Life course epidemiology and public health

Abstract

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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Life course epidemiology and public health
Cornelia Wagner, Cristian Carmeli, Josephine Jackisch, Mika Kivimäki, Bernadette W A van der Linden, Stéphane Cullati, Arnaud Chiolero
Life course epidemiology aims to study the eect 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.
Introduction
Life course epidemiology aims to study the eect of
exposures across the life course (notably early in life) on
health, looking as far back as exposures during gestation
or in previous generations.1,2 It draws on expertise
from multiple scientific disciplines, ie, epidemiology,
sociology, psychology, biomedical sciences, and other
fields related to population health sciences. In the
biomedical sciences, Barker’s hypothesis of fetal
programming3 was crucial for the early development of
life course epidemiology, stating that fetal nutrition can
contribute to the risk of adult chronic diseases, such as
diabetes or hypertension. In the social sciences, alongside
social epidemiology, interest in long-term socio-
environmental exposures was notably introduced by
Elder in his study of Californian birth cohorts to
understand the social and health impacts of the Great
Depression.4 The term life course epidemiology was
coined in the 1990s to define a field of study interested in
early-life and later-life determinants of chronic diseases.5
Since then, life course epidemiology has contributed
substantially to the study of chronic diseases and has
gained popularity across epidemiology and public health.
Barker’s fetal programming hypothesis has grown into
the developmental origins of health and disease
approach in medical research, which places emphasis on
prenatal environmental exposures as determinants of
later-life health.6 This approach expands the classic
epidemiological and biomedical perspective of the crucial
role of risk factors during midlife as the causes of chronic
diseases in later life to exposure to risk factors at other
ages or other life stages.7 Life course research has been
made possible through the availability of prospective and
retrospective birth cohort studies and other large,
population-based, longitudinal studies (within and
across generations) that collect a wide range of individual,
biological, social, and environmental data over decades of
life. Beyond the analysis of longitudinal data, life course
epidemiology is a field in its own right with unique
theories, methodologies, and public health implications.1,5
Although life course epidemiology is established in
scientific research, its application to public health policy
making is less advanced. Possible reasons are the high
context specificity of some findings, the complexity of the
mechanisms involved, and the challenge of establishing
causality across the life course. Nevertheless, policy
making already benefits from life course epi demiological
concepts and findings, notably in the form of primordial
prevention, the prevention of exposure to risk factors.8,9
Reviewing how life course epidemiology helps design
prevention strategies for chronic diseases, how it changes
the way the cause of chronic diseases is understood, and
how it informs population-based, high-risk, and vulnerable
population preventive strategies is therefore important and
timely.
From life course models to policy making
Life course models
Life course research is based on a set of five basic
principles defined by Elder and Shanahan.10 These are:
lifespan development (human development and ageing
are lifelong processes not restricted to specific life
stages); agency (people have the capability to take actions
and make choices that shape their lives within the
constraints of environmental, social, and historical
contexts); time and place (every individual life course is
embedded within and influenced by its specific historical
time and place); timing (the same events and behaviours
can have dierent eects depending on when they
happen in the life course); and linked lives (people do not
experience life alone but influence each other through
shared interdependent relationships).
