Evidence map›Paper›PMID 31915005›Full record

ArticleEnvironmental health : a global access science source2020

Climate factors driven typhus group rickettsiosis incidence dynamics in Xishuangbanna Dai autonomous prefecture of Yunnan province in China, 2005-2017.

Yuan Gao, Yanlin Niu, Wanwan Sun, Keke Liu, Xiaobo Liu, Ning Zhao, Yujuan Yue, Haixia Wu, Fengxia Meng, Jun Wang and 2 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Environmental health : a global access science source, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.8field-weighted citation impact, top 34% 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

2 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
  2. Review
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

12 authors at 2 institutions in 1 country.

Yuan GaoState Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Yanlin NiuState Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Wanwan SunState Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Keke LiuState Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Xiaobo LiuState Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Ning ZhaoState Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Yujuan YueState Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Haixia WuState Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Fengxia MengState Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Jun WangState Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Xueshuang WangState Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Qiyong LiuState Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China. liuqiyong@icdc.cn.
National Institute for Communicable Disease Control and Prevention · CNBeijing Center for Disease Prevention and Control · CN

Funding

China Prosperity Strategic Programme Fund 15LCI1National Basic Research Program of China (973 Program) 2012CB955504National Major Research and Development Program 2016YFC1200802
6 · The paper itself

Abstract

backgroundTyphus group rickettsiosis (TGR), which is a neglected vector-borne infectious disease, including epidemic typhus and endemic typhus. We explored the lag effects and nonlinear association between meteorological factors and TGR incidence in Xishuangbanna Dai autonomous prefecture from 2005 to 2017, China.

methodsA Poisson regression with a distributed lag nonlinear model (DLNM) was utilized to analyze TGR cases data and the contemporaneous meteorological data.

resultsA J-shaped nonlinear association between weekly mean temperature and TGR incidence was found. The cumulative exposure to weekly mean temperature indicated that the RR increased with the increment of temperature. Taking the median value as the reference, lower temperatures could decrease the risk of TGR incidence, while higher temperatures could increase the risk of TGR incidence and last for 21 weeks. We also found a reversed U-shaped nonlinear association between weekly mean precipitation and TGR incidence. Precipitation between 5 mm and 13 mm could increase the risk of TGR incidence. Taking the median value as the reference, no precipitation and lower precipitation could decrease the risk of TGR incidence, while higher precipitation could increase the risk of TGR incidence and last for 18 weeks.

conclusionsThe prevention and control measures of TGR should be implemented according to climatic conditions by the local government and health departments in order to improve the efficiency.

Indexed as

ClimateWeatherChinaCold TemperatureHot TemperatureHumansIncidenceNonlinear DynamicsRainTyphus, Epidemic Louse-BorneChinaClimateDistributed lag non-linear modelsTyphus group rickettsiosis

Identifiers

PMID31915005
PMCPMC6951009
OpenAlexW2999673021

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.