Cohort studies are a type of research design. They are also called longitudinal studies because they follow groups of people over time. Results from cohort studies can help people understand human health and the environmental and social factors that influence it.
The word “cohort” means a group of people. Cohort studies can be forward-looking of backward-looking.
A forward-looking cohort study is also known as a prospective cohort study. “Prospective” means that it relates to the future.
A backward-looking cohort study is also called a retrospective cohort study. “Retrospective” means that it relates to the past.
To carry out prospective cohort studies, scientists identify a group of people to study and plan the research in advance, collecting data over time. In retrospective cohort studies, scientists use data that are already available for a particular group.
Keep reading to learn more about cohort studies and their uses, advantages, and disadvantages. In addition, this article compares cohort studies with other forms of research.
Cohort studies are a powerful tool for conducting research in human populations. They are a type of longitudinal study design. Longitudinal studies follow participants over a period of time. People in cohort studies typically share some characteristics, such as their location or their age.
Researchers recruit participants in a variety of ways. They may contact people at random from a birth register, or by postal address, for example.
When people join a cohort study, the researchers gather data about them to get a more detailed picture of the group they are studying. Researchers ask questions to find out the demographics, or characteristics such as age and race, of the group. They may also gather information on the following factors:
This information forms the baseline for the study. Later, researchers collect data from different points in the participants’ lives. This is known as the follow-up period. The follow-up period could be weeks, months, or years.
By comparing data from the follow-up points to the baseline, researchers can see how different factors have affected the group members’ health. For example, in epidemiology, which is the study of disease, scientists use cohort studies to identify potential risk factors that drive disease or influence disease patterns.
Cohort studies are also good at finding relationships between health and environmental factors, such as chemicals in air, water, and food. These are issues that the
There are several types of cohort studies.
Prospective cohort studies involve recruiting a group of participants and following them over time to gather new data. Retrospective studies involve using preexisting data.
For a prospective cohort study, researchers identify a topic they want to study. They then design the study and recruit the participants that will best help them study the topic.
For example, if they wanted to study rates of heart disease in older age, they would choose an age group of younger adults with similar characteristics who do not have heart disease to use as their baseline.
For a retrospective cohort study, researchers analyze a group of people who already have certain characteristics. They then look at existing data to jump back in time. For example, they might look at a group of older adults with heart disease. Then they would analyze data about the group members’ medical history to see what factors could have contributed.
Cohort studies are a powerful tool for identifying the risk factors and causes of disease. Researchers can look at baseline data for people who did not initially have a disease and examine the factors that differed between those who developed the condition and those who did not.
For example, a
However, many things can influence physical fitness and mental health. For example, people with lower incomes could have more limited opportunities to exercise in a safe environment as well as a higher depression risk. Therefore, other factors could explain the finding.
Scientists call factors such as this “confounding” because they can potentially make the results of a cohort study inaccurate or biased. Scientists must consider confounding factors when designing the study in order to avoid this. A way to do this is through statistical methods.
In the 2020 study, researchers did this by adjusting for income, along with other possible confounding influences, such as baseline levels of depression, physical illness, and gender. This means that these factors did not affect their results. Scientists can use a similar process to examine the risk factors and causes of many diseases.
In the past, there have been some very large and long-running cohort studies that have provided a lot of data, serving researchers in different fields. These include:
Nurses’ Health Study
One famous example of a cohort study is the Nurses’ Health Study. This was a large, long-running analysis of female health that began in 1976. It investigated the potential long-term consequences of the use of oral contraceptives.
Researchers recruited the study’s second-generation cohort for the Nurses’ Health Study II in 1989. In 2010, researchers recruited the study’s third-generation cohort of nurses from across the United States and Canada.
The participants in the first cohort were married female nurses aged 30–55 years. The second and third cohorts aimed to look at more diverse cohorts.
The Nurses’ Health Study has provided many important insights. The following headlines are from news stories published by MNT. They report on some of the findings from this huge study:
Because the Nurses’ Health Study asked participants about their lifestyle choices, it yielded a lot of information about the harms and benefits of various factors, including specific types of food in the diet.
