Evidence map›Paper›PMID 39012845›Full record

ArticlePLoS neglected tropical diseases2024

Mapping the distribution of sandflies and sandfly-associated pathogens in China.

Xue-Geng Hong, Ying Zhu, Tao Wang, Jin-Jin Chen, Fang Tang, Rui-Ruo Jiang, Xiao-Fang Ma, Qiang Xu, Hao Li, Li-Ping Wang and 3 more

Abstract read
In one paragraph

Article in PLoS neglected tropical diseases, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–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

4 citing papers in PubMed.

  1. Review
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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

13 authors.

Xue-Geng HongState Key Laboratory of Pathogen and Biosecurity, Academy of Military Medical Science, Beijing, P. R. China.
Ying ZhuDepartment of Epidemiology and Biostatistics, School of Public Health, Wuhan University, Wuhan, P. R. China.
Tao WangThe 949th Chinese PLA Hospital, Altay, P. R. China.
Jin-Jin ChenState Key Laboratory of Pathogen and Biosecurity, Academy of Military Medical Science, Beijing, P. R. China.
Fang TangCenter for Disease Control and Prevention of Chinese People's Armed Police Forces, Beijing, P. R. China.
Rui-Ruo JiangInstitute of NBC Defense, PLA Army, Beijing, P. R. China.
Xiao-Fang MaQingdao Municipal Center for Disease Control and Prevention, Qingdao, P. R. China.
Qiang XuState Key Laboratory of Pathogen and Biosecurity, Academy of Military Medical Science, Beijing, P. R. China.
Hao LiState Key Laboratory of Pathogen and Biosecurity, Academy of Military Medical Science, Beijing, P. R. China.
Li-Ping WangCenter for Public Health Surveillance and Information Service, Chinese Center for Disease Control and Prevention, Beijing, P. R. China.
Yi SunState Key Laboratory of Pathogen and Biosecurity, Academy of Military Medical Science, Beijing, P. R. China.
Li-Qun FangState Key Laboratory of Pathogen and Biosecurity, Academy of Military Medical Science, Beijing, P. R. China.
Wei LiuState Key Laboratory of Pathogen and Biosecurity, Academy of Military Medical Science, Beijing, P. R. China.ORCID 0000-0002-9302-8170

Funding

National Key Research and Development Program of China 2021YFC2302004National Science Foundation for Distinguished Young Scholars of China 81825019
6 · The paper itself

Abstract

backgroundUnderstanding and mapping the distribution of sandflies and sandfly-associated pathogens (SAPs) is crucial for guiding the surveillance and control effort. However, their distribution and the related risk burden in China remain poorly understood.

methodsWe mapped the distribution of sandflies and SAPs using literature data from 1940 to 2022. We also mapped the human visceral leishmaniasis (VL) cases using surveillance data from 2014 to 2018. The ecological drivers of 12 main sandfly species and VL were identified by applying machine learning, and their distribution and risk were predicted in three time periods (2021-2040, 2041-2060, and 2061-2080) under three scenarios of climate and socioeconomic changes.

resultsIn the mainland of China, a total of 47 sandfly species have been reported, with the main 12 species classified into three clusters according to their ecological niches. Additionally, 6 SAPs have been identified, which include two protozoa, two bacteria, and two viruses. The incidence risk of different VL subtypes was closely associated with the distribution risk of specific vectors. The model predictions also revealed a substantial underestimation of the current sandfly distribution and VL risk. The predicted areas affected by the 12 major species of sandflies and the high-risk areas for VL were found to be 37.9-1121.0% and 136.6% larger, respectively, than the observed range in the areas. The future global changes were projected to decrease the risk of mountain-type zoonotic VL (MT-ZVL), but anthroponotic VL (AVL) and desert-type zoonotic VL (DT-ZVL) could remain stable or slightly increase.

conclusionsCurrent field observations underestimate the spatial distributions of main sandfly species and VL in China. More active surveillance and field investigations are needed where high risks are predicted, especially in areas where the future risk of VL is projected to remain high or increase.

Indexed as

Insect VectorsPsychodidaeAnimal DistributionAnimalsChinaHumansLeishmaniasis, Visceral

Identifiers

PMID39012845
PMCPMC11251628

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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.