Assessment of the concordance between individual-level and area-level measures of socio-economic deprivation in a cancer patient cohort in England and Wales.

OBJECTIVES: Most research on health inequalities uses aggregated deprivation scores assigned to the small area where the patient lives; however, the concordance between aggregate area-level deprivation measures and personal deprivation experienced by individuals living in the area is poorly understood. Our objective was to examine the agreement between individual and ecological deprivation. We tested the concordance between metrics of income, occupation and education at individual and area levels, and assessed the reliability of area-based deprivation measures to predict individual deprivation circumstances. SETTING: England and Wales. PARTICIPANTS: A cancer patient cohort of 9547 individuals extracted from the Office for National Statistics Longitudinal Study. OUTCOMES: We quantified the concordance between measures of income, occupation and education at individual and area level. In addition, we used ROC (receiver operating characteristic) curves and the area under the curve (AUC) to assess the reliability of area-based deprivation measures to predict individual deprivation circumstances. RESULTS: We found low concordance between individual-level and area-level indicators of deprivation (Cramer's V statistics range between 0.07 and 0.20). The most commonly used indicator in health inequalities research, area-based income deprivation, was a poor predictor of individual income status (AUC between 0.56 and 0.59), whereas education and occupation were slightly better predictors (AUC between 0.62 and 0.65). The results were consistent across sexes and across six major cancer types. CONCLUSIONS: Our results indicate that ecological deprivation measures capture only part of the relationship between deprivation and health outcomes, especially with respect to income measurement. This has important implications for our understanding of the relationship between deprivation and health, and, as a consequence, healthcare policy. The results have a wide-reaching impact for the way in which we measure and monitor inequalities, and in turn, fund and organise current UK healthcare policy aimed at reducing them.