Time Series Analysis Of Extreme Temperature On Cardiovascular Disease In Beijing Residents

BackgroundWith the sharp change in the global climate recent years, more attention has been paid to environmental health. Especially the relationship between temperature and health become the hot spot. Plenty of evidence shows that the rise or fall of ambient temperature can increase the incidence of cardiovascular events. The sympathetic nerve excitability will be enhanced in lower temperature. Thus the blood pressure, heart rate, left ventricular end-diastolic pressure will rise, increasing myocardial oxygen consumption, reducing ischemia threshold. The hemodynamics and coagulation function will be changed. Therefore, cardiovascular incidence increases. And when the temperature rises, the circulation of the blood is accelerated. Moisture loss will eventually lead to the emergence of the blood concentration, electrolyte disorder. The Global Burden of Diseases in the year 2010 listed ischemic heart disease and stroke as the top two diseases that cause the heaviest burden. Whereas Chinese cardiovascular disease report in 2014 pointed out that every year in our country the number of deaths from cardiovascular diseases reached 3.5 million, accounted for 41% of the causes of death. With the increasing of air pollution in China, more and more studies’focus tended to the health effects of air pollution. However, little attention has been paid to the meteorological conditions and health effects. China is a populous country, with a broad terrain, changeful climate, and at the same time, a high incidence of cardiovascular disease. Beijing is the capital of China, with a population of about 20 million, where a complete disease reporting system has been established. Studies conducted here can help us to determine the environmental health effects on the citizens and locking susceptible populations. Also, it can provide the epidemiological guidance for the government.ObjectiveThis study was conducted in Beijing for the first time. Indicators of exposure were daily mean temperature (Tmean), daily maximum temperature (Tmax), daily minimum temperature (Tmin), and apparent temperature (AT). Outcome indicator was the morbidity. We built distributed lag nonlinear model (DLNM) to evaluate exposure response relationship and lag-response relationship. The aim of our study was to reduce the occurrence of cardiovascular adverse events and relieve the economic burden of the government and the family through reasonable protection. And we can provide reasonable suggestions for early warning and prevention policies for the government.MethodsWe setcensus register population of Beijing as the research objects. Data collection included meteorological data, pollution data and the morbidity data of AMI during the period of January 1,2012 to December 31,2014. We calculated the incidence number of different gender, age and district,listed Tmean, Tmax, Tmin, AT, air pressure (AP), relative humidity (RH), wind speed (WS) and pollution data, built a database of time series analysis. According to characteristics of the data, we built the model of DLNM and adjusted for day of the week, long term trend, airquality index (AQI), AP, RH, and WS. We conducted ecology research on exposure-response relationship and lag-response relationship in stereo space. And we analyzed exposure of different temperature measuring methods on different gender, age, and districts.ResultsDuring the study period, the average of Tmean was 12.42℃, the average of Tmax was 18.44℃, the average of Tmin was 6.75℃, the average of AT was 5.2℃, the average of AP was 101.6kPa, the average of RH was 53.1%, and the average of WS was 9.48m/s.The resident population in Beijing during the study period was 19.612 million. There were 10.126 million male people, accounting for 51.6% of the population, 9.486 million female,48.4% of the population.16.216 millionpeople were between 18 and 64 years old, accounting for 82.7% of the population. Those who were no less than 65 were 1.709 million,8.7% of the population. Dongcheng District had a population of 919,000, Xicheng District 1.243 million, Chaoyang District 3.545 million, Fengtai District 2.113 million, Shijingshan District 616,000, Haidian District 3.281 million, Mentougou District 290,000, Fangshan District 945,000, Tongzhou District 1,184 million, Shunyi District 877,000, Changping District 1.661 million, Daxing District 1.365 million, Huairou District 373,000, Pinggu District 416,000, Miyun County 468,000, Yanqing County 317,000.The total number ofonset of AMIfor three years was 69,927 people, male 49,546, female 20,380,34,164 were 65 years or older,35,757 were between 18 and 64. The total number of the central district was 