Socioeconomic variations in the prevalence and incidence of Parkinson's disease: a population-based analysis
Bibliographic record
Abstract
BACKGROUND: For many chronic conditions, lower socioeconomic status is associated with higher rates of disease. Previous research has not investigated whether this inverse relationship exists for Parkinson's disease (PD). The purpose was to investigate the association between socioeconomic status and prevalence and incidence of PD. METHODS: The study was conducted using population-based administrative data from Manitoba, Canada. PD cases were identified from diagnoses in hospital and physician records. Area-level census data on average household income and residential postal codes in health insurance registration files were used to assign PD cases to urban and rural income quintiles. Annual adjusted prevalence and incidence estimates were calculated for fiscal years 1987/88-2006/07. Hypotheses about differences between quintiles in average estimates and average rates of change were tested using generalised linear models with generalised estimating equations. RESULTS: The estimated prevalence of PD increased over the 20-year-period but incidence remained unchanged. In urban regions, average prevalence and incidence estimates were significantly higher for the lowest income quintile than the highest quintile. In rural regions, average prevalence estimates were significantly higher for the lowest quintile than for the highest quintile but incidence estimates were not significantly different. The annual rate of increase in the PD prevalence was significantly different for the lowest urban and rural income quintiles. CONCLUSIONS: There is a greater burden of PD in low-income areas. An understanding of socioeconomic inequalities is useful when formulating hypotheses about factors associated with disease onset and developing equity-oriented policies about access to healthcare resources.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".