Fengchao Liang

Non-Linear Association between Exposure to Ambient Temperature and Children's Hand-Foot-and-Mouth Disease in Beijing, China.

Background Hand, foot and mouth disease (HFMD) was listed as a notifiable communicable disease in 2008 and is an emerging public health problem in China, especially for children. However, few data are available on the risk assessment of the potential reasons for HFMD in Beijing. This study examined the association of temperature with the incidence of children’s HFMD in Beijing at the daily scale for the first time. Methods A newly developed case-crossover design with a distributed lag nonlinear model (DLNM) was used to assess the delayed and cumulative associations of daily temperature with gender- and age-specific HFMD in Beijing, China, during 2010–2012. Relative humidity, day of the week, public holiday, season and long-term trends were controlled in the model. Results Among the total of 113,475 cases, the ratio between males and females was 1.52:1. HFMD was more prevalent in May-July. The temperature-HFMD relationships were non-linear in most age groups except for children aged 6–15 years, with a peak at 25.0~27.5°C. The high-temperature risks were greater, appeared earlier and lasted longer than the low-temperature risks. The relative risks for female children and those aged 6–15 years were higher than those among other groups. Conclusion Rising temperatures increased the incidence of children’s HFMD, with the largest association at 25.0~27.5°C. Females and children aged 6–15 years were more vulnerable to changes in temperature with regard to the transmission of HFMD than males and other age groups, respectively. Further studies are warranted to confirm these findings in other populations.

Spatiotemporal analysis of particulate air pollution and ischemic heart disease mortality in Beijing, China

BackgroundFew studies have used spatially resolved ambient particulate matter with an aerodynamic diameter of <10xa0μm (PM10) to examine the impact of PM10 on ischemic heart disease (IHD) mortality in China. The aim of our study is to evaluate the short-term effects of PM10 concentrations on IHD mortality by means of spatiotemporal analysis approach.MethodsWe collected daily data on air pollution, weather conditions and IHD mortality in Beijing, China during 2008 and 2009. Ordinary kriging (OK) was used to interpolate daily PM10 concentrations at the centroid of 287 township-level areas based on 27 monitoring sites covering the whole city. A generalized additive mixed model was used to estimate quantitatively the impact of spatially resolved PM10 on the IHD mortality. The co-effects of the seasons, gender and age were studied in a stratified analysis. Generalized additive model was used to evaluate the effects of averaged PM10 concentration as well.ResultsThe averaged spatially resolved PM10 concentration at 287 township-level areas was 120.3u2009±u200978.1xa0μg/m3. Ambient PM10 concentration was associated with IHD mortality in spatiotemporal analysis and the strongest effects were identified for the 2-day average. A 10xa0μg/m3 increase in PM10 was associated with an increase of 0.33% (95% confidence intervals: 0.13%, 0.52%) in daily IHD mortality. The effect estimates using spatially resolved PM10 were larger than that using averaged PM10. The seasonal stratification analysis showed that PM10 had the statistically stronger effects on IHD mortality in summer than that in the other seasons. Males and older people demonstrated the larger response to PM10 exposure.ConclusionsOur results suggest that short-term exposure to particulate air pollution is associated with increased IHD mortality. Spatial variation should be considered for assessing the impacts of particulate air pollution on mortality.

MAIAC-based long-term spatiotemporal trends of PM2.5 in Beijing, China

Satellite-driven statistical models have been proven to be able to provide spatially resolved PM2.5 estimates worldwide. The North China Plain has been suffering from severe PM2.5 pollution in recent years. An accurate assessment of the spatiotemporal characteristics of PM2.5 levels in this region is crucial to design effective air pollution control policy. Our objective is to estimate daily PM2.5 concentrations at 1km spatial resolution from 2004 to 2014 in Beijing and its surrounding areas using the Multi-angle implementation of atmospheric correction (MAIAC) aerosol optical depth (AOD). A high-performance three-stage model was developed with AOD, meteorological, demographic and land use variables as predictors, which includes a custom-designed PM2.5 gap-filling method. The 11-year average annual coverage increased from 177days to 279days and annual PM2.5 prediction error decreased from 14.1μg/m3 to 8.3μg/m3 after gap-filling techniques were applied. Results show that the 11-year overall mean of predicted PM2.5 was 67.1μg/m3 in our study domain. The cross-validation R2 value of our model is 0.82 in 2013 and 0.79 in 2014. In addition, the models predicted historical PM2.5 concentrations with relatively high accuracy at the seasonal and annual levels (R2 ranged from 0.78 to 0.86). Our long-term PM2.5 prediction filled the gaps left by ground monitors, which would be beneficial to PM2.5 related epidemiological studies in Beijing.

