Juliana Souza

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

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 Regiao da Grande Vitoria, 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.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.

Distribution of Serum Uric Acid in Black Africans and Its Association With Cardiovascular Risk Factors

Hyperuricemia is associated with cardiovascular disease and its prevalence is unknown in black Africans. This study reports hyperuricemia distribution and its association with cardiovascular risk factors in a selected Angolan population. A cross‐sectional study in 585 black Africans was performed. Hyperuricemia was defined as uric acid >7.0 mg/dL in men or >5.7 mg/dL in women. Overall prevalence was 25%. Hyperuricemia was associated with hypertension (odds ratio [OR], 2.20; confidence interval [CI], 95% 1.41–3.47), high waist circumference (OR, 1.67; CI, 95% 1.05–2.65), and metabolic syndrome (OR, 1.66; CI, 95% 1.07–2.57). Compared to those with uric acid levels in the first quartile, individuals in the fourth quartile showed higher body mass index, waist circumference, systolic blood pressure, and plasma levels of creatinine and triglycerides. Hypertension, high waist circumference, and metabolic syndrome were the major cardiovascular risk factors associated with hyperuricemia.