Discussion 9
Models of Causal Relationships
Drawing upon the concepts presented earlier in the chapter, this section introduces models of disease causation. Relationships between suspected disease-causing factors and outcomes fall into two general categories: not statistically associated and statistically associated.15 Among statistical associations are non-causal and causal associations. Possible types of associations are formatted in Figure 9–2.
We have already considered the role of statistical significance in evaluating an association and noted that evaluation of statistical significance is used to rule out the operation of chance in producing an observed association; a nonstatistically associated (independent) relationship is shown in box A of the diagram (left side).
FIGURE 9–2 Map of possible associations between disease-causing factors and outcomes.
Source: Data from B MacMahon and TF Pugh, Epidemiology Principles and Methods. Boston, MA: Little, Brown and Company; 1970.
As shown in Figure 9–2, a statistical association may be either noncausal or causal. What is meant by a noncausal (secondary) association? Suppose factor C is related to disease outcome A. The association may be due to the operation of a third factor B that is related to both C and A. Thus, the association between C and A is secondary to the association of C with B and C with A. For example, periodontal disease (C) is associated with chronic obstructive pulmonary disease (A).16 One possible explanation for this association is the secondary association
of smoking (B) with both periodontal disease (C) and chronic obstructive pulmonary disease (A). This model suggests that the increased risk of chronic obstructive pulmonary disease associated with periodontal disease is related to the role that smoking may play as a cofactor in both conditions. Here is a map of a secondary association: C ← B → A.1
With respect to causal associations, the relationship between factor and outcome may be indirect or direct. An indirect causal association involves the operation of an intervening variable, which is a variable that falls in the chain of association between C and A. An illustration of an indirect association is the postulated relationship between low education levels (C) and obesity (A) among men.17 Men who have lower education levels tend to be more obese than those who have higher education levels. It is plausible that the relationship between C and A operates through the intervening variable of lack of leisure time physical activity (B). An indirect association involves an intervening variable in the association between C and A. This relationship may be formatted as follows: C → B → A.1 Note that the arrow between C and B has been reversed in contrast with an indirect noncausal association.
Multiple Causality
The foregoing section provided models of causality that employ more than one factor. As stated earlier in this chapter, the measure risk difference implies multivariate causality by isolating the effects of a single exposure from the effects of other exposures. The example on NSAIDs examined the difference between risk of peptic ulcer among users and nonusers of NSAIDs, where the risk difference was 12.5 per 1,000 person-years. The risk of peptic ulcer caused by other exposures was 4.2 per 1,000 person-years.
The issue of disease causality is exceedingly complicated. To describe exposure–disease relationships, epidemiologists have developed complex models of disease causality. These models acknowledge the multifactor causality of diseases, even those that seem to have “simple” infectious agents. Often, these models involve an ecologic approach by relating disease to one or more environmental factors. “The requirement that more than one factor be present for disease to develop is referred to as multiple causation or multifactorial etiology.” 18 ( p 27 ) Examples of several influential models are the:
· •• epidemiologic triangle
· •• web of causation
· •• wheel model
· •• pie model
Web of causation
The web of causation is “… a popular METAPHOR for the theory of sequential and linked multiple causes of diseases and other health states.” 1 The web of causation implicates broad classes of events and represents an incomplete portrayal of reality. 15 Although the web of causation for most diseases is complex, one may not need to understand fully the causality of any specific disease in order to prevent it. An example of the web of causation of avian influenza is provided in Figure 9–3 . Follow the infection of the human host from the virus reservoir in wild birds. As of 2007, the virus had not mutated into a form that could be spread readily from person to person.
Wheel model
The wheel model is similar to the epidemiologic triangle and web of causation with respect to involving multiple causality ( Figure 9–4 ). Observe that the model explains the etiology of disease by calling into play host and environment interactions. Environmental components are biologic, social, and physical. The circle designated as “host” refers to human beings or other hosts affected by a disease. The circle called “genetic core” acknowledges the role that genetic factors play in many diseases. The wheel model de-emphasizes specific agent factors and, instead, differentiates between host and environmental factors in disease causation. The biologic environment is relevant to infectious agents, by taking into account the environmental dimensions that permit survival of microbial agents of disease.
FIGURE 9–3 The web of causation for avian influenza.
A wheel model may be used to account for the occurrence of childhood lead poisoning.18 In this example, preschool children are typical hosts. The physical environment provides many opportunities for lead exposure from lead-based paint in older homes, playground equipment, candy wrappers, and other sources. Some children ingest paint chips from peeling surfaces as a result of pica, the predilection to eat nonfood substances. Because lead-based paints often are located in poorer neighborhoods that have substandard housing, the social environment is associated with childhood poisoning. Limited access to medical care in such communities may restrict screening of preschool children for lead exposure. Elimination of childhood lead poisoning requires visionary public
health leadership to advocate for detection of lead-based paints and other sources of environmental lead exposure as well as the implementation of screening programs. Such efforts will help to protect vulnerable children against the sequelae of lead poisoning.
Pie model
Another model of multiple causality (multicausality) is the causal pie model.19 As Figure 9–5 shows, the model indicates that a disease may be caused by more than one causal mechanism (also called a sufficient cause), which is defined as “a set of minimal conditions and events that inevitably produce disease.”19(p S144) Each causal mechanism is denoted in Figure 9–5 by the numerals I through III. An example of different causal mechanisms for a disease is provided by the etiology of lung cancer: lung cancer caused by smoking; lung cancer caused by exposure to ionizing radiation; and lung cancer caused by inhalation of carcinogenic solvents in the workplace.
FIGURE 9–5 Three sufficient causes of disease.
Source: From KJ Rothman and S Greenland, Causation and causal inference in epidemiology, Am J Public Health, 2005; vol 95, p S145. Reprinted with permission from the American Public Health Association.
Rothman and Greenland note that, “A given disease can be caused by more than one causal mechanism, and every causal mechanism involves the joint action of a multitude of component causes.”19(p S145) The component causes, or factors, are denoted by the letters shown within each pie slice. A single letter indicates a single component cause. A single component could be common to each causal mechanism (shown by the letter A that appears in each pie); in
other cases, the component causes for each causal mechanism could be different for each mechanism (shown by the letters that differ across the pies). Returning to the lung cancer example, a common factor that could apply to all causal mechanisms for lung cancer is a genetic predisposition for cancer. Several other component causes might be different for each causal mechanism involved in the etiology of lung cancer.
In models of multicausality, most of the identified component causes are neither necessary nor sufficient causes (defined in the section on absolute effects). Accordingly, it is possible to prevent disease when a specific component cause that is neither necessary nor sufficient is removed; nevertheless, when the effects of this component cause are removed, cases of the disease will continue to occur.
Conclusion