Nursing Report: Exploring Causation in Diabetes, NURSING 4

This nursing report examines the multifaceted nature of diabetes causation, delving into biological, social, psychological, behavioral, and lifestyle factors contributing to the disease. It explores the application of epidemiological theories and the Bradford Hill criteria to determine causality, emphasizing the relevance of the biological gradient. The report also clarifies the meaning of 'correlation does not imply causation' within an epidemiological context, providing examples and highlighting the importance of understanding these concepts for registered nurses in patient assessment and evidence-based practice. The document references studies on diabetes risk factors and the impact of lifestyle choices, such as diet and physical activity, on disease outcomes. Finally, the report emphasizes the importance of understanding causation for nurses to interpret patient symptoms, assess health outcomes, and engage in evidence-based practice.

Running head: NURSING
Nursing
Name of the student:
Name of the University:
Author’s note

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Answer 1
a. Considerations for determining the cause of the disease:
Diabetes is a chronic disease that has multiple causations. To consider the cause behind this
illness, it is necessary to consider biological, social, psychological, behavioural and lifestyle
factors of an individual too. This is because diabetes risk increases because of multiple factors
like diabetes, impaired glucose tolerance, sedentary lifestyle, cigarette smoking, family history,
ethnic background, family history and gestational diabetes. Thus, while considering the cause of
diabetes, correlation between different risk factors and the disease can be explored. According to
epidemiological theory, disease causation is determines by predisposing factors, enabling factors,
precipitating factors, reinforcing factors and risk factors. The predisposing factors include all
those factors that lead to vulnerability to a disease (Costello & Angold, 2016). In case of
diabetes, the predisposing factors include being overweight, age more than 45 years and ethnic
minority groups (Spanakis & Golden, 2013).
The enabling factors determining disease causation include housing conditions and
socioeconomic status (SES) of an individual. A study investigating association between SES and
diabetes revealed that low SES and education level is associated with high prevalence of diabetes
mellitus. SES factors can be determined by looking at gender, age, marital status, income, level
of education, occupation and remaining debts of people at risk of the disease (Suwannaphant et
al., 2017). In addition, the precipitating factors are those factors that are associated with
immediate onset or exposure to the disease. It includes factors like high blood pressure and
physical inactivity. The reinforcing factors include those factors that can aggravate existing

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disease like diabetes included poor diet, limited physical activity and poor diet. This can be
followed by review of risk factors (Colberg et al., 2013; Forouhi et al., 2018).
b. Criteria to understand the causal factors related to the disease:
The Bradford Hill criteria can be a useful model to understand the cause of disease. The
Bradford Hill Criteria considers several factors like strength of the association, consistency,
specificity, biological gradient, biologic rationale, temporal sequence, experimental evidence,
coherence and analogous evidences to determine the cause behind any disease. The first factor of
association explores the association between causative agent and outcome. The consistency
factor identifies the consistency of finings across various research settings and research design.
Moreover, specificity is a criteria that demonstrates how specific a risk factor is in causing the
disease (Weed, 2018). There are many rationale for choosing the Bradford Hill Criteria for
reviewing the cause of disease. Firstly, this is a widely accepted criteria that has been widely
applied throughout medicine and research. Fedak et al. (2015) argues that the Bradford Hill
criteria is the most frequently cited framework for causal inferences in epidemiological studies.
Thus, this framework is likely to give the most reliable results.
The second rationale for choosing Bradford Hill Criteria for diabetes is that it can increase
rigor during establishing causation. This can be possible because causation between two
variables are considered by looking at temporal sequence and biological gradient. Temporal
sequence is the step to clarify that the causative agent is occurring before the outcome, whereas
the biological gradient is the step to confirm that higher amount of causative agent or higher
exposure to risk factors leads to poor outcome (Schünemann et al., 2011). Thus, through such

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rigorous method of assessing the link between diabetes and risk factors, it will be possible to
have better clarity regarding the factors contributing to diabetes.
c. Most relevant criteria for chosen health condition:
Based on the review of all the criteria listed in the Bradford Hill’s criteria, the biological
gradient factor seems most relevant to understand the causality of diabetes. This is because there
are many risk factors such as diet and physical inactivity that adversely influence blood glucose
control in patients with diabetes. Thus evaluating the dose of dietary factors and risk of diabetes
can be crucial in determining how this factor adversely affects diabetes outcome. For example,
the study by Livesey et al. (2019) considered biological gradient was assessing the causal
relation between risk of type 2 diabetes and dietary glycemic index. By the review of meta-
analyses of dose-response data related to carbohydrate consumption and diabetes, the study
considered biological gradient. Hence, finding out such data would help in interpreting better
understanding regarding how far dietary factors expose people to higher risk of diabetes. The
biological gradient can also be useful in articulating the duration of physical activity and its
impact on diabetes outcome.
d. Concept of causation for registered nurse:
Causation is a concept in epidemiology that is defined as the capacity of one variable to
influence other variable. It is mainly an investigation into the cause of any disease. All
registered nurses must be aware about the concept of causation because it can help them to
interpret the cause of behind change in symptoms of patient and evaluate how patient’s context
and past medical history influences their current health outcomes. Moreover, knowledge of
causation is necessary for registered nurse to engage in evidence based practice. Evidence based

