Investigating the association between atypical antipsychotic medication use and falls among personal care home residents in the Winnipeg Health Region
Bibliographic record
Abstract
Falls among older adults (age 65 years and older) residing in personal care homes (PCHs) are an important health concern. Atypical antipsychotic drugs (AADs) have been shown to be associated with fall risk among older adults. However, previous studies face some methodological limitations that affect the quality, consistency, and comparability of these studies. Therefore, a population-based study was undertaken to examine the effect of AAD use on the risk of falling among older PCH residents. A nested case-control study was conducted using the administrative healthcare records and Minimum Data Set for PCHs (MDS) housed at the Manitoba Centre for Health Policy in the Faculty of Medicine, University of Manitoba. The study period was from April 1, 2005 to March 31, 2007. Cases (n=626) were fallers as recorded in MDS. Using incidence density sampling, each case was matched to four controls on length of PCH stay, age, and sex (n=2,388). Exposure to AADs was obtained from the Drug Program Information Network database. Conditional logistic regression was used to model the effects of AAD use on the risk of falling while accounting for matching and for confounding of other covariates. While the adjusted odds of falling was statistically greater for AAD users versus nonusers (adjusted odds ratio = 1.60, 95% CI 1.10-2.32), this association was type and dose dependent. Compared to nonusers, the odds of falling was greater for quetiapine users, regardless of this drug's dose, and high dose risperidone users. On the other hand, low dose risperidone and olanzapine, irrespective of drug dose, use was not associated with the risk of falling. Furthermore, the effect of AAD use, in general, on the risk of falling was significantly greater for people with wandering problems (adjusted odds ratio = 1.84, 95% CI 1.09-3.09). Despite some methodological limitations, this research has provided some unique findings that enhance our understanding of AAD use as a fall risk factor. Study findings allow policymakers to further develop evidence-based interventions specific to AADs in order to better manage falls in the PCH setting. However, a great deal of research is still needed to address other important unanswered questions.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".