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ICCM impact in four African countries: project monitoring & evaluation

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A presentation by Geoffrey Namara from Malaria Consortium, which looks at the results and highlights of integrated community case management projects implemented in four African countries. The …

A presentation by Geoffrey Namara from Malaria Consortium, which looks at the results and highlights of integrated community case management projects implemented in four African countries. The presentation examines routine data collection methods, the scale and duration of data collection, results & highlights from routine data, project evaluations, evaluations conducted (methods & timelines), and results & highlights.

To see the live presentation, watch the YouTube video: http://www.youtube.com/watch?v=d2UQkhUqbP8

Published in: Health & Medicine

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  • 1. Geoffrey Namara 30th May 2013 Project Monitoring & Evaluation Results & Highlights
  • 2. Outline  Routine monitoring – Routine data collection methods – Scale and duration of data collection – Results & highlights from routine data  Project evaluations – Evaluations conducted (methods & timelines) – Results & highlights
  • 3. Project Routine Monitoring  Routine data transmission across countries Mozambique S.Sudan Collection 1 st Level CHW supervisor Summary CHW supervisor Summary Health Facility Summary Health Facility Summary Health Facility Summary Entry& Processing at Malaria Consortium CHW completes Register Submission of CHW data to Districts District Summary MC Obtain CHW data 2 nd Level Processing Feed Back Uganda Zambia
  • 4. Scale & Duration of routine data collection Mozambique S. Sudan Uganda Zambia Overall Targeted 175 871 6,800 1332 9,178 Trained 165 802 6,774 1332 9,073 % trained 94% 92% 100% 100% 99% Dropped out 3 42 485 68 Attrition 1.8% 5.2% 7.1% 5%  CHW Training  Duration of data collection Mozambique S. Sudan Uganda Zambia Months 8 15 30 25
  • 5. Monthly CHW reporting rates 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 J A S O N D J F M A M J J A S O N D J F M A M J J A S O N D 2010 2011 2012 %CHWsReporting Reporting period Mozambique S.Sudan Uganda Zambia
  • 6. Uptake of diagnostic tests & positivity rates Malaria Pneumonia 111% 96% 66% 80% 0% 20% 40% 60% 80% 100% 120% Uganda Zambia Percentage % Fevers with RDT done % RDT positive 37% 91% 61% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% S.Sudan Uganda Zambia Percentage % assessed for resp.rate found with high resp.rate
  • 7. Cases seen & treatments provided by CHWs Treatments (doses) Mozambique S. Sudan Uganda Zambia Overall ACTs 8,013 30,197 610,048 160,594 808,852 Amoxycillin 10,771 22,732 590,667 50,054 674,224 ORS 3,238 --- 252,401 25,181 280,820 Zinc --- --- 263,156 8,997 272,153 Paracetamol --- --- --- 55,700 55,700 Total Treatments 22,022 52,929 1,716,272 300,526 2,091,749 Total cases seen 25,383 47,379 1,406,342 245,038 1,724,142 Adjusted for under reporting Mozambique S. Sudan Uganda Zambia Overall Total Treatments 26,216 110,269 2,319,286 462,348 2,918,119 Total cases seen 30,218 98,706 1,900,462 376,982 2,406,368
  • 8. Relative frequency of disease treatments 36% 57% 42% 68% 46% 49% 43% 41% 21% 38% 15% 17% 11% 16% 0% 10% 20% 30% 40% 50% 60% 70% 80% Mozambique S.Sudan Uganda Zambia Overall %oftotaltreatmentsprovided Countries Malaria Pneumonia Diarrhoea
  • 9. Variation between test results & treatment -20.0% -15.0% -10.0% -5.0% 0.0% 5.0% 10.0% 15.0% 20.0% J A S O N D J F M A M J J A S O N D J F M A M J J A S O N D 2010 2011 2012 Uganda Zambia Ideal OvertreatmentUndertreatment Malaria • ACTs distributed should equal RDT positives i.e. no variation • More ACTs than RDT positives would indicate over treatment • Less ACTs than RDT positives would indicate under treatment
  • 10. Variation between test results & treatment Pneumonia -60.0% -40.0% -20.0% 0.0% 20.0% 40.0% 60.0% 80.0% J A S O N D J F M A M J J A S O N D J F M A M J J A S O N D 2010 2011 2012 Uganda Zambia Ideal OvertreatmentUndertreatment
  • 11. Project Evaluations Project was evaluated using two approaches  Surveys – Conducted at baseline and endline – Main outcome, child mortality – Other outcomes: child morbidity & treatment seeking behaviour  Modelling Impact – mortality and lives saved – Impact modelled using Lives Saved Tool (LiST)
  • 12. Surveys - Design & Methods  Cross sectional household survey at each round (baseline & endline)  Mortality sample required 4000 households (100 clusters) – Mortality survey only conducted at endline (using birth history)  Child health sample required 1600 households (40 clusters) – Similar clusters at baseline surveyed at endline  Used 2-stage cluster sampling technique – Sample clusters using probability proportionate to size, then households  Data collection, processing & analysis followed standard Demographic & Health Surveys procedures
