The Impact and Epidemiology of Non-Communicable Disease

Principles of Epidemiology and Sampling

• If scientists want to find out how many people in the UK have diabetes, they cannot possibly test all 67 million citizens.
• Epidemiology is the study of the distribution, patterns, and determinants of health and disease in populations.
• Because a full census is impractical, scientists must use a Representative Sample, which is a smaller subset that accurately reflects the characteristics of the larger population.
• A sample must be large to increase the reliability of the data and minimize the effect of anomalies or chance results.
• To ensure validity, scientists must avoid Sampling Bias (errors introduced when a sample does not reflect the population). They do this by using random sampling, where every member has an equal chance of selection, often utilizing a random number generator.
• Samples must be representative of diversity, accounting for variations in age, sex, ethnicity, and lifestyle. Scientists often use Stratification (dividing the sample into proportional subgroups) to achieve this.

Measuring Disease: Incidence vs. Prevalence

• A disease like the common cold has millions of cases a week, yet its overall burden on society at any one moment is low compared to long-term conditions like diabetes.
• Incidence is the number of new cases of a disease that develop in a population during a specific time period. It measures the risk of contracting the disease.
• Prevalence is the total number of cases (both new and existing) in a population at a specific point in time. It measures the total burden of the disease on society.
• The Bathtub Analogy: Imagine a bathtub. The water flowing into the tub represents the incidence (new cases). The water already in the tub represents the prevalence. The water draining away represents people recovering or dying.
• Diseases with short durations (like a cold) have high incidence but low prevalence. Long-duration diseases (like HIV) have high prevalence even if incidence is low, because cases accumulate over time.

Worked Example: Calculating Prevalence

A local town has a total population of 85,000. Medical records show there are 3,400 existing cases of Type 2 diabetes. Calculate the prevalence as a percentage.

Step 1: Write down the formula for prevalence.

• $\text{Prevalence (\%)} = \frac{\text{Total number of cases in population}}{\text{Total population}} \times 100$

Step 2: Substitute the known values into the equation.

• $\text{Prevalence (\%)} = \frac{3400}{85000} \times 100$

Step 3: Calculate the final answer.

• $\text{Prevalence} = 4\%$

Correlation, Causation, and Causal Mechanisms

• Just because people who carry lighters are more likely to get lung cancer, it does not mean the lighter caused the cancer.
• A Correlation is a link between two variables where a change in one is accompanied by a change in the other. However, correlation does not prove Causation (where one variable directly results in a change in another).
• To move from correlation to causation, scientists must prove a Causal Mechanism — a clear biological process explaining exactly how a Risk Factor influences a disease.
• Example 1: Smoking and Lung Cancer: Tobacco smoke contains tar, which is a known Carcinogen. The biological mechanism is that tar damages lung cell DNA, causing mutations that lead to uncontrolled cell division (mitosis), forming a malignant tumor.
• Example 2: Smoking and Pregnancy: Smoking introduces carbon monoxide into the mother's blood. This binds to Haemoglobin more strongly than oxygen, meaning less oxygen reaches the fetus. This results in lower rates of Respiration and leads to low birth weight.