Care trajectories before and after a first diagnosis of dementia in patients with schizophrenia or bipolar disorders
Bibliographic record
Abstract
Context: A first diagnosis of dementia further complexifies the healthcare needs of older adults living with severe mental illness. While these individuals have complex healthcare needs and are at higher risk of cognitive impairments, there is no evidence of how their patterns of healthcare use vary over time around the dementia diagnosis. Objective: We aimed to explore the care trajectories (CTs) of older adults living with severe mental illness one year before and one year after a first dementia diagnosis. Study Design and Analysis This is a retrospective cohort study using an innovative multidimensional state sequence analysis approach to develop a typology of CTs. Dataset We used health administrative data from the Quebec (Canada) provincial health insurance board (1996- 2016). Population Studied The cohort included all patients aged 65 years and older who received a first diagnosis of dementia (index date) in 2014-2016 and were previously diagnosed with schizophrenia or bipolar disorder. Outcome Measures We measured CTs according to 1) healthcare settings (e.g., hospital, emergency department, clinic); 2) reasons for healthcare use (e.g., dementia, other mental and non-mental diagnoses), and 3) healthcare professionals (e.g., dementia specialists, psychiatrists, home care professionals). Results A total of 3,868 patients were categorized into seven distinct types of CTs. Type 1 (47.6%) showed the lowest healthcare use and comorbidities. Type 2 (9.9%) and type 3 (63.0%) showed high healthcare and home care use after dementia diagnosis. A high comorbidity and stable healthcare use characterized type 4 (10.3%). Type 5 (9.0%) comprised younger individuals with the highest intensity of mental health-related and psychiatric consultations. Type 6 (10.9%) and type 7 (5.9%) presented a high home care use pattern for non-mental reasons before the dementia diagnosis. The year after, type 6 showed the highest long-term care (37.1%) and death (48.5%) rates, while they remained lower for type 7. Conclusions Our innovative approach provides a unique insight into the complex healthcare patterns of people living with serious mental illness and dementia. It provides an avenue to support data-driven decision-making by highlighting fragility areas in the allocation of care resources.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".