Short-Term Effect of Air Pollution on Tuberculosis Based on Kriged Data: A Time-Series Analysis
Short-Term Effect of Air Pollution on Tuberculosis Based on Kriged Data: A Time-Series Analysis
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DOI：
10.3390/ijerph17051522
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发表时间：
2020-03-01
影响因子：
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通讯作者：
Liu, Suyang
中科院分区：
文献类型：
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作者：
Huang, Shuqiong;Xiang, Hao;Liu, Suyang
结核病在世界范围内的死亡率非常高。然而，只有少数研究调查了短期接触空气污染与结核病发病率之间的关系。我们的目标是估计2015-2016年期间中国武汉市短期暴露于空气污染物与结核病发病率之间的关系。我们应用广义加性模型来获取空气污染与结核病的短期关联。采用普通克里格法测定武汉市各空气污染物日暴露量。空气污染物包括空气动力学直径小于或等于2.5微米的颗粒物（PM） （PM2.5）、空气动力学直径小于或等于10微米的PM （PM10）、二氧化硫（SO2）、二氧化氮（NO2）、一氧化碳（CO）和地面臭氧（O-3）。每日结核发病病例来源于湖北省疾病预防控制中心。使用单一和多种污染物模型来检查空气污染与结核病之间的关系。通过将全年数据分为温暖季节（5 - 10月）和寒冷季节（11 - 4月）来评估季节变化。在单一污染物模型中，滞后7时PM2.5、PM10和O-3浓度每增加10 μ g/m(3)，相关结核病风险分别增加17.03% （95% CI: 6.39, 28.74）、11.08% （95% CI: 6.39, 28.74）和16.15% （95% CI: 1.88, 32.42）。在多污染物模型中，PM2.5对结核病的影响仍然具有统计学意义，而其他污染物的影响减弱。季节分析表明，暖季和冷季空气污染对结核病的影响没有太大差异。我们的研究表明，将空气污染与结核病联系起来的机制仍然很复杂。需要进一步研究空气污染与结核病的相互作用。
Tuberculosis (TB) has a very high mortality rate worldwide. However, only a few studies have examined the associations between short-term exposure to air pollution and TB incidence. Our objectives were to estimate associations between short-term exposure to air pollutants and TB incidence in Wuhan city, China, during the 2015-2016 period. We applied a generalized additive model to access the short-term association of air pollution with TB. Daily exposure to each air pollutant in Wuhan was determined using ordinary kriging. The air pollutants included in the analysis were particulate matter (PM) with an aerodynamic diameter less than or equal to 2.5 micrometers (PM2.5), PM with an aerodynamic diameter less than or equal to 10 micrometers (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ground-level ozone (O-3). Daily incident cases of TB were obtained from the Hubei Provincial Center for Disease Control and Prevention (Hubei CDC). Both single- and multiple-pollutant models were used to examine the associations between air pollution and TB. Seasonal variation was assessed by splitting the all-year data into warm (May-October) and cold (November-April) seasons. In the single-pollutant model, for a 10 mu g/m(3) increase in PM2.5, PM10, and O-3 at lag 7, the associated TB risk increased by 17.03% (95% CI: 6.39, 28.74), 11.08% (95% CI: 6.39, 28.74), and 16.15% (95% CI: 1.88, 32.42), respectively. In the multi-pollutant model, the effect of PM2.5 on TB remained statistically significant, while the effects of other pollutants were attenuated. The seasonal analysis showed that there was not much difference regarding the impact of air pollution on TB between the warm season and the cold season. Our study reveals that the mechanism linking air pollution and TB is still complex. Further research is warranted to explore the interaction of air pollution and TB.