Influence of the COVID-19 pandemic on the incidence and exacerbation of childhood allergic diseases

Differences in the prevalence of allergy and asthma among US children and adolescents during and before the COVID-19 pandemic

BACKGROUND

The increasing prevalence of allergies and asthma has led to a growing global socioeconomic burden. Since the outbreak of the COVID-19 pandemic, the health and lifestyles of children and adolescents have changed dramatically. It's unclear how this shift impacted allergy and asthma, with limited studies addressing this question. We aim to explore the difference of the prevalence of allergies and asthma among US children and adolescents during and before the COVID-19 pandemic using a nationally representative sample of US children and adolescents.

METHODS

This cross-sectional study included 31,503 participants in the National Health Interview Survey (NHIS) between 2018 and 2021. Allergies and asthma were defined on an affirmative response in the questionnaire by a parent or guardian. Chi-square tests were used to compare baseline characteristics with allergies and asthma for categorical variables. Differences in prevalence during and before the COVID-19 pandemic were estimated with weighted logistic regression, adjusting for demographic factors. Interaction analyses explored variations across strata.

RESULTS

In US children and adolescents aged 0 to 17, prevalence of any allergy was 26.1% (95% CI, 24.8%- 27.4%) in 2018 and 27.1% (95% CI, 25.9%- 28.2%) in 2021. Thereinto, in 2018, prevalence of respiratory allergies, food allergies and skin allergies were 14.0% (95% CI, 13.1%- 15.0%), 6.5% (95% CI, 5.8%- 7.1%) and 12.6% (95% CI, 11.6%- 13.5%), respectively, and in 2021, 18.8% (95% CI, 17.8%- 19.9%), 5.8% (95% CI, 5.2%- 6.4%) and 10.7% (95% CI, 9.9%- 11.5%), respectively. And prevalence of asthma was 11.1% (95% CI, 10.5%- 11.7%) in 2018-2019 and 9.8% (95% CI, 9.2%- 10.4%) in 2020-2021. Prevalence of respiratory allergies, skin allergies and asthma during and before the COVID-19 pandemic in children and adolescents had statistically significant differences. The differences persisted after adjusting for demographic and socioeconomic variables.

CONCLUSION

Prevalence of respiratory allergies increased and the prevalence of both skin allergies and asthma decreased among US children and adolescents during the COVID-19 pandemic compared with the pre-COVID-19 pandemic. Further research is required to explore the association between allergic diseases and the pandemic, with a particular emphasis on the impact of lifestyle changes resulting from measures to prevent COVID-19 infection.

The Impact of the Pandemic on Cat and Dog Allergies

Objectives

The impact of the COVID-19 pandemic on allergic patients is not clearly understood. The aim of this study is to investigate the changes in sensitivity to cat and dog allergens in patients with respiratory allergies in Istanbul during the COVID-19 pandemic.

Methods

Before the pandemic (March 2018 - March 2020) and during and after the pandemic (March 2020 - March 2022), a total of 5499 patients who underwent skin testing were retrospectively evaluated. The patients' age, gender, diagnosis, total IgE, and eosinophil values were recorded. Patients were divided into two groups: 2-6 years old and 7 years and older. The frequency of sensitization to cats and dogs was investigated in both groups before and after the pandemic.

Results

The median age of the 5499 children included in the study was 77 months, with a range of 2 to 221 months. Furthermore, 55.7% of the children were male. Of the children, 59.1% were examined before the pandemic, and 40.9% during and after the pandemic. During the examinations, allergic rhinitis was identified in 1628 children (29.6%), asthma in 1829 children (33.3%), and both asthma and allergic rhinitis in 2042 children (37.1%). Allergies to cats were found in 247 children (4.5%), and to dogs in 166 children (3.0%). When comparing the age groups, the frequency of cat allergy in the 7-years and older group was found to have decreased compared to the pre-pandemic period, and the statistical difference was close to significance (p=0.08). However, regarding dog allergy, in the 7 years and older group, the sensitivity to dogs, which was 5.6% before the pandemic, had decreased to 2.6% during the pandemic, and this decrease was found to be statistically highly significant (p<0.001).

Conclusion

Environmental allergen exposure has a significant impact on the phenotype of allergic diseases. Changes in patients' lifestyles and increased time spent at home during the pandemic may have led to a decrease in contact with outdoor cat and dog allergens, resulting in a reduced frequency of cat and dog sensitivity. Additionally, the introduction of mask-distance-handwashing/disinfection rules during the pandemic is believed to have reduced contact with cat and dog allergens, potentially contributing to a decrease in allergy frequency.

Social distancing during the COVID-19 pandemic: Potential impact and correlation with asthma

BACKGROUND

Non-pharmaceutical interventions have been implemented globally to control the COVID-19 pandemic and have been shown to alleviate both allergies and respiratory infections. Although mask-wearing is an accepted non-pharmaceutical intervention, the effects of social distancing have not been thoroughly evaluated.

OBJECTIVES

To evaluate the effects of social distancing on asthma trends in Seoul, South Korea.

METHODS

This study included data from the National Health Insurance Service of South Korea, covering approximately 10 million people in Seoul. Daily and monthly data of patients with asthma from 2018 to 2021 were examined, and the degree of social distancing performance was measured using the number of subway users as an index. Pearson's correlation coefficient was used to determine the relationship between the two indices. The change-point detection technique, cross-correlation, and Granger causality method were used to assess the temporal causality between social distancing and asthma.

