Abstract：
OBJECTIVE: Several studies have documented the relative risk or odds of injury and fatality for females versus males in motor vehicle crashes (Parenteau et al. 2013, Forman et al. 2019, Brumbelow and Jermakian, 2022; Noh et al. 2022). Though, none combined National Automotive Sampling System-Crashworthiness Data System (NASS-CDS) and Crash Investigation Sampling System (CISS). The aim of this study was to document the relative odds of various injury outcomes for females versus males while considering a broad range of crash types, pre-crash and crash variables, and occupant characteristics.
METHODS: Multivariable logistic regression was carried out to study the odds of injury for females versus males. A select imputation method (Hot Deck, Approximate Bayesian Bootstrap) was applied as part of efforts to create multivariable logistic regression models for 25 different injury outcomes associated with occupants (age 13 years and older) involved in passenger vehicle crashes published in NASS-CDS (2000 to 2015) and CISS (2017-2021). Both pre-crash (n=7) and crashworthiness (n=22) predictor variables were considered, but only significant variables at p≤0.10 level were retained in final models. Six crash-type models were produced for each injury outcome; one that included all crashes, one for each of four different planar crash types (frontal, near-side, far-side, rear), and one for crashes involving rollover. These six sets of crash-type models were expanded further to include a model version that included both pre-crash/environment and crashworthiness predictor variables and one model limited to crashworthiness predictors only. Different than other recent studies, all crash types, occupant restraint conditions, and seating positions were considered. Occupant sex was retained in all models to facilitate female versus male injury outcome odds ratio (OR) assessments.
RESULTS: Female versus male injury OR estimates for 300 unique models are presented. Females had significantly higher odds of injury than males in 36 models (OR>1.0, p-value ≤0.05). This contrasts with 43 models where females had significantly lower odds (OR<1.0, p≤0.05). For the remaining 221 models, there was a near even split in how often the odds of injury were non-significantly higher (n=103) and non-significantly lower (n=114) for females as compared to males (p>0.05). In four cases, the OR estimate was 1.00. Amongst the results, there was a trend for females to have higher odds of AIS 2+ injuries (MAIS 2+ OR=1.75 and 1.69 for Full and Crashworthiness models, respectively for the All Crashes dataset). These increases included higher estimates for lower extremity injuries in frontal crashes, consistent with earlier studies (e.g., Forman et al. 2019). However, for certain AIS 2+ (neck, thorax) and AIS 3+ injuries (head, neck, thorax), females had significantly lower odds of injury (p≤0.05). The trends for reduced odds of injury for females were most prevalent in non-frontal crash models.
METHODS: Multivariable logistic regression was carried out to study the odds of injury for females versus males. A select imputation method (Hot Deck, Approximate Bayesian Bootstrap) was applied as part of efforts to create multivariable logistic regression models for 25 different injury outcomes associated with occupants (age 13 years and older) involved in passenger vehicle crashes published in NASS-CDS (2000 to 2015) and CISS (2017-2021). Both pre-crash (n=7) and crashworthiness (n=22) predictor variables were considered, but only significant variables at p≤0.10 level were retained in final models. Six crash-type models were produced for each injury outcome; one that included all crashes, one for each of four different planar crash types (frontal, near-side, far-side, rear), and one for crashes involving rollover. These six sets of crash-type models were expanded further to include a model version that included both pre-crash/environment and crashworthiness predictor variables and one model limited to crashworthiness predictors only. Different than other recent studies, all crash types, occupant restraint conditions, and seating positions were considered. Occupant sex was retained in all models to facilitate female versus male injury outcome odds ratio (OR) assessments.
RESULTS: Female versus male injury OR estimates for 300 unique models are presented. Females had significantly higher odds of injury than males in 36 models (OR>1.0, p-value ≤0.05). This contrasts with 43 models where females had significantly lower odds (OR<1.0, p≤0.05). For the remaining 221 models, there was a near even split in how often the odds of injury were non-significantly higher (n=103) and non-significantly lower (n=114) for females as compared to males (p>0.05). In four cases, the OR estimate was 1.00. Amongst the results, there was a trend for females to have higher odds of AIS 2+ injuries (MAIS 2+ OR=1.75 and 1.69 for Full and Crashworthiness models, respectively for the All Crashes dataset). These increases included higher estimates for lower extremity injuries in frontal crashes, consistent with earlier studies (e.g., Forman et al. 2019). However, for certain AIS 2+ (neck, thorax) and AIS 3+ injuries (head, neck, thorax), females had significantly lower odds of injury (p≤0.05). The trends for reduced odds of injury for females were most prevalent in non-frontal crash models.
