How does the increase in eating difficulties according to the Development and Well‐Being Assessment screening items relate to the population prevalence of eating disorders? An analysis of the 2017 Mental Health in Children and Young People survey
Bibliographic record
Abstract
OBJECTIVE: We examine the test accuracy of the Development and Well-Being Assessment (DAWBA) eating disorder screening items to explore whether the increased eating difficulties detected in the English National Mental Health of Children and Young People (MHCYP) Surveys 2021 reflect an increased population prevalence. METHODS: Study 1 calculated sensitivity, specificity, and positive and negative predictive values from responses to the DAWBA screening items from 4057 11-19-year-olds and their parents, in the 2017 MHCYP survey. Study 2 applied the positive predictive value to data from 1844 11-19-year-olds responding to the 2021 follow-up to estimate the prevalence of eating disorders in England compared to 2017 prevalence. RESULTS: Parental report most accurately predicted an eating disorder (93.6%, 95% confidence interval: 92.7-94.5). Sensitivity increased when parent and child answers were combined, and with a higher threshold (of two) for children. The prevalence of eating disorders in 2021 was 1% in 17-19-year-olds, and .6% in 11-16-year-olds-similar to the prevalence reported in 2017 (.8% and .6%, respectively). However, estimates for boys (.2%-.4%) and young men (.0%-.4%) increased. DISCUSSION: We found tentative evidence of increased population prevalence of eating disorders, particularly among young men. Despite this, the DAWBA screening items are useful for ruling out eating disorders, particularly when parents or carers screen negative, but are relatively poor at predicting who will have a disorder. Data from both parents and children and applying a higher cut point improves accuracy but at the expense of more missed cases. PUBLIC SIGNIFICANCE STATEMENT: The prevalence of eating disorders did not markedly change from 2017 to 2021, but we found tentative evidence of an increase, particularly among young men. This is despite larger increases in problematic eating, which need further investigation. The DAWBA screen is best suited to ruling out eating disorders which limits its clinical applications as it would provide many false positives requiring further assessment.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".