Evidence map›Paper›PMID 27174415›Full record

ArticleInternational journal of biometeorology2016

Projections of hepatitis A virus infection associated with flood events by 2020 and 2030 in Anhui Province, China.

Lu Gao, Ying Zhang, Guoyong Ding, Qiyong Liu, Changke Wang, Baofa Jiang

Abstract read
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In one paragraph

Article in International journal of biometeorology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 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

8 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Review
  4. Review
  5. Role of Climate Change in Changing Hepatic Health Maps.Current environmental health reports · 2022
    Review
  6. Review
  7. Brain diseases in changing climate.Environmental research · 2019
    Review
  8. 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

6 authors.

Lu GaoDepartment of Epidemiology, School of Public Health, Shandong University, Jinan City, Shandong Province, 250012, People's Republic of China.
Ying ZhangDepartment of Epidemiology, School of Public Health, Shandong University, Jinan City, Shandong Province, 250012, People's Republic of China.
Guoyong DingDepartment of Occupational and Environmental Health, School of Public Health, Taishan Medical College, Taian City, Shandong Province, People's Republic of China.
Qiyong LiuState Key Laboratory for Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, China CDC, Beijing, People's Republic of China.
Changke WangNational Climate Center, China Meteorological Administration, Beijing, People's Republic of China.
Baofa JiangDepartment of Epidemiology, School of Public Health, Shandong University, Jinan City, Shandong Province, 250012, People's Republic of China. bjiang2013@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Assessing and responding to health risk of climate change is important because of its impact on the natural and societal ecosystems. More frequent and severe flood events will occur in China due to climate change. Given that population is projected to increase, more people will be vulnerable to flood events, which may lead to an increased incidence of HAV infection in the future. This population-based study is going to project the future health burden of HAV infection associated with flood events in Huai River Basin of China. The study area covered four cities of Anhui province in China, where flood events were frequent. Time-series adjusted Poisson regression model was developed to quantify the risks of flood events on HAV infection based on the number of daily cases during summer seasons from 2005 to 2010, controlling for other meteorological variables. Projections of HAV infection in 2020 and 2030 were estimated based on the scenarios of flood events and demographic data. Poisson regression model suggested that compared with the periods without flood events, the risks of severe flood events for HAV infection were significant (OR = 1.28, 95 % CI 1.05-1.55), while risks were not significant from moderate flood events (OR = 1.16, 95 % CI 0.72-1.87) and mild flood events (OR = 1.14, 95 % CI 0.87-1.48). Using the 2010 baseline data and the flood event scenarios (one severe flood event), increased incidence of HAV infection were estimated to be between 0.126/10

Indexed as

FloodsChinaClimate ChangeHepatitis AHumansIncidenceModels, StatisticalRegression AnalysisHepatitis A virus infectionPoisson regressionProjectionScenario-based modelingTime series

Identifiers

What OpenQuestion holds

Textmetadata
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.