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Enregistrement W2886687706 · doi:10.1002/14651858.cd013097.pub2

Educational interventions for preventing lead poisoning in workers

2020· article· en· W2886687706 sur OpenAlexaff
Sara Allaouat, Viraj K. Reddy, Kimmo Räsänen, Sohaib Khan, Mieke E. G. L. Lumens

Notice bibliographique

RevueCochrane Database of Systematic Reviews · 2020
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueHeavy Metal Exposure and Toxicity
Établissements canadiensUniversity of ManitobaGeorge & Fay Yee Centre for Healthcare Innovation
Organismes subventionnairesnon disponible
Mots-clésMedicineLead poisoningPsychological interventionRandomized controlled trialZinc protoporphyrinIntensive care medicineInternal medicinePsychiatry

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: Occupational lead exposure can lead to serious health effects that range from general symptoms (depression, generalised ache, and digestive signs, such as loss of appetite, stomach ache, nausea, diarrhoea, and constipation) to chronic conditions (cerebrovascular and cardiovascular diseases, cognitive impairment, kidney disease, cancers, and infertility). Educational interventions may contribute to the prevention of lead uptake in workers exposed to lead, and it is important to assess their effectiveness. OBJECTIVES: To assess the effect of educational interventions for preventing lead uptake in workers exposed to lead. SEARCH METHODS: We searched CENTRAL, MEDLINE, Embase, CINAHL, and OSH UPDATE to 5 June 2020, with no language restrictions. SELECTION CRITERIA: We sought randomised controlled trials (RCT), cluster-RCTs (cRCT), interrupted time series (ITS), controlled before-after studies (CBA) and uncontrolled before-after studies that examined the effects of an educational intervention aimed at preventing lead exposure and poisoning in workers who worked with lead, for which effectiveness was measured by lead levels in blood and urine, blood zinc protoporphyrin levels and urine aminolevulinic acid levels. DATA COLLECTION AND ANALYSIS: Two review authors independently screened the search results, assessed studies for eligibility, and extracted data using standard Cochrane methods. We used the ROBINS-I tool to assess the risk of bias, and GRADE methodology to assess the certainty of the evidence. MAIN RESULTS: We did not find any RCT, cRCT, ITS or CBA studies that met our criteria. We included four uncontrolled before-after studies studies, conducted between 1982 and 2004. Blood lead levels Educational interventions may reduce blood lead levels, but the evidence is very uncertain. In the short-term after the educational intervention, blood lead levels may decrease (mean difference (MD) 9.17 µg/dL, 95% confidence interval (CI) 4.14 to 14.20; one study with high baseline blood lead level, 18 participants; very low-certainty evidence). In the medium-term, blood lead levels may decrease (MD 3.80 µg/dL, 95% CI 1.48 to 6.12; one study with high baseline blood lead level, 34 participants; very low-certainty evidence). In the long-term, blood lead levels may decrease when the baseline blood lead levels are high (MD 8.08 µg/dL; 95% CI 3.67 to 12.49; two studies, 69 participants; very low-certainty evidence), but not when the baseline blood lead levels are low (MD 1.10 µg/dL, 95% CI -0.11 to 2.31; one study, 52 participants, very low-certainty evidence). Urine lead levels In the long-term, urinary lead levels may decrease after the educational intervention, but the evidence is very uncertain (MD 42.43 µg/L, 95% CI 29.73 to 55.13; one study, 35 participants; very low-certainty evidence). Behaviour change The evidence is very uncertain about the effect of educational intervention on behaviour change. At medium-term follow-up after the educational intervention, very low-certainty evidence from one study (89 participants) found inconclusive results for washing before eating (risk ratio (RR) 1.71, 95% CI 0.42 to 6.91), washing before drinking (RR 1.37, 95% CI 0.61 to 3.06), and not smoking in the work area (RR 1.04, 95% CI 0.74 to 1.46). Very low-certainty evidence from one study (21 participants) suggested that employers may improve the provision of fit testing for all respirator users (RR 1.87, 95% CI 1.16 to 3.01), and prohibit eating, drinking, smoking, and other tobacco use in the work area (RR 4.25, 95% CI 1.72 to 10.51), however, the results were inconclusive for the adequate provision of protective clothing (RR 1.40, 95% CI 0.82 to 2.40). At long-term follow-up, very low-certainty evidence from one study (89 participants) suggested that workers may improve washing before drinking (RR 3.24, 95% CI 1.09 to 9.61), but results were inconclusive for washing before eating (RR 11.71, 95% CI 0.66 to 208.33), and for not smoking in the work area (RR 1.56, 95% CI 0.98 to 2.50). Very low-certainty evidence from one study (21 participants) suggested that employers may improve the provision of fit testing for all respirator users (RR 1.70, 95% CI 1.09 to 2.63), may provide adequate protective clothing (RR 2.80, 95% CI 1.23 to 6.37), and may prohibit eating, drinking, smoking, and other tobacco use in the work area (RR 2.13, 95% CI 1.19 to 3.81). Improved knowledge or awareness of the adverse health effects of lead The evidence is very uncertain about the effect of educational intervention on workers' knowledge. At medium-term follow-up, questionnaires found that workers' knowledge may improve (MD 5.20, 95% CI 3.29 to 7.11; one study, 34 participants; very low-certainty evidence). At long-term follow-up, there may be an improvement in workers' knowledge (MD 5.80, 95% CI 3.89 to 7.71; one study, 34 participants; very low-certainty evidence), but results were inconclusive for employers' knowledge (RR 1.67, 95% CI 0.74 to 3.75; one study, 21 participants; very low-certainty evidence). None of the studies measured the other outcomes of interest: blood zinc protoporphyrin levels, urine aminolevulinic acid levels, air lead levels, and harms. One study provided the costs of each component of the intervention. AUTHORS' CONCLUSIONS: Educational interventions may prevent lead poisoning in workers with high baseline blood lead levels and urine lead levels but this is uncertain. Educational interventions may not prevent lead poisoning in workers with low baseline blood lead levels but this is uncertain.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,009
score de la tête « metaresearch » (Gemma)0,050
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Revue systématique · Signal consensuel: Revue systématique
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,012
Score d'incertitude au seuil0,047

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0090,050
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0030,004
Bibliométrie0,0040,002
Études des sciences et des technologies0,0010,001
Communication savante0,0020,002
Science ouverte0,0020,001
Intégrité de la recherche0,0030,002
Charge utile insuffisante (le modèle a refusé de juger)0,0120,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,134
Tête enseignante GPT0,378
Écart entre enseignants0,244 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeRevue systématique
Domainenon disponible
GenreSynthèse

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations6
Publié2020
Routes d'admission1
Résumé présentoui

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