The association between cognitive ability and opioid prescribing in vulnerable older adults with chronic pain in ambulatory care: a secondary data analysis using the Medical Expenditure Panel Survey
Bibliographic record
Abstract
BACKGROUND: Vulnerable older adults living with Alzheimer's disease or Alzheimer's disease and related dementia (AD/ADRD) and chronic pain generally receive fewer pain medications than individuals without AD/ADRD, especially in nursing homes. Little is known about pain management in older adults with AD/ADRD in the community. The aim of the study was to examine opioid prescribing patterns in individuals with chronic pain by levels of cognitive ability in ambulatory care. METHODS: We used the Medical Expenditure Panel Survey (MEPS), years 2002-2017, and identified three levels of cognitive impairment: no cognitive impairment (NCI), individuals reporting cognitive impairment (CI) without an AD/ADRD diagnosis, and individuals with a diagnosis of AD/ADRD. We examined any receipt of an opioid prescription and the number of opioid prescriptions using a logistic and negative binomial regression adjusting for sociodemographic and health characteristics and stratifying by three types of chronic pain (any chronic pain, severe chronic pain, and chronic pain identified through ICD 9/10 chronic pain diagnoses). RESULTS: Among people with any chronic pain, adjusted odds of receiving an opioid for people with CI (OR 1.41, 95% confidence interval 1.31-1.52) and AD/ADRD (OR 1.23, 95% confidence interval 1.04-1.45) were higher compared to NCI. Among people with chronic pain ICD 9/10 conditions, the odds of receiving an opioid were also higher for those with CI (OR 1.43, 95% confidence interval 1.34-1.56) and AD/ADRD (OR 1.48, 95% confidence interval 1.23-1.78) compared to NCI. Among those with severe chronic pain, people with CI were more likely to receive an opioid (OR 1.17, 95% confidence interval 1.07-1.27) relative to NCI (OR 0.89, 95% confidence interval 0.75-1.06). People with AD/ADRD experiencing severe chronic pain were not more likely to receive an opioid compared to the NCI group. Adjusted predicted counts of opioid prescriptions showed more opioids in CI and AD/ADRD in all chronic pain cohorts, with the largest numbers of opioid prescriptions in the severe chronic pain and ICD 9/10 diagnoses groups. CONCLUSIONS: The results suggest increased opioid use in people living with CI and AD/ADRD in the ambulatory care setting and potentially indicate that these individuals either require more analgesics or that opioids may be overprescribed. Further research is needed to examine pain management in this vulnerable population.
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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 | 0.017 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 |
| 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".