Training on EPI Data-Management

1. Training on EPI Data-Management Mongolia Expanded Programme on Immunisation Ulaanbaatar, 24-28 August 2009

2. Training Goal provide training to EPI staff to build capacity where needed to improve data quality and strengthen surveillance and immunization coverage monitoring for vaccine preventable diseases

3. Data a set of discrete, objective facts about events and is more usefully described as structured records of transactions

4. Database computerized database is a structured collection of records or data that is stored in a computer system. to be truly functional, it must not only store large amounts of records well, but be accessed easily. In addition, new information and changes should also be fairly easy to input

5. Data Quality data quality means that the information collected adequately represents the program’s activities. Adequately representsmeans that the data are accurate and reliable Accurateis interpreted as measuring what we intend to measure (that the information is correct) Reliableimplies that data have been collected and measured in the same way (consistently) by all involved during all reporting periods

6. Data Management Data Management is the process of managing data as a resource that is valuable to an organization

7. Data Management .. • Standard Terms and Definitions • Standard Functions, Processes and Practices • Standard Roles and Responsibilities • Standard Deliverables and Metric

8. Data ManagementBASICS

9. Practices & Techniques Organisation & Culture Activities Goals & Principles Deliverables Roles & Responsibilities Technology Data Management Structure

10. Data Management Structure ..

11. Data Architecture Management Data Quality Management Data Development Database Operations Management Meta Data Management Data Governance Data Security Management Document & Content Management Data Warehousing & Business Intelligence Management Reference & Master Data Management Data Management Functions

12. Data Architecture Management Organization & Culture Data Quality Management Data Development Technology Activities Database Operations Management Meta Data Management Data Governance Goals & Principles Document & Content Management Data Security Management Practices & Techniques Deliverables Data Warehousing & Business Intelligence Management Reference & Master Data Management Roles & Responsibilities Data Management Functions Environmental Elements

13. Wisdom Joining of wholes Knowledge Formation of A whole Novelty Future Context Information Connection of parts Data Past Gathering of parts Experience Understanding Absorbing Doing Interacting Reflecting Researching Data Management

14. EPI Targets

15. Global Goals Millennium Development Goal 4 (MDG4) Reduce under-five mortality by 2/3 between 1990 and 2015 Global Immunization Vision and Strategy (GIVS) Reduce measles mortality by 90% between 2000 & 2010 Reduce childhood morbidity and mortality due to VPDs by at least 2/3 between 2000 & 2015 Global polio eradication by 2000 Global neonatal tetanus elimination by 2005

16. Regional Goals RCM Resolution (WPR/RC/56.R8) • By 2012 Member States to • Achievemeasleselimination • Reduce chronichepatitis Binfectionrates to < 2% in 5 year old children as an interim milestone towards final goal of < 1% • In addition • Maintainpolio-freestatus through high-quality AFP surveillance and high immunization coverage

17. Immunization Coverage Goals By 2010: GIVS ≥90% national coverage ≥80% coverage in all districts By 2012: regional twin goals ≥95% with two doses of MCV ≥80% coverage with HepB birth dose (<24 hrs) ≥85% coverage with HepB3

18. Monitoring Progress Towards Measles Elimination, WPR 1 Excludes imported cases, but includes import-related cases 6 MCV1 and MCV2 coverage ≥ 95% also is required in every district to prevent pockets of measles virus transmission

19. Monitoring Progress Towards Measles Elimination, WPR 2 Non-measles suspected case: excludes cases confirmed by lab, epidemiologic linkage, or clinically 3 Adequate investigation: collection of essential data elements (date of rash onset, date of specimen collection, vaccination status, date of last vaccination, date of birth or age, sex, district) and search for epidemiologically-linked cases 4 Adequate blood specimen: sufficient volume (0.5 ml) collected within 28 days after rash onset. Excludes from the denominator cases that are epidemiologically linked to confirmed measles or to other confirmed communicable diseases (e.g. rubella) 5 Transmission chain (outbreak): 2 or more cases in which rash onset in one is 7–21 days after the other Sufficient samples: oral fluid, naso-pharyngeal swabs, urine, or whole blood collected from at least two suspected cases for outbreaks with ≤ 5 cases and at least five suspected cases for outbreaks with >5 cases, early in any outbreak and every 2–3 months if transmission continues.

20. Classification of Measles and Rubella Cases Measles / Rubella IgM + Measles/Rubella Lab Confirmed Adequate Specimen Measles / Rubella IgM – Epi-linked to confirmed case of other communicable disease Suspected Case Discarded Measles/Rubella Other confirmed disease Measles/Rubella Epidemiologically Confirmed Epi-linked to measles / rubella; no other confirmed disease No/Inadequate Specimen No other confirmed disease Measles Clinically Confirmed

21. Hepatitis B Control Achieve <2% of infection in 5 years old cohorts • ≥80% coverage with HepB birth dose (<24 hrs) • ≥85% coverage with HepB3

22. Training on EPI Data-Management Mongolia Expanded Programme on Immunisation Ulaanbaatar, 24-28 August 2009

23. Data Quality data quality means that the information collected adequately represents the program’s activities. Adequately representsmeans that the information is accurate and reliable. Accurate is interpreted as measuring what we intend to measure (that the information is correct), Reliable implies that it has been collected and measured in the same way (consistently) by all involved during all reporting periods

24. Data Quality Dimensions • Accuracy: The extent to which the data is free from significant error. The measure or degree of agreement between a data value or set of values and a source assumed to be correct • Validity: The degree to which data values satisfy acceptance requirements of the validation criteria or fall within the respective domain of acceptable values; • Completeness: The extent to which enough of the required data elements are collected from the target population or sample. Completeness is typically described in terms of percentages or number of data values; • Consistency: The extent to which data is collected using the same procedures and definitions across collectors and times; • Timeliness: Whether data about recent performance is available when needed to improve program management; • Ease of use: How readily intended users can access data, aided by clear data definitions, user-friendly software and easily used access procedures.

