Patient-centred estimation of multimorbidity in chronic disease populations: a novel approach integrating global burden of disease metrics and healthcare administrative data.

Abstract

Background: Although chronic diseases represent a growing global health priority, significant gaps remain in understanding the burden of multimorbidity. This study developed an original methodology to estimate the burden of thirty major chronic diseases at the individual patient level, in terms of Disability-Adjusted Life years (DALYs), Years Lived with Disability (YLD), and Years of Life Lost due to premature death (YLL).

Methods: The Disability weights (DWs) estimated by the Global Burden of Disease (GBD) study were integrated with information from healthcare databases. A panel of medical specialists established the criteria for assigning the level of severity, and thus a specific DW, to each chronic disease. The patient-centred YLD metric was estimated as the cumulative of the combined DWs over the previous ten years. We also measured the Disability Weight Fraction of each coexisting disease (DWF). We illustrated this method using healthcare databases from a large Italian region to assess the impact of chronic diseases and multimorbidity at progressive levels of analysis: health status of the regional chronic disease population, burden of individual chronic diseases and patient clinical complexity.

Results: Unlike the standard GBD estimates, the new method provided precise metrics for multimorbidity, as shown by the comparison on the disability calculated for 4 main chronic diseases. Real-world estimates from the new method highlighted that comorbidity accounted for most of the YLD: for instance, about 88% of the YLD of patients with heart failure was explained by concomitant conditions. DALYs were higher among females than males in most age groups. In the younger groups, psychiatric conditions explained approximately 40% and 25% of YLD among males and females, respectively. Finally, the patient-centred YLD metric was a good predictor of death (c-statistic = 0.779).

Conclusions: This novel method provides insights into the measurement of multimorbidity, based on the disability fraction of each concomitant health condition, which is crucial for defining priority areas for healthcare interventions. The patient-centred estimates may serve to identify subgroups of chronic disease patients with specific healthcare needs and trajectories among a given population. Importantly, measuring the relative contribution of each disease to the patient's burden of multimorbidity favours the planning of multidisciplinary care pathways that are more responsive to individual needs.