Effect of latitude on the rate of change in incidence of Lyme disease in the United States
Bibliographic record
Abstract
BACKGROUND: Tick-borne illnesses represent an important class of emerging zoonoses, with climate change projected to increase the geographic range within which tick-borne zoonoses might become endemic. We evaluated the impact of latitude on the rate of change in the incidence of Lyme disease in the United States, using publicly available data. METHODS: We estimated state-level year-on-year incidence rate ratios (IRRs) for Lyme disease for the period 1993 to 2007 using Poisson regression methods. We evaluated between-state heterogeneity in IRRs using a random-effects meta-analytic approach. We identified state-level characteristics associated with increasing incidence using random-effects meta-regression. RESULTS: The incidence of Lyme disease in the US increased by about 80% between 1993 and 2007 (IRR per year 1.049, 95% CI [confidence interval] 1.048 to 1.050). There was marked between-state heterogeneity in the average incidence of Lyme disease, ranging from 0.008 per 100 000 person-years in Colorado to 75 per 100 000 in Connecticut, and significant between-state heterogeneity in temporal trends (p < 0.001). In multivariable meta-regression models, increasing incidence showed a linear association with state latitude and population density. These 2 factors explained 27% of the between-state variation in IRRs. No independent association was identified for other state-level characteristics. INTERPRETATION: Lyme disease incidence increased in the US as a whole during the study period, but the changes were not uniform. Marked increases were identified in northern-most states, whereas southern states experienced stable or declining rates of Lyme disease.
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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 | 0.018 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.018 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".