Racial and Ethnic Disparities in Degenerative Lumbar Disc Disease: A Population-Based Study Using the All of Us Research Program
Bibliographic record
Abstract
Background Degenerative lumbar disc disease (DLDD) can contribute to substantial low back pain and radicular leg pain. Emerging evidence suggests that racial and ethnic disparities in DLDD care could impact clinical outcomes, yet these trends remain largely unexplored. This study investigates differences in health literacy, access to care, and healthcare utilization among patients with DLDD using the All of Us (AoU) Research Program. Methods Adults diagnosed with DLDD were identified from the AoU database using ICD-9 and ICD-10 diagnostic codes. Participants were stratified by race and ethnicity into White, Black, Hispanic, and Other. Demographics, socioeconomic status, health literacy (BRIEF score), healthcare access, and treatment utilization across racial and ethnic groups were compared across cohorts using chi-square analyses. Multivariate logistic regressions evaluated these outcomes while adjusting for demographic and socioeconomic status. Results In total, 30,775 participants with DLDD were identified. Most participants were 65+ years (62.8%) and female (65.3%), with 74.0% self-identifying as White, 10.7% as Black, 8.1% as Hispanic, and 7.2% as Other. Compared to Whites, Black and Hispanic participants were significantly more likely to report limited health literacy (White = 15.3%, Black = 22.2%, Hispanic = 28.1%, Other = 21.3%, P < 0.001). In addition, they were more likely to be denied insurance coverage (9.7%, 10.5%, 14.5%, 13.8%) and report difficulty affording care, including prescription medications (11.5%, 20.9%, 19.0%, 18.9%) and follow-up visits (6.2%, 10.0%, 10.7%, 9.3%) (all P < 0.001). Finally, Black participants, in particular, were more likely to receive nonoperative treatments such as physical therapy (25.7%, 28.2%, 23.1%, 25.9%), steroid injections (14.4%, 16.7%, 12.8%, 12.4%), and opioids (49.1%, 53.4%, 42.5%, 48.3%) compared to White participants (all P < 0.001). Many of these disparities persisted in multivariate models after adjusting for demographic and socioeconomic covariates. Conclusion Disparities in DLDD are multifactorial, reflecting the intersection of age, sex, race/ethnicity, comorbidities, and social determinants of health. Despite most participants being Medicare-eligible, minority groups continued to report access and affordability barriers, suggesting the role of underinsurance and coverage gaps. These findings underscore the need for targeted interventions to improve access, promote education, and ensure equitable treatment of DLDD across minority populations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".