Cost-effectiveness analysis of a community paramedicine programme for low-income seniors living in subsidised housing: the community paramedicine at clinic programme (CP@clinic)
Bibliographic record
Abstract
OBJECTIVES: To evaluate the cost-effectiveness of the Community Paramedicine at Clinic (CP@clinic) programme compared with usual care in seniors residing in subsidised housing. DESIGN: A cost-utility analysis was conducted within a large pragmatic cluster randomised controlled trial (RCT). Subsidised housing buildings were matched by sociodemographics and location (rural/urban), and allocated to intervention (CP@clinic for 1 year) or control (usual care) via computer-assisted paired randomisation. SETTING: Thirty-two subsidised seniors' housing buildings in Ontario. PARTICIPANTS: Building residents 55 years and older. INTERVENTION: CP@clinic is a weekly community paramedic-led, chronic disease prevention and health promotion programme in the building common areas. CP@clinic is free to residents and includes risk assessments, referrals to resources, and reports back to family physicians. OUTCOME MEASURES: Quality-adjusted life years (QALYs) gained, measured with EQ-5D-3L. QALYs were estimated using area-under-the curve over the 1-year intervention, controlling for preintervention utility scores and building pairings. Programme cost data were collected before and during implementation. Costs associated with emergency medical services (EMS) use were estimated. An incremental cost effectiveness ratio (ICER) based on incremental costs and health outcomes between groups was calculated. Probabilistic sensitivity analysis using bootstrapping was performed. RESULTS: The RCT included 1461 residents; 146 and 125 seniors completed the EQ-5D-3L in intervention and control buildings, respectively. There was a significant adjusted mean QALY gain of 0.03 (95% CI 0.01 to 0.05) for the intervention group. Total programme cost for implementing in five communities was $C128 462 and the reduction in EMS calls avoided an estimated $C256 583. The ICER was $C2933/QALY (bootstrapped mean ICER with Fieller's 95% CI was $4850 ($2246 to $12 396)) but could be even more cost effective after accounting for the EMS call reduction. CONCLUSION: The CP@clinic ICER was well below the commonly used Canadian cost-utility threshold of $C50 000. CP@clinic scale-up across subsidised housing is feasible and could result in better health-related quality-of-life and reduced EMS use in low-income seniors. TRIAL REGISTRATION NUMBER: Clinicaltrials.gov, NCT02152891.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".