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. 

The Centers for Disease Control and Prevention have defined surveillance as follows:

Disease surveillance is the basic process by which epidemiologists answer questions about who, where, and when.

  Who is getting the disease? Are there differences in the rates of disease by age? sex? race?

  Where is the disease happening? Are there geographic areas with particularly high rates? extremely low rates?

  Is the occurrence of the disease changing over time? Is the disease becoming more frequent? less frequent?

Disease surveillance information is useful for:

• Estimating the magnitude of a problem
• Determining the geographic distribution of illness
• Portraying the natural history of a disease
• Detecting epidemics or defining a problem
• Generating hypotheses, stimulating research
• Evaluating control measures
• Monitoring changes in infectious agents
• Detecting changes in health practices
• Facilitating planning

A disease surveillance system should be simple, flexible, and acceptable to the population. For example, to detect hunting-related shooting injuries, the requirements for a hunter to report an episode should not be onerous or many shooting injuries will go unrecorded. The surveillance system should also be representative of the population and provide a timely alarm. Like a smoke detector without a power source, a surveillance system that is not able to recognize a disease outbreak quickly and accurately is not very useful.

Sources of public health surveillance data can include:

• notifiable diseases,
• vital records (e.g. National Infant Mortality Surveillance, birth, death records),
• registry and survey data,
• administrative databases (such as Medicare or a prescription database), and
• some laboratory records.

Below are some websites to explore available sources of public health data:

In the US, governmental agencies conduct surveys for various purposes at regular intervals. Investigate these surveys by following the links below. [Select to expand]

Even though this is not a course on survey design, a large source of public health data comes from surveys. As we saw earlier in the course, it is often not feasible to take measurements on the entire target population, so we must select a sample in which to gather data. This section introduces some advantages and disadvantages of using surveys and approaches to drawing a sample for an epidemiologic survey.

An epidemiologic survey consists of a simultaneous assessment of the health outcome and exposures as well as potential confounders and effect modifiers. A survey given at a single time point can be part of a cross-sectional study. Some epidemiologists may call it a prevalence study. The survey results provide a 'snapshot' of a population. Surveys are a useful tool for gauging the health of a population or monitoring the effectiveness of a preventative intervention or provision of emergency relief.  

While a survey may provide a relatively quick and inexpensive method for assessing the health of a population, there are both pros and cons, as noted below:

Survey Questions and Administration

Survey questions are carefully structured in order to reduce bias. Care should be given to the wording and order of questions. Using a standard questionnaire increases the reliability and validity of the results. A reliable survey has internal consistency and produces results that are replicable. The subject would answer the question in the same way if asked again. Valid questions are those which accurately assess the specific concept that is being measured.

The process of administering a survey should be standardized to reduce the potential for bias. The respondent should be informed of the purpose of the research and freely consent to participate. A survey with a low response rate is likely to have some bias.

These methods of sampling can be applied to survey studies, as well as other observational and interventional studies.

  Each of these approaches is useful, but to what population can the results be generalized? 

Public health surveillance is important for the health of any nation. In order to decide how to allocate resources, it is vital to know who is being affected, where those people live, and the timeliness of the issue. There are many sources of public health data that can be used to achieve these goals including vital records, mandatory reporting, registries, and health surveys. Surveys are used to gather information that is not standardly or systematically collected. Since we often cannot gather data on the entire population of interest, we need to select a subgroup to sample from, and different methods for sampling were outlined in this lesson.

Typical outcomes for an epidemiologic study, (sometimes referred to as the 'D's of Epidemiology) are as follows:

Outcomes of Epidemiology:

• Death Disease/Illness - Physical signs, laboratory abnormalities
• Discomfort - Symptoms (e.g., pain, nausea, dyspnea, itching, tinnitus)
• Disability - Impaired ability to do usual activities
• Dissatisfaction - Emotional reaction (e.g., sadness, anger)
• Destitution - Poverty, unemployment

In order to describe and compare measures of disease occurrence, these are the types of calculations most often used:

  Count

  Proportion

  Ratio

  Rate

  Risk

The two main ways by which the frequency of disease is measured are incidence and prevalence. These can be distinguished by differences in the time of disease onset.

