Evidence map›Paper›PMID 31796004›Full record

ArticleBMC infectious diseases2019

Weather effects on hand, foot, and mouth disease at individual level: a case-crossover study.

Zhicheng Du, Shao Lin, Tia Marks, Wangjian Zhang, Te Deng, Shicheng Yu, Yuantao Hao

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 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
1.1field-weighted citation impact, top 19% 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, 13 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

7 authors at 4 institutions in 2 countries.

Zhicheng DuDepartment of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, China.
Shao LinDepartment of Environmental Health Sciences, School of Public Health, University at Albany, State University of New York, Rensselaer, New York, 12144, USA.
Tia MarksDepartment of Environmental Health Sciences, School of Public Health, University at Albany, State University of New York, Rensselaer, New York, 12144, USA.
Wangjian ZhangDepartment of Environmental Health Sciences, School of Public Health, University at Albany, State University of New York, Rensselaer, New York, 12144, USA.
Te DengHealthcare Department, Nanshan Maternity & Child Healthcare Hospital of Shenzhen, Shenzhen, 518000, China.
Shicheng YuChinese Center for Disease Control and Prevention, Beijing, 102206, China.
Yuantao HaoDepartment of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, China. haoyt@mail.sysu.edu.cn.ORCID http://orcid.org/0000-0002-1155-8443
University at Albany, State University of New York · USSun Yat-sen University · CNChinese Center For Disease Control and Prevention · CNShenzhen Maternity and Child Healthcare Hospital · CN

Funding

Weather extremes, natural disasters, and health outcomes among vulnerable older adults: New improvements on exposure assessment, disparity identification, and risk communication strategiesR01AG070949 · NIA · STATE UNIVERSITY OF NEW YORK AT ALBANY · PI LIN, SHAO · 2022 to 2025
$1.6M
Assess and Model the Health Effects, Population, and Infrastructural Vulnerabilities of Power OutageR15ES028000 · NIEHS · STATE UNIVERSITY OF NEW YORK AT ALBANY · PI LIN, SHAO · 2018 to 2018
$444k
National Natural Science Foundation of China 81473064 & 81773543NIA NIH HHS R01 AG070949NIEHS NIH HHS R15 ES028000
6 · The paper itself

Abstract

backgroundHand, foot, and mouth disease (HFMD) raises an urgent public health issue in the Asia-Pacific region, especially in China. The associations between weather factors and HFMD have been widely studied but with inconsistent results. Moreover, previous studies utilizing ecological design could not rule out the bias of exposure misclassification and unobserved confounders.

methodsWe used case-crossover analysis to assess the associations of weather factors on HFMD. Individual HFMD cases from 2009 to 2012 in Guangdong were collected and cases located within 10 km of the meteorological monitoring sites were included. Lag effects were examined through the previous 7 days. In addition, we explored the variability by changing the distance within 20 km and 30 km.

resultsWe observed associations between HFMD and weather factors, including temperature and relative humidity. An approximately U-shaped relationship was observed for the associations of temperature on HFMD across the same day and the previous 7 days, while an approximately exponential-shaped was seen for relative humidity. Statistically significant increases in rates of HFMD were associated with each 10-unit increases in temperature [Excess rate (ER): 7.7%; 95% Confidence Interval (CI): 3.9, 11.7%] and relative humidity (ER: 1.9%; 95% CI: 0.7, 3.0%) on lag days 0-6, when assessing within 10 km of the monitoring sites. Potential thresholds for temperature (30.0 °C) and relative humidity (70.3%) detected showed associations with HFMD. The associations remained robust for 20 km and 30 km.

conclusionsOur study found that temperature and relative humidity are significantly associated with the increased rates of HFMD. Thresholds and lag effects were observed between weather factors and HFMD. Our findings are useful for planning on targeted prevention and control of HFMD.

Indexed as

WeatherAdolescentChildChild, PreschoolChinaCross-Over StudiesEcologyEnvironmental MedicineFemaleHand, Foot and Mouth DiseaseHumansHumidityIncidenceInfantInfant, NewbornLogistic ModelsCase-crossoverHand, foot, and mouth diseaseWeather effect

Identifiers

PMID31796004
PMCPMC6891988
OpenAlexW2992998528

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.