A novel comorbidity index in Italy based on diseases detected by the surveillance system PASSI and the Global Burden of Diseases disability weights.
Comorbidity
Disability weights
Global burden of disease project
Health-statistics
Surveillance system PASSI
Journal
Population health metrics
ISSN: 1478-7954
Titre abrégé: Popul Health Metr
Pays: England
ID NLM: 101178411
Informations de publication
Date de publication:
30 Oct 2023
30 Oct 2023
Historique:
received:
09
05
2023
accepted:
16
10
2023
medline:
1
11
2023
pubmed:
31
10
2023
entrez:
31
10
2023
Statut:
epublish
Résumé
Understanding 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. In 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. The 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.
Sections du résumé
BACKGROUND
BACKGROUND
Understanding 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.
METHODS
METHODS
In 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.
RESULTS
RESULTS
The 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.
Identifiants
pubmed: 37904213
doi: 10.1186/s12963-023-00317-7
pii: 10.1186/s12963-023-00317-7
pmc: PMC10617130
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
18Subventions
Organisme : Università Ca' Foscari di Venezia
ID : PON 2014-2020/DM 1062
Organisme : Università Ca' Foscari Venezia
ID : PRIN-MIUR project n. 20177BR-JXS
Informations de copyright
© 2023. The Author(s).
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