Mobile phones can improve malaria reporting in resource-limited settings like Mali. A study piloted using phones to report malaria data from health centers in Mali found it led to more timely, complete, and higher quality data compared to paper-based reporting. Health centers used basic phones to send SMS codes with monthly malaria indicator values to a central server. This allowed real-time monitoring and informed rapid decision making. While challenges remain, mobile reporting has the potential to strengthen malaria surveillance systems.

1. ASTMH 61st Annual Meeting
Atlanta, GA November 11-15, 2012
Improving malaria reporting in resource poor settings: How
cell phones affect timeliness, completion
and quality of data in Mali
Jean-Marie NGbichi, MD, MSc, ICF Mali
Cristina de la Torre, DSc ICF Calverton, MD

2. Background
 Malaria is one of the major causes of
morbidity and mortality in Mali
 99% of the population is at risk
 41% of outpatient visits in children
under five years
 Above 50% of reported deaths in
children under 5 years
 Malaria control in Mali is being
strengthened by support from multiple
partners/agencies
 Efforts requires closer monitoring

3. Background (2)
 National RHIS does not address malaria control needs
- Insufficient malaria indicators targeted
- Low timeliness, completeness and quality of data
- Data only available on annual basis
 MEASURE Evaluation started collaborating with the NMCP to
strengthen malaria routine information:
- Adapting the data collection form
- Improving capacity to collect and analyze data
- Increasing timeliness and completion
- Introducing new technologies: mobile reporting in pilot areas

4. Objectives Methods
 Coverage
Support the NMCP in : 2 health districts
(38 ComHC, 2RefHC)
- Designing and
implementing a system for  Development of basic
rapid data transfer from paper form
lower health facilities
using mobile phones  Development of the
core application
- Using data generated to
inform real time decision  Logistical support
making Mobile phones
Cell, phone network
- Exploring the feasibility of Server
a region/country wide
scale-up of the system  Training

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

6. Training: how to edit and send SMS report
1  2  3  4
5  ………………………

7. Error !


8. Central MoH
Cell phone service
Providers
Data use Server
Server
- NMCP/Regions/Districts Data
Data
- MEASURE Eval, others … +
+
Data analysis/use  decision
Data use: (
NMCP/ANTIM) Timeliness: data
Data analysis/use
 decision
transferred: 1st
through 5th of the
2
Districts following month
Validate
ComHC data via internet INTERNET
2 RefHC
38 ComHC
- Fill paper forms
- Transcribe data in SMS codes
- Send SMS

9. Central MoH (ANTIM)
Cell phone service
Providers
1
Server
Server
Data use Data
- NMCP/Regions/Districts Data
+
- MEASURE Eval, others … 1 + 2
Data analysis/use  decision making 2
Data use:
(NMCP/ANTIM)
1 Data analysis/use
 Decision
2
2
Districts INTERNET
Validate
ComHC data via 8
internet
Districts
- Validate ComHC data
- Compile/record data on Excel file
- Upload Excel file via internet
2 RefHC 1 Region
38 ComH C
Hospital
- Fill paper forms 8 RefHC
- Transcribe data in SMS code 190 ComHC
- Fill paper forms
- Send SMS 2 - Send paper forms
1

10. Results
(outputs)

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

12. Example graphs
Timeliness of reporting (%) % Suspected cases tested
Nb ITNs distributed (ANC visits) % facilities without stocks outs

13. Availability of data
 Process helps having real time
pictures on malaria routine
indicators:
- testing of malaria suspect cases
- Test positivity rate
- cases treatment with ACT
- stock outs (CTA, RTD, ITN, SP)
- malaria deaths
- ….

