Treatment landscape and caregiver status for Alzheimer’s disease patients in Canada by disease severity: results from a real‐world survey
Bibliographic record
Abstract
Abstract Background Caregiver burden associated with Alzheimer’s disease (AD) increases with AD severity, and current treatment options are limited. We aimed to describe the caregiver and treatment landscapes for patients with AD in Canada, split by disease severity. Method Data were drawn from the Adelphi Real World AD Disease Specific Programme (DSP)™, a cross‐sectional survey of physicians in Canada, from March to October 2023. Physicians reported data on caregiver involvement in the care of patients with mild cognitive impairment (MCI)/AD and on patients’ current treatment profiles in patient record forms. Non‐professional caregivers of these patients were invited to complete a voluntary caregiver survey, which included caregivers’ characteristics. Patients were defined as having MCI or mild, moderate, or severe AD, according to current diagnostic label and physician‐reported disease severity. Analyses were descriptive. Result Fifty physicians reported data on 384 patients with MCI/AD (mean age 77.6±9.1 years, 54.2% female). Data from 41 non‐professional caregivers was captured in the caregiver survey. The average number of caregivers per patient overall was 1.7 (1.0) and 2.1 (1.1) for patients with severe AD (Table 1). Professional caregivers were primarily home help (49.2%). Most patients (77.8%) received non‐professional care. Partners cared for 60.5% of patients overall and 58.6% of patients with severe AD. Non‐professional caregivers spent 87.7±65.0 hours per week on average caring for patients with severe AD and 63.7±63.8 hours per week caring for patients overall. The symptom caregivers cited as causing them the most trouble was the patient forgetting recent events (52.6%) (Table 2). Patients needed most help with preparing meals or cooking (75.6%). Physicians indicated that 49.5% of patients were receiving treatment for AD symptoms, primarily acetylcholinesterase inhibitors (91.1%; Table 3). Treatment was being received by 48.6% of patients with severe AD versus 61.8% of patients with moderate AD and 58.5% of patients with mild AD. Conclusion The burden of care on the family of patients with AD in Canada is substantial. More caregivers and caregiver time were required on average for patients with severe AD than patients overall, and fewer patients with severe AD received treatment for AD.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".