Abstract：
BACKGROUND: Environmental factors like air pollution and temperature can trigger acute myocardial infarction (AMI). However, the link between large-scale weather patterns (synoptic types) and AMI admissions has not been extensively studied. This research aimed to identify the different synoptic air types in Beijing and investigate their association with AMI occurrences.
METHODS: We analyzed data from Beijing between 2013 and 2019, encompassing 2556 days and 149,632 AMI cases. Using principal component analysis and hierarchical clustering, classification into distinct synoptic types was conducted based on weather and pollution measurements. To assess the impact of each type on AMI risk over 14 days, we employed a distributed lag non-linear model (DLNM), with the reference being the lowest risk type (Type 2).
RESULTS: Four synoptic types were identified: Type 1 with warm, humid weather; Type 2 with warm temperatures, low humidity, and long sunshine duration; Type 3 with cold weather and heavy air pollution; and Type 4 with cold temperatures, dryness, and high wind speed. Type 4 exhibited the greatest cumulative relative risk (CRR) of 1.241 (95%CI: 1.150, 1.339) over 14 days. Significant effects of Types 1, 3, and 4 on AMI events were observed at varying lags: 4-12 days for Type 1, 1-6 days for Type 3, and 1-11 days for Type 4. Females were more susceptible to Types 1 and 3, while individuals younger than 65 years old showed increased vulnerability to Types 3 and 4.
CONCLUSIONS: Among the four synoptic types identified in Beijing from 2013 to 2019, Type 4 (cold, dry, and windy) presented the highest risk for AMI hospitalizations. This risk was particularly pronounced for males and people under 65. Our findings collectively highlight the need for improved methods to identify synoptic types. Additionally, developing a warning system based on these synoptic conditions could be crucial for prevention.
METHODS: We analyzed data from Beijing between 2013 and 2019, encompassing 2556 days and 149,632 AMI cases. Using principal component analysis and hierarchical clustering, classification into distinct synoptic types was conducted based on weather and pollution measurements. To assess the impact of each type on AMI risk over 14 days, we employed a distributed lag non-linear model (DLNM), with the reference being the lowest risk type (Type 2).
RESULTS: Four synoptic types were identified: Type 1 with warm, humid weather; Type 2 with warm temperatures, low humidity, and long sunshine duration; Type 3 with cold weather and heavy air pollution; and Type 4 with cold temperatures, dryness, and high wind speed. Type 4 exhibited the greatest cumulative relative risk (CRR) of 1.241 (95%CI: 1.150, 1.339) over 14 days. Significant effects of Types 1, 3, and 4 on AMI events were observed at varying lags: 4-12 days for Type 1, 1-6 days for Type 3, and 1-11 days for Type 4. Females were more susceptible to Types 1 and 3, while individuals younger than 65 years old showed increased vulnerability to Types 3 and 4.
CONCLUSIONS: Among the four synoptic types identified in Beijing from 2013 to 2019, Type 4 (cold, dry, and windy) presented the highest risk for AMI hospitalizations. This risk was particularly pronounced for males and people under 65. Our findings collectively highlight the need for improved methods to identify synoptic types. Additionally, developing a warning system based on these synoptic conditions could be crucial for prevention.
摘要：
背景：环境因素如空气污染和温度可引发急性心肌梗死（AMI）。然而,大规模天气模式(天气类型)与AMI入院之间的联系尚未得到广泛研究.这项研究旨在确定北京不同的天气空气类型，并探讨它们与AMI发生的关系。
方法：我们分析了2013年至2019年北京的数据，包括2556天和149,632例AMI病例。利用主成分分析和层次聚类，根据天气和污染测量，对不同的天气类型进行了分类。为了评估每种类型对14天内AMI风险的影响，我们采用了分布滞后非线性模型(DLNM)，参考是最低风险类型(类型2)。
结果：确定了四种天气类型：具有温暖，潮湿天气；温度温暖的2型，低湿度,日照时间长；3型天气寒冷，空气污染严重；4型气温寒冷，干燥度,和高风速。4型在14天内表现出最大的累积相对风险（CRR）为1.241（95CI：1.150，1.339）。在不同的滞后时间观察到1、3和4型对AMI事件的显着影响：1型为4-12天，3型为1-6天，4型为1-11天。女性对1型和3型更敏感，而年龄小于65岁的个体对3型和4型的脆弱性增加。
结论：在2013年至2019年北京确定的四种天气类型中，类型4（冷，干,并且有风）是AMI住院的最高风险。这种风险在男性和65岁以下人群中尤为明显。我们的发现共同强调了需要改进的方法来识别天气类型。此外,根据这些天气条件开发预警系统对于预防至关重要。
方法：我们分析了2013年至2019年北京的数据，包括2556天和149,632例AMI病例。利用主成分分析和层次聚类，根据天气和污染测量，对不同的天气类型进行了分类。为了评估每种类型对14天内AMI风险的影响，我们采用了分布滞后非线性模型(DLNM)，参考是最低风险类型(类型2)。
结果：确定了四种天气类型：具有温暖，潮湿天气；温度温暖的2型，低湿度,日照时间长；3型天气寒冷，空气污染严重；4型气温寒冷，干燥度,和高风速。4型在14天内表现出最大的累积相对风险（CRR）为1.241（95CI：1.150，1.339）。在不同的滞后时间观察到1、3和4型对AMI事件的显着影响：1型为4-12天，3型为1-6天，4型为1-11天。女性对1型和3型更敏感，而年龄小于65岁的个体对3型和4型的脆弱性增加。
结论：在2013年至2019年北京确定的四种天气类型中，类型4（冷，干,并且有风）是AMI住院的最高风险。这种风险在男性和65岁以下人群中尤为明显。我们的发现共同强调了需要改进的方法来识别天气类型。此外,根据这些天气条件开发预警系统对于预防至关重要。
