when do causes work together? epidemiology matters: a new introduction to methodological foundations...
TRANSCRIPT
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
3Epidemiology Matters – Chapter 11
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
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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
5Epidemiology Matters – Chapter 11
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
6Epidemiology Matters – Chapter 8
Non-diseased Diseased
Non-exposed Exposed
7Epidemiology Matters – Chapter 11
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
8Epidemiology 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
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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
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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
11Epidemiology Matters – Chapter 11
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
12Epidemiology 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
13Epidemiology 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
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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?
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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
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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%
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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.
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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%
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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
20Epidemiology 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
21Epidemiology Matters – Chapter 11
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
22Epidemiology Matters – Chapter 11
Interaction across scales
The presence of interaction depends on the measure of association we are examining
23Epidemiology 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
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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.
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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.
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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
27Epidemiology Matters – Chapter 11
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
28Epidemiology Matters – Chapter 11
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
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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%
30Epidemiology 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%)
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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
32Epidemiology Matters – Chapter 11
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
33Epidemiology 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 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)
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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)
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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
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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
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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
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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
39Epidemiology Matters – Chapter 11
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
40Epidemiology Matters – Chapter 11
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
41Epidemiology Matters – Chapter 11
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
42Epidemiology 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
43Epidemiology 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
44Epidemiology Matters – Chapter 1
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