Relative moldiness index as predictor of childhood respiratory illness
Abstract
The results of a traditional visual mold inspection were compared to a mold evaluation based on the Relative Moldiness Index (RMI). The RMI is calculated from mold-specific quantitative PCR (MSQPCR) measurements of the concentration of 36 species of molds in floor dust samples. These two prospective mold evaluations were used to classify the mold condition in 271 homes of infants. Later, the development of respiratory illness was measured in the infants living in these homes and the predictive value of each classification system was evaluated. The binary classification of homes as either moldy or non-moldy by on-site visual home inspection was not predictive of the development of respiratory illness (wheeze and/or rhinitis) (P=0.27). Conversely, a method developed and validated in this paper, using the RMI index fit to a logistic function, can be used to predict the occurrence of illness in homes and allows stake-holders the choice among various levels of risk.
Symptom Clusters
Cited By (7)
- High environmental relative moldiness index during infancy as a predictor of asthma at 7 years of ageAnnals of Allergy Asthma & Immunology · 2011
- Household mold as a predictor of asthma risk: Recent progress, limitations, and future directionsJournal of Allergy and Clinical Immunology · 2012
- Indoor Bacterial and Fungal Burden in “Moldy” versus “Non-Moldy” Homes: A Case Study Employing Advanced Sequencing Techniques in a US Metropolitan AreaPathogens · 2023
- Comparison of work‐related symptoms and visual contrast sensitivity between employees at a severely water‐damaged school and a school without significant water damageAmerican Journal of Industrial Medicine · 2012
- Association of indoor dampness and molds with rhinitis risk: A systematic review and meta-analysisJournal of Allergy and Clinical Immunology · 2013
- A longitudinal study of sick building syndrome among pupils in relation to microbial components in dust in schools in ChinaThe Science of The Total Environment · 2011
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