Evidence map›Paper›PMID 31053104›Full record

ArticleBMC infectious diseases2019

Spatial distribution of tuberculosis and its association with meteorological factors in mainland China.

Yingjie Zhang, Mengyang Liu, Samuel S Wu, Hui Jiang, Junjie Zhang, Songwang Wang, Wei Ma, Qihuan Li, Yuan Ma, Yue Liu and 6 more

Open access · goldAbstract read
In one paragraph

Article in BMC infectious diseases, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
34citing papers in PubMed, 3 pooled it
4.6field-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

34 citing papers in PubMed, 3 syntheses or guidelines pooled it, 68 citations in OpenAlex.

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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

16 authors at 7 institutions in 3 countries.

Yingjie ZhangChinese Center for Disease Control and Prevention, Beijing, 102206, China.
Mengyang LiuSchool of Public Health, Capital Medical University, Beijing, 100069, China.
Samuel S WuDepartment of Statistics, University of Florida, Gainesville, FL, 32610-7450, USA.
Hui JiangNational Tuberculosis Clinical Lab of China, Beijing Chest Hospital, Capital Medical University, Beijing, 101149, China.
Junjie ZhangSchool of life sciences, Beijing Normal University, Beijing, 100875, China.
Songwang WangChinese Center for Disease Control and Prevention, Beijing, 102206, China.
Wei MaInstitute of statistics and big data, Renmin University of China, Beijing, 100872, China.
Qihuan LiSchool of Public Health, Capital Medical University, Beijing, 100069, China.
Yuan MaSchool of Public Health, Capital Medical University, Beijing, 100069, China.
Yue LiuSchool of Public Health, Capital Medical University, Beijing, 100069, China.
Wei FengSchool of Public Health, Capital Medical University, Beijing, 100069, China.
Endawoke AmsaluSchool of Public Health, Capital Medical University, Beijing, 100069, China.
Xia LiDepartment of Mathematics and Statistics, La Trobe University, Bundoora, Victoria, 3086, Australia.
Wei WangSchool of Medical Sciences and Health, Edith Cowan University, WA6027, Perth, Australia.
Weimin LiBeijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, 100069, China. lwm_18@aliyun.com.
Xiuhua GuoSchool of Public Health, Capital Medical University, Beijing, 100069, China. statguo@ccmu.edu.cn.ORCID http://orcid.org/0000-0001-6657-6940
Capital Medical University · CNChinese Center For Disease Control and Prevention · CNBeijing Normal University · CNEdith Cowan University · AULa Trobe University · AURenmin University of China · CNUniversity of Florida · US

Funding

the National Science and Technology Major Project 2018ZX10302302-001-004
6 · The paper itself

Abstract

backgroundThe incidence of tuberculosis (TB) remains high worldwide. Current strategies will not eradicate TB by 2035; instead, by 2182 is more likely. Therefore, it is urgent that new risk factors be identified.

methodsAn ecological study was conducted in 340 prefectures in China from 2005 to 2015. The spatial distribution of TB incidence was shown by clustering and hotspot analysis. The relationship between the distribution patterns and six meteorological factors was evaluated by the geographically weighted regression (GWR) model.

resultsDuring the 11 years of the study period, TB incidence was persistently low in the east and high in the west. Local coefficients from the GWR model showed a positive correlation between TB incidence and yearly average rainfall (AR) but a negative correlation with other meteorological factors. Average relative humidity (ARH) was negatively correlated with the incidence of TB in all prefectures (p < 0.05).

conclusionMeteorological factors may play an important role in the prevention and control of TB.

Indexed as

ClimateChinaCluster AnalysisHumansIncidenceRisk FactorsTuberculosisGeographically weighted regressionMeteorological factorsSpatial distributionTuberculosis

Identifiers

PMID31053104
PMCPMC6500018
OpenAlexW2947896744

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.