Individual& community factors conveying vulnerability to weather extremes

描述(由申请人提供)：这笔赠款建议确定年龄以上的人因天气原因死亡风险增加的医疗和其他个人特征，确定与空气污染的相互作用导致这种风险，并确定污染物的模式和天气参数的概况，特别是与高风险相关的。此外，我们将确定影响死亡风险的社区特征，如社会经济状况、不透水表面百分比、绿地、水域、气候带、天气变化性、空调流行率、行为风险、基线发病率等，最后确定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定义在邮政编码级别，而不是城市级别，使我们能够捕捉到真正的当地土地利用的影响。此外，通过研究一个较少被研究的天气参数，我们将研究降雨与老年人因胃肠道疾病入院治疗的关系。最后，我们将利用对2030年天气和污染分布与今天相比的预测，以及社区层面的风险修正因素变化项目，进行试点风险评估，同样是在精细范围内。这一分析的结果将有助于NIH确定传播风险的疾病状态，更广泛地帮助确定可以通过降低风险来改善公共健康的干预措施，以及从地理上讲，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，以确定风险和风险修正因素，并使用k-聚类法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.