Time Trends of the Incidence, Prevalence, and Mortality of Parkinsonism
Bibliographic record
Abstract
OBJECTIVES: We assessed trends in the incidence, prevalence, and post-diagnosis mortality of parkinsonism in Ontario, Canada over 18 years. We also explored the influence of a range of risk factors for brain health on the trend of incident parkinsonism. METHODS: We established an open cohort by linking population-based health administrative databases from 1996 to 2014 in Ontario. The study population comprised residents aged 20-100 years with an incident diagnosis of parkinsonism ascertained using a validated algorithm. We calculated age- and sex-standardized incidence, prevalence, and mortality of parkinsonism, stratified by young onset (20-39 years) and mid/late onset (≥40 years). We assessed trends in incidence using Poisson regression, mortality using negative binomial regression, and prevalence of parkinsonism and pre-existing conditions (e.g., head injury) using the Cochran-Armitage trend test. To better understand trends in the incidence of mid/late-onset parkinsonism, we adjusted for various pre-existing conditions in the Poisson regression model. RESULTS: From 1996 to 2014, we identified 73,129 incident cases of parkinsonism (source population of ∼10.5 million), of whom 56% were male, mean age at diagnosis was 72.6 years, and 99% had mid/late-onset parkinsonism. Over 18 years, the age- and sex-standardized incidence decreased by 13.0% for mid/late-onset parkinsonism but remained unchanged for young-onset parkinsonism. The age- and sex-standardized prevalence increased by 22.8%, while post-diagnosis mortality decreased by 5.5%. Adjustment for pre-existing conditions did not appreciably explain the declining incidence of mid/late-onset parkinsonism. CONCLUSION: Young-onset and mid/late-onset parkinsonism exhibited differing trends in incidence over 18 years in Ontario. Further research to identify other factors that may appreciably explain trends in incident parkinsonism is warranted.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".