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Analysis of Vaccine Safety Data

Explore the analysis of Adverse Events Following Immunization (AEFI) data to understand increased fever reports after DTPw-Hib-HepB vaccination. Learn the key steps for vaccine safety monitoring and interpretation of rates. Enhance your knowledge of causality assessment for strengthening AEFI monitoring.

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Analysis of Vaccine Safety Data

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  1. Module E Analysis of Vaccine Safety Data “Innocence vs. Ignorance” Photo: N. Wang Causality Assessment to Strengthen AEFI Monitoring (WHO country course) March 2013

  2. Objective

  3. Why analyze AEFI data?

  4. Key Steps for Vaccine Safety Monitoring Seek help with analysis early

  5. Scenario

  6. Fever (> 40o C) in 10,000 infants reported before and after DTPw-Hib-HepB vaccination DTPw-Hib-HepB vaccine Fever > 40o C cases per 10,000 infants

  7. What could explain this increase in AEFI reports of fever?

  8. Questions to answer

  9. Questions to answer continued

  10. Step 1: Characterize the cases (reports) Collect detailed information and establish the quality of data (accuracy, completeness, etc …)

  11. Step 1A: Coding, case definitions and data entry

  12. Step 1B: Analysis of the cases (reports) Descriptive epidemiology of numerator Types of reports by:

  13. Step 1B: Analysis of the cases (reports) continued Example: Analysis of AEFI reports of fever Cases defined as >400C following DTPw-HepB-HiB or DTPw vaccines (Dose 1) from October to December

  14. Basic Unit of AEFI Numerator Data for Analysis Remember: Risk ratio = Risk in vaccinated: a/a+c Risk in unvaccinated: b/b+d Passive surveillance • Only part of (a) available • ensure best data on “a” • Where possible - get background illness rates • i.e., medical events that the AEFI mimics (“b”) if possible Adapted from Auriche M, Loupi E. Drug Safety 1993; 9 (3): 30-35

  15. Step 2: From how many vaccinated individuals (denominator) do these cases originate?

  16. Step 2: From how many vaccinated individuals continued Types of denominators

  17. Step 3: Calculate the reporting rate (numerator/denominator) of this AEFI

  18. Step 4: Determine background rate and observed rate

  19. Fever (> 40o C) in 10,000 infants reported before and after DTPw-Hib-HepB vaccination DTPw-Hib-HepB vaccine Fever > 40o C cases per 10,000 infants

  20. Background rate, Vaccine reaction rate and observed rate Excess vaccine reaction rates (Z) Vaccine reactions (Y+Z) (related to vaccine) Observed rate – Background rates Known, expected vaccine reaction rates (Y) (detected in pre & post licensure studies, surveillance) Observed rates (X+Y+Z) Total number of cases reported from both vaccinated and un vaccinated groups Background rates (X) (not related to vaccine) Occur among un vaccinated, recorded prior to or simultaneously to vaccination *Rates can be expressed per 1000, 10000 or 100,000

  21. Fever in 10,000 infants: Background rate, expected rate and excess rate after vaccination DTPw-Hib-HepB vaccine Fever cases per 10,000 infants

  22. Background rate

  23. Observed rate

  24. Observed rate continued Example: AEFI reports of fever following DPTw-IPV-HepB vaccine introduced in October

  25. Step 5: Determine vaccine attributable risk

  26. Step 5: Determine vaccine attributable risk continued Example: Only the excess (observed-background) can be attributable to the vaccine

  27. Step 6: Determine the relative risk RR is used to compare the adverse event profile of one vaccine with another or risk over and above the background risk.

  28. Step 6: Determine the relative risk continued Example: The risk in November increased 5 times (2/10 per 1000) and for December 10 times (2/20 per 1000). (Relative risk)

  29. Problems Comparing one observed rate to another

  30. Step 7: Determine any confounding factors

  31. Step 7: Determine if there are any factors which modify the risk

  32. Database Review Criteria Photo: WHO February 2013

  33. Systematicreviewprocess Data analysis Review of all relevant sources of data Single casereports Single casereports Single casereports Nationaldatabase Cluster case reports Cluster casereports Causality assessment and decision on further studies to confirm causal association AND Signalling process

  34. Is there any clustering? Which are most commonly reported? Immunization errors related Coincidental Vaccine reaction Injection reaction related Which require further in-depth assessment by committee? Any other trends of interest? Some questions to ask in analysis

  35. Review of case series or cluster Determine if each AEFI case meets the cluster case definition for inclusion in the database. Check for additional cases. Conduct a systematic causality assessment for each case. Collate cases, analyze data Determine if frequency of event is as expected, increased, decreased or a newly recognized event

  36. Cluster defn Vs Case defn

  37. You should be able to: Analyze a cluster and signal using basic epidemiology: • Characterize the cases (Numerator). • Determine the denominator. • Calculate the reporting rate. • Compare observed rate to background rate. • Determine vaccine attributable risk. • Determine the relative risk. • Determine any confounding. • Determine any modifying factors.

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