When do causes work together?

When do causes work together? Epidemiology matters: a new introduction to methodological foundations Chapter 11

Seven steps • Define the population of interest • Conceptualize and create measures of exposures and health indicators • Take a sample of the population • Estimate measures of association between exposures and health indicators of interest • Rigorously evaluate whether the association observed suggests a causal association • Assess the evidence for causes working together • Assess the extent to which the result matters, is externally valid, to other populations Epidemiology Matters – Chapter 1

Component causes of disease rarely act in isolation Epidemiologic exposures are typically one of a set of component causes that have to work together in order for a change to occur in the health indicator Interaction: when multiple component causes work together to produce a particular health indicator Epidemiology Matters – Chapter 11

Interaction, conceptual • Assessing interaction in data • Interaction across scales • Additivity, multiplicativity, and interaction • Additive interaction with ratios • Random variation • Summary Epidemiology Matters – Chapter 11

Non-diseased Diseased Non-exposed Exposed Epidemiology Matters – Chapter 8

Interaction, conceptual Causes interact when they work together as part of the same sufficient cause, i.e., marble set Causes that interact are causes in which both factors are necessary to cause disease in at least one sufficient cause For example, what can ‘cause’ a sprinter to work a 100 meter dash • Only trains for years Does not win • Only has tied running shoes Does not win • Only reacts promptly to the starter’s pistol Does not win • Trains for years, tied shoes, prompt reaction Sprinter wins Epidemiology Matters – Chapter 11

Causes of Epititis Family history Exposure to toxins in utero 20 pack-years of smoking Neighborhood poverty Male sex Stressful experiences in adulthood Epidemiology Matters – Chapter 11

Interaction, conceptual: Epititis Male sex and family history are both component causes, they are components of different sufficient causes and do not interact Two components interact if they need to work together within the same sufficient cause Epidemiology Matters – Chapter 11

Comparability and interaction • Family history and in utero exposure are part of same set of marbles that cause Epititis • To develop Epititis as a result of sufficient cause 1 must always have both family history of Epititis and exposure to toxins in utero • No variation in relation between either component cause (marbles) and the outcome (Epititis) when one or the other is present • Family history and toxins interact to produce disease • Therefore, family historyis part of mechanism through which in utero exposure to toxins works - does not create non-comparability between exposed and unexposed Epidemiology Matters – Chapter 11

Interaction in theory • We could determine with certainty who would get disease if we could measure every component cause in a sufficient cause • Those exposed to all component causes would inevitably get disease • Those who do not have all the component causes, would never get disease • However, this is never the case, i.e., we can never know what all the component causes are and we therefore have to assess for causes that work together (i.e., interact) in our data Epidemiology Matters – Chapter 11

Assessing interaction, core concept We can observe interaction when measure of association for exposure and outcome varies across levels of third variable Epidemiology Matters – Chapter 11

Interaction examplealcohol consumption Question: Is consuming alcohol before driving associated with risk of dying in a motor vehicle crash? Other factors that can contribute to risk of dying in a motor vehicle crash include time of day, wearing a seatbelt Key questions of interest here are • Does alcohol consumption cause a greater risk of dying in a motor vehicle crash? • Does alcohol consumption interact with either (or both) time of day and seatbelt use in its causing motor vehicle crashes? How would we answer these questions? Epidemiology Matters – Chapter 11

Interaction examplealcohol consumption, data • Amount of alcohol consumed before driving • Subsequent death in a motor vehicle crash • Time of day that driving occurs • Driver wearing a seatbelt Epidemiology Matters – Chapter 11

Alcohol consumption and deathseatbelt use Seatbelt use Risk of death in exposed: 5% Risk of death in unexposed: 1% No seatbelt use Risk of death in exposed: 10% Risk of death in unexposed: 6% Epidemiology Matters – Chapter 11

Alcohol consumption and deathseatbelt use Alcohol use is always associated with greater risk of death Seat belt and alcohol use • Among those who did not wear a seatbelt, the risk of dying in crash was 10% among those who consumed alcohol prior to driving and 6% among those who did not consume alcohol prior to driving • Risk difference (RD) = 0.10 - 0.06 = 0.04 (95% CI 0.0162, 0.0637) • Among those who did wear a seatbelt, the risk of dying in crash was 5% among those who consumed alcohol prior to driving and 1% among those who did not consume alcohol prior to driving • Risk difference (RD) = 0.05 – 0.01 = 0.04 (95% CI 0.0238, 0.0541) Therefore there is no difference in risk difference between those who do and do not use a seatbelt. Seat belt use and alcohol use are part of different ‘marble sets’ and do not operate jointly to cause crash death. This indicates no interaction.