These principles have contributed to the development
of theoretical causal models that explain how exposures
across the life course cause health outcomes in later life.10
These models are simplistic by design to highlight
potential causal mechanisms underlying the associations
between exposures and health across the life course.11,12
In the life course epidemiology of chronic diseases, four
models are frequently used: the sensitive period model;
Lancet Public Health 2024;
9: e261–69
Population Health Laboratory
(#PopHealthLab), University of
Fribourg, Fribourg, Switzerland
(C Wagner MSc, C Carmeli PhD,
J Jackisch PhD,
B W A van der Linden PhD,
S Cullati PhD,
Prof A Chiolero MD PhD);
Department of Public Health
Sciences, Centre for Health
Equity Studies, Stockholm
University, Stockholm, Sweden
(J Jackisch); UCL Brain Sciences,
University College London,
London, UK
(Prof M Kivimäki PhD); Clinicum,
University of Helsinki, Helsinki,
Finland (Prof M Kivimäki);
Institute of Primary Health
Care (BIHAM), University of
Bern, Bern, Switzerland
(Prof A Chiolero); School of
Population and Global Health,
McGill University, Montreal,
QC, Canada (Prof A Chiolero)
Correspondence to:
Prof Arnaud Chiolero, Population
Health Laboratory
(#PopHealthLab), University of
Fribourg, Fribourg 1700,
Switzerland
arnaud.chiolero@unifr.ch

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the accumulation model; the pathway model; and the
social mobility model (figure 1).11,13
The sensitive period model focuses on the dierential
eect of an exposure depending on its timing. It posits
that there are some periods across the life course,
commonly in early life, during which an exposure has a
stronger eect on health than if it were to happen outside
of those periods. Gestation is a sensitive period for
multiple exposures.1 For instance, prenatal exposure to
maternal starvation during the Dutch famine (1944–45)
was associated with increased risk of later-life coronary
heart disease, obstructive airways disease, and decreased
glucose tolerance, depending on whether maternal
starvation happened in early, mid, or late gestation,
respectively.14 Life transitions are also typical sensitive
periods, such as the transition to motherhood, which is a
major biosocial event.15 Generally, there is the possibility
of recovering from the eect of an exposure during a
sensitive period. In contrast, an exposure during a critical
period is considered to have a more permanent eect.
For instance, lead exposure during early childhood, a
critical period for brain development, can result in
permanent cognitive impairments that persist into
adulthood.16 Identifying potential sensitive and critical
periods for disease risk factors across the life course aids
in the optimal timing of preventive interventions.
The accumulation model focuses on the accumulation
and duration of exposures rather than their timing. It
states that the accumulation of exposures across the life
course determines later disease risk. This eect can be
caused by an accumulation of dierent risk factors or by
exposure to the same risk factor over an extended period.
For instance, an accumulation across the life course of
socioeconomic disadvantage,17 low birthweight,18 physical
inactivity, and high salt intake during childhood and
adolescence,19 can result in hypertension in midlife.20
The relationship takes the form of a dose–response
association that incrementally builds towards a disease
state. Accumulation of risks can be linear or exponential.
Following this model, preventive interventions aim to
stop the accumulation of risk before the disease threshold
is attained.
The pathway model focuses on the sequential link
between multiple exposures. It is also known as the
chain-of-risk model since it states that each exposure to a
risk factor increases the likelihood of being exposed to
another risk factor.21
Finally, the social mobility model focuses on the
direction of change of an exposure and is used almost
exclusively for the study of the eects of socioeconomic
exposures. According to this model, social exposures are
states that individuals can transition in and out of:
individuals can move between dierent social classes or
income levels, and the direction of this change—upward,
downward, or non-mobile—determines their later
disease risk.22,23 In a Swedish study, the direction of
change between occupational classes between ages
25 years and 55 years was associated with myocardial
infarction risk.24 Specifically, moving from a non-manual
to a manual occupation in later life—ie, downward
mobility—was associated with an increased risk of
myocardial infarction compared with no change in
occupation class. Potential interventions could build
upon this knowledge by promoting policies that favour
upward social mobility in the population. One
shortcoming of the social mobility model is the challenge
of disentangling the eects of the final exposure, per se,
from the eects of the trajectory leading up to this last
exposure.25
These four models acknowledge the fundamental
social causes of disease that contextualise individual-level
determinants of health. Individual risk factors should be
contextualised by “attempting to understand how people
come to be exposed” or come to be put at the “risk of
risks” to design more eective preventive interventions.26
Furthermore, social factors such as socioeconomic status
can be considered fundamental causes of diseases, since
they determine people’s access to health-protective
resources, such as knowledge, money, power, prestige,
and beneficial social connections.26 If social conditions
truly put people at risk of risks, life course-informed
policies aiming to decrease health inequalities should
target social causes in addition to more proximal causes.
