Evidence map›Paper›PMID 31306984›Full record

ArticleEnvironmental research2019

A time-trend ecological study for identifying flood-sensitive infectious diseases in Guangxi, China from 2005 to 2012.

Guoyong Ding, Xiaomei Li, Xuewen Li, Baofang Zhang, Baofa Jiang, Dong Li, Weijia Xing, Qiyong Liu, Xuena Liu, Haifeng Hou

Open access · greenAbstract read
In one paragraph

Article in Environmental research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 2 pooled it
0.7field-weighted citation impact, top 30% 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

9 citing papers in PubMed, 2 syntheses or guidelines pooled it, 20 citations in OpenAlex.

  1. Pooled it
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  5. Review
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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

10 authors at 4 institutions in 1 country.

Guoyong DingSchool of Public Health, Shandong First Medical University & Shandong Academy of Medical Sciences, 271016, Taian, Shandong Province, PR China.
Xiaomei LiSchool of Public Health, Shandong First Medical University & Shandong Academy of Medical Sciences, 271016, Taian, Shandong Province, PR China.
Xuewen LiSchool of Public Health, Shandong University, 250012, Jinan, Shandong Province, PR China.
Baofang ZhangJinan Second Maternal and Child Health Hospital, 271199, Jinan, Shandong Province, PR China.
Baofa JiangSchool of Public Health, Shandong University, 250012, Jinan, Shandong Province, PR China.
Dong LiSchool of Public Health, Shandong First Medical University & Shandong Academy of Medical Sciences, 271016, Taian, Shandong Province, PR China.
Weijia XingSchool of Public Health, Shandong First Medical University & Shandong Academy of Medical Sciences, 271016, Taian, Shandong Province, PR China.
Qiyong LiuState Key Laboratory for Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, China CDC, 102206, Beijing, PR China.
Xuena LiuSchool of Public Health, Shandong First Medical University & Shandong Academy of Medical Sciences, 271016, Taian, Shandong Province, PR China. Electronic address: xuena_liu@163.com.
Haifeng HouSchool of Public Health, Shandong First Medical University & Shandong Academy of Medical Sciences, 271016, Taian, Shandong Province, PR China. Electronic address: hfhou@163.com.
Shandong First Medical University · CNShandong University · CNChinese Center For Disease Control and Prevention · CNShandong Maternal and Child Health Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundFlood-related damage can be very severe and include health effects. Among those health impacts, infectious diseases still represent a significant public health problem in China. However, there have been few studies on the identification of the spectrum of infectious diseases associated with floods in one area. This study aimed to quantitatively identify sensitive infectious diseases associated with floods in Guangxi, China.

methodsA time-trend ecological design was conducted. A descriptive analysis was first performed to exclude infectious diseases with low incidence from 2005 to 2012 in ten study sites of Guangxi. The Wilcoxon rank-sum test was applied to examine the difference in the ten-day attack rate of infectious diseases between the exposure and control periods with different lagged effects. Negative binomial, zero-inflated Poisson and zero-inflated negative binomial models were used to examine the relationship and odd ratios (ORs) of the risk of floods on infectious diseases of preliminary screening.

resultsA total of 417,271 infectious diseases were notified. There were 11 infectious diseases associated with floods in the preliminary screening process for flood-sensitive infectious diseases. The strongest effect was shown with a 0-9 ten-day lag in different infectious diseases. Multivariate analysis showed that floods were significantly associated with an increased the risk of bacillary dysentery (odds ratio (OR) = 1.268, 95% confidence interval (CI): 1.072-1.500), acute haemorrhagic conjunctivitis (AHC, OR = 3.230, 95% CI: 1.976-5.280), influenza A (H

conclusionThe spectrum of infectious diseases which are associated with floods are bacillary dysentery, AHC, influenza A (H

Indexed as

ChinaCommunicable DiseasesDysentery, BacillaryEnvironmental ExposureFloodsHumansOdds RatioFloodsInfectious diseasesSpectrum of sensitive infectious diseasesTime-trend ecological study

Identifiers

PMID31306984
PMCPMC7094502
OpenAlexW2955761746

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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.