Risk assessment of exported risk of novel coronavirus pneumonia from Hubei Province / 中华预防医学杂志

To evaluate the exported risk of novel coronavirus pneumonia (NCP) from Hubei Province and the imported risk in various provinces across China. Data of reported NCP cases and Baidu Migration Indexin all provinces of the country as of February 14, 2020 were collected. The correlation analysis between cumulative number of reported cases and the migration index from Hubei was performed, and the imported risks from Hubei to different provinces across China were further evaluated. A total of 49 970 confirmed cases were reported nationwide, of which 37 884 were in Hubei Province. The average daily migration index from Hubei to other provinces was 312.09, Wuhan and other cities in Hubei were 117.95 and 194.16, respectively. The cumulative NCP cases of provinces was positively correlated with the migration index derived from Hubei province, also in Wuhan and other cities in Hubei, with correlation coefficients of 0.84, 0.84, and 0.81. In linear model, population migration from Hubei Province, Wuhan and other cities in Hubei account for 71.2%, 70.1%, and 66.3% of the variation, respectively. The period of high exported risk from Hubei occurred before January 27, of which the risks before January 23 mainly came from Wuhan, and then mainly from other cities in Hubei. Hunan Province, Henan Province and Guangdong Province ranked the top three in terms of cumulative imported risk (the cumulative risk indices were 58.61, 54.75 and 49.62 respectively). The epidemic in each province was mainly caused by the importation of Hubei Province. Taking measures such as restricting the migration of population in Hubei Province and strengthening quarantine measures for immigrants from Hubei Province may greatly reduce the risk of continued spread of the epidemic.

Time-series analysis on the malaria morbidity affected by meteorological factors in Guangdong province / 中华预防医学杂志

<p><b>OBJECTIVE</b>To evaluate the associations between malaria risk and meteorological factors.</p><p><b>METHODS</b>A negative binomial distribution regression analysis was built between the temperature, relative humidity, rainfall capacity and the monthly incidence of malaria, based on the temperature information provided by Guangdong Meteorological Department and the malaria incidence information provided by Guangdong Center of Disease Prevention and Control during year 1980 to 2004, adopting the time-series analysis method and by distributed lag non-linear model, in order to analyze the immediate factors.</p><p><b>RESULTS</b>The number of monthly malaria cases in Guangdong province reached 4010 between year 1984 and 2004, while the monthly maximal temperature, minimal temperature, average temperature, relative humidity and average rainfall capacity was separately 26.3°C, 18.8°C, 21.9°C, 88.0% and 5.6 mm. The immediate effect of monthly maximal temperature on malaria incidence showed non-linear relationships. When the temperature reached 32.3°C, the risk was highest, the relative risk (RR) was 2.51 (95%CI: 1.99 - 3.16); when the relative humidity was 60.0%, the relative risk of malaria was highest as 1.19 (95%CI: 0.66 - 2.11) and then decreased gradually; and when the relative humidity was 86.6%, the risk of malaria was lowest at 0.51 (95%CI: 0.34 - 0.76). The risk of malaria increased while the rainfall capacity was 14.5 mm, the risk of malaria was the highest at 1.29 (95%CI: 0.87 - 1.93). Strongest delayed effects on malaria incidence was observed when the monthly maximal temperature reached 31.5°C at lagged 2 months, with the value of RR at 1.81 (95%CI: 1.02 - 3.22). When the monthly rainfall capacity was over 15.2 mm, the delayed effects was strong but short. When the monthly maximal temperature of 33.7°C, the excess risk of malaria was comparatively high, the excess risk was 92.2% (95%CI: 30.5% - 183.2%) when lagging one month. When the relative humidity was low, the delayed effect of malaria lasted for a long time, and the cumulative effect was huge. When the relative humidity reached 87.0%, the excess risk lagging 3 months was only -66.6% (95%CI: -86.4% - -17.7%). When the rainfall capacity was 15.5 mm, the cumulative effect on malaria reached the peak after 3 months, while the excess risk was 40.7% (95%CI: -30.0% - -182.6%); afterwards the cumulative effect gradually weakened. Positive and negative interaction effects were significant between malaria risk and maximal temperature and monthly rainfall capacity, and monthly rainfall capacity and relative humidity at lagged 2 months, respectively.</p><p><b>CONCLUSION</b>High temperature and large rainfall capacity might be the risk factors of malaria in Guangdong province, and there was an obvious interaction between the two factors.</p>

