Health Care Use, Costs, and Survival Trajectory of Home Mechanical Insufflation-Exsufflation
Bibliographic record
Abstract
BACKGROUND: Despite expert recommendations for use, limited evidence identifies effectiveness of mechanical insufflation-exsufflation (MI-E) in addressing respiratory morbidity and resultant health care utilization and costs for individuals with neuromuscular disorders. We examined the impact of provision of publicly funded MI-E devices on health care utilization, health care costs, and survival trajectory. METHODS: This is a retrospective pre/post cohort study linking data on prospectively recruited participants using MI-E to health administrative databases to quantify outcomes. RESULTS: We linked data from 106 participants (8 age < 15 y) and determined annualized health care use pre/post device. We found no difference in emergency department (ED) visit or hospital admission rates. Following MI-E approval, participants required fewer hospital days (median [interquartile range] [IQR]) 0 [0–9] vs 0 [0–4], P = .03). Rates of physician specialist visits also decreased (median IQR 7 [4–11] vs 4 [2–7], P < .001). Conversely, rates of home care nursing and homemaking/personal support visits increased. Following MI-E, total costs were lower for 59.4%, not different for 13.2%, and higher for 27.4%. Physician billing costs decreased whereas home care costs increased. Regression modeling identified pre-MI-E costs were the most important predictor of costs after approval. At 12 months, 23 (21.7%) participants had died. Risk of death was higher for those using more medical devices (hazard ratio 1.12, [95% CI 1.02–1.22]) in the home. CONCLUSIONS: Provision of publicly funded MI-E devices did not influence rates of ED visits or hospital admission but did shift health care utilization and costs from the acute care to community sector. Although increased community costs negated cost savings from physician billings, evidence suggests costs savings from reduced hospital days and fewer specialist visits. Risk of death was highest in individuals requiring multiple medical technologies.
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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 | 0.008 |
| 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 | 0.001 |
| Open science | 0.000 | 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".