Prescription Opioid Abuse: A Literature Review of the Clinical and Economic Burden in the United States
Bibliographic record
Abstract
Between 2002 and 2007, the nonmedical use of prescription pain relievers grew from 11.0 million to 12.5 million people in the United States. Societal costs attributable to prescription opioid abuse were estimated at $55.7 billion in 2007. The purpose of this study was to comprehensively review the recent clinical and economic evaluations of prescription opioid abuse. A comprehensive literature search was conducted for studies published from 2002 to 2012. Articles were included if they were original research studies in English that reported the clinical and economic burden associated with prescription opioid abuse. A total of 23 studies (183 unique citations identified, 54 articles subjected to full text review) were included in this review and analysis. Findings from the review demonstrated that rates of opioid overdose-related deaths ranged from 5528 deaths in 2002 to 14,800 in 2008. Furthermore, overdose reportedly results in 830,652 years of potential life lost before age 65. Opioid abusers were generally more likely to utilize medical services, such as emergency department, physician outpatient visits, and inpatient hospital stays, relative to non-abusers. When compared to a matched control group (non-abusers), mean annual excess health care costs for opioid abusers with private insurance ranged from $14,054 to $20,546. Similarly, the mean annual excess health care costs for opioid abusers with Medicaid ranged from $5874 to $15,183. The issue of opioid abuse has significant clinical and economic consequences for patients, health care providers, commercial and government payers, and society as a whole.
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 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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.021 | 0.024 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".