随着生活节奏加快以及生活压力增大，不合理的膳食结构，长期缺少运动以及环境污染等问题导致急性心肌梗死（AMI）发病率和死亡率持续上升。全球每年有1700万人死于心血管疾病，其中有一半以上死于急性心肌梗死。近10年来，我国急性心肌梗死的发病率不断上升，已接近国际平均水平。心肌梗死已成为影响人民身体健康的主要致死性疾病之一。因此，开展环境及社会经济因素对心肌梗死影响研究具有一定理论与实践价值。

前人研究证明环境和经济因素对急性心肌梗死有一定影响，但多数研究是从传统流行病与统计学视角探讨二者关系，而忽视了疾病分布的空间自相关性以及影响因素的时空异质性，导致结论有很大不确定性。很少有研究从空间流行病角度，利用地理时空数据结合顾及时空异质性的地理模型探讨影响因素与疾病之间的关系，且影响因素对疾病的交互作用往往被忽略。由于空间流行病涉及公共卫生、医学地理、环境卫生、测绘与地理信息多个领域，目前利用地理时空数据和地理模型，将地理信息系统、遥感及卫星导航用于疾病影响因素研究仍是一个巨大挑战。

鉴于此，本研究选取2014-2016年西安市AMI死亡病例，分析其与环境和经济因素之间的关系。首先对AMI死亡情况及其环境和经济因素进行时空动态分析。其次，采用莫兰指数诊断AMI死亡率的全局空间自相关性以及局部集聚特征。最后在不同时间尺度上探讨环境以及经济因素和AMI死亡率之间的关系，在天尺度，利用广义相加模型（GAM），纳入滞后效应，量化大气污染物浓度对AMI死亡率的超额危险度（ER）；在月尺度，利用时空地理加权回归模型（GTWR），探究解释变量对AMI死亡率影响的重要程度及时空异质性；在季节尺度，利用地理探测器探测，探测各影响因素在对AMI死亡率空间分异性的解释能力并识别不同影响因素间的交互作用。

研究结果为从空间流行病学的角度探讨影响因素对相关疾病的影响提供了科学参考。主要研究结论如下：

（1）2014-2016年西安市AMI死亡病例呈逐年增加趋势，男性AMI死亡病例均多于女性，该病在≥75岁这个年龄阶段AMI死亡病例明显高于其他两个年龄结构。且AMI死亡率存在一定的季节特征，在春季和夏季的AMI死亡率是高于秋季和冬季的；

（2）AMI死亡率存在显著的空间相关性。根据全局莫兰指数的结果，Moran I指数呈现小幅上升趋势，表明研究区AMI死亡率不是随机分布，其分布受到一定自然环境与社会经济因素的影响；此外，局部莫兰指数的结果表明，AMI死亡率的高-高聚集区域主要分布在长安区，鄠邑区，及周至县部分地区等郊区，低-低聚集区域主要集中在主城区，即AMI死亡率多集中在农村与郊区，AMI死亡率低值区为主城区，存在明显的区域差异；

（3）通过对AMI死亡率影响因素的分析后表明，大气污染浓度与AMI死亡存在一定的滞后效应，PM_2.5，PM₁₀，SO₂，NO₂，CO，O₃与AMI死亡率均呈正向关系，且SO₂对AMI死亡率的正向影响最大。同时考虑环境与经济因素，则人口密度（POP）对AMI死亡率的影响最大，且表现为负向影响。各解释变量对AMI死亡率的影响存在明显的时空异质性。在不同分层下，各解释变量对AMI死亡率空间分异性的解释能力不一样，造成AMI死亡率的主导因子均为人口密度（POP），且两解释变量的交互作用对AMI死亡率空间分异性的解释能力均强于单解释变量对AMI死亡率的解释能力，其中POP和各因子的交互作用最强。

With the acceleration of the pace of life and the increase of life pressure, unreasonable diet structure, long-term lack of exercise, environmental pollution and other problems possibly lead to the increasing incidence and mortality of acute myocardial infarction (AMI). AMI accounts for more than half of the 17 million deaths caused by cardiovascular disease each year. In the last decade, the incidence of AMI in China has been raising sharply, approaching the international mean level. AMI has become one of the major fatal diseases affecting mankind’s health. Thus, there is theoretical and practical value to research the impact of environmental and social economic determinants on AMI.

