Opioid use disorder among females with breast cancer: A comprehensive analysis of prevalence in the United States and associated factors.
Bibliographic record
Abstract
1118 Background: Patients with breast cancer (BC) are frequently prescribed opioids for pain management, placing them at risk of opioid use disorder (OUD). This study analyzes the prevalence of OUD and identifies factors contributing to its risk among BC patients in the United States. Methods: We conducted a retrospective analysis using the National Inpatient Sample (NIS) database, a Healthcare Cost and Utilization Project (HCUP) component. Females with BC were identified through ICD-10 codes. The Cochran-Armitage trend test assessed OUD prevalence trends from 2016 to 2022. Multivariable regression models estimated the impact of multiple patient demographics and comorbidities on the presence of OUD. Results: Among 1,189,884 females aged≥18 with BC, 2.3% (27,500) had OUD. The mean age of OUD patients was 58.38 years, compared to 64.46 years in the non-OUD cohort. OUD prevalence was highest in those aged 18–50 years (3.8%), followed by 51–60 years (3.0%), and lowest in those > 60 years (1.7%). Between 2016 and 2020, OUD prevalence increased from 1.9% to 2.8%, followed by a decline to 2.4% in 2022 (p-trend < 0.01). Factors that were linked with higher OUD involved patients with neoplasm-related pain(NRP)(aOR 5.718, 95% CI 5.549-5.893, p < 0.01), on palliative care (aOR 1.397, 95% CI 1.353-1.443, p < 0.001), with metastasis (aOR 1.573, 95% CI 1.526-1.621, p < 0.01), depression (aOR 1.447, 95% CI 1.400-1.495, p < 0.01), bipolar disorder (aOR 2.173, 95% CI 2.038-2.317, p < 0.01), suicidality (aOR 3.228, 95% CI 2.938-3.546, p < 0.01), and anxiety (aOR 1.617, 95% CI 1.572-1.664, p < 0.01). Moreover, substance use such as cocaine (aOR 5.252, 95% CI 4.708-5.859, p < 0.01) and amphetamine (aOR 3.948, 95% CI 3.443-4.527, p < 0.01) was also associated with higher odds, while cannabis users (aOR 0.876, 95% CI 0.793-0.968, p < 0.01) had lower odds of OUD. Our study further found racial disparities, with reduced odds among Blacks ( vs Whites, aOR 0.933, 95% CI 0.901-0.967, p < 0.01) and Hispanics (vs.Whites, aOR 0.866, 95% CI 0.827-0.906, p < 0.01). Socio-economic differences were also noted, with lower odds among those of the 26th-50th (vs. 0-25th, aOR 0.932, 95% CI 0.9-0.966, p < 0.01), 51st-75th (vs. 0-25th, aOR 0.951, 95% CI 0.918-0.986, p < 0.01), and 76th-100th (vs. 0-25th, aOR 0.916, 95% CI 0.882-0.951, p < 0.01) household income quartiles. Conclusions: This study showcases the significant prevalence and impact of OUD among BC patients, identifying socioeconomic and racial disparities, and key risk factors such as NRP, psychiatric comorbidities, and concurrent substance use, like cocaine and amphetamines. Interestingly, cannabis use was associated with a lower risk of OUD, which may reflect its role as an alternative pain management strategy. Overall, this study suggests the need to adopt crucial preventative measures against OUD in patients exhibiting these characteristics.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".