Comparison of national surveillance systems for Lyme disease in humans in Europe and North America: a policy review
Bibliographic record
Abstract
BACKGROUND: Lyme disease incidence is increasing in Europe, the USA, and Canada. In 2010, a comparison of surveillance systems for Lyme disease (LD) in humans in 28 European countries showed that systems highly varied, making epidemiological comparisons difficult. Details by country were not published. In 2018, one of LD clinical manifestations, neuroborreliosis, was added under European Union (EU) surveillance to standardise definitions. In this study, we identified and compared, 10 years after the European inventory, the characteristics of national surveillance systems and policies for LD in humans, with additional countries. METHODS: Thirty-four European and North American countries were included. Information on national "traditional" systems (which compile data reported by clinicians and laboratories) and "public participatory" websites and mobile applications (which collect information directly from the public) were searched in MEDLINE, a systematic evidence map, and Google. An existing framework on LD surveillance was adapted to capture information on the administration level, indicators, reporting entities, coverage, and obligation to report. RESULTS: A surveillance system was found for 29 (85%) countries. Twenty-four had a traditional system alone, one had a public participatory system alone, and the remaining had both. Among countries with traditional systems, 23 (82%) administered them at the national level. Nineteen (68%) required mandatory reporting. Sixteen (57%) used both clinicians and laboratories as reporting entities. Eighteen (64%) employed case definitions, most of which considered both neuroborreliosis and erythema migrans (n = 14). Others monitored the number of positive laboratory tests and/or patient consultations. Public participatory systems were only implemented in countries employing either also sentinels or voluntary surveys, or no traditional system, suggesting their use as a complementary tool. Only 56% of EU countries had neuroborreliosis as an indicator. CONCLUSION: The situation remains similar to 2010 with persisting heterogeneity between systems, suggesting that countries prioritise different surveillance objectives for LD. Without a common indicator in Europe, it is difficult to get a clear epidemiological picture. We discuss four factors that potentially influence LD surveillance strategies: perceptions of severity, burden on resources, two-way communication, and the medical conflicts about LD. Addressing these with countries might help moving towards the adoption of common practices.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".