Pain Treatment of Underserved Older African Americans
Bibliographic record
Abstract
Older African Americans who experience pain are especially at high risk of underassessment and undertreatment. This study examined patterns and correlates of pain medication use: severity of pain, medical conditions, and access to care. African Americans aged 65 and older were recruited from 16 churches located in south Los Angeles (N = 400). Structured face-to-face interviews and visual inspection of each participant's medications were conducted. More than 39% of participants were aged 75 and older, and 65% were female. Forty-seven percent used at least one type of pain medication. The frequency of pain medication use according to pharmaceutical class was nonopioid, 33%; opioid, 12%; adjuvant, 9%; and other drug, 8%. Seventy-seven percent of nonopioids were nonsteroidal anti-inflammatory drugs (NSAIDs), which 25% of participants with hypertension, 28% with stroke, 26% with kidney disease, and 28% with gastrointestinal problems used. Ninety-eight percent of participants who used NSAIDs, 98% experienced potentially inappropriate medication (PIM) use, 69% experienced drug duplication, and 65% experienced drug-drug interactions. This study suggests severe mismanagement of pain in underserved older African Americans, particularly those with comorbidity, multiple providers, and limited access to health care. The use of pain medication was associated with drug-drug interactions, drug duplication, and PIM use. The data show that many participants with severe pain are not taking pain medication or experience PIM use. One in four participants was taking NSAIDs, which can cause serious side effects in older African Americans with multiple chronic conditions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".