First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Science
STATISTICS FOR TECHNOLOGY AND SCIENCE
Course unit
STATISTICAL METHODS FOR EPIDEMIOLOGY
SCP4063806, A.A. 2018/19

Information concerning the students who enrolled in A.Y. 2016/17

Information on the course unit
Degree course First cycle degree in
STATISTICS FOR TECHNOLOGY AND SCIENCE
SC2094, Degree course structure A.Y. 2014/15, A.Y. 2018/19
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Number of ECTS credits allocated 9.0
Type of assessment Mark
Course unit English denomination STATISTICAL METHODS FOR EPIDEMIOLOGY
Website of the academic structure http://www.stat.unipd.it/studiare/ammissione-lauree-triennali
Department of reference Department of Statistical Sciences
E-Learning website https://elearning.unipd.it/stat/course/view.php?idnumber=2018-SC2094-000ZZ-2016-SCP4063806-N0
Mandatory attendance No
Language of instruction Italian
Branch PADOVA
Single Course unit The Course unit can be attended under the option Single Course unit attendance
Optional Course unit The Course unit can be chosen as Optional Course unit

Lecturers
Teacher in charge ALESSANDRA ROSALBA BRAZZALE SECS-S/01
Other lecturers GIOVANNA BOCCUZZO SECS-S/05

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Educational activities in elective or integrative disciplines SECS-S/01 Statistics 5.0
Educational activities in elective or integrative disciplines SECS-S/05 Social Statistics 4.0

Course unit organization
Period Second semester
Year 3rd Year
Teaching method frontal

Type of hours Credits Teaching
hours
Hours of
Individual study
Shifts
Laboratory 3.5 28 59.5 No turn
Lecture 5.5 36 101.5 No turn

Calendar
Start of activities 25/02/2019
End of activities 14/06/2019
Show course schedule 2019/20 Reg.2014 course timetable

Examination board
Board From To Members of the board
3 Commissione a.a.2018/19 01/10/2018 30/09/2019 BRAZZALE ALESSANDRA ROSALBA (Presidente)
BOCCUZZO GIOVANNA (Membro Effettivo)
VENTURA LAURA (Membro Effettivo)

Syllabus
Prerequisites: Basic knowledge of SAS.
Target skills and knowledge: This course introduces the key concepts, models and statistical techniques for use in Epidemiology. In particular, at the end of the course the students will:
i) be familiar with the main types of epidemiological studies together with the corresponding measures of occurrence and effect.
ii) master the concepts of causality, confounding and effect modification together with the required analysis tools.
iii) be able to define a suitable sample to carry out an epidemiological study.
Examination methods: Computer lab test (SAS) and oral exam with discussion of a final report. The latter focuses on a real life problem and presents the findings from the statistical analysis of a suitable set of data. The topic must be agreed with the course instructors. The analysis may be carried out independently or by a two-person team.
Assessment criteria: The evaluations takes into account: the expertise and ability shown during the computer lab test (1/5) and during the oral exam (2/5), and the final report (2/5). The final mark is a weighted average of the three marks achieved for each part.
Course unit contents: - Introduction to epidemiology: definitions and objectives.
- Causality and types of causal relations. Causal diagrams. Confounding and effect modification.
- Types of epidemiological studies: experimental studies (clinical trials, field trials, community intervention trials) and observational studies (cohort and case-control studies, cross-sectional and ecological studies, proportional mortality studies).
- Measures of disease occurrence and mortality: incidence, risk and prevalence. Relation between incidence and prevalence. Graphical representation (maps) of morbidity and mortality.
- Measures of exposure effects: absolute and relative. Relative risk, attributable risk, odds ratio. Relation between relative risk and odds ratio.
- Inference on incidence, prevalence, relative risk and odds ratio. Type 1 and type 2 error; power calculations.
- Methods to control for confounding: randomization (experimental studies), stratification, standardization, matching.
- Inference on the odds ratio in the presence of stratification (Mantel-Haenszel, logit, maximum likelihood) and matching (McNemar).
- Logistic regression for cohort studies and unmatched and 1:1 matched case-control studies.
- Further sources of bias: selection bias (self-selection, statistical bias, instrumental), misclassification (differential and non differential), representativity and generalization issues.
- Main data sources for public health studies and epidemiology and their information potential: surveys based on hospital discharge records, birth assistance certificates, death causes, information systems of the Italian Ministry for Public Health, disease registries. Definition and treatment of confidential data.
- Programming and evaluation of health care services: the Italian National Health Service, the National Health Plan, the Regional Health Plan and the local health plans. Basic assistance levels (LEA). Demand and supply of health care services.
- The evaluation of health cares services: indicators of resources, process and product. Benchmarking. Service effectiveness analysis. Composite indicators. Health care information systems.
Planned learning activities and teaching methods: This course rotates classroom lectures, hands-on exercises, SAS computer labs, reading groups and special topic talks by experts.
Additional notes about suggested reading: - Slides and further material available on the e-learning platform (which do not replace the textbooks).
- Vineis P., Duca P. e Pasquini P. (1987). Manuale di metodologia epidemiologica. Numero speciale di Epidemiologia e Prevenzione n.32-33.
- dos Santos S. (1999). Cancer Epidemiology: Principles and Methods. Monografia IARC.
Textbooks (and optional supplementary readings)
  • Kenneth J. Rothman, Sander Greenland, Timothy L. Lash, Modern Epidemiology. Philadelphia: Lippincott Williams & Wilkins, 2008. (4 copie disponibili in biblioteca: 1 del 2008 e 3 del 1986; vanno bene entrambe le edizioni) Cerca nel catalogo
  • David W. Hosmer, Stanley Lemeshow, Applied Logistic Regression. New York: Wiley, 2000. (3 copie in biblioteca: vanno bene anche le edizioni precedenti al 2000) Cerca nel catalogo

Innovative teaching methods: Teaching and learning strategies
  • Lecturing
  • Problem based learning
  • Case study
  • Interactive lecturing
  • Working in group
  • Questioning
  • Problem solving
  • Loading of files and pages (web pages, Moodle, ...)

Innovative teaching methods: Software or applications used
  • Moodle (files, quizzes, workshops, ...)

Sustainable Development Goals (SDGs)
Good Health and Well-Being Quality Education