Extreme heat events and pregnancy duration: a national study

项目摘要/摘要 最近的研究表明，环境温度过高会增加早产的风险。 怀孕)是导致婴儿死亡和长期神经功能障碍的主要原因。早产儿 与足月分娩相比，妊娠(37-38周)婴儿的发病率也更高。根据气候预测，热浪 预计会增加频率、强度和持续时间，其中许多会导致环境空气质量增加 污染物浓度。拟议的研究试图使用现有的大型数据库和强大的 在多个空间尺度上的方法论方法来检验极端高温的主要假设 事件增加了早产和早产的风险，更强的相关性被假设为 在持续时间更长、强度更大的热事件之后观察到的。使用国家出生记录数据 国家卫生统计中心，我们将在114个美国大城市评估这些关系(包括 54%的人口)，在36年期间(1981-2016年)达到县级空间分辨率。我们会 此外，还从美国人口稠密且具有地理代表性的八个州获得出生记录数据 (覆盖40%的人口)在1990-2016年间评估邮政编码或更高级别的关系 解决办法，并研究伴随空气变化对热浪联系的可能调解 污染水平。气象学的特点是综合来自多个气象站的每小时数据 网络和卫星资源，利用每个数据集的优势来最大化空间和时间 覆盖范围并将曝光预测误差降至最低。八种污染物的环境中十二种污染物浓度 选定的州还将结合社区多尺度空气质量模型(CMAQ)进行表征 带有监视器测量的输出。早产和早产的统计模型将解释 受孕的季节性模式(先前研究中的一个可能的偏差来源)，以及使用 将使用贝叶斯分层模型来组合研究地点的信息并评估 气候地区的异质性、热事件的时间(季节内或数十年间)、母体 特征(教育程度、种族/民族)和特定地点的属性(例如，背景 社会经济指标、空调普及率、都市性)。对这种异质性的准确估计是 可能是因为样本非常大，这也允许检查使用 强度和持续时间阈值高于先前评估的阈值。多尺度方法促进了 评估和传播由于暴露预测误差、空间聚集和 怀孕期间的居住流动性。结果可以用来通知当地的公共卫生警告系统，如 作为针对孕妇的热量建议，最终目标是减少早产及其后遗症。 这项研究还将为未来的气候和健康研究带来持久的好处，通过创建一个 综合的、空间和时间分辨率的、公开可用的美国大陆气象数据集。

PROJECT SUMMARY/ABSTRACT Recent studies suggest high ambient temperatures increase the risk of preterm birth (<37 completed weeks of gestation), a leading cause of infant mortality and long-term neurological disabilities. Infants born early term (37-38 weeks) also have more morbidity compared to full term births. Under climate projections, heat waves are expected to increase in frequency, intensity, and duration, and many will cause increases in ambient air pollutant concentrations. The proposed research seeks to use large existing databases and robust methodological approaches at multiple spatial scales to test the overarching hypothesis that extreme heat events increase the risk of preterm birth and early term birth, with stronger associations hypothesized to be observed following heat events of longer duration and greater intensity. Using national birth record data from the National Center for Health Statistics, we will assess these relationships in 114 large U.S. cities (covering 54% of the population) at a county-level spatial resolution over a 36-year period (1981-2016). We will additionally obtain birth record data from eight populous and geographically representative U.S. states (covering 40% of the population) over the period 1990-2016 to assess relationships at ZIP code or finer resolution and to examine possible mediation of heat wave associations by accompanying changes in air pollution levels. Meteorology will be characterized by integrating hourly data from multiple weather station networks and satellite-resources, harnessing the strengths of each dataset to maximize spatial and temporal coverage and minimize exposure prediction error. Ambient concentrations of 12 pollutants for the eight selected states will be also characterized by combining Community Multiscale Air Quality Model (CMAQ) outputs with monitor measurements. The statistical models for preterm and early term birth will account for seasonal patterns of conception (a possible source of bias in previous studies), and two-stage analyses using Bayesian hierarchical models will be used to combine information across study locations and assess heterogeneity by climate region, timing of the heat event (within season or across decades), maternal characteristics (educational attainment, race/ethnicity), and location-specific attributes (e.g., contextual socioeconomic indicators, air conditioning prevalence, urbanicity). Precise estimation of this heterogeneity is possible due to the exceptionally large sample, which also allows for examination of heat events defined using higher intensity and duration thresholds than previously assessed. The multi-scale approach facilitates assessment and propagation of uncertainty due to exposure prediction errors, spatial aggregation, and residential mobility during pregnancy. Results can be used to inform local public health warning systems such as heat advisories that target pregnant women with the ultimate goal of reducing early birth and its sequelae. The study will also yield lasting benefits for future studies of climate and health through the creation of an integrated, spatial and temporally-resolved, publically-available meteorology dataset for the continental U.S.