Societal Cost of Opioid Use in Symptomatic Knee Osteoarthritis Patients in the United States
Bibliographic record
Abstract
OBJECTIVE: Symptomatic knee osteoarthritis (SKOA) is a chronic, disabling condition, requiring long-term pain management; over 800,000 SKOA patients in the US use opioids on a prolonged basis. We aimed to characterize the societal economic burden of opioid use in this population. METHODS: We used the Osteoarthritis Policy Model, a validated computer simulation of SKOA, to estimate the opioid-related lifetime and annual cost generated by the US SKOA population. We included direct medical, lost productivity, criminal justice, and diversion costs. We modeled the SKOA cohort with a mean ± SD age of 54 ± 14 years and Western Ontario and McMaster Universities Osteoarthritis Index pain score of 29 ± 17 (0-100, 100 = worst). We estimated annual costs of strong ($1,381) and weak ($671) opioid regimens using Medicare fee schedules, Red Book, the Federal Supply Schedule, and published literature. The annual lost productivity and criminal justice costs of opioid use disorder (OUD), obtained from published literature, were $11,387 and $4,264, per-person, respectively. The 2015-2016 Medicare Current Beneficiary Survey provided OUD prevalence. We conducted sensitivity analyses to examine the robustness of our estimates to uncertainty in input parameters. RESULTS: Assuming 5.1% prevalence of prolonged strong opioid use, the total lifetime opioid-related cost generated by the US SKOA population was estimated at $14.0 billion, of which only $7.45 billion (53%) were direct medical costs. CONCLUSION: Lost productivity, diversion, and criminal justice costs comprise approximately half of opioid-related costs generated by the US SKOA population. Reducing prolonged opioid use may lead to a meaningful reduction in societal costs that can be used for other public health causes.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".