What if Acupuncture Were Covered by Insurance for Pain Management? A Cross-Sectional Study of Cancer Patients at One Academic Center and 11 Community Hospitals
Bibliographic record
Abstract
OBJECTIVE: In response to the national opioid crisis, governmental and medical organizations have called for broader insurance coverage of acupuncture to improve access to nonpharmacologic pain therapies, especially in cancer populations, where undertreatment of pain is prevalent. We evaluated whether cancer patients would be willing to use insurance-covered acupuncture for pain. DESIGN AND SETTING: We conducted a cross-sectional survey of cancer patients with pain at one academic center and 11 community hospitals. METHODS: We used logistic regression models to examine factors associated with willingness to use insurance-covered acupuncture for pain. RESULTS: Among 634 cancer patients, 304 (47.9%) reported willingness to use insurance-covered acupuncture for pain. In univariate analyses, patients were more likely to report willingness if they had severe pain (odds ratio [OR] = 1.59, 95% confidence interval [CI] = 1.03-2.45) but were less likely if they were nonwhite (OR = 0.59, 95% CI = 0.39-0.90) or had only received high school education or less (OR = 0.46, 95% CI = 0.32-0.65). After adjusting for attitudes and beliefs in multivariable analyses, willingness was no longer significantly associated with education (adjusted OR [aOR] = 0.78, 95% CI = 0.50-1.21) and was more negatively associated with nonwhite race (aOR = 0.49, 95% CI = 0.29-0.84). CONCLUSIONS: Approximately one in two cancer patients was willing to use insurance-covered acupuncture for pain. Willingness was influenced by patients' attitudes and beliefs, which are potentially modifiable through counseling and education. Further research on racial disparities is needed to close the gap in utilization as acupuncture is integrated into insurance plans in response to the opioid crisis.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".