Health‐related quality of life and economic burden of vestibular loss in older adults
Bibliographic record
Abstract
Objectives Vestibular loss is a debilitating condition, and despite its high prevalence in older adults, the quality of life (QoL) burden of vestibular loss in older individuals has not been well‐studied. This report quantifies the impact on overall QoL and identifies domains of health most affected. We hypothesize vestibular loss will be associated with impairment in diverse domains of health‐related QoL. Study Design Prospective, case‐control study. Methods A convenience sample of 27 patients age ≥60 years with vestibular physiologic loss was recruited from an academic neurotology clinic. The patients did not have any identifiable cause of their vestibular loss other than aging. The convenience sample was compared to an age‐matched cross‐sectional sample of the general US population (n = 1266). The main outcome was QoL measured by the Ontario Health Utilities Index Mark III (HUI3). Results Compared to the general population, patients with vestibular loss had significantly lower overall unadjusted HUI3 scores (−0.32, p < 0.001). Multivariate regression analysis showed vestibular loss was significantly associated with poorer performance in vision (−0.11 p < 0.0001), speech (−0.15, p < 0.0001), dexterity (−0.13, p < 0.0001), and emotion (−0.07, p = 0.0065). Adjusted aggregate HUI3 was also significantly lower for vestibular loss (−0.15, p = 0.0105). These QoL decrements resulted in an average loss of 1.30 Quality‐Adjusted Life Years (QALYs). When using a $50,000/QALY willingness‐to‐pay threshold, vestibular loss was associated with a $64,929 lifetime economic burden per affected older adult, resulting in a total lifetime societal burden of $227 billion for the US population ≥60 years of age. Conclusions Loss of vestibular function with aging significantly decreases quality of life across multiple domains of well‐being. These QoL reductions are responsible for heavy societal economic burdens of vestibular loss, which reveal potential benefits of prompt diagnosis and treatment of this condition. Level of Evidence 3
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".