Literature DB >> 11568948

Zero-inflated Poisson regression with random effects to evaluate an occupational injury prevention programme.

K K Yau1, A H Lee.   

Abstract

This study presents a zero-inflated Poisson regression model with random effects to evaluate a manual handling injury prevention strategy trialled within the cleaning services department of a 600 bed public hospital between 1992 and 1995. The hospital had been experiencing high annual rates of compensable injuries of which over 60 per cent were attributed to manual handling. The strategy employed Workplace Risk Assessment Teams (WRATS) that utilized a workplace risk identification, assessment and control approach to manual handling injury hazard reduction. The WRATS programme was an intervention trial, covering the 1988-1995 financial years. In the course of compiling injury counts, it was found that the data exhibited an excess of zeros, in the context that the majority of cleaners did not suffer any injuries. This phenomenon is typical of data encountered in the occupational health discipline. We propose a zero-inflated random effects Poisson regression model to analyse such longitudinal count data with extra zeros. The WRATS intervention and other concomitant information on individual cleaners are considered as fixed effects in the model. The results provide statistical evidence showing the value of the WRATS programme. In addition, the methods can be applied to assess the effectiveness of intervention trials on populations at high risk of manual handling injury or indeed of injury from other hazards. Copyright 2001 John Wiley & Sons, Ltd.

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Mesh:

Year:  2001        PMID: 11568948     DOI: 10.1002/sim.860

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  24 in total

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2.  A test of inflated zeros for Poisson regression models.

Authors:  Hua He; Hui Zhang; Peng Ye; Wan Tang
Journal:  Stat Methods Med Res       Date:  2017-12-28       Impact factor: 3.021

Review 3.  Distribution-free models for longitudinal count responses with overdispersion and structural zeros.

Authors:  Q Yu; R Chen; W Tang; H He; R Gallop; P Crits-Christoph; J Hu; X M Tu
Journal:  Stat Med       Date:  2012-12-12       Impact factor: 2.373

4.  Estimation of mediation effects for zero-inflated regression models.

Authors:  Wei Wang; Jeffrey M Albert
Journal:  Stat Med       Date:  2012-06-19       Impact factor: 2.373

5.  Unified Computational Methods for Regression Analysis of Zero-Inflated and Bound-Inflated Data.

Authors:  Yan Yang; Douglas Simpson
Journal:  Comput Stat Data Anal       Date:  2010-06-01       Impact factor: 1.681

6.  Estimating overall exposure effects for zero-inflated regression models with application to dental caries.

Authors:  Jeffrey M Albert; Wei Wang; Suchitra Nelson
Journal:  Stat Methods Med Res       Date:  2011-09-08       Impact factor: 3.021

7.  Marginalized zero-altered models for longitudinal count data.

Authors:  Loni Philip Tabb; Eric J Tchetgen Tchetgen; Greg A Wellenius; Brent A Coull
Journal:  Stat Biosci       Date:  2015-09-22

8.  A Bayesian model for repeated measures zero-inflated count data with application to outpatient psychiatric service use.

Authors:  Brian H Neelon; A James O'Malley; Sharon-Lise T Normand
Journal:  Stat Modelling       Date:  2010-12       Impact factor: 2.039

9.  Determination of proportionality in two-part models and analysis of Multi-Ethnic Study of Atherosclerosis (MESA).

Authors:  Anna Liu; Richard Kronmal; Xiaohua Zhou; Shuangge Ma
Journal:  Stat Interface       Date:  2011-10-01       Impact factor: 0.582

10.  Functional linear models for zero-inflated count data with application to modeling hospitalizations in patients on dialysis.

Authors:  Damla Sentürk; Lorien S Dalrymple; Danh V Nguyen
Journal:  Stat Med       Date:  2014-06-19       Impact factor: 2.373

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