Healthcare utilisation and costs of home mechanical ventilation
Bibliographic record
Abstract
BACKGROUND: Individuals using home mechanical ventilation (HMV) frequently choose to live at home for quality of life, despite financial burden. Previous studies of healthcare utilisation and costs do not consider public and private expenditures, including caregiver time. OBJECTIVES: To determine public and private healthcare utilisation and costs for HMV users living at home in two Canadian provinces, and examine factors associated with higher costs. METHODS: Longitudinal, prospective observational cost analysis study (April 2012 to August 2015) collecting data on public and private (out-of-pocket, third-party insurance, caregiving) costs every 2 weeks for 6 months using the Ambulatory and Home Care Record. Functional Independence Measure (FIM) was used at baseline and study completion. Regression models examined variables associated with total monthly costs selected a priori using Andersen and Newman's framework for healthcare utilisation, relevant literature, and clinical expertise. Data are reported in 2015 Canadian dollars ($C1=US$0.78=₤0.51=€0.71). RESULTS: We enrolled 134 HMV users; 95 with family caregivers. Overall median (IQR) monthly healthcare cost was $5275 ($2291-$10 181) with $2410 (58%) publicly funded; $1609 (39%) family caregiving; and $141 (3%) out-of-pocket (<1% third-party insurance). Median healthcare costs were $8733 ($5868-$15 274) for those invasively ventilated and $3925 ($1212-$7390) for non-invasive ventilation. Variables associated with highest monthly costs were amyotrophic lateral sclerosis (1.88, 95% CI 1.09 to 3.26, P<0.03) and lower FIM quintiles (higher dependency) (up to 6.98, 95% CI 3.88 to 12.55, P<0.0001) adjusting for age, sex, tracheostomy and ventilation duration. CONCLUSIONS: For HMV users, most healthcare costs were publicly supported or associated with family caregiving. Highest costs were incurred by the most dependent users. Understanding healthcare costs for HMV users will inform policy decisions to optimise resource allocation, helping individuals live at home while minimising caregiver burden.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".