Environmental Lead Proficiency Analytical Testing (ELPAT) Program. March 2000.

Kindergarten environments combine high microbial exposure with increased immunological vulnerability, yet antibiotic resistance genes (ARGs) in kindergarten dust remain poorly characterized. This systematic review synthesized evidence on the occurrence and potential health relevance of ARGs in kindergarten dust. Following PRISMA 2020 guidelines, PubMed, Scopus, and Web of Science were searched. Four studies from China, Hong Kong, and Norway (2018-2024) met the inclusion criteria. ARGs were detected in all kindergarten dust samples, indicating that dust is a consistent reservoir of antibiotic resistance determinants. A consensus resistome (classes detected in ≥2 studies) encompassed sulfonamide, macrolide-lincosamide-streptogramin B (MLSB), tetracycline, beta-lactam, aminoglycoside, and multidrug resistance genes; beta-lactam resistance genes were the only class reported in all four studies. Clinically important ARGs associated with last-resort antibiotics, including mecA, vanA, blaNDM, and mcr-5, were reported in three studies. Class 1 integron-integrase genes (intI1) frequently co-occurred with ARGs, suggesting potential horizontal gene transfer. Limited evidence indicated higher ARG abundance in urban and winter samples. One study reported antibiotic-resistant bacteria carrying resistance markers concordant with those in kindergarten dust in the urine of children attending the same facilities; however, this cross-sectional, single-site evidence is consistent with, but not sufficient to establish, a dust-to-child exposure pathway. The available evidence supports the plausibility that kindergarten dust may contribute to children's exposure to ARGs and ARG-carrying bacteria, but current studies do not establish causal transmission from dust to child colonization or infection. Standardized monitoring and longitudinal studies are needed to assess health risks and guide mitigation strategies in early childhood educational settings.

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PubMed2002

Designing intervention effectiveness studies for occupational health and safety: The Minnesota Wood Dust Study.

BACKGROUND: A planning model was used to guide the design of a randomized controlled study of the effectiveness of tailored interventions in lowering dust exposures in small woodworking shops. METHODS: Guided by Green's PRECEDE-PROCEED model, we used a planning committee, focus groups and a pilot study to gain information on small woodworking shops, causes of and controls for high dust levels, and barriers and incentives surrounding availability and use of dust controls. RESULTS: The planning committee identified key characteristics of small woodworking shop owners. Focus groups with owners and employees served to further elucidate why dust control was considered unimportant. The pilot study gave measures of dust exposures, tasks, and use of controls. Interventions focused on providing owners with technical and economic assistance to lower dust levels and an educational program for employees discussing health effects and effective methods of dust control. CONCLUSIONS: The PRECEDE-PROCEED model proved a useful framework for designing an intervention in the occupational setting.

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PubMed2001

The development of a stochastic physiologically-based pharmacokinetic model for lead.

This presentation describes the development of a prototype Monte Carlo module for the physiologically-based pharmacokinetic (PBPK) model for lead, created by Dr Ellen O'Flaherty. The module uses distributions for the following: exposure parameters (soil and dust concentrations, daily soil and ingestion rate, water lead concentration, water ingestion rate, air lead concentration, inhalation rate and dietary lead intake); absoption parameters; and key pharmacokinetic parameters (red blood binding capacity and half saturation concentration). Distributions can be specified as time-invariant or can change with age. Monte Carlo model predicted blood levels were calibrated to empirically measured blood lead levels for children living in Midvale, Utah (a milling/smelting community). The calibrated model was then evaluated using blood lead data from Palmerton, Pennsylvania (a town with a former smelter) and Sandy, Utah, (a town with a former smelter and slag piles). Our initial evaluation using distributions for exposure parameters showed that the model accurately predicted geometric (GM) blood lead levels of Palmerton and Sandy and slightly over predicted the GSD. Consideration of uncertainty in red blood cell parameters substantially inflated the GM. Future model development needs to address the correlation among parameters and the use of parameters for long-term exposure derived from short-term studies.

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