Multifactorial spatiotemporal analyses to evaluate environmental triggers and patient-level clinical characteristics of severe asthma exacerbations in children

多因素时空分析评估儿童严重哮喘急性发作的环境触发因素和患者水平的临床特征

• 批准号: 9884782
• 负责人: Benjamin Alan Goldstein
• 金额: $ 12.08万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-03-04 至 2021-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9884782
• 关键词: Affect; African American; Age; Air; Air Movements; Allergens; Asthma; Behavior; Biological; Biological Factors; Bronchopulmonary Dysplasia; Caring; Cessation of life; Characteristics; Child; Childhood; Childhood Asthma; Chronic; Climate; Clinical; Communities; County; Data; Data Set; Databases; Demography; Development; Disease; Disease Outbreaks; Electronic Health Record; Emergency department visit; Environment; Environmental Exposure; Environmental Risk Factor; Fibrinogen; Food; Genetic; Geographic Locations; Geography; Health; Health Personnel; Health system; Hispanics; Hormonal; Hospitalization; Hospitals; Human Biology; Humidity; Hypersensitivity; Impairment; Incidence; Individual; Inflammation; Influenza; Integration Host Factors; Investigation; Knowledge; Latino; Link; Machine Learning; Medical; Modeling; Needs Assessment; Neighborhoods; North Carolina; Obesity; Outcome; Patient Education; Patients; Persons; Play; Pollen; Pollution; Population; Predisposition; Premature Birth; Prevalence; Prevention; Prevention strategy; Preventive; Primary Health Care; Publishing; Race; Recording of previous events; Risk; Risk Factors; Role; Rural; Schools; Severities; Socioeconomic Factors; Socioeconomic Status; Steroid therapy; Symptoms; Temperature; Therapeutic; United States; Universities; Viral; Viral Load result; Visit; Weather; airborne allergen; airway hyperresponsiveness; asthma exacerbation; asthmatic; asthmatic patient; atopy; base; built environment; cohort; cost; demographics; deprivation; electronic data; health care availability; individual patient; insight; longitudinal analysis; low socioeconomic status; machine learning method; medical attention; mucus hypersecretion; novel; pediatric emergency; pollutant; prevent; respiratory virus; response; sex; spatiotemporal; stem; urgent care; violent crime

Asthma is a chronic heterogeneous airway disorder characterized by inflammation, mucus hypersecretion, airway hyperreactivity, and impaired airflow. Severe exacerbations of asthma occur frequently in children and require immediate use of systemic steroid therapy to prevent serious outcomes such as hospitalization or death. In addition to direct health risks, pediatric asthma exerts a substantial cost burden, as asthma exacerbations are a leading cause of emergency department visits, hospitalization, and missed school days. Multiple environmental factors are purported to play a role in asthma symptoms, including aeroallergens, pollutants, weather changes, and community viral outbreaks such as influenza. Additionally, asthma prevalence is greater in children of low socioeconomic status (SES) and in African-American and Hispanic/Latino children, suggesting both environmental and genetic effects on asthma incidence and severity. The existence of geographical asthma “hotspots” indicates that asthma prevalence and severity are influenced by place-based risks, including local air quality, built environment factors, access to health care providers, socioeconomic factors, culture, and behavior. To effectively prevent and treat pediatric asthma attacks, it is necessary to understand how patient-specific characteristics interact with environmental factors to render an individual susceptible to severe asthma exacerbations. Lacking sufficient power, previous studies have largely examined suspected asthma triggers in isolation; thus, there is a significant knowledge gap regarding how environmental factors interact with each other and with patient-level factors to promote severe asthma exacerbations in pediatric populations. We hypothesize that a longitudinal analysis of environmental exposures and patient-level factors will elucidate new multifactorial causes of severe asthma exacerbations. To elucidate the contributions and interactions of environmental and patient-level factors, we will apply machine learning approaches to a longitudinal (2007-2017) geocoded database of patient electronic health records detailing asthma-related health encounters and publicly available, overlapping spatiotemporal environmental data. Further, we will evaluate the interactions between person-level clinical factors, including obesity, history of premature birth/bronchopulmonary dysplasia, and atopy, to determine their effects on susceptibility to selected environmental triggers. These analyses will 1) provide an analysis of the relative contribution and interactions of environmental factors to pediatric asthma exacerbations, 2) identify geographic hotspots of asthma prevalence and severity, and 3) determine how person-level clinical factors influence susceptibility to different asthma triggers. Our findings will provide new insights into risk factors for severe asthma exacerbations, spur new studies into the biological mechanisms that underlie the interactions between human biology and the environment, inform preventive strategies and patient education efforts, and serve as a model that can be expanded to larger cohorts.

