness, low
quality of data
Data only available on annually

3. Objectives
 Update the data collection form
 Build capacity to collect and
analyze data
 Introduce innovative technologies
(mobile phone , internet)
 Improve timeliness and
completeness of reporting

4. Location of the Pilot Districts
Region of Mopti
Mopti district
Bandiagara district
Region of Ségou
Niono district
Macina district

5. Methods
 Develop the core paper form
 Develop the core application
 Provide equipment
-
Mobile phones
Cell phone network
Server from Ministry of Health
 Train health workers
 Collect and process data
 Supervise

6. Core Paper Form – Collect Data
Région Médicale
District Sanitaire
Formulaire de Collecte de données - Données sur l'Information de Routine du PNLP - Niveau District Sanitaire (Csréf/Cscom)
Mois
Année
Rupture de stock CTA pendant le
mois
(Oui, Non)
Etablissement sanitaire
Consultation
CTA Nourisson - Enfant
Classification
< 5 ans
5 ans et plus
Femmes enceintes
CTA Adolescent
Total consultation, toutes causes confondues
CTA Adulte
Nbre de Cas de paludisme (Tous suspectés)
PEC de cas de Paludisme grave
Cas de paludisme testés (GE et/ou TDR)
Rupture de soctk OUI/NON
Cas de paludisme confirmés (GE et/ou TDR)
Nbre de Cas de paludisme Simple
Nbre de Cas de paludisme Grave
Nbre de Cas traités avec CTA
Classification
< 5 ans
Décès
5 ans et plus
Cas de décès pour paludisme
Total cas de décès toutes causes confondues
Moustiquaires imprégnées d'insecticide distribuées
Classification
Nombre de moustiquaires distribuées
Arthemether injectable
Quinine Injectable
Serum Glucosé 10%
Rupture de stock pendant le
mois O/N
(Oui, Non)
MILD
TDR
Femmes enceintes
SP
CPN/SP des femmes enceintes
(nbre)
CPN
SP 1
SP 2
Femmes enceintes
< 5 ans
Femmes enceintes
Hospitalisations
+ 5 ans
< 5 ans
Total Hospitalisés Paludisme
Total Hospitalisations toutes causes
confondues
Classification
Nom et Prénom : _______________________
Le Responsable
CSCom/CSRéf
Date : ___________________/20___

7. mApplication - Enter and Send Data

8. Data Consistency Checks

9. Data Flow
Central MoH (ANTIM)
Cell phone
service
Providers
Server
Server
Data
Data
D ata use
- NMCP/Regions/Districts
- MEASURE Eval, others …
Data analysis/use  decision
making
Data use:
(NMCP/ANTIM)
Data analysis/use
 Decision
INTERNET
3 RefHC
63 ComHC
- Fill paper forms
- Transcribe data in SMS code
- Send SMS
3 Districts
Validate ComHC data via
internet

10. Outputs: Example of Table
ALL AGE
jan-12
Nb
Total
consultation
(All causes)
feb-12
(%)
59744
Nb
mar-12
(%)
61314
Nb
apr-12
(%)
Nb
70834
may-12
(%)
67244
Nb
jun-12
(%)
63688
Nb
jul-12
(%)
64204
Nb
aug-12
(%)
87157
Nb
sept-12
(%)
131373
Nb
TOTAL
(%)
11256
0
Nb
(%)
718118
Malaria
Suspect case
21980 (37%)
20291 (33%)
24466 (35%)
20812 (31%)
20578 (32%)
18601 (29%)
41356 (47%)
70462 (54%)
64802 (58%) 303348 (42%)
Suspect cases
tested
RTD/Micros)
17261 (79%)
17929 (88%)
22391 (92%)
19926 (96%)
19576 (95%)
17010 (91%)
40138 (97%) 68087 (97%)
62308 (96%) 284626 (94%)
Positive cases
12741 (74%)
13466 (75%)
16540 (74%)
12302 (62%)
13106 (67%)
11738 (69%)
29879 (74%) 55430 (81%)
50091 (80%) 215293 (76%)
Simple malaria
cases
8760 (69%)
9427 (70%)
11600 (70%)
8474 (69%)
8866 (68%)
8219 (70%)
18561 (62%
34123 (62%)
31593 (63%) 139623 (65%)
Severe
malaria cases
3870 (30%)
3928 (29%)
4708 (28%)
3801 (31%)
4207 (32%)
3426 (29%)
11312 (38%)
21083 (38%)
18390 (37%)
74725 (35%)

