Evidence map›Paper›PMID 40632802›Full record

ArticlePLoS neglected tropical diseases2025

Benefits of public awareness in mitigating cystic echinococcosis risk in Western China: A climate and socio-economic perspective.

Fang Yin, Wenrui Meng, Peiwei Fan, Yue Shi, Shuai Chen, Yongchun Liang, Jianyi Yao, Yeping Wang, Chuizhao Xue, Shuai Han and 15 more

Abstract read
In one paragraph

Article in PLoS neglected tropical diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

25 authors.

Fang YinSchool of Land Engineering, Chang'an University, Xi'an, Shaanxi, China.
Wenrui MengSchool of Land Engineering, Chang'an University, Xi'an, Shaanxi, China.
Peiwei FanInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China.
Yue ShiChinese Center for Disease Control and Prevention, Beijing, China.
Shuai ChenInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China.
Yongchun LiangSchool of Earth Science and Resources, Chang'an University, Xi'an, Shaanxi, China.
Jianyi YaoChinese Center for Disease Control and Prevention, Beijing, China.
Yeping WangChinese Center for Disease Control and Prevention, Beijing, China.
Chuizhao XueNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Beijing, China.
Shuai HanNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Beijing, China.
Mengmeng HaoInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China.
Qian WangNuffield Department of Medicine, Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, United Kingdom.
Ze MengSchool of Engineering, Tibet University, Lhasa, Tibet, China.
Jun ZhuoInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China.
Kai SunGeoAI Lab, Department of Geography, University at Buffalo, Buffalo, New York, United States of America.
Yongqing BaiState Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China.
Tingting KangSchool of Urban Planning and Design, Peking University, Shenzhen Graduate School, Shenzhen, China.
Zhenyu WangSchool of Land Science and Technology, China University of Geosciences (Beijing), Beijing, China.
Lei LiuSchool of Earth Science and Resources, Chang'an University, Xi'an, Shaanxi, China.
Dong JiangInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China.
Liqun FangAcademy of Military Medical Science State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China.
Canjun ZhengChinese Center for Disease Control and Prevention, Beijing, China.
Jiping DongInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China.
Fangyu DingInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China.
Tian MaYale Institute for Biospheric Studies, Yale University, New Haven, Connecticut, United States of America.ORCID 0000-0001-9980-3329

Funding

National Natural Science Foundation of ChinaWellcome Trust 220211
6 · The paper itself

Abstract

backgroundThe prevalence of cystic echinococcosis (CE), a widespread zoonotic disease, imposes a significant public health burden, especially in western China. However, under the background of global change, how to meet the challenge of the future risk of CE remains unclear. As global climate change, land use changes, and socio-economic factors continue to progress, the spread and intensity of CE may potentially worsen, making it crucial to assess and mitigate future risks.

methodsBy employing Bayesian additive regression trees model to develop risk models for CE in animal hosts (cattle, sheep and dogs) and humans, this study mapped the current distribution of infection risk for CE and projected future risks under the SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios. The projections considered both constant and increased rates of public awareness rates regarding CE prevention in the future.

resultsCurrent simulations indicate that the regions with a high risk of CE infection are primarily concentrated in Tibet, Qinghai, Gansu, and Xinjiang. Future projections suggest that heightened CE risks will be experienced in regions such as Yunnan, Gansu, and Sichuan will experience heightened CE risks. Notably, predictions suggest that increased public awareness is estimated to be linked to accompanied by a reduction of the population at risk by 2.72% to 3.35% in western China by 2030.

conclusionThis research offers a comprehensive understanding of the future distribution of epidemic risk for CE under climate and socio-economic changes. It highlights that enhancing public awareness regions with high-risk is a critical factor associated with reduced infection rates. Furthermore, the study offers a valuable framework for assessing the risk associated with other zoonotic diseases.

Indexed as

EchinococcosisAnimalsBayes TheoremCattleChinaClimateClimate ChangeDogsHumansSheepSocioeconomic FactorsZoonoses

Identifiers

PMID40632802
PMCPMC12240338

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.