Summer School on Health Inequalities: from Sources to Statistical Models

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Course description

This is a two-week intensive course (70hs), held in Barcelona as part of the COST-Action GREATLEAP, co-organized by the Centre for Demographic Studies (CED), the Asociación de Demografía Histórica and HiDo network.

This course is designed to strengthen students’ statistical skills in the collection, construction, and analysis of historical individual mortality data, which usually includes causes of deaths, with the aim of investigating the roots and drivers of health inequalities across regions and countries in Europe and beyond. Through hands-on work with real-world datasets, students will apply key analytical methods and explore long-term patterns in population health, and become initiated in R.

An interdisciplinary approach is central to the course, integrating insights from demography, statistics, history, sociology, economics, and political science to provide a comprehensive perspective on methods for exploring the social and structural determinants of health.

To register for this course, submit your application via jpujadasmo@uoc.edu, including a one-page CV and one-page motivation statement, and (if relevant) a request for funding support.

If you have any questions, please contact the GREATLEAP chair Tim Riswick (tim.riswick@ru.nl) or the local organiser Joana Maria Pujadas (jpujadasmo@uoc.edu).

Content

First week: The course will begin by focusing on developing skills for building databases, with particular attention to students’ own data. Students will also be introduced to using R, ensuring they are prepared to use the program in the second week for learning statistical models to study health inequalities.

Day 1: Individual-level Causes of Death Sources for Studying Health Inequalities: Exploring death parish/civil registers, hospital records, and “Bring Your Data”. R for Historians (Working with Real Data / Your Data)
Day 2: Procedures for Building Individual Databases: Handwriting recognition algorithms and relational databases. R for Historians (Working with Real Data / Your Data). Social Activity to Build Cohesion Among Students: Visiting a historical archive.
Day 3: Data Harmonization: Standardizing variables and coding (ICD10, HISCO, HISCLASS, etc.). R for Historians (Working with Real Data / Your Data).
Day 4: Record Linkage: Deterministic and probabilistic record linkage. R for Historians (Working with Real Data / Your Data)
Day 5: Individual Database Formats: Standardizing formats—Intermediate Data Structure. R for Historians (Working with Real Data / Your Data).

Second Week: The second week of the course will focus on providing students with a solid foundation for estimating statistical models to analyse health inequalities, using R.

Day 1: Direct and Indirect Methods for Analysing Mortality: Life tables, age-specific mortality rates, etc.
Day 2: Decomposition Methods: Arriaga’s method
Day 3: Essential Multivariable Methods: Linear, logit, and multinomial regressions
Day 4: Survival Analysis: Kaplan-Meier, Cox regressions, and competing risks
Day 5: Working on and presenting the final assessment

More information available at this link.

Learning Highlights

At the end of the course, students will have discussed the implications of historical mortality sources for the study of health inequalities and have learned how to build a relational database from original source materials. Furthermore, they will have acquired a solid foundation in both basic and advanced methods for the analysis of aggregated and individual-level mortality data. Finally, students will become familiar with R to fully engage with this exciting program.

Target audience

This course will be useful to PhD students, postdoctoral fellows, early-career researchers, and professionals with an interest in historical demography, public health, epidemiology, or related fields.

Required training or equipment

Basic familiarity with quantitative methods is helpful but not required.

Participants are expected to bring their own laptop.

Lecturer

Joana Maria Pujadas

Joana Maria is an ICREA Academy Researcher, Associate Professor, and Associate Dean for Research at the Universitat Oberta de Catalunya, and Head of the Historical Demography area at the CED. Her research focuses on the study of epidemic Dynamics, and; intra- and intergenerational transmission of social status; and the application of AI to the construction of demographic.

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Tim Riswick

Tim is an Assistant Professor of Historical Demography at Radboud University, Nijmegen. His work examines the emergence of health inequalities in 19th and 20th-century societies, focusing on epidemics, cause-specific mortality, and hospital patients.

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Gabriel Brea-Martinez

Gabriel is an Associate Professor at the Universitat Oberta de Catalunya. His research focuses on long-term socioeconomic inequality and social mobility, spanning from preindustrial and industrial societies to contemporary periods, with particular attention to their links with demographic behavior.

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Jordi Gumà-Lao

Jordi is currently a Ramón y Cajal Fellow Research Scientist at the Centre d’Estudis Demogràfics (CED) in Barcelona, Spain, and a Guest Researcher at the Centre for Demographic and Ageing Research (CEDAR) in Umeå (Sweden). His research is grounded in the fields of sociology of health and population studies, with a focus on gender perspectives.

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Enrique Acosta

Enrique is a demographer and Ramón y Cajal Fellow Research Scientist at the Centre d’Estudis Demogràfics (CED) in Barcelona, Spain. His research focuses on mortality analysis, with a particular emphasis on the demographic impacts of crises, including violence, armed conflict, epidemics, pandemics, and substance abuse.

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Katarina Matthes

Katarina is a Senior Researcher at the Institute of Evolutionary Medicine, University of Zurich. She has a multidisciplinary background in statistics, historical epidemiology, and (historical) demography. Her research focuses on multiple aspects of past pandemics and on social and demographic inequalities in mortality trends in Switzerland.

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Adrià Molina

Adrià is a Ph.D. Candidate in the Document Analysis Group at Universitat Autònoma de Barcelona. He holds a B.S. in Data Engineering and an M.S. degree, and his research focuses on cultural heritage, responsible AI, data governance, retrieval systems, and data visualization.

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Dídac Láinez-Moreno

Dídac is a research assistant at the Universitat Oberta de Catalunya and collaborates with the Centre d’Estudis Demogràfics, which specializes in historical demography. He holds a Master’s degree in History of Science and a Bachelor’s degree in Statistics and Sociology.

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