The Effects of Daily Mean Temperature and Diurnal Temperature Range on Ischemic Heart Disease Mortality in Hangzhou, China

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In China, the disease burden of IHD deaths has significantly increased. One of the main influencing factors of IHD is changing climates, and temperature and diurnal temperature range (DTR) are important indicators of climate change. Objective: To evaluate the effects of daily mean temperature and DTR on IHD mortality in Hangzhou, Zhejiang Province, China. Methods: We obtained daily IHD mortality data and meteorological data from mortality surveillance system from 2014 to 2016. Quasi-Poisson generalized linear regression with a distributed lag non-linear model (DLNM) was applied to estimate the associations between temperature variability and IHD deaths. Potential confounders were controlled in the analysis, including relative humidity, day of the week, public holidays, and long-term trends. Results: A total of 7423 IHD mortality data were included in this study. A J-shaped pattern of DTR and a reversely J-shaped pattern of temperature for IHD mortality were observed. Risk estimates showed that the relative risks ( RRs ) of IHD mortality with extreme high DTR at lag 0–7 days were 1.309 (95% CI : 0.985, 1.740) while RR s of IHD mortality with extreme low DTR at lag 0–2 days were 1.234 (95% CI : 1.043, 1.460). For extreme hot temperature, the highest RR s at lag 0–2 days were 1.559 (95% CI : 1.250, 1.943); for extremely cold temperatures, the RR s increased from 1.049 (95% CI : 0.930, 1.183) to 2.089 (95% CI : 1.854, 2.352). Conclusion: In Hangzhou city, short-term exposure to extreme temperature was associated with mortality for IHD. These findings have implications for policy decision-making and targeted interventions. Earth and environmental sciences/Climate sciences Health sciences/Cardiology Temperature Ischemic heart disease Mortality Distributed lag non-linear model Figures Figure 1 Figure 2 Figure 3 1. Introduction Cardiovascular disease (CVD) accounts for approximately one-third of global deaths 1 . CVD encompasses conditions affecting the vascular system supplying the heart, brain, and other vital organs 2 . Among these, ischemic heart disease (IHD) stands as the most prevalent cardiovascular disease 3 . IHD results from insufficient oxygen delivery to meet the heart's demands, often presenting as angina, acute myocardial infarction, and ischemic heart failure 4 . Merely 2%-7% of the general population exhibit no IHD risk factors, while over 70% of high-risk individuals have multiple risk factors 5 . Despite its global prominence and substantial impact on population health, the correlation between IHD mortality rates and environmental risk factors remains inadequately explored. In recent years, climate change has emerged as a pressing environmental public health concern, particularly with the escalating incidence of global warming 6 and extreme weather events 7 . Among meteorological factors, temperature has received widespread attention. It's widely acknowledged that rising temperatures escalate the risk of adverse health outcomes 8 , ranging from mild subclinical conditions to severe fatal events 9 . Heat exposure is an important but underappreciated risk factor contributing to cardiovascular disease 10 , particularly for those already afflicted by cardiovascular diseases. Increased temperatures can elevate blood pressure, augment blood viscosity, and amplify cardiac workload, consequently increasing the incidence of cardiovascular events. Diurnal temperature range (DTR) denotes the disparity between the maximum and minimum temperatures observed within a specific location over a single day 11 . Studying DTR is pivotal for comprehending climate patterns and their effects on ecosystems, agriculture, human health 12 , and various environmental facets 13 . Alterations in DTR can substantially impact ecological environment, crop cultivation, energy consumption, and the occurrence of weather-related health issues. Increasing epidemiological studies have shown that a rapid temperature change within a day is an independent risk factor for human health 14 . Nevertheless, in China, scant studies have delved into the effects of temperature variability on IHD mortality. Hangzhou, situated in the southeastern coastal area of China's Zhejiang Province, serves as the provincial capital and a hub for politics, economics, culture, and transportation within the province. Nestled on the southern bank of the Qiantang River 15 , adjacent to the East China Sea, Hangzhou lies within the Yangtze River Delta Economic Zone. The city