1. The third factor Effect modification Confounding factor FETP India

2. Competency to be gained from this lecture Identify and describe an effect modification Eliminate a confounding factor

3. Key elements Describing an effect modification Eliminating a confounding factor

4. Stratification Sub-groups can be defined according to various characteristics in a population  Age  Sex  Socio-economic status An association between a risk factor and an outcome may be studied within these various strata

5. Key elements Describing an effect modification Eliminating a confounding factor Effect modification

6. Spotting effect modification in a stratified analysis Effect modification (= Interaction) occurs when the answer about a measure of association is:  “it depends” Examples:  Efficacy of measles vaccine Variation according to the age  Risk of myocardial infarction among women taking oral contraceptives Variation according to smoking habits Effect modification

7. Describing an effect modification Conduct crude analysis Stratify data by suspected modifier Observe the association strata by strata Judge the heterogeneity of:  Odds ratios  Relative risks Test a potential difference Report the effect modification Effect modification

8. Describing an effect modification Conduct crude analysis Stratify data by suspected modifier Observe the association strata by strata Judge the heterogeneity of:  Odds ratios  Relative risks Test a potential difference Report the effect modification Effect modification

9. Describing an effect modification Conduct crude analysis Stratify data by suspected modifier Observe the association strata by strata Judge the heterogeneity of:  Odds ratios  Relative risks Test a potential difference Report the effect modification Effect modification

10. Diarrhoea ControlsTotal No breastfeeding 120136256 Breastfeeding 50204254 Total170340510 Death from diarrhoea according to breast- feeding, Brazil, 1980s (Crude analysis) Odds ratio: 3.6; 95% CI: 2.4- 5.5; p < 0.0001 Effect modification

11. Describing an effect modification Conduct crude analysis Stratify data by suspected modifier Observe the association strata by strata Judge the heterogeneity of:  Odds ratios  Relative risks Test a potential difference Report the effect modification Effect modification

12. Infants < 1 month of age CasesControlsTotal No breastfeeding 10313 Breastfeeding 76875 Total177188 Infants ≥ 1 month of age CasesControlsTotal No breastfeeding 110133243 Breastfeeding 43136179 Total153269422 Death from diarrhoea according to breastfeeding, Brazil, 1980s

13. Describing an effect modification Conduct crude analysis Stratify data by suspected modifier Observe the association strata by strata Judge the heterogeneity of:  Odds ratios  Relative risks Test a potential difference Report the effect modification Effect modification

14. CasesControlsTotal No breastfeeding 10313 Breastfeeding 76875 Total177188 Death from diarrhoea according to breast feeding, Brazil, 1980s: Analysis among infants < 1 month of age Odds ratio: 32.4; 95% CI: 6- 203; p < 0.0001 Effect modification

15. CasesControlsTotal No breastfeeding 110133243 Breastfeeding 43136179 Total153269422 Death from diarrhoea according to breast feeding, Brazil, 1980s: Analysis among infants ≥ 1 month of age Odds ratio: 2.6; 95% CI: 1.7- 4.1; p < 0.0001 Effect modification

16. Describing an effect modification Conduct crude analysis Stratify data by suspected modifier Observe the association strata by strata Judge the heterogeneity of:  Odds ratios  Relative risks Test a potential difference Report the effect modification Effect modification

17. Judge the heterogeneity of the measures of association To be a difference, a difference should make a difference  Review public health implications Odds ratios in the specific example:  Strata 1: OR = 32; 95% CI: 6.0- 200  Strata 2: OR = 2.6; 95% CI: 1.7- 4.1 Effect modification

18. Describing an effect modification Conduct crude analysis Stratify data by suspected modifier Observe the association strata by strata Judge the heterogeneity of:  Odds ratios  Relative risks Test a potential difference Report the effect modification Effect modification