11. Outputs: Example of Graphs
% Timeliness of reporting (%)
Nb malaria confirmed cases
% Suspected cases tested
% facilities without stocks outs

12. Availability of Data

Process helps to have real time
pictures on malaria routine
indicators:
-
testing of malaria suspect cases
cases treatment with ACT
stock outs (CTA, RTD, ITN, SP)
malaria deaths
….

13. Data use

Data use at district level
-

Data available at monthly basis
Help to monitor malaria core routine indicators
at district level
Help to discuss malaria control issues during
quarterly meetings: reporting gaps, data quality,
indicators trends …
 decisions to improve malaria activities

Data use at central level
-
MOH (NMCP/ANTIM) is developing a bulletin
using data generated by the system
‘‘Mobile Info’’ is used for advocacy and decision
making
-
-

14. SLIS vs. Mobile Reporting
RHIS
Mob reporting
Facilities
Timeliness of reporting
< 30%
> 95 %
Completeness of reporting
< 80%
> 95%
Work load: data transcription
on SMS codes
NA
15-30 minutes
Average time for to send data
at upper level
One to several week
Immediately

15. Challenges

Need further improvement in data quality
-
Maintain field supervision visits
Have periodic data quality assessment
•
•

quality control from registers to
monthly data collection form
from monthly data collection form to
central level data (server)
Data use at district, central levels
-
Notable progress
Needs to be reinforced

16. Way Forward

Strengthen the process in pilot
districts
-
Increase completeness of reporting
Improve analysis program to allow customized
analysis
-

Strengthen data use at district ,
central levels
Promote culture of data use through
technical support including training

17. Way Forward (2)
Ensure progressive scale up of mreporting
-
Progressive nationwide scale up: MOH (ANTIM)
intranet underway (involved other partners: UNFPA,
Red Cross …)

Explore feasibility of mreporting at
community level
Help tracking the efforts of community health workers
and improve CBIS.



18. Conclusion

Mobile reporting system set with MEASURE Evaluation assistance
in Mali improve timeliness, completeness and quality of data

The process became a reference within the health system in terms of
data production using new technologies:
-
While still improving, it already serves for data reporting needs in other health
areas.
-
Appropriate for local environment marked by turnover of health workers
-
Affordable: development of the application, follow up, and recurrent operational
costs
-
System rung despite the crisis situation

Continues giving real time pictures of core malaria indicators
needed to inform decision making

19. Acknowledgements

MOH central departments: NMCP, ANTIM, DNS, CPC

MOH decentralized entities: Health Regions (Ségou,
Mopti) health districts in Bamako & Ségou especially
Niono & Macina, Mopti, Bandiagara, health facilities
(CSComs CSRef)

Local private partners: Yeleman, Malitel, Orange Mali

USAID/PMI, WHO Mali

Yeleman

20. MEASURE Evaluation is a MEASURE program project funded by the U.S. Agency
for International Development (USAID) through Cooperative Agreement GHA-A-0008-00003-00 and is implemented by the Carolina Population Center at the
University of North Carolina at Chapel Hill, in partnership with Futures Group
International, John Snow, Inc., ICF Macro, Management Sciences for Health, and
Tulane University.
Visit us online at http://www.cpc.unc.edu/measure.
Thank You!