What is the difference between these two views of epidemiology?

In the clinical setting, epidemiologic methods are used to predict a health outcome for an individual based on scientific studies of groups of similar patients. Clinical epidemiology is integral to evidence-based medicine. Epidemiology itself is the study of disease in a population to determine the frequency and distribution of the disease as well as risk factors for the disease. Although epidemiology is defined concerning human populations, epidemiologic principles can be extended to study other problems, such as colony collapse disorder in honeybees or improving herd health for a dairy farm.

When designing epidemiologic studies, choices must be made about the role of the investigator, the purpose of the study, the hypothesis regarding exposure, and the unit of analysis. Here are some examples:

Role of investigator:

• Observational – The investigator does not manipulate the exposure of participants to risk factors. Most epidemiological studies are observational
• Experimental - According to the study design, the investigator manipulates the exposure of participants to some factor. Clinical trials and intervention studies are examples of such experiments. If the study participants themselves act to change their exposure to an influence, a natural experiment may occur. For example, a study of persons who have migrated from one environment to another could constitute a natural experiment.

Purpose of the study:

• Descriptive - describes the distribution of disease by time, place, and person; used to generate hypotheses of disease causation or for health planning
• Analytic - measures and tests the association between a hypothesized risk factor and a disease

Hypothesized Effect of Exposure:

• Harmful - exposure increases the risk or presence of disease
• Beneficial - exposure reduces the risk or presence of disease

Unit of Analysis:

• Individual - the individual (e.g., person, animal) is the unit of analysis; there is potential to ignore the impact of the community or group effect on individual risk
• Community - the community (e.g., county, hospital) is the unit of analysis. There is potential for ecological fallacy in such studies. Lacking individual data, assuming that individuals perform similarly to the average of the group may not be true.

Data for a typical epidemiologic study may be summarized in a table comparing the numbers of cases (those with the disease or condition) to non-cases in terms of their exposure to a risk factor or beneficial agent. (2x2 Epidemiologic Table)

Follow the links in the list below, and explore selected events in the history of epidemiology and population health.

1800s

• 1849-54 → John Snow formed and tested the hypothesis on the origin of cholera in London - one of the first studies in analytic epidemiology

1900s

• 1910s → Flu pandemic
• 1920 → Goldberger published a descriptive field study showing the dietary origin of pellagra
• 1940s → Fluoride supplements were added to public water supplies in randomized community trials
• 1949 → Initiation of the Framingham study of risk factors for cardiovascular disease
• 1950 → Epidemiological studies link cigarette smoking and lung cancer, demonstrating the power of case-control study design
• 1954 → Field trial of the Salk polio vaccine - the largest formal human experiment
• 1959 → Mantel and Haenszel develop a statistical procedure for stratified analysis of case-control studies
• 1960 → MacMahon published the first epidemiologic text with a systematic focus on study design
• 1964 → US Surgeon General's Report on Smoking and Health establishes criteria for evaluation of causality
• 1970s → Large community-based trials were implemented, such as Stanford Three Communities; worldwide eradication of smallpox
• 1980s → Chronic disease, injury, and occupational epidemiology; HIV epidemic
• 1990s → Behavioral risk factor epidemiology; prevention of adverse health outcomes through policies and regulations; national programs in breast and cervical cancer prevention; tobacco epidemiology; emerging infectious diseases; criticism of epidemiology for being inconsequential ('small' risk ratios); standardization of surveillance methods; Mad cow disease (BSE) in England and Europe; Variant Creutzfeld-Jacob disease; aging of USA; disaster epidemiology

2000s

• 2000s → Genetic and molecular epidemiology; health disparities; racialism; HIPAA in the USA; West Nile Virus;
• 2002 → bioterrorism; anthrax and smallpox threat and vaccinations
• 2003 → SARS, quarantines and public health law; and worldwide epidemiology; BSE in Canada
• 2004 → SARS recurrence; BSE in the USA; the flu epidemic
• 2009 → 2010 H1N1 pandemic
• 2020 → COVID-19 pandemic

The objectives of epidemiology include the ability to:

• identify the etiology or cause of disease
• determine the extent of disease
• study the progression of the disease
• evaluate preventive and therapeutic measures for a disease or condition
• develop public health policy

One objective of epidemiology is to identify the cause of a disease, with a desire to prevent or modify the severity of the condition. Consider the table below. Would you agree that this table accurately portrays the true causes of death in the U.S. population? Why or why not?

*Composite approximation drawn from studies that use different approaches to derive estimates, ranging from actual counts (eg, firearms) to population attributable risk calculations (eg, tobacco). Numbers over 100,000 rounded to the nearest 100 000; over 50 000, rounded to the nearest 10,000; below 50,000, rounded to the nearest 5000.

Table: Estimated numbers by 'Cause' of Death(From McGinnis JM, Foege, WH. 1993 JAMA, 270(18): 2207-2212.)

