GIS (Geographical Information Systems) in Environmental Epidemiology (4 days course)
6 -9 July 2020
Dr. Danielle Vienneau and Dr. Kees de Hoogh, Department of Epidemiology and Public Health, SwissTPH, University of Basel, Switzerland
The physical and social environment that surrounds us plays an important part in our health and wellbeing. The geography concept of ‘place’ thus cannot be ignored in environmental epidemiology and public health. Whether investigating the level of environmental pollution, access to recreation or health services, or patterns of disease, Geographic Information Systems (GIS) provide the standard platform for exploring spatial attributes and relationships between our environment and health.
This course offers an introduction to GIS and how it is used in environmental epidemiological research. It will introduce students to the basics including: working with and integrating spatial and non-spatial data; geographic scale and spatial precision; geocoding; visualisation; thematic mapping; and understanding spatial relationships. Specific skills and tools will also be introduced in relation to methods for spatial linkage of exposure, contextual and confounder information for epidemiological or health risk assessment studies.
This course will be a mix of lectures, demonstrations and practical time for hands-on data analysis in ArcGIS and QGIS.
No prior knowledge of GIS is required for this intensive course.
Students will gain knowledge in the fundamentals of GIS for spatial data handling and analysis. By the end of the course, students will
- Understand how GIS can be used to enhance public health and epidemiological research;
- Be able to acquire, add, manipulate, visualise and map spatial data in a GIS; and
- Be able to perform basic spatial analyses in a GIS.
Geo-spatial methods for global health applications with focus on Disease Clustering (4 days course)
6 – 9 July 2020
Dr. Annibale Biggeri, University of Florence, Florence, Italy, and Dr. Toshiro Tango, Center for Medical Statistics, Teikyo University Graduate School of Public Health, Tokio, Japan
The ultimate goal of global health science is to improve health conditions for all people worldwide. In an increasingly interconnected world, tackling the emergence of disease outbreaks requires solutions that transcend national borders. To this end, understanding the spatial variation in disease risk and the exposure to environmental hazards has become increasingly important.
In this course, we introduce state-of-the-art methods in disease clustering and disease mapping, a sub-branch of spatial statistics whose focus is on hot spots identification and on the prediction of health outcomes and exposures within a geographical area of interest. These methods have found application in public health problems both in developing and developed countries.
Scanning for hot spots of disease cases in time and/or in space is essential part of epidemiological surveillance. In the last two days of the course we shall focus our attention on case studies of disease clustering. We will review relevant literature, highlight potentially misleading approaches and introduce update methodologies.In the first part of the course we will introduce geostatistical methods and we will review popular methods for disease mapping. In low-resource settings, household surveys are a fundamental tool to quantify the disease burden, while In developed countries, disease registries provide detailed information on individuals with a specific disease or condition. Bayesian modeling will be introduced and justified.
Specific extensions to active surveillance and high risk area profiling will be discussed. This section of the course will show the connections between the two approaches and present the course topics in a unique frame.
Modern mediation analysis (4 days course)
6 – 9 July 2020
Dr.Bianca De Stavola, UCL Great Ormond Street Institute of Child Health, London, UK, and Dr. Johan Steen, Ghent University, Ghent, Belgium
In most, if not all, of the empirical sciences, mediation analysis has become the applied practitioner’s primary statistical tool to improve one’s understanding of the processes or mechanisms through which a causal effect of interest comes about. Traditional methods for mediation analysis building on the linear structural equation modelling tradition from social sciences, however, often fall short, as they tend to produce estimates for mediated and direct effects that, given adequate adjustment for confounding, can only be assigned a well-defined and causal interpretation in strictly linear settings.
In this course, we will first highlight the limitations of traditional mediation analysis and then discuss the modelling and estimation framework that builds on a more formal approach to causal inference. Settings with multiple mediators and with time-varying mediators will also be reviewed, stressing the increasing complexities that these pose, while presenting some solutions.
Practical illustrations will be carried out using Stata and R. Hence proficiency in either software is expected, as well as some familiarity with the language of modern causal inference.
Modern time series methods for public health and epidemiology (4 days course)
6 – 9 July 2020
Dr. Antonio Gasparrini, London School of Hygiene & Tropical Medicine, London, UK, Dr. Ana Maria Vicedo-Cabrera, University of Bern, Bern, Switzerland, and Dr. Francesco Sera, London School of Hygiene & Tropical Medicine, London, UK
Time series analysis has become a key tool for epidemiological and public health research. In the last two decades, there has been an intense activity to develop more sophisticated study designs and statistical models for using time series data in health studies, with applications spanning various research areas. For instance, time series methods can now be applied for evaluating public health interventions, for assessing health effect associated to environmental stressors and climate change, and for quantifying beneficial or side effects of drugs or clinical practices.
This course will offer a thorough overview of established approaches and recent advancements in time series analysis for health research, including a theoretical introduction as well as practical examples in public health, environmental, clinical, cancer, and pharmaco-epidemiology. The sessions will cover standard time series designs for aggregated data, recent extensions for small-area and individual-level analysis, and applications involving state-of-the-art technologies such as remote sensing satellite measurements and real-time smartphone data.
The course will involve lectures followed by practical sessions using the statistical software R, to illustrate the use of time series analysis in various settings using real-data examples. While no previous knowledge on time series methods is expected, having basic experience on the use of R for epidemiological analysis is an advantage.
Genetic and Epigenetic Epidemiology (5 days course)
6 -10 July 2020
Dr. David Evans, University of Queensland, Australia, Dr. Gibran Hemani, University of Bristol, Bristol, UK, Dr. Rebecca Richmond, University of Bristol, Bristol, UK, and Dr Gemma Sharp, University of Bristol, Bristol, UK
Genetic epidemiology refers to the study of the role of genetic factors in determining health and disease in families and in populations. Genetic epidemiological studies have made substantial contributions to understanding the aetiology of complex traits and diseases, and hold great promise for personalised healthcare in the future. This course provides an introduction to the design, analysis and interpretation of genetic and epigenetic epidemiological studies of disease, with a focus on genome-wide and epigenome-wide association studies (GWAS and EWAS). Topics that will be covered include design and analysis of GWAS, imputation, meta-analysis, bioinformatic follow-up, whole genome and polygenic approaches including G-REML and LD score regression, epigenetics, EWAS, and Mendelian randomization (MR). As well as lectures, participants will gain practical experience in analysing genetic and epigenetic datasets. We will use the R statistical software package for the majority of analyses and participants will get plenty of hands on training in this package. By the end of the course participants should have a good working knowledge of concepts in genetic and epigenetic epidemiology, and will be able to perform analyses of genetic and epigenetic datasets