4.17: Etiology and Contributory Cause
- Page ID
- 124753
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\(\newcommand{\avec}{\mathbf a}\) \(\newcommand{\bvec}{\mathbf b}\) \(\newcommand{\cvec}{\mathbf c}\) \(\newcommand{\dvec}{\mathbf d}\) \(\newcommand{\dtil}{\widetilde{\mathbf d}}\) \(\newcommand{\evec}{\mathbf e}\) \(\newcommand{\fvec}{\mathbf f}\) \(\newcommand{\nvec}{\mathbf n}\) \(\newcommand{\pvec}{\mathbf p}\) \(\newcommand{\qvec}{\mathbf q}\) \(\newcommand{\svec}{\mathbf s}\) \(\newcommand{\tvec}{\mathbf t}\) \(\newcommand{\uvec}{\mathbf u}\) \(\newcommand{\vvec}{\mathbf v}\) \(\newcommand{\wvec}{\mathbf w}\) \(\newcommand{\xvec}{\mathbf x}\) \(\newcommand{\yvec}{\mathbf y}\) \(\newcommand{\zvec}{\mathbf z}\) \(\newcommand{\rvec}{\mathbf r}\) \(\newcommand{\mvec}{\mathbf m}\) \(\newcommand{\zerovec}{\mathbf 0}\) \(\newcommand{\onevec}{\mathbf 1}\) \(\newcommand{\real}{\mathbb R}\) \(\newcommand{\twovec}[2]{\left[\begin{array}{r}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\ctwovec}[2]{\left[\begin{array}{c}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\threevec}[3]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\cthreevec}[3]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\fourvec}[4]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\cfourvec}[4]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\fivevec}[5]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\cfivevec}[5]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\mattwo}[4]{\left[\begin{array}{rr}#1 \amp #2 \\ #3 \amp #4 \\ \end{array}\right]}\) \(\newcommand{\laspan}[1]{\text{Span}\{#1\}}\) \(\newcommand{\bcal}{\cal B}\) \(\newcommand{\ccal}{\cal C}\) \(\newcommand{\scal}{\cal S}\) \(\newcommand{\wcal}{\cal W}\) \(\newcommand{\ecal}{\cal E}\) \(\newcommand{\coords}[2]{\left\{#1\right\}_{#2}}\) \(\newcommand{\gray}[1]{\color{gray}{#1}}\) \(\newcommand{\lgray}[1]{\color{lightgray}{#1}}\) \(\newcommand{\rank}{\operatorname{rank}}\) \(\newcommand{\row}{\text{Row}}\) \(\newcommand{\col}{\text{Col}}\) \(\renewcommand{\row}{\text{Row}}\) \(\newcommand{\nul}{\text{Nul}}\) \(\newcommand{\var}{\text{Var}}\) \(\newcommand{\corr}{\text{corr}}\) \(\newcommand{\len}[1]{\left|#1\right|}\) \(\newcommand{\bbar}{\overline{\bvec}}\) \(\newcommand{\bhat}{\widehat{\bvec}}\) \(\newcommand{\bperp}{\bvec^\perp}\) \(\newcommand{\xhat}{\widehat{\xvec}}\) \(\newcommand{\vhat}{\widehat{\vvec}}\) \(\newcommand{\uhat}{\widehat{\uvec}}\) \(\newcommand{\what}{\widehat{\wvec}}\) \(\newcommand{\Sighat}{\widehat{\Sigma}}\) \(\newcommand{\lt}{<}\) \(\newcommand{\gt}{>}\) \(\newcommand{\amp}{&}\) \(\definecolor{fillinmathshade}{gray}{0.9}\)Etiology, is a term with roots in Greek that means the cause of disease. Within an evidence-based framework of public health, the concept of contributory cause is a specific type of etiology, which requires demonstrating causality beyond mere association through three key criteria (Riegelman & Kirkwood, 2025 pp 35-39):
- In an individual level, the "cause" is associated with the "effect". For example, the "cause" = lung cancer and the "effect" = smokers, it would be required that people diagnosed with lung cancer tend to be smokers as compared with individuals without lung cancer.
- In an individual level, the "cause" comes before the "effect". For example, the "cause" = not enough sleep and the "effect" = increase risk of obesity, it would be required that not enough sleep comes before the onset of obesity.
- In an individual level, altering the "cause" also alters the "effect". For example, the "cause" = reduction of cigarette smoking and the "effect" = lung cancer, it would be required that reducing cigarette smoking also reduces the incidence of lung cancer.
- In a community level, a group association of "cause" and "effect". For example, history shows that men started to smoke before women and the incidence of lung cancer in men increased years before women.
It is important to note that epidemiologists use certain studies to analyze the relationship between the "causes" and the "effects" to infer contributory cause. These studies are case-control studies, cohort studies, randomized controlled trials, population comparison or ecological studies. Let's look at how the studies help in determining the contributory cause:
- Case-control studies are the most helpful in associating the first requirement; the cause is associated with an effect.
- Cohort studies are the most helpful in associating the second requirement; the cause comes before the effect.
- Randomized controlled trials are the most helpful in associating the third requirement; altering the cause also alters the effect.
- Population comparison or ecological studies are the most helpful in comparing population incidence between cause and effect.
It is important to note that the above requirement for identifying contributory cause is the same as the one to establishing efficacy. Efficacy is the ability to produce the desired positive effect of an intervention.
There are times when it is not possible to have a concrete contributory cause, and in those cases, analysis of supportive evidence needs to take place to establish cause and effect. The most well-accepted criteria are:
- Strength of the relationship: this looks at how closely related the risk factor (what increases the likelihood of a negative outcome) is to the disease. To measure the strength of the relationship, it is necessary to calculate the relative risk, which is calculated by: probability of the disease for those with the risk factor/ probability of the disease for those "without" the risk factor. The larger the quotient (result of the division), the larger the relative risk.
- Dose-response relationship: this looks at whether quantity is a factor in developing the disease. For instance, considering the opioid crisis and knowing that prescription drugs could be a contributory cause, one can analyze to see if those that have developed opioid addiction disease through prescription drugs were more addicted due to the time they were on medication or the strength of the pain medication.
- Consistency of the relationship: this looks at whether different groups in different geographical areas produce similar results. Again, take the opioid crisis and addiction formed due to prescribed pain medication, would an analysis of the people afflicted in different geographic areas yield the same result?
- Biological plausability: this looks at explaining the disease based on accepted biological means. Looking at the opioid addiction, morphine derived drugs act upon the brain to change to produce drug liking, tolerance, dependence, and addiction (Korsten & George, 2002).
- Specificity: this considers how closely tied a specific exposure is to a specific disease. For example, if excessive exposure to sunlight was observed as a factor in many different illnesses, not just skin cancer, it would be harder to draw a causal link.
- Temporality: this one seems almost obvious -- does the exposure happen BEFORE the disease develops? If the exposure happens after the disease develops, it cannot be a cause of the disease.
Much of the above information was obtained from Riegelman & Kirkwood, 2025 pp35-39.
Reference:
Korsten, T., George, T. (2002). The neurobiology of the opioid dependence: implications for treatment. Science Practice Perspect July 1(1):13–20. https://pmc.ncbi.nlm.nih.gov/articles/PMC2851054/
Riegelman, R., & Kirkwood, B. (2025). Public Health 101 Improving Community Health (4th ed). Jones & Bartlett Learning LLC.