Evidence map›Paper›PMID 39443644›Full record

ArticleScientific reports2024

To study the impact of COVID-19 on the epidemiological characteristics of allergic rhinitis based on local big data in China.

Ruru Tian, Jianhua Jiang, Jinv Ding, Jieling Zhao, Xinru Zhou

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Ruru TianAffiliated Hospital of Hangzhou Normal University, Hangzhou, China. tianrrall@yeah.net.
Jianhua JiangAffiliated Hospital of Hangzhou Normal University, Hangzhou, China.
Jinv DingAffiliated Hospital of Hangzhou Normal University, Hangzhou, China.
Jieling ZhaoAffiliated Hospital of Hangzhou Normal University, Hangzhou, China.
Xinru ZhouAffiliated Hospital of Hangzhou Normal University, Hangzhou, China.

Funding

Health Science and Technology Project of Hangzhou city (A20220403)
6 · The paper itself

Abstract

To investigate the big data characteristics and trend changes of patients with allergic rhinitis (AR) who sought medical attention at our hospital before (from 2018 to 2019) and after (from 2020 to 2023) COVID-19, and provide reference basis for the treatment of AR. This study used a descriptive epidemiological method to analyze the big data and trend changes of AR patients. A total of 62,196 AR patients were collected, of whom 32,874 were male and 29,322 were female, with an age range of 1-89 years. The monthly change trend of AR patients showed a marked seasonality. The number of AR patients increased year by year. There was no significant difference in the number of patients between different genders. There was a significant difference in the number of patients between different age groups. The number of AR patients increased markedly from 2018 to 2023, and COVID-19 seems to have accelerated this process. There is a clear seasonal pattern. The number of boy patients is significantly higher than that of girl patients, and the change in female hormones may affect the incidence of AR. Therefore, it is necessary to update management measures and formulate relevant policies in response to the changing trend of AR since the COVID-19 epidemic.

Indexed as

Big DataCOVID-19Rhinitis, AllergicAdolescentAdultAgedAged, 80 and overChildChild, PreschoolChinaFemaleHumansIncidenceInfantMaleMiddle AgedAllergic rhinitisBig dataCOVID-19Epidemiological studiesPrevalence

Identifiers

PMID39443644
PMCPMC11500020

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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.