Skip to main content
NCBI home page
Allergologie Select logoLink to Allergologie Select
. 2026 Apr 2;10:36–48.

Comparing pre- and post-COVID-19 chronic allergy prevalence in children using National Health and Nutrition Examination Survey in 2019 and 2021

Bomi Kim 1, Sunyeob Choi 2
PMCID: PMC13055419  PMID: 41953499

Abstract

Objective: To investigate changes in demographic characteristics, parental smoking habits, and the prevalence of asthma, atopic dermatitis, and rhinitis among children in South Korea before and after the COVID-19 pandemic. Materials and methods: A retrospective analysis of national health survey data from 2019 and 2021 was conducted, including children aged 3 – 18 years. Factors such as gender, age, location, housing type, family size, income, body mass index, subjective health status, influenza vaccination, family structure, and parental smoking habits were analyzed. Results: No significant differences were found in most demographic characteristics and parental features between 2019 and 2021, except for influenza vaccination rates and mothers’ age at first childbirth. The influenza vaccination rate increased from 69.3% in 2019 to 77.8% in 2021, and the average maternal age at first birth increased from 28.46 years to 29.22 years. Asthma diagnoses showed no significant differences between the 2 years after adjusting for general and parent-related characteristics. For atopic dermatitis, significant differences in gender distribution were observed in 2021. Rhinitis diagnoses showed significant differences in age, area, and breastfeeding status between the two years. Conclusion: The COVID-19 pandemic may have influenced certain demographic characteristics, such as influenza vaccination rates and mothers’ age at first childbirth, but the prevalence of asthma, atopic dermatitis, and rhinitis among children remained largely unchanged between 2019 and 2021. This study underscores the importance of monitoring the impact of social changes on children’s health, particularly during significant events like the COVID-19 pandemic. Further research is required to understand the long-term effects of these changes on child health.

Keywords: COVID-19, chronic allergic diseases, children, asthma, atopic dermatitis, allergic rhinitis, KNHANES

Introduction

Chronic allergic diseases, including asthma, atopic dermatitis, and allergic rhinitis, which occur during childhood, are gradually increasing due to increased triggering factors resulting from changes in living environments and weakened immune systems caused by a decrease in pediatric infectious diseases [1]. The prevalence of chronic allergic diseases in childhood is higher than in adulthood [2], and the prevalence rates of atopic dermatitis, asthma, and allergic rhinitis were 4.3, 3.2 [3], and 16.3% [4], respectively. Chronic allergic diseases are the most common chronic diseases in children [5, 6]. The World Allergy Organization defines children as the group with the highest burden related to allergic diseases [7]. Chronic diseases in children affect overall aspects of a child’s life, such as growth and development, emotional development, academics, and peer relationships [8, 9, 10, 11].

The Coronavirus Disease 2019 (COVID-19), an acute respiratory illness caused by the SARS-CoV-2 virus, which began in Wuhan, China, in December 2019, has become a global pandemic [12]. In Korea, since the first confirmed case of COVID-19 in January 2020, 250,000 people have been diagnosed with the virus, and 25,000 have died due to the virus [13]. Chronic allergic diseases such as asthma, atopic dermatitis, and allergic rhinitis have the characteristic of worsening due to respiratory-related viruses [14]. Also, mechanisms that trigger allergic reactions, such as cytokines, play a role in increasing the prevalence of COVID-19 in patients with allergic diseases [15], suggesting that COVID-19 may affect the prevalence of chronic allergic diseases in children. Recent findings indicate that pediatric patients with allergic rhinitis and aeroallergen sensitivity may exhibit milder COVID-19 symptoms, highlighting a complex interaction between allergic diseases and the virus. However, comprehensive data on the full impact of COVID-19 in children with chronic allergic conditions are still lacking [16].

In Korea, the prevalence of asthma and allergic rhinitis in adolescents significantly decreased in 2020 compared to 2019, but there was no change in the prevalence of atopic dermatitis [4]. However, a study targeting adults reported an increasing trend of asthma patients due to COVID-19, and a longer treatment period was required when diagnosed with COVID-19 [17]. In a study related to allergic rhinitis and COVID-19, 27% of COVID-19 patients experienced worsening rhinitis symptoms [18]. Although there was no relationship between the prevalence of atopic dermatitis and COVID-19 [19], some studies suggested that COVID-19 could exacerbate atopic dermatitis by triggering allergic reactions [15]. Therefore, there are varying results across studies regarding the relationship between COVID-19 and chronic allergic diseases. In this context, this study aims to clarify the difference in the prevalence of chronic allergic diseases in children before and after the outbreak of COVID-19 by using raw data from the Korea National Health and Nutrition Examination Survey in 2019 and 2021. By comparing the prevalence and risk factors of these diseases before and after the pandemic, the study seeks to provide a comprehensive understanding of the impact of COVID-19 on chronic allergic diseases in children and identify areas that may require further research or intervention.

Materials and methods

Data collection

This study is a descriptive survey comparing raw data from the 8th Korea National Health and Nutrition Examination Survey (KNHANES) [20] conducted between 2019 and 2021 by the Korea Centers for Disease Control and Prevention. KNHANES has been conducted annually since 1998, and it investigates health status, prevalence of chronic diseases, food intake and nutritional status, and health-related habits based on Article 16 of the National Health Promotional Act. The survey targets different age groups, including children (1 – 11 years old), adolescents (12 – 18 years old), and adults (19 years old and above), and investigates items specific to each group. The first case of COVID-19 in Korea was reported on January 20, 2020. Therefore, this study analyzes the raw data from 2019 and 2021 to compare the pre- and post-COVID-19 outbreak periods.

Study population

The study population includes children aged 3 – 18 years old. Chronic allergic diseases are typically diagnosed during childhood and can have lifelong impacts [2]. This study focuses on children aged 3 years and older who are prone to chronic allergic diseases. The study’s inclusion criteria are children who have been diagnosed with asthma, atopic dermatitis, or allergic rhinitis by a doctor or are currently suffering from these diseases. To clarify the relationship between the prevalence of children’s chronic allergic diseases and parental factors, this study also includes children whose parents have completed the survey. A total of 791 participants from 2019 and 562 participants from 2021 were included in the study.

Variables

The study includes variables related to the prevalence of chronic allergic diseases, such as children’s characteristics, family characteristics, and parental characteristics, as collected by the KNHANES. Children’s characteristics include gender, age (pre-school, school-age, adolescence), body mass index (BMI) (< 5th percentile, 5 – 85th percentile, 85 – 95th percentile, ≥ 95th percentile), self-perceived health status (good, average, poor), and influenza vaccination status. Family characteristics include residential area (city, province), type of housing (general housing, apartment), family income level (low, lower-middle, middle, upper-middle, high), and family generation structure (2 generations, 3 generations or more). Parental factors include age at childbirth and breastfeeding status. Outcome variables include the current status of doctor’s diagnosis.

