环境条件对遵义市呼吸系统疾病的影响及预测研究

为探究气象和空气污染等环境条件对呼吸系统疾病发病的影响，为遵义地区相关疾病预防提供科学依据，采用分布滞后非线性模型和广义线性、相加模型，利用当地气象和污染资料，分析了2012~2016年遵义市气象环境要素对呼吸系统疾病发病的影响.结果显示，遵义市呼吸系统疾病发病状况与当地长期气候状态基本保持一致，气候效应对其的影响占主导作用，其中，冬季为呼吸系统疾病高发期，立秋至处暑前后其发病人数最少，表明此时间段内当地气候条件对呼吸系统疾病患者有气候疗养效应.气温对呼吸系统疾病发病的影响以低温滞后效应为主，在其敏感阈值附近气温每变化1℃，发病人数将累积增加31.6%（95% CI：4.4%~65.8%）；气压以高压滞后效应为主，相对湿度则在低湿部分同时有即时和滞后效应.舒适度对呼吸系统疾病的影响，在冷、热斜胁迫下其发病人数明显多于舒适状况时.PM_2.5、SO₂和NO₂三种污染物的影响都以即时效应为主，而CO则在累积滞后lag04时相对危险度最高，PM_2.5与呼吸系统疾病发病人数的暴露-反应曲线呈单调线性分布，SO₂、NO₂和CO均为“J”型分布.低温与高浓度NO₂或者低湿与高浓度SO₂的协同作用对呼吸系统疾病的影响较大.建立的全年和季节多元逐步回归方程的试预报准确率在75%以上（夏季除外），其中分季节建模预测效果显著优于全年预测效果.

Study about the impact of environmental conditions on respiratory diseases and prediction in Zunyi City

To explore environmental impacts such as the meteorological and air pollution factors on respiratory diseases and to provide scientific basis for the prevention of these diseases in Zunyi City, distributed lag non-linear model together with generalized linear and additive models are applied to analyse the exposure-response relationship between environmental factors and respiratory diseases from 2012 to 2016 in Zunyi City. Results show that the changes in respiratory diseases are mainly consistent with the local long-term climatic conditions, and the impact of climate change is dominant. Winter and spring are the peak periods with high respiratory diseases number, and during the Start of Autumn and Stopping the heat periods, the respiratory diseases number is the lowest, indicating that local climatic conditions have positive climatic effects on patients with respiratory diseases during this time period. The impact of temperature on respiratory disease is mainly low-temperature lagged effect with the patients increasing by 31.6% (95%CI:4.4%~65.8%) if the temperature changes by 1℃.The pressure mainly has the high-pressure lagged effect on respiratory diseases, while the relative humidity has both immediate and lagged effects in lower humidity. The number of respiratory diseases is significantly higher under cold and hot uncomfortable levels than that of comfort levels. PM_2.5, SO₂, and NO₂ mainly show immediate effects on respiratory diseases, while the CO had the highest risk if lagged for four days. The exposure-response relationship between respiratory diseases and PM_2.5 shows an monotonously linear distribution, while that of SO₂, NO₂, and CO are J-type distribution, and the synergistic effects between low temperature and high concentration NO₂ or low humidity and high concentration SO₂ both have significant impact on respiratory diseases. The accuracy of annual and seasonal regression equations is over 75% (except for summer equation), and the seasonal equations' prediction effect is better than that of the annual equation.