Children's Health Care Utilization and Cost in the Last Year of Life: A Cohort Comparison with and without Regional Specialist Pediatric Palliative Care
Bibliographic record
Abstract
Background: Research remains inconclusive regarding the impact of specialist pediatric palliative care (SPPC) on health care utilization and cost. Objective: To better understand and quantify the impact of regional SPPC services on children's health care utilization and cost near end of life. Design: A retrospective cohort study used administrative databases to compare outcomes for child decedents (age 31 days to 19 years) from two similar regions in Ontario, Canada between 2010 and 2014, wherein one region had SPPC services (SPPC+) and the other did not (SPPC−). Measurements: Administrative databases provided demographics, health care utilization (days), and costs Canadian dollars) across settings in the last year of life, and location of death. Multivariable analyses produced relative rates (RRs) of health care days (acute and home care), intensive care unit (ICU) days, and health care costs (inpatient, outpatient, home, and physician) as well as the odds ratio (OR) of in-hospital death. Counterfactual analysis quantified the differences in utilization and costs. Results: A total of 807 children were included. On multivariable analysis, residence in the SPPC+ region ( n = 363) was associated with fewer mean health care days (RR = 0.73; 95% confidence interval [CI]: 0.59–0.90); fewer mean ICU days (RR = 0.64; 95% CI: 0.44–0.94); lower mean health care costs (RR = 0.71; 95% CI: 0.56–0.91); and lower likelihood of in-hospital death (OR = 0.67; 95% CI: 0.49–0.92). The counterfactual analysis estimated mean reductions of 16.2 days (95% CI: 14.4–18.0) and $24,940 (95% CI: $21,703–$28,177) per child in the SPPC+ region. Conclusions: Although not a causal study, these results support an association between regional SPPC services and decreased health care utilization, intensity, and cost for children near end of life.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 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".