Incidence quantifies the development of disease. Incidence can be estimated using data from a disease registry data or a cohort trial. There is an implicit assumption of a period of time, such as new cases within a month (or a year).

A summary incidence rate can estimate the risk (e.g., probability of disease in an individual) if the risk is constant across the summarized groups.

As defined, incidence is a count of new cases. However, it is often expressed as a proportion of those at risk. The denominator includes all persons at risk for the disease or condition, i.e. disease-free or condition-free individuals in the population at the start of the time period. Persons in the denominator, those at-risk, should be able to appear in the numerator. Obviously, the denominator would not include persons who already have the disease or condition. Incidence can also be expressed in terms of person-time at risk.

Rates are usually expressed per 100, 1,000, or 100,000 persons. In a strict application, "rate" should only be used when the denominator is an estimate of the total person-time at risk. (You will find the term "rate" used inconsistently in epidemiologic reports. It is better to seek the source of the numbers than to rely on the nomenclature.)

Two Common Measures of Incidence

Since prevalence counts both new and existing cases, the duration of the disease affects the prevalence. Diseases with a long duration will be more prevalent than those with a shorter duration. Chronic, non-fatal conditions are more prevalent than conditions with high mortality. The prevalence of disease is directly related to the duration of the disease. Prevalence is not an apt descriptor of an acute condition.

Similar to incidence, persons included in the denominator must have the potential for being in the numerator, i.e. at-risk for the disease or condition. Prevalence is often expressed after multiplication by 100 (%), 1000, or 100,000.

The prevalence pool is the subset of the population with the condition of interest. The prevalence pool is not generally useful for hypothesis-driven epidemiologic research because these are not new cases, but can be useful in tracking the natural history of the disease, evaluating effects of treatments, or disease burden.

For most etiologic research, incidence is the more appropriate measure. Studying the incidence of a rare condition, however, poses a challenge. Given a small number of new cases, it can be preferable to estimate prevalence instead of incidence in these situations. For example, birth defect rates reported as the number of cases/live births is a prevalent measure. Similarly, an autopsy rate is a prevalent measure.

Two common measures of prevalence

The difference is whether the estimate is made over a period of time or at one specific time as illustrated below:

In this course, we have often assumed that investigators have knowledge of a potentially harmful exposure coincidentally with or prior to observing the disease or illness. In other situations, the first indication of harmful exposure is a report of a potential outbreak of disease or illness. Increased numbers of cases of disease or illness may necessitate an outbreak investigation. Questions to be answered in an outbreak investigation include the following:

When an increase in the number of cases of a disease is reported, a speedy response is critical. At the same time, it is also of utmost importance to end up with an answer that will appropriately protect public health and safety. A systematic approach to outbreak investigation helps assure timely and accurate answers:

• Prepare for fieldwork
• Establish the existence of an outbreak
• Verify the diagnosis
• Define and identify cases
• Measure the frequency of adverse outcomes and describe the data in terms of time, place, and person
• Develop hypotheses
• Evaluate hypotheses
• Refine hypotheses and carry out additional studies
• Implement control and prevention measures
• Communicate findings

Characterizing by time: Constructing an Epi-Curve

An epidemic curve, frequently referred to as an 'epi-curve', is used to examine and characterize the occurrence of a possible outbreak. By constructing and examining an accurate epi-curve, an investigator can consider questions such as:

An epi-curve is a histogram with the number of cases of the adverse health outcome on the y-axis (ordinate) and dates of onset of the outcome on the x-axis (abscissa). Dates of onset may be grouped by days, weeks, or months, depending on the nature of the potential outbreak. A typical time period used is 1/4 to 1/3 the incubation period for the disease. If the incubation or lag time from exposure to outcome is unknown, it is valuable to experiment with different lengths of time.

A typical epi-curve is a simple chart with one series of data, the onset of cases. In other situations, several layers of data are displayed on the curve. For example, the investigator may want to examine the date of onset in more than one location (e.g. 2 or more cities, states or countries) or in different groups of people (e.g. stratified by age or race).

