Evidence map›Paper›PMID 33598765›Full record

ArticleInternational journal of biometeorology2021

Extreme weather events and dengue outbreaks in Guangzhou, China: a time-series quasi-binomial distributed lag non-linear model.

Jian Cheng, Hilary Bambrick, Francesca D Frentiu, Gregor Devine, Laith Yakob, Zhiwei Xu, Zhongjie Li, Weizhong Yang, Wenbiao Hu

Abstract read
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In one paragraph

Article in International journal of biometeorology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 3 pooled it
–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

21 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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  20. [Dengue virus E protein-based luciferase immunosorbent assay for detecting dengue virus IgG antibody].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2021
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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

9 authors.

Jian ChengSchool of Public Health and Social Work, Queensland University of Technology, Brisbane, Queensland, 4059, Australia.
Hilary BambrickSchool of Public Health and Social Work, Queensland University of Technology, Brisbane, Queensland, 4059, Australia.
Francesca D FrentiuCentre for Immunology and Infection Control, School of Biomedical Sciences, Queensland University of Technology, Brisbane, Australia.
Gregor DevineMosquito Control Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, Australia.
Laith YakobDepartment of Disease Control, London School of Hygiene and Tropical Medicine, London, UK.
Zhiwei XuSchool of Public Health and Social Work, Queensland University of Technology, Brisbane, Queensland, 4059, Australia.
Zhongjie LiDivision of Infectious Disease, Key Laboratory of Surveillance and Early Warning of Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing, China.
Weizhong YangDivision of Infectious Disease, Key Laboratory of Surveillance and Early Warning of Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing, China.
Wenbiao HuSchool of Public Health and Social Work, Queensland University of Technology, Brisbane, Queensland, 4059, Australia. w2.hu@qut.edu.au.

Funding

National Health and Medical Research Council APP 1138622
6 · The paper itself

Abstract

Dengue transmission is climate-sensitive and permissive conditions regularly cause large outbreaks in Asia-Pacific area. As climate change progresses, extreme weather events such as heatwaves and unusually high rainfall are predicted more intense and frequent, but their impacts on dengue outbreaks remain unclear so far. This paper aimed to investigate the relationship between extreme weather events (i.e., heatwaves, extremely high rainfall and extremely high humidity) and dengue outbreaks in China. We obtained daily number of locally acquired dengue cases and weather factors for Guangzhou, China, for the period 2006-2015. The definition of dengue outbreaks was based on daily number of locally acquired cases above the threshold (i.e., mean + 2SD of daily distribution of dengue cases during peaking period). Heatwave was defined as ≥2 days with temperature ≥ 95th percentile, and extreme rainfall and humidity defined as daily values ≥95th percentile during 2006-2015. A generalized additive model was used to examine the associations between extreme weather events and dengue outbreaks. Results showed that all three extreme weather events were associated with increased risk of dengue outbreaks, with a risk increase of 115-251% around 6 weeks after heatwaves, 173-258% around 6-13 weeks after extremely high rainfall, and 572-587% around 6-13 weeks after extremely high humidity. Each extreme weather event also had good capacity in predicting dengue outbreaks, with the model's sensitivity, specificity, accuracy, and area under the receiver operating characteristics curve all exceeding 86%. This study found that heatwaves, extremely high rainfall, and extremely high humidity could act as potential drivers of dengue outbreaks.

Indexed as

DengueExtreme WeatherAsiaChinaDisease OutbreaksHumansNonlinear DynamicsWeatherDengueExtremely high humidityExtremely high rainfallExtreme weatherHeatwave

Identifiers

PMID33598765

What OpenQuestion holds

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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.