59 Fetal Alcohol Spectrum Disorder in Canada’s Children: Pediatric Advocacy for Optimal Physical and Mental Health
Bibliographic record
Abstract
Abstract Introduction/Background Fetal Alcohol Spectrum Disorder (FASD) is a diagnostic term used to describe the range of physical and neurobehavioural effects that may result from prenatal exposure to alcohol. With school prevalence figures of approximately 4%, this may represent as many as 224,000 children across Canada. The pediatrician is key to identifying children who may be at risk based on exposure and in providing regular health and developmental surveillance to families caring for these children. To date, information about the range of specific co-morbidities in the paediatric population has not been clearly established for the Canadian population. The Canadian National FASD Dataform has been collecting diagnostic and assessment data from specialty FASD clinics across Canada for the last 6 years. Objectives The purpose of this abstract is to describe the physical and mental health conditions seen in children and adolescents with FASD in Canada. Design/Methods The Canadian National Dataform collects information from 29 Canadian FASD diagnostic clinics. Dataform started in 2011 as a project funded by the Public Health Agency of Canada to provide national clinical information on FASD in Canada. The database is hosted on the RedCap platform. De-identified clinical data collected includes information on FASD diagnoses, other prenatal exposures, brain domains of impairment and physical/mental health co-morbidities. Descriptive and quantitative analyses were used to compare individuals with and without FASD in the sample. Results Of the 1,684 records in the database, 58% had FASD, 11% were designated as At Risk for FASD and 31% did not receive an FASD-related diagnosis. Nine percent (N=152) were between the ages of 0-5 years, 46% (N=780) were 6-12 years and 24% (N=402) were 13-17 years of age. Of all individuals with FASD, 53% were also exposed prenatally to other substances including nicotine (43%), cannabis (29%) and cocaine/crack (18%), which did not significantly differ from the exposures of those who do not have FASD in the sample. Eighty-eight percent of the sample had confirmed prenatal alcohol exposure (PAE). Data show that children and adolescents across all age groups who meet criteria for FASD had significantly more impairment across each of the 10 brain domains measured when compared to those who have PAE but do not meet criteria for an-FASD diagnosis (Figure 1). The children and adolescents with FASD had significantly higher physical and mental health co-morbidities across all age cohorts (Tables 1 and 2). It is important to note that, in most cases, the rates of co-morbidities are higher than in the general Canadian population. Conclusion Children with FASD/PAE are at risk for physical and mental health co-morbidity and on-going risk for developing new and significant health challenges. They should be followed by a community pediatrician. Appropriate anticipatory guidance should be provided to families at check-ups, including referrals for early intervention. A community team to support families caring for complex children optimizes developmental outcomes, reducing the burden of care. Understanding complexities of PAE changes how we consider public health policy/service delivery.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".