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Journal Article

Making sense of symptom checklists: a latent class approach to the 9 years of the British Household Panel Study


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Background In health inequalities research there is a growing impetus to examine the development of inequalities in health over time. However, many of the sources of longitudinal data in Britain are not designed specifically for health research. Typically, health status is assessed by self-reported problems and the use of symptom checklists.
Methods The British Household Panel Survey (BHPS) is an annual survey of approximately 5500 private households containing 9000 men and women, which began in 1991. Each year, the BHPS contains a checklist of 13 health problems and symptoms. The findings presented here are based on adult participants aged 16 years and over in 1991. Using eight waves of data from the BHPS, we use latent class analysis (LCA) to model latent health status from a set of observed binary variables. Individuals are assigned to a latent health class on the basis of LCA estimated probabilities of class membership given their response patterns and the estimated unconditional class frequencies. The predictive value of latent health class membership is assessed for self-reported health status and functioning, health and welfare service use, and mortality 1 year later.
Results The LCA supported a suitable four-class model of health status representing good health, psychosomatic health problems, physical health problems and comorbid health problems. Members of the good latent health class were predicted to have better self-reported health and functioning, less health and welfare service use, and lower risk of mortality 1 year later than members of the three problem health classes. Those with comorbid health problems were predicted to have particularly poor outcomes.
Conclusions A latent class approach to modelling self-reported health problems and symptoms has allowed for both quantitative and qualitative dimensions of health status to be captured. This may motivate better informed models of health by users of general population surveys.

Published in

Journal of Public Health Medicine


25 (3):215-222


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