EPIET Introductory Course
October 2010

Surveillance system
Health Care Services
Indicator
Public Health Authority
Reporting
Collection
Data
Intervention
Collation,
Analysis,
Interpretation
& Presentation
Information
Dissemination
2

 Monitor trends (PPT)
 Monitor control programmes
 Detect unusual events

 Define steps in surveillance analysis
 Perform descriptive analysis
 Use surveillance data for alert
 Understand mechanisms of more complex analysis

Knowledge of surveillance system / surveillance data
– Changes over time
– Multiple sources of information
– Data entry and validation
– Problem of quality and completeness
Evaluation of the system

Surveillance indicators & Denominator issues
Choice of indicator according to availability of denominator :
• no denominator available:
 crude number of cases
 proportional morbidity
• denominator available:
 calculation of rates
 standardisation

Albanian and refugee populations,
Albania, week 16/1999

Surveillance indicators
Distribution of attendance at health facility by diagnosis
Albania, Week 19 (10-16/05/1999)

Diarrhoea by age groups, weeks 15-19, 1999, Albania
1400
Number of notified cases
1200
< 5 years
5 years and +
1000
800
600
400
200
0
15 16 17
Week
18 19

Notifying healh-care centres and notified out-patients
Albania, weeks 15-19, 1999
140
120
100
80
60
40
20
0
15
NGO
Number of health centres
MoH
16 17
Week
18 19
Number of out-patients
30000
25000
20000
15000
10000
5000
0
15 16 17
Week
18 19

800
600
400
Number of notified cases
1400
1200
< 5 years
5 years and +
Diarrhoea by age groups, weeks 15-19, 1999, Albania
30
Proportional morbidity
< 5 years
5 years and +
20
1000 10
0
2
15 16 17 18
Diarrhoea / cardiovasc.
19
1
200
0
15 16 17
Week
18 19
0
15 16 17 18 19

Surveillance indicators
& denominator issues
Choice of indicator according to availability of denominator :
• no denominator available:
 crude number of cases
 proportional morbidity
• denominator available:
 calculation of rates
 standardisation

Incidence Rate of Disease =
XX.X / 1000
Incidence Rate of Disease =
YY.Y / 1000
Compare XX.X to YY.Y
Disease frequency varies according to age
Age structure of the 2 populations is different
Standardisation

 Frequency distributions
– Tables
– Histograms for quantitative classification: age…
– Bars for ordinal or nominal qualitative classification: uneven age-groups, suspectconfirmed…
– Pie for nominal qualitative classification: sex, strain, region
14

 Mapping
– Dot/Spot map
– Chloropleth map
 Choice of
– Colors
– Scale

Asthma cases in Barcelona by district
January 21, 1986 l l l l l l l 7
4 l
5 l l l l
3 l l l l l l l l l l l l l l l l l
2 l l l l l l l l l l l l l l l l l l l l l l l l
1 l l l l l l l l l l l l l l l l l
6 l l l l l l l l l l l l l l l l l l
9 l
10
Mediterranean sea
8 l

H1N1 Map by number of confirmed cases
Wikipedia May 9, 2009
50 000+ confirmed cases
5 000+ confirmed cases
500+ confirmed cases
50+ confirmed cases
5+ confirmed cases
1+ confirmed cases
17

18

• Equal amplitude scale : (120 / 4)
INCIDENCE | Freq Percent Cum
--------------+---------------------
1 – 30 | 10 33.3% 33.3%
31 – 60 | 16 53.3% 86.6%
61 – 90 | 2 6.7% 93.3%
91 – 120 | 2 6.7% 100.0%
--------------+---------------------
Total | 30 100.0%
• Equal frequency scale : (30 / 3)
INCIDENCE | Freq Percent Cum
--------------+---------------------
1 – 30 | 10 33.3% 33.3%
31 – 39 | 10 33.3% 66.6%
40 – 120 | 10 33.3% 100.0%
--------------+---------------------
Total | 30 100.0%
•
Convenience scale
INCIDENCE | Freq Percent Cum
--------------+---------------------
< 100 | 28 93.3% 93.3%
>= 100 | 2 6.7% 100.0%
--------------+---------------------
Total | 30 100.0%

Seasonal influenza: incidence rate (%) by region
France, January-March 2003
Incidence Rate (%)
0,00 – 2,85
2,86 – 5,69
5,70 – 8,54
Equal amplitude scale
Incidence Rate (%)
0,00 – 1,02
1,03 – 2,52
2,53 – 8,54
Equal frequency scale

Diarrhoea, week 40, 2008
Estimated number of cases / 100,000

 Graphical analysis
 Requires aggregation on appropriate time unit
 Choice of time variable
Date of onset
Date of notification
 Use rates when denominator changes over time
 Describe trend, seasonality