14. 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, indicatros trends …
  decisions to improve malaria
interventions
 Data use at central level
- MOH (NMCP/ANTIM) started developing
a bulletin using data generated by the
system
- ‘‘Mobile Info’’ is used for advocacy and
decision making

15. Malaria RI strengthening process
Paper form vs. mobileReporting
Paper form Mob reporting
Facilities Facilities
Timeliness of reporting 80% > 95 %
Completeness of reporting 80% > 95%
Work load: data transcription NA 15-30 minutes
on SMS codes

16. Malaria Routine information strengthening process
Paper form vs. mobileReporting
Paper form MobileReporting
Average time for data One to several days
sending Immediately
- Direct record to database
Overall advantages from No evident advantages - Better timeliness of reporting
field experience - Better completeness of reporting
- Suitable for hard to reach areas
- Data instantly available on server
- Lower timeliness
- Lower completeness
Overall disadvantages - Risk of form loosing - Possible failure with mob phone net
- Risk of errors in data transcription work coverage
from field experience from paper form to computer
database (district level)

17. Malaria Routine information strengthening process
Paper form vs. mReporting
Paper form mReporting
- Registers (consultation, ANC, Lab, - Cell phones
Pharmacy) - Air times
- Data collection forms - Phone network coverage
-Training - Server
Inputs - Staff transportation (HF  - Registers (consultation, ANC, Lab, Pharmacy)
district) : - Data collection forms
time and cost -Training

18. Challenges
 Periodic report writing: MOH
counterparts (central, regional, district levels)
- Still needs continuous technical support
 Feed back toward field workers
- Needs availability of report/s brief notes (at
central and district levels) presenting data
collected
- Focus on specific notices/recommendations for
field workers
 Data use at district, central levels
- Notable progress
- Needs to be reinforced

19. Challenges
 Data quality : Notable progress , needs
continuous improvement
- Field supervision visits
- Periodic data quality assessment
1. quality control from registers (consultation,
antenatal cares, pharmacy, stock
management sheets) to monthly data
collection form
2. from monthly data collection form to central
level (server).
 NMCP leadership and management
capacity: to coordinate and help sustain the
process

20. Way forward
 Strengthen data use at district ,
central levels
Promote culture of data use through
continuous technical support; Including training
 Ensure feed back toward field
workers
Feed back through SMS
 Consolidate the process in current
covered districts
- Increase completeness of reporting
- Improve analysis program to allow customized
analysis
- Review/adapt some definitions

21. Way forward (2)
 Integrate paper form into the RHIS
reporting form (RTA): RHIS review
 Ensure progressive scale up of
mreporting
- Mopti malaria epidemic surveillance and

response (USAID/PMI)
- Progressive nationwide scale up: MOH (ANTIM)
intranet underway (involved other partners:
UNFPA, Red Cross …)
 Expand mreporting at community level
Help tracking the efforts of community health
workers and improve CBIS.

22. Conclusion
 Mobile reporting system set with MEASURE Evaluation assistance
in Mali improves timeliness, completion 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.
- Fit with local environment marked by turnover of health workers
- Affordable: cost for the development of the application, field follow up
requirements, and recurrent operational costs
- System still running despite a crisis situation
 Continues giving real time pictures of core malaria routine
indicators needed to inform decision making

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

24. MEASURE Evaluation is a MEASURE program project funded by the U.S.
Agency for International Development (USAID) through Cooperative
Agreement GHA-A-00-08-00003-00 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.

25. Thanks
for Your Attention !

26. Annex: Technical features
 Phone  Server
- Java J2ME Application for phone
- Python, Django, MySQL (similar to
(MIDP2)
Rapid SMS)
- Data completeness checks
- SMS handled by Gammu or Kannel
- Data coherence checks on Phone
- Data Collection surveillance:
- Off-coverage storage or report
Progresses, late reports, late
- Phone to server transmission by SMS
validation, list of messages,
- Per-provid Java J2ME Application for
bulk/individual message sending.
phone (MIDP2). er number of
- Raw data visualization
messages sent/received.
- Raw Data exports (Excel, same
 Non Phone format as collection Form)
- Excel-based (OpenOffice compatible)
Nokia 2690 - Report upload (for districts on behalf
data-entry for non-phones districts. of Health Centers)
- District Java J2ME Application for - Report modification & validation (for
phone (MIDP2) district & region levels)
- Upload of excel files via Web - Indicator-oriented data visualization
(optimized website for very poor
(tables, charts)
connections)