The vulnerable population preventive strategy is built
partly on this concept.26,27
Testing the value of these models has been made
possible by the availability of large, population-based
cohort studies in, for example, the UK (eg, the 1958
National Child Development Study and Lothian birth
cohort studies), Finland (eg, the Northern Finnish Birth
Cohort Study), and New Zealand (eg, the Christchurch
Figure 1: Life course models for the causes of chronic diseases
A, B, C, and D are exposure at different times during the life course. Arrows show
causal effects; dotted arrows signify weaker causal effects.11
Sensitive period model
Accumulation model
Pathway model
Social mobility model
Sensitive
period
Preconception Early life Midlife Later life
Chronic disease
A
A
A
B
B
B
C
C
C
D
ABC
ABC

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Health and Development Study).28 Birth cohorts typically
consist of population-based samples of individuals born
in a given period of time and followed up from birth or
later in life over many years, if not across generations.
The advancement of life course epidemiology has also
been facilitated by biobanks linked to cohorts, aiming to
assess the exposome of large samples of the population.29
The strength of such large-scale cohorts lies in the
collection of individual, biological, social, and environ-
mental data over long periods of time, spanning decades
of life. Together with the accumulation of these data
came the development of advanced statistical methods
for multicohort and big data research that make their
analysis possible.30
Life course perspective on chronic diseases
A life course epidemiological approach can be applied to
the study of any type of disease, but it has been especially
useful in the understanding and prevention of chronic
diseases. Life course epidemiology oers a framework
for examining the cause of chronic disease across life
stages with appropriate concepts and vocabulary
(appendix pp 1–2) and, as a result, shapes disease
definitions, health-related beliefs and fears, and
preventive strategies.31 In this section, we review how life
course epidemiology has changed the way hypertension,
breast cancer, and dementia are understood and its
impact on their prevention (figure 2). These examples
were chosen due to their high public health burden and
suitability for a life course perspective.
Hypertension
Hypertension is a state of sustained elevated blood
pressure and is a major modifiable risk factor for cardio-
vascular diseases—the leading cause of death world-
wide.20,32–34 The study of cardiovascular diseases lends
itself particularly well to a life course approach since
most cardiovascular diseases happen in later life and are
typically understood as the outcomes of a lifetime
exposure to causal risk factors, including smoking,
dyslipidaemia, obesity, diabetes, and hypertension.33,35
Although these risk factors were initially focused on
during midlife, a growing number of studies have
pointed at early life as a sensitive period for the
development of these factors, establishing their eect on
cardiovascular diseases in later life.9
Causes of hypertension can be identified across the
entire life course, starting at conception and the first
1000 days of life.36–38 Hypertension has been associated
with fetal exposure to maternal smoking,39 under-
nutrition while in utero,3 low birthweight,40 and
increased salt intake in the first months of life.19,41 In
midlife and later life, elevated blood pressure is a major
cause of cardiovascular diseases42 and a large number of
drug trials have shown that lowering blood pressure
reduces the occurrence of these diseases (and related
mortality)33,43 and dementia.44 Additionally, exposure to
hypertensive risk factors is at its peak during midlife
and later life, including high alcohol intake,45 high salt
intake,46 and high BMI.47
With the identification of risk factors across the life
course comes the opportunity for targeted life stage-
specific interventions, with the aim of directing the
health–disease trajectory towards an optimal path
(figure 3). An extensive and in-depth guide to possible
interventions is listed in the Lancet Commission on
hypertension’s call to action20 for a life course strategy to
address the global burden of hypertension. To
summarise, intervention strategies should be multi-
faceted and target prevention, diagnosis, and treatment
at the population and individual levels depending on the
absolute risk of cardiovascular diseases. Clinical
approaches at the individual level, including drug
treatments, should target subpopulations at high risk (ie,
people with a high absolute risk of cardiovascular
disease) typically in midlife and later life. At the
population level, interventions can be tailored to the life
course. At all life stages, primordial prevention can be
achieved via reduced salt intake, increased physical
activity, and improved dietary habits. Early in life and
during adolescence, the focus should be on eective
health education; screening for hypertension is not
Figure 2: Selected determinants of hypertension, breast cancer, and dementia risks across the life course
Some determinants have an effect during a specific period of the life course and others during multiple periods.
