Understanding the epidemiology of firearm injuries in a large urban county: a guide for targeted intervention efforts

摘要 枪支暴力是美国的一个主要公共卫生问题，与枪支有关的死亡率很高 超过了世界上所有其他国家。该项目的目标是了解流行性感冒的流行病学。 在一个庞大而多样化的人口中心(德克萨斯州哈里斯县)发生的枪击伤亡事件，以确定 为预防工作提供信息的趋势和风险因素。我们建议对以下数据进行为期3年的回顾 哈里斯县枪支致伤或死亡儿童和成人检查枪支范围 并界定风险因素，以针对未来的干预努力。为了最大限度地捕获数据 受影响人数，当代枪支暴力综合数据库，包括 来自创伤中心、法医办公室和警方记录的非致命和致命事件将 发展起来的。综合数据库将包括个人、社会、社会经济和环境变量 基于预防枪支暴力的社会生态模型的关于枪击受害者的信息。这将被用来 确定个人级别和邻里级别的地理、人口、时间 与枪支有关的暴力的社会和社会经济风险因素。将使用地统计模型来 根据受害者居住地确定社区层面的社会经济预测因素。最后，枪支暴力 “热点”将根据与枪支有关的事件(两人受伤)的官方犯罪数据来确定 和非损伤)使用地理单元分析的空间聚类法。了解哪些亚群是 无论是受伤还是死亡，枪击的背景是什么，事件发生的地点都将提供丰富的 数据，以制定战略干预措施，减少地方一级的枪支暴力。整合了 在一个庞大而多样化的人口中心，来自多个数据源的枪支暴力数据将提供独特的 分析伤害集群并提供基于证据的风险因素优先顺序的平台，以帮助社区 设计有针对性的干预措施。

ABSTRACT Firearm violence is a major public health problem in the United States (US), and firearm-related mortality far surpasses that in every other country in the world. The goal of this project is to understand the epidemiology of firearm injuries and deaths in a large and diverse population center (Harris County, Texas) in order to identify trends and risk factors to inform prevention efforts. We propose a 3-year retrospective review of data on children and adults who were injured or killed by firearms in Harris County to examine the scope of firearm violence and define risk factors to target future intervention efforts. In order to capture data on the maximum number of affected individuals, an integrated database for contemporary firearm violence that includes non-fatal and fatal events from trauma centers, medical examiner's office and police records will be developed. The integrated database will include personal, social, socioeconomic, and environmental variables about the shooting victims based on a social-ecological model for firearm violence prevention. This will be used to identify and categorize individual-level and neighborhood-level geographic, demographic, temporal, social, and socioeconomic risk factors for firearm-related violence. Geostatistical models will be used to identify neighborhood-level socioeconomic predictors based on victim residence. Finally, firearm violence “hot spots” will be characterized based on official crime data for firearm-related incidents (both injury and non-injury) using spatial clustering by geographic unit analysis. Understanding which subpopulations are being injured or killed, what the shooting context was, and where the incidents occurred will provide a wealth of data to devise strategic interventions for decreasing firearm violence at the local level. The integration of firearm violence data from multiple data sources in a large and diverse population center will provide a unique platform to analyze injury clusters and provide evidence-based prioritization of risk factors to help communities design targeted interventions.