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Dong Y, Zhou G, Cao W, Xu X, Zhang Y, Ji Z, Yang J, Chen J, Liu M, Fan Y, Kong J, Wen S, Li B, Yue P, Liu A, Bao F. Global seroprevalence and sociodemographic characteristics of Borrelia burgdorferi sensu lato in human populations: a systematic review and meta-analysis. BMJ Glob Health 2022; 7:bmjgh-2021-007744. [PMID: 35697507 PMCID: PMC9185477 DOI: 10.1136/bmjgh-2021-007744] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 02/16/2022] [Indexed: 12/13/2022] Open
Abstract
Introduction Borrelia burgdorferi sensu lato (Bb) infection, the most frequent tick-transmitted disease, is distributed worldwide. This study aimed to describe the global seroprevalence and sociodemographic characteristics of Bb in human populations. Methods We searched PubMed, Embase, Web of Science and other sources for relevant studies of all study designs through 30 December 2021 with the following keywords: ‘Borrelia burgdorferi sensu lato’ AND ‘infection rate’; and observational studies were included if the results of human Bb antibody seroprevalence surveys were reported, the laboratory serological detection method reported and be published in a peer-reviewed journal. We screened titles/abstracts and full texts of papers and appraised the risk of bias using the Cochrane Collaboration-endorsed Newcastle-Ottawa Quality Assessment Scale. Data were synthesised narratively, stratified by different types of outcomes. We also conducted random effects meta-analysis where we had a minimum of two studies with 95% CIs reported. The study protocol has been registered with PROSPERO (CRD42021261362). Results Of 4196 studies, 137 were eligible for full-text screening, and 89 (158 287 individuals) were included in meta-analyses. The reported estimated global Bb seroprevalence was 14.5% (95% CI 12.8% to 16.3%), and the top three regions of Bb seroprevalence were Central Europe (20.7%, 95% CI 13.8% to 28.6%), Eastern Asia (15.9%, 95% CI 6.6% to 28.3%) and Western Europe (13.5%, 95% CI 9.5% to 18.0%). Meta-regression analysis showed that after eliminating confounding risk factors, the methods lacked western blotting (WB) confirmation and increased the risk of false-positive Bb antibody detection compared with the methods using WB confirmation (OR 1.9, 95% CI 1.6 to 2.2). Other factors associated with Bb seropositivity include age ≥50 years (12.6%, 95% CI 8.0% to 18.1%), men (7.8%, 95% CI 4.6% to 11.9%), residence of rural area (8.4%, 95% CI 5.0% to 12.6%) and suffering tick bites (18.8%, 95% CI 10.1% to 29.4%). Conclusion The reported estimated global Bb seropositivity is relatively high, with the top three regions as Central Europe, Western Europe and Eastern Asia. Using the WB to confirm Bb serological results could significantly improve the accuracy. More studies are needed to improve the accuracy of global Lyme borreliosis burden estimates. PROSPERO registration number CRD42021261362.
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Affiliation(s)
- Yan Dong
- The Institute for Tropical Medicine, Kunming Medical University, Kunming, China
| | - Guozhong Zhou
- The Institute for Tropical Medicine, Kunming Medical University, Kunming, China
| | - Wenjing Cao
- The Institute for Tropical Medicine, Kunming Medical University, Kunming, China
| | - Xin Xu
- The Institute for Tropical Medicine, Kunming Medical University, Kunming, China
| | - Yu Zhang
- The Institute for Tropical Medicine, Kunming Medical University, Kunming, China
| | - Zhenhua Ji
- The Institute for Tropical Medicine, Kunming Medical University, Kunming, China
| | - Jiaru Yang
- The Institute for Tropical Medicine, Kunming Medical University, Kunming, China
| | - Jingjing Chen
- The Institute for Tropical Medicine, Kunming Medical University, Kunming, China
| | - Meixiao Liu
- The Institute for Tropical Medicine, Kunming Medical University, Kunming, China
| | - Yuxin Fan
- The Institute for Tropical Medicine, Kunming Medical University, Kunming, China
| | - Jing Kong
- The Institute for Tropical Medicine, Kunming Medical University, Kunming, China
| | - Shiyuan Wen
- The Institute for Tropical Medicine, Kunming Medical University, Kunming, China
| | - Bingxue Li
- The Institute for Tropical Medicine, Kunming Medical University, Kunming, China.,Yunnan Province Key Laboratory for Tropical Infectious Diseases in Universities, Kunming Medical University, Kunming, China
| | - Peng Yue
- The Institute for Tropical Medicine, Kunming Medical University, Kunming, China
| | - Aihua Liu
- The Institute for Tropical Medicine, Kunming Medical University, Kunming, China .,Yunnan Province Key Laboratory for Tropical Infectious Diseases in Universities, Kunming Medical University, Kunming, China
| | - Fukai Bao
- The Institute for Tropical Medicine, Kunming Medical University, Kunming, China .,Yunnan Province Key Laboratory for Tropical Infectious Diseases in Universities, Kunming Medical University, Kunming, China
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Predictive Model of Lyme Disease Epidemic Process Using Machine Learning Approach. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12094282] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Lyme disease is the most prevalent tick-borne disease in Eastern Europe. This study focuses on the development of a machine learning model based on a neural network for predicting the dynamics of the Lyme disease epidemic process. A retrospective analysis of the Lyme disease cases reported in the Kharkiv region, East Ukraine, between 2010 and 2017 was performed. To develop the neural network model of the Lyme disease epidemic process, a multilayered neural network was used, and the backpropagation algorithm or the generalized delta rule was used for its learning. The adequacy of the constructed forecast was tested on real statistical data on the incidence of Lyme disease. The learning of the model took 22.14 s, and the mean absolute percentage error is 3.79%. A software package for prediction of the Lyme disease incidence on the basis of machine learning has been developed. Results of the simulation have shown an unstable epidemiological situation of Lyme disease, which requires preventive measures at both the population level and individual protection. Forecasting is of particular importance in the conditions of hostilities that are currently taking place in Ukraine, including endemic territories.
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