Evidence map›Paper›PMID 33735304›Full record

ArticlePLoS neglected tropical diseases2021

Prevalence and prediction of Lyme disease in Hainan province.

Lin Zhang, Xiong Zhu, Xuexia Hou, Huan Li, Xiaona Yang, Ting Chen, Xiaoying Fu, Guangqing Miao, Qin Hao, Sha Li

Open access · goldAbstract read
In one paragraph

Article in PLoS neglected tropical diseases, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.6field-weighted citation impact, top 20% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 11 citations in OpenAlex.

  1. Article
  2. The Prevalence ofChina CDC weekly · 2024
    Article
  3. Article
  4. Article
  5. Review
  6. Electrochemical Detection ofACS measurement science au · 2023
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 3 institutions in 1 country.

Lin ZhangState Key Laboratory for Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.ORCID 0000-0001-5404-5464
Xiong ZhuPeople's Hospital of Sanya, Hainan province, China.
Xuexia HouState Key Laboratory for Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Huan LiPeople's Hospital of Sanya, Hainan province, China.
Xiaona YangState Key Laboratory for Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Ting ChenPeople's Hospital of Sanya, Hainan province, China.
Xiaoying FuPeople's Hospital of Sanya, Hainan province, China.
Guangqing MiaoAffiliated Hospital of Hangzhou Normal University, Hangzhou, China.
Qin HaoState Key Laboratory for Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Sha LiPeople's Hospital of Sanya, Hainan province, China.ORCID 0000-0001-8325-1945
Sanya Central Hospital · CNNational Institute for Communicable Disease Control and Prevention · CNAffiliated Hospital of Hangzhou Normal University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lyme disease (LD) is one of the most important vector-borne diseases worldwide. However, there is limited information on the prevalence and risk analysis using correlated factors in the tropical areas. A total of 1583 serum samples, collected from five hospitals of Hainan Province, were tested by immunofluorescence assay (IFA) and western blot (WB) analyses using anti-Borrelia burgdorferi antibodies. Then, we mapped the distribution of positive rate (by IFA) and the spread of confirmed Lyme patients (by WB). Using ArcGIS, we compiled host-vector-human interactions and correlated data as risk factor layers to predict LD risk in Hainan Province. There are three LD hotspots, designated hotspot I, which is located in central Hainan, hotspot II, which contains Sanya district, and hotspot III, which lies in the Haikou-Qiongshan area. The positive rate (16.67% by IFA) of LD in Qiongzhong, located in hotspot I, was higher than that in four other areas. Of confirmed cases of LD, 80.77% of patients (42/52) whose results had been confirmed by WB were in hotspots I and III. Hotspot II, with unknowed prevalence of LD, need to be paid more attention considering human-vector interaction. Wuzhi and Limu mountains might be the most important areas for the prevalence of LD, as the severe host-vector and human-vector interactions lead to a potential origin site for LD. Qiongzhong is the riskiest area and is located to the east of Wuzhi Mountain. In the Sanya and Haikou-Qiongshan area, intervening in the human-vector interaction would help control the prevalence of LD.

Indexed as

Arachnid VectorsAnimalsAntibodies, BacterialBorrelia burgdorferiChinaDisease ReservoirsHumansIxodidaeLyme DiseaseMuridaePrevalenceSeroepidemiologic StudiesSpatial AnalysisAntibodies, Bacterial

Identifiers

PMID33735304
PMCPMC8009380
OpenAlexW3136763921

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.