Your SlideShare is downloading. ×
Improving Malaria Reporting in Resource Poor Settings: How Cell Phones Affect Timeliness, Completion and Quality of Data in Mali
Improving Malaria Reporting in Resource Poor Settings: How Cell Phones Affect Timeliness, Completion and Quality of Data in Mali
Improving Malaria Reporting in Resource Poor Settings: How Cell Phones Affect Timeliness, Completion and Quality of Data in Mali
Improving Malaria Reporting in Resource Poor Settings: How Cell Phones Affect Timeliness, Completion and Quality of Data in Mali
Improving Malaria Reporting in Resource Poor Settings: How Cell Phones Affect Timeliness, Completion and Quality of Data in Mali
Improving Malaria Reporting in Resource Poor Settings: How Cell Phones Affect Timeliness, Completion and Quality of Data in Mali
Improving Malaria Reporting in Resource Poor Settings: How Cell Phones Affect Timeliness, Completion and Quality of Data in Mali
Improving Malaria Reporting in Resource Poor Settings: How Cell Phones Affect Timeliness, Completion and Quality of Data in Mali
Improving Malaria Reporting in Resource Poor Settings: How Cell Phones Affect Timeliness, Completion and Quality of Data in Mali
Improving Malaria Reporting in Resource Poor Settings: How Cell Phones Affect Timeliness, Completion and Quality of Data in Mali
Improving Malaria Reporting in Resource Poor Settings: How Cell Phones Affect Timeliness, Completion and Quality of Data in Mali
Improving Malaria Reporting in Resource Poor Settings: How Cell Phones Affect Timeliness, Completion and Quality of Data in Mali
Improving Malaria Reporting in Resource Poor Settings: How Cell Phones Affect Timeliness, Completion and Quality of Data in Mali
Improving Malaria Reporting in Resource Poor Settings: How Cell Phones Affect Timeliness, Completion and Quality of Data in Mali
Improving Malaria Reporting in Resource Poor Settings: How Cell Phones Affect Timeliness, Completion and Quality of Data in Mali
Improving Malaria Reporting in Resource Poor Settings: How Cell Phones Affect Timeliness, Completion and Quality of Data in Mali
Improving Malaria Reporting in Resource Poor Settings: How Cell Phones Affect Timeliness, Completion and Quality of Data in Mali
Improving Malaria Reporting in Resource Poor Settings: How Cell Phones Affect Timeliness, Completion and Quality of Data in Mali
Improving Malaria Reporting in Resource Poor Settings: How Cell Phones Affect Timeliness, Completion and Quality of Data in Mali
Improving Malaria Reporting in Resource Poor Settings: How Cell Phones Affect Timeliness, Completion and Quality of Data in Mali
Improving Malaria Reporting in Resource Poor Settings: How Cell Phones Affect Timeliness, Completion and Quality of Data in Mali
Improving Malaria Reporting in Resource Poor Settings: How Cell Phones Affect Timeliness, Completion and Quality of Data in Mali
Improving Malaria Reporting in Resource Poor Settings: How Cell Phones Affect Timeliness, Completion and Quality of Data in Mali
Improving Malaria Reporting in Resource Poor Settings: How Cell Phones Affect Timeliness, Completion and Quality of Data in Mali
Improving Malaria Reporting in Resource Poor Settings: How Cell Phones Affect Timeliness, Completion and Quality of Data in Mali
Improving Malaria Reporting in Resource Poor Settings: How Cell Phones Affect Timeliness, Completion and Quality of Data in Mali
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

Improving Malaria Reporting in Resource Poor Settings: How Cell Phones Affect Timeliness, Completion and Quality of Data in Mali

983

Published on

Presented at the November 2012 ASTMH Conference in Atlanta, GA.

Presented at the November 2012 ASTMH Conference in Atlanta, GA.

0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
983
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
0
Comments
0
Likes
0
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide
  • 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
  • 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. ASTMH 61st Annual Meeting Atlanta, GA November 11-15, 2012Improving 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  CoverageSupport 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 lInformation de Routine du PNLP - Niveau District Sanitaire (Csréf/Cscom)Région MédicaleDistrict Sanitaire Mois Année Rupture de stock CTA pendant le moisEtablissement sanitaire (Oui, Non) Consultation CTA Nourisson - Enfant Classification < 5 ans 5 ans et plus Femmes enceintes CTA AdolescentTotal consultation, toutes causes confondues CTA AdulteNbre de Cas de paludisme (Tous suspectés)Cas de paludisme testés (GE et/ou TDR) PEC de cas de Paludisme graveCas de paludisme confirmés (GE et/ou TDR) Rupture de soctk OUI/NONNbre de Cas de paludisme Simple Arthemether injectableNbre de Cas de paludisme Grave Quinine InjectableNbre 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 MILDTotal Hospitalisés Paludisme TDRTotal Hospitalisations toutes causesconfondues SP Décès CPN/SP des femmes enceintes Classification < 5 ans 5 ans et plus Femmes enceintes (nbre)Cas de décès pour paludisme CPNTotal cas de décès toutes causes confondues SP 1 SP 2 Moustiquaires imprégnées dinsecticide distribuées Classification < 5 ans Femmes enceintes Nom et Prénom : _______________________ Le ResponsableNombre de moustiquaires distribuées CSCom/CSRéf Date : ___________________/20___
    • 6. Training: how to edit and send SMS report1  2  3  45  ………………………
    • 7. Error ! 
    • 8. Central MoHCell 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 TOTALALL AGE Nb (%) Nb (%) Nb (%) Nb (%) Nb (%) Nb (%) Nb (%) Nb (%) Nb (%) Nb (%) Total 11256consultation 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 testedNb 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 FacilitiesTimeliness of reporting 80% > 95 %Completeness of reporting 80% > 95%Work load: data transcription NA 15-30 minuteson SMS codes
    • 16. Malaria Routine information strengthening process Paper form vs. mobileReporting Paper form MobileReportingAverage time for data One to several dayssending Immediately - Direct record to databaseOverall advantages from No evident advantages - Better timeliness of reportingfield experience - Better completeness of reporting - Suitable for hard to reach areas - Data instantly available on server - Lower timeliness - Lower completenessOverall disadvantages - Risk of form loosing - Possible failure with mob phone net - Risk of errors in data transcription work coveragefrom 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 - ServerInputs - 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 CooperativeAgreement GHA-A-00-08-00003-00 and is implemented by the CarolinaPopulation Center at the University of North Carolina at Chapel Hill, inpartnership 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. Thanksfor 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)

    ×