Another variation of the epi-curve is stacking the bars in order to show different characteristics of the cases. For example, you may decide to separate confirmed cases from suspect cases, using stacked bars to assess whether an outbreak is truly occurring.

Interpreting an Epi-Curve

The following shows the outbreak of COVID-19 cases in Pennsylvania:

The first consideration is the overall shape of the curve which is determined by the pattern of the outbreak (common source or person-to-person transmission). The shape also indicates the period of time over which susceptible people are exposed and the minimum, average and maximum incubation periods for the disease.
If the duration of exposure is prolonged, the epidemic is called a "continuous common source epidemic," and the epidemic curve will have a plateau instead of a peak. Person-to-person spread (a "propagated" epidemic) should have a series of progressively taller peaks one incubation period apart.

Cases that stand apart ("outliers") provide valuable information. An early case can represent a background (unrelated) case, a source of the epidemic, or a person who was exposed earlier than others. Similarly, late cases may be unrelated to the outbreak, may have especially long incubation periods, may indicate exposure later than most of the people affected, or maybe secondary cases (the person who becomes ill after being exposed to someone who was part of the initial outbreak). Examine any outliers that are part of the outbreak carefully because they may point directly to the source. For example, a prep chef could be the first case of strep in an epidemic among party-goers eating food prepared by this person.
In a point-source epidemic of a disease with a known incubation period, the epidemic curve can also identify the likely period of exposure.

Characterizing by place

A simple technique for looking at geographic patterns is to plot on a 'spot map' the locations where the affected people live, work, or may have been exposed. A map of cases in a community may show clusters or patterns that reflect water supplies, wind currents, or proximity to a restaurant or grocery store. A classic example is John Snow's detection of the Broad St. water pump as the source of a cholera epidemic. On a spot map of a hospital, nursing home, or another residential facility, clustering may indicate either a primary source or person-to-person spread. The scattering of cases throughout a facility is more consistent with a common source such as a dining hall.

If the size of the overall population varies among the areas being compared, the spot map with the number of cases can be misleading. Indicating the proportion affected or the attack rate for each area would be a better approach.

Characterizing by person

Define the populations at risk for the disease by characterizing an outbreak by personal characteristics such as age, race, sex, medical status, etc, and/or by exposures (e.g., occupation, leisure activities, use of medications, tobacco, drugs, etc.). Age and sex are characteristics often strongly related to exposure and risk; thus these factors are often assessed first. Other factors to be assessed are those possibly related to susceptibility to the disease and to opportunities for exposure to the disease being investigated and in the setting of the outbreak.

Lesson 3 was a big lesson!  It introduced the main calculations for disease occurrence and frequency including counts, proportions, ratios, rates, and risks. An important component of epidemiologic measures is the concept of time, which is incorporated into rates and risks.  The two main measures of disease frequency are incidence (new cases) and prevalence (new + old cases) which both are important for understanding the landscape of public health issues.  Finally, we learned about outbreak investigations which are needed when a new public health concern arises - again, focusing on the who, when, and where to best understand the issue.

Suppose our goal is to compare two populations with regard to disease or exposure-disease frequency. We wish to use precise valid measures of disease frequency such as point prevalence, period prevalence, cumulative incidence, incidence density, etc. The two populations must be distinct in location or time or exposure status. We'd like to apply statistical tests to these measures to see if any difference is likely to have occurred by chance.

There are many examples of hypotheses that are comparative in nature, such as:

Consider this cohort study, An Association between Air Pollution and Mortality in Six U.S. cities, which investigated the relationship between air pollution and mortality in six US cities.  The researchers were interested in the exposure of air pollution and the outcome of mortality.  

Exposure refers to the characteristic of interest that the researcher hypothesizes may be associated with or causing a certain outcome.  Often in epidemiological studies, the outcome of interest is a certain disease. Those who develop the disease are often referred to as cases, while those that do not are referred to as non-cases.  

Data from a study that includes a risk factor (exposure) and indicators of the presence or absence of disease is often summarized as shown below:

For the air pollution cohort study, the following tables can be constructed.

*this column was calculated by subtracting the number alive in Table 1 of the manuscript from the total number of participants. See...

