The life course prospective design: an example of benefits and problems associated with study longevity

Soc Sci Med. 2003 Dec;57(11):2193-205. doi: 10.1016/s0277-9536(03)00083-2.


Although the life course prospective study design has many benefits, and information from such studies is in increasing demand for scientific and policy purposes, it has potential inherent design problems associated with its longevity. These are in particular the fixed sample structure and the data collected in early life, which are each determined by the scientific principles of another time and the risk over time of increased sample loss and distortion through loss. The example of a national birth cohort in Britain, studied from birth so far to age 53 years is used to address these questions. Although the response rate is high, avoidable loss, which was low in childhood, increased in adulthood, and was highest in those in adverse socio-economic circumstances and those with low scores on childhood cognitive measures. Recent permanent refusal rate rises may be the result of better tracing and/or a response to increased requests for biological measurement. Nevertheless, the responding sample continues in most respects to be representative of the national population of a similar age. Consistency of response over the study's 20 data collections has been high. The size of the sample responding in adulthood is adequate for the study of the major costly diseases, and for the study of functional ageing and its precursors. This study's continuation has depended not only on scientific value but also policy relevance. Although the problems inherent in the prospective design are unavoidable they are not, in the study described, a barrier to scientific and policy value. That seems also likely in Britain's two later born national birth cohort studies that have continued into adulthood.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adolescent
  • Adult
  • Child
  • Child, Preschool
  • Demography*
  • Emigration and Immigration / statistics & numerical data
  • Female
  • Humans
  • Longitudinal Studies*
  • Male
  • Middle Aged
  • Mortality
  • Patient Dropouts
  • Population Surveillance / methods*
  • Socioeconomic Factors
  • Time Factors
  • United Kingdom / epidemiology