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Record W4388479567 · doi:10.1016/j.envint.2023.108226

Global, regional and national burdens of non-melanoma skin cancer attributable to occupational exposure to solar ultraviolet radiation for 183 countries, 2000–2019: A systematic analysis from the WHO/ILO Joint Estimates of the Work-related Burden of Disease and Injury

2023· article· en· W4388479567 on OpenAlexaff
Frank Pega, Natalie C. Momen, Kai Nino Streicher, Maria Léon-Roux, Subas Neupane, Mary K. Schubauer‐Berigan, Joachim Schüz, Marissa G. Baker, Tim Driscoll, Irina Guseva Canu, Hannah M. Kiiver, Jian Li, Jamaji C. Nwanaji‐Enwerem, Michelle C. Turner, Susana Viegas, Paul J. Villeneuve

Bibliographic record

VenueEnvironment International · 2023
Typearticle
Languageen
FieldMedicine
TopicSkin Protection and Aging
Canadian institutionsCarleton University
FundersCenters for Disease Control and PreventionWorld Health OrganizationInternational Labour OrganisationAgencia Española de Cooperación Internacional para el DesarrolloNational Institute for Occupational Safety and HealthBundesministerium für Gesundheit
KeywordsSkin cancerMedicineEnvironmental healthAttributable riskPopulationDemographyGeographyCancer

Abstract

fetched live from OpenAlex

BACKGROUND: A World Health Organization (WHO) and International Labour Organization (ILO) systematic review reported sufficient evidence for higher risk of non-melanoma skin cancer (NMSC) amongst people occupationally exposed to solar ultraviolet radiation (UVR). This article presents WHO/ILO Joint Estimates of global, regional, national and subnational occupational exposures to UVR for 195 countries/areas and the global, regional and national attributable burdens of NMSC for 183 countries, by sex and age group, for the years 2000, 2010 and 2019. METHODS: We calculated population-attributable fractions (PAFs) from estimates of the population occupationally exposed to UVR and the risk ratio for NMSC from the WHO/ILO systematic review. Occupational exposure to UVR was modelled via proxy of occupation with outdoor work, using 166 million observations from 763 cross-sectional surveys for 96 countries/areas. Attributable NMSC burden was estimated by applying the PAFs to WHO's estimates of the total NMSC burden. Measures of inequality were calculated. RESULTS: Globally in 2019, 1.6 billion workers (95 % uncertainty range [UR] 1.6-1.6) were occupationally exposed to UVR, or 28.4 % (UR 27.9-28.8) of the working-age population. The PAFs were 29.0 % (UR 24.7-35.0) for NMSC deaths and 30.4 % (UR 29.0-31.7) for disability-adjusted life years (DALYs). Attributable NMSC burdens were 18,960 deaths (UR 18,180-19,740) and 0.5 million DALYs (UR 0.4-0.5). Men and older age groups carried larger burden. Over 2000-2019, attributable deaths and DALYs almost doubled. CONCLUSIONS: WHO and the ILO estimate that occupational exposure to UVR is common and causes substantial, inequitable and growing attributable burden of NMSC. Governments must protect outdoor workers from hazardous exposure to UVR and attributable NMSC burden and inequalities.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.032
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.012
Bibliometrics0.0080.011
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.012
GPT teacher head0.268
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreEmpirical

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".

Quick stats

Citations86
Published2023
Admission routes1
Has abstractyes

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