在三个国家公共使用数据集中,比较了受伤的自行车骑行者和摩托车骑行者的酒精参与
Kathryn G Burford1, Andrew G Rundle2, Spiros Frangos3
1Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, New York.
Traffic injury prevention
|June 26, 2024
概括
酒精显著影响致命和非致命伤害的摩托车和踏板车骑手. 来自三个国家数据集的数据显示,酒精参与率各不相同,强调需要标准化监测,以改善伤害预防工作.
科学领域:
- 公共卫生监督是对公共卫生的监督.
- 伤害流行病学 伤害流行病学
- 酒精涉及交通事故中的伤害
背景情况:
- 自2008年以来,酒精涉及致命的摩托车 (超过38%) 和自行车 (超过20%) 撞车事故是一个持续的问题.
- 对酒精在这些伤害中的作用的公共卫生监测仍然不发达.
- 这项研究通过检查酒精涉及致命和非致命的踩踏车和摩托车骑行者的伤害来弥补这一差距.
研究的目的:
- 为了确定酒精涉及致命和非致命伤害在踩踏车和摩托车骑手 (21岁以上) 的流行率.
- 为了比较三个不同的国家公共使用数据集中的酒精参与调查结果.
- 通过准确的酒精相关骑自行车和摩托车事故数据,为伤害预防策略提供信息.
主要方法:
- 利用了来自国家紧急医疗服务信息系统 (NEMSIS),死亡分析报告系统 (FARS) 和国家电子伤害监测系统 (NEISS) 的2019年数据.
- 根据临床医生的评估 (NEMSIS,NEISS) 或血中酒精含量 (BAC) ≥0.01 (FARS) 的定义酒精参与.
- 使用特定的ICD-10代码,产品代码和美国国家标准协会在数据集上的分类来识别踏板车和摩托车受伤.
主要成果:
- 据NEMSIS数据显示,10%的自行车骑行者和8.5%的摩托车伤害涉及酒精;NEISS显示分别为7.3%和6.1%.
- FARS数据显示,酒精参与致命伤害的比例较高:自行车骑行者占27%,摩托车骑行者占42.0%.
- 对于两种骑手类型的所有数据集中,中年成人和男性的酒精参与更为常见.
结论:
- 在国家数据集中,如何衡量踩踏车和摩托车受伤以及酒精参与的情况存在显著差异.
- 在这些人群中,需要标准化,有效和可行的方法来监测酒精参与.
- 改善数据收集对于开发有效的伤害预防倡议对于骑自行车者和摩托车者至关重要.
相关概念视频
Introduction to Test of Independence
In statistics, the term independence means that one can directly obtain the probability of any event involving both variables by multiplying their individual probabilities. Tests of independence are chi-square tests involving the use of a contingency table of observed (data) values.
The test statistic for a test of independence is similar to that of a goodness-of-fit test:
The test statistic for a test of independence is similar to that of a goodness-of-fit test:
Hypothesis Test for Test of Independence
The test of independence is a chi-square-based test used to determine whether two variables or factors are independent or dependent. This hypothesis test is used to examine the independence of the variables. One can construct two qualitative survey questions or experiments based on the variables in a contingency table. The goal is to see if the two variables are unrelated (independent) or related (dependent). The null and alternative hypotheses for this test are:
H0: The two variables (factors)...
H0: The two variables (factors)...
Determination of Expected Frequency
Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...


