Evidence map›Paper›PMID 27097318›Full record

ArticlePLoS neglected tropical diseases2016

Mapping the Distribution of Anthrax in Mainland China, 2005-2013.

Wan-Jun Chen, Sheng-Jie Lai, Yang Yang, Kun Liu, Xin-Lou Li, Hong-Wu Yao, Yu Li, Hang Zhou, Li-Ping Wang, Di Mu and 4 more

Erratum issuedOpen access · goldAbstract read
In one paragraph

Article in PLoS neglected tropical diseases, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 38 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
38citing papers in PubMed, 1 pooled it
6.0field-weighted citation impact, top 4% of its field
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

38 citing papers in PubMed, 1 synthesis or guideline pooled it, 73 citations in OpenAlex.

  1. Pooled it
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  15. Seasonality and Ecological Suitability Modelling for Anthrax (Animals : an open access journal from MDPI · 2022
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

14 authors at 4 institutions in 4 countries.

Wan-Jun ChenThe State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, People's Republic of China.
Sheng-Jie LaiDivision of Infectious Disease, Key Laboratory of Surveillance and Early-warning on Infectious Disease, Chinese Centre for Disease Control and Prevention, Beijing, People's Republic of China.
Yang YangDepartment of Biostatistics, College of Public Health and Health Professions, and Emerging Pathogens Institute, University of Florida, Gainesville, Florida, United States of America.
Kun LiuThe State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, People's Republic of China.
Xin-Lou LiThe State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, People's Republic of China.
Hong-Wu YaoThe State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, People's Republic of China.
Yu LiDivision of Infectious Disease, Key Laboratory of Surveillance and Early-warning on Infectious Disease, Chinese Centre for Disease Control and Prevention, Beijing, People's Republic of China.
Hang ZhouDivision of Infectious Disease, Key Laboratory of Surveillance and Early-warning on Infectious Disease, Chinese Centre for Disease Control and Prevention, Beijing, People's Republic of China.
Li-Ping WangDivision of Infectious Disease, Key Laboratory of Surveillance and Early-warning on Infectious Disease, Chinese Centre for Disease Control and Prevention, Beijing, People's Republic of China.
Di MuDivision of Infectious Disease, Key Laboratory of Surveillance and Early-warning on Infectious Disease, Chinese Centre for Disease Control and Prevention, Beijing, People's Republic of China.
Wen-Wu YinDivision of Infectious Disease, Key Laboratory of Surveillance and Early-warning on Infectious Disease, Chinese Centre for Disease Control and Prevention, Beijing, People's Republic of China.
Li-Qun FangThe State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, People's Republic of China.
Hong-Jie YuDivision of Infectious Disease, Key Laboratory of Surveillance and Early-warning on Infectious Disease, Chinese Centre for Disease Control and Prevention, Beijing, People's Republic of China.
Wu-Chun CaoThe State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, People's Republic of China.
Chinese Center For Disease Control and Prevention · CNInstitute of Microbiology · CNUniversity of Florida · USUniversity of Southampton · GB

Funding

Virology &ImmunologyU19AI051915 · NIAID · NATIONAL CENTER FOR AIDS/STD CONTROL AND PREVENTION · PI SHAO, YIMING · 2002 to 2009
$21.6M
Understanding Transmission with Integrated Genetic and Epidemiologic InferenceU54GM111274 · NIGMS · FRED HUTCHINSON CANCER RESEARCH CENTER · PI BEDFORD, TREVOR BC · 2014 to 2018
$12.5M
NIAID NIH HHS U19 AI051915NIGMS NIH HHS U54 GM111274NIGMS NIH HHS U54-GM111274
6 · The paper itself

Abstract

backgroundAnthrax, a global re-emerging zoonotic disease in recent years is enzootic in mainland China. Despite its significance to the public health, spatiotemporal distributions of the disease in human and livestock and its potential driving factors remain poorly understood. METHODOLOGY/PRINCIPAL

findingsUsing the national surveillance data of human and livestock anthrax from 2005 to 2013, we conducted a retrospective epidemiological study and risk assessment of anthrax in mainland China. The potential determinants for the temporal and spatial distributions of human anthrax were also explored. We found that the majority of human anthrax cases were located in six provinces in western and northeastern China, and five clustering areas with higher incidences were identified. The disease mostly peaked in July or August, and males aged 30-49 years had higher incidence than other subgroups. Monthly incidence of human anthrax was positively correlated with monthly average temperature, relative humidity and monthly accumulative rainfall with lags of 0-2 months. A boosted regression trees (BRT) model at the county level reveals that densities of cattle, sheep and human, coverage of meadow, coverage of typical grassland, elevation, coverage of topsoil with pH > 6.1, concentration of organic carbon in topsoil, and the meteorological factors have contributed substantially to the spatial distribution of the disease. The model-predicted probability of occurrence of human cases in mainland China was mapped at the county level. CONCLUSIONS/SIGNIFICANCE: Anthrax in China was characterized by significant seasonality and spatial clustering. The spatial distribution of human anthrax was largely driven by livestock husbandry, human density, land cover, elevation, topsoil features and climate. Enhanced surveillance and intervention for livestock and human anthrax in the high-risk regions, particularly on the Qinghai-Tibetan Plateau, is the key to the prevention of human infections.

Indexed as

Topography, MedicalAdultAge FactorsAnimal HusbandryAnimalsAnthraxCattleChinaClimateEpidemiological MonitoringFemaleHumansIncidenceLivestockMaleMiddle Aged

Identifiers

PMID27097318
PMCPMC4838246
OpenAlexW2341741445

What OpenQuestion holds

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LicenceCC BY
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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.