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The present volume contributes to life course research with a specific emphasis on health trajectories as they unfold along individual lives in specific socio-historical contexts. It brings together a range of contributions from different disciplines to shed light on the complex social and biological processes which influence people’s health. The Introduction addresses the goals of life course epidemiology and the contribution which insights from the life course perspective can bring to the study of health. Theoretical, methodological and policy considerations associated with life course epidemiology are presented to discuss the current state of the field and expected developments to come. The Introduction finally presents the different contributions of this volume.

The last few decades have seen a dramatic rise in the prevalence of overweight and obesity in most high-income countries. This rise has been seen across all ages, even in very young children. Childhood obesity has both immediate and long-term health consequences and is now recognised as one of the most important public health concerns of our time. In this chapter, we take a life course perspective to review the evidence on trajectories and transitions in childhood and adolescent obesity. Understanding the life course aetiology of obesity is essential to inform prevention strategies – for example, knowing how the incidence of obesity changes across childhood and adolescence can be informative about the potential impact of intervening in different age groups. In this chapter, we review the evidence on the ages at which children tend to become overweight or obese, and the degree to which obesity persists once established. We then go on to discuss the importance of applying a life course perspective to the study of childhood and adolescent obesity, including the possibility that distinct adiposity trajectories have different long-term health consequences and the fact that longitudinal studies enable a finer analysis of the determinants of obesity than is possible from cross-sectional studies, improving aetiological insight. In the concluding part of this chapter, we reflect on the importance of a life course perspective in studies of childhood and adolescent obesity, and discuss future directions for life course studies in this area.

Oral diseases refer to conditions of the teeth, gums and mouth, and include dental caries, periodontal disease, and oral cancers. The impact of these conditions on quality of life is high, they are very common and their treatment is costly, therefore they are considered a major public health problem. Oral diseases are socially patterned, disproportionately affecting socially disadvantaged and marginalised populations. Because oral health and general health are inextricably linked and share common risk factors, caries and periodontal disease are useful markers of general health, and overall patterns of health inequalities. The literature on life course epidemiology applied to oral health is still limited, although there is a strong argument for studying oral diseases within a dynamic life course framework: they are chronic in nature and cumulative over time. Critical periods, as well as accumulation of risk models are applicable to oral diseases. Given that childhood diet and oral hygiene are related to socioeconomic and psychosocial factors, and that tooth loss is irreversible, adult oral health is rooted in early life conditions, while upward and downward social mobility influences oral health trajectories. In this chapter we review the theoretical and empirical developments in life course research on oral health, and suggest ways forward.

The growth of a child and the amount of weight gained across the life course is associated with risk for many chronic degenerative diseases. The life course approach to this area of epidemiological research has burgeoned over the last 25 years, since the initial observation of an inverse relationship between birth weight and coronary heart disease. We review the substantial amount of published research to demonstrate which age-related body size are indicative of increased risk of cardio-metabolic diseases such as coronary heart disease, stroke, and type two diabetes. Association does not mean causation. The chapter thus also provides an overview of the key sociocultural and other factors responsible for deleterious trajectories, and the biological pathways through which they act, to show how the trajectories are driven by biology but modifiable by the environment. Attention is paid to critical periods and transitions, both biological (e.g., puberty) and behavioural (e.g., marriage), and the influences that these might have on an individual’s trajectory. We highlight the importance of life course trajectories of body size and their associations with trajectories of markers of cardio-metabolic health such as blood pressure and lipids, and the need to integrate biological and social research to move towards a more complete understanding of cardio-metabolic disease processes and, ultimately, how to delay the onset of disease.

This chapter describes our longitudinal registry studies examining the effect of social deprivation on longitudinal clinical outcomes, healthcare use and employment opportunities in people with cystic fibrosis (CF). Because CF is genetically determined, this offers an opportunity to investigate the impact of deprivation on health and social outcomes, in a chronic condition without a socio-economic gradient in incidence. It helps in understanding how and when health inequalities are generated, in a chronic disease of childhood onset. The analyses show that children and adults with CF from more disadvantaged areas in the UK have worse growth and lung function compared with people from more affluent areas. These inequalities appear early, but do not widen with advancing age in the UK. In terms of use and access to treatments in the British National Health Service (NHS), more disadvantaged CF patients are more likely to receive antibiotic and nutritional treatments, after adjusting for disease severity, but less likely to receive inhaled therapies such as DNase. This inequality becomes more evident after transition to adult care. In adults with CF, greater deprivation, disease severity, and time in hospital all reduce employment chances. We discuss the implications of these findings for public health policy and care in cystic fibrosis.