What temperature index is the best predictor for the impact of temperature on mortality / 中华预防医学杂志

<p><b>OBJECTIVE</b>To explore the suitable temperature index to establish temperature-mortality model.</p><p><b>METHODS</b>The mortality and meteorological information of Guangzhou between year 2006 and 2010 were collected to explore the association between sendible temperature, heat index and deaths by adopting distributed lag non-linear model to fit the daily maximum, mean and minimum temperature with and without humidity. Q-Q plots based on the standardized residuals of each model were used to qualitatively access the goodness of fitting. The minimum Akaike information criterion (AIC) and residual sum of squares (RSS) value were used to explore the most suitable temperature index for model establishment, and to further analyze the fittest temperature index for different diseases, ages and cold and hot effect.</p><p><b>RESULTS</b>Guangzhou features a subtropical monsoon climate, with an annual average temperature at 22.9°C and daily average relative humidity of 71%. The standardized residuals of all models followed normal distribution. For all death, death from circulation system diseases, the 65-84 years old aging groups and cold effect models, the daily average temperature fit better, whose AIC (RSS) values were the smallest as 11 537 (1897), 9527 (1928), 10 595 (2018) and 11 523 (1899), respectively. However, for death from respiratory system disease, groups aging under 65 years old or over 85 years old and hot effect models, the daily average sendible temperature fit better, whose AIC (RSS) values were the smallest as 8265(1854), 675 (1739), 8550 (1871) and 11 687 (1938), respectively. In comparison with the model controlling both temperature and relative humidity, different diseases, aging groups and cold and hot effect models fitted by sendible temperature index showed smaller AIC (RSS) values. The relative risk (RR) value of the cold effect lagging 0 - 3 days fitting by daily maximal temperature was < 1, and the RR value of it fitting by daily minimum temperature was > 1.04. The RR value of the hot effect lagging 0 - 1 days fitting by daily maximal temperature was < 1.16, and the RR values of it fitting by daily minimum temperature and daily average temperature were > 1.16.</p><p><b>CONCLUSION</b>There were no best temperature indicators for different diseases, ages and cold and hot effect. The model using sendible temperature index better fit the model including relative humidity as a covariable.</p>

The impact of temperature on cardiovascular disease deaths in 4 cities, China: a time-series study / 中华流行病学杂志

Objective To estimate the effects of temperature on cardiovascular disease (CVD) deaths in 4 cities-Kunming,Changsha,Guangzhou and Zhuhai,from southern part of China.Methods Daily CVD deaths,meteorological and air pollution data were used to explore the association between temperature and mortality.Distributed lag non-linear model was fitted for each city to access the delayed and cumulative effects of low,median and high temperature on CVD deaths.Cold and hot effects of temperature on CVD deaths were then accessed,based on the linear threshold model.Results The city-specific exposure-response functions appeared to be non-linear.Temperatures that associated with the lowest mortality for Changsha,Kunming,Guangzhou and Zhuhai were 22.0 ℃,20.0 ℃,26.0 ℃,and 25.5 ℃.The greatest cumulative RRs (95％CI) for CVD deaths of low temperature during the delayed period of the study in the 4 cities were 1.858 (1.089-3.170),1.537 (1.306-1.809),2.121 (1.771-2.540) and 1.934 (1.469-2.548),while 1.100 (0.816-1.483),1.061 (0.956-1.177),1.134 (1.047-1.230) and 1.259 (1.104-1.436) for high temperatures in Changsha,Kunming,Guangzhou and Zhuhai respectively.The hot effect was greater than the cold effect on the current days.The hot effect was restricted to the first week,whereas the cold effect increased over the lag days,and then last for 3-4 weeks.Conclusion The city-specific exposure-response functions appeared to be non-linear.Both high and cold temperatures were associated with increased CVD deaths,but the impact of low temperature was more notable.Cold effect was delayed by several days but last for a longer period than the hot effect did.