The Impacts of Mosquito Density and Meteorological Factors on Dengue Fever Epidemics in Guangzhou, China, 2006-2014: a Time-series Analysis / 生物医学与环境科学（英文）

<p><b>OBJECTIVE</b>To explore the associations between the monthly number of dengue fever(DF) cases and possible risk factors in Guangzhou, a subtropical city of China.</p><p><b>METHODS</b>The monthly number of DF cases, Breteau Index (BI), and meteorological measures during 2006-2014 recorded in Guangzhou, China, were assessed. A negative binomial regression model was used to evaluate the relationships between BI, meteorological factors, and the monthly number of DF cases.</p><p><b>RESULTS</b>A total of 39,697 DF cases were detected in Guangzhou during the study period. DF incidence presented an obvious seasonal pattern, with most cases occurring from June to November. The current month's BI, average temperature (Tave), previous month's minimum temperature (Tmin), and Tave were positively associated with DF incidence. A threshold of 18.25 °C was found in the relationship between the current month's Tmin and DF incidence.</p><p><b>CONCLUSION</b>Mosquito density, Tave, and Tmin play a critical role in DF transmission in Guangzhou. These findings could be useful in the development of a DF early warning system and assist in effective control and prevention strategies in the DF epidemic.</p>

The impact of relative humidity and atmospheric pressure on mortality in Guangzhou, China / 生物医学与环境科学（英文）

<p><b>OBJECTIVE</b>Although many studies have examined the effects of ambient temperatures on mortality, little evidence is on health impacts of atmospheric pressure and relative humidity. This study aimed to assess the impacts of atmospheric pressure and relative humidity on mortality in Guangzhou, China.</p><p><b>METHODS</b>This study included 213,737 registered deaths during 2003-2011 in Guangzhou, China. A quasi-Poisson regression with a distributed lag non-linear model was used to assess the effects of atmospheric pressure/relative humidity.</p><p><b>RESULTS</b>We found significant effect of low atmospheric pressure/relative humidity on mortality. There was a 1.79% (95% confidence interval: 0.38%-3.22%) increase in non-accidental mortality and a 2.27% (0.07%-4.51%) increase in cardiovascular mortality comparing the 5th and 25th percentile of atmospheric pressure. A 3.97% (0.67%-7.39%) increase in cardiovascular mortality was also observed comparing the 5th and 25th percentile of relative humidity. Women were more vulnerable to decrease in atmospheric pressure and relative humidity than men. Age and education attainment were also potential effect modifiers. Furthermore, low atmospheric pressure and relative humidity increased temperature-related mortality.</p><p><b>CONCLUSION</b>Both low atmospheric pressure and relative humidity are important risk factors of mortality. Our findings would be helpful to develop health risk assessment and climate policy interventions that would better protect vulnerable subgroups of the population.</p>

Impact of heat wave in 2005 on mortality in Guangzhou, China / 生物医学与环境科学（英文）

<p><b>OBJECTIVE</b>To assess the impact of the heat wave in 2005 on mortality among the residents in Guangzhou and to identify susceptible subpopulations in Guangzhou, China.</p><p><b>METHODS</b>The data of daily number of deaths and meteorological measures from 2003 to 2006 in Guangzhou were used in this study. Heat wave was defined as ⋝7 consecutive days with daily maximum temperature above 35.0 °C and daily mean temperature above the 97th percentile during the study period. The excess deaths and rate ratio (RR) of mortality in the case period compared with the reference period in the same summer were calculated.</p><p><b>RESULTS</b>During the study period, only one heat wave in 2005 was identified and the total number of excess deaths was 145 with an average of 12 deaths per day. The effect of the heat wave on non-accidental mortality (RR=1.23, 95% CI: 1.11-1.37) was found with statistically significant difference. Also, greater effects were observed for cardiovascular mortality (RR=1.34, 95% CI: 1.13-1.59) and respiratory mortality (RR=1.31, 95% CI: 1.02-1.69). Females, the elderly and people with lower socioeconomic status were at significantly higher risk of heat wave-associated mortality.</p><p><b>CONCLUSION</b>The 2005 heat wave had a substantial impact on mortality among the residents in Guangzhou, particularly among some susceptible subpopulations. The findings from the present study may provide scientific evidences to develop relevant public health policies and prevention measures aimed at reduction of preventable mortality from heat waves.</p>

Effect of influenza-like diseases on the number of outpatient visits / 南方医科大学学报

<p><b>OBJECTIVE</b>To estimate the effect of influenza-like illness (ILI) on outpatient visits and assess its impact on public health.</p><p><b>METHODS</b>We analyzed the data of weekly number of ILI and outpatient visits in Departments of Internal Medicine, Pediatrics and Emergency at two influenza surveillance hospitals during a period of 137 weeks in Guangzhou. Spectral analysis and time-series analysis were performed to evaluate the variation of outpatient visits over time. The predictive model was fitted with weekly outpatient visits as the dependent variable and weekly number of ILI as the independent variable. The optimal model was established according to the coefficient of determination, Akaike-information criterion and residual analysis. The validity of the model was assessed prospectively using the 31-week data that were not used for the model establishment.</p><p><b>RESULTS</b>The outpatient visits increased significantly over time and showed significant seasonality (P<0.001). A significant correlation was found between the weekly number of ILI and outpatient visits (r=0.568, P<0.001). The residuals of the fitted autoregression model were white-noise series and the coefficient of determination was 75% for the data used to establish the model and 56% for the subsequent 31-week data.</p><p><b>CONCLUSIONS</b>The autoregression model can be used to estimate the effect of weekly number of outpatient visits based on the weekly number of ILI and thus assess the effects of influenza on public health.</p>