Alcohol consumption and deathtime of day Daytime Risk of death in exposed: 5% Risk of death in unexposed: 1% Nighttime Risk of death in exposed: 15% Risk of death in unexposed: 6% Epidemiology Matters – Chapter 11

Alcohol consumption and deathtime of day Alcohol use is always associated with greater risk of death Time of day and alcohol use • Among those who drove at night, the risk of dying in crash was 15% among those who consumed alcohol prior to driving and 6% among those who did not consume alcohol prior to driving • Risk difference (RD) = 0.15 – 0.06 = 0.09 (95% CI 0.0634, 0.1165) • Among those who drove during the day, the risk of dying in crash was 5% among those who consumed alcohol prior to driving and 1% among those who did not consume alcohol prior to driving • Risk difference (RD) = 0.05 – 0.01 = 0.04 (95% CI 0.0238, 0.0541) Therefore there is a difference in risk differences associated with alcohol consumption for nighttime drivers and for daytime drivers; this indicates the presence of interaction Epidemiology Matters – Chapter 11

Looking for interaction in data • Examine the evidence for interaction in data by comparing magnitude of association between exposure and disease across a third variable • If measure of association differs across levels of the third variable, there is evidence of interaction for that measure • If measure of association does not differ across levels of third variable - is not evidence of interaction Epidemiology Matters – Chapter 11

Interaction across scales The presence of interaction depends on the measure of association we are examining Epidemiology Matters – Chapter 11

Interaction across scales, example Question: Is consumption of green tea associated with reduced risk of stomach cancer? Does the relationship vary by whether individuals have diets that are rich in smoked and cured food? Purposive sample of 4000 individuals without stomach cancer • 1000 drink green tea and do not eat smoked/cured foods • 1000 drink green tea and eat smoked/cured foods • 1000 do not drink green tea but eat smoked/cured foods • 1000 do not drink green tea and eat smoked/cured foods • All follow forward for twenty years to determine which individuals develop stomach cancer Epidemiology Matters – Chapter 11

Green tea and cancerno smoked/cured food Interpretation: Among those who do not eat smoked/cured foods, green tea consumption is associated with 0.5 times the odds of stomach cancer compared with those who do not consume green tea. Epidemiology Matters – Chapter 11

Green tea and cancersmoked/cured food Interpretation: Among those who consume smoked/cured foods, green tea consumption is associated with 0.8 times the odds of stomach cancer compared with those who do not consume green tea. Epidemiology Matters – Chapter 11

Green tea and cancerinteraction scale Based on the risk ratio and the odds ratio, green tea consumption has a stronger protective effect among those who do not consume smoked/cured meats than among those who do consume such food. Therefore, there is evidence of interaction between green tea and smoked/cured foods However, based on risk differences across the two strata indicates that green tea consumption is associated with 5 fewer cases of stomach cancer for every 1,000 individuals who consume green tea, regardless of whether an individual consumes smoked/cured foods or not, i.e., no evidence of interaction between green tea and smoked/cured foods Interaction is dependent on whether we use relative measures or difference measure Epidemiology Matters – Chapter 11

Interaction is scale dependent Additive:if two exposures do not interact, the risk of disease among exposed to both exposures = sumof risk of disease given exposure to one factor + risk of disease given exposure to the other factor Multiplicative:If two exposures do not interact, the risk of disease among those exposed to both = productof risk of disease given exposure to one factor * risk of disease given exposure to the other factor Epidemiology Matters – Chapter 11

Interaction is scale dependent, example A Risk among those exposed to both X and Y: 10% Risk among those exposed to X but not Y: 6% Risk among those exposed to Y but not X: 5% Risk among those exposed to neither X nor Y: 1% There is no evidence of additive interaction.The risk of disease among those exposed to both X and Y is = sum of the risk associated with exposure to X alone, plus Y alone, minus the exposure associated with neither exposure (10=6+5-1) This is evidence of multiplicative interaction. The risk of disease among those exposed to both X and Y to be 30% if there were no multiplicative interaction, because 6x5=30 - observed risk is 10% < 30% Epidemiology Matters – Chapter 11