*For incidence rate, the number of non-diseased (i.e. alive) participants is not necessary. Instead we need person-years for all people who experienced the outcome. 

There are two ways to compare measures between groups: ratios and differences. The next few sections will outline both methods and show examples using the air pollution study. Also, note that for these examples, estimates for the cumulative incidences and incidence rates are similar, but that is not always the case. In this study, person-years were similar across groups, resulting in similar estimates.

A ratio may be used to convey the strength of an effect or association between two population groups or the relative 'risk' of the study (e.g. exposed) group compared to a comparison group (e.g. unexposed.). A ratio is not dependent on the prevalence of exposure among the study population.

A ratio can be reported with upper and lower bounds. We will learn some formulas for these calculations in a later lesson.  When there is no significant difference between groups, the ratio will equal 1 and/or include 1 in its confidence interval.

Alternatively, differences can be calculated between the estimates for the two groups. The difference can be reported with a confidence interval that includes upper and lower bounds.  If the confidence interval includes 0, this indicates that there is no significant difference between the groups. If the interval does not include 0, there is an increased risk for one population compared to the other (or conversely, a decreased risk). The difference can convey an excess or decreased risk among the exposed group due to exposure, possibly an excess or decreased risk that would be removed if the exposure ends, a potential reduction in risk for exposed individuals, or the absolute risk of the exposure.

When comparing groups, it is important to make sure we are making a fair comparison. Thus, it is helpful to standardize the rates, in order to remove the effect of a potential confounder (often age), which might differ between populations and could distort the results. Standardization is also helpful when comparing rates of one population over time, such as monitoring disease in a population over many years.

Disease can be measured in one population or compared between populations. Within one population, it is common to summarize disease burden with the number of cases. Another measure is the crude rate (i.e., x cases / y population at-risk), which you will also recognize as the cumulative incidence rate. If the distribution of a modifier of disease frequency (such as age) is different between two populations, however, a comparison of the crude rates in the two populations can mask the rate.

• A standardized rate is a measure of disease frequency that facilitates comparisons of populations with a different distribution of one or more potential confounding variables. (e.g., x cases / y population at-risk, adjusted to remove the effect of potential confounder [e.g., age]).
• Age-specific rates (i.e.., x cases in a specific age group/(population at-risk in same age group) are also useful in summarizing the health status of a population.

There are two different approaches to standardizing a rate:

Direct standardization

Direct standardization, more commonly used, creates a summary disease rate for a population that would be expected if the study population had a population distribution identical to that of an arbitrarily chosen standard population. A reference population is used as the standard population. The standardized rate is the sum of weighted group-specific rates, with weights derived from the standard population. The weights sum to 1.0. A standardized rate is essentially a weighted average of age-specific rates.

\(I_{W}=\dfrac{\sum W_{i} I_{i}}{\sum W_{i}}\)
where I_i is a group-specific rate and \(\sum W_{i}=1\)

The necessary data for direct standardization is the group-specific disease rates for the study population and the population distribution from the standard population.

Indirect standardization

Indirect standardization also produces a weighted average, through the production of a summary disease rate for the study population which would be expected if the disease experience of the study population were identical to that of a standard population. The standard population is arbitrarily chosen, but should be as similar as possible to the study population Indirect adjustment is used when accurate group-specific rates for the study population are not available. If these rates are available, direct adjustment is preferred because it uses more information from the study population. Indirect adjustment produces an expected rate. Observed and expected rates are typically compared as a standardized ratio. Indirect adjustment is often used in occupational health to calculate standardized mortality ratios, which is dividing the observed death rate by the expected death rate.

The data required for indirect standardization is the crude rate for the study population; the population distribution for the study population and group-specific rates for the standard population.

Describing the state of public health is an important component of epidemiology, which was presented in Lesson 3.  The next step of comparing outcomes between groups was introduced in Lesson 4.  We saw some examples of situations when the goal is to compare groups, and learned the two main ways to do so:  absolute (differences) and relative (ratios).  An additional point to consider is that the groups may differ on a characteristic that affects the measure, most often age, so standardization is needed in order to make fair comparisons.