Using rush hour and daytime exposure indicators to estimate the short-term mortality effects of air pollution: A case study in the Sichuan Basin, China.

BACKGROUND: Daily mean concentrations of air pollutants have been widely used as exposure indicators to estimate the short-term mortality effects of outdoor air pollution. However, daily mean concentrations might insufficiently represent the true exposure level because of the diurnal variations of air pollutants and various human activity patterns. Daytime or rush-hour concentrations may lead to better estimations. OBJECTIVE: Our study aimed to imitate the true exposure level under assumptions about human activity patterns and to examine the short-term mortality effects of the exposure to air pollution during a) the morning-evening rush hours (ME), b) the morning-lunch-evening rush hours (MLE), and c) the whole daytime (WDT) in Chengdu, Sichuan Basin, China. METHODS: We investigated the diurnal variations of PM2.5, SO2, and O3 and examined the associations between the three pollutants and nonaccidental mortality, cardiovascular mortality, respiratory mortality using generalized additive model. Three novel exposure indicators (ME, MLE, and WDT) were employed to imitate the most probable exposure levels. Relative change of excess risk (ER) was used to compare effects estimated from models with different exposure indicators. RESULTS: In the relationship of PM2.5 and mortality, ERs estimated from the novel-indicator models decreased by 4.88%-11.89% in comparison with ERs from the daily-indicator models. All the three novel indicators of SO2 offered lower ERs of respiratory mortality than the daily indicator did. Significant associations were observed in O3-nonaccidental mortality at lag0 in both winter and spring, and O3-cardiovascular mortality at lag0 in winter. Overall, majority of effect estimates based on rush-hour or daytime indicators were lower than the estimates based on daily mean concentrations. CONCLUSION: The use of daily mean concentrations may bias exposure assessment and thus inflating effect estimates. This study highlights the importance of rush-hour and daytime exposure and provides alternative indicators for estimating acute effects of air pollution.

Does temperature modify the effect of PM10 on mortality? A systematic review and meta-analysis.

Large and growing literature has explored whether temperature modified the effect of particular matter (PM) on mortality, but results of the modification effect are inconsistent. In this study, we reviewed information from 29 studies to get the qualitative evidence of the modification effects of temperature on PM to mortality, and the data from 16 of the 29 studies were extracted to conduct a meta-analysis. Temperatures were grouped into three level: "low", "middle" and "high" according to the original studies. The random effect model was used in the meta-analysis with the relative risk (RR) as the measure indicator. The RRs (95% confidence intervals, CIs) for non-accidental death, cardiovascular death and respiratory death per 10 µg/m3 increase in PM10 were 1.004 (1.003, 1.006), 1.005 (1.003,1.007), and 1.005 (1.000,1.010) in the low temperature level, 1.005 (1.004,1.006), 1.005 (1.004,1.007), and 1.008 (1.006, 1.010) in the middle temperature level, and 1.012 (1.010, 1.015), 1.016 (1.010, 1.022) and 1.019 (1.010,1.028) in the high temperature level, respectively. In conclusion, moderate evidence exists that temperature modifies the effect of PM10 on mortality. The effect of PM10 on respiratory death was the greatest, while the effect on non-accidental death was the smallest in the same temperature level. In addition, the effects of PM10 on all the three kinds of mortality were the biggest in the high-temperature level, and the smallest in the low-temperature level.

Describing interaction effect between lagged rainfalls on malaria: an epidemiological study in south-west China.

BACKGROUND: When discussing the relationship between meteorological factors and malaria, previous studies mainly focus on the interaction between different climatic factors, while the possible interaction within one particular climatic predictor at different lag periods has been largely neglected. In this study, this issue was investigated by exploring the interaction of lagged rainfalls and its impact on malaria epidemics, which is a typical example of those meteorological variables. METHODS: The weekly data of malaria cases and three climatic variables of 30 counties in southwest China from 2004 to 2009 were analysed with the varying coefficient-distributed lag non-linear model. The correlation patterns of the 6th, 9th and 12th week lags would vary over different rainfall levels at the 4th-week lag. RESULTS: The non-linear patterns for rainfall at different rainfall levels are distinct from each other. In the low rainfall level at the 4th week lag, the increasing rainfall may promote the transmission of malaria. However, for the high rainfall level at the 4th week lag, evidence shows that the excessive rainfall decreases the risk of malaria. CONCLUSION: This study reports for the first time that the interaction effect between lagged rainfalls on malaria exists, and highlights the importance of integrating the interaction between lagged predictors in relevant studies, which could help to better understand and predict malaria transmission.

Attributable risk of ambient PM10 on daily mortality and years of life lost in Chengdu, China.

