1. Data that Walk and Talk Presented by: JSI/Boston Brown Bag 22 February 2008

2. Introduction on concepts and use of immunization data

3. 3 Key Concepts Data should be useful to YOU Avoid collecting data that you will not use In most cases, unorganized data do not provide sufficient information for decision- making Collected and well-organized data provide a «snapshot » and/or message that can be used to make decisions

4. 4 Use of Data for Decision Making INFORMATION To “talk”, data need to be organized: Tables, Graphs, Figures, Maps Data Data collection forms Training on data collection Reporting forms Number of measles cases Number of deaths Number of births Number of inhabitants Number of missed children Number of children vaccinated District with low health coverage Areas with low access to the immunization service Decision-making Many children have not received vaccine doses Plan outreach sessions Train and supervise Create new health centers

5. 5 Key elements for data analysis Where is the population? –Population distribution in a given territory Where are the hard-to-reach populations? –Low coverage areas Where are the unreached populations? –Areas with the highest number of unimmunized children Where are there problems with access to immunization services? –Catchment areas with DPT1 < 80% Where is utilization of services low? –Areas with high drop-outs

6. 6 Definitions DPT1, DPT3: Vaccine given in 3 doses Left-outs: unimmunized, unreached, … Drop-outs: started but didn’t complete series Access to immunization services: DPT1 rate Utilization of immunization services: Drop-outs Categorization of districts: –Category 1: good access and good utilization –Category 2: good access and poor utilization –Category 3: poor access and good utilization –Category 4: poor access and poor utilization

7. 7 DR Congo’s experience on data use and decision-making

8. 8 Background Since 1998, EPI technical staff and ICC technical partners meet annually to: –review the annual EPI plan and data - immunization coverage drop out rates unimmunized children disease incidence, reported outbreaks categorization of health districts (accessibility & utilization of services) completeness of reporting –develop a new EPI plan for the next period –review MOU and develop another to define roles, responsibilities, and support from ICC partners

9. 9 Data use and decision-making process Review Review EPI plan / MOU EPI macro plan Annual EPI macro plan & MOU Mid year review update Macro plan update micro plan EPI micro plan Implementation & monitoring Quarterly monitoring

10. 10 Examples of Data Use: problems, priorities, actions for a maximum results and impact Focus in provinces with more target age group children Focus on areas with high left-out children Children who have not received DPT1, by province DRC, January-May 2005 Proportion of target population by province

11. 11 Unimmunized Children with DPT1 by health districts, DR Congo, Jan-May 2005

12. 12 Concentrate on provinces with more less performing health districts Focus on areas with low immunization Coverage Examples of Data Use: problems, priorities, actions for a maximum results and impact Proportion of health districts by DPT3 strata by province, DRC, January-May 2005 Cumulative DPT3 coverage by province, DRC, January-May 2005

13. 13 DPT3 coverage by health districts, DR Congo, 2000-2005 2000 2001 2002 No reports CV < 50 CV 50 - 80 CV > 80 2003 2004 2005

14. 14 DPT1 & DPT3 coverage follow-up, DRC, Goma health district, January-December 2005 05 juillet 2005 Left-outs Drop-outs

15. 15 Identify problems and make the data “walk” What is the situation? – look at coverage data What are the problems? – access; utilization Where are the problems? –Health districts with low coverage –Health districts with unvaccinated children (left-outs) –Health districts with high drop-out rate Where to focus and have quick impact –Health districts with high population density –Where opportunities exist

16. Use of data in program support – India example

17. 17 Improving quality of services Supportive Supervision –Conducted by trained teams from partner agencies and govt. –Supervisory visits to all health care facilities and selected immunization sites in a district for: On-site corrections Orientation of health functionaries Collection of critical information for making managerial decisions and providing IMMEDIATE FEEDBACK. –Activity followed by graphed feedback and suggestive actions to block, district and state managers. –Initial round followed by second and third rounds to determine status and further action.

