This CU is transversal in the first year of the curriculum.
Intended learning outcomes (knowledge, skills and competences to be developed by the students)
- Understand and apply essential concepts of Epidemiology
- Knowledge and apply about epidemiological studies
- Interpret the basic demographic characteristics of a population through a population pyramid
- Prepare a contingency table to be able to interpret study results and calculate relative risks, odds ratios, sensitivity, specificity, positive predictive values, negative predictive values and precision
- Calculate measures of frequency
- Understand the Bradford-Hill causality criteria
- Understand the definition and purposes of epidemiological surveillance
- Understand essential biostatistical concepts, data collection methods and sampling
- Comparison between two proportions in independent samples and paired samples, logistic regression, linear regression, and rate comparison
- Correctly interpret the various hypothesis tests
- Understand Database principles, concepts, models and technologies
- Understand statistical techniques using SPSS software
Syllabus
- Review of basic concepts used in Epidemiology
- The role of the Epidemiology and Biostatistics in research Population dynamics and health: factors of population dynamics
- Descriptive and analytical observational epidemiological studies
- Epidemiological studies: experimental and quasi-experimental studies
- Meta-analysis: definition and interpretation of the results of a study Bradford-Hill causality criteria
- Diagnostic tests
- Epidemiological characters: person, time, place
- Epidemiological surveillance
- Problems in epidemiological studies: bias and confounding
- Validity and accuracy
- Introduction to Databases and framing in the area of Information Systems Database Management Systems
- Planning a study and Exploratory Data Analysis
- Concept of a Statistical Test
- Tests to compare independent samples
- Confidence intervals
- Relative risk, odds ratio
- Survival analysis: Kaplan-Meyer, log-rank test
