Global seroprevalence and sociodemographic characteristics of <i>Borrelia burgdorferi sensu lato</i> in human populations: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Borrelia burgdorferi sensu lato (Bb) infection, the most frequent tick-transmitted disease, is distributed worldwide. No consensus exists regarding the global prevalence of Bb in humans, but Northern Hemisphere residents have the highest Lyme borreliosis (LB; i.e., Lyme disease) burden. This systematic review of the literature and meta-analysis addresses this knowledge gap.Methods: We searched PubMed, EMBASE and Web of Science for studies reporting the seroprevalence of Bb in humans through July 19, 2021. Anti-Bb seroprevalence was estimated using a binomial mixed model, weighting for study quality and population size. Bb seroprevalence was summarized in three general populations: high-risk, tick-bitten, and LB-like symptoms. Subgroup analyses were performed according to sociodemographic characteristics (sex, age, residence, tick bites, continent of residence) and testing methods to identify risk factors for Bb infection. The Quality Assessment of Diagnostic Accuracy Studies checklist was used to assess the quality of included studies. Heterogeneity was evaluated using the I2 statistic.Findings: Eighty-seven studies with moderate to high methodological quality were included, comprising 155,339 individuals; estimated Bb seroprevalence based on the studies was 14·0% (95% confidence interval: 12·3%–15·9%). Reported seroprevalence was highest in the LB-like symptoms population (21·3%) and lowest in the general population (5·4%). Meta-regression analyses showed that the reported pooled Bb seroprevalence of studies using methods confirmed by Western blot (WB) was lower than that of studies using methods not confirmed by WB after eliminating confounding risk factors. Potential risk factors associated with Bb infection were male sex, age >40 years, residence in rural area, and suffering tick bites.Interpretation: To our knowledge, this is the first study describing the reported global seroprevalence and specific risk factors of Bb infection. Using WB to confirm Bb serological results could significantly improve the accuracy. More quality data are needed to improve the precision of LB burden estimates.Funding: National Natural Science Foundation of China (No. 32060180, 81860644, 81560596, and 31560051) and Joint Fundation of Yunnan Province Department of Science, Technology-Kunming Medical University [No. 2019FE001 (-002) and 2017FE467 (-001)] and the Science Research Fund Project of Yunnan Provincial Department of Education (2021Y323).Declaration of Interest: We declare no competing interests.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.036 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".