Background & aims Hepatitis C (HCV) is associated with several extrahepatic manifestations, and estimates of the hospitalization burden related to these comorbidities are still limited. The aim of this study is to quantify the hospitalization risk associated with comorbidities in an Italian cohort of HCV-infected patients and to assess which of these comorbidities are associated with high hospitalization resource utilization. Methods Individuals aged 18 years and older with HCV-infection were identified in the Abruzzo’s and Campania’s hospital discharge abstracts during 2011–2014 with 1-year follow-up. Cardio- and cerebrovascular disease, diabetes and renal disease were grouped as HCV-related comorbidities. Negative binomial models were used to compare the hospitalization risk in patients with and without each comorbidity. Logistic regression model was used to identify the characteristics of being in the top 20% of patients with the highest hospitalization costs (high-cost patients). Results 15,985 patients were included; 19.9% had a liver complication and 48.6% had one or more HCV-related comorbidities. During follow-up, 36.0% of patients underwent at least one hospitalization. Liver complications and the presence of two or more HCV-related comorbidities were the major predictors of hospitalization and highest inpatient costs. Among those, patients with cardiovascular disease had the highest risk of hospitalization (Incidence Rate Ratios = 1.42;95%CI:1.33–1.51) and the highest likelihood of becoming high-cost patients (Odd Ratio = 1.37;95%CI:1.20–1.57). Conclusion Beyond advanced liver disease, HCV-related comorbidities (especially cardiovascular disease) are the strongest predictors of high hospitalization rates and costs. Our findings highlight the potential benefit that early identification and treatment of HCV might have on the reduction of hospitalization costs driven by extrahepatic conditions.

The inpatient hospital burden of comorbidities in HCV-infected patients: A population-based study in two Italian regions with high HCV endemicity (The BaCH study)

Citarella A.;Conti V.;Naccarato C.;
2019-01-01

Abstract

Background & aims Hepatitis C (HCV) is associated with several extrahepatic manifestations, and estimates of the hospitalization burden related to these comorbidities are still limited. The aim of this study is to quantify the hospitalization risk associated with comorbidities in an Italian cohort of HCV-infected patients and to assess which of these comorbidities are associated with high hospitalization resource utilization. Methods Individuals aged 18 years and older with HCV-infection were identified in the Abruzzo’s and Campania’s hospital discharge abstracts during 2011–2014 with 1-year follow-up. Cardio- and cerebrovascular disease, diabetes and renal disease were grouped as HCV-related comorbidities. Negative binomial models were used to compare the hospitalization risk in patients with and without each comorbidity. Logistic regression model was used to identify the characteristics of being in the top 20% of patients with the highest hospitalization costs (high-cost patients). Results 15,985 patients were included; 19.9% had a liver complication and 48.6% had one or more HCV-related comorbidities. During follow-up, 36.0% of patients underwent at least one hospitalization. Liver complications and the presence of two or more HCV-related comorbidities were the major predictors of hospitalization and highest inpatient costs. Among those, patients with cardiovascular disease had the highest risk of hospitalization (Incidence Rate Ratios = 1.42;95%CI:1.33–1.51) and the highest likelihood of becoming high-cost patients (Odd Ratio = 1.37;95%CI:1.20–1.57). Conclusion Beyond advanced liver disease, HCV-related comorbidities (especially cardiovascular disease) are the strongest predictors of high hospitalization rates and costs. Our findings highlight the potential benefit that early identification and treatment of HCV might have on the reduction of hospitalization costs driven by extrahepatic conditions.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12079/51587
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