RFA-CE-22-008, ASTRAL (Aquatic Safety Through Research, Association, and Linkage of Data)

RFA-CE-22-008，ASTRAL（通过数据研究、关联和链接实现水生安全）

• 批准号: 10585362
• 负责人: Rohit Shenoi
• 金额: $ 34.9万
• 依托单位: BAYLOR COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-30 至 2025-09-29
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10585362
• 关键词: RFA; CE; 22; 008; ASTRAL

Abstract The long-term goal of the project is to understand conditions affecting morbidity and mortality from unintentional drowning, especially in subpopulations that experience higher rates of drowning. The specific aims are: 1) Develop an analysis database by linking data from EMS, hospital and fatality recordsverified by syndromic surveillance data and enriched with socio-economic contextual information pertaining to risk factors of drowning; 2a) Examine gender, racial and ethnic differences in drowning rates and identify risk factors that account for these differences; 2b) Perform spatial- temporal analysis of drownings in order to identify locations and time periods when these events are most likely to occur and 3) Compute the aggregate medical and other costs associated with fatal and non-fatal drowning by age, minority status and aquatic body. This is a cross-sectional study of all victims or patients of unintentional drowning that occurred in HarrisCounty, Texas between 2016-2023. We will link data from fatality reports, EMS and hospitalization dataand add contextual information on drowning from other data sources to prepare an analysis database. Individual and environmental information of drowning victims will be matched at the neighborhood level. We will match demographic, exposure and outcome variables through spatial overlap and will identify demographic, medical and drowning characteristics associated with fatal and nonfatal drowning. Spatial regression modeling will produce a model to predict fatal and nonfatal drowning, further broken down by gender, race, Hispanic ethnicity and other ethnic differences. We will examine associations of drowning with neighborhood characteristics, proximity to swimming venues and swim lesson participation. Drowning incidents will be examined for spatial concentrations using the Nearest Neighbor Hierarchical Cluster algorithm. To relate drownings to predictive factors, a Markov Chain Monte Carlo Poisson-Lognormal-Conditional Autoregressive spatial regression model willbe tested at the census block group level. Hospital billing records will be analyzed to compute the direct medical costs, comprehensive (lifetime) costs, and disability adjusted life years associated with drowning. This project will integrate informationfrom multiple data sources to identify key factors that contribute to increased drowning rates. The results will inform injury prevention efforts and help prioritize strategies to safeguard people from drowning.

抽象的 该项目的长期目标是了解影响发病率和死亡率的条件 无意的溺水，尤其是在溺水率更高的亚群中。这 具体目的是：1）通过连接EMS，医院和死亡的数据来开发分析数据库 由综合征监视数据介绍的记录，并具有社会经济背景 与溺水的风险因素有关的信息； 2a）检查性别，种族和种族差异 在溺水率并确定造成这些差异的风险因素； 2b）执行空间 溺水的时间分析，以确定这些事件的位置和时间段 最有可能发生，3）计算总体医疗以及与致命和致命相关的其他费用 非致命的溺水，年龄，少数群体和水生身体。 这是对所有发生在 德克萨斯州哈里斯卡特（Harriscounty）2016 - 2023年之间。我们将从死亡报告，EMS和 住院数据并添加有关从其他数据源溺水准备的上下文信息 分析数据库。溺水受害者的个人和环境信息将与 在邻里。我们将通过人口统计，曝光和结果变量匹配 空间重叠，并将确定与人口，医学和溺水特征 致命的和非致命的溺水。空间回归建模将产生一个模型来预测致命和 非致命溺水，进一步被性别，种族，西班牙种族和其他种族分解 差异。 我们将研究溺水与邻里特征，靠近游泳的关联 场地和游泳课。将检查溺水事件的空间浓度 使用最近的邻居分层群集算法。为了将溺水与预测因素联系起来， 马尔可夫链蒙特卡洛泊松托尼 - 通知条件自回归空间回归 模型将在人口普查块组级别进行测试。 医院计费记录将进行分析以计算直接医疗费用，全面 （终生）成本和残疾调整了与溺水有关的生命年。这个项目将集成 信息从多个数据源来识别导致溺水增加的关键因素 费率。结果将为预防伤害提供依据，并帮助优先考虑保护策略以维护策略 溺水的人。