Evidence map›Paper›PMID 42763540›Full record

ArticleJournal of tropical medicine2026

Regression Analysis of the Main Environmental Risk Factors for Mountainous Subtype of Zoonotic VL in China.

Zhongqiu Li, Haobo Ni, Zhengbin Zhou, Shuxun Wang, Zixin Wei, Yi Zhang, Junhu Chen, Shizhu Li

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of tropical medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Zhongqiu LiNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), NHC Key Laboratory of Parasite and Vector Biology, WHO Collaborating Center for Tropical Diseases, National Center for International Research on Tropical Diseases, National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Shanghai 200025, China.ORCID https://orcid.org/0000-0001-7405-0383
Haobo NiNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), NHC Key Laboratory of Parasite and Vector Biology, WHO Collaborating Center for Tropical Diseases, National Center for International Research on Tropical Diseases, National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Shanghai 200025, China.
Zhengbin ZhouNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), NHC Key Laboratory of Parasite and Vector Biology, WHO Collaborating Center for Tropical Diseases, National Center for International Research on Tropical Diseases, National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Shanghai 200025, China.
Shuxun WangNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), NHC Key Laboratory of Parasite and Vector Biology, WHO Collaborating Center for Tropical Diseases, National Center for International Research on Tropical Diseases, National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Shanghai 200025, China.
Zixin WeiShanghai Center for Disease Control and Prevention, Shanghai 200025, China.
Yi ZhangNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), NHC Key Laboratory of Parasite and Vector Biology, WHO Collaborating Center for Tropical Diseases, National Center for International Research on Tropical Diseases, National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Shanghai 200025, China.
Junhu ChenNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), NHC Key Laboratory of Parasite and Vector Biology, WHO Collaborating Center for Tropical Diseases, National Center for International Research on Tropical Diseases, National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Shanghai 200025, China.ORCID https://orcid.org/0000-0002-3765-0248
Shizhu LiNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), NHC Key Laboratory of Parasite and Vector Biology, WHO Collaborating Center for Tropical Diseases, National Center for International Research on Tropical Diseases, National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Shanghai 200025, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Mountain-type zoonotic visceral leishmaniasis (MT-ZVL) has re-emerged in China in recent years, posing a growing public health concern. Canines are the primary reservoir hosts, yet the environmental determinants of canine infection remain insufficiently understood. This study aimed to identify key environmental factors associated with MT-ZVL infection. Methods: A total of 1484 canine blood samples were collected from 89 villages across five endemic provinces in China. After excluding cases with missing data, 1374 cases were ultimately included in the analysis. Candidate predictors included canine hair length, age, sex, weight, NDVI, altitude, and temperature. We developed a model using ridge-penalized logistic regression, tuned hyperparameters, and validated internally through 30 iterations of 10-fold stratified cross-validation. Model discriminatory power was evaluated using the area under the receiver operating characteristic (AUC) curve and its 95% confidence interval. Additionally, the optimal cutoff value corresponding to the maximum Youden's index was determined, and sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were calculated. Variable importance was ranked based on the absolute values of the standardized coefficients. Results: The final model, built using ridge regression (optimal Conclusion: Environmental factors, particularly vegetation coverage, play a critical role in shaping the risk of canine infection in MT-ZVL endemic areas. These findings provide important evidence for improving surveillance strategies and implementing targeted control measures.

Indexed as

altitudelogistic regressionmountain-type zoonotic VLNDVItemperaturevisceral leishmaniasis

Identifiers

PMID42763540
PMCPMC13589195

What OpenQuestion holds

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Registered trials

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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.