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Life course epidemiology and public health
Open accessAbstract
Life course epidemiology aims to study the effect of exposures on health outcomes across the life course from a social, behavioural, and biological perspective. In this Review, we describe how life course epidemiology changes the way the causes of chronic diseases are understood, with the example of hypertension, breast cancer, and dementia, and how it guides prevention strategies. Life course epidemiology uses complex methods for the analysis of longitudinal, ideally population-based, observational data and takes advantage of new approaches for causal inference. It informs primordial prevention, the prevention of exposure to risk factors, from an eco-social and life course perspective in which health and disease are conceived as the results of complex interactions between biological endowment, health behaviours, social networks, family influences, and socioeconomic conditions across the life course. More broadly, life course epidemiology guides population-based and high-risk prevention strategies for chronic diseases from the prenatal period to old age, contributing to evidence-based and data-informed public health actions. In this Review, we assess the contribution of life course epidemiology to public health and reflect on current and future challenges for this field and its integration into policy making.
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“This approach recognizes that health and disease are influenced by the complex interplay of various factors across an individual’s life span and that the timing and duration of these exposures can have critical implications for future health outcomes [1].”
From Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions (2024) · cites this paper as [1]
In the citing paper
“Life-course epidemiology is a field of study that examines the long-term effects of biological, behavioral, and social exposures during gestation, childhood, adolescence, and adulthood on the development of chronic diseases later in life [1].”
From Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions (2024) · cites this paper as [1]
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Metadata
- Authors
- Wagner, C., Carmeli, C., Jackisch, J., Kivimäki, M., van der Linden, B. W. A., Cullati, S., & Chiolero, A.
- Affiliations
- University of Fribourg (CH)
- Venue
- The Lancet Public Health
- Publisher
- Elsevier BV
- Published
- 2024-04
- Volume
- 9
- Issue
- 4
- Pages
- e261-e269
- ISSN
- 2468-2667 (ISSN-L)
- OpenAlex
- W4393246597
- Cited by
- 188
- References
- 94
- Citations by year
- 2026: 102 · 2025: 68 · 2024: 18
- FWCI
- 17.94
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- The Lancet Public Health (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Bern Open Repository and Information System (University of Bern) (submittedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Työväentutkimus Vuosikirja (submittedVersion)
- Open Access CRIS of the University of Bern
- Funders
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (208205)
- Medical Research Council (R024227)
- National Institute on Aging (R01AG062553, R01AG056477)
- Wellcome Trust (221854/Z/20/Z)
- Research Council of Finland (350426)
- Topics
- Social Sciences › Health › Health disparities and outcomes
- Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Health, Environment, Cognitive Aging
- Health Sciences › Medicine › Epidemiology › Historical and modern epidemiology studies
- Keywords
- life course epidemiology, chronic disease prevention, primordial prevention, hypertension, breast cancer, dementia, causal inference, social determinants of health, health behavior, socioeconomic conditions, prenatal exposure, public health policy
- MeSH terms
- Chronic Disease, Female, Humans, Life Change Events, Pregnancy, Public Health, Risk Factors, Causality
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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