25. Data Quality and Routine vaccination Routine vaccination coverage rates are generally used to describe the proportion of the targeted population that has been vaccinated. This information is valuable at every level of the programme; it provides a rough estimate of the proportion of the population that remains susceptible to the disease targeted by the vaccine

26. Coverage estimates based on routine/administrative data are sensitive to two major biases; • those in the numerator (the number of doses administered) and • those in the denominator (the size of the target population). This assumes that all areas have reported Underestimate Depends on the # of children vaccinated in the non-reporting areas ?

27. Date of birth Date of last MCV dose Date of notification Date of rash onset < < ≤ (dob) (dlastvac) (donset) (dnoti) Date of case invest Date of spec collection Date of lab receipt Date of lab report ≤ ≤ ≤ ≤ (dcaseinv) (dscoll) (dlabrec) (dlabrept)

28. Suspected Measles Cases with Reported Core Variables. Western Pacific Region 2007 - 2009†

29. Measles IgM Results*, WPR 2007

30. District 2 District 3 District 1 Monthly Report Monthly Report Monthly Report District 4 Monthly Report District 1 RS 6 RS 1 RS 3 TOTAL District 3 RS 2 District 4 TOTAL District 2 RS 4 TOTAL RS 5 75 75 45 200 50 20 65 45 75 45 200 250 45 TOTAL 65 Reporting Site 5 Reporting Site 1 Reporting Site 4 Monthly Report Monthly Report Reporting Site 6 Reporting Site 3 Reporting Site 2 Monthly Report Susp. cases 50 Susp. cases 45 Monthly Report Monthly Report Monthly Report Susp. cases 75 Susp. cases Susp. cases Susp. cases 75 200 20 Source Document 1 Source Document 1 Source Document 1 Source Document 1 Source Document 1 Source Document 1 Tracing and Verifying Report Totals from the Service Delivery Site Through Intermediary Reporting Sites to the Program/project M&E Unit M&E Unit/National Monthly Report TOTAL 385

33. Reporting completeness, 2000-2007 % of JRFs received

34. Data collected in EPI • Annual data collection • Annual (WHO-UNICEF Joint reporting Form on Immunization JRF) • Surveillance data collection • Epidemiologic and laboratory data for selected vaccine preventable diseases (quarterly, monthly, weekly) • Ad hoc data collection • Supplementary immunization activities

35. Standard forms (for annual data collection) Electronic data transfer - predefined file format (for monthly data collection Manual data entry (from e-mails, reports) for ad hoc data collection Instruments for Data collection

36. The JRF? http://www.who.int/immunization_monitoring/data/data_subject/en/index.html#c • JRF =WHO/UNICEF Joint Reporting Form on immunization for the period January-December, YYYY • Annual process • Sent to and received from all Member States • Filled in by MoH • Joint WHO/UNICEF process started in 1998 • Core set of items with additional regional adaptations http://www.who.int/immunization_monitoring/en/globalsummary/timeseries/tswucoveragedtp3.htm

37. Objectives of the JRF? • Global level: primary monitoring tool • Consistency between international agencies • Same data to both UNICEF & WHO • Reduce reporting burden on National programmes • Annual only • One format • Single schedule (January each year) • Same core set of questions, with regionalmodifications

38. Reporting 2009

39. Reported Immunization CoverageWPR, 2001-2007 Source: WHO-UNICEF JRF 2001-2007 * JRF 2008 data from 33 reporting countries with data on coverage, as of June 30.

42. 1st Viremia IgG Meales specific 2nd Viremia Koplik spots IgM Measles specific Virus detection Relative antibody activity negative 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 2 3 4 5 Days Months after infection Incubation Prodrome Rash Recovery Infectiousness Clinical and Serological Patterns of Measles Virus Infection Type of specimens: Serum, swab from (throat, nose, conjunctives), urine, lymphocytes

43. Serological Patterns of Measles Virus Infection Source: from WHO Manual for the Laboratory diagnosis of Measles and Rubella virus infection. 2007

44. Clinical Patterns of Measles Virus Infection Source: from WHO Manual for the Laboratory diagnosis of Measles and Rubella virus infection. 2007

45. Clinical Patterns of Rubella Virus Infection Source: from WHO Manual for the Laboratory diagnosis of Measles and Rubella virus infection. 2007

46. Access to immunization services High Coverage with DPT1 Low coverage with DPT1 Low drop out High dropout Low dropout High dropout (<10%) (>10%) (<10%) (>10%) Interpretation Category 1 Category 2 Category 3 Category 4 Good access Good access Low access Low access Good utilization ° Poor utilization ° Good utilization ° Poor utilization Utilization of Immunization Services