experiences a subtropical monsoon climate characterized by four distinct seasons: sultry and humid summers, mild and pleasant springs and autumns, and chilly and damp winters. The rapid economic expansion and burgeoning population exert profound influence on the trajectories of annual average and extreme temperature indices 16 . Therefore, studying temperature variability has become an urgent task. Consequently, this research endeavors to scrutinize the correlation between daily mean temperature and Diurnal Temperature Range (DTR) as representative indicators of temperature variation, and mortality from IHD. Using daily meteorological data and mortality statistics from Hangzhou, China to explore this association through time-series analysis. 2. Methods 2.1. Mortality data Daily data on IHD deaths were retrieved from the mortality surveillance system of Zhejiang Provincial Center for Disease Prevention and Control from 1 January 2014 to 31 December 2016. IHD deaths were classified according to the International Classification of Diseases and Related Health Problems, 10th revision (ICD-10,120–125). Moreover, demographic information (including age and gender) was also collected from the surveillance system. 2.2. Meteorological and air pollution data Daily meteorological data including temperature and relative humidity were obtained from Zhejiang Meteorological Bureau during the study period. Daily mean temperature was the 24-hour average temperature. DTR can be calculated in each day from maximum temperature and minimum temperature by means of: DTR = T max − T min . ( 1 ) Over the same period, daily air pollution data on particulate matter less than 2.5 µm in aerodynamic diameter (PM2.5), nitrogen dioxide (NO 2 ) and sulfur dioxide (SO 2 ) were obtained from eight environmental monitoring stations in urban areas. The 24-hour average concentrations were applied for air pollutants. 2.3. Statistical analysis Frist, a descriptive review of the distribution of IHD deaths, pollutant datasets, and meteorological were presented, then the data were summarized as the mean, standard deviation, and percentile for continuous variables, as well as the absolute and relative frequencies for categorical variables. Next, time series analysis is applied to estimate the relationship between temperature exposure and IHD deaths. Daily IHD deaths are a small probability event for the general population, with a statistical distribution similar to the Poisson distribution. Thus, this study used a Poisson generalized linear model (GLM) for time series data, which incorporated a distributed lag nonlinear model (DLNM) with natural cubic spline function and a maximum lag of 7 days to account for the relationship between delay and non-linear effects 17,18 . Specifically, we modeled the cross-basis function for the lag effect on mortality or morbidity for temperature at different day. The quasi-Akaike information criterion (QAIC) was used for model selection to determine the number of degrees of freedom (df) for temperature and DTR 19 . The lowest QAIC determined the best model. Thereafter, 3 df for temperature and lag were adopted, and the df for DTR and lag were set to 3 as they would generate the best model, as well as df for long-term trend was 2 df per year. Besides, the moving average of lag 0–3 and relative humidity were adjusted using a natural cubic spline with 3 degrees of freedom. We also adjusted for the control variables mentioned in previous studies, including the day of the week and public holidays 20 . In this study, the model used is represented by equation: Y t ∼ Poisson (µ t ): log(µ t ) = α + β(DTR t , Lag)+γ(temperature t , Lag) + ns(humidity 03 , df) + ns(time t , df) + DOW + Holiday ( 2 ) where, µ t is the daily death number of IHD on specific day t; α and β represents the intercept of the model and the vector of the coefficient for DTR, respectively; γ refers to the vector of the coefficient for temperature. The minimum mortality temperature with the lowest risk was defined as the reference value 21 , and the 99th percentile of the data distribution were treated as the extreme high temperature and extreme DTR. To evaluate cumulative the time lag and correlation between temperature change and IHD deaths, we calculated relative risks ( RRs ) and 95% confidence intervals ( CIs ) of the data, followed by conducting age stratification analysis with the purpose of investigating whether there is a potential differential association between subgroups of exposure 22 . Seasonal modification for DTR was also examined. Consistent with the previous literature, warm season was defined as April to September, and cold season was defined as October to March 23 . The 95% CI was used to test the significant difference between subgroups. Several sensitivity analyses were performed to assess the robustness of our results. On the one hand, we varied the df of calendar time and relative humidity to examine the relative risks of temperatures. On the other hand, we added air pollutants in our analysis, including PM2.5, NO 2 , and SO 2 in the model, respectively. All the statistical analyses were performed using R software (4.1.0). Statistical significance was considered when a two-sided p value was less than 0.05 ( p < 0.05). 