25
20
15
10
5
0
37
Descriptive analysis – Time –
Graphical analysis
Number of cases
Foodborne Intoxications Clusters
19
17 17
12
11
12
9
6
5
66
16
15
12 12
9
8 8
6
8 8
7
3
2
5
44
55
44
2
3
4
2
44
3
1
3
2
4
6
12
11
7
14
13
12
11
6
0
14 14
7
6
7 7
4
5
6
7
8
12
10
1
10
2
77
6
333
2
5
44
5
1
4
3
2
3
4
9
8
3
4
5
6
12
10 10
11
12
11
12
10
11
10
6
7
6
8
7
3
9
50 11 24 11 24 37 37
Weeks
50
24

Descriptive analysis – Time –
Graphical analysis
15
10
5
25
Number of cases
20
0
37 50 11 24 37
Weeks
50 11 24 37

100
80
60
40
20
0
50
25
0
50
25
-25
0
50
25
0
Descriptive Analysis of Time
Components of Surveillance Data
Signal
Trend
Seasonality
Residuals

Smoothing techniques:
Moving average
Notifications
Moving average of 12 weeks
Moving average of 52 weeks
30 -
Number of notified cases
25 2005 2006 2007 2008
20 -
15 -
10 -
5 -
0 -
26 39 52 13 26 39 52 13 26 39 52 13 26 39 52 13
Weeks

Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Descriptive Analysis of Time
Smoothing Techniques
822
654
546
728
872
890
692
465
869
726
834
945
1000
500
3622/5=724,4
3690/5=738.0
3728/5=745.6
0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Effect of the Moving Average Window Size
Weekly Notifications of Salmonellosis, Georgia, 1993-1994
3 weeks
5 weeks
7 weeks
10 weeks
29

Steps in Surveillance Analysis
 Prerequisite to Surveillance Analysis
– Knowledge of surveillance system (evaluation)
• Nature of surveillance data
• Data quality
– Choice of indicator & Denominator issue
 Analysis
– Descriptive (PPT)
– Detection of unusual variations / test hypothesis
30

Methodological considerations when testing for time hypothesis
 Surveillance data
– Does not result from sampling cases
– Can be viewed as a sample of time units
“ Ecological analysis ”
– Time units are not independent
“ Correlated over time ”
– Specific testing methods need to be applied

Testing for time hypothesis
 Convert to rates (if needed)
 Remove time dependency
– Trend and seasons
– By restriction or modelling
 Test for detection of outbreaks
– More cases than expected?
 Test for changes in trend
– Departure from historical trend?

Accounting for Time Dependency
400
300
200
100
700
600
500
Is the red dot consistent with the data?
0
1 10 19 28 37 46 55 64 73 82 91 100 109 118 127 136

Tests not accounting for time dependency
Mean + 1.96 Standard Deviations
700
600
500
400
300
200
100
-10
0
10 30
Yes
110 130
95% CI
Mean
150 50 70 90
Randomly ordered data

Tests accounting for time dependency
95% CI
0,0
-0,1
-0,2
-0,3
-0,4
0,3
0,2
0,1
400
300
200
100
700
600
500
0
1
1
10
12
19 28
23
37 46
No
Chronologically ordered data
55 64 73 82 91 100 109 118 127 136
34 45 56 67
Mois
78 89
Residuals, after removing trend and seasonality
100 111 122
95% CI
Mean

Statistical tests for time series
 For time series with no trend and seasonality: random series
– Tests not accounting for time dependency (TD)
– Chi square, Poisson
 For time series with seasonality and no trend
– Tests accounting for TD by restriction
– Similar historical period mean/median
 For all time series
– Tests accounting for TD by modeling

Olympic Games Surveillance, Athens 2004
Septic Shocks, Syndromic Surveillance
 Poisson test
– Count of cases/average previous 7 days ( l
)
P-value between 1-4% <1%
37

Restriction approach:
Historical mean
2009 X 0
2008 X 1
2007 X 4
X 2
X 5
2006 X 7 X 8
2005 X 10 X 11
2004 X 13 X 14
X
X
3
6
X 9
X 12
X 15
12-15 16-19 20-23
Mean and standard deviation
X = X i
/ 15
Std(X) = (X i
X)² / n
Test
X 0 >X + 1.96*Std(X)
Only applicable if data does not present a significant trend


Know the system / the data
– role of artefacts, errors, …
 First step = graphic description
– PPT
 More complex analysis
– Statistical testing
– Chance, bias, truth?
 Data analysis by epidemiologist
– Added value +++
 Hypothesis must be validated
– Specific investigation / study

Analysis of surveillance data
= Translating data into information
 Provides the basis for public health action
 Requires sound analysis and interpretation
 Extracts meaningful, actionable findings
 Requires clear presentation of complex issues
41

To know more about surveillance data analysis taking into account time dependency
…
EPIET TSA Module!
42