RESULTS

The number of patients with asthma decreased by 42.4 % from 2019 to 2020, while that of subway users decreased by 26.3 % during this period. Pearson's correlation analysis revealed significant positive correlations. Asthma and subway users showed a significant change in incidence following the implementation of social distancing; subway users showed a causal relationship with patients with asthma.

CONCLUSION

Our results showed that the number of subway users decreased after the implementation of strict social distancing, coinciding with a decrease in the number of patients with asthma. These findings suggest that social distancing measures implemented to control COVID-19 may reduce the incidence and exacerbation of asthma.

Development and validation of a deep learning model to diagnose COVID-19 using time-series heart rate values before the onset of symptoms

One of the effective ways to minimize the spread of COVID-19 infection is to diagnose it as early as possible before the onset of symptoms. In addition, if the infection can be simply diagnosed using a smartwatch, the effectiveness of preventing the spread will be greatly increased. In this study, we aimed to develop a deep learning model to diagnose COVID-19 before the onset of symptoms using heart rate (HR) data obtained from a smartwatch. In the deep learning model for the diagnosis, we proposed a transformer model that learns HR variability patterns in presymptom by tracking relationships in sequential HR data. In the cross-validation (CV) results from the COVID-19 unvaccinated patients, our proposed deep learning model exhibited high accuracy metrics: sensitivity of 84.38%, specificity of 85.25%, accuracy of 84.85%, balanced accuracy of 84.81%, and area under the receiver operating characteristics (AUROC) of 0.8778. Furthermore, we validated our model using external multiple datasets including healthy subjects, COVID-19 patients, as well as vaccinated patients. In the external healthy subject group, our model also achieved high specificity of 77.80%. In the external COVID-19 unvaccinated patient group, our model also provided similar accuracy metrics to those from the CV: balanced accuracy of 87.23% and AUROC of 0.8897. In the COVID-19 vaccinated patients, the balanced accuracy and AUROC dropped by 66.67% and 0.8072, respectively. The first finding in this study is that our proposed deep learning model can simply and accurately diagnose COVID-19 patients using HRs obtained from a smartwatch before the onset of symptoms. The second finding is that the model trained from unvaccinated patients may provide less accurate diagnosis performance compared with the vaccinated patients. The last finding is that the model trained in a certain period of time may provide degraded diagnosis performances as the virus continues to mutate.

Nonpharmaceutical interventions for COVID-19 disrupt the dynamic balance between influenza A virus and human immunity

During the COVID-19 epidemic, nonpharmaceutical interventions (NPIs) blocked the transmission route of respiratory diseases. This study aimed to investigate the impact of NPIs on the influenza A virus (IAV) outbreak. The present study enrolled all children with respiratory tract infections who came to the Children's Hospital of Zhejiang University between January 2019 and July 2022. A direct immunofluorescence assay kit detected IAV. Virus isolation and Sanger sequencing were performed. From June to July 2022, in Hangzhou, China, the positive rate of IAV infection in children has increased rapidly, reaching 30.41%, and children over 3 years old are the main infected population, accounting for 75% of the total number of infected children. Influenza A (H3N2) viruses are representative strains during this period. In this outbreak, H3N2 was isolated from a cluster of its own and is highly homologous with A/South_Dakota/22/2022 (2021-2022 Northern Hemisphere). Between isolated influenza A (H3N2) viruses and A/South_Dakota/22/2022, the nucleotide homology of the HA gene ranged from 97.3% to 97.5%; the amino acid homology was 97%-97.2%, and the genetic distance of nucleotides ranged from 0.05 to 0.052. Compared with A/South_Dakota/22/2022, the isolated H3N2 showed S156H, N159Y, I160T, D186S, S198P, I48T, S53D, and K171N mutations. There was no variation in 13 key amino acid sites associated with neuraminidase inhibitor resistance in NA protein. Long-term NPIs have significantly affected the evolution and transmission of the influenza virus and human immunity, breaking the dynamic balance between the IAV and human immunity.

Incidence of Asthma, Atopic Dermatitis, and Allergic Rhinitis in Korean Adults before and during the COVID-19 Pandemic Using Data from the Korea National Health and Nutrition Examination Survey

The prevalence of allergic diseases has been increasing globally prior to COVID-19. The pandemic resulted in changes in lifestyle and personal habits such as universal mask-wearing and social distancing. However, there is insufficient information on the impact of the COVID-19 pandemic on the prevalence of allergic conditions such as asthma, atopic dermatitis, and allergic rhinitis. We analyzed the incidence rate for self-reported and doctor-diagnosed cases of allergic diseases of asthma, atopic dermatitis, and allergic rhinitis. A total of 15,469 subjects were registered from a national cohort dataset of the National Health and Nutrition Examination Survey. Using multiple logistic regression analysis, we calculated the adjusted odds ratio (OR) for each disease in 2020 compared to 2019. Subgroup analyses were performed according to age and sex. There were no statistically significant differences between the incidence of doctor-diagnosed and current allergic diseases in 2019 and 2020 (asthma, p = 0.667 and p = 0.268; atopic dermatitis, p = 0.268 and p = 0.973; allergic rhinitis, p = 0.691 and p = 0.942, respectively), and subgroup analysis showed consistent results. Among the Korean population from 2019 to 2020, the incidence of the allergic diseases asthma, atopic dermatitis, and allergic rhinitis did not decrease as expected.