RESUMO
Health impacts of air pollution may differ depending on sex, education, socioeconomic status (SES), location at time of death, and other factors. In São Paulo, Brazil, questions remain regarding roles of individual and community characteristics. We estimate susceptibility to air pollution based on individual characteristics, residential SES, and location at time of death (May 1996-December 2010). Exposures for particulate matter with an aerodynamic diameter ≤ 10 µm (PM10), nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), and ozone (O3) were estimated using ambient monitors. Time-stratified case-crossover analysis was used with individual-level health data. Increased risk of non-accidental, cardiovascular, and respiratory mortality were associated with all pollutants (P < 0.05), except O3 and cardiovascular mortality. For non-accidental mortality, effect estimates for those with > 11 years education were lower than estimates for those with 0 years education for NO2, SO2, and CO (1.66% (95% confidence interval: 0.23%, 3.08%); 1.51% (0.51%, 2.51%); and 2.82% (0.23%, 5.35%), respectively). PM10 cardiovascular mortality effects were (3.74% (0.044%, 7.30%)) lower for the high education group (> 11 years) compared with the no education group. Positive, significant associations between pollutants and mortality were observed for in-hospital deaths, but evidence of differences in air pollution-related mortality risk by location at time of death was not strong.
Assuntos
Poluentes Atmosféricos/efeitos adversos , Poluição do Ar/efeitos adversos , Doenças Cardiovasculares/mortalidade , Doenças Respiratórias/mortalidade , Adulto , Idoso , Idoso de 80 Anos ou mais , Poluentes Atmosféricos/análise , Poluição do Ar/análise , Brasil/epidemiologia , Monóxido de Carbono/efeitos adversos , Monóxido de Carbono/análise , Estudos de Casos e Controles , Atestado de Óbito , Monitoramento Ambiental , Feminino , Humanos , Modelos Logísticos , Masculino , Pessoa de Meia-Idade , Dióxido de Nitrogênio/efeitos adversos , Dióxido de Nitrogênio/análise , Ozônio/efeitos adversos , Ozônio/análise , Tamanho da Partícula , Material Particulado/efeitos adversos , Material Particulado/análise , Fatores de Risco , Distribuição por Sexo , Fatores Socioeconômicos , Dióxido de Enxofre/efeitos adversos , Dióxido de Enxofre/análiseRESUMO
Understanding how weather impacts health is critical, especially under a changing climate; however, relatively few studies have investigated subtropical regions. We examined how mortality in São Paulo, Brazil, is affected by cold, heat, and heat waves over 14.5 years (1996-2010). We used over-dispersed generalized linear modeling to estimate heat- and cold-related mortality, and Bayesian hierarchical modeling to estimate overall effects and modification by heat wave characteristics (intensity, duration, and timing in season). Stratified analyses were performed by cause of death and individual characteristics (sex, age, education, marital status, and place of death). Cold effects on mortality appeared higher than heat effects in this subtropical city with moderate climatic conditions. Heat was associated with respiratory mortality and cold with cardiovascular mortality. Risk of total mortality was 6.1% (95% confidence interval 4.7, 7.6%) higher at the 99th percentile of temperature than the 90th percentile (heat effect) and 8.6% (6.2, 11.1%) higher at the 1st compared to the 10th percentile (cold effect). Risks were higher for females and those with no education for heat effect, and males for cold effect. Older persons, widows, and non-hospital deaths had higher mortality risks for heat and cold. Mortality during heat waves was higher than on non-heat wave days for total, cardiovascular, and respiratory mortality. Our findings indicate that mortality in São Paulo is associated with both cold and heat and that some subpopulations are more vulnerable.
Assuntos
Mortalidade , Temperatura , Adolescente , Adulto , Idoso , Poluição do Ar/análise , Brasil/epidemiologia , Criança , Pré-Escolar , Cidades/epidemiologia , Feminino , Humanos , Umidade , Lactente , Recém-Nascido , Masculino , Pessoa de Meia-Idade , Ozônio/análise , Material Particulado/análise , Adulto JovemRESUMO
Developing exposure estimates is a challenging aspect of investigating the health effects of air pollution. Pollutant levels recorded at centrally located ambient air quality monitors in a community are commonly used as proxies for population exposures. However, if ample intraurban spatial variation in pollutants exists, city-wide averages of concentrations may introduce exposure misclassification. We assessed spatial heterogeneity of particulate matter with an aerodynamic diameter < or = 10 microm (PM10) and ozone (O3) and evaluated implications for epidemiological studies in São Paulo, Brazil, using daily (24-hr) and daytime (12-hr) averages and 1-hr daily maximums of pollutant levels recorded at the regulatory monitoring network. Monitor locations were also analyzed with respect to a socioeconomic status index developed by the municipal government. Hourly PM10 and O3 data for the Sao Paulo Municipality and Metropolitan Region (1999-2006) were used to evaluate heterogeneity by comparing distance between monitors with pollutants' correlations and coefficients of divergence (CODs). Both pollutants showed high correlations across monitoring sites (median = 0.8 for daily averages). CODs across sites averaged 0.20. Distance was a good predictor of CODs for PM10 (p < 0.01) but not O3, whereas distance was a good predictor of correlations for O3 (p < 0.01) but not PM10. High COD values and low temporal correlation indicate a spatially heterogeneous distribution of PM10. Ozone levels were highly correlated (r > or = 0.75), but high CODs suggest that averaging over O3 levels may obscure important spatial variations. Of municipal districts in the highest of five socioeconomic groups, 40% have > or = 1 monitor, whereas districts in the lowest two groups, representing half the population, have no monitors. Results suggest that there is a potential for exposure misclassification based on the available monitoring network and that spatial heterogeneity depends on pollutant metric (e.g., daily average vs. daily 1-hr maximum). A denser monitoring network or alternative exposure methods may be needed for epidemiological research. Findings demonstrate the importance of considering spatial heterogeneity and differential exposure misclassification by subpopulation.