The treatment gap for deep brain stimulation in Parkinson’s disease: a comparative analysis of cost and utilisation in high-income countries
Bibliographic record
Abstract
OBJECTIVE: Parkinson's disease (PD) is one of the most prevalent neurodegenerative disorders, globally affecting approximately 120 per 100,000 people by age 70. Deep brain stimulation (DBS) is a US Federal Drug Administration (FDA)-approved and highly effective treatment for late-stage PD. However, country-specific reimbursement regulations and health policies may affect access to PD-DBS. We aimed to evaluate the uptake rate and 'treatment gap' for DBS across high-income countries. METHODS: We reviewed previous literature to investigate the cost and utilisation of PD-DBS in high-income countries across Asia, Europe, Oceania, and North America (Australia, Canada, France, Germany, Hong Kong, Japan, Korea, the Netherlands, New Zealand, Norway, Spain, Switzerland, UK, and USA). Using previous estimates of DBS candidate eligibility rates, we calculated theoretical DBS uptake rates and treatment gaps nationally. RESULTS: PD-DBS utilisation was highest in Australia and the USA and lowest in Korea and New Zealand. The total cost of PD-DBS in the first 12 months was highest in the USA and France and lowest in the UK and Germany. The utilisation rate (i.e. uptake rate) of PD-DBS (% DBS surgeries per PD case) was highest in Australia and the USA, and lowest in New Zealand and the UK, where the treatment gap reflected these trends. CONCLUSIONS: Our results highlight differences in access to DBS for PD patients among high-income countries, which we discuss in the context of health systems. Better access to effective PD treatments such as DBS is critical given the increasing prevalence of PD in an ageing world and the associated, avoidable morbidity.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".