Analysis & graphical display of surveillance data

EPIET Introductory Course

October 2010

2

Health Care Services Public Health Authority

Indicator Data

InformationIntervention

Reporting

Collection

Collation, Analysis,

Interpretation & Presentation

Dissemination

Surveillance system

Purpose of surveillance

Monitor trends (PPT)

Monitor control programmes

Detect unusual events

Objectives

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

Denominators

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

Number of notified cases

Week

0

200

400

600

800

1000

1200

1400

15 16 17 18 19

< 5 years5 years and +

Notifying healh-care centres and notified out-patientsAlbania, weeks 15-19, 1999

0

5000

10000

15000

20000

25000

30000

15 16 17 18 19

Number of out-patients

Week

Number of health centres

Week

0

20

40

60

80

100

120

140

15 16 17 18 19

NGO MoH

Diarrhoea by age groups, weeks 15-19, 1999, Albania

Number of notified cases

Week

0

200

400

600

800

1000

1200

1400

15 16 17 18 19

< 5 years5 years and +

Proportional morbidity

0

10

20

30

15 16 17 18 19

< 5 years 5 years and +

Diarrhoea / cardiovasc.

0

1

2

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

Standardisation

Disease frequency varies according to age

Age structure of the 2 populations is different

Incidence Rate of Disease = XX.X / 1000

Incidence Rate of Disease = YY.Y / 1000

Compare XX.X to YY.Y

14

Descriptive Analysis of Person Characteristics

Frequency distributions– Tables– Histograms for quantitative classification: age…– Bars for ordinal or nominal qualitative

classification: uneven age-groups, suspect-confirmed…

– Pie for nominal qualitative classification: sex, strain, region

Descriptive analysis- Place -

Mapping– Dot/Spot map– Chloropleth map

Choice of– Colors– Scale

Mediterranean sea

3

4

2

5

1 10

9

7 8

6

Asthma cases in Barcelona by district January 21, 1986

17

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

18

Combination?

• 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%

• 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%

• Convenience scaleINCIDENCE | Freq Percent Cum--------------+--------------------- < 100 | 28 93.3% 93.3% >= 100 | 2 6.7% 100.0%--------------+--------------------- Total | 30 100.0%

Seasonal influenza: incidence rate (%) by regionFrance, January-March 2003

Equal amplitude scale Equal frequency scale

0,00 – 2,85

2,86 – 5,69

5,70 – 8,54

Incidence Rate (%)

0,00 – 1,02

1,03 – 2,52

2,53 – 8,54

Incidence Rate (%)

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

Descriptive analysis- Time -

Descriptive analysis of time

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

24

Descriptive analysis – Time – Graphical analysis

17

1211

17

9

12

6566

12

15

9

68

12

8

32

544

5544

23

7

4244

313

8

24

86

1112

7

1616

11

6

1416

13

0

12

767

4

75

14

6781012

12

10

776

3332

544

5

1

43234

98

3456

10

6

1210

7

15

11

15

1112

6

19

1210

87

111011

14

3

9

37 50 11 24 37 50 11 24 37

Weeks

0

5

10

15

20

25

Number of cases

Foodborne Intoxications Clusters

37 50 11 24 37 50 11 24 37

Weeks

0

5

10

15

20

25Number of cases

Descriptive analysis – Time – Graphical analysis

Descriptive Analysis of TimeComponents of Surveillance Data

0

25

50

-25

Seasonality

0

25

50 Residuals

0

25

50Trend

0

20

40

60

80

100

Signal

Smoothing techniques:Moving average

2005 2006 2007 2008

26 39 52 13 26 39 52 13 26 39 52 13 26 39 52 13

0 -

5 -

10 -

15 -

20 -

25 -

30 -

Moving average of 52 weeks

Moving average of 12 weeks

Notifications

Number of notified cases

Weeks

Descriptive Analysis of TimeSmoothing Techniques

JanFebMar

Jun

AugJul

SepOct

Dec

AprMay

Nov

869726

945834

465

822654

872

546728

692890

0

500

1000

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

3622/5=724,43690/5=738.03728/5=745.6

29

Effect of the Moving Average Window SizeWeekly Notifications of Salmonellosis, Georgia, 1993-1994

3 weeks

7 weeks

5 weeks

10 weeks

30

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

Methodological considerationswhen 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?

0

100

200

300

400

500

600

700

1 10 19 28 37 46 55 64 73 82 91 100 109 118 127 136

Accounting for Time Dependency

Is the red dot consistent with the data?

Tests not accounting for time dependencyMean + 1.96 Standard Deviations

0

100

200

300

400

500

600

700

-10 10 30 50 70 90 110 130 150

Yes

95% CI

Mean

Randomly ordered data

Chronologically ordered data

Tests accounting for time dependency

0

100

200

300

400

500

600

700

1 10 19 28 37 46 55 64 73 82 91 100 109 118 127 136

No95% CI

Residuals, after removing trend and seasonality

-0,4

-0,3

-0,2

-0,1

0,0

0,1

0,2

0,3

1 12 23 34 45 56 67 78 89 100 111 122Mois

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

37

Olympic Games Surveillance, Athens 2004Septic Shocks, Syndromic Surveillance

Poisson test– Count of cases/average previous 7 days ()

between 1-4% <1%P-value

Restriction approach:Historical mean

X = Xi / 15

Mean and standard deviation

Test

X0 >X + 1.96*Std(X)

Std(X) = (Xi-X)² / n

2009 X0

2008 X1 X2 X3

2007 X4 X5 X6

2006 X7 X8 X9

2005 X10 X11 X12

2004 X13 X14 X15

12-15 16-19 20-23 Only applicable if data does not present a significant trend

Conclusion

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

41

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

42

To know moreabout surveillance

data analysistaking into account time dependency

…

EPIET TSA Module!