摘要：
目的：一些研究记录了机动车碰撞中女性与男性的相对伤害和死亡风险或几率（Parenteau等人。2013年，福尔曼等人。2019年，Brumbelow和Jermakian，2022年；Noh等人。2022年）。不过,None结合了国家汽车采样系统-耐撞性数据系统（NASS-CDS）和碰撞调查采样系统（CISS）。这项研究的目的是记录女性与男性的各种伤害结果的相对几率，同时考虑广泛的碰撞类型。崩溃前和崩溃变量，和乘员特征。
方法：采用多变量logistic回归分析研究女性与男性的损伤几率。一种精选的插补方法(热甲板，应用近似贝叶斯Bootstrap）作为努力创建多变量逻辑回归模型的一部分，该模型适用于NASS-CDS（2000年至2015年）和CISS（2017年至2021年）发表的与乘用车碰撞有关的25种不同伤害结果的乘员（13岁及以上）。考虑了碰撞前(n=7)和耐撞性(n=22)预测变量，但最终模型中只保留了p≤0.10水平的显著变量.为每种伤害结果生产了六个碰撞类型的模型；一个包括所有碰撞，四种不同的平面碰撞类型(正面，近侧,远端，后),还有一场涉及翻车的撞车事故。这六组碰撞类型模型进一步扩展为包括一个模型版本，该模型版本包括碰撞前/环境和耐撞性预测变量，以及一个仅限于耐撞性预测变量的模型。与其他最近的研究不同，所有崩溃类型，乘员约束条件,并考虑了座位位置。在所有模型中都保留了职业性别，以促进女性与男性受伤结果比值比（OR）评估。
结果：提供了300个独特模型的女性与男性损伤或估计值。在36个模型中，女性受伤的几率明显高于男性（OR>1.0，p值≤0.05）。这与43个模型形成对比，其中女性的几率显着降低（OR<1.0，p≤0.05）。对于其余221个型号，与男性相比，女性受伤的几率非显着较高（n=103）和非显着较低（n=114）的几率几乎是平均的（p>0.05）。在四个案例中，OR估计值为1.00。在结果中，女性AIS2+受伤的几率更高(完全和耐撞性模型的MAIS2+OR=1.75和1.69，分别用于所有崩溃数据集)。这些增加包括对正面碰撞中下肢受伤的更高估计，与早期研究一致(例如，福尔曼等人。2019)。然而,对于某些AIS2+(颈部，胸部)和AIS3+损伤(头部，脖子,胸部)，女性受伤几率显著较低(p≤0.05).在非正面碰撞模型中，女性受伤几率降低的趋势最为普遍。
方法：采用多变量logistic回归分析研究女性与男性的损伤几率。一种精选的插补方法(热甲板，应用近似贝叶斯Bootstrap）作为努力创建多变量逻辑回归模型的一部分，该模型适用于NASS-CDS（2000年至2015年）和CISS（2017年至2021年）发表的与乘用车碰撞有关的25种不同伤害结果的乘员（13岁及以上）。考虑了碰撞前(n=7)和耐撞性(n=22)预测变量，但最终模型中只保留了p≤0.10水平的显著变量.为每种伤害结果生产了六个碰撞类型的模型；一个包括所有碰撞，四种不同的平面碰撞类型(正面，近侧,远端，后),还有一场涉及翻车的撞车事故。这六组碰撞类型模型进一步扩展为包括一个模型版本，该模型版本包括碰撞前/环境和耐撞性预测变量，以及一个仅限于耐撞性预测变量的模型。与其他最近的研究不同，所有崩溃类型，乘员约束条件,并考虑了座位位置。在所有模型中都保留了职业性别，以促进女性与男性受伤结果比值比（OR）评估。
结果：提供了300个独特模型的女性与男性损伤或估计值。在36个模型中，女性受伤的几率明显高于男性（OR>1.0，p值≤0.05）。这与43个模型形成对比，其中女性的几率显着降低（OR<1.0，p≤0.05）。对于其余221个型号，与男性相比，女性受伤的几率非显着较高（n=103）和非显着较低（n=114）的几率几乎是平均的（p>0.05）。在四个案例中，OR估计值为1.00。在结果中，女性AIS2+受伤的几率更高(完全和耐撞性模型的MAIS2+OR=1.75和1.69，分别用于所有崩溃数据集)。这些增加包括对正面碰撞中下肢受伤的更高估计，与早期研究一致(例如，福尔曼等人。2019)。然而,对于某些AIS2+(颈部，胸部)和AIS3+损伤(头部，脖子,胸部)，女性受伤几率显著较低(p≤0.05).在非正面碰撞模型中，女性受伤几率降低的趋势最为普遍。
