Environmental Epidemiology

This module provides students with knowledge on how to formulate hypotheses, design, implement, analyse and interpret an environmental epidemiology study. Through the lectures, our students understand why and when an environmental epidemiology study is necessary and useful.

The module is divided into three sections:

• In the first section the history and achievements of environmental epidemiology, formulation of hypotheses, study design, overview of analysis and interpretation are covered.
• In the second section, students learn how to apply the statistical analysis of studies aiming to evaluate effects of short-term exposures, emphasising time series and case-crossover studies; and long-term exposures with a focus on panel and cohort studies. Practical hands-on applications in R are included.
• In the third section, students are introduced in how to manage and export data developed in geographical information systems in order to use them in the analysis of environmental epidemiological studies.

Environmental health problems are discussed with focused reviews of the literature and critical appraisal of published papers.

Module Aim and Learning Objectives

This module aims to provide students with knowledge on how to recognise the need, formulate hypotheses, design, analyse data and interpret results from an environmental epidemiology study.

Specific Learning objectives:

By the end of this module, the students should be able to:

1. Understand the type of problems that environmental epidemiology addresses and the related study designs.
2. Understand how the various indices for disease occurrence, effect estimates and statistical methods correspond with the type of data and study design at hand. Interpret and critically review the results of environmental epidemiology studies.
3. Be able to design and write the protocol of an environmental epidemiology study.
4. Analyse, using R, time series and case-crossover studies for applications in environmental health research and interpret their results.
5. Use Geographical Information Systems for producing spatial variables and manage spatially resolved data for use in environmental health studies.
6. Analyse, using R, panel and cohort studies data for applications in environmental health research and interpret their results.

Professor Klea Katsouyanni, Professor of Public Health (Team Lead Air Pollution Epidemiology Team)

Dr Dimitris Evangelopoulos (Deputy Head of the ERG Air Pollution Epidemiology Team) and Module co-leaders.

The module is also supported by Associate Professor Daniela Fecht of Geospatial Health and by a team from the Environmental Research Group with expertise in Epidemiology, Biostatistics and Environmental Policy.

People

Timetable

The module runs over five weeks every Thursday 10:00 to 13:00 and 14:00 to 17:00 in Term 2.

It is offered to students enrolled in the MSc Epidemiology and Master of Public Health courses at Imperial College. All sessions are held in person. There are formative assessments usually prepared as group work, such as paper presentations and quizzes. Additionally, students implement supervised analysis of datasets during course hours. The summative assessment of the module includes supervised analysis of a dataset with the aim to answer a specific research hypothesis and submitting a report of 1000-1500 words describing briefly methods and results, at a later date.

Comments from Previous Students

The students enjoy the module and find it informative and engaging. They particularly like the workshops and practical's, particularly the data analysis sessions.  Students’ feedback quotes from the 2024/25 academic year:

“I liked the practical sessions. All assessments are rather clearly elaborated on such that we know what is expected of us, especially for the individual project”

“The teaching team was really knowledgeable and it was exciting to be taught by experts in the field”

“I enjoyed the introduction to the different types of epidemiological study designs”

“The GIS tutorial and introduction to geospatial health was really interesting”

“The group project was extremely meaningful and a good way to assess us”