Heat waves are associated with increased mortality, however, few studies have examined the added effect of heat waves. Moreover, there is limited evidence for the influence of different heat wave definitions (HWs) on cardiovascular mortality in Beijing, the capital of China. The aim of this study was to find the best HW definitions for cardiovascular mortality, and we examined the effect modification by an individual characteristic on cardiovascular mortality in Beijing, a typical northern city in China. We applied a Poisson generalized additive approach to estimate the differences in cardiovascular mortality during heat waves (using 12 HWs) compared with non-heat-wave days in Beijing from 2006 to 2009. We also validated the model fit by checking the residuals to ensure that the autocorrelation was successfully removed. In addition, the effect modifications by individual characteristics were explored in different HWs. Our results showed that the associations between heat waves and cardiovascular mortality differed from different HWs. HWs using the 93th percentile of the daily average temperature (27.7 °C) and a duration ≥5 days had the greatest risk, with an increase of 18% (95% confidence interval (CI): 6%, 31%) in the overall population, 24% (95% CI: 10%, 39%) in an older group (ages ≥65 years), and 22% (95% CI: 3%, 44%) in a female group. The added effect of heat waves was apparent after 5 consecutive heat wave days for the overall population and the older group. Females and the elderly were at higher risk than males and younger subjects (ages <65 years). Our findings suggest that heat wave definitions play a significant role in the relationship between heat wave and cardiovascular mortality. Using a suitable definition may have implications for designing local heat early warning systems and protecting the susceptible populations during heat waves.

We estimated PM 2.5 concentrations using satellite data and population mortality values for cause-specific diseases and employed the integrated exposure-response model to obtain the associations between exposure and response. PM 2.5 source apportionment data were then used to evaluate the excess mortality attributable to PM 2.5 from different emission sources. In 2013, 1.07 million excess deaths were attributed to PM 2.5 exposure in China. The potentially avoidable excess deaths would be 279 000, 459 000, 731 000 and 898 000 if the PM 2.5 concentrations were reduced to meet WHO interim target (IT)-1 (35 g m −3 , also the Chinese standard), IT-2 (25 g m −3 ), IT-3 (15 g m −3 ) and the air quality guidelines (10 g m −3 ), respectively, compared with concentrations experienced in 2013. There were 249 000 (95% CI: 115-337), 228 000 (95% CI: 105-309), 203 000 (95% CI: 94-274), 197 000 (95% CI: 91-266), and 193 000 (95% CI: 88-262) excess deaths attributed to PM 2.5 from coal burning, vehicle emissions, industry-related emissions, dust and other sources in 2013, respectively. Coal burning was the main source of atmospheric PM 2.5 ; it contributed the most to excess mortalities and the health effects were likely to have been conservatively estimated. Considerable health benefits could be achieved if more stringent ambient PM 2.5 standards were achieved in China.

No abstract