Enhancing data tracking for evidence based programming

Enhancing data tracking for evidence based programming

Describing the needs and funding for ACTs and RDTs in Africa 350,000,000 300,000,000 250,000,000 200,000,000 150,000,000 Need funded Gaps 100,000,000 50,000,000 0 RDTs ACTs RDTs ACTs 2010 2011

Scaling up RDTs and ACTs Countries identified need for RDTs: 42% increase in required number of RDTs from 2010 2011 35% increase in reported number financed from 2010 2011 Indicates positive trend, continued rapid scale up of RDTs Diagnosis is still falling short of universal coverage targets Countries identified need for ACTs: Leveling off in identified requirements for ACTs from 2010 2011 Leveling off in identified financing for ACTs from 2010 2011 This likely reflects an increase in coverage of health facilities, iccm and private sector coupled with decreasing consumption as a result of increased vector control and parasitological diagnosis

Implications: Need for Improved Routine Monitoring Improved data for routine monitoring will: Improve quantification of need Avoid stock outs or over-consumption Support all levels of management decision making Allow monitoring of implementation against targets Facility District / Programmatic National / International Example: SMS For Life Example: Stock / Case data used in HMIS / LMIS for management Example: ALMA Scorecard

ALMA Scorecard for Accountability and Action With the support of RBM partners 2011, ALMA 13 May, 2011 DRAFT Policy Financial control Commodities financed Implementation Impact Tracer indicators for maternal and child health Country Angola 3 2 1 ND 2.4 84 73 100 50 ND 18 47 11 73 Benin 2 3 2 3.3 100 42 87 100 ND 44 74 43 83 Botswana 2 3 1 ND ND ND 100 100 ND 3 94 94 34 96 Burkina Faso 2 3 1 4.0 100 70 100 86 ND 32 54 7 82 Burundi 1 3 1 ND 2.6 100 97 100 100 ND 11 34 45 92 Cameroon 1 3 3 2.9 79 100 100 58 ND 58 63 21 80 Cape Verde 2 2 ND ND 4.0 ND ND ND ND 3 61 ND ND 99 Central African Republic 1 1 1 2.4 30 13 35 53 ND 32 53 23 54 Chad 1 2 1 2.2 73 100 100 100 ND 7 14 2 23 Comoros 2 3 ND ND 2.4 64 100 100 87 ND 25 ND ND 83 Congo 1 2 1 ND 2.6 73 0 29 100 ND 18 83 19 91 Cote d'ivoire 2 3 2 ND 2.6 82 100 100 11 ND 50 57 4 81 Djibouti 1 3 1 ND 2.8 22 31 100 22 ND 7 61 1 89 DRC 1 3 3 2.2 100 52 59 62 ND 2 74 36 77 Equatorial Guinea 1 1 3 ND 83 100 100 16 2 29 65 24 33 Eritrea 1 3 2 2.7 100 100 100 84 3 25 28 52 99 Ethiopia 2 3 2 3.2 58 100 100 69 3 8 6 49 79 Gabon 2 3 3 ND 33 0 33 16 ND 26 86 6 45 Gambia 1 2 3 2.9 100 100 100 93 2 21 57 41 98 Ghana 2 3 3 3.8 83 100 100 59 ND 28 57 63 94 Guinea 3 3 1 2.6 68 26 100 57 ND 14 46 48 57 Guinea Bissau 2 3 1 2.6 100 100 100 74 3 24 39 16 68 Kenya 3 3 1 ND 3.3 100 25 100 84 2 72 44 32 75 Liberia ND 3 2 ND 2.8 100 60 88 95 ND 17 46 29 64 Madagascar 2 3 3 3.3 100 100 100 100 3 1 51 67 78 Malawi 2 2 3 3.4 50 0 44 29 ND 39 54 0 93 Mali 3 3 3 3.4 73 51 100 62 ND 37 49 38 74 Mauritania 2 3 1 ND 3.0 76 5 52 28 ND 9 61 0 64 Mozambique 3 3 2 3.4 100 0 36 57 ND 46 55 37 76 Namibia 2 2 1 ND ND 33 100 100 38 3 58 ND ND 83 Niger 2 3 3 3.1 44 20 100 29 ND 55 33 4 70 Nigeria 2 3 3 2.9 77 39 80 63 ND 19 39 13 54 Rwanda 2 3 3 3.5 100 54 100 100 3 68 52 88 97 Sao Tome and Principe ND 2 ND 3.1 100 100 