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

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

Publication date: Oct 23, 2024

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.

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Concepts Keywords
China Adolescent
Hospital Adult
Rhinitis Aged
Seasonal Aged, 80 and over
Allergic rhinitis
Big Data
Big data
Child
Child, Preschool
China
COVID-19
COVID-19
Epidemiological studies
Female
Humans
Incidence
Infant
Male
Middle Aged
Prevalence
Rhinitis, Allergic
SARS-CoV-2
Seasons
Young Adult

Semantics

Type Source Name
disease MESH COVID-19
disease MESH allergic rhinitis
disease IDO process
disease IDO host
disease MESH lifestyles
drug DRUGBANK Coenzyme M
drug DRUGBANK Diphenylpyraline
drug DRUGBANK Medical air
disease MESH post traumatic stress disorder
disease MESH psychological stress
drug DRUGBANK Etoperidone
disease MESH uncertainty
disease MESH infection
pathway KEGG Circadian rhythm
disease IDO intervention
pathway REACTOME Circadian Clock
disease MESH dysbacteriosis
disease MESH inflammation
disease MESH allergy
pathway REACTOME Influenza Infection
disease MESH viral infections
drug DRUGBANK Acetaminophen
disease MESH infertility
disease IDO country
disease MESH chronic diseases
pathway REACTOME Immune System
disease MESH critical illness
disease IDO innate immune response
drug DRUGBANK Progesterone
drug DRUGBANK Testosterone
disease MESH asthma
pathway KEGG Asthma
disease MESH depression
disease MESH loneliness
drug DRUGBANK Pentaerythritol tetranitrate
disease MESH atopic dermatitis
disease MESH food allergy
disease MESH severe acute respiratory syndrome
disease MESH syndromes
disease IDO blood
disease MESH rhinitis
disease MESH Anosmia
disease MESH anxiety
disease IDO cell
disease MESH viral load
disease MESH Influenza
disease MESH infectious disease
pathway REACTOME Infectious disease
drug DRUGBANK Dermatophagoides pteronyssinus
disease MESH seasonal allergic rhinitis
drug DRUGBANK Estradiol
pathway REACTOME Reproduction

Original Article

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