Ethnic and Immigrant Variations in the Time Trends of Dementia and Parkinsonism
Bibliographic record
Abstract
OBJECTIVE: We assessed long-term incidence and prevalence trends of dementia and parkinsonism across major ethnic and immigrant groups in Ontario. METHODS: Linking administrative databases, we established two cohorts (dementia 2001-2014 and parkinsonism 2001-2015) of all residents aged 20 to 100 years with incident diagnosis of dementia (N = 387,937) or parkinsonism (N = 59,617). We calculated age- and sex-standardized incidence and prevalence of dementia and parkinsonism by immigrant status and ethnic groups (Chinese, South Asian, and the General Population). We assessed incidence and prevalence trends using Poisson regression and Cochran-Armitage trend tests. RESULTS: Across selected ethnic groups, dementia incidence and prevalence were higher in long-term residents than recent or longer-term immigrants from 2001 to 2014. During this period, age- and sex-standardized incidence of dementia in Chinese, South Asian, and the General Population increased, respectively, among longer-term immigrants (by 41%, 58%, and 42%) and long-term residents (28%, 7%, and 4%), and to a lesser degree among recent immigrants. The small number of cases precluded us from assessing parkinsonism incidence trends. For Chinese, South Asian, and the General Population, respectively, prevalence of dementia and parkinsonism modestly increased over time among recent immigrants but significantly increased among longer-term immigrants (dementia: 134%, 217%, and 117%; parkinsonism: 55%, 54%, and 43%) and long-term residents (dementia: 97%, 132%, and 71%; parkinsonism: 18%, 30%, and 29%). Adjustment for pre-existing conditions did not appear to explain incidence trends, except for stroke and coronary artery disease as potential drivers of dementia incidence. CONCLUSION: Recent immigrants across major ethnic groups in Ontario had considerably lower rates of dementia and parkinsonism than long-term residents, but this difference diminished with longer-term immigrants.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".