As you may have noticed, the causes of death in Table 1 are all related to modifiable factors. The percentages do not total 100, but if these results are accurate, a large percentage of deaths can be postponed. The opportunity to prevent or ameliorate disease is an exciting component of epidemiologic study.
Epidemiologists follow pre-determined procedures in deciding whether to attribute a particular factor as a cause of a disease or condition. In the late 19th century, a German microbiologist, Robert Koch, devised a scheme for deciding whether or not a particular microbe caused a disease.

Koch's Postulates

One organism leads to one disease. (one-to-one)

• A specific organism must always be observed in association with the disease. (regular presence)
• The organism must be isolated from an infected host and grown in pure culture in the laboratory. (exclusive presence)
• When organisms from the pure culture are inoculated into a susceptible host organism, it must cause the disease. (sufficient cause)
• The infectious organism must be re-isolated from the diseased organism and grown in pure culture.

  Do you see any problem with applying Koch's postulates to determine the cause of all diseases?

  Consider asthma or lung cancer: can one micro-organism be isolated as causing the development of these conditions?

Modern Epidemiology

Modern epidemiology accommodates multiple exposures contributing to increased risk for one disease (many-to-one) and situations where one risk factor contributes to multiple diseases (one-to-many).

Considerations When Assessing Possible Causal Role of a Risk

Obviously, there are many factors to assess when considering whether a potential risk factor causes a disease or condition:

• How strong is the association? (odds ratio, relative risk)
• Is there a dose-response relationship?
• If exposure ceases, what happens? Does the condition change?
• Can the findings be replicated?
• Is there biological plausibility?
• Are there alternative explanations?
• How specific is the association?
• Is this consistent with other knowledge?
• Is there a statistical association? If so, is the association
• Spurious, due to chance or bias
• Non-causal OR Causal?
• Is a temporal relationship observed?
• Was the study design adequate?

A traditional model of infectious disease causation, known as the Epidemiologic Triad is depicted in Figure 2. The triad consists of an external agent, a host, and an environment in which the host and agent are brought together, causing the disease to occur in the host. A vector, an organism that transmits infection by conveying the pathogen from one host to another without causing the disease itself, could be part of the infectious process.

A classic example of a vector is the Anopheles mosquito. As the mosquito ingests blood from an infected host, it picks up the parasite plasmodium. The plasmodium is harmless to the mosquito. However, after being stored in the salivary glands and then injected into the next human upon which the mosquito feeds, the plasmodium can cause malaria in the infected human. Thus, the Anopheles mosquito serves as a vector for malaria. Another familiar example of a vector is ticks of the genus Ixodes which can be vectors for Lyme disease.

In the traditional epidemiologic triad model, transmission occurs when the agent leaves its reservoir or host through a portal of exit and is conveyed by a mode of transmission to enter through an appropriate portal of entry to infect a susceptible host. Transmission may be direct (direct contact host-to-host, droplet spread from one host to another) or indirect (the transfer of an infectious agent from a reservoir to a susceptible host by suspended air particles, inanimate objects (vehicles or fomites), or animate intermediaries (vectors).

Can the epidemiologic triad be applied to a disease that is not infectious? Consider a smoking-related disease (Figure 3). If smoking (or more specifically, a carcinogen in the smoke of the cigarette) causes the disease, those who manufacture, sell and distribute cigarettes are vectors, bringing the disease-causing agent to the susceptible host. Diagramming the epidemiologic triad also indicates potential interventions to reduce disease in the population. In this example, clean indoor air legislation, advertising potential harm from smoking, or establishing workplace smoking cessation programs could change the environment and reduce the exposure of the host to the agent. Conversely, increased advertising from cigarette manufacturers or increased numbers of vendors would increase the exposure of the host to the agent.

Thus, the traditional model of disease transmission can be useful to identify areas of potential intervention to reduce disease prevalence, whether infectious or non-infectious.

An epidemiologic hypothesis is a testable statement of a putative relationship between exposure and disease. The hypothesis should be:

• Clear
• Testable or resolvable
• State the relationship between exposure and disease
• Limited in scope
• Not inconsistent with known facts
• Supported by literature, theory, references

Hierarchy of Epidemiologic Study Designs in the Demonstration of Causality/Prevention

The design of a study contributes to the strength of its findings. Below are the types of studies, in order of increased strength for testing the relevant hypothesis. We will study some of these designs further later in this course.

Causation Hypothesis

• Case Study (describing one person with the condition, a case)
• Case Series (series of cases)
• Ecological Study (analysis of group statistics..for example, comparing rates of disease between two countries)
• Cross-Sectional Study (assessing individuals at one time, such as a survey)
• Case-Control Study (studying those with the condition vs. those without)
• Cohort Study (following subjects over time to study the initiation and progression of a condition)

Often, it is not feasible to conduct a study where we collect data from all affected individuals. Thus, we need to select a subset of those individuals for our study. The following defines populations and samples.

Obviously, the method for selecting the sample can greatly influence the study results. In Lesson 2, we will learn more about methods to select samples.

This lesson laid the groundwork for the study of epidemiology by introducing key terms, providing an overview of the history of epidemiology, introducing the concept of causality, and presenting examples of hypotheses that can be evaluated using epidemiologic principles.  

In Lesson 2, we’ll build upon these fundamentals to explore how epidemiology is used to inform public health practice.