To Assess Effect Of Temperature On Cardiovascular Diseases Out-patient Visits In Shanghai Residents With Medical Insurance

Objective:To assess the association between climate factors and the number of cardiovascular diseases out-patient visits in Shanghai residents with medical insurance.Significance:Climate change impacts on human health are a global concern. Recent extreme weather events have attracted renewed interseted on weather-related health effects. To our knowledge, the vast majority of researchers selected mortality or hospital admission rate as indicators of health effects in time-series studies. So far, the papers selecting outpatient visits are not found. In Shanghai, the beds in hospitals, especially for three-level hospitals are often saturated, so hospital admission rate could not reflect the real effect of temperature change on the population. The mortality rate always reflects the population's health status with serious condition, therefore, it is not suitable for reflecting the effect of temperature on the general population's health. Basically, hospital out-patient can meet the health need of the common residents, so daily out-patient visits can be used to effectively evaluate the medium-term and short-term effect of temperature change on the common residents' health. Due to increasing morbidity and mortality of cardiovascular disease, the mortality of cardiovascular diseases are the leading cause of death for Chinese and higher than cancer and other diseases, which poses a huge threat to the people's health. If we can understand the relation between temperature change and the number of cardiovascular, it will be conducive to control the occurrence and development of cardiovascular disease and have a very important public health significance outpatient visits.Methods:We investigated the association between temperature and daily cardivascular out-patient with medical insurance in Shanghai using generalized additive model(GAM), with a Poisson link function from January 1,2005 to December 31,2010. Natural cubic spline smoothing function was used to controlled for medium-term and long term time trend, pressure, huimdity, wind, rain from 8am to 8pm, PM10,SO2 and NO2. Dummy variables was used to adjusted for weekday, national holidays, and the day regarding medical insurance policy. Due to the correlation of time-series data, an auto-regression term(AR(P)) was added in the model to control for auto-regression based on ACF(auto-regression function).Main Results and Conclusion:We used 3-day moving average temperature of current and previous two days values as the indicator reflecting the effect of temperature. An auto-regression term AR(P) was added in the model to control for auto-regression because ACF was great than 0.1. A GAM+AR(P) model was performed to assess the relation between temperature and cardiovascular out-patient visits. As temperature decreased, cardiovascular out-patient visits with medical insurance increased.:Low temperature is the risk factor of cardiovascular diseases.When temperature is less than 9℃or great than 26℃, as temperature increased,the relative risk curve of cardiovascular diseases decreased rapidly. The result indicated that the relative risk of cardiovascular diseases reduced significantly with the increasing of temperature.