Impact of air pollution on benign paroxysmal positional vertigo incidence: a retrospective study of the citizens of Seoul, South Korea

Climatic variations and pollution on benign paroxysmal positional vertigo in Kars, Türkiye

Benign paroxysmal positional vertigo (BPPV) is the most common diagnosis for peripheral vertigo. Although pathophysiological mechanisms remain unclear, BPPV is mostly idiopathic and factors related to BPPV are still being investigated. Knowing these factors can contribute to the prevention and management of BPPV. In this study, we investigated the correlations between climatic variations, pollution, and BPPV retrospectively. 262 patients diagnosed with BPPV between 2019 and 2021 in Kars, Türkiye, were included in our study. Meteorological parameters were obtained from Turkish State Meteorological Service. Horizontal BPPV increased significantly with the humidity (p < 0.05). In addition, carbon monoxide levels significantly increased the potantial of BPPV (p < 0.05). Surprisingly, BPPV increased in the summertime and showed a significant relationship with humidity. We believe this change is related with the city-specific features as it is the coldest place in the country, emigrant province and crowded in the summer times.

Acute effects of ambient air pollution on daily neurology clinic visits for vertigo: a time-series study in Wuhan, China

This study aimed to disclose the relationship between ambient air pollution and neurology clinic visits (NCVs) for vertigo. A time-series study was conducted to examine relationships between six different criteria air pollutants (SO₂, NO₂, PM_2.5, PM₁₀, CO, and O₃) and daily NCVs for vertigo in Wuhan, China, from January 1st, 2017 to November 30th, 2019. Stratified analyses were computed according to gender, age, and season. A total of 14,749 records of NCVs for vertigo were enrolled in this study. Data showed that the increase in daily NCVs for vertigo corresponding to 10 μg/m³ increase of respective pollutants are: SO₂ (- 7.60%; 95% CI: - 14.25 to - 0.44%), NO₂ (3.14%; 95% CI: 0.23 to 6.13%), PM_2.5 (0.53%; 95% CI: - 0.66 to 1.74%), PM₁₀ (1.32%; 95% CI: - 0.36 to 3.06%), CO (0.00%; 95% CI: - 0.12 to 0.13%), and O₃ (0.90%; 95% CI: - 0.01% to 1.83%). Males were more susceptible to acute exposure to SO₂ and NO₂, compared to females (SO₂: - 11.91% vs. - 4.16%; NO₂: 3.95% vs. 2.92%), whereas the acute effect of O₃ exposure was more significantly obvious in females than males (0.94% vs. 0.87%). Moreover, correlations between daily NCVs for vertigo and acute exposure to SO₂, NO₂, and O₃ were all stronger in individuals under 50 years old (SO₂: - 12.75% vs. - 4.41%; NO₂: 4.55% vs. 2.75%; O₃: 1.27% vs. 0.70%). Short-term exposure to PM_2.5 was more significantly associated with daily NCVs for vertigo in cool seasons (1.62% vs. - 0.68%), while the correlation between CO exposure and daily NCVs for vertigo was stronger in warm seasons (0.21% vs. - 0.03%). Our study demonstrated acute exposure to ambient NO₂ and O₃ positively associated with daily NCVs for vertigo. Acute effects of air pollution on daily NCVs for vertigo varied according to gender, age, and season.

Development of prediction models for the incidence of pediatric acute otitis media using Poisson regression analysis and XGBoost

Otitis media has profound health and economic impact, and its occurrence is known to be influenced by air pollution and climate. The purpose of this study was to develop prediction models using climate and air pollution indicators for the occurrence of acute otitis media (AOM). The study was conducted from January 1, 2014, to December 31, 2019, and included pediatric patients (age < 12 years) diagnosed on their emergency room visit in our tertiary medical institution. We obtained data on the weekly number of AOM patients and the weekly average values of air pollution and climate indicators. Poisson regression analysis and eXtreme Gradient Boosting (XGBoost) were used to develop prediction models for the overall pediatric patients and for the patients classified according to sex (male and female) and age (< 2 years and ≥ 2 years). For the overall population, the correlation coefficients between the original and estimated data in the testing set were 0.441 (p < 0.001) and 0.844 (p < 0.001) for the models developed using Poisson regression analysis and XGBoost, respectively. The root-mean-square errors in the testing set were 3.094 and 1.856, respectively. For patients classified according to sex and age, the prediction models developed using XGBoost showed better performance than the models developed using Poisson regression analysis. In conclusion, this study successfully developed prediction models with air pollution and climate indicators for the incidence of pediatric AOM, using XGBoost. This model can be further developed to prevent pediatric AOM.