Ethical considerations

This study was conducted using publicly available data from the Korean National Health and Nutrition Examination Survey (KNHANES) [20]. The KNHANES is conducted annually and is approved by the Institutional Review Board of the Korea Centers for Disease Control and Prevention (Approval No. 2018-01-03-3C-A). The data used in this study was downloaded from the KNHANES website after following the appropriate procedures, and all provided data was de-identified and cannot be used to identify individual subjects.

Data analysis

To evaluate the general population characteristics of 2019 and 2021, we conducted linear regression analysis using complex sampling, and applied the χ2-test with Rao-Scott correction to the population data with weighted values. All results were presented as weighted values using the weights recommended by KNHANES. The χ2-test was used to compare the prevalence and diagnosis of chronic allergic diseases by year based on individual characteristics, family characteristics, and parental characteristics.

Two sets of adjusted analyses were performed. Adjusted1 included only the general characteristics of the child as confounding variables, while Adjusted2 included the general characteristics of the child, as well as parental smoking-related variables, mother’s age at first childbirth, and breastfeeding status. The analysis was further adjusted for potential confounders, such as age, gender, and other relevant factors.

All statistical analyses were conducted using SPSS 28.0 version. A p-value of less than 0.05 was considered statistically significant. The results of the data analysis were used to identify any differences in the prevalence and risk factors of chronic allergic diseases before and after the COVID-19 outbreak. Additionally, the findings were compared and contrasted with existing literature to provide context and further understanding of the impact of COVID-19 on childhood chronic allergic diseases.

Results

Comparison of general and parent-related characteristics of children in 2019 and 2021

In this study, we compared the distribution of various factors between 2019 and 2021. These factors include gender, age, location (urban/rural), type of housing, family size, income, BMI, subjective health status, influenza vaccination, family structure, parental smoking habits, and maternal breastfeeding. There were no significant differences between the 2 years for most factors, except for the influenza vaccination rate (p = 0.005) and maternal age at first birth (p = 0.046). The influenza vaccination rate increased from 69.3% in 2019 to 77.8% in 2021, and the average maternal age at first birth increased from 28.46 years to 29.22 years (Table 1).

Table 1. Comparison of general and parent-related characteristics of children in 2019 and 2021.

Variable Category Year χ 2 or t p-value
M ± SD, n (%)
2019 (n = 791) 2021 (n = 562)
Gender Male 408 (51.6) 278 (49.5) 0.717 0.398
Female 383 (48.4) 284 (50.5)
Age Mean ± SD 9.46 ± 4.134 9.63 ± 4.000 0.157 0.875
3 – 6 277 (28.7) 145 (25.8) 0.240 0.780
7 – 11 308 (38.9) 219 (39.0)
12 – 17 256 (32.4) 198 (35.2)
Rural/urban Urban 669 (84.6) 471 (83.8) 0.007 0.932
Rural 122 (15.4) 91 (16.2)
Type of housing General housing 169 (21.4) 140 (24.9) 0.007 0.935
Apartment 622 (78.6) 422 (75.1)
Number in family 4.17 ± 0.739 4.14 ± 0.750 –1.012 0.313
Income Low 14 (1.8) 6 (1.1) 0.978 0.414
Lower middle 129 (16.3) 58 (10.3)
Middle 209 (26.4) 173 (30.8)
Upper middle 263 (33.2) 187 (33.3)
High 176 (22.3) 138 (24.6)
Body mass index < 5th percentile 69 (8.7) 34 (6.0) 1.308 0.271
5 – 85th percentile 563 (71.2) 382 (68.0)
85 – 95th percentile 73 (9.2) 64 (11.4)
≥ 95th percentile 85 (10.7) 76 (13.5)
Subjective health status Good 586 (74.1) 429 (76.3) 0.535 0.580
Fair 182 (23.0) 114 (20.3)
Poor 23 (2.9) 19 (3.4)
Influenza vaccination Yes 548 (69.3) 437 (77.8) 8.040 0.005
No 243 (30.7) 125 (22.2)
Household composition 2 generations 741 (93.7) 535 (95.2) 1.919 0.167
3 generations or more 50 (6.3) 27 (4.8)
Father
   Lifetime smoking amount < 5 packs 9 (1.1) 15 (2.7) 1.066 0.344
≥ 5 packs 602 (76.1) 425 (75.6)
Never smoked 180 (22.8) 122 (21.7)
   Current smoking status Daily 247 (31.2) 177 (31.5) 0.215 0.885
Occasionally 38 (4.8) 29 (5.2)
Former smoker 326 (41.2) 234 (41.6)
Never smoked 180 (22.8) 122 (21.7)
Mother
   Lifetime smoking amount < 5 packs 20 (2.5) 19 (3.4) 0.544 0.581
≥ 5 packs 84 (10.6) 57 (10.1)
Never smoked 687 (86.9) 486 (86.5)
   Current smoking status Daily 14 (1.8) 17 (3.0) 0.289 0.807
Occasionally 16 (2.0) 6 (1.1)
Former smoker 74 (9.4) 53 (9.4)
Never smoked 687 (86.9) 486 (86.5)
   Mother’s age at first childbirth Mean (SD) 28.46 ± 0.136 29.22 ± 3.731 2.002 0.046
   Mother’s breastfeeding status Yes 711 (89.9) 504 (89.7)
No 80 (10.1) 58 (10.3) 0.240 0.625
Open in a new tab

Comparison of general and parent-related characteristics of children diagnosed with asthma in 2019 and 2021

We compared the general and parent-related characteristics of children diagnosed with asthma in 2019 and 2021. After adjusting for general and parent-related characteristics, there were no significant differences found in asthma diagnoses between the 2 years. Gender, age, residence, housing type, household composition, household income, BMI, subjective health status, and influenza vaccination status were similar in both years. Parental smoking habits and other parent-related variables also showed no significant differences between the 2 years (Table 2).

Table 2. Comparison of general and parent-related characteristics of children diagnosed with asthma in 2019 and 2021.

