Exploring the Implications of Small-Area Variation in the Incidence of Multiple Sclerosis
Bibliographic record
Abstract
In this study, we describe the geospatial variation in the incidence of multiple sclerosis (MS) in Manitoba, Canada, and the sociodemographic characteristics associated with MS incidence. By using administrative health data, we identified all incident cases of MS in Manitoba from 1990 to 2006 (n = 2,290) and geocoded them to 230 neighborhoods in the City of Winnipeg and 268 municipalities in rural Manitoba. Age-standardized incidence rates for 1990-2006 (combined) were calculated for each region. By using the spatial scan statistic, we identified high-rate clusters in southwestern (incidence rate ratio (IRR) = 1.48) and central Winnipeg (IRR = 1.54) and low-rate clusters in north-central Winnipeg (IRR = 0.52) and northern Manitoba (IRR = 0.48). Multivariable Poisson regression showed a positive association between MS incidence rates and socioeconomic status. Despite our finding that MS incidence varied geographically and by socioeconomic status, the low Gini coefficient of 0.152 for MS incidence identified in this study suggests that the causes of MS are pervasive across all population groups. Searching for local-level causes of the disease may therefore not be as productive as investigating etiological factors operating at the population level. This may require an examination of macro-level differences in environmental exposures between high- and low-incidence regions of the world.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".