Previous researches have proved that environmental and economic factors have a certain impact AMI, but most studies have explored the relationship between them from the traditional epidemiological and statistical perspectives, ignoring the spatial autocorrelation of disease distribution and the spatial and temporal heterogeneity of influencing factors, leading to great uncertainties in the conclusions. From the perspective of spatial epidemic, few studies have explored the relationship between influencing factors and diseases by using geographic spatiotemporal data combined with geographical models considering spatiotemporal heterogeneity. Meanwhile, the interaction between influencing factors and diseases was often ignored. Spatial epidemics involve many fields such as public health, medical geography, environmental health, surveying and mapping and geographic information. So, it is still a great challenge to use the technology of geographic information system (GIS), remote sensing (RS) and global positioning system (GPS) to research the influencing factors of diseases based on geographic spatiotemporal data and geographic models.

Here, AMI deaths in Xi’an from 2014 to 2016 were selected to analyze the relationship between AMI deaths and environmental and economic factors in current study. Firstly, the temporal and spatial dynamics of AMI deaths and its environmental and economic factors were analyzed. Secondly, global Moran’s I and local Moran’s I were used to diagnose the spatial autocorrelation and local aggregation features of AMI mortality, respectively. Finally, the relationship between environmental and economic factors and AMI mortality was analyzed at different time scales and research methods. At the daily scale, generalized additive model (GAM) was used to quantify the excess risk (ER) of AMI mortality caused by air pollutant concentrations in lag days. At the monthly scale, geographically & temporally weighted regression model (GTWR) was used to explore the importance ranking and spatiotemporal heterogeneity of the impact of explanatory variables on AMI mortality. At the seasonal scale, Geodetector were used to quantify the explanatory ability of potential influencing factors to spatial variability of AMI mortality at different stratification levels, and to identify the interaction between different potential influencing factors.

The results provided a scientific reference for discussing the influence of influencing factors on related diseases from the perspective of spatial epidemiology. Some conclusions were obtained as follows:

(1) AMI deaths increased year by year from 2014 to 2016 in Xi’an. The number of AMI deaths in males was more than in females, and the number of AMI deaths in ≥75 years of age structure was significantly higher than in the other age structure. What’s more, AMI mortality had certain seasonal characteristics, and AMI mortality in spring and summer was higher than in autumn and winter;

(2) AMI mortality had obvious spatial correlation in study area. According to the results of global Moran’s I, the Moran I index showed a slight upward trend, indicating that AMI mortality was not randomly distributed in the study area, and its distribution was affected by certain natural environment and social economic factors. In addition, the results show that local Moran’s I AMI mortality of High-High Cluster areas were mainly distributed parts of Chang’an district in Xi’an, Huyi district and Zhouzhi district, Low-Low Cluster areas were distributed in the main urban areas, that is, the areas with high incidence of AMI are suburbs, and the areas with low incidence are main urban areas, with regional differences.

(3) Through the analysis of influencing factors of AMI mortality, it was found that there was a certain lag effect between air pollution concentrations and AMI mortality. PM_2.5, PM₁₀, SO₂, NO₂, CO, O₃ and AMI mortality all had a positive impact, and SO₂ and AMI mortality showed the greatest positive impact. When environmental and economic factors were considered, POP had the greatest negative influence on AMI mortality. Furthermore, the impact of each explanatory variable on AMI mortality had obvious spatial and temporal heterogeneity. Under different stratification, the explanatory ability of each factor to spatial differentiation of AMI mortality was different, but the leading factor causing AMI mortality was POP, but DEM had no statistical significance to explain the spatial distribution of AMI mortality. The effect of interaction of two factors on spatial differentiation of AMI mortality was stronger than that of single factor, and the interaction between POP and each factor was the strongest.

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