Depression in children and youth in British Columbia : an analysis of prevalence and pharmacological treatment trends
Bibliographic record
Abstract
Depression often manifests prior to young adulthood. Rates of depression and associated rates of treatment with antidepressants in children and youth have long been on the rise, but pre-pandemic estimates in Canada in general and British Columbia (BC) more specifically are largely out of date. It is also unknown to what extent prescriptions of antidepressants follow Canadian evidence-based guidelines. Trends in depression diagnoses and antidepressant treatment in children and youth in BC were examined. Using administrative data including medical service payment information and prescriptions, provided by Population Data BC, this work describes yearly incidence and prevalence of depression diagnoses in children and young adults in BC from 2008 to 2019, examining each by socio-demographic characteristics including age, administrative sex, health service delivery area, and neighbourhood income decile. Prescribing trends in the same period were examined using descriptive analyses and a logistic regression model to examine on- versus off-guideline prescriptions by the socio-demographic characteristics of interest. The sensitivity of the results to an adjusted operational definition of depression was also explored. Yearly incidence of depression increased from 1.2% of children and young adults in BC in 2008 to 1.9% in 2019 while lifetime prevalence of depression increased from 6.3% to 11.0%. Estimated “active” depression prevalence increased too, affecting 5.8% of BC’s children and young adults in 2019 compared to 3.2% in 2008. Antidepressant prescriptions to children and young adults with incident depression nearly doubled from 27% of newly diagnosed cases in 2008 to 41% in 2019. In line with guidelines, escitalopram and fluoxetine were the most prescribed first line antidepressants, but concordance with guidelines was overall lower than expected at just over 75% from 2008-2019. Concordance with guidelines differed significantly by age, administrative sex, health service delivery area, and to a lesser extent, neighbourhood income decile. Depression diagnoses in children and young adults in BC increased significantly leading up to the pandemic, as did antidepressant prescriptions. However, not all prescribing followed the best-evidence guidelines, and odds of receiving a best-evidenced first prescription differed by socio-demographic characteristics. Further research into these disparities is warranted.
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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 | 0.003 | 0.010 |
| 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) | 0.002 | 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".