Evidence map›Paper›PMID 30413773›Full record

ArticleScientific reports2018

Geographical and temporal distribution of the residual clusters of human leptospirosis in China, 2005-2016.

Pandji Wibawa Dhewantara, Abdullah Al Mamun, Wen-Yi Zhang, Wen-Wu Yin, Fan Ding, Danhuai Guo, Wenbiao Hu, Ricardo J Soares Magalhães

Abstract read
In one paragraph

Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 2 pooled it
–field-weighted citation impact
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

13 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. PathogenicMicroorganisms · 2022
    Article
  10. Article
  11. Article
  12. Article
  13. 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

8 authors.

Pandji Wibawa DhewantaraUQ Spatial Epidemiology Laboratory, School of Veterinary Science, The University of Queensland, Gatton, QLD, 4343, Australia. p.dhewantara@uq.edu.au.ORCID http://orcid.org/0000-0002-9461-0648
Abdullah Al MamunInstitute for Social Science Research, The University of Queensland, Indooroopilly, QLD, 4068, Australia.
Wen-Yi ZhangCenter for Disease Surveillance and Research, Institute of Disease Control and Prevention of PLA, Beijing, 100071, People's Republic of China.
Wen-Wu YinChinese Center for Disease Control and Prevention, Beijing, 102206, People's Republic of China. yinww@chinacdc.cn.
Fan DingChinese Center for Disease Control and Prevention, Beijing, 102206, People's Republic of China.
Danhuai GuoComputer Network Information Center, Chinese Academy of Sciences, Beijing, 100190, People's Republic of China.
Wenbiao HuSchool of Public Health and Social Work, Queensland University of Technology, Kelvin Grove, QLD, 4059, Australia.
Ricardo J Soares MagalhãesUQ Spatial Epidemiology Laboratory, School of Veterinary Science, The University of Queensland, Gatton, QLD, 4343, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Human leptospirosis outbreaks still persistently occur in part of China, indicating that leptospirosis remains an important zoonotic disease in the country. Spatiotemporal pattern of the high-risk leptospirosis cluster and the key characteristics of high-risk areas for leptospirosis across the country are still poorly understood. Using spatial analytical approaches, we analyzed 8,158 human leptospirosis cases notified during 2005-2016 across China to explore the geographical distribution of leptospirosis hotspots and to characterize demographical, ecological and socioeconomic conditions of high-risk counties for leptospirosis in China. During the period studied, leptospirosis incidence was geographically clustered with the highest rate observed in the south of the Province of Yunnan. The degree of spatial clustering decreased over time suggesting changes in local risk factors. However, we detected residual high-risk counties for leptospirosis including counties in the southwest, central, and southeast China. High-risk counties differed from low-risk counties in terms of its demographical, ecological and socioeconomic characteristics. In high-risk clusters, leptospirosis was predominantly observed on younger population, more males and farmers. Additionally, high-risk counties are characterized by larger rural and less developed areas, had less livestock density and crops production, and located at higher elevation with higher level of precipitation compare to low-risk counties. In conclusion, leptospirosis distribution in China appears to be highly clustered to a discrete number of counties highlighting opportunities for elimination; hence, public health interventions should be effectively targeted to high-risk counties identified in this study.

Indexed as

AdultAnimalsChinaDisease OutbreaksEpidemiological MonitoringFemaleGeographyHumansIncidenceLeptospirosisMaleMiddle AgedRisk FactorsTime FactorsYoung AdultZoonoses

Identifiers

PMID30413773
PMCPMC6226456

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.