Interaction is scale dependent, example B Risk among those exposed to both X and Y: 30% Risk among those exposed to X but not Y: 6% Risk among those exposed to Y but not X: 5% Risk among those exposed to neither X nor Y: 1% There is no evidence of multiplicative interaction. The risk of disease among those exposed to both X and Y = to product of the risk associated with exposure to X alone, times Y alone (30=6*5) There is evidence of additive interaction. 30% is greater than the sum of risks for those exposed to X but not Y (6%) and Y but not X (5%) (minus the risk among those exposed to neither, 1%) Epidemiology Matters – Chapter 11

Interaction, use of additive scale When two factors are causal partners in the same sufficient cause, the resulting measures of association will depart from additivity, but not necessarily from multiplicativity The general recommendation is that interaction, or the search for factors that co-occur in the same sufficient cause, should be assessed on an additive scale Epidemiology Matters – Chapter 11

Additive interaction with ratio Interaction arises when there are two (or more) component causes of the same sufficient cause influencing outcome of interest Evidence of interaction in our data comes when we asses measure of association between exposure and outcome differs across levels of third variable Evidence for interaction will be dependent on measure of association used (additive interaction scale best) Epidemiology Matters – Chapter 11

Additive interaction with ratio What if we are unable to estimate risk or rate differences? The odds ratio is an appropriate measure of association for some study designs We can therefore estimate interaction with ratio measures (odds ratio, risk ratio, or rate ratio) Epidemiology Matters – Chapter 11

Additive interaction, with ratio, example • We are interested in the association between consumption of aspartame and stroke • Purposive sample - 200 cases of stroke newly diagnosed at hospitals and 600 individuals who have never had a stroke from communities of hospitals • Hypothesize that individuals with a family history of stroke are vulnerable to effects of aspartame, i.e., that aspartame and family history are causal partners in a sufficient cause for stroke Epidemiology Matters – Chapter 11

Aspartame and stroke No family history of stroke Family history of stroke This does not give us information about presence of additive interaction between aspartame use and family history - we are examining variation in the odds ratio- a multiplicative measure Epidemiology Matters – Chapter 11

Aspartame and stroke To assess whether additive interaction is present, divide the sample into • Family history of stroke and regular aspartame user (A+F+) • Regular aspartame user with no family history of stroke (A+F-) • Family history of stroke but not an aspartame user (A-F+) • No family history and no aspartame use (A-F-) Then estimate three odds ratios and compare each to the fourth category Epidemiology Matters – Chapter 11

Aspartame and stroke Aspartame+ Family- to Aspartame- Family- Aspartame- Family+ to Aspartame- Family- Aspartame+ Family+ to Aspartame- Family- • Estimate magnitude of interaction between family history and aspartame • Interaction contrast ratio (ICR):ICR=OR++ - OR+- - OR-+ + 1 • Hypothetical study • ICR= OR++ - OR+- - OR-+ + 1 • ICR = 2.15 - 1.03 - 1.04 + 1 = 1.08 This suggests some, if not much, additive interaction between aspartame and family history Epidemiology Matters – Chapter 11

Random variation Appearance of interaction can arise due to chance in sampling process We may collect a sample in which there were, by chance, a large proportion of individuals with disease in a certain subgroup Therefore confidence intervals around interaction measures are important Epidemiology Matters – Chapter 11

Interaction summary • Interaction occurs when two causes are both components of the same sufficient cause • When two causes interact this means that at least some individuals become diseased through a certain sufficient cause that includes both component causes • We can observe interaction when measure of association for exposure and outcome varies across levels of third variable • Different measures of association will evidence difference variation over a third variable depending on the scale (additive or multiplicative) • Epidemiology we are principally concerned with additive interaction Epidemiology Matters – Chapter 11

Seven steps • Define the population of interest • Conceptualize and create measures of exposures and health indicators • Take a sample of the population • Estimate measures of association between exposures and health indicators of interest • Rigorously evaluate whether the association observed suggests a causal association • Assess the evidence for causes working together • Assess the extent to which the result matters, is externally valid, to other populations Epidemiology Matters – Chapter 1

epidemiologymatters.org Epidemiology Matters – Chapter 1

When do causes work together?