Estimating differentials in return to work after injury from two surveys

描述(由申请人提供)：评估工人从工作场所受伤和疾病中恢复的全部经济成本对于有效制定和确定减少工作场所事故和工作场所危险暴露的政策和方案、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，经常被用来研究工作场所伤害的后果和背景。与行政数据相比，多用途、具有全国代表性的家庭调查数据提供了关于工作场所伤害的更广泛的覆盖范围，并提供了关于其背景和后果的更广泛的信息。纵向调查对于调查影响进程的因素的作用特别有用，例如从可能延长的时间段内发生的工伤或疾病中恢复过来。特别是，纵向调查数据更有可能准确地捕捉到在哪些情况下重返工作岗位与伤残津贴的结束不重合，或者在受伤导致多次停工的情况下。然而，任何单一的纵向调查都有缺点，可能包括受伤工人的样本较少、观察的频率或持续时间有限、调查的年龄范围有限以及受访者的自然减员和自我报告中的错误。本研究通过对调查之间的伤害及其后果进行比较评估，以及开发和测试各种调查中对受伤工人的观察结果的方法来解决这些局限性。这项研究应用了危险建模和多种归因方法，将来自两个具有全国代表性的小组调查的数据结合在一起，每个小组调查都包括工伤自我报告以及由此产生的健康状况和工作限制。根据职业、行业、健康状况和受伤工人的社会经济特征，分析了重返工作岗位的持续时间和可持续性方面的差异。这两个小组研究的设计是重叠的，但在人口覆盖率、观察持续时间和数据获取模式(小组观察和回顾观察)方面不相同。将这两项调查结合使用，可望提高对包括疾病类型在内的关键变量重返工作岗位的差异的统计推断能力；并通过在分析工伤后重返工作岗位的分析中尽可能广泛地纳入潜在重要变量，加强我们对重返工作岗位过程的了解。根据职业、行业、健康状况和受伤工人的社会经济特征，分析了重返工作岗位的持续时间和可持续性方面的差异。危险建模和多种归因方法被应用于合并来自两个具有全国代表性的小组调查的数据，每个小组调查都包括工伤和由此产生的健康状况和工作限制的自我报告。将这两项调查结合使用，可望增加统计推论的力量，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过程的了解。

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.