  • 13. 2 yrs J J A S O N D J F M A M J J A S O N D J F M A M J J A S O N D J F M A M J J A S O N D 2009 2010 2011 2012 Surveys - Timelines Mozambique South Sudan Uganda Zambia J J A S O N D J F M A M J J A S O N D J F M A M J J A S O N D J F M A M J J A S O N D 2009 2010 2011 2012 J J A S O N D J F M A M J J A S O N D J F M A M J J A S O N D J F M A M J J A S O N D 2009 2010 2011 2012 J J A S O N D J F M A M J J A S O N D J F M A M J J A S O N D J F M A M J J A S O N D 2009 2010 2011 2012 2 yrs, 4mths 3 yrs 2 yrs, 7 mths
  • 14. Child Morbidity: 2 week disease prevalence 1. Reported Fever country Baseline Endline Mozambique 23.2 [19.1,28.0] 27.9 [25.4,30.5] Uganda 37.5 [32.5,42.8] 30.8 [25.5,36.7] Zambia 37.6 [31.2,44.4] 37.7 [33.5,42.1] 2. Reported ARI country Baseline Endline Mozambique 20.5 [16.6,25.1] 9.5 [8.0,11.1] Uganda 21.8 [19.1,24.7] 31.5 [29.1,33.9] Zambia 16.1 [13.5,19.1] 15.6 [13.2,18.4] 3. Reported Diarrhoea country Baseline Endline Mozambique 9.9 [7.9,12.4] 10.5 [8.9,12.5] Uganda 16.9 [14.4,19.7] 14.8 [12.8,17.1] Zambia 17.5 [14.7,20.7] 14.7 [11.6,18.4]
  • 15. Treatment seeking behaviour: 0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0 100.0 Fever ARI Diarrhoea %soughttreatment Disease/symptom baseline endline Fever ARI Diarrhoea Disease/Symptom baseline endline Any condition Disease/Symptom baseline endline Zambia Uganda Mozambique  Percentage of sick children who sought treatment
  • 16. Treatment seeking behaviour First point of contact in seeking care 1. Uganda Source Fever Baseline Endline Public facilities 25.8 9.6 CHW 1.9 40.4 Private sector 68.9 44.3 Other 2.4 5.7 ARI Baseline Endline 28.2 9.0 1.7 33.8 69.2 51.4 1.0 5.8 Diarrhoea Baseline Endline 25.0 17.9 0.7 38.8 69.1 38.9 5.2 4.4 2. Zambia Source Fever Baseline Endline Public facilities 78.3 24.7 CHW 16.1 68.8 Private sector 3.7 3.2 Other 1.9 3.4 ARI Baseline Endline 96.6 25.8 1.3 62.7 0.0 4.2 2.1 7.3 Diarrhoea Baseline Endline 75.6 23.4 17.5 66.4 1.6 4.1 5.3 6.2
  • 17. Received appropriate treatment Percentage of children that received appropriate treatment Zambia MozambiqueUganda Baseline Endline 0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 Fever ARI Diarrhoea %tookappropriatetreatment 0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0 100.0 Fever ARI Diarrhoea %tookappropriatetreatment 0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0 45.0 50.0 Fever ARI %tookappropriatetreatment
  • 18. Timing of treatment Treatment within 24 hours of onset of fever or ARI 0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 Fever ARI %soughttreatmentwithin24hrs Zambia 0.0 5.0 10.0 15.0 20.0 25.0 30.0 Fever ARI %soughttreatmentwithin24hrs Mozambique 0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0 45.0 50.0 Fever ARI %soughttreatmentwithin24hrs Uganda Baseline Endline
  • 19. Impact Modelling-LiST  LiST (Lives Saved Tool) – Part of a compendium of modelling modules (SPECTRUM) that aid projection of impact of existing interventions – Computer programme module focusing on child survival – Developed under collaboration of – Futures Institute, – CHERG – International Child Development Steering Group – How it works: projects changes child survival based on changes of coverage of child health interventions
  • 20. LiST: Modelling Impact of ICCM  Model Inputs – Population covered by age category, population growth rate – Child health indicators before & after ICCM implementation – Expected trend in non implementation areas  Model outputs – Changes in mortality estimates during the period – Lives saved (deaths averted) – Projected changes over a longer period (5 years)
  • 21. LiST: Modelling Impact of ICCM  Results 1. Uganda indicator Implementation period projections 2009 2010 2011 2012 2013 2014 2015 U5 Mortality rate 100 96 90 86 79 75 72 Lives saved (1-59 months) 0 151 297 439 574 614 629 % deaths averted 4% 7% 2. Zambia indicator Implementation period projections 2009 2010 2011 2012 2013 2014 2015 U5 Mortality rate 102 96 88 81 75 73 72 Lives saved (1-59 months) 0 103 207 317 430 479 409 % deaths averted 7% 12%
  • 22. LiST: Modeling Impact of ICCM  Results 1. Mozambique indicator Implementation period projections 2009 2010 2011 2012 2013 2014 2015 U5 Mortality rate 117 111 104 97 91 87 84 Lives saved (1-59 months) 0 139 278 418 555 656 612 % deaths averted 5% 9%
  • 23. Learning  It is feasible for non-medical community-based agents to deliver life saving medicines according to national guidelines  Community-based agents can provide a complementary & acceptable source of effective treatment  Access to timely treatment of sick children has increased with the introduction of ICCM  Model projections illustrate a potential for mortality reductions with sustained ICCM
  • 24. ICCM is a feasible approach for increasing access to effective treatment for childhood illnesses; urgent scale up is needed to enable countries to achieve MDG 4
  • 25. www.malariaconsortium.org Thank you