Health Care Burden and Cost Associated with Fetal Alcohol Syndrome: Based on Official Canadian Data
Bibliographic record
Abstract
BACKGROUND: Fetal Alcohol Spectrum Disorder (FASD) is a group of disorders caused by prenatal alcohol exposure. From this group, Fetal Alcohol Syndrome (FAS) is the only disorder coded in the International Classification of Diseases, version 10 (ICD-10). This coding was used to gain an understanding on the health care utilization and the mortality rate for individuals diagnosed with FAS, as well as to estimate the associated health care costs in Canada for the most recent available fiscal year (2008-2009). METHODS: Health care utilization data associated with a diagnosis of FAS were directly obtained from the Canadian Institute for Health Information (CIHI). Mortality data associated with a diagnosis of FAS were obtained from Statistics Canada. RESULTS: The total direct health care cost of acute care, psychiatric care, day surgery, and emergency department services associated with FAS in Canada in 2008-2009, based on the official CIHI data, was about $6.7 million. The vast majority of the most responsible diagnoses, which account for the majority of a patient's length of stay in hospital, fall within the ICD-10 category Mental and Behavioural Disorders (F00-F99). It was evident that the burden and cost of acute care hospitalizations due to FAS is increasing -1.6 times greater in 2008-2009, compared to 2002-2003. The mortality data due to FAS, obtained from Statistics Canada (2000-2008), may be underreported, and are likely invalid. DISCUSSION: The official data on the utilization of health care services by individuals diagnosed with FAS are likely to be underreported and therefore, the reported cost figures are most likely underestimated. The quantification of the health care costs associated with FAS is crucial for policy developers and decision makers alike, of the impact of prenatal alcohol exposure, with the ultimate goal of initiating preventive interventions to address FASD.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.016 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".