Statistics and Epidemiology

Data collection: measurement scale of the variables, coding, setting of the detection in a questionnaire.
Descriptive statistics: the distributions and their graphical representation (bar diagrams, pie charts, histograms, box-plot diagrams), the cumulated distributions and their interpretation, numerical synthesis indices (mean, median, quartiles, standard deviation, range interquartile).
Association between qualitative variables (contingency tables and indices to estimate the association strength).
Linear correlation and linear regression.
The Gaussian distribution.
Sampling schemes (simple random, stratified, clusters).
Sample distributions (averages and proportions).
Confidence intervals for averages and proportions.
Hypothesis tests for averages and proportions.

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 syntheses: the central trend and the dispersion of data. Examples, exercises and cases.
3. Classification and representation of data. Analysis of a frequency distribution. Purposes, types and possible risks of graphic representations. Relationships between variables (correlation and regression). Detection techniques and sampling methods.
4. Statistics laboratory: use of statistical functions with Excel. Analysis of numerical and categorical variables through the calculation of the main synthesis measures, the construction of frequency distribution tables, the creation of graphs and boxplots with Excel. Practises.