Epidemiological Paradigms: Life-Course (KUH)

Epidemiological paradigms are examples or models of how things can be interpreted and explained. There are number of different paradigms but this section focuses on three epidemiological influences throughout life:

• Programming
• Adult risk factor approaches
• Life-course

3. Life-Course (KUH)

Theory & Evidence

The life-course approach is defined as the long term effects on later health or disease risk of physical or social exposures during gestation, childhood, adolescence, young adulthood and later adult life. It provides a model to conceptualize how various biological and social factors, experienced at different life-course stages, can independently, cumulatively and interactively influence health and disease in adult life. Life-course epidemiology acknowledges that different processes come into play in relation to different health outcomes at different stages of life. The model, therefore, requires detailed data for periods covering the entire life course in order to identify the contribution of exposures acting at particular periods of time during an individual's life.

The life-course theory does not dismiss the findings of either fetal programming or adult risk factor approach or believe that there should be an either/or model but combines the contribution of early life factors (programming) with later life factors (adult risk factors) and integrates both biological and social risk processes.

Life course epidemiology aims to build and test theoretical models that postulate pathways linking exposures across the life-course to later life health outcomes. Ben Shlomo and Kuh (2002) have developed conceptual life-course models:

3.1 Critical period model
With or without later-life risk factors
With later-life effect modifiers

The critical period model sees the time window of exposure as key. It involves a particular period of time when a specific exposure will lead to devastating permanent developmental changes whereas if they were experienced just a few days/weeks earlier or later, they would have no long term impact. The critical period can take place at any period of time e.g. before conception or throughout life. The 'fetal origins of adult disease' (programming) hypothesis can be considered one example of the critical period model. Another example is poliomyelitis which requires infection at a specific period in childhood and leads to serious permanent developmental changes. In addition, absence of an exposure during a critical period may adversely influence health in later life, such as the absence or late exposure to dirt possibly leading to asthma later in life.

3.2 Accumulation of risk
With independent and uncorrelated insults
With correlated insults
'Risk clustering'
'Chains of risk' with additive or trigger effects

The accumulation of risk model occurs such that an adverse exposure early in life that increases risk has an additive effect with adverse influences in later life i.e. the life course exposures gradually accumulate throughout life and may cause long term damage. The adverse exposures are classed as either uncorrelated or correlated. Uncorrelated are those exposures that have an independent effect on the disease risk irrespective of the later exposure. For example, high alcohol intake is not strongly related to adulthood social class. Correlated exposures are when exposures are 'clustered' or 'chains of risk'. Correlated or 'risk clustering' involves the accumulation of exposures that are related to a number of other exposures. These are often connected to socioeconomic factors. A 'chain of risk' refers to a sequence of linked exposures that raise disease risk because one negative exposure leads to another and then another. It is known as an 'additive effect' when the adverse experience increases the risk of disease in a cumulative manner, and as a 'trigger effect' when it is only the final link in the chain that has a marked effect on disease risk.

The coexistence of a series of exposures within one individual's life may generate greater health problems than would be anticipated for the known effect of a single exposure. For example, the combination of occupational exposure to asbestos and smoking results in a greater risk of lung cancer than would be expected from the single addition of the known effects experienced individually.

The collection of detailed data for periods covering the entire life course enables the identification of the contribution of exposures acting at particular time-periods - statistics are able to identify the key exposures at key time-periods for specific diseases. For example, which is the key exposure for lung cancer out of the following life-course exposures: low birth weight, smoked as teenager, worked in coalmines as an adult?

Limitations

The approach requires detailed data but few researchers will have access to a sufficient wide range of biological and social data on cohorts of subjects covering the entire life course (from preconception to the outcome of interest).

References

• Ben-Shlomo, Y and Kuh, D. (2002). A life-course approach to chronic disease epidemiology: conceptual models, empirical challenges and interdisciplinary perspectives. International Journal of Epidemiology. 31: 285-293.
• Davey Smith G. (2007). Life-course approaches to inequalities in adult chronic disease risk. Proceedings of the Nutrition Society. 66: 216-236.
• Gibney M., Margetts B., Kearney J., Arab L. Public Health Nutrition. The Nutrition Society. Blackwell Publishing.
• Kuh D. and Ben-Shlomo Y. (1997) A Life Course Approach to Chronic Disease Epidemiology. Oxford University Press.
• Kuh D., Ben-Shlomo Y., Lynch J., Hallqvist J. and Power C. (2003) Life course epidemiology. Journal of Epidemiology and Community Health. 57: 778-783.
• Lewis, G. Sheringham, J. Kalim, K. Crayford, T. (2008) Mastering Public Health: A postgraduate guide to examinations and revalidation. The Royal Society of Medicine Press Limited.
• Sheiham A. and Watt R.G. (2000). The Common Risk Factor Approach: a rational basis for promoting oral health. Community Dentistry and Oral Epidemiology. 28: 399-406.

© Hannah Pheasant 2008