Socioeconomic status
Maternal blood pressure
Socioeconomic status
Birthweight
Physical activity and salt intake
Adiposity
Physical activity and salt intake
Alcohol intake
Physical activity and salt intake
Alcohol intake
Preconception Early life Midlife Later life
Hypertension
Family history Adiposity
Age at menarche
Age at first birth
Diet and physical activity
Alcohol intake
Adiposity
Breast cancer
Socioeconomic status
Family history
Socioeconomic status
Education
Blood pressure
Hearing loss
Alcohol intake
Depression
Physical activity
Social isolation
Dementia
See Online for appendix

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recommended during this period of life.48 In midlife and
later life, education should continue in the form of
wide distributions of evidence-based knowledge and
recommendations to promote cardiovascular health, and
screening for hypertension and treatment programmes
should be implemented.
Breast cancer
Breast cancer constitutes approximately 25% of all
cancer diagnoses in women and roughly 16% of cancer
deaths in women.49 A classic life course perspective on
breast cancer follows reproductive stages—ie, pre-
menarche, menarche to first birth, pregnancy, and post-
menopause.50 The eect of breast cancer risk factors
diers among these sensitive periods. For example,
adiposity in early life, during premenarche, has been
associated with lower risk of breast cancer,51,52 whereas
adiposity after menopause has been associated with
higher risk.53 Breast cancer can also be seen as the result
of accumulated risk factors, particularly in relation to
the timings of births and menarche. The Pike model
postulates that the rate of breast tissue ageing, a risk
factor for cancer development, slows down with each
birth and after menopause, and is highest in the period
between menarche and first birth.50,54 Thus, depending
on when a woman experiences menarche, and when
and whether she gives birth once or multiple times
could change her lifetime breast cancer risk. This
relationship exemplifies how identifying risk factors for
breast cancer is not enough—a life course perspective
can add the context needed to design targeted prevention
strategies.55
For the prevention of breast cancer, multiple windows
for intervention exist along the life course. Much
emphasis has been put on secondary prevention through
screening for early disease detection in midlife.
The US Preventive Services Task Force recommends
mammography screenings for women aged 40–74 years.56
The timings for screenings based on this traditional
approach of early disease detection are informed by
clinical trials.56 Developments in the life course
epidemiology of breast cancer oer new perspectives for
primordial prevention strategies that are set earlier in
life. Apart from genetic susceptibility and hormonal risk
factors, large population-based studies have suggested
that health behaviours (eg, alcohol intake, diet, and
physical inactivity) could be modifiable risk factors for
breast cancer and thus potential targets for preventive
strategies.50 Some studies suggest that environmental
exposures (eg, dioxins, air pollution, and heavy metals)
might also be involved.57 Hence, rather than being
limited to screening in midlife and later life, breast
cancer prevention could start in early life within an
eco-social preventive approach that targets both
health behaviours and environmental risk factors at a
population level.
Dementia
Worldwide, people live longer and are thus more
exposed to age-related diseases such as dementia.
Dementia is the loss of cognitive function typically
attributable to vascular and neurodegenerative brain
damage.58 The occurrence of dementia in later life is
aected by exposure to risk factors across the life course
that diminish cognitive reserves—ie, an individual’s
ability to cope with brain damage.59 Increasing and
maintaining cognitive reserves throughout the life
course could therefore prevent or delay the onset of
dementia.60
In early life, education (as a form of mental activity)
stands out as a target for intervention, since there is
consistent evidence for education having a protective
eect on later-life cognition,61,62 for example through its
association with healthier behaviours.63,64 Nevertheless,
mental activity might be beneficial across the entire life
course and not only in the form of formal education.
