0289 ”david´s cheese bread” method: workload quantitative exposure thresholds detection using adjusted hazard multivariate parametric modelling, useful in cumulative-trauma disorders prevention and within their causal assessment

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Abstract

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 analysed 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 modelling. Huber’s M-estimator was used warranting robust estimators 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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