Economic burden and secondary complications of influenza-related hospitalization among adults in the US: a retrospective cohort study
Bibliographic record
Abstract
OBJECTIVE: This study aims to describe the healthcare resource utilization (HCRU) and direct medical cost of influenza-related hospitalizations to illustrate the persistent economic burden of influenza among adults in the US. METHODS: A retrospective cohort study was conducted using the PINC AI Healthcare Database. Adults hospitalized with a diagnosis of influenza between August 1-May 31 from 2016-2023 were identified and stratified by age (18-49, 50-64 and ≥65 years). The index hospitalization was defined as the individual's first influenza-related hospitalization during each season. Patient demographics, comorbidities, and hospitalization characteristics were assessed during the index hospitalization. Index hospitalization length of stay (LOS), in-hospital mortality, intensive care unit (ICU) admissions, mechanical ventilation (MV) usage, and costs were evaluated overall and by MV usage, ICU admission, and secondary complication status. Pre-index influenza-related outpatient and emergency department (ED) visits (7 days prior) were also evaluated. RESULTS: Primarily initiated in the ED, the median LOS for influenza-related hospitalizations was 3-4 days. Inpatient mortality increased with age (2.2-4.4%). Combined mean hospitalization and initial ED visit costs were $12,556-$14,494 (2017/18; high severity season) and $11,384-$12,896 (2022/23; most recent season). Compared to other age groups, adults ≥65 years had higher proportions of hospitalization with no MV or ICU usage. Adults 18-49 years had the highest proportion of ICU admission only, whereas adults 50-64 years had the highest MV usage only and both MV and ICU admission. MV and/or ICU usage was associated with higher hospitalization costs. Increasing proportionally with age, the majority of influenza-related hospitalizations had a secondary complication diagnosis, which were associated with elevated costs. LIMITATIONS: Analysis of this hospital-based administrative database relied on coding accuracy. Only hospital system-associated outpatient/ED visits were captured; the full scope of HCRU was under-ascertained. CONCLUSIONS: The economic burden of influenza-related hospitalizations remains substantial, driven by underlying conditions, MV/ICU usage and secondary complications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".