The Prevalence of Cognitive Impairment and Dementia in Incarcerated Older Adults
Bibliographic record
Abstract
OBJECTIVES: In view of the growing number of older incarcerated persons in the United States, cognitive impairment represents one of the most challenging and costly health care issues facing the U.S. correctional system. This study examined the prevalence and correlates of this growing public health issue in the nation's largest prison system. METHODS: In this study of a random sample of 143 older (≥55 years) adults incarcerated in the Texas prison system, we assessed-using the Montreal Cognitive Assessment (MoCA)-the percentage of inmates who met the MoCA thresholds for mild cognitive impairment (MCI; <23) and dementia (<18). Due to sample size limitations, our multivariable analysis assessed the binary outcome, MoCA <23. RESULTS: Overall, 35.0% of our random sample of incarcerated older adults in Texas met the threshold for MCI and 9.1% met the threshold for dementia. After adjusting for covariates, study participants who were Black (odds ratio [OR] = 4.12, 95% confidence interval [CI] = 1.57-10.82), Hispanic (OR = 4.34, 95% CI = 1.46-12.93), and those with a diagnosis of major depressive disorder (8.56, 95% CI = 1.21-60.72) all had higher prevalence of a positive screen for MCI or dementia. Dementia was underdiagnosed in our study sample of incarcerated adults, with 15.4% of MoCA-diagnosed dementia patients having a dementia diagnosis documented in their medical records. DISCUSSION: Future studies of cognitive impairment in prisons and jails can inform health care planning and resource allocation, such as expansion of access to palliative care, advance care planning, and targeted cognitive screening in older age groups.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".