| With the background of climate change and frequent extreme weather events, the impacts of climate on human health become more and more highlight which have been paid more attention by researcher, doctor and public. As one of the main diseases in human, respiratory disease is more sensitive to climate change. So far, research on the impacts of meteorological elements on respiratory disease is still in the starting stage in China. Limited studies are qualitative rather than quantitative. Therefore, three representative cities, including Beijing, Lanzhou, and Nanjing, were selected from different climatic regions to evaluate the response of respiratory disease to meteorological elements. Generalized additive model was used to analyze the association between respiratory disease and meteorological factors, which will be expected to provide theory and technique supporting for promoting medical meteorological forecasting for respiratory disease in China and also to provide scientific basis for government to draft prevention and control measures for human health. The main study results as follows:1. From 1951 to 2010, the change of daily average temperature shown an increaseing trend in Beijing, Lanzhou and Nanjing with the rising rate of 0.41℃/10a,0.44℃/10a and 0.23 ℃ /10a, respectively. Moreover, the upgrade tendency was most obvious in winter and spring. For relative humidity, it presented a decreasing tendency in three cities with the decending rate of 1.53%/10a,2.0%/10a,0.95%/10a. For wind, it shown different tendency in three cities. With the rate of 0.09m/s/10a and 0.18m/s/10a wind demonstrated a fall trend in Lanzhou and Nanjing. However, it was no obvious change trend in Beijing. The change of annual precipitation in Beijing and Lanzhou shown a decreasing trend with the rate of 3.9mm and 0.30mm per decade; while it presented an increasing trend in Nanjing with the rate of 3.2mm per decade. In summary, the climate presented a warming and drying tendency in Beijing and Lanzhou, while it shown warming and precipitation increasing trend in Nanjing.2. The daily number of emergency, hospitalization and mortality in Beijing, Lanzhou and Nanjing presented a periodic variation with the characteristic of greater in winter half year and lower in summer half year. This illustrated cold effect is the most important factor which harm to the morbidity and mortality of respiratory disease. In addition, the seasonal distribution of respiratory morbidity and mortality in three cities shown more obvious in autumn, winter and spring. The population distribution presented that male and less than 60 years were greater than female and older than 60 years.3. The impacts of temperature on morbidity and mortality of respiratory disease presented a U- or fall J- shaped which means there was a temperature threshold, namely both of high and low temperature may increase morbidity or mortality. The temperature thresholds were 12℃,16℃,26℃ in Beijing, Lanzhou and Nanjing, respectively. Below thresholds, a 1℃ decease in daily average temperature were 2.42%,1.48%,3.01% increasing for respiratory in Beijing, Lanzhou and Nanjing, respectively. While above thresholds, a 1℃ incease in daily average temperature were 20.95%,0.48%,0.60% increasing for respiratory in three cities. In addition, daily minimum temperature in winter and daily maximum temperature in summer shown most obvious effect on respiratory disease. Female and less than 60 years were more sensitive to temperature than male and older than 60 years. In summary, cold effect on respiratory disease was more significant, however, it can not be ignored hot effect.4. Similar to temperature effect, the impacts of relative humidity on respiratory disease also shown a U-shape, excepte for all respiratory and lower respiratory tract infection in Lanzhou. The relative humidity thresholds were 49% and 62% in Beijing and Nanjing. Below relative humidity thresholds, a 10% decease in relative humdity were 11.32% increasing for respiratory in Beijing, while it was not significant in Nanjing. Above relative humidity threshold, a 10% incease in relative humdity were 4.42% and 3.65% increasing for respiratory in Beijing and Nanjing. There was no threshold of relative humidity effect on daily hospitalization of respiratory in Lanzhou. It only shown 3.79% decease for daily hospitalization of respiratory with 10% relative humidity increasing. Female and less than 60 years were more sensitive to temperature than male and older than 60 years. In summary, dry climate have a negative effect on the morbidity and mortality of respiratory disease. Appropriate relative humidity will protect human from respiratory disease, while high humidity was also a risk factor for respiratory disease.5. There was a synergistic effect of temperature and humidity on respiratory diseases which mean temperature effect was different with diverse relative humidity levels. The modifying effect of humidity on temperature was disparity in different temperature condition. In Beijing, temperature effect was more obvious in low humidity level when temperature below threshold, while it was more significant in high humidity level when temperature above threshold. In Lanzhou, no matter how temperature condition, temperature effect was all more noteable in high humidity level. In Nanjing, as the climate is wet all year around, the modifying effect of humidity on temperature is not obvious. In a word, both of low and high humidity level could modify the temperature effect on respiratory disease.6. Generalized additive model was used to establish prediction models for respiratory disease in three represent cities and the results of the back substitution test were good. The established models were used to forcast daily emergency, hospitalization and mortality of respiratory disease in Beijing, Lanzhou, Nanjing. The prediction veracities for respiratory disease were more than 85% in Beijing, more than 70% in Lanzhou (except for lower respiratory tract infection), and 73.5% in Nanjing. This indicate that it is suitable to forcast the morbidity and mortality of respiratory disease by using generalized additive model. |
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