26,192, where 2,654 for Dongcheng District,4,372 for Xicheng District,8,702 for Chaoyang District,4,046 for Haidian District,2,217 for Shijingshan District and 4,201 for Fengtai District. The daily average number during the study period was 63.8; the maximum number was 124 for a single day. Male incidence was significantly higher than female, but divided according to age, the difference was insignificant. Strong correlation could be found betweendifferentmeasurement including Tmean, Tmax, Tmin and AT. RH and other indicators had the correlation of low intensity, whereas AP and other measurements had highly negative correlation. WS had highly negative correlation with RH.First, we conducted 21 days cumulative effect analysis. In the overall analysis, we found no difference between different measurement such as Tmean, Tmax, Tmin and AT. And we found a U-shaped relationship between temperature and AMI morbidity. In another word, an increase or decrease in temperature would lead to the rise of the AMI morbidity. When the temperature was’extreme cold’, cumulative relative risk (CRR) was 2.08 [95% confidence interval (CI),1.28-3.39], while in an ’extremehot’condition, CRR was 1.12 (95% CI,0.92-1.37). Males were more susceptible to the influence of ambient temperature than females. AT reached the biggest effect among all the measurement. CRR was 2.38 (95%CI,1.48-3.83) in the ’extreme cold’condition, and 1.28 (95 %CI,0.81-2.00) in the’extreme hot’. The old were more susceptible than the youth. Tmean reached the biggest effect among all the measurement. CRR was 2.13 (95% CI,1.19-3.84) in the’extreme cold’condition, and 1.11(95% CI,0.89-1.41) in the’extreme hot’. When we take the central six districts as a whole, the effect was similar to the whole population.Then, we conduct the lag-effect analysis. The similar relationship curve was found in the study. The duration of the cold effect would last for two weeks, whereas the hot effect would disappear within one week. In the overall analysis, we found that extreme temperature would have a higher influence than others. That was to say, the estimate effects were larger in the first and nighty ninth percentile of the temperature than the tenth and ninetieth percentile. Using the Tmean as the measurement, we found that males were influenced more. The largest estimate effect appeared on lag five. The CRRs were1.08(95% CI,1.03-1.12)and 1.05(95% CI,1.01-1.09)for extreme cold and cold separately. Hot weather had little influence on the morbidity. When the measurement changed to Tmax, the largest estimate effect appeared on lag four. Compared to the young people, the elderly had more sensitive response to changes in temperature. When measured in Tmean, the largest estimate effect appeared on lag seven, RR1.09 (95%CI,1.04-1.13). The younger group was little influenced. Under hot weather, no matter what kind of measurementwe used, people of different age groups showed no statistical significance. The effect of the central six districts was similar to the whole population in Beijing.ConclusionCold weather could lead to the increase of AMI morbidity, which would last for a longer time than the hot weather. Males had a higher risk than the females. The old would be affected more than the younger. There were no absolute advantages and disadvantages between different temperature measurement methods.Background and ObjectiveThe relationship between stroke and short-term temperature changes remains controversial. Therefore, we conducted a systemic review and meta-analysis to investigate the association between stroke and both high and low temperatures, and health assessment.MethodsWe searched PubMed, Embase, Cochrane, China National Knowledge Infrastructure (CNKI) and Wanfang Data up to September 14th,2014. Study selection, quality assessment, and author-contractions were steps before data extraction. We converted all estimates effects into RR per 1 ℃ increase/decrease in temperature from 75th to 99th or 25th to 1st percentiles. Then conducted meta-analyses to combine the ultimate RRs.Results20 articles were included in the final analysis. The overall analysis showed a positive relationship between 1 ℃ change and the occurrence of major adverse cerebrovascular events (MACBE),1.1%(95% CI,0.6 to 1.7) and 1.2%(95% CI,0.8 to 1.6) increase for hot and cold effects separately. The same trends can be found in both effects of mortality and the cold effect for morbidity. Hot temperature acted as a protective factor of hemorrhage stroke (HS),-1.9%(95% CI,-2.8 to-0.9), however, a risk factor for ischemic stroke (IS),1.2%(95% CI,0.7 to 1.8).ConclusionShort-term changes of both low and high temperature had statistically significant impacts on MACBE and cause excessive death for stroke.