3.5.9 Correlations & Causal Relationships - The Heart

Correlations:

• A correlation is when two variables show a relationship or trend, but one does not necessarily cause the other.
• Example:
  • A study may show a correlation between high-fat diets and increased risk of heart disease.
  • However, this does not prove that a high-fat diet directly causes heart disease—it could be influenced by other factors like lack of exercise or genetics.

Causal Relationships:

• A causal relationship exists when one variable directly causes a change in another.
• Example:
  • Smoking is causally linked to increased blood pressure and atherosclerosis due to the direct effects of nicotine and carbon monoxide on blood vessels.

Key Considerations:

1. Identifying Correlation:

• Look for trends in data, such as an increase in one variable corresponding with an increase or decrease in another.
• Example: Higher levels of LDL cholesterol correlate with a greater incidence of heart attacks.

2. Proving Causation:

• Requires controlled experiments or studies to eliminate confounding variables.
• Example: Randomised controlled trials showing that reducing LDL cholesterol levels lowers the risk of heart attacks provide evidence for causation.

3. Confounding Variables:

• Factors that may influence both variables and create a false correlation.
• Example: Sedentary lifestyle may increase both body weight and heart disease risk, creating a misleading link between the two.

Examples in Cardiovascular Health:

1. Exercise and Heart Rate:

• Correlation: People who exercise regularly have lower resting heart rates.
• Causation: Regular exercise strengthens the heart muscle, increasing stroke volume and reducing the need for a higher heart rate at rest.

2. Smoking and Heart Disease:

• Correlation: Smokers are more likely to develop heart disease.
• Causation: Smoking damages blood vessels and increases plaque formation, leading to atherosclerosis.

3. Obesity and High Blood Pressure:

• Correlation: Obese individuals are more likely to have high blood pressure.
• Causation: Excess body fat increases blood volume and resistance in arteries, directly causing hypertension.

Data Analysis Skills:

• Scatter Graphs:
  • Used to identify correlations.
  • Positive correlation: As one variable increases, so does the other.
  • Negative correlation: As one variable increases, the other decreases.
  • No correlation: No clear relationship between the variables.
• Statistical Tests:
  • Spearman's rank or Pearson's correlation coefficient can quantify the strength of a correlation.
  • Use p-values to determine if a correlation is statistically significant.

Key Terms:

• Correlation: A relationship between two variables without proof that one causes the other.
• Causal Relationship: Direct evidence that one variable causes a change in another.
• Confounding Variables: External factors that may affect the relationship between two variables.
• Statistical Significance: A measure of whether observed results are likely to be due to chance.