Extreme temperature, humidity, air pollution and spontaneous preterm birth

极端温度、湿度、空气污染和自发性早产

• 批准号: 10360841
• 负责人: Allan C. Just
• 金额: $ 50.22万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-01 至 2026-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10360841
• 关键词: Acute; Address; Air; Air Pollutants; Air Pollution; Assessment tool; Big Data; Birth; Cessation of life; Chronic; Chronic Disease; Climate; Code; Communication; Communities; Complex; Crossover Design; Data; Data Set; Environmental Epidemiology; Environmental Exposure; Environmental Risk Factor; Epidemiology; Event; Functional disorder; Future; Gestational Age; Goals; Health; Health Planning; Health Resources; Heat Waves; Home; Hospitals; Human; Humidity; Individual; Infant; Link; Literature; Machine Learning; Manuals; Measurement; Medical; Membrane; Methods; Modeling; Modification; Neighborhoods; New York; Outcome; Particulate Matter; Pattern; Perinatal Epidemiology; Persons; Phenotype; Philadelphia; Policies; Population; Population Characteristics; Positioning Attribute; Predisposition; Pregnancy; Premature Birth; Premature Labor; Public Health; Race; Registries; Reproductive Health; Research; Resolution; Risk; Role; Rupture; Rural; Science; Series; Subgroup; Surface; System; Technology; Temperature; Testing; Time; Training; United States National Aeronautics and Space Administration; Variant; Weather; Work; administrative database; adverse pregnancy outcome; air monitoring; anthropogenesis; base; career; career development; climate change; climate-related health; cohort; cost estimate; data registry; deprivation; epidemiologic data; epidemiological model; epidemiology study; extreme temperature; extreme weather; fine particles; follow-up; greenhouse gases; health application; health warning; improved; instrument; large datasets; mobile application; multidisciplinary; novel; perinatal health; predictive modeling; public health relevance; remote sensing; reproductive outcome; response; sex; spatiotemporal; stressor; temporal measurement; ultra high resolution; water vapor

PROJECT SUMMARY This project will extend methods for estimating daily temperature, humidity, and fine particulate matter air pollution at high resolution across large regions by utilizing NASA and new USGS satellite measurements to generate estimated exposures at the neighborhood level across the Northeast US. This detailed exposure record can be used in health studies to further consider extreme weather and air pollutant as a health risk. These new daily models will be developed for 2007-2023 at a <1 km resolution and thus include billions of point-day temperature, humidity, and particulate matter estimates. While it is well established that particulate matter increases the risk of preterm birth, less is known about how extreme weather events (including temperature and humidity) and air pollution contribute to the onset of spontaneous preterm birth. Pathophysiology builds throughout a lifetime & during pregnancy, yet the onset of preterm labor and/or rupture of membranes is acute. In this way, we investigate potential environmental triggers to ask, “Why today?”. The epidemiologic application of these novel exposure models will be demonstrated by testing the association of temperature, humidity, and particulate matter with a dataset of spontaneous preterm births and residential addresses from a comprehensive New York statewide administrative database. We will use address-level exposures in the 7 days prior to a spontaneous preterm delivery with matched days in the same fixed two- week period as a comparison (time stratified case-crossover design) for 17 years of spontaneous preterm births statewide – making this among the largest environmental epidemiology studies of spontaneous preterm birth with more than 170,000 cases. Given the large number of cases and the variation in population characteristics across New York State, the association between extreme weather, air pollution patterns and spontaneous preterm birth will be further tested for effect modification by sex, race, gestational age, urban/rural, and neighborhood deprivation. A follow-up analysis will use carefully phenotyped spontaneous preterm cases from large hospital systems in Philadelphia. Our epidemiologic models will improve on prior work on the acute impacts of extreme temperature and air pollution on preterm birth that have relied on coarse exposure assignments via ecological time series models, inappropriately long time strata for case-crossover comparison, or overly broad outcome definitions that included medically-indicated preterm births. Given the global coverage of satellite remote sensing, our approach – which can generate daily exposure estimates that are highly spatially resolved (<1 km) to capture ambient exposure at the home address – is broadly applicable to better understand the role of extreme weather and air pollution as a stressor in both chronic and acute health outcomes using big data registries.

项目摘要 该项目将延长估计每日温度，湿度和精美特定物质空气的方法 通过使用NASA和新的USGS卫星测量值 在美国东北部的邻里水平上产生估计的暴露。这种详细的曝光 可以在健康研究中使用记录，以进一步将极端天气和空气污染物视为健康风险。 这些新的每日模型将于2007 - 2023年以<1公里的分辨率开发，因此包括数十亿美元 点日温度，湿度和特定物质估计值。虽然这是一个特殊的 物质增加了早产的风险，对极端天气事件的了解较少（包括 温度和湿度）和空气污染有助于发作早产。 病理生理在一生中及其怀孕期间建立，但早产和/或破裂的发作 膜的急性。通过这种方式，我们调查了潜在的环境触发器，问“为什么今天？”。 这些新型暴露模型的流行病学应用将通过测试的关联来证明 具有赞助早产的数据集和居民的温度，湿度和特定物质 来自纽约州全州行政数据库的地址。我们将使用地址级 在赞助早产前的7天内，在同一固定的两天中的匹配日期的曝光 比较17年的比较（时间分层的案例交叉设计） 全州出生 - 使其成为赞助早产的最大环境流行病学研究 生日有超过170,000例。鉴于大量案件和人口的差异 纽约州的特征，极端天气，空气污染模式与 赞成早产将进一步测试，以通过性别，种族，胎龄修改， 城市/农村和邻里剥夺。后续分析将使用仔细的表型赞助商 费城大型医院系统的早产。我们的流行病学模型将在先验上有所改善 对极端温度和空气污染对早产的急性影响的工作 通过生态时间序列模型的暴露分配，病例交叉不适当的时间层 比较或过度广泛的结果定义，包括医学指示的早产。鉴于 卫星遥感的全球覆盖范围，我们的方法 - 可以产生每日暴露估计 在空间上得到高度解决（<1 km）以捕获家庭地址的环境暴露 - 广泛适用 更好地理解极端天气和空气污染的作用，作为慢性和急性的压力源 使用大数据注册表的健康结果。