Concept, distinction among growth, development and maturation
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What is a Life Course Approach to Chronic Disease Epidemiology?
Over the last few years there has been increasing interest in conceptualizing disease aetiology within a life course framework.1,2 This approach is not new to Public Health or unique to epidemiology (see below). However, its current resonance and interest within epidemiology reflects the challenging theoretical framework this approach provides. This issue of the International Journal of Epidemiology has several papers with a ‘life course theme’. This accompanying editorial is intended to highlight what we believe are the key conceptual issues around life course epidemiology. We have chosen to use examples from chronic disease epidemiology, but this approach is also applicable within the context of infectious diseases3 and wider notions of health and wellbeing.4
We have defined a life course approach to chronic disease epidemiology1 as the study of long-term effects on chronic disease risk of physical and social exposures during gestation, childhood, adolescence, young adulthood and later adult life. It includes studies of the biological, behavioural and psychosocial pathways that operate across an individual's life course, as well as across generations, to influence the development of chronic diseases.
Conceptual Models in Life Course Epidemiology
Conventionally, chronic disease cohort studies recruit subjects in mid-life and follow them up for future disease end-points. The risk of developing disease is then related to baseline exposures or changes in exposure measures ascertained at further follow-ups. Even when baseline measures include early life exposures, such as birthweight and childhood socioeconomic position, these would usually be entered into a multivariable model without much attention to the temporal relationship between variables. Merely the collection of exposure data across the life course is not synonymous with a life course model of disease causation. Surprisingly few epidemiological publications explicitly state the temporal ordering of exposure variables and their inter-relationships, both directly or through intermediary variables, with the outcome measure. One example, where this approach was explicitly undertaken was a study testing the influence of early and later life factors on carotid intima thickness.5 This diagramatically ordered classes of variables across the life course. Such an approach is commonplace in structural equation modelling, path analysis and graphical models where prior conceptual representations, before statistical modelling, are standard practice.6,7
Figure 1 illustrates such a conceptualization with respect to adult respiratory disease and/or impaired respiratory function. This figure illustrates many potential pathways between intrauterine growth and adult disease. Such a life course model enables the researcher to explicitly test not only early life course exposures with later disease, but possible pathways with potential intermediaries or confounding factors. Path (a) would represent a predominantly biological pathway whereby impaired fetal development of the lung architecture is associated with future respiratory insults from infectious agents and greater susceptibility to impaired lung function in adulthood and/or chronic obstructive airways disease. Path (b) would be a predominantly social pathway whereby adverse childhood socioeconomic position influences adverse childhood exposures as well as adult socioeconomic position and smoking behaviour. Path (c) reflects a socio-biological pathway whereby adverse childhood socioeconomic position is associated with post-natal lung function and subsequently with poor adult lung function through its effects on immune function and the likelihood of exposure to infectious agents. Path (d) is a bio-social pathway so that repeated childhood infections results in adverse educational attainment and lower adult socioeconomic position. Even such a crude model highlights the complex inter-relationships and rather arbitrary differentiation between biological and social mechanisms. As Krieger8 asserts a ‘simplistic division of the social and biological will not suffice’. Even such a simplified model (see Strachan9 for detailed discussion) is daunting but importantly challenges both clinical epidemiologists and social scientists to operationalize exposures and conceptualize their inter-relationships across the life course.