A novel comorbidity index in Italy based on diseases detected by the surveillance system PASSI and the Global Burden of Diseases disability weights

Population Health Metrics

Research

Stefano Campostrini¹  Angela Andreella¹  Lorenzo Monasta² 

[1] Department of Economics, Ca’ Foscari University of Venice, Venice, Italy;Institute for Maternal and Child Health, IRCCS Burlo Garofolo, Trieste, Italy;

关键词: Health-statistics;    Comorbidity;    Disability weights;    Surveillance system PASSI;    Global burden of disease project;   

DOI  :  10.1186/s12963-023-00317-7

 received in 2023-05-09, accepted in 2023-10-16,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundUnderstanding comorbidity and its burden characteristics is essential for policymakers and healthcare providers to allocate resources accordingly. However, several definitions of comorbidity burden can be found in the literature. The main reason for these differences lies in the available information about the analyzed diseases (i.e., the target population studied), how to define the burden of diseases, and how to aggregate the occurrence of the detected health conditions.MethodsIn this manuscript, we focus on data from the Italian surveillance system PASSI, proposing an index of comorbidity burden based on the disability weights from the Global Burden of Disease (GBD) project. We then analyzed the co-presence of ten non-communicable diseases, weighting their burden thanks to the GBD disability weights extracted by a multi-step procedure. The first step selects a set of GBD weights for each disease detected in PASSI using text mining. The second step utilizes an additional variable from PASSI (i.e., the perceived health variable) to associate a single disability weight for each disease detected in PASSI. Finally, the disability weights are combined to form the comorbidity burden index using three approaches common in the literature.ResultsThe comorbidity index (i.e., combined disability weights) proposed allows an exploration of the magnitude of the comorbidity burden in several Italian sub-populations characterized by different socioeconomic characteristics. Thanks to that, we noted that the level of comorbidity burden is greater in the sub-population characterized by low educational qualifications and economic difficulties than in the rich sub-population characterized by a high level of education. In addition, we found no substantial differences in terms of predictive values of comorbidity burden adopting different approaches in combining the disability weights (i.e., additive, maximum, and multiplicative approaches), making the Italian comorbidity index proposed quite robust and general.

【 授权许可】

CC BY   
© The Author(s) 2023

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