1.3 Basic Principles of Epidemiology
1.3.1 Outcome of Disease
Key Concept: What’s the “outcome” of disease?
Example (Practical):
A patient recovering from a heart attack may experience improvements in their clinical outcomes (e.g., heart function) and patient-reported outcomes (e.g., quality of life) but could still face economic outcomes such as hospitalization costs and lost workdays.
Quiz II
Which clinical outcome is most appropriate for tracking effectiveness?
Key Takeaway:
In clinical epidemiology, it’s important to consider how a treatment will work not just in the general population, but also in specific subgroups with different lifestyles and socioeconomic conditions. For this patient, you need to evaluate the availability, cost and contextual factors (e.g., rural healthcare infrastructure) to make a well-informed decision on whether the treatment is feasible and effective for them.
1.3.2 Variables in Research: The Building Blocks of Data
- Independent Variables (IVs): These are the factors or treatments you manipulate or study to see if they cause a change in the dependent variable.
- Dependent Variables (DVs): These are the outcomes you measure to see if they change when the independent variable is applied.
- Confounding Variables : These are external factors that can distort or interfere with the relationship between the independent and dependent variables.
For a variable to be classified as a confounder, it must fulfill three key conditions:
a). Association with the IV: The confounding variable must be related to the independent variable (the factor being manipulated or studied).
Example: If you’re studying the effect of a new anti-hypertension drug on blood pressure, a confounder like age could be related to the drug because older people may be more likely to take the drug, or may need different dosages.
b). Association with the DV: The confounding variable must also have a relationship with the dependent variable (the outcome you’re measuring). This means the confounder influences the outcome, potentially masking or exaggerating the effect of the independent variable on the dependent variable.
Example: In the hypertension drug study, age can affect blood pressure directly (older age is often associated with higher blood pressure), regardless of the drug being tested. This means age is related to the outcome, which is blood pressure.
c). Not an Intermediate Variable (i.e., not in the causal pathway): The confounder must not be an intermediate variable in the causal pathway between the independent and dependent variables. This means that the confounder should not be affected by the independent variable or act as a step between the IV and DV in the cause-effect chain.
Example: In the hypertension study, age is not caused by the drug (IV) nor does it affect how the drug works. However, age directly influences blood pressure, which makes it a confounder.
Example (Practical)
Quiz III
Key Takeaways:
By identifying and controlling for confounding variables, researchers can ensure their study results are as accurate and reliable as possible.
1.3.3 Population and Sample in Research
Key Concepts:
- Population: The entire group you want to understand or generalize about (e.g., people with diabetes).
- Sample: A subset of the population selected for a study (e.g., 500 people with diabetes in a city).
Example (Practical):
If you are conducting a study on a new vaccine, your population is all individuals at risk of the disease, but you’ll study a sample (a group of patients). How well this sample represents the population impacts the generalizability of the study results.
Activity III
Choose the Best Sampling Method for Antibiotic Resistance Study.
1.3.4 Error in Research: Systematic vs. Random
Key Concepts:
- Systematic Error: Errors that are consistent and predictable, like measurement bias.
- Random Error: Unpredictable and caused by chance, such as human error or environmental factors.
Example (Practical):
In a study measuring blood pressure changes after a new treatment, systematic error could occur if the device used to measure blood pressure is improperly calibrated, while random error could be caused by fluctuations in patient readings due to varying stress levels.
Activity IV
1.3.5 Internal vs. External Validity
Key Concepts:
- Internal Validity: The degree to which the study’s design, execution, and analysis avoid bias, ensuring that the results are accurate for the study group.
- External Validity: The extent to which the study results apply to broader populations or settings.
Example (Practical):
A clinical trial, conducted only on young, healthy volunteers has high internal validity but may have low external validity when applying the results to older or sicker patients.
Activity V
1.3.6 Putting It All Together in Real-Time Practice
Activity VI
Scenario:
Select the true statement A 60-year-old woman with hypertension, diabetes, and a history of stroke presents with a sudden headache. You need to use clinical epidemiology to decide on the next steps for her care.