POSB126 Inpatient Burden of Influenza in the Elderly in Germany

Abstract

In Germany, currently available information on resource utilization of hospitalized influenza patients is limited and underlying chronic diseases are often not reported. Our study aimed at providing detailed insights into the inpatient burden of influenza/influenza-like illness (ILI) in the elderly. We used claims data of a large German health insurance fund covering three seasons (2016/17 to 2018/19) to describe the disease burden and resource utilization of influenza/ILI patients in persons ≥60 years of age. ICD-10 codes (J09, J10, J11) were used to identify people with an influenza/ILI diagnosis. We differentiated between individuals with and without underlying chronic diseases. We included 18,530 (2016/17), 27,833 (2017/18) and 16,490 (2018/19) individuals with an outpatient or inpatient influenza/ILI diagnosis. Influenza/ILI incidence was 691 per 100,000 in the 2016/17 season, 1,081 per 100,000 in the 2017/18 season and 633 per 100,000 in the 2018/19 season. The hospitalization rate using principal and secondary influenza/ILI inpatient diagnoses ranged from 14% (2016/17 season) to 20% (2018/19 season). When limited to principal diagnoses, the hospitalization rate ranged from 8% (2016/17 season) to 12% (2018/19 season). Hospitalization rates were higher in patients with chronic conditions compared to patients without chronic diseases. Mean length of hospital stay with a principal influenza/ILI diagnosis was 9 days in all seasons. The average cost per hospital stay (based on inpatient cases with a principal influenza/ILI diagnosis) ranged from €3,631 in the 2016/17 season to €4,106 in the 2018/19 season. Hospital costs of patients with underlying diseases were higher than those of patients without chronic conditions (e.g. €4,205 vs. €2,756 in the 2018/19 season). Our analysis showed that hospitalization rates in older people with influenza/ILI are high. Underlying chronic conditions led to higher hospitalization rates and costs. Future analyses could therefore benefit from differentiating between individuals with and without chronic diseases.