| BackgroundWith the development of global industrialization process,the air pollution situation has become more severe,and extreme temperature events are frequent.The impact of air pollutants and temperature changes on human health is also gradually expanding and aggravating.Acute Myocardial Infarction(AMI)is characterized by high incidence of disability and high mortality rates,which pose a huge burden on the global economy.Previous research has shown that many air pollutants and temperature changes are associated with cardiovascular health.However,the exposure indicators used in previous studies were relatively single,and case data rarely covered all of the city’s hospitalization data during the study period,while there was no screening of urban residents,resulting in insufficient accuracy in assessing temperature changes and the health effects of air pollutants.Therefore,the impact of air pollutants and temperature changes on cardiovascular health deserves further attention and exploration.Beijing,located in the north of the Chinese plain,belongs to the warm and moist and semi-dry seasonal climate,the four seasons are clear,and at the same time is China’s political,economic and cultural center,with sufficient medical resources and clinical treatment level,suitable for such environmental factors and health-related research.ObjectivesThe purpose of this study was to analyze the impact of six main air pollutants[particulate matter Less than 2.5μm in aerodynamic diameter(PM2.5),particulate matter Less than 10μm in aerodynamic diameter(PM10),Sulfur Dioxide,(SO2),Nitrogen Dioxide(NO2),Carbon Monoxide(CO),Ozone(O3)]and six kinds of temperature change indicators[① Temperature Range(TR n,temperature difference between daily maximum and mean temperature within n days);② Temperature Difference between daily maximum and mean temperature(TDmax);③Temperature Difference between daily mean and minimum temperature(TDmin);④Daily Mean Temperature Difference(DTDmean n,temperature difference between daily mean temperature on current day and mean temperature n days ago);⑤ Daily Maximum Temperature Difference(DTDmax n,temperature difference between daily maximum temperature on current day and maximum temperature n days ago);⑥ Daily Minimum Temperature Difference(DTDmin n,temperature difference between daily minimum temperature on current day and minimum temperature n days ago)]on the risk of AMI hospitalization among residents in Beijing to find out more meaningful risk prediction indicators and identify the susceptible population,so as to provide theoretical support for the formulation of public prevention measures in the future.MethodsAir pollutants and AMI hospitalization:Time series analysis method was used in the section.Data on AMI hospitalization among Beijing permanent residents,hourly concentrations of air pollutants(PM2.5,PM10,SO2,NO2,CO,O3),meteorological data(daily mean temperature,relative humidity),influenza epidemic data,and public holiday data were all gathered between May 1,2014,and December 31,2019.The relationship between the hourly peak,average,and valley concentrations of six different types of air pollutants and AMI hospitalization exposure response was quantified by using the generalized additive model,and the lag effect analysis and population susceptibility analysis were performed to investigate the differences in different lag days and subgroups.Temperature change and AMI hospitalization:Time series analysis method was also used in the section.Data on AMI hospitalization among Beijing permanent residents,meteorological data(daily mean temperature,maximum temperature,minimum temperature,average wind speed,relative humidity),air quality index,influenza epidemic data,and public holiday data were collected between January 1,2013,and December 31,2016.To examine the impact of temperature change on the relative risk of AMI hospitalization,the temperature differences were calculated to describe the temperature change,and the distributed hysteresis nonlinear model was constructed.To investigate the differences in the lag effect,multi-day cumulative relative risk(CRR),and population susceptibility,the lag effect analysis,21-day CRR calculation,and population susceptibility analysis were all performed.ResultsAir pollutants and AMI hospitalization:The daily peak average and valley concentrations of PM2.5,PM10,SO2,NO2,CO were significantly positively correlated with AMI hospitalization events,and the risk of AMI hospitalization increased by 0.50%(95%CI:0.35%-0.66%),0.44%(95%CI:0.32%-0.56%),0.84%(95%CI:0.47%-1.22%),1.86%(95%CI:0.73%-3.01%)and 44.6%(95%CI:28.99%-62.10%)for every 10 units increase of the daily valley concentration.The effect of the valley concentration of air pollutants was greater than the average or peak concentration.Meanwhile,it was found that air pollutants had a greater impact on women and people aged 65 and above.Temperature change and AMI hospitalization:All of the six temperature differences were significantly correlated with AMI hospitalization.In general,there was a U-shaped curve between DTDmeanl,DTDmax,1,DTDminl and risk of AMI hospitalization and with the increase of absolute these temperature differences,risk of AMI hospitalization gradually increased.There was a J-shaped curve between TR1,TD max,TDmin temperature difference and risk of AMI hospitalization,and with the increase of these temperature differences,the risk of AMI hospitalization gradually increased.When DTDmean1,DTDmax1 and DTDmin1 were in the extremely low percentile(1st),the 21-day CRRS were 2.73(95%CI:1.564.79),1.86(95%CI:1.10-3.14)and 9.94(95%CI:4.44-22.23),respectively.When DTDmean1,DTDmax1 and DTDminl were in the extremely high percentile(99th),the 21-day CRRS were 2.15(95%CI:1.54-3.01)、1.56(95%CI:1.04-2.35)、2.09(95%CI:1.433.08),respectively.When TR1,TDmax and TDmin were in the extremely high percentile(99th),the 21-day CRRS were 2.00(95%CI:1.73-2.85)1.71(95%CI:1.40-2.09)2.73(95%CI:2.04-3.66),respectively.At the same time,the effect of DTD max1 was more significant than DTDmax2 and DTDmax3.DTDmin1 DTDmeanl and TR1 were similar.The stratified analysis results showed that the effect of DTDmean1 was more significant on female population and the elderly population aged 65 and above;The effect of TR1,TDmin and TDmax was more significant in younger people between the ages of 20 and 65.ConclusionsThe daily peak,average and valley concentrations of PM2.5,PM10,SO2,NO2 and CO were significantly positively correlated with AMI hospitalization events among permanent residents in Beijing.The effect of valley concentration of air pollutants was more significant than the average or peak concentration.The effect of air pollutants on the risk of AMI hospitalization was more significant in women and 65 years and older.The temperature changeswere significantly correlated with AMI hospitalization events among permanent residents in Beijing,especially the extreme temperature changes and the temperature changes in short period.The effect of DTDmean1 on the female population and the elderly population aged 65 and above was more significant,and the effect of TR1,TDmin and TDmax on the young population between the ages of 20 and 65 was more significant. |
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