Evidence map›Paper›PMID 28593078›Full record

ArticleSafety and health at work2017

Non-chemical Risk Assessment for Lifting and Low Back Pain Based on Bayesian Threshold Models.

Sudha P Pandalai, Matthew W Wheeler, Ming-Lun Lu

Abstract read
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Article in Safety and health at work, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Sudha P PandalaiEducation and Information Division, Risk Evaluation Branch, National Institute for Occupational Safety and Health, Cincinnati, OH, USA.
Matthew W WheelerEducation and Information Division, Risk Evaluation Branch, National Institute for Occupational Safety and Health, Cincinnati, OH, USA.
Ming-Lun LuDivision of Applied Research and Technology, Organizational Science and Human Factors Branch, Human Factors and Ergonomics Research Team, National Institute for Occupational Safety and Health, Cincinnati, OH, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSelf-reported low back pain (LBP) has been evaluated in relation to material handling lifting tasks, but little research has focused on relating quantifiable stressors to LBP at the individual level. The National Institute for Occupational Safety and Health (NIOSH) Composite Lifting Index (CLI) has been used to quantify stressors for lifting tasks. A chemical exposure can be readily used as an exposure metric or stressor for chemical risk assessment (RA). Defining and quantifying lifting nonchemical stressors and related adverse responses is more difficult. Stressor-response models appropriate for CLI and LBP associations do not easily fit in common chemical RA modeling techniques (e.g., Benchmark Dose methods), so different approaches were tried.

methodsThis work used prospective data from 138 manufacturing workers to consider the linkage of the occupational stressor of material lifting to LBP. The final model used a Bayesian random threshold approach to estimate the probability of an increase in LBP as a threshold step function.

resultsUsing maximal and mean CLI values, a significant increase in the probability of LBP for values above 1.5 was found.

conclusionA risk of LBP associated with CLI values > 1.5 existed in this worker population. The relevance for other populations requires further study.

Indexed as

Bayes theoremmusculoskeletal diseasesoccupational exposurerisk assessment

Identifiers

PMID28593078
PMCPMC5447412

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.