Unit 1468: Comprehensive Epidemiology and Biostatistics

1. Introduction to Epidemiology

1.1 Definition and Scope

• What is epidemiology?
• Role in public health

1.2 Historical Perspective

• Key milestones in epidemiology
• Evolution of epidemiological methods

1.3 Importance of Epidemiology

• Disease prevention and control
• Informing health policy

1.4 Key Terms and Principles

• Population, exposure, outcome
• Risk, rate, ratio

2. Measures of Disease Frequency

2.1 Prevalence

• Definition and types (point, period)
• Calculation and interpretation

2.2 Incidence

• Incidence proportion (risk)
• Incidence rate
• Differences and uses

2.3 Mortality Rates

• Crude, cause-specific, age-specific mortality
• Case fatality rate

2.4 Practical Examples and Calculations

• Worked examples
• Interpretation in public health context

3. Study Designs in Epidemiology

3.1 Observational Studies

• Cross-sectional studies: design, uses, pros and cons
• Case-control studies: selection of cases and controls, strengths, limitations
• Cohort studies: prospective vs retrospective, advantages, challenges

3.2 Experimental Studies

• Randomized controlled trials (RCTs): design, randomization, blinding
• Ethical considerations

3.3 Comparative Analysis

• When to use which design
• Impact on causal inference

4. Bias and Confounding in Epidemiological Studies

4.1 Types of Bias

• Selection bias
• Information bias (misclassification, recall bias)

4.2 Confounding

• Definition and examples
• Identifying confounders

4.3 Strategies to Control Bias and Confounding

• Design phase: randomization, restriction, matching
• Analysis phase: stratification, multivariable analysis

5. Introduction to Biostatistics

5.1 Role of Biostatistics in Epidemiology

• Data analysis and interpretation

5.2 Basic Statistical Concepts

• Variables: types and scales
• Descriptive statistics

5.3 Measures of Central Tendency

• Mean, median, mode

5.4 Measures of Variability

• Range, variance, standard deviation

6. Probability and Sampling Distributions

6.1 Fundamentals of Probability

• Basic rules and concepts

6.2 Discrete Probability Distributions

• Binomial distribution: definition and applications
• Poisson distribution: definition and uses

6.3 Continuous Probability Distributions

• Normal distribution: properties and importance

6.4 Sampling Distributions

• Concept and significance
• Central limit theorem

7. Hypothesis Testing in Epidemiology

7.1 Formulating Hypotheses

• Null and alternative hypotheses

7.2 Significance Levels and P-values

• Interpretation and common thresholds

7.3 Types of Errors

• Type I and Type II errors
• Power of a test

7.4 Steps in Hypothesis Testing

• Test selection
• Calculations and decision making

8. Regression Analysis in Epidemiology

8.1 Introduction to Regression

• Purpose and basic concepts

8.2 Linear Regression

• Model assumptions
• Interpretation of coefficients

8.3 Logistic Regression

• Use with binary outcomes
• Odds ratios and interpretation

8.4 Applications in Public Health

• Examples of exposure-outcome relationship analyses