Epidemiological Studies: Methods and Benefits (HSC SSCE Biology): Revision Notes

Epidemiological Studies: Methods and Benefits

Overview of epidemiological study types

Epidemiological studies help us understand the patterns and causes of diseases in populations. There are three major types of epidemiological studies:

• Descriptive studies: provide information about disease patterns
• Analytical studies: test hypotheses about disease causes
• Intervention studies: test effectiveness of treatments or public health campaigns

Descriptive studies

Descriptive studies are usually the first investigation carried out when examining the cause of a disease. These studies help answer the questions: who, what, where and when?

Purpose and methods

Descriptive studies collect information about:

• The frequency of the disease
• Which groups are affected (age, gender, occupation, socioeconomic status)
• Geographical location of cases
• Time periods when individuals were affected

Researchers gather data from people with the disease and look for common factors to identify possible causes. This information is used to develop hypotheses about what might be causing the disease.

Analytical studies

Once a descriptive study has been completed, analytical studies collect more detailed data to test hypotheses about the likely cause of a disease. These studies help answer the questions: why and how?

Key measures used

Analytical studies examine several important indicators:

• Morbidity: the number of cases of the disease
• Mortality: the percentage of the population that dies from the disease
• Incidence: the number of new cases occurring in a specific time period
• Prevalence: the total number of people affected at any one time

The data is then statistically analysed to identify the most likely causes of the disease.

Types of analytical studies

There are two main types of analytical studies: case-control studies and cohort studies.

Case-control studies

Case-control studies compare two groups of people:

• Cases: people who have the disease
• Controls: people who do not have the disease

Method

Researchers collect a wide range of data from both groups, including:

• Age and sex
• Diet and lifestyle
• Location and occupation
• Exercise habits

The data is then analysed to find differences between the two groups that might explain why one group developed the disease.

Cohort studies

Cohort studies follow two or more similar groups of people over a long period of time. All participants are free of the disease at the start of the study.

Method

The groups differ in one main factor: their exposure to a potential cause of the disease:

• One group is exposed to the possible cause (test group)
• The other group is not exposed (control group)

Researchers track both groups over many years and compare how many people in each group develop the disease.

Intervention studies

Intervention studies test whether a particular treatment or public health campaign is effective. The aim is to change behaviour across a population to reduce disease incidence.

Experimental studies

In experimental studies, researchers test the effectiveness of new treatments:

• Participants with a particular condition are randomly placed into two groups
• One group receives the trial drug or treatment
• The other group receives a placebo (an inactive substance that looks like the real treatment)
• The effects on both groups are recorded and statistically analysed

This helps determine whether the new treatment is actually effective.

Quasi-experimental studies

When it is not possible to randomly assign participants, a quasi-experimental study is carried out. In this type of study, the researcher chooses which subjects receive the treatment.

Examples

• Testing a new vaccine for influenza in hospital workers (one department receives the vaccine, another does not)
• Evaluating campaigns like 'Quit now – smoking while pregnant' to reduce the number of pregnant women who smoke

Requirements for valid epidemiological studies

Good epidemiological studies should meet several important criteria:

Time and sample size

• Be conducted over a long period of time (often many years)
• Study very large sample sizes (thousands of participants)

Data collection

• Collect a range of relevant data from both affected and unaffected people
• Include information about age, sex, diet, occupation, lifestyle and exercise habits
• Ensure participants represent the wider population

Study design

• Use control groups who are not exposed to the potential cause but are similar in all other ways
• Collect data on incidence, prevalence, mortality and morbidity rates

Analysis and outcomes

• Statistically analyse data to identify patterns and trends
• Identify possible causes and risk factors
• Develop management plans with strategies to control or eliminate the disease
• Educate the public about findings
• Evaluate the effectiveness of control and treatment programmes

Errors in epidemiological studies

Even well-designed studies can contain errors that affect the reliability of results. There are two major types of errors:

• Random errors
• Systematic errors

Random errors

Random errors are unpredictable variations in data that have an inconsistent effect on measurements within a study.