Asthma diagnosis
Year 2019 (n = 791) 2021 (n = 562)
M ± SD, n (%) M ± SD, n (%)
Yes No χ 2 (p) Yes No χ 2 (p) OR 95% CI p
General characteristics
   Gender Male 19 (2.4) 389 (49.2) 7.183 (0.008) 7 (1.2) 271 (48.2) 5.368 (0.022) 0.522 0.030 0.089 0.211
Female 3 (0.4) 380 (48.0) 3 (0.5) 281 (50.0) 0.577 0.109 3.039 0.515
   Age M ± SD 10 ± 4.220 9.44 ± 4.133 0.167 (0.868) 10.30 ± 3.302 9.62 ± 4.013 0.828 (0.408) 0.518 0.212 1.266 0.148
3 – 6 4 (0.5) 223 (28.2) 0.317 (0.716) 1 (0.2) 144 (25.6) 0.712 (0.478) 0.203 0.021 1.933 0.165
7 – 11 11 (1.4) 297 (37.5) 6 (1.1) 213 (37.9) 0.550 0.198 1.525 0.249
12 – 17 7 (0.9) 249 (31.5) 3 (0.5) 195 (34.7) 0.646 0.118 3.529 0.612
   Residential area Urban 19 (2.4) 650 (82.2) 1.308 (0.255) 7 (1.2) 464 (82.6) 0.547 (0.461) 0.442 0.159 1.225 0.116
Rural 3 (0.4) 119 (15.0) 3 (0.5) 88 (15.7) 1.520 0.296 7.811 0.614
   Type of housing General housing 4 (0.5) 165 (20.9) 0.447 (0.505) 3 (0.5) 137 (24.4) 0.033 (0.856) 0.789 0.178 3.510 0.755
Apartment 18 (2.3) 604 (76.4) 7 (1.2) 415 (73.8) 0.462 0.155 1.373 0.164
   Number in family 3.91 ± 0.684 4.18 ± 0.739 –1.735 (0.085) 4 ± 0.667 4.14 ± 0.752 –0.954 (0.341) 0.493 0.205 1.185 0.113
   Income Low 0 (0.0) 14 (1.8) 0.413 (0.778) 0 (0.0) 6 (1.1) 0.744 (0.454)
Lower middle 5 (0.6) 124 (15.7) 2 (0.4) 56 (10.0) 0.632 0.107 3.714 0.610
Middle 2 (0.3) 207 (26.2) 4 (0.7) 169 (30.1) 1.437 0.181 11.387 0.731
Upper middle 11 (1.4) 252 (31.69) 1 (0.2) 186 (33.1) 0.110 0.014 0.869 0.036
High 4 (0.5) 172 (21.7) 3 (0.5) 135 (24.0) 0.558 0.097 3.227 0.514
   Body mass index < 5th percentile 1 (0.1) 68 (8.6) 0.150 (0.918) 0 (0.0) 34 (6.1) 0.698 (0.539)
5 – 85th percentile 17 (2.2) 546 (69.1) 9 (1.6) 373 (67.1) 0.642 0.236 1.747 0.384
85 – 95th percentile 2 (0.3) 71 (9.0) 1 (0.2) 63 (11.3) 0.509 0.038 6.775 0.608
≥ 95th percentile 2 (0.3) 83 (10.5) 0 (0.0) 76 (13.7)
   Subjective health status Good 15 (1.9) 571 (72.2) 0.504 (0.598) 7 (1.2) 422 (75.1) 6.455 (0.004) 0.547 0.179 1.674 0.289
Fair 7 (0.9) 175 (22.1) 1 (0.2) 113 (20.1) 0.068 0.008 0.573 0.014
Poor 0 (0.0) 23 (2.9) 2 (0.4) 17 (3.0)
   Influenza vaccination Yes 15 (1.9) 533 (67.4) 0.806 (0.371) 7 (1.2) 430 (76.5) 0.005 (0.945) 0.443 0.147 1.330 0.146
No 7 (0.9) 234 (29.8) 3 (0.5) 122 (21.7) 0.761 0.171 3.391 0.719
   Household composition 2 generations 21 (2.7) 720 (91.0) 0.421 (0.518) 10 (1.8) 525 (93.4) 0.218 (0.642) 0.524 0.212 1.292 0.160
3 generations or more 1 (0.1) 49 (6.2) 0 (0.0) 27 (4.8)
Parental characteristics
   Father’s smoking history < 5 packs 0 (0.0) 9 (1.1) 0.147 (0.769) 0 (0.0) 15 (2.7) 0.808 (0.443)
≥ 5 packs 11 (1.4) 591 (74.7) 8 (1.4) 417 (74.2) 0.868 0.314 2.400 0.785
Never smoked 11 (1.4) 169 (21.4) 2 (0.4) 120 (21.4) 0.275 0.042 1.786 0.175
   Father’s current smoking status Daily 1 (0.1) 246 (31.1) 0.169 (0.886) 3 (0.5) 174 (31.0) 0.091 (0.945) 2.056 0.206 20.478 0.538
Occasionally 2 (0.3) 36 (4.6) 3 (0.2) 28 (5.0) 0.579 0.039 8.527 0.690
Former smoker 8 (1.0) 318 (40.2) 4 (0.7) 230 (40.9) 0.746 0.225 2.476 0.631
Never smoked 11 (1.4) 169 (21.4) 2 (0.4) 120 (21.4) 0.275 0.042 1.786 0.175
   Mother’s smoking history < 5 packs 1 (0.1) 19 (2.4) 0.120 (0.787) 1 (0.2) 18 (3.2) 1.968 (0.143) 3.215 0.168 61.59 0.437
≥ 5 packs 2 (0.3) 82 (10.4) 0 (0.0) 57 (10.1) < 0.001 5.29E-12 8.06E-11 < 0.001
Never smoked 19 (2.4) 668 (84.5) 9 (1.6) 477 (84.9) 0.55 0.21 1.442 0.223
   Mother’s current smoking status Daily 0 (0) 14 (1.8) 0.201 (0.866) 0 (0.0) 17 (3.0) 0.135 (0.931)
Occasionally 0 (0) 16 (2.0) 0 (0.0) 6 (1.1)
Former smoker 3 (0.4) 71 (9.0) 1 (0.2) 52 (9.3) 0.297 0.027 3.254 0.319
Never smoked 19 (2.4) 668 (84.5) 9 (1.6) 477 (84.9) 0.55 0.21 1.1442 0.223
   Mother’s age at first childbirth 27.82 (3.78) 28.56 (3.90) –0.165 (0.869) 30.10 (2.73) 29.20 (3.75) 1.025 (0.308) 0.52 0.213 1.266 0.149
   Mother’s breastfeeding history Yes 22 (2.8) 689 (87.1) 2.045 (0.155) 9 (1.6) 495 (88.1) 0.020 (0.889) 0.373 0.165 0.845 0.018
No 0 (0) 80 (10.1) 1 (0.2) 57 (10.1)
Crude 0.519 0.213 1.265 0.148
Adjusted1 (includes general characteristics) 0.471 0.199 1.117 0.087
Adjusted2 (includes general and parental characteristics) 0.541 0.231 1.265 0.541
Open in a new tab

Comparison of general and parent-related characteristics of children diagnosed with atopic dermatitis in 2019 and 2021

Gender distribution was not significantly different in 2019 data, but was in 2021 data (p = 0.014). Age distribution, type of housing, and parental variables showed no significant differences in both years. However, family size showed a significant difference in 2021 (p = 0.019), which was not observed in 2019. Significant differences were observed in income levels between the 2 years (p < 0.001 in 2021) and in BMI percentiles (p = 0.021 in 2019 and p = 0.418 in 2021). Subjective health status also showed significant differences in both years (p = 0.023 in 2019 and p < 0.001 in 2021).

