Analyzing COVID-19 and Air Pollution Effects on Pediatric Asthma Emergency Room Visits in Taiwan.
Yan-Lin ChenYen-Yue LinPi-Wei ChinCheng-Chueh ChenChun-Gu ChengChun-An ChengPublished in: Toxics (2024)
(1) Background: An asthma exacerbation that is not relieved with medication typically requires an emergency room visit (ERV). The coronavirus disease 2019 (COVID-19) pandemic began in Taiwan in January of 2020. The influence of the COVID-19 pandemic on pediatric ERVs in Taiwan was limited. Our aim was to survey pediatric asthma ERVs in the COVID-19 era; (2) Methods: Data were collected from the health quality database of the Taiwanese National Health Insurance Administration from 2019 to 2021. Air pollution and climatic factors in Taipei were used to evaluate these relationships. Changes in the rates of pediatric asthma ERVs were assessed using logistic regression analysis. Poisson regression was used to evaluate the impact of air pollution and climate change; (3) Results: The rate of pediatric asthma ERVs declined in different areas and at different hospital levels including medical centers, regional and local hospitals. Some air pollutants (particulate matter ≤ 2.5 µm, particulate matter ≤ 10 µm, nitrogen dioxide, and carbon monoxide) reduced during the COVID-19 lockdown. Ozone increased the relative risk (RR) of pediatric asthma ERVs during the COVID-19 period by 1.094 (95% CI: 1.095-1.12) per 1 ppb increase; (4) Conclusions: The rate of pediatric asthma ERVs declined during the COVID-19 pandemic and ozone has harmful effects. Based on these results, the government could reduce the number of pediatric asthma ERVs through healthcare programs, thereby promoting children's health.
Keyphrases
- air pollution
- particulate matter
- lung function
- coronavirus disease
- healthcare
- chronic obstructive pulmonary disease
- sars cov
- public health
- allergic rhinitis
- climate change
- health insurance
- young adults
- machine learning
- mental health
- hydrogen peroxide
- nitric oxide
- health information
- adverse drug
- social media
- risk assessment
- childhood cancer
- intensive care unit
- deep learning