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Chapter 9
Causality
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
Cause
 A cause is a specific event, condition, or
characteristic that precedes the health outcome
and is necessary for its occurrence
 If an environmental exposure is required for the outcome
to occur, the causative factor is “necessary”
 If the health-related state or event always occurs
because of the exposure, the causative factor is
“sufficient”
Related Terms
 Risk factor
 At-risk behavior – Stop it
 Predisposing factor
Epidemiology Triangle
 The epidemiology triangle is a traditional model
that characterizes infectious disease causation
by showing the interaction and interdependence
of agent, host, environment, and time
Epidemiology Triangle
 The agent is a causative factor, such as a
pathogen or chemical
 The host is an organism and usually a human
 Host factors are intrinsic factors that influence a
person’s exposure, susceptibility, or response to a
causative agent, such as age or race
 Environmental factors are extrinsic factors that
affect the agent and the opportunity for exposure
 Climate and geology, biological factors, psychosocial
factors
Epidemiology Triangle
 The agent, host, and environment interact in
complex ways to produce adverse health-related
states or events in humans
 Each component has time-related issues
 The interrelatedness of the four epidemiological
factors influences birth defects and other adverse
reproductive health outcomes
Rothman’s Causal Pies (1976)
 Rothman’s causal pies explains the multifactorial
nature of causation for many noninfectious diseases
 The factors that cause the adverse health outcome are
pieces of a pie, with the entire pie making up the sufficient
cause for a health problem or one causal mechanism
 Component causes – contributing factors to the
cause of a health-related state or event
 Component causes include agent, host factors, and
environmental factors
What Is Causal Inference?
 A conclusion about the presence of a healthrelated 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
What Is 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
Types of Causal Associations
 Direct causal association
 No intermediate factor and is more obvious
 Eliminating the exposure will eliminate the adverse
health outcome
 Example?
 Indirect causal association
 Involves one or more intervening factors
 Often much more complicated
 Example?
Direct Causal Association
 Example 1 – A trauma to the skin results in a
bruise or infection
 Example 2 – Salmonella results in enteritis
(inflammation of the small intestine)
Indirect Causal Association
 Example 1 – Poor diet and stress may cause high
blood pressure, which in turn causes heart disease
 Example 2 – Early pregnancy may cause
molecular changes that stabilize p53 (a tumor
suppressor gene). With p53 stabilized, it remains
functionally active longer to repair cumulative DNA
damage and to prevent cellular proliferation
induced by carcinogens.
Factors of Causation
 Predisposing factors
 Enabling factors
 Precipitating factors
 Reinforcing factors
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
 Example 1 – Age
 Example 2 – Immune status
 Example 3 – Li-Fraumeni syndrome predisposes the
person to a greater susceptibility of sarcomas, brain
cancer, breast cancer, and leukemia
 Other examples – Peoples’ knowledge, attitudes,
beliefs, preferences, skills, and self-efficacy beliefs
Reinforcing Factors
 The factors that help aggravate and perpetuate
behaviors, disease, conditions, disability, or death
 Positive reinforcing factors
 Social support, health education and economic
assistance
 Negative reinforcing factors
 Negative peer influence or poor economic conditions
Enabling Factors
 Antecedents to behaviors, disease, conditions,
disability, or death that allow it to be realized
 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
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 on motorcycles
Three methods of hypothesis
formulation in disease etiology
(John Stuart Mill, 1856)
 Method of difference
 Method of agreement
 Method of concomitant variation
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
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
Method of Concomitant Variation
 The frequency or strength of a risk factor varies
with the frequency of the disease or condition
 Increased numbers of children not immunized against
measles causes the incidence rate for measles to go up
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
Causal Criteria
 Strength of association
 Consistency of association
 Temporality
 Biological plausibility
 Experimental evidence
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
Confidence Intervals
 A range of reasonable values in which a population
parameter lies, based on a random sample from
the population
 As sample size increases, the role of chance
decreases, as reflected by the confidence interval
 Can also be used to evaluate statistical
significance
The Role of Bias
 The deviation of the results from the truth can
explain an observed association between
exposure and outcome variables
 Minimized by properly designing and conducting
the research study
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
The Role of Confounding (cont’d)
 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
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
Example