3. Results 3.1. Descriptive analysis Table 1 presents a concise overview of the study population, air pollutants, and meteorological conditions. Over the survey period spanning from January 1, 2014, to December 31, 2016, encompassing 1,096 days, a total of 7,423 deaths were documented within the study area, averaging 6.79 deaths per day (standard deviation, SD : 3.03). Usually, individuals aged 65 and above, commonly categorized as elderly, comprised a substantial proportion of the deceased, accounting for 92.12% of all recorded deaths. Among the deceased, males constituted 52.46% of the total, slightly outnumbering females at 47.54%. Table 1 Summary statistics for daily weather and air pollution variables in Hangzhou during the study period (January 1, 2014 to December 31, 2016). Variables N (%) Mean Standard Deviation Minimum P 10 P 25 P 50 P 75 P 90 Maximum IHD death Total 7423(100) 6.79 3.03 1.00 18.00 3.00 5.00 6.00 8.75 11.00 Male 3894(52.46) 3.56 1.98 0.00 11.00 1.00 2.00 3.00 5.00 6.00 Female 3529(47.54) 3.23 1.94 0.00 11.00 1.00 2.00 3.00 4.00 6.00 Old 6838(92.12) 6.25 2.94 0.00 18.00 3.00 4.00 6.00 8.00 10.00 Air pollution PM25 (µg/m 3 ) - 56.83 31.45 8.00 24.70 34.08 50.20 72.24 97.55 228.56 NO 2 (µg/m 3 ) - 47.90 17.63 10.89 27.20 34.76 45.85 58.03 72.40 118.30 SO 2 (µg/m 3 ) - 16.29 8.91 4.33 7.60 9.70 14.00 20.10 28.40 81.20 Meteorological data Temperature (℃) - 17.77 8.43 -5.00 5.85 10.50 19.15 24.43 28.20 34.40 DTR (℃) - 7.57 3.60 0.90 2.80 4.70 7.50 10.20 12.20 18.50 Relative humidity (%) - 74.41 13.86 27.00 54.00 65.00 76.00 85.00 93.00 98.00 The daily mean concentrations of PM2.5, NO 2 , and SO 2 stood at 56.83 µg/m 3 (ranging from 8.00 to 228.56 µg/m 3 ), 47.9 µg/m 3 (ranging from 10.89 to 118.30 µg/m 3 ), and 16.29 µg/m 3 (ranging from 4.33 to 81.20 µg/m 3 ), respectively, in Hangzhou, China. Furthermore, the daily mean temperature averaged at 17.77°C (ranging from − 5.00 to 34.40°C), with a diurnal temperature range (DTR) of 7.57°C (ranging from 0.90 to 18.50°C), and a relative humidity of 74.41% (ranging from 27.00 to 98.00%). Table 2 illustrates the Spearman correlation coefficients between weather conditions and key pollutants. The criteria for interpreting correlation coefficients were established based on previous research (low for | r | 0.6) 24 . Notably, air pollutants exhibited strong intercorrelations, while demonstrating only slight correlations with IHD deaths. Daily mean temperature displayed negative correlations with both IHD deaths and air pollutants. Conversely, there were low correlations observed between DTR and both air pollutants and temperature. Relative humidity exhibited a slight negative correlation with both IHD deaths and air pollutants, yet demonstrated a notably high negative correlation with DTR. Table 2 Spearman correlation coefficients between weather conditions in Hangzhou during the study period. IHD death PM25 NO 2 SO 2 Temperature DTR Relative humidity IHD death 1 PM25 0.142** 1 NO 2 0.161** 0.703** 1 SO 2 0.142** 0.677** 0.643** 1 Temperature -0.339** -0.378** -0.519** -0.447** 1 DTR -0.007 0.246** 0.130** 0.252** 0.168** 1 Relative humidity -0.097** -0.225** -0.069* -0.487** 0.085** -0.642** 1 * p < 0.05 ** p < 0.01 3.2. Time‑lag and cumulative effects Figure 1 depicts a three-dimensional graph illustrating the RRs for IHD deaths concerning DTR and temperature across various lag days. The results indicate a non-linear exposure-response relationship with increased RRs observed at both high and low temperatures. Moreover, high DTR appears to mitigate adverse effects on IHD deaths, while substantial temperature increases exhibit delayed adverse effects on IHD deaths. Figure 2 presents exposure-response curves detailing the cumulative effects over 7 days of both DTR and temperature on IHD deaths. The association between DTR and IHD deaths exhibits a J-shaped pattern, while the association with temperature demonstrates a reverse J-shaped pattern, with no clear threshold identified. Using median values as reference points, the minimum mortality