In the 2019 data, there was no significant difference in gender distribution, but in the 2021 data, there was a significant difference (p = 0.014), indicating a change in gender distribution in 2021. Age distribution, type of housing, family size, and parental variables showed no significant differences in both years. Income levels showed significant differences between the 2 years (p < 0.001). BMI percentiles showed a significant difference in 2019 (p = 0.021), indicating a decrease in 2021, but no significant difference in 2021 (p = 0.418). Subjective health status also showed significant differences in both years (p = 0.023 in 2019 and p < 0.001 in 2021), with a decline in subjective health status observed in 2019 and an improvement in 2021 (Table 3).

Table 3. Comparison of general and parent-related characteristics of children diagnosed with atopic dermatitis diagnosis in 2019 and 2021.

Atopic dermatitis diagnosis
Year 2019 (n = 791) 2021 (n = 562)
M ± SD, n (%) M ± SD, n (%)
Yes No χ 2 (p) Yes No χ 2 (p) OR 95% CI p
General characteristics
   Gender Male 56 (7.1) 352 (44.5) 0.005 (00.945) 45 (8.0) 233 (41.5) 6.305 (0.014) 1.260 0.724 2.192 0.413
Female 52 (6.6) 331 (41.8) 33 (5.9) 251 (44.7) 0.702 0.397 1.240 0.222
   Age M ± SD 10.07 ± 4.0 9.36 ± 4.15 1.980 (0.050) 10.09 ± 4.35 9.56 ± 3.94 1.688 (0.095) 0.966 0.617 1.511 0.879
3~6 22 (2.8) 205 (25.9) 2.688 (0.074) 19 (3.4) 126 (22.4) 0.887 (0.413) 1.426 0.698 2.913 0.329
7~11 47 (5.9) 261 (33.0) 31 (5.5) 188 (33.5) 0.720 0.378 1.370 0.316
12~17 39 (4.9) 217 (27.4) 28 (5.0) 170 (30.2) 0.995 0.510 1.943 0.989
   Residential area Urban 89 (11.3) 580 (73.3) 0.057 (0.812) 65 (11.6) 406 (72.2) 3.024 (0.085) 0.905 0.565 1.448 0.675
Rural 19 (2.4) 103 (13.0) 13 (2.3) 78 (13.9) 1.359 0.470 3.925 0.570
   Type of housing General housing 28 (3.5) 141 (17.8) 1.109 (0.294) 19 (3.4) 121 (21.5) 2.744 (0.101) 1.099 0.473 2.554 0.826
Apartment 80 (10.1) 542 (68.5) 59 (10.5) 363 (64.6) 0.913 0.454 1.531 0.730
   Number in family 4.13 ± 0.75 4.18 ± 0.74 –1.046 (0.298) 4.10 ± 0.73 4.14 ± 0.75 –2.393 (0.019) 0.949 0.607 1.485 0.820
   Income Low 1 (0.1) 13 (1.6) 0.333 (0.819) 0 (0.0) 6 (1.1) 5.465 (< 0.001)
Lower middle 21 (2.7) 108 (13.7) 10 (1.8) 48 (8.5) 1.319 0.541 3.218 0.541
Middle 30 (3.8) 179 (22.6) 15 (2.7) 158 (28.1) 0.460 0.204 1.033 0.060
Upper middle 33 (4.2) 230 (29.1) 22 (3.9) 165 (29.4) 0.702 0.362 1.361 0.293
High 23 (2.9) 153 (19.3) 31 (5.5) 107 (19.0) 2.103 0.864 5.119 0.101
   Body mass index < 5th percentile 5 (0.6) 64 (8.1) 3.344 (0.021) 7 (1.3) 27 (4.9) 0.939 (0.418) 2.622 0.73 9.416 0.139
5 – 85th percentile 76 (9.6) 487 (61.6) 55 (9.9) 327 (58.8) 1.196 0.728 1.964 0.479
85 – 95th percentile 11 (1.4) 62 (8.7) 8 (1.4) 56 (10.1) 0.643 0.205 2.013 0.446
≥ 95th percentile 16 (2.0) 69 (8.7) 8 (1.4) 68 (12.2) 0.302 0.109 0.843 0.022
   Subjective health status Good 71 (9.0) 515 (65.1) 3.857 (0.023) 45 (8.0) 384 (68.3) 8.142 (< 0.001) 0.826 0.479 1.424 0.490
Fair 31 (3.69) 151 (19.1) 27 (4.8) 87 (15.5) 1.386 0.756 2.541 0.290
Poor 6 (0.8) 17 (2.1) 6 (1.1) 13 (2.3) 1.086 0.275 4.281 0.906
   Influenza vaccination Yes 75 (9.5) 473 (59.8) 0.001 (0.976) 56 (10.0) 381 (67.8) 4.713 (0.032) 0.830 0.513 1.341 0.444
No 33 (4.2) 210 (26.5) 22 (3.9) 103 (18.3) 1.452 0.635 3.320 0.375
   Household composition 2 generations 103 (13.0) 638 (80.7) 0.110 (0.741) 74 (13.2) 461 (82.0) 1.654 (0.201) 0.978 0.616 1.551 0.924
3 generations or more 5 (0.6) 45 (5.7) 4 (0.7) 23 (4.1) 0.551 0.101 3.002 0.489
Parental characteristics
   Father’s smoking history < 5 packs 1 (0.1) 8 (1.0) 1.832 (0.175) 1 (0.2) 14 (2.5) 0.621 (0.524) 0.472 0.032 7.076 0.586
≥ 5 packs 83 (10.5) 519 (65.6) 61 (10.9) 364 (64.8) 1.029 0.613 1.727 0.913
Never smoked 24 (3.0) 156 (19.7) 16 (2.8) 106 (18.9) 0.820 0.34 1.978 0.657
   Father’s current smoking status Daily 34 (4.3) 213 (26.9) 1.499 (0.200) 26 (4.6) 151 (26.9) 1.275 (0.284) 0.982 0.473 2.039 0.961
Occasionally 7 (0.9) 31 (3.9) 2 (0.4) 27 (4.8) 0.154 0.024 0.999 0.05
Former smoker 43 (5.4) 283 (35.8) 34 (6.0) 200 (35.6) 1.168 0.600 2.274 0.646
Never smoked 24 (3.0) 156 (19.7) 16 (2.8) 106 (18.9) 0.820 0.340 1.978 0.657
   Mother’s smoking history < 5 packs 2 (0.3) 18 (2.3) 2.261 (0.124) 3 (0.5) 16 (2.8) 0.119 (0.887) 0.833 0.116 5.998 0.855
≥ 5 packs 18 (2.3) 66 (8.3) 7 (1.2) 50 (8.9) 0.561 0.192 1.641 0.290
Never smoked 88 (11.1) 599 (75.7) 68 (12.1) 418 (74.4) 1.071 0.649 1.768 0.787
   Mother’s current smoking status Daily 1 (0.1) 13 (1.6) 1.424 (0.235) 1 (0.2) 16 (2.8) 0.261 (0.847) 1.065 0.071 15.884 0.964
Occasionally 2 (0.3) 14 (1.8) 0 (0.0) 6 (1.1)
Former smoker 17 (2.1) 57 (7.2) 9 (1.6) 44 (7.8) 0.524 0.178 1.545 0.241
Never smoked 88 (11.1) 599 (75.7) 68 (12.1) 418 (74.4) 1.071 0.649 1.768 0.787
   Mother’s age at first childbirth 27.82 (3.38) 28.56 (3.90) –1.840 (0.068) 29.47 (3.58) 29.18 (3.76) 1.250 (0.214) 0.958 0.619 1.483 0.848
   Mother’s breastfeeding history Yes 101 (12.8) 610 (77.1) 1.467 (0.228) 74 (13.2) 430 (76.5) 0.933 (0.336) 0.998 0.634 1.571 0.994
No 7 (0.9) 93 (9.2) 4 (0.7) 54 (9.6) 0.633 0.155 2.58 0.522
Crude 0.966 0.617 1.513 0.879
Adjusted1 (includes general characteristics) 0.929 0.596 1.446 0.743
Adjusted2 (includes general and parental characteristics) 0.93 0.603 1.434 0.742
Open in a new tab

