OBJECTIVE To describe the interrelationship of post-injury employment and substance abuse (SA) among individuals with traumatic brain injury (TBI). DESIGN Structural equation model (SEM) and logistic regression analytic approach using a merged database of the National Trauma Data Bank (NTDB) and TBI Model Systems National Database (TBIMS-NDB), with acute care and rehabilitation hospitalization data and 1, 2, and 5-year follow-up data. SETTING United States Level I/II trauma centers and inpatient rehabilitation centers with telephone follow-up. PARTICIPANTS Individuals in the TBIMS-NDB successfully matched to their NTDB data, aged 18-59 years, with trauma severity, age, sex, employment, and SA data at 1, 2, and/or 5 years post-injury (n=2,890). INTERVENTIONS Not applicable MAIN OUTCOME MEASURE: Employment status (employed/unemployed) and SA (present/absent) at year-1, year-2, and year-5 post-injury RESULTS: SEM analysis showed older age at injury predicted lower likelihood of employment at all time-points post-injury (βYR1=-0.016; βYR2=-0.006; βYR5=-0.016; all p<0.001), while higher injury severity score (ISS) predicted lower likelihood of employment (β=-0.008, p=0.027) and SA (β=-0.007; p=0.050) at year-1. Being male predicted higher likelihood of SA at each follow-up (βYR1=0.227; βYR2=0.184; βYR5=0.161; all p<0.10). Despite associations of pre-injury unemployment with higher pre-injury SA, post-injury employment at year-1 predicted SA at year-2 (β=0.118, p=0.028). Employment and SA during the previous follow-up period predicted subsequent employment and SA respectively. CONCLUSIONS Employment and SA have unique longitudinal interrelationships and are additionally influenced by age, sex, and ISS. The present work suggests the need for more research on causal, confounding, and mediating factors and appropriate screening and intervention tools that minimize SA and facilitate successful employment related outcomes.

中文翻译：

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“TBI 后就业与药物滥用之间的相互关系：交叉滞后的结构方程建模分析”

目的 描述创伤性脑损伤 (TBI) 患者受伤后就业和药物滥用 (SA) 的相互关系。设计结构方程模型 (SEM) 和逻辑回归分析方法，使用国家创伤数据库 (NTDB) 和 TBI 模型系统国家数据库 (TBIMS-NDB) 的合并数据库，以及急性护理和康复住院数据以及 1、2 和5年随访数据。设置美国 I/II 级创伤中心和住院康复中心，并提供电话随访。参与者 TBIMS-NDB 中的个人成功匹配了他们的 NTDB 数据，年龄在 18-59 岁之间，具有创伤严重程度、年龄、性别、就业和受伤后 1、2 和/或 5 年的 SA 数据（n=2,890 ）。干预 不适用 主要结果测量：受伤后第 1 年、第 2 年和第 5 年的就业状况（就业/失业）和 SA（存在/缺席） 结果：SEM 分析显示，受伤时年龄较大的预测在所有时间点就业的可能性较低- 伤害（βYR1=-0.016；βYR2=-0.006；βYR5=-0.016；所有 p<0.001），而较高的伤害严重程度评分 (ISS) 预测较低的就业可能性（β=-0.008，p=0.027）和 SA（ β=-0.007；p=0.050）在第 1 年。男性在每次随访时预测 SA 的可能性更高（βYR1=0.227；βYR2=0.184；βYR5=0.161；所有 p<0.10）。尽管受伤前失业率与较高的受伤前 SA 相关，但第 1 年的受伤后就业预测第 2 年的 SA（β=0.118，p=0.028）。前一个随访期间的就业和 SA 分别预测了随后的就业和 SA。结论 就业和 SA 具有独特的纵向相互关系，另外还受年龄、性别和 ISS 的影响。目前的工作表明需要对因果、混杂和中介因素以及适当的筛选和干预工具进行更多研究，以最大限度地减少 SA 并促进成功的就业相关结果。