Evidence map›Paper›PMID 31790406›Full record

ArticlePLoS neglected tropical diseases2019

Spatio-temporal patterns of scrub typhus in mainland China, 2006-2017.

Yujuan Yue, Dongsheng Ren, Xiaobo Liu, Yujiao Wang, Qiyong Liu, Guichang Li

Abstract read
In one paragraph

Article in PLoS neglected tropical diseases, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
34citing papers in PubMed, 1 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

34 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Nasopharyngeal Swabs forPathogens (Basel, Switzerland) · 2026
    Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Predicting the potential distribution areas ofFrontiers in public health · 2025
    Article
  8. Cavitary pulmonary tuberculosis withFrontiers in medicine · 2025
    Article
  9. Mite Infestation onFrontiers in veterinary science · 2025
    Article
  10. Article
  11. Article
  12. Article
  13. The Prevalence ofChina CDC weekly · 2024
    Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Review
  20. 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

6 authors.

Yujuan YueState Key Laboratory of Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, People's Republic of China.
Dongsheng RenState Key Laboratory of Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, People's Republic of China.
Xiaobo LiuState Key Laboratory of Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, People's Republic of China.
Yujiao WangState Key Laboratory of Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, People's Republic of China.
Qiyong LiuState Key Laboratory of Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, People's Republic of China.
Guichang LiState Key Laboratory of Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, People's Republic of China.ORCID 0000-0002-6286-6366

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundScrub typhus, a serious public health problem in the Asia-Pacific area, is endemic in the "tsutsugamushi triangle" area. Scrub typhus has been widespread and has become a significant health concern in China. However, spatiotemporal patterns need to be investigated further.

objectiveThis study aims to explore spatiotemporal patterns, diffusion characteristics and regional distribution differences of scrub typhus cases in mainland China from January 2006 to December 2017.

methodMonthly cases of scrub typhus reported at the county level during 2006-2017 were obtained. Time-series analyses, spatial distribution analyses, spatial diffusion analyses, spatial autocorrelation analyses and space-time scan statistic analyses were used to explore spatiotemporal characteristics of scrub typhus.

resultsA total of 121 251 scrub typhus cases were reported in 30 provinces (or municipalities) of mainland China during 2006-2017, which rose exponentially. There were seasonal characteristics from June to November for scrub typhus. Scrub typhus had been diffused from south, southwest, southeast and eastern coasts to center, north, northeast and northwest in mainland China. Scrub typhus occurrences were from point to surrounding regions, and from south to north every year. The peak periods of scrub typhus became longer and longer from north to southwest to south in mainland China. There existed a single peak in Southwest region and North region, respectively, but existed a bimodal peak for South region. Scrub typhus cases were clustered in Yunnan, Guangdong, Guangxi, Fujian and Anhui among June to November. The scrub typhus epidemics in Guangdong and Yunnan were the most serious.

conclusionsThe results in this study can be guide targeted public health interventions against scrub typhus at the county level.

Indexed as

EpidemicsTopography, MedicalChinaHumansScrub TyphusSpatio-Temporal Analysis

Identifiers

PMID31790406
PMCPMC6917297

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.