Choose one of the causality models in Chapter 9 of Epidemiology for public health practice,textbook and use it to explain the relationships between the risk factors associated with the leading cause of death you selected in Module Fourand the outcome (death from that cause).

 

Choose one of the causality models in Chapter 9 of Epidemiology for public health practice,textbook and use it to explain the relationships between the risk factors associated with the leading cause of death you selected in Module Fourand the outcome (death from that cause). How do Hill’s criteria for causation apply to these relationships? Remember to cite references where necessary.

Leading cause of death you selected in Module Four and the outcome (death from that cause):

A big population of people in both developed and developing countries are affected by Diabetes. Diabetes refers to a health condition that is related to the amount of sugar in the blood. Glucose is such a crucial element in a human body as it is the body’s main source of energy(Colberg et. al., 2016). Diabetes occurs when a person’s blood sugar is too high. Insulin, a hormone that ensures the body cells get glucose from food is made by the pancreas. Sometimes, the body fails to generate enough insulin for conversion of glucose into absorbable element for energy generation(Basher, 2017). This results into a condition of having too much sugar in the blood. Diabetes has no cure but necessary steps can be taken to manage it and stay healthy.

There are various types in based on various cause related to insulin. Type 1 diabetes is a condition brought by the inability of the body to make insulin. Type 2 diabetes is as a result of the body not making good use of the available insulin(Farrar et. al., 2016). Gestational diabetes usually affects pregnant women and disappears after the woman has given birth. This might sound less dangerous but it increases chances of developing type 2 diabetes. The other type of diabetes is monogenic, which is an inherited type of diabetes. There are various symptoms of diabetes based on the type. In type 1 and type 2 cases of diabetes, these symptoms are unclear vision, too much hunger, recurrent urination, too much thirst, regular illnesses, dawdling healing sores, fatigue and irritability. People over the age of 40 years are the most vulnerable to diabetes though some types can develop at any age.

In conclusion, diabetes is such a serious disease that must be managed at all costs. Over some time, high amounts of glucose in the blood can stroke, kidney disease, blindness, dental disease, nerve problem, foot problem, heart diseases or even death.

Chapter 9 causality models attached.

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. Among statistical associations are non-causal and causal associations. Possible types of associations are formatted in .

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 , 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). 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.

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.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. 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.”() 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.” The web of causation implicates broad classes of events and represents an incomplete portrayal of reality. 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 . 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 (). 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. 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. As  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.”(p S144) Each causal mechanism is denoted in  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.”(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

This chapter covered two new measures of effect—absolute and relative effects—that may be used as aids in the interpretation of epidemiologic studies. In addition, the chapter presented guidelines that should be taken into account when one is interpreting an epidemiologic finding. Absolute effects, the first variety of which is called risk differences, are determined by finding the difference in measures of disease frequency between exposed and nonexposed individuals. A second type of absolute effect, called population risk difference, is found by computing the difference in measures of disease frequency between the exposed segment of the population and the total population. Relative effects are characterized by the inclusion of an absolute effect in the numerator and a reference group in the denominator. One type of relative effect, the etiologic fraction, attempts to quantify the amount of a disease that is attributable to a given exposure. The second type of relative effect, the population etiologic fraction, provides an estimate of the possible impact on the population rates of disease that can be anticipated by removal of the offending exposure. With respect to interpretation of epidemiologic findings, one should be cognizant of the influence of sample size upon the statistical significance of the results. Large sample sizes may lead to clinically unimportant, yet statistically significant, results; small sample sizes may yield statistically nonsignificant results that are clinically important. Therefore, we presented a series of five questions that should be asked when one attempts to interpret an epidemiologic observation. The chapter closed with a discourse on causal models, which may be particularly instructive when trying to interpret epidemiologic data.