An individual participant data meta-analysis65 found
that people who perform cognitively challenging jobs
have a lower risk of dementia, regardless of their
education. Furthermore, there is evidence that the
longer people are exposed to socioeconomic hardships,
the lower their level of memory function and the higher
their rate of later-life memory decline.66 This evidence
indicates that interventions are possible at every life
stage, making dementia prevention a lifelong prospect
that should combine widespread social and public
health policies with individually tailored interventions
at dierent life stages.60,67
A life course perspective for the prevention of dementia
in early life, midlife, and later life has been adopted in
policy and clinical guidelines. The 2020 report of the Lancet
Commission on dementia prevention, intervention, and
care, for example, identified 12 potentially modifiable risk
Figure 3: Life course trajectories across the continuum of health and disease and how they are modified by
interventions applied during different periods of life
Depending on the timing and type of intervention, and the causal process at stake, the effect on the trajectory will
be different. The arrows signify which trajectory the interventions apply to.
Preconception Early life Midlife Later life
Intervention
Health
Disease
Average trajectory
Optimal trajectory
Trajectory following
an intervention
before conception
Trajectory following
an intervention in
midlife
Intervention

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factors and incorporated these into a life course model of
dementia prevention. These risk factors are, in early life:
low educational attainment; in midlife: elevated blood
pressure, hearing impairment, traumatic brain injury,
high alcohol intake, and obesity; and in later life:
depression, physical inactivity, diabetes, smoking, social
isolation, and air pollution.60 This model illustrates the
value of intervening early and continuously throughout
the life course.
Causality and the life course
Since the turn of the 21st century, developments in causal
inference methods based on observational data have
helped life course epidemiology move from a rich
conceptual way of thinking towards a truly preventive
strategy information tool. The three main data science
tasks in epidemiology are description, prediction, and
causality.68 Description aims to describe the world as it is,
prediction aims to predict how the world might be, and
causality aims to estimate how an outcome would change
if we were to intervene on an exposure. One major issue
in epidemiology is the enduring confusion between
association and causality when explicit causal inference
is necessary to guide prevention.7 This issue is especially
true in life course, social, and environmental
epidemiology in which evidence stems largely from
observations and rarely from experiments such as
randomised trials.69–71
Within observational studies, an increasingly adopted
approach is based on the potential outcomes or
counterfactual framework, with statistical models
informed by expert knowledge encoded into graphical
causal models and statistical estimation (notably via
G methods).72,73 This approach has advantages for life
course epidemiology compared with traditional
regression-based or adjustment-based methods, as these
informed statistical models can better handle exposure-
induced or time-varying measured confounding and
reduce over-adjustment bias or mutual adjustment
fallacies through appropriate covariate selection.74,75 Other
methods useful for life course research encompass
causal evaluation of risk factors via instrumental
variables (eg, genetic and non-genetic instruments) and
policy evaluations via econo metric methods (eg,
dierence-in-dierence, regression dis continuity, and
interrupted time series).76–79
For instance, the eect of BMI on all-cause mortality is a
classic and highly complex question in life course research,
which is plagued by confounding and reverse causation
issues that are intractable by typical epidemiological
methods.80 Instrumental variables help overcome these
limitations. In a large, population-based, intergenerational
prospective study, when ospring BMI was used as an
instrumental variable for paternal BMI, the estimated
association between BMI and paternal cardio vascular
disease mortality (hazard ratio [HR] per standard deviation
of BMI 1·82, 95% CI 1·17–2·83) was stronger than that
indicated by the directly observed association between
individuals’ own BMIs and their cardiovascular disease
mortality (HR 1·45, 1·31–1·61).80 Another example is how
the life course mendelian randomisation technique can
enlighten complex time-varying eects of age-dependent
lifestyle factors on risk of chronic disease.81
Furthermore, advances in biobanks and omics have
provided capacity for the joint measurement of thousands
of biomarkers, such as proteins and metabolites, from a
single stored sample. This increased availability of
biomarkers has allowed for a better understanding of
biological mechanisms across the life course, linking an
exposure and a disease through the analysis of the
mediating role of these biomarkers.65,81
Policy implications
To translate life course research into policies for chronic
disease prevention, what determines health on a
population level and how to intervene to improve it
must both be made clear.82 A relevant framework is the
eco-social perspective that frames how health stems
from interactions with the social environment.83,84 In
this perspective, the individual is embedded within
multiple social circles, starting from the immediate
family, and moving on to include peers, neighbourhoods,
cities, and countries of residence. Each level has an
influence on health at a personal level and therefore
determines the patterns of chronic diseases at a
population level.