19. Woolf’s test for heterogeneity of the odds ratios Statistical testing of the heterogeneity of the odds ratios Lacks statistical power Calculation:  In statistical textbooks  In the software’s analysis output Judgement is important Effect modification

20. Handling heterogeneous measures of association

21. Describing an effect modification Conduct crude analysis Stratify data by suspected modifier Observe the association strata by strata Judge the heterogeneity of:  Odds ratios  Relative risks Test a potential difference Report the effect modification Effect modification

22. Conclusion of the Brazilian case-control study on breastfeeding and death from diarrhoea The protective efficacy of breastfeeding is more marked among infants under the age of one month This may correspond to a biological phenomenon that must be reported as part of the results Effect modification

23. Reporting results in the presence of an effect modification Once the effect modification was detected the study population is split Results for the risk factor considered are reported stratum by stratum Effect modification

24. Vaccination against hepatitis B among institutionalized children in Romania Hepatitis B is highly endemic in Romania Many children live in institutions Institutionalized children are at higher risk 1995: Hepatitis B immunization initiated 1997: Evaluation through serologic survey Effect modification

25. Hepatitis B vaccine efficacy among institutionalized children over 6 months of age *, Romania, 1997 Anti-HBc (+) Anti-HBc (-) RR95% C.I. 3 doses 15 383 0.480.17-1.4 < 3 doses 4 47 Ref. * Born after implementation of routine vaccination Vaccine efficacy, 52%, 95% CI 0-83% HBV Vaccine Effect modification

26. Hepatitis B vaccine efficacy among institutionalized children over 6 months of age *, by district, Romania, 1997 Anti-HBc (+) Anti-HBc (-) RR95% C.I. 3 doses 12612.00.28-14 < 3 doses111Ref. 3 doses 33220.120.0-0.6 < 3 doses336Ref. Wolf test for evaluation of interaction: p = 0.03 * Born after implementation of routine vaccination District X Others Effect modification

27. Hepatitis B vaccine efficacy among Romanian children in institutions: Conclusions The protective efficacy of hepatitis B vaccine appears low overall This overall low efficacy does not correspond to a biological phenomenon In fact, the efficacy is:  Normal in most districts (88%)  Low in district X This points towards programme errors that must be identified and prevented Effect modification

28. Describing an effect modification: Summary The analysis plan:  Anticipates effect modifiers to collect data The analysis:  Looks for effect modification to test it The report:  Breaks down the population in strata to report the effect modification Effect modification

29. Key elements Describing an effect modification Eliminating a confounding factor Confounding factor

30. What may explain an association between a risk factor and an outcome? ?Chance ?Bias ?Third factor ?Causal association Confounding factor

31. What may explain an association between a risk factor and an outcome? ?Chance ?Bias ?Third factor ?Causal association Confounding factor

32. Exposure Characteristics of a third, confounding factor Associated with the exposure  Without being a consequence of exposure Associated with the outcome  Independently from the exposure Outcome Confounding factor

33. The nuisance introduced by confounding factors May simulate an association May hide an association that does exist May alter the strength of the association  Increased  Decreased Confounding factor

34. Outcome Example of confounding factor Exposure 1 Apparent association Confounding factor

35. Pneumonia Example of confounding factor (1) Ethnicity Apparent association Crowding Confounding factor

36. Pneumonia Example of confounding factor (2) Crowding Altered strength of association Malnutrition Confounding factor

37. Eliminating confounding in the pneumonia example Estimate the strength of the association between malnutrition and pneumonia Estimate the strength of the association between crowding and pneumonia  Adjusted for the effect of malnutrition Eliminate the confounding effect of crowding on the false association between ethnicity and pneumonia Confounding factor

38. Controlling a confounding factor Stratification Restriction Matching Randomization Multivariate analysis Confounding factor

39. Controlling a confounding factor Stratification Restriction Matching Randomization Multivariate analysis Confounding factor

