Lesson 1 - Introduction to Epidemiology

Lesson 1 - Introduction to Epidemiology

Lesson 1 Objectives

Upon completion of this lesson, you should be able to:

  • Distinguish between epidemiology and clinical epidemiology
  • Apply the terminology of the Epidemiologic Triad to an infectious disease
  • Explore selected events in the history of epidemiology and population health
  • State five objectives of epidemiologic research
  • Compare Epidemiologic Study Designs in the Demonstration of Causality
  • Differentiate between different types of populations

1.1 - Defining Terms

1.1 - Defining Terms

Major Definitions for the Study of Epidemiology

The study of the distribution of disease and determinants of health-related states or events in specified human populations and the application of this study to the control of human health problems. (JM Last. Dictionary of Epidemiology. 2nd edition)
Clinical Epidemiology
The science of making predictions about individual patients by counting clinical events in similar patients, using strong scientific methods for studies of groups of patients to ensure that the predictions are accurate. (Fletcher, Fletcher, Wagner. Clinical Epidemiology. 1996)

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.

General Dichotomies in Epidemiological Studies

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)

2x2 Epidemiologic Table
  Case Non-Case Total
Exposed A B Texposed
No Exposed C D Tnon-exposed
Total Tcases Tnon-cases  

1.2 - History of Epidemiology

1.2 - History of Epidemiology

Selected History of Epidemiology and Population Health

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


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



  • 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


1.3 - Objectives, Causality, Models

1.3 - Objectives, Causality, Models

Objectives of Epidemiology

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

Causality in Epidemiology

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?

Table: Deaths
Cause Estimated No.* Percentage of Total Deaths
Tobacco 400 000 19
Diet/ Activity Patterns 300 000 14
Alcohol 100 000 5
Microbial Agents 90 000 4
Toxic Agents 60 000 3
Firearms 35 000 2
Sexual Behavior 30 000 1
Motor Vehicles 25 000 1
Illicit Use of Drugs 20 000 <1
Total 1 060 000 50

*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.

Infectious Disease Model

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?


Epidemiologic Triad

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).

Figure 2

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.

HOST VECTOR AGENT ENVIRONMENT Genetic suseptibility, income, resilience Clean indoor air policy, advertising, peer pressure Manufacturers, distributors, vendors Filtered cigarettes, Safe cigarettes
Figure 3

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.


1.4 - Epidemiologic Hypotheses, Designs, and Populations

1.4 - Epidemiologic Hypotheses, Designs, and Populations


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.

Target Population
Population to which inferences from the study are to be made.
A target population may be defined by geography, demography, health status, or some other factor.
Study Population
Population from which study subjects are selected
The sampling frame is the actual list that will be used to select the sample (ex. list of hospital admissions, household addresses, people with a certain disease or outcome)
Careful consideration should go into identifying the sampling frame for a study. If one cannot be created that mostly covers the population, bias can occur.
Subjects that provide data to the study
Data from the study participants are used to make estimates and draw conclusions about the population

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.

1.5 - Lesson 1 Summary

1.5 - Lesson 1 Summary

Lesson 1 Summary

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. 

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