Scheda insegnamento

Anno Accademico 2022/2023

Conoscenze e abilità da conseguire

By the end of the course, the student should have gained a fundamental understanding of the objectives, theory and application of statistical methods for the production of health indicators. The learning process will include an updated overview of the aims and definitions adopted internationally to monitor population health and health care, particularly at EU level. The course will provide detailed explanations on the statistical methods applied for the continuous improvement of health and social policies, with practical examples showing how to produce indicators from large databases using R software.


This course is intended to support the student towards a fundamental understanding of the societal value of health information and the methods and tools used to report, evaluate and continuously improve policies.

In particular, the course will present solutions to current challenges involving the use of large scale routine databases available at national and international level. Practical cases of data analysis will be presented using relevant statistical software. Issues in the correct communication of health statistics will be also discussed.

At the end of the course, the student should be able:

  • to calculate and interpret health indicators used in regional, national and international reports (in particular, the EU European Core Health Indicators, European Sustainable Development Indicators and State of Health in the EU): from life expectancy to quality of care, access and efficiency measures.

  • to apply advanced techniques for health systems performance evaluation: from risk adjustment and standardization through the use of multivariate models (generalized linear models, generalized estimating equations and multilevel models), to modern approaches using person-centered statistical models (risk prediction and stratification for population health management).

  • to apply principles of study design (experimental vs observational) and analytical techniques (e.g. propensity scores, difference-in-difference) to plan and evaluate health interventions and policies, considering social determinants of health and prevention.

Lectures will cover the following subjects:

  • STREAM 1. Regional, national and international health statistics

    • International data sources, projects, classification and coding systems (Health status and quality of life, Surveys, Reports and Health Databases - WHO, European Commission/EUROSTAT, OECD Health at a Glance).

    • Standardized health care data sources in the Italian National Health System.

    • Theory and applications of health systems performance assessment. The “Triple Aim” and the future of health statistics: Patient Reported Outcome Measures (PROMs) for value-based health care.

    • R Labs: analysis of health indicators

  • STREAM 2. Risk stratification and standardization

    • Standardization methods in AHRQ and OECD indicators. Multivariate models for complex data: GEE logistic regression and multilevel analysis.

    • Healthcare performance intelligence: modern approaches for international comparisons.

    • R Labs: risk adjustment and standardization techniques

  • STREAM 3. Statistical methods and tools to plan and evaluate health policies

    • Study design (experimental vs observational, cluster clinical trials, etc) and related statistical techniques (propensity scores, difference-in-difference, etc).

    • R Labs: propensity scores and difference-in-difference.


Course notes (available during the lectures)

Main References (extracts)

  • Kleinbaum DG, Logistic Regression. A self learning text. 3rd Edition, Springer, 2010. 

Papers (provisional list)

  • Carinci F et al., Towards actionable international comparisons of health system performance: expert revision of the OECD framework and quality indicators, International Journal for Quality in Health Care, Apr; 27(2):137-46, 2015. Available at: http://intqhc.oxfordjournals.org/content/27/2/137.long.

  • Carinci F et al. Lower extremity amputation rates in people with diabetes as an indicator of health systems performance. A critical appraisal of the data collection 2000-2011 by the Organization for Economic Cooperation and Development (OECD), Acta Diabetologica, 2016 Oct;53(5):825-32. Available at: http://link.springer.com/article/10.1007%2Fs00592-016-0879-4.

Metodi didattici

Lectures with open discussion + Computer labs + Selected readings

Modalità di verifica e valutazione dell'apprendimento

The exam will evaluate the achievements of the student through a direct assessment of the following learning outcomes:

  • ability to identify the most suitable methods required to carry out the analysis and interpretation of health-related data

  • ability to conduct a critical appraisal of the methodology adopted in a research project or scientific publications

The exam will consist in the preparation and presentation of a project of data analysis chosen by the student (individually or in a group), using datasets available in the public domain, followed by questions concerning the statistical techniques and the underlying theory related to the specific study program. An understanding of the fundamental societal goals relevant to population health and health policies will be also requested for the examination.

Strumenti a supporto della didattica

Course notes

Slide power point

R - scripts

Orario di ricevimento

Consulta il sito web di Fabrizio Carinci