Using spatio-temporal surveillance data to test the infectious environment of children before type 1 diabetes diagnosis
Bibliographic record
Abstract
The "hygiene hypothesis" postulates that reduced exposure to infections favours the development of autoimmunity and childhood type 1 diabetes (T1D). But on the other side, viruses, notably enteroviruses, are suspected to trigger T1D. The assessment of the possible relationships between infections and T1D still defies the classical tools of epidemiology. We report the methods and results of a geographical approach that maps the addresses of patients to a communicable diseases surveillance database. We mapped the addresses of patients at birth, infancy and T1D diagnosis to the weekly estimates of the regional incidences of 5 frequent communicable diseases routinely collected since 1984 by the French Sentinel network. The pre-diagnostic infectious environment of 3548 patients with T1D diagnosed between 0.5 and 15 years was compared to those of 100 series of age-matched "virtual controls" drawn randomly on the map. Associations were classified as "suggestive" (summer diarrhea, SD, and varicella, V) when p< 0.05, or "significant" (influenza-like infections, ILI) when they passed the Bonferroni correction for FDR. Exposure to ILI and SD were associated with T1D risk, while V seemed protective. In the subset of 2521 patients for which we had genome wide data, we used a case-only approach to search for interactions between SNPs and the infectious environment as defined by the Sentinel database. Two SNPs, rs116624278 and rs77232854, showed significant interaction with exposure to V between 1 and 3 years of life. The infectious associations found should be taken as possible markers of patients' environment, not as direct causative factors of T1D. They require replication in other populations. The increasing public availability of geographical environmental databases will expand the present approach to map thousands of environmental factors to the lifeline of patients affected by various diseases.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".