78853 - EPIDEMIOLOGIA AMBIENTALE

Learning outcomes

At the end of this course students know how to handle statistical methods and models to study the relationship between health and exposure to environmental stressors. In particular, students will learn about: geostatistics for spatial predictions of pollutants; disease mapping in presence of small areas; poisson regression models for counts of disease to study the link between pollution and health; the most popular R packages to analyse spatial data.

Course contents

Module I

Statistics for environmental epidemiology. Spatial data. Mapping areal risks, rates and proportions. 

Areal data analysis for environmental epidemiology applications. Standardized mortality rates. Internal standardization. Poisson regression for counts of disease. Disease mapping. Smoothing methods: local mean, empirical Bayes. Global clustering of disease risks; Moran index.

Geostatistics tools useful in environmental epidemiology. Stochastic spatial processes. Covariogram. Matern covariance functions. Spatial prediction.

Ecological regression models for studying the relationship between pollution and health.

 

Module II

Atmospheric pollutants. Correlation between time series of pollutants.

Literature review on pollutant effects on health. Short-term effects versus long-term effects. Response-dose relationships. Health hazards associated to waste management systems.

Assessing exposure to atmospheric pollutants. Direct methods versus indirect methods. Statistical models to assess exposure. 

Impact on health evaluation. Quantify risks and impacts. Attributable fractions and conterfactual scenarios. Impact indicators (AC, YLL, YLD, DALYs). Exposure-response functions quantifying impact of  particulate matter on mortality. 

 

 

Readings/Bibliography

Module I:

• Applied Spatial Statistics for Public Health Data (2004). Lance A. Waller, Carol A. Gotway. Wiley
• Peter Diggle, Paulo Ribeiro (2007). "Model-based Geostatistics". Springer
  

Module II:

• Epidemiologia ambientale: Metodi di studio e applicazioni in sanità pubblica. Dean Baker , Fabio Barbone , Rebecca Calderon , Tord Kjellstrom , Harris Pastides (2004). Edizione italiana del testo: Environmental Epidemiology A Textbook on Study Methods and Public Health Applications. WHO-USEPA (WHO/SPE/OEH/99.7). Disponibile all’indirizzo: http://www.arpat.toscana.it/documentazione/catalogo-pubblicazioni-arpat/epidemiologia-ambientale?searchterm=epidemiologia+ambientale
• D. Baker, M.J. Nieuwenhuijsen(eds). Environmental Epidemiology: study methods and application, Oxford University Press, 2008.

Teaching methods

Frontal lectures; LAB tutorial with RStudio

Assessment methods

The exam aims at evaluating students' understanding of the all topics included in the syllabus; it will be evaluated the ability to:

- produce statistical analysis in R;

- interpreting the output of the statistical analysis;

- draw conclusions.

Exams for module I and II are separate and independent. The final grade is the average of the two exam grades.

Module I

Exam is done in two steps: a take-home assignment and a computer-based with questions on all the topics included in the syllabus; the computer-based exam is administered in a university LAB (April 4th). 

Exam questions can be true/false; multiple choice with three options; numerical. Some of the multiple choice questions require to 'select only one option', others require 'select one or more'. The numerical questions require the student to input a number (typically it is required to compute the answer using the software R).  

For each correct answer you get 1 point, 0 points in case of mistake. A positive grade is expressed in a scale between 18 (sufficient) and 30L (excellent). A grade below 18 means you have failed the exam (denoted as 'Respinto' in the webpage 'almaesami').

 

Module II

The exam is done through a take-home assignment. The students must contact the teacher a couple of weeks before the exam date (please put Massimo Ventrucci in cc). The teacher will provide the assignment so that there is enough time for the student to complete it within the exam date.

 

REGISTRATION

A couple of days after the exam the teacher will comunicate grades (via almaesami) and the date set for registration.

Teaching tools

All material will be provided through the virtuale platform (login in to https://virtuale.unibo.it/). There you will find slides, lab tutorial in pdf, further readings and material discussed in class. I suggest students who have a laptop to bring it in class. Softwares used in this course are all open source: we will use R (http://www.r-project.org/) and RStudio (https://rstudio.com/).

Office hours

See the website of Massimo Ventrucci

See the website of Andrea Ranzi