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practices requires nurse to review various research literature to solve or clarify any clinical issue
(Costa & Yakusheva, 2016). Thus, awareness about this concept can help them to make causal
inferences while reviewing research papers. By this knowledge they can control the cause behind
any health issue and develop plans to promote improvement in patient outcomes by identifying
causal pathways of relation between two variables.
Answer 2
a. The phrase ‘correlation does not imply causation’ means that observing association or
correlation between two variables alone does not determine the cause and effect
relationship between two variables. This phrase is relevant to epidemiology because this
field mainly deals with study if incidence, distribution and factors linked to any disease.
Thus, causation and correlation is most frequently utilized in epidemiology. It mainly
mandates that any causation must be very specific so that it distinguishes from mere
correlation. Correlation is measure that determines the association between risk factors
and disease. However, the problem is that correlation can never prove the causation of
any disease (Perraillon, Welton & Jenkins, 2019). Thus, the above phrase is very
relevant to the field of epidemiology as the main goal of epidemiology is to understand
the factors that contribute to any disease.
b. The phrase ‘‘correlation does not imply causation’ is used to denote or notify that any
correlation between two variables does not confirm that there is a cause and effect
relation between two variables. The phrase is termed to validate any explanation and give
the notion that no conclusion has yet been received regarding the causation of the disease.
Thus, this phrase is useful in making a correct conclusion at the end of any
epidemiological study. This can be further understood by the following examples:

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For example, correlation between sale of processed food products and number of diabetes
case means that as sale of processed food is increasing, so is the incidence of diabetes. However,
this does not means that processed food is causing diabetes. Causation can be an outcome such
as when a person is engaging in daily rigorous walk for 1 hour every day, his blood glucose level
is decreasing. Thus, this is an indication of causation. The phrase can be used to conclude for the
link between processed food product sales and incidence of diabetes.
c. The phrase ‘correlation does not imply causation’ is often misunderstood and
misinterpreted. For example, most people think that this phrase denotes that correlation is
related with causation. However, this is not correct as causation and correlation are two
different concepts. Any two quantitative variables are said to be correlated if they
increase or decrease together (Fattorini, 2017). However, this does not means that the one
variable is causing the other variable to change. An example of incorrect use of this
phrase is as follows:
With the increase in cloudy weather, there is a high chance of rainfall. This is an
example of correlation as the two variables are positively correlated.
However, many people may assume that cloudy weather is the cause of rainfall. This is
an example of misinterpretation of the above phrase.
d. It is equally important for a nurse to have full understanding about the terms ‘correlation’
and ‘causation’. This is because they are often involved in conducting patient assessment
and they may often come across clinical data that can help them to interpret the cause
behind the illness. Correlation is a statistical technique to identify linear relationship
between two continuous variables (Fattorini, 2017). During nursing assessment, they are
required to conduct assessment of vital signs and record pain scores. They can compare

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this data with that of current symptoms or health status to understand the correlation
between them. For example, nurse can correlate the use of pain medications with that of
decrease or increased in pain scores of patient. Moreover, nurses can interpret causation
of any symptoms by assessing how change in one variables such as level of physical
activity or consumption of drug improves the symptoms of diabetes. Thus, knowledge
related to the above two terms can help them in tracking improvement in symptoms of
patient and understanding the causation factor in illness.

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References:
Colberg, S. R., Sigal, R. J., Yardley, J. E., Riddell, M. C., Dunstan, D. W., Dempsey, P. C., ... &
Tate, D. F. (2016). Physical activity/exercise and diabetes: a position statement of the
American Diabetes Association. Diabetes care, 39(11), 2065-2079.
Costa, D. K., & Yakusheva, O. (2016). Why causal inference matters to nurses: The case of
nurse staffing and patient outcomes. OJIN: The Online Journal of Issues in
Nursing, 21(2).
Costello, E. J., & Angold, A. (2016). Developmental epidemiology. Developmental
psychopathology, 1-35.
Fattorini, S. (2017). Cause and Correlation in Biology. A User's Guide to Path Analysis,
Structural Equations and Causal Inference with R, Bill Shipley, Cambridge University
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Fedak, K. M., Bernal, A., Capshaw, Z. A., & Gross, S. (2015). Applying the Bradford Hill
criteria in the 21st century: how data integration has changed causal inference in
molecular epidemiology. Emerging themes in epidemiology, 12(1), 14.
Forouhi, N. G., Misra, A., Mohan, V., Taylor, R., & Yancy, W. (2018). Dietary and nutritional
approaches for prevention and management of type 2 diabetes. Bmj, 361, k2234.
Livesey, G., Taylor, R., Livesey, H. F., Buyken, A. E., Jenkins, D. J., Augustin, L. S., ... &
Willett, W. C. (2019). Dietary glycemic index and load and the risk of type 2 diabetes:
Assessment of causal relations. Nutrients, 11(6), 1436.

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Perraillon, M. C., Welton, J. M., & Jenkins, P. (2019). Measuring Nursing Care Value, Big Data,
and the Challenges of Estimating Causal Effects. Nursing Economics, 37(4), 213-215.
Schünemann, H., Hill, S., Guyatt, G., Akl, E. A., & Ahmed, F. (2011). The GRADE approach
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Spanakis, E. K., & Golden, S. H. (2013). Race/ethnic difference in diabetes and diabetic
complications. Current diabetes reports, 13(6), 814-823.
Suwannaphant, K., Laohasiriwong, W., Puttanapong, N., Saengsuwan, J., & Phajan, T. (2017).
Association between socioeconomic status and diabetes mellitus: the National
Socioeconomics Survey, 2010 and 2012. Journal of clinical and diagnostic research:
JCDR, 11(7), LC18.
Weed, D. L. (2018). Annals of Epidemiology Analogy in causal inference: rethinking Austin
Bradford Hill's neglected consideration.