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Harnessing mHealth to Monitor Different Epidemics within One Country: Experience from Mali
Harnessing mHealth to Monitor Different Epidemics within One Country: Experience from Mali
Harnessing mHealth to Monitor Different Epidemics within One Country: Experience from Mali
Harnessing mHealth to Monitor Different Epidemics within One Country: Experience from Mali
Harnessing mHealth to Monitor Different Epidemics within One Country: Experience from Mali
Harnessing mHealth to Monitor Different Epidemics within One Country: Experience from Mali
Harnessing mHealth to Monitor Different Epidemics within One Country: Experience from Mali
Harnessing mHealth to Monitor Different Epidemics within One Country: Experience from Mali
Harnessing mHealth to Monitor Different Epidemics within One Country: Experience from Mali
Harnessing mHealth to Monitor Different Epidemics within One Country: Experience from Mali
Harnessing mHealth to Monitor Different Epidemics within One Country: Experience from Mali
Harnessing mHealth to Monitor Different Epidemics within One Country: Experience from Mali
Harnessing mHealth to Monitor Different Epidemics within One Country: Experience from Mali
Harnessing mHealth to Monitor Different Epidemics within One Country: Experience from Mali
Harnessing mHealth to Monitor Different Epidemics within One Country: Experience from Mali
Harnessing mHealth to Monitor Different Epidemics within One Country: Experience from Mali
Harnessing mHealth to Monitor Different Epidemics within One Country: Experience from Mali
Harnessing mHealth to Monitor Different Epidemics within One Country: Experience from Mali
Harnessing mHealth to Monitor Different Epidemics within One Country: Experience from Mali
Harnessing mHealth to Monitor Different Epidemics within One Country: Experience from Mali
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Harnessing mHealth to Monitor Different Epidemics within One Country: Experience from Mali

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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 …

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.

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  • Reports writing by MOH counterparts (central, regional, district levels) still remains a challenge to overcome by continuous technical support to partners
    Data collected should serve to write sound reports providing relevant information to guide decision making at central, district and even facilities levels Reports writing by MOH counterparts (central, regional, district levels) still remains a challenge to overcome by continuous technical support to partner
  • Reports writing by MOH counterparts (central, regional, district levels) still remains a challenge to overcome by continuous technical support to partners
    Data collected should serve to write sound reports providing relevant information to guide decision making at central, district and even facilities levels Reports writing by MOH counterparts (central, regional, district levels) still remains a challenge to overcome by continuous technical support to partner
  • Transcript

    • 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!

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