Comparison of general and parent-related characteristics of children diagnosed with rhinitis in 2019 and 2021

In 2019, several general characteristics showed statistically significant differences in rhinitis diagnosis, including gender (p < 0.001), residential area (p = 0.040), and influenza vaccination status (p = 0.035). Specifically, male children had a significantly higher rate of diagnosis in 2019. However, in 2021, these variables were distributed similarly between the two groups, showing no statistical significance. Instead, the 2021 data revealed new significant factors: age (p = 0.012) and family size (p < 0.001). The average age was higher in the diagnosed group in 2021, and the diagnosis rate was notably lower in the 3 – 6 years age group (p = 0.026). Regarding parental characteristics, breastfeeding history was a significant factor only in 2019 (p = 0.005), whereas mother’s age at first childbirth showed a significant difference only in 2021 (p = 0.007). Other variables, such as housing type, household income, BMI, and parental smoking status, showed no statistically significant differences in either year (Table 4).

Table 4. Comparison of general and parent-related characteristics of children diagnosed with rhinitis diagnosis in 2019 and 2021 .

Rhinitis diagnosis
Year 2019 (n = 791) 2021 (n = 562)
M ± SD, n (%) M ± SD, n (%)
Yes No F (p) Yes No F (p) OR 95% CI p
General characteristics
   Gender Male 130 (16.4) 278 (35.1) 10.120 (< 0.001) 80 (14.2) 198 (35.2) 0.650 (0.422) 0.842 0.547 1.297 0.434
Female 84 (10.6) 299 (37.8) 72 (12.8) 212 (37.7) 1.210 0.728 2.009 0.461
   Age M ± SD 9.29 ± 3.70 9.52 ± 4.28 –1.350 (0.180) 10.21 ± 3.67 9.42 ± 4.10 2.556 (0.012) 0.986 0.679 1.432 0.941
3 – 6 54 (6.8) 173 (21.9) 1.707 (0.184) 26 (4.6) 119 (21.2) 3.723 (0.026) 0.583 0.306 1.111 0.101
7 – 11 94 (11.9) 214 (27.1) 71 (12.6) 148 (26.3) 0.960 0.548 1.683 0.886
12 – 17 66 (8.3) 190 (24.0) 55 (9.8) 143 (25.4) 1.344 0.782 2.308 0.283
   Residential area Urban 185 (23.4) 484 (61.2) 4.325 (0.040) 135 (24.0) 336 (59.58) 0.826 (0.365) 0.954 0.647 1.408 0.813
Rural 29 (3.7) 93 (11.8) 17 (3.0) 74 (13.2) 1.319 0.410 4.242 0.641
   Type of housing General housing 40 (5.1) 129 (16.3) 2.123 (0.148) 27 (4.8) 113 (20.1) 2.465 (0.119) 0.967 0.456 2.052 0.930
Apartment 174 (22.0) 448 (56.6) 125 (22.2) 297 (52.8) 0.993 0.653 1.511 0.975
   Number in family 4.11 ± 0.68 4.20 ± 0.76 –1.125 (0.263) 4.01 ± 0.71 4.18 ± 0.76 –3.423 (< 0.001) 0.963 0.667 1.391 0.841
   Income Low 2 (0.3) 12 (1.5) 0.649 (0.614) 4 (0.7) 2 (0.4) 0.928 (0.442) 12.588 0.797 198.944 0.072
Lower middle 30 (3.8) 99 (12.5) 14 (2.5) 44 (7.8) 0.872 0.278 2.732 0.814
Middle 60 (7.6) 149 (18.8) 43 (7.7) 130 (23.1) 0.777 0.412 1.466 0.435
Upper middle 70 (8.8) 193 (24.4) 55 (9.8) 132 (23.5) 1.135 0.668 1.928 0.639
High 52 (6.6) 124 (15.7) 36 (6.4) 102 (18.1) 0.948 0.477 1.885 0.880
   Body mass index < 5th percentile 15 (1.9) 54 (6.8) 0.769 (0.507) 11 (2.0) 23 (4.1) 1.217 (0.303) 2.739 0.851 8.817 0.091
5 – 85th percentile 146 (18.5) 417 (52.8) 101 (18.2) 281 (50.5) 0.943 0.612 1.453 0.789
85 – 95th percentile 24 (3.0) 49 (6.2) 21 (3.8) 43 (7.7) 1.170 0.544 2.518 0.686
≥ 95th percentile 29 (3.7) 56 (7.1) 19 (3.4) 57 (10.3) 0.675 0.278 1.636 0.382
   Subjective health status Good 149 (18.8) 437 (55.2) 1.635 (0.199) 113 (20.1) 316 (56.2) 0.048 (0.949) 1.041 0.679 1.598 0.852
Fair 57 (7.2) 125 (15.8) 33 (5.9) 81 (14.4) 0.917 0.502 1.678 0.779
Poor 8 (1.0) 15 (1.9) 6 (1.1) 13 (2.3) 0.590 0.15 2.322 0.449
   Influenza vaccination Yes 163 (20.6) 385 (48.7) 4.553 (0.035) 129 (23.0) 308 (54.8) 3.264 (0.074) 0.927 0.606 1.418 0.727
No 51 (6.4) 192 (24.3) 23 (4.1) 102 (18.1) 0.961 0.46 2.006 0.915
   Household composition 2 generations 202 (25.5) 539 (68.1) 0.001 (0.978) 142 (25.3) 393 (69.9) 0.892 (0.347) 0.958 0.664 1.383 0.818
3 generations or more 12 (1.5) 38 (4.8) 10 (1.8) 17 (3.0) 1.765 0.371 8.403 0.474
Parental characteristics
   Father’s smoking history < 5 packs 4 (0.5) 5 (0.6) 1.524 (0.220) 7 (1.2) 8 (1.4) 2.546 (0.083) 1.031 0.094 11.305 0.980
≥ 5 packs 154 (19.5) 448 (56.6) 121 (21.5) 304 (54.1) 1.177 0.798 1.735 0.410
Never smoked 56 (7.1) 124 (15.7) 24 (4.3) 98 (17.4) 0.509 0.246 1.054 0.069
   Father’s current smoking status Daily 71 (9.0) 176 (22.3) 1.776 (0.152) 54 (9.6) 123 (21.9) 0.783 (0.494) 1.005 0.578 1.748 0.985
Occasionally 11 (1.4) 27 (3.4) 6 (1.1) 23 (4.1) 0.710 0.132 3.823 0.689
Former smoker 76 (9.6) 250 (31.6) 68 (12.1) 166 (29.5) 1.440 0.872 2.377 0.153
Never smoked 56 (7.1) 124 (15.7) 24 (4.3) 98 (17.4) 0.509 0.246 1.054 0.069
   Mother’s smoking history < 5 packs 6 (0.8) 14 (1.8) 0.785 (0.454) 2 (0.4) 17 (3.0) 1.225 (0.296) 0.249 0.016 3.810 0.317
≥ 5 packs 27 (3.4) 57 (7.2) 17 (3.0) 40 (7.1) 0.662 0.277 1.582 0.352
Never smoked 181 (22.9) 506 (64.0) 133 (23.7) 353 (62.8) 1.090 0.736 1.616 0.665