18. 18 Trained Supervisor Visit 2 PHCs a day Visit 3 to 4 Immunization Sessions 1.On-site correction 2.Orientation &Training H/W 3.Collection of critical information for action Feedback to H/W & MO PHC locally Analysis Feedback to District & State Plan Re-visit ACTIVITY

19. 19 Essential Elements of Supportive Supervision “STaR” set-up: Supervisor Tools Resources Planning 3 ‘W’s: Where When What

20. 20 Improved Programme Management & Service Delivery (District : Lohardagga)

21. 21 Improved Programme Management & Service Delivery (District : Lohardagga, Jharkhand )

22. 22 Improved Programme Management & Service Delivery (District : Lohardagga)

23. 23 Supportive Supervision – Impact Completed in selected districts of 3 States by IMMUNIZATIONbasics (IB) India team and partners. Improvement seen in subsequent visits, which were shared with MOH officials Further support through development of standardized supervisory checklists and tool for data entry with ready analysis & graphs. Results have motivated both National & State Govts. to include the activity as part of Programme Implementation Plans for the years 2007-2008.

24. 24 COMBINING DATA: Supplemental & Routine Data Example: Diphtheria Pertussis Tetanus (DPT) vaccine DPT given at 6 weeks (DPT1), 10 wks (DPT2), 14 wks (DPT3) in a routine immunization schedule (children under 1 yr) DPT3 coverage may indicate continuity of use by parents, client satisfaction with services, and capability of the system to deliver a series of vaccinations DPT1-3 dropout may indicate perceived quality of service and quality of communication between parents and health workers — this is the classic drop-out indicator (DPT1 - DPT3 / DPT1 x 100) NIGERIA case study

25. 25 Background Information NIGERIA Immunization Plus Days (IPDs) began in May 2006 deliver multiple antigens (including DPT) through mass campaign-like events held every 4-6 weeks target multiple age cohorts (children under 5 yrs) Definition of “routine immunization” becomes unclear ….closer to “routine doses” rather than the regular and predictable delivery of doses through a routine system IPD (supplemental immunization activity or SIA) and RI (routine immunization) data are combined or aggregated in records and reports

26. 26 Source: National Immunization Coverage Survey 2006, Preliminary report National Coverage Trends by Antigens NIGERIA

27. 27 NIGERIA DPT3 Vaccination Coverage by LGA, State Z, Jan – Nov 2006 What could be some reasons for coverage over 100 percent?

28. 28 Making sense of the data – tracking coverage Why the sudden rise in coverage? Why the sudden drop in DPT1 coverage? What information can this graph provide? NIGERIA

29. 29 DPT1 – DPT3 Dropout Rates by LGA, X State Jan – Nov 2006 What does this chart tell us? Source: State X administrative reports NIGERIA Drop-out = DPT1 – DPT3 x 100 DPT1

30. 30 Comparing DPT3 Coverage in One State >=65% Coverage 50-64% Coverage < 50% Coverage Reflects coverage through SIAs Reflects coverage through RI system NIGERIA Source: extrapolated from administrative reports What are the implications for M&E?

31. 31 Reported “Routine” EPI Performance DPT3 2003 24.8% - DPT3 2005 ~38% -- Source: ERC presentation, (1) 2003 Data Coverage Survey Data; (2&3) 2005/2006 Routine Immunization Administrative Data <50% 50 - 79% ≥80% DPT3 2006 77%

32. 32 Reported DPT3 Coverage Source: Administrative reports submitted to NPI National Immunization Coverage Survey (NICS) results: DPT3 coverage 19% in 2002 and 26% in 2005 NIGERIA What are the implications for IMR and U5MR?

33. 33 NIGERIA SUMMARY Combining supplemental and routine data: Masks system’s weaknesses (RI) Magnifies data quality issues Numerator and denominator challenges Different age cohorts targeted (<1s and <5s) Campaigns are chaotic…hard to track a child (double, triple counting), compounded by no card culture Means surveys can’t measure RI coverage …community unclear on SIA vs RI SIAs are costly, especially IPDs (every 4-6 wks). How sustainable are IPDs? What happens when they stop? Other M&E implications ???

34. Remember…. Data should be useful to YOU Avoid collecting data that you will not use In most cases, unorganized data do not provide sufficient information for decision- making Collected and well-organized data provide a «snapshot » and/or message that can be used to make decisions

35. T HANK Y OU