Interpreting and Describing Data

General Considerations Correct interpretation depends on your being very familiar with your data –Ongoing process that gets easier with time –Understand factors that can influence the data Incomplete reporting, holidays, changes in human behavior Don’t assume that others have the same detailed understanding of the data –Explain everything very clearly, including data limitations

Objectives of Influenza Surveillance Determine which influenza viruses are circulating, where are they circulating, when are they circulating, and who is affected Contribute to vaccine selection Determine intensity and impact of influenza activity Detect unusual events –Infection by unusual viruses –Unusual syndromes caused by influenza viruses –Unusually large/severe outbreaks of influenza Understand the impact of influenza on populations to guide policy and resource decisions for each country and globally

Objectives of Influenza Surveillance Determine which influenza viruses are circulating, where are they circulating, when are they circulating, and who is affected Determine intensity and impact of influenza activity Detect unusual events –Infection by unusual viruses –Unusual syndromes caused by influenza viruses –Unusually large/severe outbreaks of influenza

What Viruses are Circulating Where and When? Straight forward analysis of lab data –# of viruses detected per week or month by type and subtype –Show and/or discuss geographic differences Possible causes of misinterpretation –Large # of specimens from a single outbreak –Reporting by test date rather than collection data Batch testing –Tests that don’t detect all influenza viruses

U.S. WHO/NREVSS Collaborating Laboratories, National Summary,

Sentinel Surveillance in Thailand

Regional Variation of Influenza Viruses in Thailand Chittaganpitch et al. Influ Other Resp Viruses 2012;6(4):276-83

Intensity and Impact of Influenza Activity Interpretation can be more difficult and may require more detailed explanation Age-specific population-based rates are probably the ideal but: –Can be expensive –Feasibility differs with health care system –May be difficult to define the population under surveillance –Case ascertainment may not be the same at all sites

FluSurvNet – Cumulative Rate of Influenza Hospitalizations,

FluSurvNet – Cumulative Rate of Influenza Hospitalizations,

Intensity and Impact of Influenza Activity Comparison to historical data –Use known “bad” years and known “mild” year for comparison –Have to have historical data collected in a relatively stable manner over time Site to site comparisons –Strength of surveillance may vary –Population under surveillance may not be the same –Baseline activity may differ

Site to Site Comparisons

US Region-Specific ILI Baselines Season GroupGroup Name BaselinesBaseline MeanStd DevMean + 2 Std Dev National Federal RegionsRegion Region Region Region Region Region Region Region Region Region

Data Interpretation Challenges Holidays Significant variation in a subset of data that is hidden by the majority (finding an important needle in a really big haystack) Activity outside the normal timeframe Changes in human behavior

Data Interpretation – Holiday Effect Christmas/New Year’s holiday Same or increased number of ill patients but fewer routine visits

Data Interpretation Challenges Holidays Significant variation in a subset of data that is hidden by the majority (finding an important needle in a really big haystack) Activity outside the normal timeframe Changes in human behavior

Epidemic Threshold Seasonal Baseline Pneumonia and Influenza Mortality for 122 U.S. Cities Week Ending 06/04/ H1N1 pandemic

Epidemiology/Surveillance Number of Influenza-Associated Pediatric Deaths by Week of Death: season to present Number of Deaths Reported = Number of Deaths Reported =133 Deaths Reported Current Week Deaths Reported Previous Weeks Number of Deaths Reported= Number of Deaths Reported=116 Date Influenza A (2009 H1N1) Influenza A (H3N2) Influenza A (Subtype Unknown) Influenza BTotal # Deaths Current Week – # Deaths Since October 1,

Data Interpretation Challenges Holidays Significant variation in a subset of data that is hidden by the majority (finding an important needle in a really big haystack) Activity outside the normal timeframe Changes in human behavior

U.S. WHO/NREVSS Collaborating Laboratories, National Summary, Problem: % positive higher during the 1 st pandemic wave than the 2 nd larger wave

Problem: increase in ILI at the start of the pandemic – real or not?

U.S. WHO/NREVSS Collaborating Laboratories, National Summary, Corresponding virus data: “worried ill”

Conclusions Correct interpretation requires detailed knowledge of the data You have to guide others to the correct interpretation through clear explanation and visual presentation –Sometimes this is much easier to do retrospectively –Sometimes the best you can do is confirm that the data is correct, admit you don’t know why you are seeing what you’re seeing, give possible explanations (internally), and keep investigating

Questions?