   Mother’s current smoking status Daily 9 (1.1) 5 (0.6) 1.909 (0.139) 3 (0.5) 14 (2.5) 0.916 (0.433) 0.104 0.013 0.845 0.034
Occasionally 5 (0.6) 11 (1.4) 0 (0.0) 6 (1.1)
Former smoker 19 (2.4) 55 (7.0) 16 (2.8) 37 (6.6) 0.942 0.347 2.555 0.906
Never smoked 181 (22.9) 506 (64.0) 133 (23.7) 353 (62.8) 1.090 0.736 1.616 0.665
Mother’s age at first childbirth 28.57 ± 3.65 28.42 ± 3.90 0.437 (0.663) 30.0 ± 3.16 28.93 ± 3.88 2.764 (0.007) 0.942 0.651 1.364 0.751
   Mother’s breastfeeding history Yes 182 (23.0) 529 (66.9) 8.286 (0.005) 135 (24.0) 369 (65.7) 0.847 (0.360) 1.060 0.722 1.554 0.766
No 32 (4.0) 48 (6.1) 17 (3.0) 41 (7.3) 0.591 0.219 1.596 0.298
Crude 0.986 0.678 1.431 0.941
Adjusted1 (includes general characteristics) 0.918 0.634 1.329 0.649
Adjusted2 (includes general and parental characteristics) 0.862 0.596 1.248 0.431
Open in a new tab

Discussion

This study compared the demographic characteristics and parental smoking habits of children in 2019 and 2021. The results found little difference between the years in most demographic characteristics and parental features, but there were significant differences in influenza vaccination and mothers’ age at first childbirth. Influenza vaccination rates were relatively higher in 2021 compared to 2019, which has been observed in previous studies during the COVID-19 pandemic [21]. This change may be interpreted as an increased interest in influenza vaccination as awareness of vaccines due to COVID-19 increased. There is limited research on the effect of the pandemic on mothers’ age at first childbirth, but it is believed to be associated with a temporary decrease in birth rates. Social and economic changes during the COVID-19 pandemic may have influenced decisions related to childbirth [22]. Unconsidered factors may have contributed to the observed changes in the age of first childbirth in this study.

This study aimed to compare the demographic characteristics and parental smoking habits of children diagnosed with asthma in 2019 and 2021, reflecting the social changes before and after the COVID-19 pandemic in South Korea. The results of this study did not reveal any significant differences in demographic characteristics or parental smoking habits between the 2 years. Similar findings have been reported in previous studies [23, 24]. Considering the social changes due to the COVID-19 pandemic, several interpretations can be drawn from these findings.

First, the main factors influencing asthma diagnosis may not have changed significantly between 2019 and 2021 despite the pandemic. However, it is important to consider that the prevalence of atopic dermatitis diagnoses tends to decrease as children age. Studies have demonstrated that younger children exhibit higher rates of atopic dermatitis diagnosis compared to older children and adolescents. For instance, the prevalence rates of atopic dermatitis and wheeze decrease with age, while the prevalence of allergic rhinitis tends to increase [25, 26]. This trend suggests that age is a significant factor in the diagnosis of atopic dermatitis, highlighting the importance of targeted prevention and management strategies that address the needs of younger children who are more likely to be diagnosed with the condition. Furthermore, existing prevention and management strategies may still be effective during this period, particularly among younger children. This aligns with findings from previous studies indicating that early intervention can mitigate the severity and progression of atopic dermatitis [27, 28].

Second, other factors influenced by social changes brought about by the pandemic may have affected asthma diagnosis. Increased indoor living, changes in air pollution levels, and alterations in lifestyle habits are among the factors that have been identified. For example, the pandemic led to significant lifestyle changes, including more time spent indoors and reduced physical activity, which can exacerbate symptoms of asthma and atopic dermatitis [29, 30]. Additionally, changes in environmental factors, such as air pollution, during the pandemic have had a notable impact on the prevalence and severity of atopic dermatitis in children [31].

In both 2019 and 2021, our study found that the prevalence of atopic dermatitis diagnosis was highest among children aged 7 – 11 years. However, it is essential to recognize that while some children may experience a decrease in symptoms or complete resolution of atopic dermatitis as they grow older, others may continue to experience symptoms into adolescence and adulthood [32]. The persistence or resolution of atopic dermatitis may be influenced by various factors, including genetic predisposition, environmental exposures, and the individual’s immune response [27].

