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:/.

1. 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

2. 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=e
ds-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).

3. 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.

4. 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
Discussion: Models of Causation( one page )
Choose one of the causality models in Chapter 9 of the textbook
and use it to explain the relationships between the risk factors
associated with the leading cause of death you selected in
Module Four and the outcome (death from that cause). How do
Hill’s criteria for causation apply to these relationships?
Remember to cite references where necessary
Response one (Models of Causation)
The Pie model can be used to explain the relationships between
the risk factors associated with influenza. This model shows
how there are more than one “causal mechanisms” for a disease
to cause harm or death in an individual and can be different in
all individuals (Friis and Sellers, 2014). There are many
components which contribute a piece of the pie when dealing
with determining the risk a person has to get influenza which
include age, gender, chronic issues, antibody titers,
environments, contact with already sick individuals, and
preventative measures (Mansiaux et al., 2014). Even if some of
these factors are added or removed from individuals they might

5. still come done with influenza (Friis and Sellers, 2014). In
following Hill’s criteria of causality, all these factors follow the
criteria showing “strength of the association, temporality, dose-
response, consistency, biologic plausibility, specificity,
analogy, and coherence” (Friis and Sellers, 2014).
A model used in analyzing influenza, which is not in the book,
is structural equation modeling which is defined as a “flexible
and comprehensive methodology for representing, estimating,
and test a theoretical model with the objective of explaining as
much of the variance as possible (Ramlall, 2017). By using this
model, Mansiaux and associates were “able to obtain a coherent
quantitative picture of the complex mechanisms” which
occurred during the H1N1 pandemic in 2009. This model is
very valuable in developing public health policies (Mansiaux et
al., 2014). A diagram follows below showing the model.
References
Friis, R. H., & Sellers, T. A. (2014). Epidemiology for public
health practice. Burlington, MA: Jones & Bartlett Learning.
Mansiaux, Y., Salez, N., Lapidus, N., Setbon, M., Andreoletti,
L., Leruez-Ville, M., Cauchemez, S., Gougeon, M. L., Vély, F.,
Schwarzinger, M., Abel, L., Delabre, R. M., Flahault, A., de
Lamballerie, X., … Carrat, F. (2014). Causal analysis of
H1N1pdm09 influenza infection risk in a household
cohort. Journal of epidemiology and community health, 69(3),
272-7.
Ramlall, Indranarain. 2017. Applied Structural Equation
Modelling for Researchers and Practitioners : Using R and Stata
for Behavioural Research. Vol. First edition. Bingley: Emerald
Group Publishing Limited.
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