哮喘是一种慢性异质性气道疾病，其特征为炎症、粘液分泌过多、 气道过度反应和气流受损经常发生在儿童和儿童中。 需要立即使用全身类固醇治疗，以防止住院或死亡等严重后果。 除了直接的健康风险外，小儿哮喘还造成巨大的成本负担，因为哮喘恶化会导致儿童哮喘发作。 急诊就诊、住院和旷课的主要原因。 据称，影响哮喘症状的因素包括空气过敏原、污染物、天气变化、 此外，低收入儿童的哮喘患病率更高。 失业状况（SES）以及非裔美国人和西班牙裔/拉丁裔儿童，表明两者 环境和遗传对哮喘发病率和严重程度的影响 地理哮喘的存在。 “热点”表明哮喘患病率和严重程度受到地方风险的影响，包括当地空气 质量、建筑环境因素、获得医疗保健提供者的机会、社会经济因素、文化和行为。 为了有效预防和治疗小儿哮喘发作，有必要了解患者的具体情况 特征与环境因素相互作用，使个体易患严重哮喘 由于缺乏足够的证据，之前的研究主要检查了可疑的哮喘触发因素。 因此，对于环境因素如何相互作用，存在着巨大的知识差距。 其他因素以及患者层面的因素会促进儿科人群哮喘严重发作。 很快，对环境暴露和患者因素的纵向分析将阐明新的 哮喘严重发作的多因素原因 阐明哮喘严重发作的贡献和相互作用。 环境和患者层面的因素，我们将应用机器学习方法进行纵向（2007-2017） 患者电子健康记录的地理编码数据库，详细说明与哮喘相关的健康状况并公开 此外，我们将评估可用的、重叠的时空环境数据。 个人临床因素，包括肥胖、早产史/支气管肺发育不良和特应性， 以确定它们对选定环境触发因素的敏感性的影响。这些分析将 1) 提供一个结果。 环境因素对小儿哮喘发作的相对贡献和相互作用的分析，2) 确定哮喘患病率和严重程度的地理热点，以及 3) 确定个人层面的临床因素 影响对不同哮喘触发因素的易感性，我们的研究结果将为了解哮喘的危险因素提供新的见解。 严重的哮喘恶化，激发了对相互作用背后的生物学机制的新研究 人类生物学与环境、预防策略和患者教育工作之间的关系，以及 作为可以扩展到更大群体的模型。

项目成果

Well-Child Care Attendance and Risk of Asthma Exacerbations.

儿童保健出勤率和哮喘恶化的风险。

• 发表时间: 2020
• 影响因子: 8
• 作者: Lang, Jason E;Tang, Monica;Zhao, Congwen;Hurst, Jillian;Wu, Angie;Goldstein, Benjamin A
• 通讯作者: Goldstein, Benjamin A

Development of an electronic health records datamart to support clinical and population health research.

开发电子健康记录数据集市以支持临床和人口健康研究。

• 发表时间: 2020-06-23
• 作者: Hurst, Jillian H;Liu, Yaxing;Maxson, Pamela J;Permar, Sallie R;Boulware, L Ebony;Goldstein, Benjamin A
• 通讯作者: Goldstein, Benjamin A