interpreting and describing data. general considerations correct interpretation depends on your...
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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
0
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12000
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30
40
50
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80
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100
A(2009 H1N1)
A(Unable to Subtype)
A(H3)
A(Subtyping not performed)
B
Percent Positive
Week ending
Num
ber
of P
ositi
ve S
peci
men
s
Per
cent
Pos
itive
U.S. WHO/NREVSS Collaborating Laboratories, National Summary, 2009-11
Sentinel Surveillance in Thailand
Regional Variation of Influenza Viruses in Thailand
Se
p-0
4
No
v-0
4
Jan
-05
Ma
r-0
5
Ma
y-0
5
Jul-
05
Se
p-0
5
No
v-0
5
Jan
-06
Ma
r-0
6
Ma
y-0
6
Jul-
06
Se
p-0
6
No
v-0
6
Jan
-07
Ma
r-0
7
Ma
y-0
7
Jul-
07
Se
p-0
7
No
v-0
7
Jan
-08
Ma
r-0
8
Ma
y-0
8
Jul-
08
Se
p-0
8
No
v-0
8
Jan
-09
Ma
r-0
9
Ma
y-0
9
Jul-
09
Se
p-0
9
No
v-0
9
Jan
-10
Ma
r-1
0
Ma
y-1
0
Jul-
10
Se
p-1
0
No
v-1
0
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
North
Northeast
Central
East
South
Per
cent
pos
itive
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, 2010-11
FluSurvNet – Cumulative Rate of Influenza Hospitalizations, 2009-10
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
2011-12 Season
Group Group Name 2010-11 Baselines Baseline Mean Std Dev Mean + 2 Std Dev
National 2.5 1.52 0.45 2.4
Federal Regions Region 1 1.4 0.71 0.20 1.1
Region 2 2.4 1.52 0.47 2.5
Region 3 2.6 1.57 0.45 2.5
Region 4 2.3 1.33 0.47 2.3
Region 5 1.8 0.96 0.33 1.6
Region 6 4.9 2.37 0.96 4.3
Region 7 2.3 1.05 0.63 2.3
Region 8 1.7 1.27 0.42 2.1
Region 9 4.1 2.26 0.84 3.9
Region 10 2.7 1.27 0.45 2.2
Region 1 - CT, ME, MA, NH, RI, VT
0
2
4
6
8
10
Week
% o
f V
isits fo
r IL
I
Region 6 - AR, LA, NM, OK, TX
0
2
4
6
8
10
12
14
Week
% o
f V
isits fo
r IL
I
Region 2 - NJ, NY, USVI
0
2
4
6
8
10
Week
% o
f V
isits fo
r IL
I
Region 3 - DE, DC, MD, PA, VA, WV
0
2
4
6
8
10
12
Week
% o
f V
isits fo
r IL
I
Region 4 - AL, FL, GA, KY, MS, NC, SC, TN
0
2
4
6
8
Week
% o
f V
isits fo
r IL
I
Region 5 - IL, IN, MI, MN, OH, WI
0
2
4
6
8
10
Week
% o
f V
isits fo
r IL
I
Region 7 - IA, KS, MO, NE
0
2
4
6
8
10
12
Week
% o
f V
isits fo
r IL
I
Region 8 - CO, MT, ND, SD, UT, WY
0
2
4
6
8
10
12
Week
% o
f V
isits fo
r IL
I
Region 9 - AZ, CA, HI, NV
0
2
4
6
8
Week
% o
f V
isits fo
r IL
I
Region 10 - AK, ID, OR, WA
0
2
4
6
8
10
Week
% o
f V
isits fo
r IL
I
NOTE: Scales differ between regions
*Use of the regional baselines for state data is not appropriate.
Baseline*% ILI
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 patientsbut 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
4
6
8
10
12
40 50 10 20 30 40 50 10 20 30 40 50 10 20 30 40 50 10 20 30 40 50 10 20
% o
f All
De
ath
s D
ue t
o P
&I
Weeks
Epidemic Threshold
Seasonal Baseline
Pneumonia and Influenza Mortalityfor 122 U.S. CitiesWeek Ending 06/04/2011
2007 20082006 2009 2010 2011
2009 H1N1 pandemic
Epidemiology/SurveillanceNumber of Influenza-Associated Pediatric Deaths
by Week of Death: 2007-08 season to present
0
5
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15
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25
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35
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20
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-40
20
07
-46
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07
-52
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08
-06
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08
-12
20
08
-18
20
08
-24
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08
-30
20
08
-36
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08
-42
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08
-48
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09
-01
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09
-07
20
09
-13
20
09
-19
20
09
-25
20
09
-31
20
09
-37
20
09
-43
20
09
-49
20
10
-03
20
10
-09
20
10
-15
20
10
-21
20
10
-27
20
10
-33
20
10
-39
20
10
-45
20
10
-51
20
11
-05
20
11
-11
20
11
-17
20
11
-23
20
11
-29
20
11
-35
Week of Death
Nu
mb
er o
f d
eath
s
2007-08
Number of Deaths Reported = 88
2008-09
Number of Deaths Reported =133
Deaths Reported Current Week Deaths Reported Previous Weeks
2009-10
Number of Deaths Reported=282
2010-11
Number of Deaths Reported=116
DateInfluenza A (2009 H1N1)
Influenza A (H3N2)
Influenza A (Subtype Unknown)
Influenza B Total
# Deaths CurrentWeek – 39
0 0 0 0 0
# Deaths SinceOctober 1, 2010
30 21 20 45 116
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
0
2000
4000
6000
8000
10000
12000
0
10
20
30
40
50
60
70
80
90
100
A(2009 H1N1)
A(Unable to Subtype)
A(H3)
A(Subtyping not performed)
B
Percent Positive
Week ending
Num
ber
of P
ositi
ve S
peci
men
s
Per
cent
Pos
itive
U.S. WHO/NREVSS Collaborating Laboratories, National Summary, 2009-11
Problem: % positive higher during the 1st pandemic wave than the 2nd larger wave
Problem: increase in ILI at the start of the pandemic – real or not?
0
2000
4000
6000
8000
10000
12000
0
10
20
30
40
50
60
70
80
90
100
A(2009 H1N1)
A(Unable to Subtype)
A(H3)
A(Subtyping not performed)
B
Percent Positive
Week ending
Num
ber
of P
ositi
ve S
peci
men
s
Per
cent
Pos
itive
U.S. WHO/NREVSS Collaborating Laboratories, National Summary, 2009-11
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?