The association between gender and atopic dermatitis diagnosis was not significant in 2019 but became significant in 2021. This finding aligns with a recent study that reported higher prevalence rates of atopic dermatitis among boys compared to girls, with significant differences observed particularly in younger age groups, such as infants and toddlers [33]. These findings suggest that boys may be more susceptible to developing atopic dermatitis in early childhood due to various factors, including differences in immune system development and environmental exposures. Regarding the living environment, the type of housing did not reach statistical significance in either year. This result diverges from some previous studies [34] that have strongly linked specific housing types or increased urbanization to the prevalence of atopic dermatitis. Children living in urban areas, particularly in high-density housing, have higher exposure to indoor allergens and pollutants, which can exacerbate or trigger atopic dermatitis symptoms. Furthermore, the number of people in the family was not significantly associated with atopic dermatitis diagnosis in 2019 but showed a significant association in 2021. This finding is consistent with a study by Pelucchi et al. [35], which reported that a larger family size may have a protective effect against atopic dermatitis, possibly due to increased exposure to diverse microorganisms that can modulate the immune system.

Regarding variables related to rhinitis, the significant difference in breastfeeding status between the 2 years could be related to changes in healthcare practices or parental behavior during the COVID-19 pandemic. A study conducted by La Verde et al. [36] found that the pandemic may have affected the availability of healthcare services, including breastfeeding support, as well as the accessibility of information on breastfeeding, leading to altered breastfeeding practices. The authors reported that breastfeeding initiation rates decreased during the pandemic, possibly due to the increased stress and anxiety experienced by mothers. The COVID-19 pandemic might have also contributed to the observed differences in age, area, and number of family members among children diagnosed with rhinitis. Changes in environmental factors, increased time spent indoors, and altered hygiene practices during the pandemic could have affected the development of rhinitis in children [37]. According to a study by Li et al. [38], increased exposure to indoor allergens and irritants during the pandemic could have contributed to a higher incidence of allergic diseases, including rhinitis, in children. Additionally, the study found that families living in crowded conditions were more likely to have children diagnosed with rhinitis.

This study provides a comprehensive comparison of the general and parent-related characteristics of children diagnosed with asthma, atopic dermatitis, and rhinitis in 2019 and 2021, taking into account the potential impact of the COVID-19 pandemic. The research design allows for a thorough analysis of various factors, including demographic characteristics, parental smoking habits, and environmental factors, that might influence the prevalence and diagnosis of these diseases. By linking the results to previous research and offering plausible explanations for the observed trends, this study contributes to the existing knowledge on the factors affecting childhood asthma, atopic dermatitis, and rhinitis in South Korea.

The main limitations of this study are as follows. First, the study relies on self-reported measures instead of objective indicators for diagnosing and determining the prevalence of chronic allergic diseases. This can lead to the possibility of underreporting or overreporting, reducing the reliability of the findings. Second, the study is cross-sectional, which makes it difficult to establish causal relationships. In particular, it is impossible to completely rule out the influence of selection bias and confounding variables, and there might be other uncontrolled confounding factors that could affect the results. Third, the study aimed to examine the relationship between COVID-19-related characteristics, such as vaccination status, handwashing, and mask-wearing, and chronic allergic diseases. However, the original dataset did not include these variables, limiting the analysis. This makes it difficult to accurately estimate the impact of the COVID-19 pandemic on chronic allergic diseases. Last, the sample size is relatively small, and the study focuses on South Korea, which may limit the generalizability of the findings to other countries or populations with different cultural, socioeconomic, or environmental contexts. This limitation restricts the applicability of the study’s results.

Conclusion

In conclusion, this study provides a comprehensive comparison of the general and parent-related characteristics of children diagnosed with asthma, atopic dermatitis, and rhinitis in South Korea in 2019 and 2021, considering the potential impact of the COVID-19 pandemic. The results revealed several significant differences between the 2 years in certain aspects, while other factors remained stable. This highlights the complex interplay of demographic, parental, environmental, and social factors that influence the prevalence and diagnosis of these diseases in children. Furthermore, the study emphasizes the importance of considering the broader social and environmental context, including the potential influence of the COVID-19 pandemic, when developing and implementing prevention and management strategies for childhood asthma, atopic dermatitis, and rhinitis. As our understanding of these diseases continues to evolve, it is crucial to adapt and refine prevention and management approaches to address the changing landscape and emerging challenges. Overall, this study contributes valuable insights to the existing knowledge on factors affecting childhood asthma, atopic dermatitis, and allergic rhinitis in South Korea and underscores the need for continued research and collaboration among researchers, healthcare professionals, policymakers, and other stakeholders to improve the health and well-being of children affected by these conditions.

Authors’ contributions

Bomi Kim: Contributed to conceptualization, data curation, formal analysis, and writing of the original draft.

Sunyeob Choi: Contributed to supervision, methodology, and review and editing of the manuscript.

Funding

This research received no external funding.

Conflict of interest

There is no conflict of interest.

