Response to <scp>H</scp>ser <i>et al</i>. (2014): The necessity for more and better data on the global epidemiology of opioid dependence
Bibliographic record
Abstract
We agree with Hser et al. 1 on the importance of renewed efforts to address the major public health burden of opioid dependence 2. Opioid dependence made the greatest by far estimated contribution to disease burden via premature death and disability of all illicit drugs considered in the 2010 Global Burden of Disease Study 3. The need for a concerted, evidence-based response is heightened by the estimated increase in the burden over the past two decades, due largely to increased prevalence of these disorders 2. We also agree with Hser et al. 1 about the major limitations of existing global data on the epidemiology of this disorder, especially in regions other than western Europe, North America and Australia. As we documented in the systematic reviews that informed our modelling 4-6, and have noted elsewhere 7, 8, there are major gaps in the data that vary with the parameter being estimated. There are many more estimates of the prevalence and mortality of opioid dependence than there are of incidence and remission of the disorder. These estimates are also geographically limited, as indicated above. Hser et al. suggest that it is counter-intuitive that disability adjusted life year (DALY) rates were highest in North America and Australasia because treatment and health services are most developed and accessible in these countries. We disagree for two reasons: first, the DALY burden is directly related to the prevalence of the disorder, which was much higher in these countries than most others; and second, even in high-income countries such as the United States, only a minority of opioid-dependent people are offered evidence-based treatment 9. Hser et al. 1 observed that our point estimate of the prevalence of opioid dependence in GBD 2010 was similar to the estimated prevalence of opiate use in the past year (excluding pharmaceutical opioids) in the United Nations Office on Drugs and Crime's World Drug Report 10. It is important to note that both estimates have wide uncertainty intervals around them, and we encourage all readers to take this into account when interpreting our findings, especially in developing countries. As noted by Hser et al. 1, the sources of our estimates of opioid dependence in global burden of disease (GBD) 2010 did not exclude problems related to pharmaceutical opioids. These opioids are important to consider in the United States, Canada and Australia and are important drivers of opioid burden in some countries in South Asia and eastern Europe. Despite these acknowledged limitations, there is public health value in producing estimates of global burden that are based upon conservative assumptions and include clearly described levels of uncertainty. First, GBD 2010 has an important role in heightening attention to opioid dependence at global, regional and national levels. The updates that will be provided through GBD 2.0 11 will improve upon these estimates by estimating the epidemiology and health burden of a full range of injuries and diseases, using comparable metrics and a consistent estimation framework for each disorder. If no estimates are made while we await better data to be collected, then we run the risk that the burden of opioid dependence will be ignored by policy makers. Secondly, the quantification of the uncertainty around our estimates also serves to highlight the gaps in epidemiological data. This should stimulate the efforts to undertake better studies that will produce more reliable and less uncertain estimates of the epidemiological parameters needed to estimate burden, prevalence, incidence, mortality and remission. L.D. has received untied educational grants from Reckitt Benckiser for the post-marketing surveillance of opioid substitution therapy medications in Australia. All such studies' design, conduct and interpretation of findings are the work of the investigators; the funders had no role in those studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".