A comparison of different methods for estimating the prevalence of problematic drug misuse in Great Britain
Bibliographic record
Abstract
AIMS: The European Monitoring Centre for Drugs and Drug Addiction (EMCDDA) has produced methodological guidelines for national drug prevalence estimation. This paper pilots the methods to estimate prevalence for Great Britain and provides a commentary on the methods and resulting estimates. DESIGN: Three types of methodology were used to estimate prevalence: (a) the multiple indicator (MI) method, (b) multipliers applied to (i) drug-treatment records (ii) HIV estimates and (iii) mortality statistics and (c) the British/Scottish Crime Surveys. SETTING: England, Scotland and Wales. PARTICIPANTS: Aggregated data on people recorded on databases and respondents in household surveys. MEASUREMENTS: Prevalence estimates of different forms of problematic drug use. FINDINGS: The estimates are 161,133 (range: 120,850-241,700) for people at risk of mortality due to drug overdose; 161,000-169,000 for people who have ever injected drugs; 202,000 (range: 162,000-244,000) problem opiate users and 268,000 problem drug users (all types). CONCLUSIONS: The multiple indicator method offers a comprehensive approach to estimating the prevalence of problematic drug use in the United Kingdom. Simple multiplier methods and household surveys also provide a range of estimates corresponding to different types of drug use in the United Kingdom. The current study suggests that previous national estimates of 100,000-200,000 were conservative. The new estimate of 161,000-266,000 should enable a more focused response. For further development of this method, reliable and timely estimates of anchor points are required for specific geographical areas such as cities or Drug Action Teams (DAT), as well as routine aggregation of drug indicators for these areas.
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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 | 0.097 | 0.264 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".