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Record W1727275461 · doi:10.1111/add.12308

Variations in prescription opioids and related harms: a key to understanding and effective policy

2014· article· en· W1727275461 on OpenAlexaboutno aff
Jeffrey H. Samet, Judith I. Tsui

Bibliographic record

VenueAddiction · 2014
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsPsychological interventionMedical prescriptionDilemmaHealth careMedicineProfit (economics)Public economicsPublic relationsBusinessPsychologyPsychiatryPolitical scienceNursingEconomicsLaw

Abstract

fetched live from OpenAlex

Why does North America have the dubious distinction of excelling in the non-medical use of prescription opioids (POs) and incurring their related harms? Fischer et al. present an analysis comparing North America to the rest of the world and identify factors that resonate with common sense 1. The three factors identified (i.e. consumption of more POs, less regulatory access restrictions and patient expectations for effective treatment within a ‘for-profit’ oriented health-care system) lead to an unfortunate reality: exposure to opioids seems omnipresent in North America. These authors have utilized an established health services research tool, namely identifying variation in practice patterns, to identify big issues that probably directly and indirectly impact the indisputable finding of opioid prescribing problems in North America. However, the anticipated chorus of calls to ‘shut down the candy store’ risk oversimplifying the solution to the dilemma: how do we adequately treat pain and simultaneously avoid such pervasive exposure to opioids that, invariably, adverse consequences ensue? While Fischer and colleagues present a substantive and thought-provoking descriptive review of differences in health systems, policy, regulations and culture between North America and other high-income countries which may explain the disparity in non-medical use of POs and subsequent harms, there is still a need for detail about the evidence linking those factors to harms. What are the relative contributions of the factors that they outline? What does variation in practices to mitigate such harms reveal about potential interventions to address these problems? This information is key in order to devise effective strategies to combat prescription opioid abuse. One approach that merits serious consideration is to pursue further the path that these authors initiated, by applying the methodology of studying small area variations in the non-medical use of POs and their related harms 2. A logical next step is to circle back to North America to examine what is happening in different regions within the United States and Canada. It is very likely that what happens in Tampa, Florida does not occur in Sacramento, California. Indeed, prior research has demonstrated state and regional differences in opioid prescribing in the United States 3-5. However, we still lack detailed evidence regarding whether these variations translate into harms related to non-medical use of prescription opioids, although some preliminary research may suggest that this is the case 6. Research on other medical conditions has uncovered geographic variation in the United States which many have suggested reflects patterns of overuse that do not translate into tangible benefits (and may even be linked to harm) 7, 8. Others have pointed out that variation may relate more to ‘discretionary decision-making’ (i.e. medical decisions for which there is little evidence to provide guidance) 9. Certainly the lack of high-quality evidence as to whether opioids are effective and safe for chronic pain 10, 11 creates a scenario ripe for practice heterogeneity. In the absence of firm guidelines, providers may be pushed and pulled by the various forces described in this review. The challenge that must be taken up is to measure these relationships in a quantifiable way. Therein lies an opportunity for further understanding of a perplexing and tragic phenomenon, the implication of medical practice contributing to the most common cause of death of young people, overdose and poisoning, exceeding that of motor vehicle crashes in the United States since 2008 12, 13. What variations might be uncovered? What findings might lead to practical policy changes? How can such new knowledge alter public opinion and health-care providers practice patterns? We will offer a few possibilities. One could compare states with and without more strict regulatory policies for opioid prescribing or that have enacted Physician Monitoring Programs 14, 15. Such an analytical approach has been conducted effectively in past research related to driving and legal limits for alcohol use 16. These analyses might yield insight about the impact of such interventions on non-medical use of prescription opioids and related harms. Further examination is possible of small area (e.g. zipcode) variation in the prescription of opioids to determine if it indeed tracks directly to prescription opioid-related harms. If such a finding were uncovered, it would be potent data to share with prescribers as, in reality, physicians do not want to be unaware co-conspirators in this epidemic. ‘Primum non nocere—first do no harm’, as stated in the Hippocratic oath, is in fact taken seriously by most physicians. A third example could compare regions that require continuing medical education on safe opioid prescribing in order to be re-credentialed by the jurisdiction's medical board to those that have no comparable stipulation. Finally, mapping health-care teams that follow recommended safe opioid prescribing practices 17 and comparing such geographic regions to those that do not use such consensus guidelines might reveal differences in key outcomes. These are four examples of many potential approaches to examine practice variation as a means to provide further insight about how best to address a recognized problem that has indisputably and disproportionately adversely impacted North America. None.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.486
Threshold uncertainty score0.306

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.265
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2014
Admission routes1
Has abstractyes

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