Lyme Disease Surveillance Using Sampling Estimation: Evaluation of an Alternative Methodology in New York State
Bibliographic record
Abstract
In the 14-year period from 1993 to 2006, New York State (NYS) accounted for over one-quarter (27.1%) of all confirmed Lyme disease (LD) cases in the United States. During that time period, a nine-county area in south-east NYS accounted for 90.6% of the reported LD cases in the state. Based on concerns related to diminishing resources at both the state and local level and the increasing burden of traditional LD surveillance, the NYS Department of Health (DOH) sought to develop an alternative to traditional surveillance that would reduce the investigative workload while maintaining the ability to track LD trends by developing a system to estimate county-level LD cases based on a 20% random sample of positive laboratory reports. Estimates from this system were compared to observed cases from traditional surveillance for select counties in 2007-2009 and 2011. There were no significant differences between the two methodologies in six of nine evaluations conducted. In addition, in 93 of 98 (94.9%) demographic, symptom and other variable proportion comparisons made between the two methodologies in 2009 and 2011, there were no significant differences found. Overall, using sampling estimates was accurate and efficient in estimating LD cases at the county level. Use of case estimates for LD should be considered as a useful surveillance alternative by health policy makers for states with endemic LD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".