Prevalence of Parkinson’s disease across North America
Bibliographic record
Abstract
Estimates of the prevalence of Parkinson's disease in North America have varied widely and many estimates are based on small numbers of cases and from small regional subpopulations. We sought to estimate the prevalence of Parkinson's disease in North America by combining data from a multi-study sampling strategy in diverse geographic regions and/or data sources. Five separate cohort studies in California (2), Minnesota (1), Hawaii USA (1), and Ontario, Canada (1) estimated the prevalence of PD from health-care records (3), active ascertainment through facilities, large group, and neurology practices (1), and longitudinal follow-up of a population cohort (1). US Medicare program data provided complementary estimates for the corresponding regions. Using our age- and sex-specific meta-estimates from California, Minnesota, and Ontario and the US population structure from 2010, we estimate the overall prevalence of PD among those aged ≥45 years to be 572 per 100,000 (95% confidence interval 537-614) that there were 680,000 individuals in the US aged ≥45 years with PD in 2010 and that that number will rise to approximately 930,000 in 2020 and 1,238,000 in 2030 based on the US Census Bureau population projections. Regional variations in prevalence were also observed in both the project results and the Medicare-based calculations with which they were compared. The estimates generated by the Hawaiian study were lower across age categories. These estimates can guide health-care planning but should be considered minimum estimates. Some heterogeneity exists that remains to be understood.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".