Framingham Heart Study
Another example of a long-running cohort study is the Framingham Heart Study. This study recruited over 5,209 male and female participants in 1948 from around the area of Framingham, MA. Since then, the study has served as a source of data for cardiovascular risk factors.
A second cohort began in 1971 and a third in 2002. The study has made important contributions to the understanding of heart health. The researchers are now looking into how genetic factors may affect cardiovascular health risks.
In 1958, researchers in the United Kingdom launched a large-scale birth cohort study. In 2003, a paper reported that the study had followed 17,000 people born in the same week across the country.
Since then, researchers from the U.K.’s Centre for Longitudinal Studies have launched more studies with new large groups of babies.
The latest is the Millennium Cohort Study, which is following 19,000 babies born in the U.K. between 2000 and 2001. In addition to data on the health of these children and their parents, the study is also looking into child behavior and cognitive development, as well as a range of social factors.
Cohort studies are one of the most robust forms of medical research. They are well-suited to identifying causes of disease because they look at groups of people before they develop an illness. This means scientists can examine whether there might be cause and effect between people’s lifestyle choices and health outcomes.
Another advantage is that cohort studies can collect a wide variety of data that researchers can use in many ways. A study on the impact of smoking, for example, might reveal links with multiple types of disease. Researchers can also assess how risky a factor is in comparison with others.
Cohort studies also allow researchers to conduct studies that would otherwise be unethical. For example, an experiment where researchers deliberately expose participants to cigarette smoke would be unethical. A cohort study allows scientists to study people who have chosen to smoke on their own.
This type of research does have some limitations though. Cohort studies are:
- more time-consuming and often more expensive than other types of studies
- less well-suited to finding clues about rare diseases since these do not develop in a large number of people
- potentially prone to bias if participants drop out of the study over time or if researchers select an unrepresentative group of people
- unable to explore how or why a factor is associated with a disease — for this, experimental studies are necessary
Retrospective studies can be much cheaper than prospective studies since the data are already available. However, if the original data do not include all the information the researchers need, these studies can be less useful.
Randomized controlled trials (RCTs) are one of the best and most rigorous ways of investigating medical interventions, such as new drugs. However, they have some key differences compared with cohort studies.
Cohort studies are observational. This means scientists observe what happens to a group of people without intervening. This allows researchers to study potential risk factors for disease as they naturally occur.
By contrast, RCTs are interventional. They involve scientists influencing the group of participants, often by giving a drug or therapy to determine its impact. Scientists then compare this data with the data they collect from a group of people who are receiving a placebo.
It is difficult to use RCTs to determine the causes and risk factors for disease because this would involve intentionally exposing participants to something that could make them ill. This would be unethical.
While there is some risk involved in drug trials too, scientists only test drugs on humans when they are reasonably sure they are beneficial and when participants are fully aware of the risk.
Learn more about RCTs here.
Case-control studies involve identifying people who already have a disease (the “case”) and comparing them with people who are similar across many characteristics but who do not have the disease (the “control”). This helps scientists identify potential risk factors for the disease without spending a long time following the same group.
However, case-control studies only allow scientists to calculate the odds ratio of developing the disease. They do not assess how often a particular factor causes the disease, which would help scientists understand how dangerous that factor is.
Cross-sectional studies are similar to cohort studies, but they only collect data from one point in time or over a short period. They can identify potential risks or causes for disease, but they are unable to examine whether something causes disease over a longer period.
Many national surveys are cross-sectional, such as the
Cohort studies are one of the most powerful tools researchers have to understand human health. They involve following groups of people for long periods of time and examining trends in the data. These trends can be important for identifying causes and risk factors for diseases.
RCTs provide stronger evidence when it comes to medical interventions, such as medications. However, cohort studies are more practical and more ethical for examining health risks.
Cohort studies have several limitations though. They can be more time-consuming than other options, such as cross-sectional or case-control studies.