Jainonthee Chalita, Wang Ying-Lin, Chen Colin W K, Jainontee Karuna
Veterinary Public Health and Food Safety Centre for Asia Pacific (VPHCAP), Faculty of Veterinary Medicine, Chiang Mai University, Chiang Mai 50100, Thailand.
Center of Excellence in Veterinary Public Health, Faculty of Veterinary Medicine, Chiang Mai University, Chiang Mai 50100, Thailand.
Trop Med Infect Dis. 2022 Oct 31;7(11):341. doi: 10.3390/tropicalmed7110341.
The unfavorable effects of global climate change, which are mostly the result of human activities, have had a particularly negative effect on human health and the planet's ecosystems. This study attempted to determine the seasonality and association of air pollution, in addition to climate conditions, with two respiratory infections, influenza and pneumonia, in Chiang Mai, Thailand, which has been considered the most polluted city on Earth during the hot season. We used a seasonal-trend decomposition procedure based on loess regression (STL) and a seasonal cycle subseries (SCS) plot to determine the seasonality of the two diseases. In addition, multivariable negative binomial regression (NBR) models were used to assess the association between the diseases and environmental variables (temperature, precipitation, relative humidity, PM, and PM). The data revealed that influenza had a clear seasonal pattern during the cold months of January and February, whereas the incidence of pneumonia showed a weak seasonal pattern. In terms of forecasting, the preceding month's PM and temperature (lag1) had a significant association with influenza incidence, while the previous month's temperature and relative humidity influenced pneumonia. Using air pollutants as an indication of respiratory disease, our models indicated that PM lag1 was correlated with the incidence of influenza, but not pneumonia. However, there was a linear association between PM and both diseases. This research will help in allocating clinical and public health resources in response to potential environmental changes and forecasting the future dynamics of influenza and pneumonia in the region due to air pollution.
全球气候变化的不利影响主要是人类活动造成的，对人类健康和地球生态系统产生了特别负面的影响。本研究试图确定泰国清迈空气污染的季节性和关联性，以及气候条件与两种呼吸道感染疾病（流感和肺炎）之间的关系。清迈在炎热季节被认为是地球上污染最严重的城市。我们使用基于黄土回归的季节性趋势分解程序（STL）和季节性周期子序列（SCS）图来确定这两种疾病的季节性。此外，使用多变量负二项回归（NBR）模型来评估疾病与环境变量（温度、降水、相对湿度、PM和PM）之间的关联。数据显示，流感在1月和2月的寒冷月份有明显的季节性模式，而肺炎的发病率季节性模式较弱。在预测方面，前一个月的PM和温度（滞后1）与流感发病率有显著关联，而前一个月的温度和相对湿度影响肺炎。以空气污染物作为呼吸道疾病的指标，我们的模型表明，滞后1的PM与流感发病率相关，但与肺炎无关。然而，PM与这两种疾病之间存在线性关联。这项研究将有助于根据潜在的环境变化分配临床和公共卫生资源，并预测该地区因空气污染导致的流感和肺炎的未来动态。
