Estimating differentials in return to work after injury from two surveys

描述（由申请人提供）：评估工人从工伤和疾病中恢复的全部经济成本，对于有效制定和针对减少工伤事故和工作场所危险的政策和方案，成功地将受伤工人带回工作场所和劳动力市场，以及评估工人补偿的充分性和公平性至关重要。行政数据，如工人索赔数据库中的数据，经常用于研究工伤的后果和背景。与行政数据相比，具有全国代表性的多用途家庭调查数据提供了更广泛的工伤覆盖面，以及关于其背景和后果的更广泛信息。纵向调查对于调查影响某个过程的因素的作用特别有用，例如在可能延长的时间内发生的工伤或疾病的恢复。特别是，纵向调查数据更有可能准确地反映重返工作与残疾福利结束不一致的情况，或受伤导致多次失业的情况。然而，任何单一的纵向调查都有缺点，可能包括受伤工人样本量小、观察频率或持续时间有限、调查年龄范围有限以及受访者自我报告中的损耗和错误。本研究解决了这些局限性的比较评估调查之间的伤害及其后果，并通过开发和测试方法，汇集观察受伤工人的调查。该研究采用危险模型和多重插补方法，结合两个全国代表性的小组调查的数据，每个调查都包括工伤的自我报告，以及由此产生的健康状况和工作限制。不同的职业，行业，健康状况和受伤工人的社会经济特征，在返回工作的持续时间和可持续性进行了分析。两项面板研究的设计重叠，但在人群覆盖范围、观察持续时间和数据采集模式（面板和回顾性观察）方面不相同。同时使用这两项调查，预计将增加统计推断的力量，对关键变量，包括受伤或疾病的类型，返回工作的差异;并加强我们对返回工作过程的理解，包括尽可能广泛的潜在重要变量在分析工作场所受伤后返回工作。根据职业、行业、健康状况和受伤工人的社会经济特征，分析了恢复工作的持续时间和可持续性的差异。风险建模和多重插补方法被应用到两个全国代表性的面板调查，每个包括工伤和由此产生的健康状况和工作限制的自我报告的数据相结合。同时使用这两项调查，预计将增加统计推断的力量，并通过在分析中纳入广泛的变量和工人年龄，加强我们对重返工作岗位过程的理解。

DESCRIPTION (provided by applicant): Evaluating the full economic costs to workers of recovery from workplace injury and illness is crucial for the effective formulation and targeting of policy and programs for reducing workplace accidents and exposure to workplace hazards, for successfully bringing back injured workers into the workplace and the workforce, and for evaluating the adequacy and equity of workers' compensation. Administrative data, such as those found in workers' compensation claim databases, are frequently used to study the consequences and contexts of workplace injuries. Compared with administrative data, multi-purpose, nationally representative household survey data offer a far broader coverage of workplace injuries, and a broader range of information on their context and consequences. Longitudinal surveys are especially useful for investigating the roles of factors that influence a process such as recovery from a workplace injury or illness that occurs over a potentially extended time period. In particular, longitudinal survey data are more likely to accurately capture circumstances where return to work does not coincide with the end of disability benefits, or where an injury leads to multiple spells of time out of work. Any single longitudinal survey, however, has disadvantages that may include small samples of injured workers, limited frequency or duration of observation, a limited range of ages in the survey, and respondent attrition and error in their self-reports. The present study addresses these limitations by comparative evaluation of injuries and their consequences between surveys, and by developing and testing methods for pooling observations of injured workers across surveys. The study applies hazard modeling and multiple imputation methods to the combining of data from two nationally representative panel surveys, each including self-reports of work injury and resulting health condition and work limitation. Differentials in duration and sustainability of return to work are analyzed by occupation, industry, health condition, and the socio- economic characteristics of injured workers. The two panel studies' designs are overlapping but non-identical with respect to their population coverage, duration of observation, and mode of data capture (panel and retrospective observation). Using the two surveys together is expected to increase the power of the statistical inference about the differentials in return to work on key variables including type of injury of illness; and to strengthen our understanding of the return-to-work process by including the widest possible range of potentially important variables in the analysis of returning to work after a workplace injury. Differentials in duration and sustainability of return to work are analyzed by occupation, industry, health condition, and the socio-economic characteristics of injured workers. Hazard modeling and multiple imputation methods are applied to the combining of data from two nationally representative panel surveys, each including self-reports of work injury and resulting health condition and work limitation. Using the two surveys together is expected to increase the power of the statistical inference and to strengthen our understanding of the return-to- work process by including a broad range of variables and worker ages in the analysis.