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OP69 Understanding the biological pathways that mediate the associations between social position and health: allostatic load
  1. Adisetu Malih
  1. Institute for Social and Economic Research, University of Essex, Colchester, UK


Background Psychosocial pathways have been found to play a role in explaining socio-economic differences in health. Allostatic load, or the multiphysiological response to chronic stress, is proposed as the biological basis of this pathway.

However, the use of allostatic load has been criticised as there has been inconsistency in its operationalisation: different studies use different biomarkers. They often exclude measures of stress such as neuroendocrine biomarkers.

This study investigates differences in the operationalisation of allostatic load. And its association with social position. Three research questions are addressed. 1: Do differences in specific system biomarkers alter allostatic load factor structure? 2: Do mean factor scores vary by social class? 3: Does including neuroendocrine biomarkers change the mean factor scores by social class?

Methods This study uses cohort and longitudinal panel data from seven United Kingdom datasets, comprising approximately 8239 adults over 17 years old. Social class was measured using the nine major groupings of the standard occupational classifications SOC90 and SOC2000. Allostatic load factor scores were calculated using 15–18 biomarkers.

Exploratory factor analysis was conducted using StataMP 16, and confirmatory factor analysis was performed using Mplus8.7. The association of resulting mean factor scores and social class was examined using regression analysis.

Results Six-to-seven factors with factor loadings above 0.3 were extracted. Model fit statistics suggested a good fit to the data. The root mean square error of approximation (0.067) and the standardised root mean square residual (0.065) is below 0.08. Preliminary results show that differences in specific system biomarkers did not alter allostatic load factor structure. This implies that different biomarkers representing the same physiological system can be used to express that particular physiological system. Mean factor scores vary by social class. For example, the mean inflammatory factor score was higher in participants from a disadvantaged social class. The neuroendocrine factor was apparent in the data, but inclusion did not change the mean factor scores by social class.

Conclusion In conclusion, factor analysis could be a valuable way of operationalising allostatic load. It could also aid the understanding of how the social gets under the skin to understand social differences in health.

  • Biomarkers Class Health

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