Culqui Lévano Dante R, Díaz Julio, Blanco Alejandro, Lopez José A, Navas Miguel A, Sánchez-Martínez Gerardo, Luna M Yolanda, Hervella Beatriz, Belda Fernando, Linares Cristina
Reference Unit On Climate Change, Health and Urban Environment National School of Health, Carlos III Health Institute, Monforte de Lemos 5, ZIP 28029 Madrid, Spain.
The UNEP DTU Partnership, Copenhagen, Denmark.
Environ Sci Eur. 2022;34(1):39. doi: 10.1186/s12302-022-00617-z. Epub 2022 Apr 26.
The objective of this study was to identify which air pollutants, atmospheric variables and health determinants could influence COVID-19 mortality in Spain. This study used information from 41 of the 52 provinces in Spain (from Feb. 1, to May 31, 2021). Generalized Linear Models (GLM) with Poisson link were carried out for the provinces, using the Rate of Mortality due to COVID-19 () per 1,000,000 inhabitants as dependent variables, and average daily concentrations of PM and NO as independent variables. Meteorological variables included maximum daily temperature (max) and average daily absolute humidity (HA). The GLM model controlled for trend, seasonalities and the autoregressive character of the series. Days with lags were established. The relative risk (RR) was calculated by increases of 10 g/m in PM and NO and by 1 ℃ in the case of max and 1 g/m in the case of HA. Later, a linear regression was carried out that included the social determinants of health.
Statistically significant associations were found between PM, NO and the . These associations had a positive value. In the case of temperature and humidity, the associations had a negative value. PM being the variable that showed greater association, with the followed of NO in the majority of provinces. Anyone of the health determinants considered, could explain the differential geographic behavior.
The role of PM is worth highlighting, as the chemical air pollutant for which there was a greater number of provinces in which it was associated with . The role of the meteorological variables-temperature and HA-was much less compared to that of the air pollutants. None of the social determinants we proposed could explain the heterogeneous geographical distribution identified in this study.
The online version contains supplementary material available at 10.1186/s12302-022-00617-z.
本研究的目的是确定哪些空气污染物、大气变量和健康决定因素会影响西班牙的新冠肺炎死亡率。本研究使用了西班牙52个省份中41个省份的信息(从2021年2月1日至5月31日)。对这些省份进行了具有泊松链接的广义线性模型(GLM)分析,将每100万居民中因新冠肺炎导致的死亡率()作为因变量,将PM和NO的日均浓度作为自变量。气象变量包括每日最高温度(max)和日均绝对湿度(HA)。GLM模型对趋势、季节性和序列的自回归特征进行了控制。确定了有滞后的天数。通过PM和NO每增加10 μg/m³以及max每增加1℃和HA每增加1 g/m³来计算相对风险(RR)。随后,进行了一项包括健康社会决定因素的线性回归分析。
发现PM、NO与死亡率之间存在统计学上的显著关联。这些关联具有正值。在温度和湿度的情况下,关联具有负值。PM是显示出更强关联的变量,在大多数省份中其次是NO。所考虑的任何健康决定因素都可以解释不同的地理行为差异。
PM的作用值得强调,因为它是与死亡率相关的省份数量更多的化学空气污染物。与空气污染物相比,气象变量——温度和HA——的作用要小得多。我们提出的社会决定因素都无法解释本研究中确定的异质地理分布。
在线版本包含可在10.1186/s12302-022-00617-z获取的补充材料。