The Impact of Dementia and Language on Hospitalizations. A Retrospective Cohort of Long-Term Care Residents.
Bibliographic record
Abstract
Abstract Background: Hospitalizations carry considerable risks for frail, elderly patients; this is especially true for patients with dementia, who are more likely to experience delirium, falls, functional decline, iatrogenic complications, and infections when compared to their peers without dementia. Since up to two thirds of patients in long-term care (LTC) facilities have dementia, there is interest in identifying factors associated with transitions from LTC to hospitals. The purpose of this study was to investigate the association between dementia status and incidence of hospitalization among residents in LTC in Ontario, Canada, and to determine whether this association is modified by linguistic factors. Methods: We used linked administrative databases to establish a prevalent cohort of 81,188 residents in 628 LTC facilities from April 1 st 2014 to March 31, 2017. Diagnoses of dementia were identified with a previously validated algorithm; all other patient characteristics were obtained from in-person assessments. Residents’ primary language was coded as English or French; facility language (English or French) was determined using language designation status according to the French Language Services Act . We identified all hospitalizations within 3 months of the first assessment performed after April 1 st 2014. We performed multivariate logistic regression analyses to determine the impact of dementia and resident language on the incidence of hospitalization; we considered interactions between dementia and both resident language and resident-facility language discordance. Results: The odds of hospitalization were 39% lower for residents with dementia compared to residents without dementia (OR 0.61, 95% CI 0.57–0.65). Francophones had lower odds of hospitalization than Anglophones, but this difference was not statistically significant (OR 0.91, 95% CI 0.81 – 1.03). However, Francophones without dementia were significantly less likely to be hospitalized compared to Anglophones without dementia (OR 0.71, 95% CI 0.53 – 0.94). Resident-facility language discordance did not significantly affect hospitalizations. Conclusions: Residents in LTC facilities were generally less likely to be hospitalized if they had dementia, or if their primary language was French and they did not have dementia. These findings could be explained by differences in end-of-life care goals; however, they could also be the result of poor patient-provider communication.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".