The third factor Effect modification Confounding factor FETP India

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

Key elements Describing an effect modification Eliminating a confounding factor

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

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

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

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

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

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

Diarrhoea ControlsTotal No breastfeeding Breastfeeding Total Death from diarrhoea according to breast- feeding, Brazil, 1980s (Crude analysis) Odds ratio: 3.6; 95% CI: ; p < Effect modification

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

Infants < 1 month of age CasesControlsTotal No breastfeeding Breastfeeding Total Infants ≥ 1 month of age CasesControlsTotal No breastfeeding Breastfeeding Total Death from diarrhoea according to breastfeeding, Brazil, 1980s

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

CasesControlsTotal No breastfeeding Breastfeeding Total Death from diarrhoea according to breast feeding, Brazil, 1980s: Analysis among infants < 1 month of age Odds ratio: 32.4; 95% CI: ; p < Effect modification

CasesControlsTotal No breastfeeding Breastfeeding Total Death from diarrhoea according to breast feeding, Brazil, 1980s: Analysis among infants ≥ 1 month of age Odds ratio: 2.6; 95% CI: ; p < Effect modification

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

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:  Strata 2: OR = 2.6; 95% CI: Effect modification

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

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

Handling heterogeneous measures of association

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

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

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

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

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

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 < 3 doses111Ref. 3 doses < 3 doses336Ref. Wolf test for evaluation of interaction: p = 0.03 * Born after implementation of routine vaccination District X Others Effect modification

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

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

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

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

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

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

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

Outcome Example of confounding factor Exposure 1 Apparent association Confounding factor

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

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

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

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

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

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

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

Incidence of malaria according to the presence of a radio set, Kahinbhi Pradesh Crude data MalariaNo malariaTotal Radio No radio Total RR: 0.7; 95% CI: ; p < 0.02 Confounding factor

Incidence of malaria according to the presence of a radio set, Kahinbhi Pradesh Strata 1: Sleeping under a mosquito net MalariaNo malariaTotal Radio No radio Total RR: 1.02; 95% CI: ; p < 0.97 Confounding factor

Incidence of malaria according to the presence of a radio set, Kahinbhi Pradesh Strata 2: Sleeping without a mosquito net MalariaNo malariaTotal Radio No radio Total RR: 0.98; 95% CI: ; p < 0.95 Confounding factor

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

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

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

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

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

CasesControls Total Transfusion314 Non-transfusion Total Odds ratio: 8.2; 95% CI : Acute hepatitis B and receiving a transfusion in Moldova, Confounding factor

Acute hepatitis B and receiving a transfusion in Moldova, (According to receiving injections) Case ControlTotal Transfusion316 No transfusion22628 Total25732 CaseControlTotal Transfusion 000 No transfusion Total Odds ratio: - InjectionsNo injections Odds ratio: 0.8, 95% CI: Confounding factor

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

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

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

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

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

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

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

Analyzing a third factor

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