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INTRODUCTION TO
EPIDEMIOLOGY
FIFTH EDITION
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2. Statistical and Causal Associations
Chapter 9
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Objectives
Understand the distinction between causal inference and statistical inference.
Be familiar with selected criteria for establishing causal associations.
Understand the roles of chance, bias, and confounding in statistical associations.
Know the steps for hypothesis testing and be able to apply hypothesis testing in the search for causal associations.
Understand how webs of causation can be used as tools in epidemiology.
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Aim in epidemiology
At the heart of epidemiology is the notion of causality:
The idea is that when a causal association is established, a protection and control attitude can occur rather than a mere reaction to the public health crises
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Related terms
Risk factor
At-risk behavior
Predisposing factor
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Causal inference
A conclusion about the presence of a health-related state or event and reasons for its existence
Causal inferences provide a scientific basis for medical and public health action.
Made with methods comprising lists of criteria or conditions applied to the results of scientific studies.
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7. Statistical Inference
Draws a conclusion about a population based on information from sampled data
Probability is used to indicate the level of reliability in the conclusion
The possibility that chance, bias, or confounding explain a statistical association should always be considered
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8. Types of Causal Associations
Direct Causal Association
No intermediate factor and is more obvious
Eliminating the exposure will eliminate the adverse health outcome
Indirect causal association
Involves one or more intervening factors
Often much more complicated
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Factors of causation
Predisposing factors
Enabling factors
Precipitating factors
Reinforcing factors
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Predisposing factors
Factors or conditions already present that produce a susceptibility or disposition in a host to a disease or condition without actually causing it
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Reinforcing factors
Can be negative or positive
Positive reinforcing factors
Support, enhance, and improve the control and prevention of the causation of disease
Negative reinforcing factors
The factors that help aggravate and perpetuate disease, conditions, disability, or death
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Enabling factors
Factors that can affect health through an environmental factor
Services, living conditions, programs, societal supports, skills, and resources that facilitate a health outcome’s occurrence
Can also be a result of a lack of services or medical programs
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13. Precipitating factors
Factors essential to the development of diseases, conditions, injuries, disabilities, and death
An infectious agent
Lack of seat belt use in cars
Drinking and driving
Lack of helmet use by motorcycle
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14. Three methods of hypothesis formulation in disease etiology
Method of difference
Method of agreement
Method of concomitant variation
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Method of Difference
The frequency of disease occurrence is extremely different under different situations or conditions.
If a risk factor can be identified in one condition and not in a second, it may be that factor, or the absence of it, that causes the disease.
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Method of Agreement
If risk factors are common to a variety of different circumstances and the risk factors have been positively associated with a disease, then the probability of that factor being the cause is extremely high.
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17. Method of Concomitant Variation
The frequency or strength of a risk factor varies with the frequency of the disease or condition.
Example. Increased numbers of children not immunized against measles causes the incidence rate for measles to go up.
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18. Statistical association does not mean causal association
For example, ice cream consumption and murder are strongly correlated.
Does eating ice cream make people want to kill or does killing result in a desire for ice cream?
The explanation may be that hot temperatures are related to both ice cream consumption and murder and that it is the heat, not the ice cream causally associated with murder
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Causal criteria
Strength of association
Consistency of association
Specificity
Temporality
Biologic gradient
Biological plausibility
Coherence
Analogy
Experimental evidence
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20. Direct and indirect causal associations
Direct causal association has no intermediate factor and is more obvious
Exposure to staphylococcal pathogens results in illness
Indirect causal association involves one or more intervening factors and is often much more complicated
A high fat diet is associated with polyps and polyps are associated with colon cancer
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21. The role of chance The “luck of the draw”
Most epidemiologic studies rely on sampled data
Characteristics of subjects in a sample may vary from sample to sample. As a result, an association between an exposure and outcome, or lack thereof, may be the result of chance
Sample size is directly related to chance
To minimize chance, increase the sample size
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Confidence intervals
A range of reasonable values in which a population parameter lies, based on a random sample from the population
As sample size goes up, the role of chance goes down, as reflected by the confidence interval.
Can also be used to evaluate statistical significance
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The role of bias
The deviation of the results from the truth and can explain an observed association between exposure and outcome variables
Minimized by properly designing and conducting the research study
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24. The role of confounding
Occurs when the relationship between an exposure and a disease outcome is influenced by a third factor, which is related to the exposure and, independent of this relationship, is also related to the health outcome.
Only the randomized experimental study allows us to balance out confounding among groups.
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25. Confounding (continued)
Should always be considered as a possible explanation for an observed association, particularly descriptive epidemiologic studies and non-randomized analytic epidemiologic studies
May over or underestimate a true association
Possible to control for at the design and analysis levels of a study
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Web of causation
Webs are graphic, pictorial, or paradigm representations of complex sets of events or conditions caused by an array of activities connected to a common core or common experience or event
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Web of causation
Webs have many arms, branches, sources, inputs, causes, etc., that are somehow interconnected or interrelated to the core
Webs can also have a chain-of-events where some events must occur before others can happen
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Decision Trees
The yes–no response of decision trees leads the epidemiologist closer to discovering the cause than a web of causation alone.
Helps eliminate possibilities of causation while leading the investigator down the correct path toward discovery, assuming the questions are answered correctly.
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