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救援服务部署数据作为德国法兰克福/美因(2014-2022 年)热发病的指标 - 与各种热暴露指标的趋势关联以及外展工作的考虑因素。

Rescue service deployment data as an indicator of heat morbidity in Frankfurt / Main, Germany (2014-2022) - Trend association with various heat exposure indicators and considerations for outreach.

机构信息

University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckstr. 1, 55131, Mainz, Germany.

Institute for Medical Informatics, Biometry and Epidemiology, University Hospital Essen, Germany, Universitätsstr. 12, 45141, Essen, Germany.

出版信息

Int J Hyg Environ Health. 2023 Sep;254:114250. doi: 10.1016/j.ijheh.2023.114250. Epub 2023 Sep 6.

Abstract

Many publications dealt with the monitoring of heat-related mortality. Fewer analyses referred to indicators of heat-related morbidity. The aim of this work was to describe the heat-related morbidity using rescue service data from the city of Frankfurt/Main, Germany for the time period 2014-2022, with regard to the questions: 1) How do rescue service deployments develop over the years? Is there a trend identifiable towards a decrease in deployments over the years, e.g. as an effect of either (physiological) adaptation of the population or of the measures for prevention of heat-related morbidity? 2) Which heat parameters (days with a heat warning, heat days, heat weeks, heat waves) are most strongly associated with heat-related morbidity in terms of rescue service deployments and might therefore be additionally used as an easily communicable and understandable heat-warning indicator? Rescue service data were provided by the interdisciplinary medical supply compass system "IVENA" and adjusted for population development including age development. The effect of various indicators for heat exposure, such as days with a heat warning from the German meteorological service based on the scientific concept of "perceived heat", heat days, heat wave days and heat week days on different endpoints for heat morbidity (deployments in total as well as for heat associated diagnoses) was calculated using both difference-based (difference ± 95% CI) and ratio-based (ratio ± 95% CI) effect estimators. Rescue services deployments in summer months increased overall from 2014 to 2022 in all age groups over the years (2698 to 3517/100.000 population). However, there was a significant decrease in 2020, which could be explained by the special situation of the COVID-19 pandemic, probably caused by the absence of tourists and commuters from the city. In addition, no data are available on the actual implementation of the measures by the population. Therefore, an effect of the measures taken to prevent heat-associated morbidity in Frankfurt am Main could not be directly demonstrated, and our first question cannot be answered on the basis of these data. Almost all heat definitions used for exposure (day with a heat warning, heat day, heat wave day, heat week day) showed significant effects on heat-associated diagnoses in every year. When analysing the effect on all deployments, the effect was in part strongly dependent on individual years: Heat wave days and heat week days even showed negative effects in some years. The definition heat day led to a significant increase in rescue service deployments in all single years between 2014 and 2022 (ratio 2014-2022 1.09 (95CI 1.07-1.11); with a range of 1.05 (95CI 1.01-1.09) in 2020 and 1.14 (95CI 1.08-1.21) in 2014), this was not the case for days with a heat warning (ratio 2014-2022 1.04 (95CI1.02-1.05); with a range of 1.01 (95CI 0.97-1.05) in 2017 and 1.16 (95CI 1.10-1.23). Thus being not inferior to the heat warning day, the "heat day" defined as ≥32 °C maximum temperature, easily obtainable from the weather forecast, can be recommended for the activities of the public health authorities (warning, surveillance etc.) regarding heat health action planning.

摘要

许多出版物都涉及与热相关的死亡率监测。但较少的分析涉及与热相关发病率的指标。本研究的目的是使用德国法兰克福市的救援服务数据,描述 2014 年至 2022 年期间与热相关的发病率,研究问题为:1)救援服务的部署随着时间的推移如何发展?是否存在部署逐年减少的趋势,例如由于(生理)人群适应或预防与热相关发病率的措施?2)哪些热参数(炎热天气警告日、炎热日、炎热周、热浪)与救援服务部署的与热相关发病率最相关,因此可以额外用作易于传播和理解的炎热天气警告指标?救援服务数据由跨学科医疗供应指南针系统“IVENA”提供,并根据包括年龄发展在内的人口发展情况进行了调整。使用基于差异(差异±95%置信区间)和基于比值(比值±95%置信区间)的效应估计器,计算了德国气象服务基于“感知热”科学概念的炎热天气警告日、炎热日、热浪日和炎热周等各种热暴露指标对不同热发病率终点(总部署以及与热相关的诊断)的影响。2014 年至 2022 年,各年龄段的夏季救援服务部署总体呈上升趋势(每 10 万人口从 2698 人增加到 3517 人)。然而,2020 年出现了显著下降,这可能是由于 COVID-19 大流行的特殊情况造成的,可能是由于城市游客和通勤者的缺席。此外,没有关于人口实际实施这些措施的数据。因此,不能直接证明法兰克福采取的预防与热相关发病率的措施的效果,我们的第一个问题不能基于这些数据回答。几乎所有用于暴露的热定义(炎热天气警告日、炎热日、热浪日、炎热周)在每年都与与热相关的诊断显著相关。当分析所有部署的影响时,影响在一定程度上取决于个别年份:热浪日和炎热周甚至在某些年份产生负面影响。炎热日的定义导致 2014 年至 2022 年期间救援服务部署的所有单年均显著增加(2014-2022 年比值为 1.09(95%置信区间 1.07-1.11);2020 年的范围为 1.05(95%置信区间 1.01-1.09),2014 年为 1.14(95%置信区间 1.08-1.21)),炎热天气警告日则并非如此(2014-2022 年比值为 1.04(95%置信区间 1.02-1.05);2017 年范围为 1.01(95%置信区间 0.97-1.05),2022 年为 1.16(95%置信区间 1.10-1.23))。因此,定义为最高温度≥32°C 的“炎热日”与炎热天气警告日一样有效,可从天气预报中轻松获得,可推荐用于公共卫生当局的活动(警告、监测等),以制定与热健康行动相关的计划。

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