1. Disentangling / unmasking Alain Moren 1995-2005 Pawel Stefanoff, 2006 What is behind: The epidemic curve The controls

2. Behind the epidemic curve ?

3. Hepatitis A by date of onset Ogemaw county, Michigan, April - May 1968 281420262814202617 Days 0 5 10 15 Number of cases one case 30 days 15 days 50 days Exposure

4. Cases of Legionnaires disease and control measures implementation at plant N, Lens, France, 2004 Source: InVS, France

5. Cases of Legionnaires disease and control measures implementation at plant N, Lens, France, 2004

6. Risk factors Restaurant Hotel Shop

8. Number of cases One case Cases of gastroenteritis among residents of a nursing home, by date of onset, Pennsylvania, October 1986 181920212223242526271716151314 Days 0 5 10

9. ProteinTotalCasesAR%RR suppl. YES 29 22763,3 NO 74 1723 Total103 3938 Cases of gastroenteritis among residents of a nursing home according to protein supplement consumption, Pa, 1986

10. Number of cases One case Early cases of gastroenteritis among residents of a nursing home, by date of onset, Pennsylvania, October 1986 181920212223242526271716151314 0 5 10 Days

11. ProteinTotalEarly AR%RRCI 95% suppl.cases YES 29 14 486.93,2-15,8 NO 74 5 7 Total103 19 19 Early cases of gastroenteritis according to protein suppl. consumption Early cases = onset < 21 October

12. Number of cases One case Late cases of gastroenteritis among residents of a nursing home, by date of onset, Pennsylvania, October 1986 181920212223242526271716151314 Days

13. ProteinTotalLate cases ARRRCI 95% suppl. % YES103302,50,7 - 8,9 NO 65812 Total751115 Late cases of gastroenteritis according to protein suppl. consumption, Pa, 1986 Late cases = onset > 20 October

15. Epidemic curves are useful tools to generate hypotheses on disease transmission Analysis of the total cases included in the epidemic curve can mask the source of exposure

16. Behind the controls?

20. Cases Exposed Unexposed Source population Controls = Sample of the denominator Representative with regard to exposure Controls Sample

21. Total Cases Non C. RateRR E 28010 412796914.61.9 E 19017 1519002 7.9Ref. Incidence of breast cancer after radiation

22. Random TotalCasesNon C. Rate RRContr. OR 28010 4127969 14.61.9280 1.9 19017 1519002 7.9Ref.190 Ref.

23. Incidence of breast cancer after radiation Non Random Cases. TotalCasesNon C.RateRRContr. OR Contr. OR 28010 412796914.61.9280 1.9 279 1.9 19017 1519002 7.9Ref.190 Ref. 190 Ref.

24. Cases E E 4 Source Population 60 40 Outbreak of food borne disease in a nursing home 100 residents, 40 cases 36 RR = 6 24 36 Non cases Cohort

25. Cases E E 4 Source Population 60 40 Outbreak of food borne disease in a nursing home 100 residents, 40 cases 36 RR = 6 24 36 Non cases Cohort Non cases 1212 18 OR = 13.5 Potential control groups

26. Cases Non cases E E 1212 4 18 Source Population 60 40 Outbreak of food borne disease in a nursing home 100 residents, 40 cases 36 Source Population 30 20 RR = 6OR = 13.5 OR = 6 24 36 Non cases Potential control groupsCohort

27. Case control study design I 1 = a / P 1 I 0 = c /P 0 CasesControls E E a b c d Since d/b = P 0 / P 1 E E a c P1P1 P0P0 Source population Pop.Cases a/P 1 a.P 0 a.d RR = ------ = ------- = ------ c/P 0 c.P 1 c.b b P 1 --- = --- dP 0

28. A member of the source population is a suitable control Rare disease assumption = wrong issue Issue = selection of controls

29. Controls We should always think of a case control study as extracted from a cohort Cases always come from a cohort of people Controls: Represent the population giving rise to cases (in terms of exposure) Probability to be selected as a control is proportional to the time spent by individual in the denominator Should have a possibility to become cases Each member of the source population is a potential control

30. If AR is high we do a cohort study