Trends in Office-Based Anxiety Treatment Among US Children, Youth, and Young Adults: 2006–2018
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Anxiety disorder diagnoses in office-based settings increased for children through the mid-2010s, but recent changes in diagnosis and treatment are not well understood. The objectives of the current study were to evaluate recent trends in anxiety disorder diagnosis and treatment among children, adolescents, and young adults. METHODS: This study used serial cross-sectional data from the National Ambulatory Medical Care Survey (2006-2018), a nationally representative annual survey of US office-based visits. Changes in anxiety disorder diagnosis and 4 treatment categories (therapy alone, therapy and medications, medications alone, or neither) are described across 3 periods (2006-2009, 2010-2013, 2014-2018). Multinomial logistic regression compared differences in treatment categories, adjusting for age group, sex, and race/ethnicity, contrasting the last and middle periods with the first. RESULTS: The overall proportion of office visits with an anxiety disorder diagnosis significantly increased from 1.4% (95% confidence interval [CI] 1.2-1.7; n = 9 246 921 visits) in 2006 to 2009 to 4.2% (95% CI 3.4-5.2; n = 23 120 958 visits) in 2014 to 2018. The proportion of visits with any therapy decreased from 48.8% (95% CI 40.1-57.6) to 32.6% (95% CI 24.5-41.8), but there was no significant change in the overall use of medications. The likelihood of receiving medication alone during office visits was significantly higher in the last, relative to the first period (relative risk ratio = 2.42, 95% CI 1.24-4.72). CONCLUSIONS: The proportion of outpatient visits that included a diagnosis of anxiety increased over time, accompanied by a decrease in the proportion of visits with therapy.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".