References

  • 1. Ministry of Health and Welfare. 2022 Community Integrated Health Promotion Project Guide. Ministry of Health and Welfare, Sejong.
  • 2.Yi Y Kim J Factors affecting asthma and atopic dermatitis in Korean children: a population-based cross-sectional survey. Child Health Nurs Res. 2015; 21: 20–27. [Google Scholar]
  • 3. Korea Disease Control and Prevention Agency. 2020 National Health Statistics. Available from: https://knhanes.kdca.go.kr/knhanes/sub04/sub04_04_01.do [Last accessed: 1/20/2023].
  • 4.Choi HG Kong IG Asthma, allergic rhinitis, and atopic dermatitis incidence in Korean adolescents before and after COVID-19. J Clin Med. 2021; 10: 3446. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Yang CF Yang CC Wang IJ Association between allergic diseases, allergic sensitization and attention-deficit/hyperactivity disorder in children: A large-scale, population-based study. J Chin Med Assoc. 2018; 81: 277–283. [DOI] [PubMed] [Google Scholar]
  • 6.Im YJ Park ES Oh WO Suk MH Parenting and relationship characteristics in mothers with their children having atopic disease. J Child Health Care. 2014; 18: 215–229. [DOI] [PubMed] [Google Scholar]
  • 7.Pawankar R Allergic diseases and asthma: a global public health concern and a call to action. World Allergy Organ J. 2014; 7: 12. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Park ES Lee KH Oh WO Im Y Cho E Parenting experience of parents with chronic illness children. Child Health Nurs Res. 2015; 21: 272–284. [Google Scholar]
  • 9.Beacham BL Deatrick JA Children with chronic conditions: perspectives on condition management. J Pediatr Nurs. 2015; 30: 25–35. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Mendes EV Interview: The chronic conditions approach by the Unified Health System. Cien Saude Colet. 2018; 23: 431–436. [DOI] [PubMed] [Google Scholar]
  • 11.Spurr S Danford CA Roberts KJ Sheppard-LeMoine D Machado Silva-Rodrigues F Darezzo Rodrigues Nunes M Darmofal L Ersig AL Foster M Giambra B Lerret S Polfuss M Smith L Somanadhan S Fathers’ Experiences of Caring for a Child with a Chronic Illness: A Systematic Review. Children (Basel). 2023; 10: 197. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Park SE Epidemiology, virology, and clinical features of severe acute respiratory syndrome -coronavirus-2 (SARS-CoV-2; Coronavirus Disease-19). Clin Exp Pediatr. 2020; 63: 119–124. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Park J Min J Song JH Park MY Yoo H Kwon O Yang M Kim S Lee J Myong J-P The COVID-19 Pandemic Response and Its Impact on Post-Corona Health Emergency and Disaster Risk Management in Republic of Korea. Sustainability (Basel). 2023; 15: 3175. [Google Scholar]
  • 14.Juhn YJ Risks for infection in patients with asthma (or other atopic conditions): is asthma more than a chronic airway disease? J Allergy Clin Immunol. 2014; 134: 247–257, quiz 258-259.. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Yang JM Koh HY Moon SY Yoo IK Ha EK You S Kim SY Yon DK Lee SW Allergic disorders and susceptibility to and severity of COVID-19: A nationwide cohort study. J Allergy Clin Immunol. 2020; 146: 790–798. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Vezir E Hizal M Cura Yayla B Aykac K Yilmaz A Kaya G Oygar PD Ozsurekci Y Ceyhan M Does aeroallergen sensitivity and allergic rhinitis in children cause milder COVID-19 infection? Allergy Asthma Proc. 2021; 42: 522–529. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Mahdavinia M Foster KJ Jauregui E Moore D Adnan D Andy-Nweye AB Khan S Bishehsari F Asthma prolongs intubation in COVID-19. J Allergy Clin Immunol Pract. 2020; 8: 2388–2391. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Asadi-Pooya AA Simani L Central nervous system manifestations of COVID-19: A systematic review. J Neurol Sci. 2020; 413: 116832. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Birdi G Cooke R Knibb RC Impact of atopic dermatitis on quality of life in adults: a systematic review and meta-analysis. Int J Dermatol. 2020; 59: e75–e91. [DOI] [PubMed] [Google Scholar]
  • 20. Korea National Health and Nutrition Examination Survey 2023. Korea Disease Control and Prevention Agency; [cited February 20, 2023]. Available from: https://knhanes.kdca.go.kr/knhanes/eng/index.do.
  • 21.Gallant AJ Nicholls LAB Rasmussen S Cogan N Young D Williams L Changes in attitudes to vaccination as a result of the COVID-19 pandemic: A longitudinal study of older adults in the UK. PLoS One. 2021; 16: e0261844. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Ullah MA Moin AT Araf Y Bhuiyan AR Griffiths MD Gozal D Potential effects of the COVID-19 pandemic on future birth rate. Front Public Health. 2020; 8: 578438. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Beasley R Semprini A Mitchell EA Risk factors for asthma: is prevention possible? Lancet. 2015; 386: 1075–1085. [DOI] [PubMed] [Google Scholar]
  • 24.Patel S Henderson J Jeffreys M Associations between socioeconomic position and asthma: findings from a historical cohort. Eur J Epidemiol. 2017; 32: 797–805. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Belgrave DC Granell R Simpson A Guiver J Bishop C Buchan I Henderson AJ Custovic A Developmental profiles of eczema, wheeze, and rhinitis: two population-based birth cohort studies. PLoS Med. 2014; 11: e1001748. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Lee S Wang HY Kim E Hwang HJ Choi E Lee H Choi EH Clinical characteristics and genetic variation in atopic dermatitis patients with and without allergic contact dermatitis. Eur J Dermatol. 2018; 28: 637–643. [DOI] [PubMed] [Google Scholar]
  • 27.Bantz SK Zhu Z Zheng T The atopic march: Progression from atopic dermatitis to allergic rhinitis and asthma. J Clin Cell Immunol. 2014; 5: 202. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Paller AS Spergel JM Mina-Osorio P Irvine AD The atopic march and atopic multimorbidity: Many trajectories, many pathways. J Allergy Clin Immunol. 2019; 143: 46–55. [DOI] [PubMed] [Google Scholar]
  • 29.Lee JY Kim J Ahn K Time trends in the prevalence of atopic dermatitis in Korean children according to age. Allergy Asthma Immunol Res. 2022; 14: 123–130. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Wollenberg A Werfel T Ring J Ott H Gieler U Weidinger S Atopic dermatitis in children and adults: Diagnosis and treatment. Dtsch Arztebl Int. 2023; 120: 224–234. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Guo Y Zhang KY Zou YF Yu B National situation, trends, and predictions of disease burden of atopic dermatitis in Chinese children and adolescents. Front Microbiol. 2023; 14: 1161969. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Silverberg JI Hanifin JM Simpson EL Climatic factors are associated with childhood eczema prevalence in the United States. J Invest Dermatol. 2015; 135: 1905–1907. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Bylund S Kobyletzki LB Svalstedt M Svensson Å Prevalence and incidence of atopic dermatitis: a systematic review. Acta Derm Venereol. 2020; 100: adv00160. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Flohr C Mann J New insights into the epidemiology of childhood atopic dermatitis. Allergy. 2014; 69: 3–16. [DOI] [PubMed] [Google Scholar]
  • 35.Pelucchi C Galeone C Bach JF La Vecchia C Chatenoud L Pet exposure and risk of atopic dermatitis at the pediatric age: a meta-analysis of birth cohort studies. J Allergy Clin Immunol. 2013; 132: 616–622. [DOI] [PubMed] [Google Scholar]
  • 36.La Verde M Torella M Riemma G Narciso G Iavarone I Gliubizzi L Palma M Morlando M Colacurci N De Franciscis P Incidence of gestational diabetes mellitus before and after the Covid-19 lockdown: A retrospective cohort study. J Obstet Gynaecol Res. 2022; 48: 1126–1131. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Bousquet J Anto JM Iaccarino G Is diet partly responsible for differences in COVID-19 death rates between and within countries? Clin Transl Allergy. 2021; 11: 1–11. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Li X Li T Chen N Wang X Yang J Bi L Influence of COVID-19 pandemic on inhaled allergens in children with allergic diseases, Henan, China. J Med Virol. 2023; 95: e28409. [DOI] [PMC free article] [PubMed] [Google Scholar]

Articles from Allergologie Select are provided here courtesy of Dustri-Verlag

RESOURCES