Publicly funded healthcare costs associated with orofacial clefts for children born in Alberta, Canada between 2002 and 2018
Bibliographic record
Abstract
BACKGROUND: Orofacial clefts (OFCs) include cleft palate (CP), cleft lip (CL), and cleft lip with cleft palate (CLP) and require multidisciplinary healthcare services. Alberta, Canada has a publicly funded, universal access healthcare system. This study determined publicly funded healthcare costs for children with an OFC and compared these costs to children without congenital anomalies. METHODS: This retrospective population-based cohort analysis used the Alberta Congenital Anomalies Surveillance System to identify children born between 2002 and 2018 with an isolated OFC. They were matched 1:1 to a reference cohort based on sex and year of birth. The study population included 1614 children, from birth to 17 years of age linked to administrative databases to estimate annual inpatient and outpatient costs. Average annual all-cause costs were compared using two-sample independent t tests. RESULTS: The mean total cleft-related costs per patient were highest for children with CLP ($74,138 CAD, standard deviation (SD) $43,447 CAD), followed by CP ($53,062 CAD, SD $74,366 CAD), and CL ($35,288 CAD, SD $49,720 CAD). The mean total all-cause costs per child were statistically significantly higher (p < .001) in children with an OFC ($56,305 CAD, SD $57,744 CAD) compared to children without a congenital anomaly ($18,600 CAD, SD $61,300 CAD). CONCLUSIONS: Despite public health strategies to mitigate risk factors, the trend for OFCs has remained stable in Alberta, Canada for over 20 years. The costs reported are useful to other jurisdictions for comparison, and to families, healthcare professionals, service planners, and policy makers.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".