The aim of this book chapter is to outline how the life course perspective can move forward the debate on socioeconomic inequalities in preventive health care use. Recent theoretical developments in medical sociology, including health lifestyle theory and cultural health capital theory, have implicitly encapsulated a longer-term view of an individual’s life, in order to develop a better understanding of the social causes of good health and conversely illness. I will elaborate more explicitly on how the five central principles of the life course perspective apply to preventive health care use, using the empirical example of mammography screening. Central and unanswered questions pertain to (i) the life stages that are important in the development of cultural health capital or a healthy lifestyle (life-span development); (ii) the temporality of socioeconomic inequalities in preventive health care (timing); (iii) the impact of different socialization contexts for healthy lifestyles or cultural health capital (structure-agency debate); (iv) the change in preventive health care use across policy implementations (time and place); and (v) the role of significant others for health care use (linked lives).

Body mass index (BMI) is an important health indicator that changes with age and may be shaped in important ways by prior military service. While a high proportion of older men in the United States served in the military, to date, there has been no longitudinal, population-representative study of veteran status differences in men’s mid- to late-life BMI trajectories. In this chapter, we use data from the 1992–2010 Health and Retirement Study (HRS) and growth curve models to examine veteran status differences in mid- to late-life BMI trajectories for cohorts of men born in the United States during the first half of the twentieth century. Without any controls in the models, veterans exhibit lower BMI, on average, than non-veterans. Once we add controls for birth cohort, early-life characteristics that occur prior to military service, potentially mediating mid- to late-life characteristics, and methodological controls for proxy report, attrition, and death during the study period, the effect of veteran status is small, marginally significant, and positive—net of other factors, veterans are marginally heavier than their non-veteran counterparts. Taken together, our analyses demonstrate the large effect of the secular trend in increased weight across the population—younger cohorts are substantially heavier than older cohorts regardless of their veteran status—and a substantively small but consistent, positive intra-cohort effect of veteran status.

Life course researchers often collect longitudinal data by assessing repeatedly, over long time spans, the same individuals. Such data are inherently dependent, and thus cannot be analyzed with standard classical models, like ordinary least squares regression, because statistical inference about the estimated parameters would be incorrect. In this chapter we present two related families of statistical models for longitudinal data: linear mixed-effects models and structural equation models. Both classes of models allow analyzing quantitative longitudinal data and explicitly define parameters related to both stability and change processes. The models also allow studying interactions between individual and contextual characteristics, both of which may be stable in time or vary across time. Advantages and recent extensions of these models include, among others, (a) statistical advances to cope with incomplete data, without the need to impute incomplete data, nor to limit analyses to complete cases; (b) multivariate specifications, to study how multiple dimensions of one’s life may change in parallel or even exert reciprocal influences; (c) multiple group analyses, to compare groups of known membership; (d) latent class analyses, to uncover previously unknown group membership according to specific statistical features. We will discuss similarities, advantages, and drawbacks of both families of models and illustrate them by analyzing public health data from the Swiss Household Panel.

When analysing individual trajectories across the life course, the fact that repeated observations of individuals are not independent (i.e. correlated) should be taken into account. Several techniques to do this are available, such as mixed models, MM and latent growth models, LGM. These models can also elegantly incorporate different stages of the life course, including childhood, adolescence and adulthood in the modelling process. MM do so by the inclusion of a ‘time’ variable denoting each stage in the model and LGM can be conducted in a piecewise manner, where each ‘piece’ represents a life course stage. Moreover, both techniques can further be extended to allow for possible heterogeneity in health trajectory (shape), but do so in different ways. MM can include random slopes to account for heterogeneity in growth; LGM can be extended into latent class growth models to allow for the possible revelation of subgroups of individuals determined by the data with distinct health trajectories across the life course.

This chapter will explain and compare the two techniques using existing life course data of the Amsterdam Growth and Health Study cohort and combines a methodological focus with empirical findings.

This chapter considers age, period and cohort (APC) as different sources of health-related change. Age (or, life course) effects are individual, often biological, sources of change, whilst periods and cohorts can be thought of as social contexts affecting individuals that reside within them. Due to the mathematical confounding of age, period and cohort, careful consideration of each is important – otherwise what appears to be, for example, a period (year) effect could in fact be a mixture of age and cohort processes. Naive life course approaches could thus produce misleading results when APC effects are not all considered. However, the mathematical confounding also often makes modelling all three effects together impossible, and the dangers of attempting to do so, or of ignoring one effect without critical forethought, is illustrated through the example of the obesity epidemic. This example uses Yang and Land’s Hierarchical APC model which it is claimed (incorrectly) solves the identification problem. Finally, we suggest a flexible multilevel framework that extends Yang and Land’s model, and by making relatively strong assumptions (in this case that there are no long-run period trends) can model age, period and cohort effects robustly and explicitly, so long as those assumptions are correct. This is illustrated using health data from the British Household Panel Survey. We argue that this theory driven approach is often the most appropriate for conceptualising APC effects, and producing valid empirical inference about both individual life courses and the spatial and temporal contexts in which they exist.