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Analysis & graphical display of surveillance data EPIET Introductory Course October 2010.

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Presentation on theme: "Analysis & graphical display of surveillance data EPIET Introductory Course October 2010."— Presentation transcript:

1 Analysis & graphical display of surveillance data EPIET Introductory Course October 2010

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

3 Purpose of surveillance Monitor trends (PPT) Monitor control programmes Detect unusual events

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

5 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

6 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

7 Denominators Albanian and refugee populations, Albania, week 16/1999

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

9 Diarrhoea by age groups, weeks 15-19, 1999, Albania Number of notified cases Week 0 200 400 600 800 1000 1200 1400 1516171819 < 5 years 5 years and +

10 Notifying healh-care centres and notified out-patients Albania, weeks 15-19, 1999 Number of out-patients Week Number of health centres Week 0 20 40 60 80 100 120 140 1516171819 NGO MoH

11 Diarrhoea by age groups, weeks 15-19, 1999, Albania Number of notified cases Week 0 200 400 600 800 1000 1200 1400 1516171819 < 5 years 5 years and + Proportional morbidity 0 10 20 30 1516171819 < 5 years 5 years and + Diarrhoea / cardiovasc. 0 1 2 1516171819

12 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

13 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 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

15 Descriptive analysis - Place - Mapping –Dot/Spot map –Chloropleth map Choice of –Colors –Scale

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

17 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 18 Combination?

19 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 scale INCIDENCE | Freq Percent Cum --------------+--------------------- < 100 | 28 93.3% 93.3% >= 100 | 2 6.7% 100.0% --------------+--------------------- Total | 30 100.0%

20 Seasonal influenza: incidence rate (%) by region France, 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 (%)

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

22 Descriptive analysis - Time -

23 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 24 Descriptive analysis – Time – Graphical analysis

25 375011243750112437 Weeks 0 5 10 15 20 25 Number of cases Descriptive analysis – Time – Graphical analysis

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

27 Smoothing techniques: Moving average 2005 2006 2007 2008 26395213263952132639521326395213 0 - 5 - 10 - 15 - 20 - 25 - 30 - Moving average of 52 weeks Moving average of 12 weeks Notifications Number of notified cases Weeks

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

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

30 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

31 Methodological considerations when testing for time hypothesis Surveillance data –Does not result from sampling cases –Can be viewed as a sample of time unitsEcological analysis –Time units are not independentCorrelated over time –Specific testing methods need to be applied

32 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?

33 0 100 200 300 400 500 600 700 110192837465564738291100109118127136 Accounting for Time Dependency Is the red dot consistent with the data?

34 Tests not accounting for time dependency Mean + 1.96 Standard Deviations 0 100 200 300 400 500 600 700 -101030507090110130150 Yes 95% CI Mean Randomly ordered data

35 Chronologically ordered data Tests accounting for time dependency Residuals, after removing trend and seasonality

36 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 37 Olympic Games Surveillance, Athens 2004 Septic Shocks, Syndromic Surveillance Poisson test –Count of cases/average previous 7 days ( ) between 1-4%<1%P-value

38 Restriction approach: Historical mean X = X i / 15 Mean and standard deviation Test X 0 >X + 1.96*Std(X)Std(X) = (X i -X)² / n 2009 X 0 2008X 1 X 2 X 3 2007X 4 X 5 X 6 2006X 7 X 8 X 9 2005X 10 X 11 X 12 2004X 13 X 14 X 15 12-1516-1920-23 Only applicable if data does not present a significant trend

39 Conclusion

40 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 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 42 To know more about surveillance data analysis taking into account time dependency … EPIET TSA Module!


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