Naja Hulvej Rod
Bartholinsgade 6Q, 2. sal, 1356 København K, 24 Øster Farimagsgade 5, Bygning: 24-2-16
Naja Hulvej Rod is Professor of Epidemiology and Chair of the Section of Epidemiology, University of Copenhagen. Her research deals with large public health challenges including sleep, health inequality, young adult health, and early life adversity and it is focused on causal inference, complexity, and life course mechanisms in health research. She is leading the interdisciplinary Complexity and Big Data Group, which aims at studying the social and biological factors determining health and disease across the life span. She has a particular interest in causal inference theory and how it intersects with methodological insights from complex systems theory. She has extensive expertise in working with longitudinal datasets, register-based research and complex modelling including social influences and group dynamics. To embrace complexity in epidemiology, she actively explores new sources (e.g., smartphone tracking and geocoding) of ‘big data’, incorporate systems thinking and leverage insights across disciplines, and she has been involved in several citizen science projects with a direct societal engagement and impact. Naja Hulvej Rod has participated in numerous scientific boards and committees across Europe, including the Swedish Research Council, the Finnish Research Council, and the French Health Data Hub. In 2022, she was awarded the Elite Researcher Prize, one of the highest academic honours in Denmark.
Primary Investigator of the Danish Life Course (DANLIFE) Cohort (www.danlife.ku.dk), which is a register-based cohort of more than 2 million individuals with multi-dimensional exposome data covering the totality of measured lifetime exposures across multiple social, environmental and biological dimensions to study the health consequences of social and environmental adversities.
Primary investigator for the citizen science research project ‘Standing together – at a distance: How Danes are living with the Corona Crisis’ (https://coronaminds.ku.dk/), which documents the public health effects of the crisis.
Primary investigator for the SmartSleep program (www.smartsleep.ku.dk), which is a multi-sourced study on mobile phone use, sleep and health.
FIVE KEY PUBLICATIONS
- Rod NH, Bengtsson J, Budtz-Jørgensen E, Clipet-Jensen C, Taylor-Robinson D, Nybo Andersen AM, PhD, Dich N, Rieckmann A. Trajectories of childhood adversity and mortality in early adulthood: A population-based cohort study. The Lancet 2020;396:489-497.
- Elsenburg LK, Rieckmann A, Nguyen TL, Bengtsson J, Andersen AN, Taylor-Robinson D, Lange T, Rod NH. Mediation of the parental education gradient in early adult mortality by childhood adversity: a population-based cohort study of more than 1 million children. Lancet Public Health. 2022 Feb;7(2):e146-e155. doi: 10.1016/S2468-2667(21)00275-9. PMID: 35122758.
- Otte Andersen T, Skovlund Dissing A, Rosenbek Severinsen E, Kryger Jensen A, Thanh Pham V, Varga TV, Hulvej Rod N. Predicting stress and depressive symptoms using high-resolution smartphone data and sleep behavior in Danish adults. Sleep 2022 Jun 13;45(6):zsac067. doi: 10.1093/sleep/zsac067. PMID: 35298650.
- Rod NH, Bengtsson J, Elsenburg LK, Taylor-Robinson D, Rieckmann Hospitalisation patterns among children exposed to childhood adversities: a population-based cohort study of half a million children. The Lancet Publ Health 2021. https://doi.org/10.1016/S2468-2667(21)00158-4.
- Varga TV, Bu F, Dissing AS, Elsenburg LK, Bustamante JJH, Matta J, van Zon SKR, Brouwer S, Bültmann U, Fancourt D, Hoeyer K, Goldberg M, Melchior M, Strandberg-Larsen K, Zins M, Clotworthy A, Rod NH. Loneliness, worries, anxiety, and precautionary behaviours in response to the COVID-19 pandemic: A longitudinal analysis of 200,000 Western and Northern Europeans. Lancet Reg Health Eur. 2021 Mar;2:100020. doi: 10.1016/j.lanepe.2020.100020. PMID: 33870246; PMCID: PMC8042675.
Undervisnings- og vejledningsområder
- From Research Idea to Scientific Paper in Public Health
- Drawing Causal Inference from Epidemiological Data
- Introduction to Directed Acyclic Graphs