Oral health care’s contribution to catastrophic spending in Canada: a descriptive study
Bibliographic record
Abstract
Background: Oral health care (OHC) in Canada is largely financed through employer-sponsored insurance and out-of-pocket (OOP) payments and is generally excluded from its system of universal health coverage, although public financing will increase substantially with the introduction of the Canadian Dental Care Plan (CDCP). We generate estimates of catastrophic health expenditure (CHE) in Canada and assess the contribution of OOP spending in OHC on CHE between 2010 and 2019. Methods: We examined the Survey of Household Spending from 2010 to 2019 by year and in pooled cross-sections and followed the WHO/Europe methodology to determine CHE. Spending OOP in OHC was compared to medicines, medical products, outpatient care, diagnostic tests, and inpatient care. We assessed CHE and the share of OOP spending annually, nationally, provincially, across income quintiles and presence of private insurance including oral health coverage. Results: Estimates in CHE dropped from 5 % (2010) to 3.4 % (2019) and was more common among lower income groups, those without private insurance and Québec residents. Oral health care was the second highest contributor to CHE (after medicines) especially among the lowest income groups. Having private insurance yielded a higher share of OOP spending among lower than higher income groups. Conclusions: From 2010 to 2019, OOP spending in OHC was the second-highest contributor to CHE in Canada. Further monitoring is warranted to ensure financial protection is achieved for OHC after the full implementation of the CDCP.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.015 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 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".