Combining education and income into a socioeconomic position score for use in studies of health inequalities

• Published in: BMC Public Health
• Main Authors: Lindberg, Marie Hella, Chen, Gang, Olsen, Jan Abel, Abelsen, Birgit
• Format: Article in Journal/Newspaper
• Language: English
• Published: BMC 2022
• Subjects: Norway; Northern Norway
• Online Access: https://hdl.handle.net/10037/25264; https://doi.org/10.1186/s12889-022-13366-8

Background: In studies of social inequalities in health, there is no consensus on the best measure of socioeconomic position (SEP). Moreover, subjective indicators are increasingly used to measure SEP. The aim of this paper was to develop a composite score for SEP based on weighted combinations of education and income in estimating subjective SEP, and examine how this score performs in predicting inequalities in health-related quality of life (HRQoL). Methods: We used data from a comprehensive health survey from Northern Norway, conducted in 2015/16 (N=21,083). A composite SEP score was developed using adjacent-category logistic regression of subjective SEP as a function of four education and four household income levels. Weights were derived based on these indicators’ coeffcients in explaining variations in respondents’ subjective SEP. The composite SEP score was further applied to predict inequalities in HRQoL, measured by the EQ-5D and a visual analogue scale. Results: Education seemed to infuence SEP the most, while income added weight primarily for the highest income category. The weights demonstrated clear non-linearities, with large jumps from the middle to the higher SEP score levels. Analyses of the composite SEP score indicated a clear social gradient in both HRQoL measures. Conclusions: We provide new insights into the relative contribution of education and income as sources of SEP, both separately and in combination. Combining education and income into a composite SEP score produces more comprehensive estimates of the social gradient in health. A similar approach can be applied in any cohort study that includes education and income data.