German Influenza Burden Study: Claims Data Analysis 2016-2019

Three consecutive influenza seasons from 2016–2017 to 2018–2019 are covered in this cohort study analyzing German statutory health insurance claims data. The dataset includes approximately 7 million insured individuals per season, with incidence, hospitalization, complication, and direct cost data stratified by age and underlying conditions. Authored by Anna C. Meyer and published by Taylor & Francis, it quantifies the healthcare burden of medically attended influenza and influenza-like illness.

Use Cases

• Estimating age-stratified incidence rates of medically attended influenza/ILI using ICD-10 codes J09-J11.
• Analyzing hospitalization rates and complication risks for individuals with underlying chronic conditions.
• Calculating direct per-case healthcare costs (e.g., €133.28 to €218.99) and comparing resource use against a matched control group.
• Modeling the disproportionate economic burden of influenza on older adults and high-risk populations.

Strengths

• Large cohort scale with approximately 7 million individuals included per influenza season.
• Longitudinal coverage across three consecutive influenza seasons (2016-2017 to 2018-2019).
• Use of a matched control group methodology to estimate influenza-attributable resource use and costs.
• Detailed stratification of analyses by age and prevalence of underlying conditions.

Limitations

• Row count is inconsistently reported: one source lists a size of 634349, but row counts are 'unknown' or 'None' elsewhere, creating a conflict.
• The dataset examines only selected complications, excluding cardiovascular and neurological outcomes, which likely underestimates the full disease burden.
• Relies on administrative claims data where underdiagnosis and misclassification of influenza, especially in outpatient settings, may occur.

Provenance

Source

Claims data from a large German statutory health insurance fund.

Collection Method

Cohort study identifying influenza/ILI via ICD-10 codes (J09-J11) and comparing outcomes with a matched control group.

Time Range

2016–2017 to 2018–2019

Freshness

2026-03-25 18:00:04

Geography

Germany