Waiting for hip fracture surgery: Analysis of individual heterogeneity and discriminatory accuracy

Abstract Background Evaluation of healthcare inequality could be improved by considering (i) the intersection of socioeconomic axes of inequality, (ii) the existence of individual heterogeneity and (iii) criteria to quantify group differences and how those differences translate into recommendations for universal or targeted interventions. In this study we illustrate how to achieve these improvements. Methods We applied analysis of individual heterogeneity and discriminatory accuracy (AIHDA) to analyze data from the Swedish patient registry for the quality indicator hip fracture (HF) surgery within the same day. We analyzed 57,340 patients residing in Sweden during 2011-2014 and belonging to 36 socioeconomic strata defined by age, sex, income, and country of birth and the 21 Swedish regions. We calculated prevalences and 95% confidence intervals as well as the absolute number of cases. The benchmark was set to 80%. To quantify group differences, we evaluated the discriminatory accuracy (DA) using the area under the ROC curve (AUC). Results 68% (39,073/57,340) of the patients were operated in time. The prevalence across the 36 sociodemographic strata ranged from 51% (43-58%) to 81% (67-91%). However, the DA was low (AUC= 0.562) indicating very small group differences. About half of the operated HP (n = 20,287) occurs in 18/36 strata with the lowest prevalences of operations in time. The regions’ prevalences ranged from 37% (36-38%) to 88% (88-89%) with a rather high DA (AUC= 0.736) indicating larger differences. About half of the operated HP (n = 19,677) occurs in 12/21 regions with the lowest prevalence of operations in time. Conclusions The 80% benchmark was only achieved in one socioeconomic stratum and the group differences were very small. 6/21 regions achieved the benchmark and the group differences were large. Interventions aiming to reach the benchmark in the entire population should proportionately target regions and be universal and tailored to sociodemographic strata. Key messages • The AIHDA-framework improves evaluations of healthcare inequalities. • Both differences between group averages and the groups’ DA need be considered when planning healthcare interventions.