Attributable risk is an important indicator for planning and evaluating public health interventions. However, most current measures of the attributable risk of air pollutants have not considered temporal relationships between exposure and risk. More importantly, limited information is available regarding the attributable risk due to ambient air pollutants in basin regions like the Sichuan Basin, China. To quantify the association between PM10 and deaths in the Basin region, we used a measure proposed recently within the framework of the distributed lag non-linear model to estimate the attributable risk in Chengdu, China. Meanwhile, we examined the association between PM10 and years of life lost (YLL). Our analysis showed that population-attributable fractions for non-accidental, respiratory, and cardiovascular mortality were 0.569% (95% CI: -3.474%, 4.374%), 0.695% (95% CI: -5.260%, 6.457%), and 0.631% (95% CI: -6.973%, 7.390%), respectively. On average, a 1µg/m3 increase in PM10 was associated with cumulative increases of 0.26893, 0.30437, and 0.21924 YLL for non-accidental, respiratory, and cardiovascular mortality, respectively, referring to 20µg/m3. In addition, we found an inverse U-shaped pattern for the cumulative risk with 350µg/m3 as the reverse point. With a 1µg/m3 increase in PM10, YLL changed more significantly than mortality. Moreover, PM10 demonstrated remarkable effects on YLL among men and the elderly.

The effects of Sulphur dioxide on acute mortality and years of life lost are modified by temperature in Chengdu, China.

The effect modification of meteorological factors on the association between ambient Sulphur dioxide (SO2) and mortality is critical for designing intervention policy. Existing studies did not result in consistent conclusions on the effect modification, and Years of life lost (YLLs) was rarely used as a health impact indicator to examine the modifying effect. This study aims to estimate the mean air temperature and relative humidity modification effects on the impact of SO2 on daily mortality and YLLs in Chengdu, China. Mortality, YLLs, air pollution and meteorological data were collected for 2011-2014. Three analytical approaches based on generalized additive models (GAMs) were used, including bivariate response surface model, product term model, and stratification model. We found that the effects of SO2 on mortality and YLLs depended on temperature at various lags, but did not depend on relative humidity. SO2 exhibited larger adverse effects on mortality in high temperature level (22.8-29.4°C) days than in low temperature level (-0.3-9.3°C) days, with a 10µg/m3 increment in SO2, non-accidental death increased by 0.8% (0.001, 0.015)at low temperature level, but increased by 1.4% (0.005, 0.024) at high temperature level. On the contrary, SO2 showed greater adverse effects on YLLs in low temperature days than in high temperature days, with a 10µg/m3 increment in SO2, non-accidental YLL increased by 40.580 (31.478, 49.682) at high temperature level, but increased by -2.703 (-14.668, 9.261) at low temperature level. We concluded that the effect of SO2 on mortality and YLLs may depend on temperature in Chengdu, China. Our results highlight the importance of considering the interaction between SO2 and temperature on health outcomes in future research. Also, policy makers should enhance the emission control of SO2 in extreme temperature days in Chengdu.

Temporal and long-term trend analysis of class C notifiable diseases in China from 2009 to 2014.

OBJECTIVES: Time series models are effective tools for disease forecasting. This study aims to explore the time series behaviour of 11 notifiable diseases in China and to predict their incidence through effective models. SETTINGS AND PARTICIPANTS: The Chinese Ministry of Health started to publish class C notifiable diseases in 2009. The monthly reported case time series of 11 infectious diseases from the surveillance system between 2009 and 2014 was collected. METHODS: We performed a descriptive and a time series study using the surveillance data. Decomposition methods were used to explore (1) their seasonality expressed in the form of seasonal indices and (2) their long-term trend in the form of a linear regression model. Autoregressive integrated moving average (ARIMA) models have been established for each disease. RESULTS: The number of cases and deaths caused by hand, foot and mouth disease ranks number 1 among the detected diseases. It occurred most often in May and July and increased, on average, by 0.14126/100 000 per month. The remaining incidence models show good fit except the influenza and hydatid disease models. Both the hydatid disease and influenza series become white noise after differencing, so no available ARIMA model can be fitted for these two diseases. CONCLUSION: Time series analysis of effective surveillance time series is useful for better understanding the occurrence of the 11 types of infectious disease.

Association between air pollution and sperm quality: A systematic review and meta-analysis.

Exposure to ambient air pollution has been clearly linked to adverse reproductive outcome and fecundation index, but its effects on male semen quality are still uncertain. In this study, we reviewed information from ten studies to get the qualitative evidence of the influence of the ambient air pollution on sperm quality and collected data from six of the ten studies to conduct meta-analysis. The original studies classified participants into different exposure levels and the highest and lowest expose levels were chosen as high expose and low expose groups, respectively. The random-effect model was used in the meta-analysis with the weight mean difference (WMD) as the measure indicator. The WMDs (95% confidence intervals, CIs) of sperm volume, sperm count, semen concentration, sperm progressive motility, total motility, and normal morphology were 0.09 (-0.04, 0.23), 0.46 (-4.47, 5.39), -8.21 (-20.38, 3.96), -7.76 (-16.26, 0.74), -7.61 (-16.97, 1.74) and -3.40 (-7.42, 0.62), respectively. In conclusion, although the differences are not statistically significant between the two groups, the overall trends and evidence from this review indicate the chronic exposure to ambient pollutants at high level may alter men sperm quality.