1. Chapter 9
Causality

2. 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

3. 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”

4. Related Terms Risk factor At-risk behavior – Stop it
Predisposing factor

5. 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

6. 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

7. 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

8. 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

9. What Is 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

10. 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

11. 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

12. 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)

13. 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.

14. Factors of Causation Predisposing factors Enabling factors
Precipitating factors
Reinforcing factors

15. 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

16. 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

17. 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

18. 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

19. Three methods of hypothesis formulation in disease etiology (John Stuart Mill, 1856)
Method of difference
Method of agreement
Method of concomitant variation

20. 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

21. 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

22. 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

23. 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

24. Causal Criteria Strength of association Consistency of association
Temporality
Biological plausibility
Experimental evidence

25. 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

26. 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

27. 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

28. 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

29. 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

30. 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

31. Example