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

When do causes work together?

Epidemiology matters: a new introduction to methodological foundations

Chapter 11

2Epidemiology Matters – Chapter 1

Seven steps

1. Define the population of interest2. Conceptualize and create measures of exposures and health

indicators3. Take a sample of the population4. Estimate measures of association between exposures and health

indicators of interest

5. Rigorously evaluate whether the association observed suggests a causal association

6. Assess the evidence for causes working together7. Assess the extent to which the result matters, is externally valid, to

other populations


Component causes of disease rarely act in isolationEpidemiologic 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 indicatorInteraction: when multiple component causes work together to produce a particular health indicator


1. Interaction, conceptual

2. Assessing interaction in data

3. Interaction across scales

4. Additivity, multiplicativity, and interaction

5. Additive interaction with ratios

6. Random variation

7. Summary







6. Random variation

7. Summary


Non-diseased Diseased

Non-exposed Exposed


Interaction, conceptual

Causes interact when they work together as part of the same sufficient cause, i.e., marble setCauses that interact are causes in which both factors are necessary to cause disease in at least one sufficient causeFor 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


Causes of Epititis

Family history

Exposure to toxins in utero

20 pack-years of smoking

Neighborhood poverty

Male sex

Stressful experiences in adulthood


Interaction, conceptual: Epititis

Male sex and family history are both component causes, they are components of different sufficient causes and do not interactTwo components interact if they need to work together within the same sufficient cause


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 history is part of mechanism through which in utero exposure

to toxins works - does not create non-comparability between exposed and

unexposed







6. Random variation

7. Summary


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


Assessing interaction, core concept

We can observe interaction when measure of association for exposure and outcome varies across levels of third variable


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 seatbeltKey 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?


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


Alcohol consumption and deathseatbelt use

Seatbelt use

Risk of death in exposed: 5%

Risk of death in unexposed: 1%

No seatbelt use



17

Alcohol consumption and deathseatbelt use

Alcohol use is always associated with greater risk of deathSeat 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



Nighttime




Alcohol consumption and deathtime of day

Alcohol use is always associated with greater risk of deathTime 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


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







6. Random variation

7. Summary


Interaction across scales

The presence of interaction depends on the measure of association we are examining


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


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.


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.


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







6. Random variation

7. Summary


Interaction is scale dependent

Additive: if two exposures do not interact, the risk of disease among exposed to both exposures = sum of risk of disease given exposure to one factor + risk of disease given exposure to the other factorMultiplicative: If two exposures do not interact, the risk of disease among those exposed to both = product of risk of disease given exposure to one factor * risk of disease given exposure to the other factor


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%


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%)


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 multiplicativityThe 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







6. Random variation

7. Summary


Additive interaction with ratio

Interaction arises when there are two (or more) component causes of the same sufficient cause influencing outcome of interestEvidence 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)


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 designsWe can therefore estimate interaction with ratio measures (odds ratio, risk ratio, or rate ratio)


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


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


Aspartame and stroke

To assess whether additive interaction is present, divide the sample into1. Family history of stroke and regular aspartame user (A+F+)2. Regular aspartame user with no family history of stroke (A+F-)3. Family history of stroke but not an aspartame user (A-F+)4. No family history and no aspartame use (A-F-)Then estimate three odds ratios and compare each to the fourth category


Aspartame and strokeAspartame+ 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







6. Random variation

7. Summary


Random variation

Appearance of interaction can arise due to chance in sampling processWe may collect a sample in which there were, by chance, a large proportion of individuals with disease in a certain subgroupTherefore confidence intervals around interaction measures are important







6. Random variation

7. Summary


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


Seven steps

1. Define the population of interest2. Conceptualize and create measures of exposures and health

indicators3. Take a sample of the population4. Estimate measures of association between exposures and health

indicators of interest

5. Rigorously evaluate whether the association observed suggests a causal association

6. Assess the evidence for causes working together7. Assess the extent to which the result matters, is externally valid, to

other populations


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