Additionally, removing the floor dust variable did not change the estimate (beta) for the renovation variable.
Within our approach it was designed and implemented a geospatial-based index − Urban Energy Renovation (UER) − integrating socio-economic variables of households, buildingś characteristics and energy savings potential, with the purpose of benchmarking the capacity and opportunity for energy renovation in historic centres.
We analyzed the association of renovation activities with the continuous outcome variable B-Pb using a mixed effects linear model with subjects (children) as a random effect and subject characteristics as fixed effects.
Several variables, including PIR, smoking in the home, renovation, and survey year, were significant in the linear regression model but were not significant in the logistic model.
The information that may be obtained through the measurement and analysis of such variables may be particularly important to informing approaches to renovation of older dwellings as opposed to the construction of new dwellings.
Income, race/ethnicity, floor surface/condition, windowsill PbD, year of construction, recent renovation, smoking, and survey year were significant predictors of floor PbD [the proportion of variability in the dependent variable accounted for by the model (R) = 35%].
In this analysis, we included the XRF lead index variable to account for the paint lead in the setting of renovation.
The only significant associations between urinary arsenic and categorical exposure variables were visiting a local orchard (p = 0.002) or a home undergoing renovation (p = 0.027) within a week of sampling (Table 5).
In another separate analysis to explore potential associations of renovation with floor dust lead, we evaluated the association of renovation activity with the continuous variable log transformed floor lead loading using a mixed effects linear model.
Renovation delay followed renovation delay.
