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January 30, 2016 – Everywhere we look today, the threat of emerging contagious diseases has captured our attention. Movies, books, and video games about out-of-control diseases have piqued society’s interest in how diseases arise and spread. Epidemiology is the study of how diseases affect populations. Epidemiologists study data in order to describe disease distribution, and to quantify risk associated with potentially disease-causing or disease-preventing factors. Epidemiology is an observational field, which means that no interventions are performed; it is a natural experiment in the real world.

Modern epidemiology got its start during a cholera outbreak in London in the middle of the 19th century. John Snow, a London physician, was skeptical of how the outbreak was being handled, and the explanation given to residents. He started his own investigations, which led to the discovery that the source of the infections was a single water pump. Snow’s methods became the basis of modern epidemiology.

The field has expanded beyond infectious disease to include many noninfectious diseases (including cancer) and many different populations (such as golden retriever dogs).

Epidemiologic investigation is a multi-step process. The first step is to establish the distribution and extent of disease, and to identify populations at increased risk for disease. The next step is to determine if an association exists between exposure(s) and outcome (disease) and, if there is an association, decide if the exposure caused the outcome.

Demonstrating causality is an incremental process that involves considering the entire body of scientific evidence in order to make a firm conclusion.

When epidemiologists are looking at causality, they consider several factors: 

  1. Temporality – the time factor. Exposure must precede disease; this is the only criterion that must be present to demonstrate a causal relationship.
  2. Strength of the association – the stronger the association between a risk factor and disease the more likely it is causal.
  3. Dose-response relationship – more exposure is associated with more outcome (either more pronounced outcomes and/or more outcomes in a population).
  4. Consistency – the association can be replicated in other studies and other populations.
  5. Plausibility – the association has an explanation consistent with current scientific knowledge.
  6. Consideration of alternate explanations – what other factors might produce the outcome?
  7. Experiment – test to see whether the outcome can be avoided or ameliorated by either removing or preventing exposure.
  8. Specificity – a single exposure produces a specific outcome.
  9. Coherence – the association is consistent with existing theory and knowledge.

The Golden Retriever Lifetime Study is a prospective study, meaning that data is being collected in real time. This approach has several important advantages. Although we are primarily interested in cancer outcomes, we can study many different health outcomes using the collected data. In addition, we’ll learn the timing of exposures in relation to disease outcomes, which is the most important step in making causal inferences.

We all love the spine-tingling thrill of watching a good zombie movie, or reading a great novel about a new infection. Epidemiology is the discipline that examines life as the ultimate natural experiment in providing insight into the interplay between exposures and outcomes.