Associations of PM2.5 and Black Carbon with Hospital Emergency Room Visits during Heavy Haze Events: A Case Study in Beijing, China

In January 2013, severe haze events over northeastern China sparked substantial health concerns. This study explores the associations of fine particulate matter less than 2.5 μm (PM2.5) and black carbon (BC) with hospital emergency room visits (ERVs) during a haze season in Beijing. During that period, daily counts of ERVs for respiratory, cardiovascular and ocular diseases were obtained from a Level-3A hospital in Beijing from 1 December 2012 to 28 February 2013, and associations of which with PM2.5 and BC were estimated by time-stratified case-crossover analysis in single- and two-pollutant models. We found a 27.5% (95% confidence interval (CI): 13.0, 43.9%) increase in respiratory ERV (lag02), a 19.4% (95% CI: 2.5, 39.0%) increase in cardiovascular ERV (lag0), and a 12.6% (95% CI: 0.0, 26.7%) increase in ocular ERV (lag0) along with an interquartile range (IQR) increase in the PM2.5. An IQR increase of BC was associated with 27.6% (95% CI: 9.6, 48.6%) (lag02), 18.8% (95% CI: 1.4, 39.2%) (lag0) and 11.8% (95% CI: −1.4, 26.8%) (lag0) increases for changes in these same health outcomes respectively. Estimated associations were consistent after adjusting SO2 or NO2 in two-pollutant models. This study provides evidence that improving air quality and reducing haze days would greatly benefit the population health.

Projecting temperature-related years of life lost under different climate change scenarios in one temperate megacity, China

Temperature has been associated with population health, but few studies have projected the future temperature-related years of life lost attributable to climate change. To project future temperature-related disease burden in Tianjin, we selected years of life lost (YLL) as the dependent variable to explore YLL attributable to climate change. A generalized linear model (GLM) and distributed lag non-linear model were combined to assess the non-linear and delayed effects of temperature on the YLL of non-accidental mortality. Then, we calculated the YLL changes attributable to future climate scenarios in 2055 and 2090. The relationships of daily mean temperature with the YLL of non-accident mortality were basically U-shaped. Both the daily mean temperature increase on high-temperature days and its drop on low-temperature days caused an increase of YLL and non-accidental deaths. The temperature-related YLL will worsen if future climate change exceeds 2xa0°C. In addition, the adverse effects of extreme temperature on YLL occurred more quickly than that of the overall temperature. The impact of low temperature was greater than that of high temperature. Men were vulnerable to high temperature compared with women. This analysis highlights that the government should formulate environmental policies to reach the Paris Agreement goal.

The impact of power generation emissions on ambient PM2.5 pollution and human health in China and India

Emissions from power plants in China and India contain a myriad of fine particulate matter (PM2.5, PMu202f≤u202f2.5u202fμm in diameter) precursors, posing significant health risks among large, densely settled populations. Studies isolating the contributions of various source classes and geographic regions are limited in China and India, but such information could be helpful for policy makers attempting to identify efficient mitigation strategies. We quantified the impact of power generation emissions on annual mean PM2.5 concentrations using the state-of-the-art atmospheric chemistry model WRF-Chem (Weather Research Forecasting model coupled with Chemistry) in China and India. Evaluations using nationwide surface measurements show the model performs reasonably well. We calculated province-specific annual changes in mortality and life expectancy due to power generation emissions generated PM2.5 using the Integrated Exposure Response (IER) model, recently updated IER parameters from Global Burden of Disease (GBD) 2015, population data, and the World Health Organization (WHO) life tables for China and India. We estimate that 15 million (95% Confidence Interval (CI): 10 to 21 million) years of life lost can be avoided in China each year and 11 million (95% CI: 7 to 15 million) in India by eliminating power generation emissions. Priorities in upgrading existing power generating technologies should be given to Shandong, Henan, and Sichuan provinces in China, and Uttar Pradesh state in India due to their dominant contributions to the current health risks.

