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SOCIAL POSITION AND HEALTH: ARE OLD AND NEW OCCUPATIONAL CLASSIFICATIONS INTERCHANGEABLE?

Published online by Cambridge University Press:  08 December 2004

PETER CRAIG
Affiliation:
School of Clinical Sciences and Community Health, University of Edinburgh
JOHN FORBES
Affiliation:
School of Clinical Sciences and Community Health, University of Edinburgh

Abstract

There is growing international interest in the choice of socioeconomic indicators for health research. This study used a combination of standard and novel methods to compare three occupation-based measures of social position in terms of their ability to explain variation and measure inequality in self-assessed health. The recently developed National Statistics Socioeconomic Classification (NS-SEC) is compared with its predecessor, the Registrar General’s Social Class schema (RGSC), and with another occupation-based measure, the Cambridge Social Interaction and Stratification Scale (CAMSIS). With data from two large, independent, nationally representative samples of adults aged 16–64 living in private households in Scotland, logistic regression models are used to compare the classifications' ability to predict self-assessed health. Concentration indices are estimated to compare how well they capture inequality in self-assessed health. The study shows that all three classifications are strongly associated with self-assessed health, though the associations are heavily attenuated by adjustment for one another and for other measures of social position. Despite their differing theoretical bases, the three are closely related. No evidence is found that any of them systematically under- or overstate the extent of inequality in self-assessed health in either men or women, and the extent to which they measure independent dimensions of social inequality is questioned. It is concluded that the availability of the new classifications is unlikely to transform our understanding of the extent or the causes of socioeconomic inequality in health, but provides useful opportunities for sensitivity analysis.

Type
Regular Articles
Copyright
© 2004 Cambridge University Press

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