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0289 ‘David’s cheese bread’ method: workload quantitativeexposurethresholds detection using adjusted hazardmultivariate parametric modelling, useful incumulative-trauma disorders prevention and within their causal assessment
  1. David Alvarez Rincón1,2,3,
  2. Luisa Perez4
  1. 1ICOH membership, Milano, Italy
  2. 2EPICOH member, Utrecht, The Netherlands
  3. 3Provincial Workers’ Compensation Board, Cali, Colombia
  4. 4University of Alberta OH&S postgraduate certificate, Edmonton, Canada


Background Qualitative methods are frequently used for workload assessment due to their relative low-cost but their evidence lack, high subjectivity and inaccurate conclusions lead to develop quantitative evidence-based methods for Cumulative Trauma Disorders evaluation. This research aims to generate robust and reliable evidence useful in prevention systems and within workers’ compensation processes (causal assessment) by measuring cumulative effective working time to define suitable exposure thresholds.

Methods A retrospective cohort study was assembled with workers from different positions. Inclusion/exclusion criteria were rigorously applied to finally accept 328 workers (656 shoulders). Entire clinic history was analyzed towards obtaining important clinical variables. Each shoulder workload was assessed independently getting cumulative exposure time to movement angles, repetitive motions, load lifting, exertion and vibration, adjusting by rest/break periods and other important covariates, controlling confusing effects. The exposure thresholds were obtained using an adjusted multivariate Weibull regression modeling. Huber's M-estimator was used warranting robustestimators to correct both shoulders non-completely independent measures. Final model was built according with Hosmer-Lemeshow-May’s covariates purposeful selection principles.

Findings/conclusions Within the adjusted multivariate final model, we could set hazard rate ratio (HRR) into five different clusters across cohort exposure time-line: ‘D’ or baseline hazard zone; ‘a’ zone (HRR≈1; p-value≥0.05); ‘v’ or risk zone (HRR>1; p-value<0.05); ‘i’ or survivors zone (HRR≈1; p-value≥0.05); and ‘d’ or super-survivors zone (HRR<1; p-value<0.05). Shortest cumulative times within ‘v’ zone were selected as exposure thresholds. For workload factors, we were able to clearly define zones and thresholds. We’ve also named ‘v’ cluster as ‘cheese’ zone and others as ‘no-cheese’ areas.

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