1. Analysis of Surveillance Data Philippe Dubois From Denis Coulombier, Julia Fitzner, Augusto Pinto & Marta Valenciano, WHO-HQ/LYON April 8 – 12, 2013 Phom Penh, Cambodia

2.  Data characteristics  Data validation  Descriptive analysis  Hypothesis testing Analysis of Surveillance Data

3. Data Characteristics  Various sources of notification  Various levels of qualification  Continuous data collection subject to change

4. Surveillance Data Validation  Frequency distributions missing values expected distribution digit attraction  Cross-Tabulations age, sex, logical errors by source: collect bias ?

5. Notifications of All Notifiable Diseases by Date of Onset, USA, 1989

6. Birth weight Distribution, in Pounds Fermattes Hospital, Haiti, February 1994

7. Descriptive Approach  Time  Place  Persons  Generating hypotheses

8. Analyzing Time Characteristics  Graphical analysis  The 3 data components secular trend seasonal variations accidental variations

9. Notifications of Foodborne Outbreaks in France, 1996-1998

10. 199619971998

11. Components of Surveillance Data  Signal secular trend seasonal variations accidental variations

12. Decomposition of Surveillance Data Signal

13. Descriptive analysis of Components  Moving averages empirical method for reducing variability same area under the curve  Logarithmic scale dynamic analysis of changes difficult to interpret

14. Calculation of moving averages 1993 Jan Feb Apr Jun Aug Jul Sep Oct Dec Mar May Nov 869 726 945 834 465 822 654 872 546 728 692 3622/5=724,4 3690/5=738.0 3836/5=767.2 5 month window 890

15. Notification of giardiasis in Delaware, 03/1991-03/1995 Crude Weekly Data

16. 12 Week Moving Average Notification of giardiasis in Delaware, 03/1991-03/1995

17. 52 Week Moving Average

18. Notification of giardiasis in Delaware, 03/1991-03/1995 Aggregated data

19. Size of the Moving Average Window  empirical approach: the visual impression  inversely proportional to the number of cases  increases as the variance increases 1. Showing cyclical variations by removing accidental variations

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

21. Size of the Moving Average Window  Cycle span 52 for weekly data 12 for monthly data 4 for quarterly data 2. Showing secular trend by removing cyclical variations

22. Cases of Gonorrhea in Michigan Week 10 of 1994 and 208 Previous Weeks

23. Cases of Gonorrhea in Michigan Week 10 of 1994 and 208 Previous Weeks

24. Descriptive Analysis of the 3 Components  Moving average empirical method variability reduction same area under the curve  Logarithmic scale dynamic analysis of changes difficult to interpret

25. Annual Rate of Tuberculosis, United States, 1940-1990

27. MALARIA- By year, United States, 1930-1992

28. MALARIA- By Year, United States, 1930-1992 MALARIA- By year, United States, 1930-1992

29. GONORRHEA - By race and ethnicity, United States, 1981-1993 Arithmetic scale

30. GONORRHEA - By race and ethnicity, United States, 1981-1993 Logarithmic scale Source: Summary of Notifiable Diseases, United States 1993 GONORRHEA - By race and ethnicity, United States, 1981-1993

31. Typhoid Notifications in France

33. Interpreting the results  Role of chance  Role of bias  True disease pattern

34. Conclusions  Analysis to draw attention  Validation by investigation

35. Questions? Comments? Discussions?