Module 3: Fundamentals of Epidemiology

The World Health Organization defines epidemiology as “the study of the distribution and determinants of health-related states or events (including disease), and the application of this study to the control of diseases and other health problems”.(1) Epidemiology is a science that involves surveillance, data collection and analysis, and the use of data to create interventions that improve the health of the target population. All public health activities hinge on the work of epidemiologists in the field and in the laboratory as they collect data and generate meaningful, comparable health statistics.

On a global scale, epidemiology is dependent on the standardization of data across countries. The International Classification of Diseases (ICD) is the classification system that aims to standardize diagnoses globally.(2) It was developed and is revised periodically by the WHO. ICD is used by health care workers around the world to report and monitor health events. ICD-10 (the most current version) is available in 43 languages and is used in 117 countries.(3) Its goal is to ensure that health events reported in one country are reported in the same way in another country. This helps minimize biases and errors in data comparison. Standardization with ICD ensures, for example, that a person diagnosed with Tuberculosis in the United States is reported to the WHO health surveillance system in the same way that a person diagnosed with Tuberculosis in Ghana would be reported. The WHO compiles health measurement data from many surveillance systems worldwide, and this collected data is analyzed and reported in their “World Health Statistics”publication.(4)

Epidemiology Terms and Definitions

Epidemiology is a science that encompasses extensive and diverse material. In terms of surveillance and detection of disease, the following terms are important to understand:(5)

Incidence: number of new cases that occur in a population during a certain time period; can also be called an attack rate (especially when used during an epidemic)

Prevalence: number of cases that exist in a population during a certain time period (point prevalence is number of cases at a single point in time)

Mortality rate: number of deaths in a population during a specific time interval

Years of Potential Life Lost (YPLL):  the number of years lost due to premature mortality; measures the effect of premature death on a population

Disability-Adjusted Life Year (DALY): the number of years lost due to disability, illness, or premature death; measures the burden of a disease in a population

Adjusted rates: when rates are statistically modified to eliminate the effect of certain characteristics of the population on the data (like age, sex, or race). For example, higher death rates in one city may be due to a higher proportion of elderly people in the area, so this mortality data might be age-adjusted to account for differences in age when comparing it with data from other cities.

Crude rates: non-adjusted rates

Case-fatality rate (CFR): the proportion of cases that die from a particular disease/health issue (number of cause-specific deaths over number of incident cases)

Cluster: when cases of a disease or health condition are closely tied by time and place (particularly important when looking at cancers, birth defects, and outbreaks)

Endemic: the usual incidence or prevalence of a certain disease in a population; the baseline or expected number of cases in a population; can vary between geographic areas

Epidemic: when the number of cases of a disease rises above endemic levels in a specific area or among a specific population over a particular time period

Pandemic: an epidemic occurring over several countries or continents and usually affecting a large proportion of the population

Health indicator: measurement that reflects the health state of people in a population; infant mortality rate is often used as a health indicator of entire countries because it is influenced by multiple factors such as maternal health, environmental conditions, education levels, and access to medical care

Validity: the degree that a measurement accurately measures what it purports to measure

Vital statistics: information about births, marriages, divorces, and deaths in a population that comes from their systematic registration by local health authorities

Sensitivity: the proportion of people with the disease that a test finds positive; a highly sensitive test has a low rate of false negatives (an ideal test is 100% specific and 100% sensitive)

Specificity: the proportion of people without the disease that a test finds negative; a highly specific test has a low rate of false positives (an ideal test is 100% specific and 100% sensitive)

Outbreak Investigation: The Application of Surveillance System Data

Outbreak investigation is one of the most important components of surveillance and detection systems. Sensitive surveillance systems can detect when abnormal health events are occurring, and can enable health authorities (such as local health departments, the CDC, and the WHO) to know when their investigation and intervention is necessary. WHO and CDC experts provide aid to foreign Ministries of Health to help in the investigation of epidemics.(6) According to the Office of Surveillance, Epidemiology, and Laboratory Services at the CDC, there are ten steps of an outbreak investigation:(7)

Before arriving at an outbreak site, an investigator should make sure that he or she is prepared for the work ahead. The investigator should make sure to collect all relevant information concerning the reported outbreak, bring all materials (like lab equipment for taking samples), and bring all safety equipment needed to protect him or her from harm in the field.

To avoid wasting resources, it is important that surveillance and detection systems analyze the reported rise in health events to make sure that an outbreak is actually occurring. The amount of cases reported should be significantly higher than normal or endemic levels in the population. Other questions to consider are: was there a recent change in the way the disease is classified that might have led to the increased amount of cases? Was there a recent change in the size of the population that could account for more cases?

