Naja Hulvej Rod

• Presentation
• CV
• Research outputs
• Keywords
• Most downloaded

Primary fields of research

My primary research interests are centered around complexity and lifecourse approaches to understand long-term health consequences of stress, impaired sleep and social adversities. I also have a keen interest in epidemiological methododology including causal inference approaches.   

Current research

I am Head of the Section of Epidemiology, which is a strong interdisciplinary and international environment that contributes to the development of the field of theoretical epidemiology, with a specific focus on causal inference, complexity and life course epidemiology. 

I am also heading an interdisciplinary research group in Complexity and Big Data in Epidemiology. Health is a complex phenomenon and the aim of the research group is to study the social and biological factors determining health and disease and to elucidate the underlying behavioral, psychological, and physiological mechanisms that might explain these effects. I specifically aim to investigate the health consequences of the accumulation of childhood social adversities and the complex interactions between work, life and health as they evolve across the life span. I have solid experience in working with longitudinal datasets, register-based research and complex modelling including social influences and group dynamics. I also have a particular interest in causal inference and in development and application of new methods in epidemiology, such as interaction and mediation analyses. To embrace complexity in epidemiology I actively explore new sources (e.g. smartphones) of ‘big data’, incorporate system theory thinking and leverage insights across disciplines. I have also been involved in several citizen science project with a direct societal engagement and impact.

For details about my research group please visit: https://publichealth.ku.dk/about-the-department/section-epidemiology/research-epi/complexity-and-big-data/

Teaching

PhD courses

• From Research Idea to Scientific Paper in Public Health
• Drawing Causal Inference from Epidemiological Data
• Introduction to Directed Acyclic Graphs