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首页> 外文期刊>Accident Analysis & Prevention >A bivariate zero-inflated Poisson regression model to analyze occupational injuries.
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A bivariate zero-inflated Poisson regression model to analyze occupational injuries.

机译:用于分析职业伤害的双变量零膨胀Poisson回归模型。

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摘要

The aim of many occupational safety interventions is to reduce the incidence of injury. However, when measuring intervention effectiveness within a period, population-based accident count data typically contain a large proportion of zero observations (no injury). This situation is compounded where injuries are categorized in a binary manner according to an outcome of interest. The distribution thus comprises a point mass at zero mixed with a non-degenerate parametric component, such as the bivariate Poisson. In this paper, a bivariate zero-inflated Poisson (BZIP) regression model is proposed to evaluate a participatory ergonomics team intervention conducted within the cleaning services department of a public teaching hospital. The findings highlight that the BZIP distribution provided a satisfactory fit to the data, and that the intervention was associated with a significant reduction in overall injury incidence and the mean number of musculoskeletal (MLTI) injuries, while the decline in injuries of a non-musculoskeletal (NMLTI) nature was marginal. In general, the method can be applied to assess the effectiveness of intervention trials on other populations at high risk of occupational injury.
机译:许多职业安全干预措施的目的是减少伤害发生率。但是,在一段时间内测量干预效果时,基于人群的事故计数数据通常包含很大一部分零观测值(无伤害)。如果根据关注的结果以二进制方式对伤害进行分类,则情况会更加复杂。因此,分布包括零点质量与非退化参数分量(例如双变量泊松)混合。在本文中,提出了一个二元零膨胀泊松(BZIP)回归模型来评估在公立教学医院清洁服务部门内进行的参与式人机工程学团队干预。研究结果突出表明,BZIP分布对数据提供了令人满意的拟合,并且该干预措施与总体损伤发生率和平均肌肉骨骼(MLTI)损伤的显着减少有关,而非肌肉骨骼的损伤减少了(NMLTI)性质微不足道。通常,该方法可用于评估对其他具有高职业伤害风险的人群进行干预试验的有效性。

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