Introduction: This study analyzed workers’ compensation (WC) claims among private employers insured by the Ohio state-based WC carrier to identify high-risk industries by detailed cause of injury. Methods: A machine learning algorithm was used to code each claim by U.S. Bureau of Labor Statistics (BLS) event/exposure. The codes assigned to lost-time (LT) claims with lower algorithm probabilities of accurate classification or those LT claims with high costs were manually reviewed. WC data were linked with the state’s unemployment insurance (UI) data to identify the employer’s industry and number of employees. BLS data on hours worked per employee were used to estimate full-time equivalents (FTE) and calculate rates of WC claims per 100 FTE. Results: 140,780 LT claims and 633,373 medical-only claims were analyzed. Although counts and rates of LT WC claims declined from 2007 to 2017, the shares of leading LT injury event/exposures remained largely unchanged. LT claims due to Overexertion and Bodily Reaction (33.0%) were most common, followed by Falls, Slips, and Trips (31.4%), Contact with Objects and Equipment (22.5%), Transportation Incidents (7.0%), Exposure to Harmful Substances or Environments (2.8%), Violence and Other Injuries by Persons or Animals (2.5%), and Fires and Explosions (0.4%). These findings are consistent with other reported data. The proportions of injury event/exposures varied by industry, and high-risk industries were identified. Conclusions: Injuries have been reduced, but prevention challenges remain in certain industries. Available evidence on intervention effectiveness was summarized and mapped to the analysis results to demonstrate how the results can guide prevention efforts. Practical Applications: Employers, safety/health practitioners, researchers, WC insurers, and bureaus can use these data and machine learning methods to understand industry differences in the level and mix of risks, as well as industry trends, and to tailor safety, health, and disability prevention services and research.

简介：本研究分析了俄亥俄州 WC 运营商投保的私人雇主的工人赔偿 (WC) 索赔，以通过详细的伤害原因识别高风险行业。方法：使用机器学习算法对美国劳工统计局 (BLS) 事件/曝光的每项索赔进行编码。分配给准确分类算法概率较低的损失工时 (LT) 索赔或成本高的 LT 索赔的代码是人工审查的。WC 数据与该州的失业保险 (UI) 数据相关联，以确定雇主的行业和雇员人数。BLS 关于每位员工工作时间的数据用于估算全职当量 (FTE) 并计算每 100 FTE 的 WC 索赔率。结果：分析了 140,780 项 LT 索赔和 633,373 项纯医疗索赔。尽管 LT WC 索赔的数量和比率从 2007 年到 2017 年有所下降，但主要 LT 伤害事件/暴露的份额基本保持不变。由于过度劳累和身体反应 (33.0%) 导致的 LT 索赔最常见，其次是跌倒、滑倒和绊倒 (31.4%)、接触物体和设备 (22.5%)、交通事故 (7.0%)、接触有害物质或环境 (2.8%)、人或动物的暴力和其他伤害 (2.5%) 以及火灾和爆炸 (0.4%)。这些发现与其他报告的数据一致。伤害事件/暴露的比例因行业而异，并确定了高风险行业。结论：伤害已经减少，但某些行业仍然存在预防挑战。总结了有关干预有效性的可用证据并将其映射到分析结果中，以证明结果如何指导预防工作。实际应用：雇主、安全/健康从业者、研究人员、WC 保险公司和局可以使用这些数据和机器学习方法来了解行业在风险水平和组合方面的差异以及行业趋势，并定制安全、健康、残疾预防服务和研究。