Evidence map›Paper›PMID 39140274›Full record

ArticleJMIR public health and surveillance2024

Increased Risk of Influenza Infection During Cold Spells in China: National Time Series Study.

Haitao Wang, Mengjie Geng, Tamara Schikowski, Ashtyn Tracey Areal, Kejia Hu, Wen Li, Micheline de Sousa Zanotti Stagliorio Coelho, Paulo Hilário Nascimento Saldiva, Wei Sun, Chengchao Zhou and 3 more

Abstract read
In one paragraph

Article in JMIR public health and surveillance, 2024. 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
–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

2 citing papers in PubMed.

  1. Article
  2. Article
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

13 authors.

Haitao WangDepartment of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China.ORCID 0000-0001-9787-0835
Mengjie GengChinese Center for Disease Control and Prevention, Beijing, China.ORCID 0000-0003-2578-940X
Tamara SchikowskiDepartment of Epidemiology, IUF-Leibniz Research Institute for Environmental Medicine, Düsseldorf, Germany.ORCID 0000-0002-4559-9374
Ashtyn Tracey ArealDepartment of Epidemiology, IUF-Leibniz Research Institute for Environmental Medicine, Düsseldorf, Germany.ORCID 0000-0002-6272-6726
Kejia HuDepartment of Big Data in Health Science, School of Public Health, Zhejiang University, Hangzhou, China.ORCID 0000-0002-1175-3580
Wen LiDepartment of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China.ORCID 0000-0002-8128-9904
Micheline de Sousa Zanotti Stagliorio CoelhoFaculty of Medicine, University of São Paulo, São Paulo, Brazil.ORCID 0000-0002-9732-1278
Paulo Hilário Nascimento SaldivaFaculty of Medicine, University of São Paulo, São Paulo, Brazil.ORCID 0000-0003-2005-8253
Wei SunTaierzhuang Center for Disease Control and Prevention, Zaozhuang, China.ORCID 0009-0008-3896-3300
Chengchao ZhouManagement and Policy, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China.ORCID 0000-0002-9364-3579
Liang LuShandong University Climate Change and Health Center, Jinan, China.ORCID 0000-0001-6372-0054
Qi Zhao *Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China.ORCID 0000-0003-0840-677X
Wei Ma *Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China.ORCID 0000-0002-8092-3280

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Studies have reported the adverse effects of cold events on influenza. However, the role of critical factors, such as characteristics of cold spells, and regional variations remain unresolved. Objective: We aimed to systematically evaluate the association between cold spells and influenza incidence in mainland China. Methods: This time series analysis used surveillance data of daily influenza from 325 sites in China in the 2014-2019 period. A total of 15 definitions of cold spells were adopted based on combinations of temperature thresholds and days of duration. A distributed lag linear model was used to estimate the short-term effects of cold spells on influenza incidence during the cool seasons (November to March), and we further explored the potential impact of cold spell characteristics (ie, intensity, duration, and timing during the season) on the estimated associations. Meta-regressions were used to evaluate the modification effect of city-level socioeconomic indicators. Results: The overall effect of cold spells on influenza incidence increased with the temperature threshold used to define cold spells, whereas the added effects were generally small and not statistically significant. The relative risk of influenza-associated with cold spells was 3.35 (95% CI 2.89-3.88), and the estimated effects were stronger during the middle period of cool seasons. The health effects of cold spells varied geographically and residents in Jiangnan region were vulnerable groups (relative risk 7.36, 95% CI 5.44-9.95). The overall effects of cold spells were positively correlated with the urban population density, population size, gross domestic product per capita, and urbanization rate, indicating a sterner response to cold spells in metropolises. Conclusions: Cold spells create a substantial health burden on seasonal influenza in China. Findings on regional and socioeconomic differences in the health effects of cold spells on seasonal influenza may be useful in formulating region-specific public health policies to address the hazardous effects of cold spells.

Indexed as

Cold TemperatureInfluenza, HumanChinaHumansIncidenceSeasonsChinacold spelldefinitioninfluenzamodification effectvulnerable population

Identifiers

PMID39140274
PMCPMC11336504

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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.