Estimated health and economic impact of using high-dose influenza vaccine on respiratory and circulatory plus respiratory hospitalizations of older adults in Australia
Bibliographic record
Abstract
Background: Standard dose influenza vaccine provides moderate protection from infection, but with lower effectiveness among the elderly. High dose and adjuvanted vaccines (HD-TIV and aTIV) were developed to address this. This study aims to estimate the incremental health and economic impact of using HD-TIV (high dose trivalent vaccine) instead of aTIV (adjuvanted trivalent vaccine) on respiratory and circulatory plus respiratory hospitalizations of older people (≥65 years) in Australia. Methods: This is a modelling study comparing predicted hospitalization outcomes in people receiving HD-TIV or aTIV during an average influenza season in Australia. Hospitalization records of Australian adults ≥65 years of age from 01 April to 30 November during 15 influenza seasons (2002-2017 excluding 2009, which was a pandemic) were extracted from the Australian Institute of Health and Welfare [AIHW] and used to calculate hospitalisation rates during an average season. Relative vaccine effectiveness data for aTIV and HD-TIV were used to estimate morbidity burden related to influenza. Results: Between 2002 and 2017, the average respiratory hospitalization rate among older people during influenza season (April-November) was 3,445/100,000 population-seasons, with an average cost of AU$ 7,175 per admission. The average circulatory plus respiratory hospitalization rate among older Australian people during that time was 10,393/100,000 population-seasons, with an average cost of AU$ 7829 per admission. For older Australians, HD-TIV may avert an additional 6,315-9,410 respiratory admissions each year, with an incremental healthcare cost saving of AU$ 15.9-38.2 million per year compared to aTIV. Similar results were also noted for circulatory plus respiratory hospitalizations. Conclusions: From the modelled estimations, HD-TIV was associated with less economic burden and fewer respiratory, and circulatory plus respiratory hospitalizations than aTIV for older Australians.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".