Environmental epidemiological studies of the health effects of air pollution frequently utilize the generalized additive model (GAM) as the standard statistical methodology, considering the ambient air pollutants as explanatory covariates. Although exposure to air pollutants is multi‐dimensional, the majority of these studies consider only a single pollutant as a covariate in the GAM model. This model restriction may be because the pollutant variables do not only have serial dependence but also interdependence between themselves. In an attempt to convey a more realistic model, we propose here the hybrid generalized additive model–principal component analysis–vector auto‐regressive (GAM–PCA–VAR) model, which is a combination of PCA and GAMs along with a VAR process. The PCA is used to eliminate the multicollinearity between the pollutants whereas the VAR model is used to handle the serial correlation of the data to produce white noise processes as covariates in the GAM. Some theoretical and simulation results of the methodology proposed are discussed, with special attention to the effect of time correlation of the covariates on the PCA and, consequently, on the estimates of the parameters in the GAM and on the relative risk, which is a commonly used statistical quantity to measure the effect of the covariates, especially the pollutants, on population health. As a main motivation to the methodology, a real data set is analysed with the aim of quantifying the association between respiratory disease and air pollution concentrations, especially particulate matter PM10, sulphur dioxide, nitrogen dioxide, carbon monoxide and ozone. The empirical results show that the GAM–PCA–VAR model can remove the auto‐correlations from the principal components. In addition, this method produces estimates of the relative risk, for each pollutant, which are not affected by the serial correlation in the data. This, in general, leads to more pronounced values of the estimated risk compared with the standard GAM model, indicating, for this study, an increase of almost 5.4% in the risk of PM10, which is one of the most important pollutants which is usually associated with adverse effects on human health.

OBJECTIVE To analyze the association between fine particulate matter concentration in the atmosphere and hospital care by acute respiratory diseases in children.METHODS Ecological study, carried out in the region of Grande Vitória, Espírito Santo, in the winter (June 21 to September 21, 2013) and summer (December 21, 2013 to March 19, 2014). We assessed data of daily count for outpatient care and hospitalization by respiratory diseases (ICD-10) in children from zero to 12 years in three hospitals in the Region of Grande Vitória. For collecting fine particulate matter, we used portable samplers of particles installed in six locations in the studied region. The Generalized Additive Model with Poisson distribution, fitted for the effects of predictor covariates, was used to evaluate the relationship between respiratory outcomes and concentration of fine particulate matter.RESULTS The increase of 4.2 µg/m3 (interquartile range) in the concentration of fine particulate matter increased in 3.8% and 5.6% the risk of medical care or hospitalization, respectively, on the same day and with six-day lag from the exposure.CONCLUSIONS We identified positive association between outpatient care and hospitalizations of children under 12 years due to acute respiratory diseases and the concentration of fine particulate matter in the atmosphere.

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.

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.

RESUMO: Objetivo: Comparar a prevalência de fatores de risco cardiovascular na população de Vitória (ES) em pesquisa autorreferida por contato telefônico (VIGITEL) ou por exames clínicos e laboratoriais realizados na Pesquisa Nacional de Saúde (PNS). Método: Os inquéritos foram realizados na população adulta de Vitória (≥18anos). No VIGITEL foram entrevistados 1996 indivíduos (homens = 38%). Na PNS foi feita visita domiciliar seguida de exames clínicos e laboratoriais em 318 indivíduos (homens = 48%) selecionados em 20setores censitários da cidade. Nos dois inquéritos, as prevalências foram ajustadas para a estrutura populacional estimada para o ano de 2013. Os dados são fornecidos como porcentagens e intervalo de confiança de 95% (IC95%). Resultados: Foram encontradas prevalências similares no VIGITEL e na PNS, respectivamente, para tabagismo (8,2%; IC95% 6,7 - 9,7% versus 10,0; IC95% 6,4 - 13,6%) e hipertensão (24,8%; IC95% 22,6- 27,0% versus 27,2%; IC95% 21,8 - 32,5%). Houve diferença estatística (p < 0,01) entre o VIGITEL e a PNS, respectivamente, para as prevalências de obesidade (16,8%; IC95% 14,1 - 18,1% versus 25,7%; IC95% 20,4- 30,9%) e colesterol elevado (≥ 200mg/dL) no sangue (20,6%; IC95% 18,6 - 22,6% versus 42,3%; IC95% 36,9- 47,7%). A prevalência de diabetes também foi maior (p < 0,05) na PNS (6,7 versus 10,7%). Conclusão: A prevalência populacional de hipertensão e tabagismo foi estimada adequadamente no VIGITEL. Isso não ocorreu com a obesidade por provável viés de informação do peso corporal no VIGITEL. Os dados mostram a necessidade de melhorar a cobertura diagnóstica das dislipidemias em vista da importância do controle desse fator de risco na prevenção primária das doenças cardiovasculares.