Individual& community factors conveying vulnerability to weather extremes

描述（由申请人提供）：该补助金提议确定64岁以上人员的医疗和其他个人特征，这些特征使他们因天气而死亡的风险增加，确定与空气污染的相互作用，并确定污染物模式的概况和与风险升高特别相关的天气参数。此外，我们将确定社区的特征，如社会经济地位，不渗透表面的百分比，绿色空间，水，气候带，天气的变化，空调的普及率，行为风险，基线疾病率等修改死亡的风险，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而不是城市级别上定义，从而使我们能够捕捉到真正的当地土地使用的影响。此外，我们将研究一个较少探索的天气参数，研究降雨量与老年人胃肠道疾病住院率的关系。最后，我们将进行一项试点风险评估，使用预测，同样是在一个精细的尺度上，与今天相比，2030年的天气和污染分布，以及风险修正因子变化的社区级项目。这项分析的结果将通过识别传达风险的疾病状态来帮助NIH，并更广泛地帮助识别可以通过降低风险改善公共卫生的干预措施的任务，以及在地理上，这些干预措施将最有效。所使用的方法将是案例交叉分析和仅案例分析，以确定风险和风险修正因素，以及k-聚类来分组天气和空气污染参数。

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.