Evidence to improve heat warning effectiveness in reducing morbidity and mortality.

项目总结/摘要 当暴露于高环境温度（即，热）长期以来被认为是对公众的威胁 在美国，由于炎热而导致的疾病和死亡负担仍然很高。为了减少热量- 相关的死亡率和发病率，美国国家气象局（NWS）提前发布高温警报， 预测极端高温事件，向公众和政府官员传达这些风险。但 在很大程度上是未知的：（1）什么是热应激的最佳指标，以通知何时发出热警报，（2）如何 高温警报在保护公众健康方面是有效的;（3）什么因素使高温警报相对 在某些地方或某些人身上或多或少有效。由于缺乏这些信息，我们 将无法最大限度地提高高温警报对公众健康的益处。 本提案的目标是确定最佳的基于健康和特定于位置的指标， 发布发热警报，以估计发热警报的因果效益，并识别个体的特征，或 与高温警报后发病率或死亡率降低最大相关的社区。具体地说， 使用国家索赔数据的死亡和住院人数在大，地理上不同的， 2001年至2015年期间登记的年龄≥65岁的美国医疗保险受益人人数> 6000万， 急诊科就诊人数超过1.3亿，来自美国最大的健康中心之一的所有年龄段的参与者。 保险公司，我们建议：（目标1）使用新的机器学习方法来识别热量指标（例如，热 指数、环境温度、空间天气分类、湿球地球仪温度、绝对湿度）， 最好的预测过热相关的死亡，紧急住院，和急诊科就诊， 每个地点，（目标2）估计NWS热警报对死亡率，住院率， 全国各地和按健康结果、性别和 年龄组，以及（目标3）评估高温警报的好处如何因社区的特点而异。 该提案的主要创新包括非常大的样本量，地理多样性， 整个美国，多个健康终点和年龄组的评估，以及使用复杂的 统计学习和因果推理的方法。总的来说，本提案的结果将提供 气象学家、公共卫生和应急管理官员以及当地决策者， 在极端高温事件期间更好地保护公众健康，并指导未来更有针对性的研究 减轻高温对健康的不利影响的策略。

Project Summary/Abstract While exposure to high ambient temperature (i.e., heat) has long been recognized as a threat to public health, the burden of illness and death attributable to heat in the US remains high. In an effort to reduce heat- related mortality and morbidity, the US National Weather Service (NWS) issues heat alerts in advance of forecasted extreme heat events to communicate these risks to the public and government officials. However, it is largely unknown: (1) what are the optimal metrics of heat stress to inform when to issue heat alerts, (2) how effective are heat alerts in protecting the public’s health and (3) what factors make heat alerts comparatively more or less effective in some places or in some people versus others. In the absence of such information, we will fail to maximize the public health benefits of heat alerts. The goals of this proposal are to identify the optimal health-based and location-specific metrics for issuing heat alerts, to estimate the causal benefits of heat alerts, and to identify characteristics of individuals or communities associated with the greatest reductions in morbidity or mortality following heat alerts. Specifically, using national claims data on deaths and hospital admissions among the large, geographically diverse population of >60 million US Medicare beneficiaries age ≥65 years enrolled between 2001 and 2015, and on emergency department visits among >130 million participants of all ages from one of the nation’s largest health insurers, we propose to: (Aim 1) Use novel machine learning methods to identify the heat metric(s) (e.g., heat index, ambient temperature, spatial synoptic classification, wet bulb globe temperature, absolute humidity) that best predict excess heat-related deaths, emergency hospitalizations, and emergency departments visits in each location, (Aim 2) estimate the causal effects of NWS heat alerts on rates of mortality, hospitalizations, and emergency department visits across the country and within groups stratified by health outcome, sex, and age group, and (Aim 3) assess how the benefits of heat alerts vary across characteristics of communities. Key innovations of this proposal include a very large sample size, geographic diversity encompassing the entire US, the assessment across multiple health endpoints and age groups, and the use of sophisticated methods in statistical learning and causal inference. Collectively, the findings from this proposal will provide meteorologists, public health and emergency management officials, and local policy-makers with critical information to better protect public health during extreme heat events and guide more targeted future research on strategies to mitigate the adverse health effects of heat.