Individual& community factors conveying vulnerability to weather extremes

描述（由申请人提供）：该赠款旨在确定64岁以上老年人因天气导致死亡风险增加的医疗和其他个人特征，确定导致这种风险的空气污染的相互作用，并确定与风险增加特别相关的污染物模式和天气参数的概况。此外，我们将确定社区的特征，如社会经济地位、不透水地表百分比、绿地、水、气候带、天气可变性、空调普及率、行为风险、基线疾病率等，这些特征会改变死亡风险，最后，社区水平和个人特征之间的相互作用。重要的是，这些社区级别的特征将在邮政编码级别定义，而不是在城市级别定义，从而使我们能够捕捉到真正的当地土地使用的影响。此外，研究较少探索的天气参数，我们将研究降雨与老年人胃肠疾病住院率的关系。最后，我们将进行一项试点风险评估，同样是在精细尺度上，利用2030年与今天相比的天气和污染分布预测，以及社区层面的风险调节因素变化项目。这一分析的结果将有助于NIH识别疾病状态的风险，更广泛地说，有助于识别干预措施的任务，这些干预措施可以通过降低风险来改善公众健康，以及在地理上，这些干预措施将是最有效的。使用的方法将是病例交叉分析和仅病例分析，以确定风险和风险调节剂，并使用k聚类对天气和空气污染参数进行分组。

DESCRIPTION (provided by applicant): This grant proposes to identify medical and other individual characteristics of persons aged over 64 years that put them at increased risk of dying due to weather, to identify interactions with air pollution that contribute to that risk, and to identify profiles of patterns of pollutants and weather parameters that are particularly associated with elevated risk. Further, we will identify characteristics of community, such as socio-economic status, percent of impermeable surface, of green space, of water, climate zone, variability of weather, air conditioning prevalence, behavioral risk, baseline disease rates, etc which modify the risk of dying, and finally, interactions between the community level and individual characteristics. Importantly, these community level characteristics will be defined on the zip code level, not the city level, allowing us to capture the impact of true local land use. In addition, examining a less explored weather parameter, we will examine the association of rainfall with hospital admissions for gastrointestinal illness in the elderly. Finally, we will conduct a pilot risk assessment using projections, again on a fine scale, of the distribution of weather and pollution in 2030 compared to today, as well as community level projects of changes in risk modifiers. The results of this analysis will aid NIH by identifying disease states that convey risk, and more broadly aid the task of identifying interventions that can improve public health by reducing risk, and where, geographically, those interventions will be most efficacious. The methods used will be case-crossover and case-only analyses to identify risk and risk modifiers, and k-clustering to group weather and air pollution parameters.