Evaluation of a data fusion approach to estimate daily PM2.5 levels in North China

Abstract PM2.5 air pollution has been a growing concern worldwide. Previous studies have conducted several techniques to estimate PM2.5 exposure spatiotemporally in China, but all these have limitations. This study was to develop a data fusion approach and compare it with kriging and Chemistry Module. Two techniques were applied to create daily spatial cover of PM2.5 in grid cells with a resolution of 10 km in North China in 2013, respectively, which was kriging with an external drift (KED) and Weather Research and Forecast Model with Chemistry Module (WRF‐Chem). A data fusion technique was developed by fusing PM2.5 concentration predicted by KED and WRF‐Chem, accounting for the distance from the central of grid cell to the nearest ground observations and daily spatial correlations between WRF‐Chem and observations. Model performances were evaluated by comparing them with ground observations and the spatial prediction errors. KED and data fusion performed better at monitoring sites with a daily model R2 of 0.95 and 0.94, respectively and PM2.5 was overestimated by WRF‐Chem (R2=0.51). KED and data fusion performed better around the ground monitors, WRF‐Chem performed relative worse with high prediction errors in the central of study domain. In our study, both KED and data fusion technique provided highly accurate PM2.5. Current monitoring network in North China was dense enough to provide a reliable PM2.5 prediction by interpolation technique. HighlightsKED and data fusion model predicted daily PM2.5 with high accuracy.WRF‐Chem performed worse in PM2.5 prediction compared with KED and data fusion.The PM2.5 monitoring network in North China was able to support reliable PM2.5 interpolation.

Satellite-based short- and long-term exposure to PM2.5 and adult mortality in urban Beijing, China

Severe and persistent haze accompanied by high concentrations of fine particulate matter (PM2.5) has become a great public health concern in urban China. However, research on the health effects of PM2.5 in China has been hindered by the lack of high-quality exposure estimates. In this study, we assessed the excess mortality associated with both short- and long-term exposure to ambient PM2.5 simultaneously using satellite-derived exposure data at a high spatiotemporal resolution. Adult registries of non-accidental, respiratory and cardiovascular deaths in urban Beijing in 2013 were collected. Exposure levels were estimated from daily satellite-based PM2.5 concentrations at 1u202fkm spatial resolution from 2004 to 2013. Mixed Poisson regression models were fitted to estimate the cause-specific mortality in association with PM2.5 exposures. With the mutual adjustment of short- and long-term exposure of PM2.5, the percent increases associated with every 10u202fμg/m3 increase in short-term PM2.5 exposure were 0.09% (95% CI:xa0-0.14%, 0.33%; lag 01), 1.02% (95% CI: 0.08%, 1.97%; lag 04) and 0.09% (95% CI:xa0-0.23%, 0.42%; lag 01) for non-accidental, respiratory and cardiovascular mortality, respectively; those attributable to every 10u202fμg/m3 increase in long-term PM2.5 exposure (9-year moving average) were 16.78% (95% CI: 10.58%, 23.33%), 44.14% (95% CI: 20.73%, 72.10%) and 3.72% (95% CI:xa0-3.75%, 11.77%), respectively. Both associations of short- and long-term exposure with the cause-specific mortality decreased after they were mutually adjusted. Associations between short-term exposure to satellite-based PM2.5 and cause-specific mortality were larger than those estimated using fixed measurements. Satellite-based PM2.5 predictions help to improve the spatiotemporal resolution of exposure assessments and the mutual adjustment model provide better estimation of PM2.5 associated health effects. Effects attributable to long-term exposure of PM2.5 were larger than those of short-term exposure, which should be more concerned for public health.

Spatio-temporal analysis of the relationship between meteorological factors and hand-foot-mouth disease in Beijing, China

BackgroundHand-foot-mouth disease (HFMD) is a common infectious disease in China and occurs mostly in infants and children. Beijing is a densely populated megacity, in which HFMD has been increasing in the last decade. The aim of this study was to quantify spatio-temporal characteristics of HFMD and the relationship between meteorological factors and HFMD incidence in Beijing, China.MethodsDaily counts of HFMD cases from January 2010 to December 2012 were obtained from the Beijing Center for Disease Prevention and Control (CDC). Seasonal trend decomposition with Loess smoothing was used to explore seasonal patterns and temporal trends of HFMD. Bayesian spatiotemporal Poisson regression models were used to quantify spatiotemporal patterns of HFMD incidence and associations with meteorological factors.ResultsThere were 114,777 HFMD cases reported to Beijing CDC from 1 January 2010 to 31 December 2012 and the raw incidence was 568.6 per 100,000 people. May to July was the peak period of HFMD incidence each year. Low-incidence townships were clustered in central, northeast and southwest regions of Beijing. Mean temperature, relative humidity, wind velocity and sunshine hours were all positively associated with HFMD. The effect of wind velocity was significant with a RR of 3.30 (95%CI: 2.37, 4.60) per meter per second increase, as was sunshine hours with a RR of 1.20 (95%CI: 1.02, 1.40) per 1 hour increase.ConclusionsThe distribution of HFMD in Beijing was spatiotemporally heterogeneous, and was associated with meteorological factors. Meteorological monitoring could be incorporated into prediction and surveillance of HFMD in Beijing.