When considering eco-social and life course
perspectives together, dierent strategies for chronic
disease prevention emerge that target dierent eco-social
levels across the life course, and these preventive
interventions can either work together or independently
of each other. We give an example of prevention strategies
for hypertension from an eco-social and life course
perspective (figure 4).20 In early life and in immediate
social surroundings, policies targeting socioeconomic
inequalities can create healthy family environments that
allow children to engage in education and leisure
activities, and to learn health-promoting behaviours early
in life. Moving up a level, community-based projects can
raise awareness of hypertension and grant universal
access to screening and anti-hypertensive drugs in
midlife and later life. On a city level, health-promoting
urban spaces (eg, cycle lanes and walkable cities) can
facilitate an active lifestyle in the entire population from
childhood to old age. On a country level, legislators can
protect the health of the population at all life stages via
regulations, such as mandated salt limits in food
production. Finally, at all ages and on a country-wide
level, eective surveillance of hypertension and its risk
factors are needed to ensure that prevention works.
Challenges
Research in life course epidemiology faces several
challenges. Longitudinal cohorts are expensive and

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time-consuming to establish, and long periods of follow-
up are needed before valuable data are available. These
requirements, in turn, often result in life course
research being questioned for its reproducibility and
generalisability—can findings from a generation born
50 years ago be applied to the current generation?85
Other study designs, such as case–control studies or
trials, are limited when it comes to addressing life course
epidemiological research questions.86 Measure ment of
exposures across the life course is also a major source of
bias. This results in life course research often having to
rely on incomplete or poor-quality data.
Another major challenge is that life course
epidemiology often deals with weak eect sizes at an
individual level.87 As for all fields of epidemiology, weak
eects can be dicult to distinguish from bias
introduced by study design, measurement errors,
analyses, and residual confounding.88 The best ways to
mitigate these issues are the same as for other
epidemiological fields: use multiple approaches to verify
results; strengthen statistical knowledge to prevent
misuse of analyses; place importance on transparent
and reproducible study protocols; and give researchers
the right incentives to favour quality over quantity when
publishing research.88,89 However, even with adequate
study designs and analyses, weak eect sizes across the
life course complexify policy making in terms of
deciding where, when, and how to intervene, especially
within a consequentialist perspective.90
One important question is to what extent life course
epidemiology informs population-level or individual-level
preventive interventions. Epidemiology in general, and
life course epidemiology in particular, is primarily focused
on population-level or group-level eects, providing
evidence for population-wide and high-risk preventive
interventions. Even a small eect size at an individual
level might have major impacts at a population level if a
large share of the population is exposed to the determinant
in question. This fact is a major argument for the
population-based preventive strategy advocated by Rose,82
which is built on the insight that both risk and health are
a continuum distributed in the population, implying that
targeting the whole population rather than only the people
at high risk of a disease is better for reducing disease
burden. Many exposures examined in life course studies
are highly prevalent, and this prevalence is part of the
reason why a life course perspective is increasingly
adopted for optimising the timing of population-level
preventive programmes.91,92
The life course approach is also increasingly mentioned
in clinical guidelines93 and family medicine,94 but the
potential benefit at this level should not be over estimated
because it can lead to inecient pseudo-high-risk
preventive strategies.95 Evidence from life course and
social epidemiology also informs vulnerable population
preventive strategies, promoting the mitigation of health
inequities by tailoring preventive strategies towards
vulnerable populations.27
Finally, the translation of life course epidemiological
findings into preventive strategies is a balance between
precision and simplicity. Policy makers could aim for
precision, for example by acting early in life to reduce
risk factor exposure during a sensitive period, but they
might do so at the cost of simplicity (ie, by not acting at
all ages to reduce overall risk exposure). For example,
identifying smoking as a major risk for cardiovascular
diseases in midlife does not mean that smoking
prevention should not target other life periods. This
translational challenge extends towards populations as
well, for which the right balance between segmentation
into subpopulations with targeted needs and wide
population-based interventions needs to be found.96 A
further challenge with implementing a life course
approach in policy is the diculty of persuading both
the public and policy makers to embrace preventive
interventions whose benefits can take decades to
appear.
