The prevalence of occupational exposure to ergonomic risk factors: A systematic review and meta-analysis from the WHO/ILO Joint Estimates of the Work-related Burden of Disease and Injury
Bibliographic record
Abstract
BACKGROUND: The World Health Organization (WHO) and the International Labour Organization (ILO) are developing joint estimates of the work-related burden of disease and injury (WHO/ILO Joint Estimates), with contributions from a large network of experts. Evidence from mechanistic and human data suggests that occupational exposure to ergonomic (or physical) risk factors may cause osteoarthritis and other musculoskeletal diseases (excluding rheumatoid arthritis, gout, and back and neck pain). In this paper, we present a systematic review and meta-analysis of the prevalence of occupational exposure to physical ergonomic risk factors for estimating the number of disability-adjusted life years from these diseases that are attributable to exposure to this risk factor, for the development of the WHO/ILO Joint Estimates. OBJECTIVES: We aimed to systematically review and meta-analyse estimates of the prevalence of occupational exposure to ergonomic risk factors for osteoarthritis and other musculoskeletal diseases. DATA SOURCES: We searched electronic bibliographic databases for potentially relevant records from published and unpublished studies, including Ovid Medline, EMBASE, and CISDOC. We also searched electronic grey literature databases, Internet search engines and organizational websites; hand-searched reference list of previous systematic reviews and included study records; and consulted additional experts. STUDY ELIGIBILITY AND CRITERIA: We included working-age (≥15 years) workers in the formal and informal economy in any WHO and/or ILO Member State but excluded children (<15 years) and unpaid domestic workers. The exposure was defined as any occupational exposure to one or more of: force exertion, demanding posture, repetitive movement, hand-arm vibration, kneeling or squatting, lifting, and/or climbing. We included all study types with an estimate of the prevalence of occupational exposure to ergonomic risk factors. STUDY APPRAISAL AND SYNTHESIS METHODS: At least two review authors independently screened titles and abstracts against the eligibility criteria at a first stage and full texts of potentially eligible records at a second stage, followed by extraction of data from qualifying studies. We combined prevalence estimates using random-effect meta-analysis. Two or more review authors assessed the risk of bias and the quality of evidence, using the ROB-SPEO tool and QoE-SPEO approach developed specifically for the WHO/ILO Joint Estimates. RESULTS: 100%, low quality of evidence). Subgroup analyses found no statistically significant differences in exposure by sex but differences by age group, occupation and country. No evidence was found for publication bias. We assessed this body evidence to be of low quality, based on serious concerns for risk of bias due to exposure assessment only being based on self-report and for indirectness due to evidence from two WHO regions only. CONCLUSIONS: Our systematic review and meta-analysis found that occupational exposure to ergonomic risk factors is highly prevalent. The current body of evidence is, however, limited, especially by risk of bias and indirectness. Producing estimates for the burden of disease attributable to occupational exposure to ergonomic risk factors appears evidence-based, and the pooled effect estimates presented in this systematic review may perhaps be used as input data for the WHO/ILO Joint Estimates. Protocol identifier:https://doi.org/10.1016/j.envint.2018.09.053. PROSPERO registration number: CRD42018102631.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.034 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".