Prevalence of Mental Disorders in Children Living in Alberta, Canada, as Determined From Physician Billing Data
Bibliographic record
Abstract
BACKGROUND: The prevalence of mental disorders is often assessed using survey techniques. Although providing good estimates of prevalence, these techniques are time-consuming and expensive. OBJECTIVE: To estimate the prevalence of mental disorders among children aged 0 to 17 years living in Alberta, Canada, using health care administrative data. DESIGN: This was a cross-sectional study. International Classification of Diseases, Ninth Revision, Clinical Modification chapter 5 diagnostic codes from physician billing data were used. Codes were grouped into 10 categories. Prevalence rates for each category were calculated, stratified by age, sex, and premium subsidy status (a proxy for socioeconomic status). The age pattern, times of greatest risk, and the effect of sex on type and prevalence of mental disorder were estimated. SETTING: All fee-for-service health care venues in Alberta between April 1, 1995, and March 31, 1996, providing services to children registered with the Alberta Health Care Insurance Commission on March 31, 1996. RESULTS: Prevalence of mental disorders varied by disorder category, age, sex, and premium subsidy status. For boys, maximum prevalence of 9.5% occurred at age 10 years; for girls, maximum prevalence of 12.0% occurred at age 17 years. Mental disorders were most common in young boys and adolescent girls and among children receiving welfare. Distinct patterns of disorder were evident and comorbidity was common. CONCLUSIONS: Administrative data can be used to estimate the prevalence of mental disorders in a pediatric population. The estimates made are lower than those obtained by using surveys of similar populations, perhaps indicating the difference between treated and untreated prevalence. Strengths of this study are that the estimates reflect the entire population, are more easily and obtained at less cost, and are useful for the planning of mental health services.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".