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.

摘要