As a certain pathogen or disease is implicated in the outbreak, it is important to make sure that it has been correctly identified. Things to consider are: has an error in the laboratory been made? Do the laboratory results and clinical findings in the field match up?

Establishing a working case definition is a crucial step in an outbreak investigation. A case definition is defined as “a standard set of criteria for deciding whether, in this investigation, a person should be classified as having the disease or health condition under study”.(8) Parts of a case definition can include clinical information about the disease (specific symptoms), characteristics of the disease, location and place where incidence of the disease has spiked, and the time frame during which the outbreak is thought to have occurred. Creating a case definition helps in case reporting and classification. Cases during an outbreak are often classified as possible (having a few symptoms), probable (having most symptoms), and confirmed (symptoms and lab confirmation) case categories.


Time: Describing the cases in the context of time is very important in an outbreak investigation. The number of cases is plotted on a graph in relationship to time to show the rate at which the outbreak has progressed in the population. This graph is known as an Epi Curve. Epi Curves can help investigators discern when the exposure that caused the outbreak likely occurred, how the disease is being spread (common source), the average incubation period for the disease (which can help identify unknown pathogens), and the likely future course that the disease may take. Below is an example of an Epi Curve during a Salmonella outbreak.

Description: Infections with the outbreak strain of Salmonella Typhimurium, by date of illness onset







Place: Epidemiologists will often plot cases on a map to help identify clusters and patterns of disease. These maps are called spot maps. They can show potential places of exposure and help narrow down the at-risk geographic population. It is important to remember that some clustering on spot maps might be random variation, or could be due to differences in population sizes between regions. For example, a large metropolitan city may have 1,000 cases of the disease, while a small rural town may have three. This difference could be from variation in exposures or from differences in population size. The proportion of people affected in a population should be used when comparing spot maps. Below is a spot map created by British physician John Snow (who is often called the “Father of Modern Epidemiology”) during his famous investigation of a Cholera outbreak in London that he eventually linked to the Broad Street water pump in 1849.(9)


After collecting data on potential and confirmed cases, hypotheses as to when, where, and what type of exposure is occurring can be developed. Investigators will look at common exposures among patients and try to identify the source.

Investigators can evaluate their hypotheses in many different ways, depending on what type of outbreak is occurring. This stage usually involves a visit to the hypothesized source location, along with taking samples and conducting laboratory tests. It may also involve analytic epidemiologic studies.

A cohort study is done by dividing people into test and control groups based on their exposures. Cohort studies generate a number that is known as “relative risk” that shows how closely associated an exposure is with the disease. Cohort studies are usually conducted when there is a small population effected that is easily defined.

Case control studies divide people into groups for comparison based on who is sick and who is not, and then look at their exposure histories. Case control studies generate a number that describes the relationship between exposure and illness called an “odds ratio”. Case control studies are usually conducted when the outbreak affects a large population and cohort studies are not possible.

Disease outbreak investigators are constantly refining and changing their hypothesis to make sure they are correct about disease source and attributes. For example, an outbreak of a food-borne illness may be linked to a certain restaurant, but upon inspection, is only associated with the eating of pickles in that restaurant. It is important that hypotheses are as concise and accurate as possible so that control measures and announcements to the public do not create unnecessary panic.

Timing of this step is crucial during an outbreak investigation. While an investigator wants to make sure that he or she intervenes in a timely manner to prevent further disease transmission, he or she also wants to make sure that the hypothesis is correct and does not create unnecessary fear or panic among the public.

It is important in the public health field that all findings and conclusions are reported during and after an investigation. Reports need to be communicated to the parties involved, including local health authorities, cases, and potential victims. Recording what an investigator observed and acted upon also guides future investigators, and helps illustrate where surveillance systems are achieving both success and failure.

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(1) World Health Organization. (2012). Epidemiology. Retrieved May 18, 2012.

(2)The WHO Family of International Classifications. (2012). Retrieved May 18, 2012, from World Health Organization.

(3) Ibid.

(4) World Health Organization. (2012). World Health Statistics 2012. Retrieved May 18, 2012.

(5) Columbia University. (n.d.). Glossary of Epidemiology Terms. Retrieved May 18, 2012.

(6) Centers for Disease Control and Prevention. (2011, February 28). International Outbreak Investigations. Retrieved May 18, 2012.

(7) Office of Surveillance, Epidemiology, and Laboratory Services. (2004, November 17). Steps of an Outbreak Investigation. Retrieved May 18, 2012, from Centers for Disease Control and Prevention.

(8) Ibid.

(9)Who is John Snow? (n.d.). Retrieved May 31, 2012, from UCLA Department of Epidemiology, School of Public Health.