Search strategy and selection criteria
The starting point of study selection for this Review was based on the expertise of all
authors, who listed important life course epidemiological concepts that needed to be
addressed. We identified key studies, reviews, or textbooks in our fields (appendix p 3),
which were summarised and placed into context with the other papers included in this
Review. We conducted a broad search on MEDLINE and Google Scholar to identify
additional papers and reviews on the topic of life course epidemiology. We considered only
full-text articles published in English; there were no limitations regarding article
publication dates. We concentrated our Review on influential concepts within life course
epidemiology from the past three decades, aware of the potential bias stemming from a
subjective study selection. Furthermore, we particularly considered studies and reviews on
the life course epidemiology of hypertension, breast cancer, and dementia. These diseases
were selected due to their high public health burden and suitability for a life course
perspective. References were chosen for their importance, ease of access, and usefulness to
readers who might want further high-quality reading options in this field.
Figure 4: Examples of hypertension prevention across eco-social and life course dimensions83
Perinatal
period
AdolescenceChildhood Midlife Later life
Life course perspective
Eco-social perspective
Individual behaviour
Family
Peers
Country
City
Reduce socioeconomic
inequalities to create
healthy family
environments for
children to thrive
Raise awareness of
hypertension via
community-based
projects
Give universal access to
hypertension screening
and treatment
Build health-promoting urban spaces that facilitate and
promote physical activity (eg, cycle lanes and walkable cities)
Enact laws in food production that reduce salt intake and monitor
hypertension and its risk factors via surveillance systems

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e267
Conclusions
Over the past three decades, research in life course
epidemiology has flourished. The origins of many
chronic diseases can now be traced back to early life,
allowing for new intervention strategies that target
specific times during the life course. With examples
from research on hypertension, breast cancer, and
dementia, we have described how the field has grown
from the idea of fetal programming and findings from
social epidemiology to a multidisciplinary research
approach that informs public health policy making.
Life course epidemiology oers the evidence needed
to design primordial prevention of chronic diseases. It
has brought new understanding of the transitions
between health and disease, which are now conceived
more than ever as continuums, linking the life course
exposome to biomarkers, diseases, disability, and death.
This field refines Rose’s population-based preventive
strategy82 by tailoring interventions to distinct life stages
and, to a lesser extent, informs high-risk preventive
strategies.
The future of the field is promising and will most likely
be characterised by even stronger multidisciplinary
collaborations, particularly by advances in causal
inference and a broadening of research foci to capture
disease trajectories and multimorbidity in addition to
single diseases, facilitating a more comprehensive
evaluation of morbidity associated with life course
exposures.
Contributors
CW, CC, SC, and AC conceptualised this Review. CW wrote the first draft.
All authors were involved in draft revisions and approving the final draft
for submission. All authors approved the final manuscript and accept
responsibility for the decision to submit for publication.
Declaration of interests
We declare no competing interests.
Acknowledgments
JJ was supported by the Swiss National Science Foundation (grant
number 208205). MK was supported by the Wellcome Trust (grant
number 221854/Z/20/Z), Medical Research Council (grant number
R024227), National Institute on Aging (grant numbers R01AG062553
and R01AG056477), and Academy of Finland (grant number 350426).
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