Healthcare Use and Costs in Children Receiving Home Mechanical Ventilation in Ontario: A 14-Year Cohort Study
Bibliographic record
Abstract
Abstract Rationale Home mechanical ventilation (HMV) is an advanced medical therapy offered to children with medical complexity. Despite the growing pediatric HMV population in North America, there are limited studies describing healthcare use and predictors of highest costs using robust health administrative data. Objectives To describe patterns of healthcare use and costs in children receiving HMV over a 14-year period in Ontario, Canada. Methods We conducted a retrospective population-based cohort study (April 1, 2003, to March 31, 2017) of children aged 0–18 years receiving HMV via invasive mechanical ventilation or noninvasive ventilation. Paired t tests compared healthcare system use and costs 2 years before and 2 years after HMV approval. We developed linear models to analyze variables associated with children in the top quartile of health service use and costs. Results We identified 835 children receiving HMV. In the 2 years after HMV approval compared with the 2 years prior, children had decreased hospitalization days (median, 9 [interquartile range, 3–30] vs. 29 [6–99]; P < 0.0001) and intensive care unit admission days (6.6 [1.9–18.0] vs. 17.1 [3.3–70.9]; P < 0.0001) but had increased homecare service approvals (195 [24–522] vs. 40 [12–225]; P < 0.0001) and outpatient pulmonology visits (3 [1–4] vs. 2 [1–3]; P < 0.0001). Total healthcare costs were higher in the 2 years after HMV approval (mean, CAD$164,892 [standard deviation, CAD$214,187] vs. CAD$128,941 [CAD$194,199]; P < 0.0001). However, all-cause hospital admission costs were reduced (CAD$66,546 [CAD$142,401] vs. CAD$81,578 [CAD$164,672]; P < 0.0001). The highest total 2-year costs were associated with invasive mechanical ventilation (odds ratio [OR], 3.45; 95% confidence interval [CI], 2.24–5.31; reference noninvasive ventilation), number of medical devices at home (OR, 1.63; 95% CI, 1.35–1.96; reference no technology), and increased healthcare costs in the year before HMV initiation (OR, 2.23; 95% CI, 1.84–2.69). Conclusions Children progressing to the need for HMV represent a worsening in their respiratory status that will undoubtedly increase healthcare use and costs. We found that the initiation of HMV in these children can reduce inpatient healthcare use and costs but can still increase overall healthcare expenditures, especially in the outpatient setting.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".