Disparities in Pain Among Nursing Home Residents: Race, Cognitive Impairment, and Nursing Home Racial Composition
Bibliographic record
Abstract
Abstract Pain is common among nursing home (NH) residents and can significantly impact quality of end-of-life experiences, yet inequities in pain assessment and treatment persist and are less studied among racial groups with smaller population sizes. This study examined associations between resident race, cognitive impairment, and NH racial composition with any pain documented in the last five days. We conducted a longitudinal multi-level logistic regression using MDS 3.0, LTCFocus, and MBSF data. We focused on residents who died in 2018 or 2019, regardless of place of death (n = 617,922). Analyses were stratified by staff- and self-reported pain, controlling for resident age, function, comorbidity, gender, and NH percent Medicaid, bed number, staffing, ownership, and rurality. In the initial adjusted models, both staff- and self-reported pain increased over the year before death. Staff- and self-reported pain decreased as cognitive impairment increased, and this was more dramatic in the self-reported model. In both models, pain varied by race, with American Indian or Alaskan Native residents having the highest rates, followed by Hispanic and White residents at similar rates; Black residents had slightly lower rates, followed by Asian residents, and Native Hawaiian or other Pacific Islander residents at the lowest. Pain was more frequently reported in predominately White NHs than in more racially diverse NHs, especially in the staff-reported model. Disparities may reflect underassessment of pain and inappropriate pain assessment methods, particularly at the NH-level in facilities that included more racially minoritized residents. Further research is needed to clarify these disparities and ensure equitable pain management.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".