Discussion-09

RESPONSE TWO

9-3 Discussion: Models of Causation

MacMahon and Pugh, in 1970, wrote that “the word cause is an abstract noun and, like beauty, will have different meanings in different contexts” (Parascandola & Weed, 2001, p. 905). Cause is also a scientific term and it is important that epidemiologists have common thinking about what is meant in saying “X causes Y” (Parascandola & Weed, 2001). Epidemiologists have developed complex models of disease causality to describe exposure-disease relationships. These models use an ecologic approach by relating disease to one or more environmental factors. Multiple causation in epidemiology refers to the requirement that more than one factor be present for disease to develop. There are several models used by epidemiologists such as epidemiologic triangle, web of causation, wheel of model and pie model (Friis & Sellers, 2014).

The web of causation, one of the several models used, arose out of the study of chronic diseases like cancer or heart disease. In a casual web, interconnections of casual components in a population are emphasized. There are direct and indirect causes comprising casual webs. Proximal to pathogenic events are direct causes. Distal from pathological events are indirect causes. Indirect and direct causes form a hierarchical causal web often with reciprocal relations among factors. An example of a causal web model is shown below in Figure 1 (Gerstman, 2003). When looking at heart disease, the concept of a “necessary” condition is rarely, if ever, meaningful. There is no “necessary” cause of heart disease; rather the idea of a “causal web” has been introduced and applied. To induce heart disease a concurrence of different “exposures” or conditions are required, and the casual web reflects this fact. Heart disease can be induced by a casual web, including tobacco smoking, genetics, high-fat diet, physical inactivity, and stress. The web of causation implies that even though heart disease is well-defined from a clinical point of view, the etiologic perspective is more complex. Not all heart disease cases can be linked to the same exposures but may share partially overlapping causes (Vineis & Kriebel, 2006). Hill's criteria for causation applies to this relationship as each of his criteria are carefully considered when determining a cause-and-effect relationship.

Figure 1.0. Causal-web model for myocardial infarction.

References

Friis, R. H., & Sellers, T. A. (2014). Epidemiology for Public Health Practice (5th ed.). Burlington, MA: Jones & Bartlett Learning.

Gerstman, B. B. (2003). Epidemiology kept simple: an introduction to traditional and modern epidemiology (2nd ed). Hoboken, N.J: Wiley-Liss. Retrieved from http://www.sjsu.edu/faculty/gerstman/hs161/Ch2-EKS-2ed.pdf

Parascandola, M., & Weed, D. (2001). Causation in epidemiology. Journal of Epidemiology and Community Health, 55(12), 905–912. https://doi.org/10.1136/jech.55.12.905 Retrieved from http://ezproxy.snhu.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=edsjsr&AN=edsjsr.40543358&site=eds-live&scope=site

Vineis, P., & Kriebel, D. (2006). Causal models in epidemiology: past inheritance and genetic future.Environmental Health: A Global Access Science Source, 5, 21. https://doi.org/10.1186/1476-069X-5-21

RESPONSE THREE

Heart disease is one of the most detrimental reasons of illness that it has been ranked the number one leading cause of mortality (Centers for Disease Control and Prevention, 2017). There are multiple risk factors that cause or lead to heart disease including high blood pressure, high cholesterol, diabetes, obesity, lack of exercise, unhealthy diets, alcohol or tobacco use (Centers for Disease Control and Prevention, 2017). The pie model can be caused by minimal conditions and by different causal mechanisms (Friis & Sellers, 2014). According to Friis & Sellers (2014) “a given disease can be caused by more than one casual mechanism, and every causal mechanism involves the joint action of a multitude of component causes. The component causes, or factors, are denoted by the letters shown within each pie slice” (Friis & Sellers, 2014). For example, heart disease caused by lack of exercise; heart disease caused by high cholesterol intake; heart disease caused by unhealthy diets are all relationships between the risk factors associated with heart disease. The pie slices all indicate different things for example according to Friis & Sellers (2014) “a single letter indicates a single component cause , a single component could be common to each causal mechanism, the component causes for each causal mechanism could be different” (Friis & Sellers, 2014). 

Hill’s criteria for causation applies to these relationships because although his theories were not fully developed at the time his questions and suggestions on how to develop the relationships between potential causes of disease became a pivotal point in history that strengthened throughout the years. Fedak et al. (2015) mentions “how could they effectively practice preventative occupational medicine without a basis for determining which occupational hazards ultimately cause sickness and injury, he proceeded to propose nine aspects of association for evaluating traditional epidemiology” (Fedak et al, 2015). These nine aspects have been used to evaluate hypothesized relationships between occupational and environmental exposures all correlate to the way in which we study and tackle heart disease (Fedak et al, 2015). In order to prevent heart disease, we must be aware of what causes it, and what we can do to lessen the risks associated with getting it. Hill’s criteria has set a foundation and basis to apply to future use of epidemiology and how we hypothesize.

 

Reference:

Centers for Disease Control and Prevention. (2017). Retrieved from            https://www.cdc.gov/dhdsp/data_statistics/fact_sheets/fs_heart_disease.htm

Friis, R. H., Sellers, T. A. (2014). Epidemiology for Public Health Practice. Burlington, MA: 

Jones and Bartlett Learning. 

Fedak, K. M., Bernal, A., Capshaw, Z. A., & Gross, S. (2015). Applying the Bradford Hill criteria in the 21stcentury: how data integration has changed causal inference in molecular epidemiology. Emerging themes in epidemiology, 12, 14.doi: 10.1186/s12982-015-0037-4