Institute of Landscape Ecology, Climatology Group, University of Münster, Heisenbergstraße 2, 48149, Münster, Germany.
ERIN-Environmental Research and Innovation Department, Luxembourg Institute of Science and Technology (LIST), 5, avenue des Hauts-Fourneaux, Esch/Alzette, 4362, Luxembourg.
Int J Biometeorol. 2018 Jan;62(1):17-27. doi: 10.1007/s00484-017-1326-0. Epub 2017 Feb 28.
Air quality and thermal stress lead to increased morbidity and mortality. Studies on morbidity and the combined impact of air pollution and thermal stress are still rare. To analyse the correlations between air quality, thermal stress and morbidity, we used a two-stage meta-analysis approach, consisting of a Poisson regression model combined with distributed lag non-linear models (DLNMs) and a meta-analysis investigating whether latitude or the number of inhabitants significantly influence the correlations. We used air pollution, meteorological and hospital admission data from 28 administrative districts along a north-south gradient in western Germany from 2001 to 2011. We compared the performance of the single measure particulate matter (PM10) and air temperature to air quality indices (MPI and CAQI) and the biometeorological index UTCI. Based on the Akaike information criterion (AIC), it can be shown that using air quality indices instead of single measures increases the model strength. However, using the UTCI in the model does not give additional information compared to mean air temperature. Interaction between the 3-day average of air quality (max PM10, max CAQI and max MPI) and meteorology (mean air temperature and mean UTCI) did not improve the models. Using the mean air temperature, we found immediate effects of heat stress (RR 1.0013, 95% CI: 0.9983-1.0043) and by 3 days delayed effects of cold stress (RR: 1.0184, 95% CI: 1.0117-1.0252). The results for air quality differ between both air quality indices and PM10. CAQI and MPI show a delayed impact on morbidity with a maximum RR after 2 days (MPI 1.0058, 95% CI: 1.0013-1.0102; CAQI 1.0068, 95% CI: 1.0030-1.0107). Latitude was identified as a significant meta-variable, whereas the number of inhabitants was not significant in the model.
空气质量和热应激导致发病率和死亡率上升。关于发病率以及空气污染和热应激综合影响的研究仍然很少。为了分析空气质量、热应激和发病率之间的相关性，我们使用了两阶段荟萃分析方法，包括泊松回归模型与分布式滞后非线性模型（DLNMs）相结合，以及一项荟萃分析，以调查纬度或居民数量是否显著影响相关性。我们使用了 2001 年至 2011 年德国西部沿南北梯度的 28 个行政区的空气污染、气象和住院数据。我们比较了单测量颗粒物（PM10）和空气温度与空气质量指数（MPI 和 CAQI）和生物气象指数 UTCI 的表现。基于赤池信息量准则（AIC），可以表明使用空气质量指数而不是单一措施可以提高模型的强度。然而，与平均空气温度相比，在模型中使用 UTCI 并不能提供额外的信息。空气质量（最大 PM10、最大 CAQI 和最大 MPI）和气象（平均空气温度和平均 UTCI）的 3 天平均值之间的相互作用并没有改善模型。使用平均空气温度，我们发现热应激的即时效应（RR 1.0013，95%CI：0.9983-1.0043）和 3 天后冷应激的延迟效应（RR：1.0184，95%CI：1.0117-1.0252）。空气质量的结果在两个空气质量指数和 PM10 之间有所不同。CAQI 和 MPI 显示出对发病率的延迟影响，最大 RR 出现在 2 天后（MPI 1.0058，95%CI：1.0013-1.0102；CAQI 1.0068，95%CI：1.0030-1.0107）。纬度被确定为一个重要的元变量，而居民数量在模型中并不显著。
