Risk factors are linked to an increased rate of disease. They can be categorized as:
Lifestyle factors: For example, lack of exercise, poor diet, or smoking.
Substances in the body: Such as chemicals from cigarette smoke or certain foods.
Substances in the environment: For example, exposure to harmful chemicals or radiation.
Not all correlations indicate a cause-and-effect relationship. For example:
Causal link: Spending time in the sun (UV exposure) increases the risk of skin cancer.
Correlation: Eating ice cream and skin cancer rates rise together in summer, but eating ice cream does not cause skin cancer.
Some risk factors have clear causal links to diseases:
Diet: High-fat diets can raise bad cholesterol, increasing the risk of cardiovascular disease.
Smoking:
Damages lung cells, leading to lung disease and cancer.
Raises blood pressure, increasing the risk of cardiovascular disease.
Harms unborn babies during pregnancy, affecting tissue and organ development.
Lack of exercise: Leads to high blood pressure, increasing the risk of cardiovascular disease.
Obesity:
Reduces insulin sensitivity, causing type 2 diabetes.
Increases the risk of certain cancers.
Alcohol:
Damages liver and nerve cells, affecting liver function and brain health.
Affects the development of unborn babies during pregnancy.
Exposure to harmful substances:
Carcinogenic chemicals and ionising radiation damage DNA, increasing the risk of cancer.
Non-communicable diseases often result from the interaction of multiple factors, not just one. Examples include:
Geographical and economic factors:
Developed countries: Higher fat diets but lower smoking rates.
Deprived areas: Higher rates of smoking and poor diets.
Certain industries: Jobs like mining may involve exposure to harmful chemicals or radiation.
Financial impact:
On individuals: Inability to work due to illness, leading to loss of income.
On communities and nations: High healthcare costs for treating diseases like lung cancer.
Scientists use sampling to study risk factors when testing an entire population is impractical. Key points about sampling:
Random sampling:
Uses tools like random number generators to select individuals or areas to study.
Allows scientists to test a subset and scale up results to estimate for the entire population.
Practicality:
Collecting data from every individual is too expensive and time-consuming.
Sampling ensures manageable and accurate data analysis.