Time Trends in Opioid Use Disorder Hospitalizations in Gout, Rheumatoid Arthritis, Fibromyalgia, Osteoarthritis, and Low Back Pain
Bibliographic record
Abstract
OBJECTIVE: To examine opioid use disorder (OUD)-related hospitalizations and associated healthcare utilization outcomes in people with 5 common musculoskeletal diseases (MSD). METHODS: We used the US National Inpatient Sample (NIS) data from 1998 to 2014 to examine the rates of OUD hospitalizations (per 100,000 NIS claims overall), time trends, and outcomes in 5 common rheumatic diseases: gout, rheumatoid arthritis (RA), fibromyalgia (FM), osteoarthritis (OA), and low back pain (LBP). RESULTS: OUD hospitalization rate per 100,000 total NIS claims in 1998-2000 vs 2015-2016 (and increase) were as follows: gout, 0.05 vs 1.88 (36-fold); OA, 0.68 vs 10.22 (14-fold); FM, 0.53 vs 6.98 (12-fold); RA, 0.30 vs 3.16 (9.5-fold); and LBP, 1.17 vs 7.64 (5.5-fold). The median hospital charges and hospital stays for OUD hospitalizations were as follows: gout, $18,363 and 2.5 days; RA, $17,398 and 2.4 days; FM, $15,772 and 2.1 days; OA, $16,795 and 2.4 days; and LBP, $13,722 and 2.0 days. In-hospital mortality rates ranged from 0.9% for LBP and FM to 1.7% for gout with OUD hospitalizations. Compared to those without each MSD, age-, sex-, race-, and income-adjusted total hospital charges (inflation-adjusted) for OUD hospitalizations with each rheumatic disease were as follows: gout, $697 higher; OA, $4759 lower; FM, $2082 lower; RA, $1258 lower; and LBP, $4944 lower. CONCLUSION: OUD hospitalizations increased in all 5 MSD studied, but the rate of increase differed. Awareness of these OUD hospitalization trends in 5 MSD among providers, policy makers, and patients is important. Development and implementation of interventions, policies, and practices to potentially reduce OUD-associated effects in people with rheumatic diseases is needed.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".