Characteristics

• Make the study less precise
• Do not shift results in a particular direction
• Can usually be corrected using statistics

Reducing random errors

• Ensure groups being studied are similar (homogenous)
• Use large sample sizes

Systematic errors (bias)

Systematic errors, also called bias, occur when any process during the study causes a consistent deviation from the true value. These errors result in incorrect estimates of how exposure to a factor affects disease risk.

Selection bias

Selection bias occurs when the subjects chosen for the study do not properly represent the population being studied.

Types of selection bias include:

• Sampling bias: the method of choosing subjects does not lead to a representative sample
• Volunteer bias: volunteers may have a vested interest and already be at higher risk than non-volunteers
• Healthy worker bias: employed participants are generally healthier than those not working, which can skew results in workplace studies
• Prevalence/incidence bias: only current cases are included, while those who have recovered or died are excluded

Information bias

Information bias involves errors in measurement or recording information. This affects study groups differently.

Types of information bias include:

• Misclassification bias: some subjects already have the undiagnosed condition at the start
• Recall bias: people with the condition remember factors better than those without it
• Ascertainment bias: not all study participants are followed up equally
• Interviewer bias: the interviewer unintentionally leads participants to particular answers
• Measurement bias: measurements are consistently wrong (always too high or too low)
• Loss to follow-up bias: not all subjects who began the study are available at the end

Confounding factors

A confounding factor is an unrecognised factor that may be affecting study results and leading to bias. A particular factor may appear to cause a disease, but another factor could also be contributing.

Evaluating an epidemiological study

To evaluate means to make a judgement about something and use evidence to support that judgement. When evaluating epidemiological studies, we assess whether they follow accepted scientific principles.

Criteria for evaluation

The validity of an epidemiological study depends on:

• Whether it follows accepted epidemiological principles for that type of study
• Consideration of any errors and bias, including confounding variables
• Large sample size and long study period
• Use of scientifically approved methods for conducting the study, collecting data and analysing results
• Appropriate study design (case-control, cohort or experimental)

Case study: The Pima Indian population study

An excellent example of evaluating a study is the cohort study of the Pima Indian population in Arizona, which examined the role of physical activity in developing type 2 diabetes.

Evaluation of validity

This study meets the criteria for a valid epidemiological study because:

Sample size and time: The study included 1,728 individuals over 13 years, satisfying requirements for large sample size and long study period. This reduced sampling bias.

Cohort study requirements: The study examined similar groups (non-diabetic Pima Indians from the same area, aged 15-59) who differed mainly in physical activity levels, which is appropriate for a cohort study.

Objective diagnosis: Diabetes was diagnosed using scientifically approved testing at each visit, reducing measurement bias.

Valid data collection: Trained interviewers used a scientifically valid questionnaire to assess activity levels, reducing interviewer and measurement bias.

Mathematical modelling: Physical activities were weighted for intensity using accepted models, and results were analysed using scientifically tested models.

Quality control: Data was excluded if participants were thought to have incorrectly reported activity levels, reducing recall bias.

Peer review: The written report was peer reviewed before publication.

Benefits of epidemiological studies

Epidemiological studies provide numerous benefits that can be classified in different ways:

Types of benefits

Benefits can be:

• Short-term: advantages received immediately
• Long-term: advantages that may take many years to realise
• Direct: benefits that can be quantified or measured
• Indirect: benefits that cannot be directly quantified but still have an impact

Major benefits

Epidemiological studies provide several important advantages:

1. Identify disease causes: Studies help determine what factors contribute to disease development, allowing for better prevention strategies
2. Inform public health policy: Results guide government decisions about health campaigns, funding and resource allocation
3. Develop treatment strategies: Understanding disease causes helps researchers develop more effective treatments and interventions
4. Educate the public: Study findings can be used to inform communities about risk factors and prevention methods
5. Track disease patterns: Studies help monitor how diseases spread through populations over time
6. Evaluate interventions: Research can assess whether treatments or public health campaigns are working effectively

Who benefits?

Different groups benefit from epidemiological studies:

• Individuals: gain knowledge to make healthier lifestyle choices
• Communities: receive targeted health interventions
• Healthcare providers: get evidence-based treatment guidelines
• Governments: can develop effective public health policies
• Future generations: benefit from long-term prevention strategies