Statistics and Epidemiology

Introduction to statistics: variability of biological and clinical data. Study planning and data collection.
Qualitative and quantitative variables. Frequencies and relative frequencies distribution.
Graphical representations: bar-chart and pie-charts, histograms.
Synthesis of the data: Measure of central tendency (modal value, median, mean) and variability (range, interquartile range, standard deviation, variance, variation coefficient).
Probability. Empirical distribution and theoretical models:
Bernoulli, Binomial and Gaussian distributions.
Incidence and prevalence.
Diagnostic tests: screening, sensitivity and specificity; positive and negative likelihood ratio, positive and negative predictive value.
Statistical inference and sampling. Sample mean distribution, standard error of the mean and confidence interval for the sampling mean.
The logic of the hypothesis test. Comparison test of a mean and a proportion with a known value. P-value.
Contingency table analysis: chi-square test.
Correlation and regression.

1. Sources of epidemiological data
- Demographic data: the census, the age pyramid, the personal registers
- Health data: notification of infectious diseases, hospital sources, ad hoc investigations, pathology registers
- Other sources
2. Use of frequency measures and health indicators
3. Knowledge of the determinants of health and disease
4. Risk factor and disease
- Causality
- Risk measures
- The association measures: Relative Risk, Attributable Risk, Odds Ratio
5. Types of epidemiological studies
- Observational studies for the formulation of hypotheses: ecological or descriptive studies and transversal studies. Epidemiological surveillance.
- Analytical studies for hypothesis testing: cohort studies and case-control studies
- Experimental studies to support the hypothesis: randomized controlled trials and randomized controlled population studies
- Practical application of risk measures in public health.

1. Importance and use of statistics, etymology and definition, variables and data (aspects, matrices, quality, methods of collection and use), limits of statistics. Examples and practical cases.
2. Produce effective data summaries: central trend and data dispersion. Examples, exercises and cases.
3. Classification and data representation. Analysis of a frequency distribution. Potential, choice, types and possible risks of graphic representations.
4. Statistics laboratory: use of statistical functions with Excel. Analysis of numerical and categorical variables through the calculation of the main summary measures, the construction of frequency distribution tables and the creation of graphs with Excel.
5. Advantages and risks of using a sample, planning and sampling methods, question of the error in inference. Association and relationships between variables (correlation and regression).