Retrospective database analysis on the demographics and resource utilization of patients with Parkinson’s disease in Quebec, Canada
Bibliographic record
Abstract
Introduction: Parkinson's disease (PD) is the most prevalent neurodegenerative movement disorder. Despite its recognized significance, there remains a paucity of recent studies reporting treatment utilization and the economic impact of PD in a real-world setting, especially in Canada. This study aimed to analyze real-world treatment patterns and health care resource utilization (HCRU) of patients with PD in Quebec, Canada. Methods: This was a retrospective observational study using data between 2010-2019 from the Régie de l'assurance maladie du Québec (RAMQ) databases. Patients with PD were compared to age- and sex-matched controls. Treatment adherence and persistence were measured over 24 months. All-cause and PD-related HCRU and costs were characterized on an annual basis. Results: Overall, 303 PD patients and 909 age- and sex-matched controls were selected. Adherence rates were high (≥85 %) among all drug classes, but lower with dopamine agonists. Persistence to PD treatment declined over time, with nearly 50 % discontinuation rates at 24 months in all PD drug classes, except the levodopa class (discontinuation rate: 20.4 %). PD patients had a significantly higher total costs per year than the matched control group ($17,405 vs. $6,431), mainly driven by higher inpatient costs. Conclusion: Many pharmacological options exist for PD patients and, though patients are adherent while on therapy, treatment discontinuation rates are high. This suggests potential long-term challenges in PD management, especially since PD continues to place a substantial burden on the health care system. This study underscores the need for enhanced therapeutic strategies, particularly for patients inadequately controlled with standard therapies.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".