Principal components and generalized linear modeling in the correlation between hospital admissions and air pollution

Abstract

OBJECTIVE To analyze the association between concentrations of air pollutants and admissions for respiratory causes in children.METHODS Ecological time series study. Daily figures for hospital admissions of children aged < 6, and daily concentrations of air pollutants (PM10, SO2, NO2, O3 and CO) were analyzed in the Região da Grande Vitória, ES, Southeastern Brazil, from January 2005 to December 2010. For statistical analysis, two techniques were combined: Poisson regression with generalized additive models and principal model component analysis. Those analysis techniques complemented each other and provided more significant estimates in the estimation of relative risk. The models were adjusted for temporal trend, seasonality, day of the week, meteorological factors and autocorrelation. In the final adjustment of the model, it was necessary to include models of the Autoregressive Moving Average Models (p, q) type in the residuals in order to eliminate the autocorrelation structures present in the components.RESULTS For every 10:49 μg/m3 increase (interquartile range) in levels of the pollutant PM10 there was a 3.0% increase in the relative risk estimated using the generalized additive model analysis of main components-seasonal autoregressive – while in the usual generalized additive model, the estimate was 2.0%.CONCLUSIONS Compared to the usual generalized additive model, in general, the proposed aspect of generalized additive model − principal component analysis, showed better results in estimating relative risk and quality of fit.

Association between the concentration of fine particles in the atmosphere and acute respiratory diseases in children

“…Another study, also in RGV, presented an increase of 3.0% in the RR of hospital care by respiratory diseases in children younger than six years for the increase of 10.49 µg/m 3 in the concentration of PM 10 21 . These results together suggest that the finest PM seems to be more aggressive for the respiratory system of children, mainly for the younger ones.…”

Coarse particles and hospital admissions due to respiratory diseases in children. An ecological time series study

“…Souza et al 23 Other differences between these two fractions relate to the longer half-life of the fine fraction (of the order of days to weeks), in contrast to the half-life of the coarse fraction (of the order of a few hours), and the greater distance that the fine fraction can reach, in comparison with the coarse fraction. 24 Separate analysis on the fine and coarse fractions was justified in a study carried out in São José dos Campos (SP) where the compositions of these fractions were different.…”

The Relationship between Urban Land Surface Material Fractions and Brightness Temperature Based on MESMA

“…R 2 of the regression Equation (8) OriginPro 8.6 was used to generate scatter plots (Figure 10a,b), which showed a relatively clear linear relationship between the aestival LSBT and the fraction of vegetation (or the impervious surface fraction). Then, SPSS Statistics 19 was used to fit the multivariate linear regression equations to determine the mathematical relationship between the aestival LSBT data and the endmember fractions [48][49][50]. The fitted results (Equations (8)- (10)) indicated a significant linear relationship, of which the probability values (respectively, in the F test, for the regression equation's significance, as well as the T test, for the regression coefficients' significance) were less than the 0.05 significance level ( From Figure 10a, it can be seen that the vegetation had a significant regulatory effect on temperature (high temperatures can be reduced when there is a large vegetation fraction).…”

“…Then, SPSS Statistics 19 was used to fit the multivariate linear regression equations ine the mathematical relationship between the aestival LSBT data and the endmember [48][49][50]. The fitted results (Equations (8)- (10) …”