40. Adjustment to eliminate confounding Examine strength of association across strata Check for the absence of effect modification  If there is an effect modification, break in various strata, report. End of the story Observation of a strength of association:  Homogeneous across strata  Different from the crude measure Calculate weighted average of stratum- specific measures of association Confounding factor

41. Malaria and radio sets Hypothesis: Could radio waves be a repellent for female anopheles? Cohort study on the risk factors for malaria in an endemic area Confounding factor

42. Incidence of malaria according to the presence of a radio set, Kahinbhi Pradesh Crude data MalariaNo malariaTotal Radio80440520 No radio2208601080 Total30013001600 RR: 0.7; 95% CI: 0.6- 0.9; p < 0.02 Confounding factor

43. Incidence of malaria according to the presence of a radio set, Kahinbhi Pradesh Strata 1: Sleeping under a mosquito net MalariaNo malariaTotal Radio30370400 No radio50630680 Total8010001080 RR: 1.02; 95% CI: 0.7- 1.6; p < 0.97 Confounding factor

44. Incidence of malaria according to the presence of a radio set, Kahinbhi Pradesh Strata 2: Sleeping without a mosquito net MalariaNo malariaTotal Radio5070120 No radio170230400 Total220300520 RR: 0.98; 95% CI: 0.8- 1.2; p < 0.95 Confounding factor

45. Mantel-Haenszel adjusted relative risk RR M-H =  a i xL0 i ) / T i ]  c i xL1 i ) / T i ] Confounding factor

46. Malaria and radio sets: Conclusion No association between radio and malaria within each strata The new adjusted relative risk replaces the crude one Malaria Radio sets Apparent association Mosquito nets Confounding factor

47. OR M-H =  a i.d i ) / Ti]  b i.c i ) / Ti] Mantel-Haenszel adjusted odds ratio Confounding factor

48. Controlling a confounding factor Stratification Restriction Matching Randomization Multivariate analysis Confounding factor

49. Hepatitis B and blood transfusion in Moldova Hepatitis B virus infection is highly endemic in Moldova Routes of transmission are unknown A case control study was initiated to assess potential modes of transmission Confounding factor

50. CasesControls Total Transfusion314 Non-transfusion69189258 Total72190262 Odds ratio: 8.2; 95% CI : 0.8-220 Acute hepatitis B and receiving a transfusion in Moldova, 1994-1995 Confounding factor

51. Acute hepatitis B and receiving a transfusion in Moldova, 1994-1995 (According to receiving injections) Case ControlTotal Transfusion316 No transfusion22628 Total25732 CaseControlTotal Transfusion 000 No transfusion 47183230 Total47183230 Odds ratio: - InjectionsNo injections Odds ratio: 0.8, 95% CI: 0.1-24.9 Confounding factor

52. Controlling a confounding factor Stratification Restriction Matching Randomization Multivariate analysis Confounding factor

53. Matching Stratification conducted initially at the stage of the study design of a case control study Stratified analysis (matched) necessary Confounding factor

54. Controlling a confounding factor Stratification Restriction Matching Randomization Multivariate analysis Confounding factor

55. Randomization Distribution of exposure of interest at random in the study population for a prospective cohort An association between an exposure and a confounding factor will be:  Secondary to chance alone  Improbable Confounding factor

56. Controlling a confounding factor Stratification Restriction Matching Randomization Multivariate analysis Confounding factor

57. Multivariate analysis Mathematical model Simultaneous adjustment of all confounding and risk factors Can address effect modification Confounding factor

58. Taking into account a third factor in practice 1.Think of potential confounding factors 2.Collect accurate data on them 3.Conduct crude analysis 4.Stratify 5.Look for effect modification Are the RR or OR different to each other? 6.If effect modification: Report Do not adjust 7.Control confounding factors through adjustment If applicable Before the study During the analysis

59. Analyzing a third factor

60. Take-home messages Describe effect modifications  The analysis must TEST for their occurrences Control confounding factors  The analysis must ELIMINATE their influence