ABSTRACT
Objective To understand the effect of temperature on the risk of mortality and the modification effect of latitude,in China.Methods Relevant papers were searched and Meta-analysis was used to determine the exposure-response relationship for each health outcome which was associated with the exposure to temperature.Meta-regression analysis was used to evaluate the effect modification by latitude.Results Ten studies in 15 cities were included in the study.When temperature increased by one centigrade,the risks of mortality showed the following changes:deaths from non-accidental increased by 2％ (95％CI:1％,3％),from cardiovascular disease increased by 4％ (95％CI:2％,6％)and from the respiratory disease increased by 2％ (95％CI:1％,4％).As temperature decreased by one centigrade,the mortality risks of the following diseases showed the changes as:non-accidental death increased by 4％ (95％CI:2％,7％),cardiovascular disease increased by 4％ (95％CI:1％,7％) and the respiratory diseases increased by 2％ (95％CI:0％,4％).When latitude ranged from 0 to 25,26 to 30,31 to 39 degree or over 40 degrees,respectively and the temperature decreased by one centigrade,the mortality risks of the general population increased by 6.5％ (95％CI:-2.7％,15.6％),5.8％(95％ CI:2.4％,9.3％),0.8％(95％CI:0.4％,1.2％),0.5％(95％CI:-0.5％,1.5％).As temperature increased by one centigrade,mortality risk of the general population increased by 0.6％ (95％ CI:-0.3％,1.4％),1.9％ (95％ CI:0.7％,3.1％),2.0％ (95％ CI:1.0％,3.0％) and 5.8％ (95％CI:-3.2％,14.8％).As latitude increased by five degrees with high temperature,the mortality risk of general people increased by 0.3％ (95％CI:0.1％,0.8％) while decreased by 0.8％ (95％ CI:0.5％,0.9％) under low temperature.Conclusion In China,the mortality risk increased along with the changes of temperature.The adaptability to cold ness among people living in high latitude areas seemed to be stronger than those living in other areas of latitudes.Who were more vulnerable to high temperature.
ABSTRACT
<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>