Isolated and synergistic effects of PM10 and average temperature on cardiovascular and respiratory mortality

OBJECTIVE To analyze the effect of air pollution and temperature on mortality due to cardiovascular and respiratory diseases. METHODS We evaluated the isolated and synergistic effects of temperature and particulate matter with aerodynamic diameter < 10 µm (PM10) on the mortality of individuals > 40 years old due to cardiovascular disease and that of individuals > 60 years old due to respiratory diseases in Sao Paulo, SP, Southeastern Brazil, between 1998 and 2008. Three methodologies were used to evaluate the isolated association: time-series analysis using Poisson regression model, bidirectional case-crossover analysis matched by period, and case-crossover analysis matched by the confounding factor, i.e., average temperature or pollutant concentration. The graphical representation of the response surface, generated by the interaction term between these factors added to the Poisson regression model, was interpreted to evaluate the synergistic effect of the risk factors. RESULTS No differences were observed between the results of the case-crossover and time-series analyses. The percentage change in the relative risk of cardiovascular and respiratory mortality was 0.85% (0.45;1.25) and 1.60% (0.74;2.46), respectively, due to an increase of 10 μg/m3 in the PM10 concentration. The pattern of correlation of the temperature with cardiovascular mortality was U-shaped and that with respiratory mortality was J-shaped, indicating an increased relative risk at high temperatures. The values for the interaction term indicated a higher relative risk for cardiovascular and respiratory mortalities at low temperatures and high temperatures, respectively, when the pollution levels reached approximately 60 μg/m3. CONCLUSIONS The positive association standardized in the Poisson regression model for pollutant concentration is not confounded by temperature, and the effect of temperature is not confounded by the pollutant levels in the time-series analysis. The simultaneous exposure to different levels of environmental factors can create synergistic effects that are as disturbing as those caused by extreme concentrations.