Educational interventions for preventing lead poisoning in workers
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,009 | 0,050 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,004 |
| Bibliométrie | 0,004 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,012 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».