100 69 3 28 ND nd 98 Senegal 2 3 3 3.4 89 100 100 98 ND 22 52 34 86 Sierra Leone 1 3 3 2.9 88 34 76 100 ND 18 42 11 75 Somalia ND 1 1 ND ND 38 100 50 15 ND 0 33 9 31 South Africa 2 3 1 ND ND NA 100 100 NA 3 83 91 8 69 Sudan 2 3 3 ND 2.2 84 76 77 35 2 2 49 34 84 Swaziland 2 2 1 ND 100 100 100 79 3 69 69 32 95 Tanzania 3 3 1 3.5 100 100 100 94 2 68 43 41 85 Togo 2 2 3 2.4 86 82 100 100 ND 26 62 48 89 Uganda 3 3 3 3.3 93 100 100 56 ND 52 42 60 64 Zambia 2 3 2 3.2 100 ND ND 74 3 61 47 61 81 Zimbabwe 2 2 1 ND 2.0 100 100 100 74 2 43 69 22 73 Data Source Removal of tariffs on antimalaria commodities (# of tariff-free commodities) Oral Artemisinin Based Monotherapy Ban status Community case management Pneumonia Pneumoni a Malaria Malaria World Bank rating on public sector mgmt and institutions 2009 (CPIA Cluster D) LLIN financing 2011 projection (% of need) Public sector RDT financing 2011 projection (% of need) Public sector ACT financing 2011 projection (% of need) Operational ITN coverage (% of at risk population) MTAP www.mtap.org/ www.who.int/malaria/ WHO-GMP and http://siteresources.w RBM Roadmap 2011 RBM Roadmap 2011 RBM Roadmap 2011 RBM Harmonisation marketing_of_oral_art http://www.countdown orldbank.org/ida/res (www.rollbackmalaria. (www.rollbackmalaria. (www.rollbackmalaria. working group/ emisinin_monotherapi 2015mnch.org ources/73153- org/) org/) org/) Alliance for malaria es 1181752621336/3878 prevention 278-1277851499224/ICP R_2009_Alpha_Table 1.pdf Reduced malaria deaths by >50% by 2009 (vs 2000) PMTCT coverage 2009 (% pregnant HIV pts receiving ARVs) % deliveries Exclusive DPT3 vaccination assisted by skilled breastfeeding (% among 12-23 mo. birth attendant children < 6 months) olds WHO WMR 2010 http://www.aidsinfoonl www.healthmetricsan http://www.unicef.org/ http://www.unicef.org/ ine.org/ devaluation.org/ rightsite/sowc/statistic rightsite/sowc/statistic www.mdgmonitor.org s.php s.php Produced by WHO on behalf of ALMA and RBM Partners Target achieved on track Progress but more effort required Not on track No data/data to be obtained

Requirements for enhanced data Wave 1 collection Wave 2 Wave 3 Metric cluster (examples) Actionable metrics based on currently available data RDTs RDT financing 2011 projection (% funding secured for universal coverage of the public sector) ACTs ACT financing 2011 projection (% funding secured for universal coverage of the public sector) Data sources RBM roadmaps Metrics improved using better data sources RDT Stockouts, at national and health facility level) ACT Stockouts, at national and health facility level) SMS Stock Data SMS Case Mgt Info National Volumes Data LMIS Addition of /switch to newer metrics that best track progress of antimalaria efforts % of suspected cases receiving a malaria diagnostic % of confirmed cases receiving treatment with appropriate and effective antimalarial drugs. HMIS 6

Conclusions Need for increased funding and emphasis on data collection to inform programming and enhance accountability Innovative methodologies to increase availability of data SMS etc Enhanced data improves accountability and promotes efficient use of resources We are seeing results! E.g. impact on malaria deaths in Zanzibar, Zambia etc