Incremental healthcare utilisation and costs among new senior high-cost users in Ontario, Canada: a retrospective matched cohort study
Bibliographic record
Abstract
OBJECTIVES: To describe healthcare use and spending before and on becoming a new (incident) senior high-cost user (HCU) compared with senior non-HCUs; to estimate the incremental costs, overall and by service category, attributable to HCU status; and to quantify its monetary impact on the provincial healthcare budget in Ontario, Canada. DESIGN: We conducted a retrospective, population-based comparative cohort study using administrative healthcare records. Incremental healthcare utilisation and costs were determined using the method of recycled predictions allowing adjustment for preincident and incident year values, and covariates. Estimated budget impact was computed as the product of the mean annual total incremental cost and the number of senior HCUs. PARTICIPANTS: Incident senior HCUs were defined as Ontarians aged ≥66 years who were in the top 5% of healthcare cost users during fiscal year 2013 (FY2013) but not during FY2012. The incident HCU cohort was matched with senior non-HCUs in a ratio of 1 HCU:3 non-HCU. RESULTS: Senior HCUs (n=175 847) reached the annual HCU threshold of CAD$10 192 through different combinations of incurred costs. Although HCUs had higher healthcare utilisation and costs at baseline, HCU status was associated with a substantial spike in both, with prolonged hospitalisations playing a major role. Twelve per cent of HCUs reached the HCU expenditure threshold without hospitalisation. Compared with non-HCUs (n=5 27 541), HCUs incurred an additional CAD$25 527 per patient in total healthcare costs; collectively CAD$4.5 billion or 9% of the 2013 Ontario healthcare budget. Inpatient care had the highest incremental costs: CAD$13 427, 53% of the total incremental spending. CONCLUSIONS: Costs attributable to incident senior HCU status accounted for almost 1/10 of the provincial healthcare budget. Prolonged hospitalisations made a major contribution to the total incremental costs. A subgroup of patients that became HCU without hospitalisation requires further investigation.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".