temperature for DTR and temperature were found to be 6.5°C and 22.0°C, respectively. Subsequently, these values were used as references for further analysis. Figure 3 illustrates the cumulative effects of extremely DTR and temperature over a 7-days period. Regarding DTR, a steadily increasing association was observed between extreme high DTR (99th percentile: 16.0°C) and IHD deaths throughout the lag period, with a significant association noted in lag 0–7 days ( RR = 1.309, 95% CI : 0.985, 1.740). Conversely, the greatest RR was observed between extreme low DTR (1st percentile: 1.4°C) and IHD deaths in lag 0–2 days ( RR = 1.234, 95% CI : 1.043, 1.460). Similarly, temperature (1st percentile: 1.86°C; 99th percentile: 33.0°C) displayed significant associations with IHD deaths across the entire lag period, with the highest RR observed at lag 0–2 days ( RR = 1.559, 95% CI : 1.250, 1.943) for extremely hot temperatures. Conversely, for extremely cold temperatures, the RR increased from 1.049 (95% CI : 0.930, 1.183) to 2.089 (95% CI : 1.854, 2.352). Table 3 presents an analysis of the overall cumulative effect among various sub-groups based on gender and age. Notably, females exhibited greater susceptibility than males across different lag days, except for extreme high DTR, although no significant associations were found when comparing both extreme DTR and temperature with IHD deaths. In terms of age, the elderly demonstrated stronger sensitivity compared to the younger population. Table 3 Cumulative effect of extremely DTR and Temperature on IHD death by different population characteristics in various lag structures. Variables Characteristics Lag 0 Lag 0–2 Lag 0–4 Lag 0–7 p -value DTR Extremely low (1th) Total 1.158(1.064–1.261) 1.234(1.043–1.460) 1.113(0.891–1.389) 1.209(0.918–1.592) Male 1.095(0.977–1.229) 1.116(0.889-1.400) 1.006(0.746–1.357) 0.997(0.687–1.447) 0.145 Female 1.232(1.091–1.391) 1.377(1.084–1.749) 1.240(0.904–1.701) 1.489(1.009–2.199) Ref. Old 1.153(1.053–1.262) 1.223(1.024–1.462) 1.106(0.874–1.399) 1.214(0.908–1.624) Extremely high (99th) Total 1.044(0.945–1.154) 1.138(0.932–1.390) 1.230(0.952–1.589) 1.309(0.985–1.740) Male 1.110(0.973–1.265) 1.251(0.962–1.627) 1.293(0.920–1.818) 1.306(0.892–1.911) 0.965 Female 0.969(0.837–1.122) 1.011(0.755–1.354) 1.147(0.791–1.662) 1.290(0.857–1.942) Ref. Old 1.067(0.960–1.185) 1.189(0.964–1.468) 1.291(0.984–1.692) 1.380(1.021–1.865) Temperature Extremely cold (1th) Total 1.049(0.930–1.183) 1.219(1.011–1.470) 1.494(1.230–1.815) 2.089(1.854–2.352) Male 1.004(0.853–1.181) 1.133(0.880–1.460) 1.417(1.090–1.842) 1.970(1.674–2.320) 0.661 Female 1.096(0.924–1.299) 1.311(1.005–1.710) 1.580(1.197–2.084) 2.216(1.876–2.617) Ref. Old 1.053(0.927–1.195) 1.268(1.040–1.545) 1.618(1.318–1.987) 2.132(1.880–2.418) Extremely hot (99th) Total 1.250(1.074–1.456) 1.559(1.250–1.943) 1.533(1.222–1.923) 1.233(1.042–1.459) Male 1.165(0.948–1.430) 1.401(1.039–1.891) 1.464(1.076–1.994) 1.289(1.027–1.616) 0.558 Female 1.348(1.086–1.674) 1.743(1.276–2.381) 1.604(1.166–2.207) 1.168(0.919–1.483) Ref. Old 1.290(1.098–1.515) 1.644(1.302–2.075) 1.592(1.253–2.023) 1.243(1.040–1.485) 3.3. Sensitivity analysis To test the robustness of our results, several sensitivity analyses were conducted and presented in Table 4 . Initially, we adjusted the df from 3 to 5 for long-term and seasonal trends, 4 to 7 for relative humidity. The results indicated minimal changes in the estimation values, suggesting that our findings remained stable. Furthermore, we conducted sensitivity analyses by incorporating additional air pollutants into the model. Overall, when PM2.5 and NO 2 were added to the model, the RRs slightly decreased, whereas the inclusion of SO 2 led to a moderate increase in RRs . However, these adjustments did not significantly alter the results. Consequently, we have confidence that the model specifications employed in our study accurately reflect the main effects of DTR and temperature on IHD deaths. Table 4 Sensitivity analysis of different df for long-term and seasonal trend, relative humidity and controlling air pollution factors. Variables Characteristics Baisc model Adjusting df of long-term and seasonal trend Adjusting df of relative humidity Introducing other air pollution factors 3 5 4 5 6 7 PM 2.5 NO 2 SO 2 DTR Extremely low (1th) Total 1.209(0.918–1.592) 1.226(0.930–1.616) 1.213(0.921–1.597) 1.210(0.919–1.595) 1.213(0.919–1.601) 1.206(0.913–1.592) 1.187(0.899–1.567) 1.214(0.918–1.605) 1.186(0.896–1.571) 1.235(0.931–1.639) Male 0.997(0.687–1.447) 1.007(0.694–1.462) 1.003(0.691–1.457) 0.994(0.684–1.445) 1.011(0.694–1.473) 1.003(0.688–1.461) 0.979(0.672–1.425) 1.006(0.689–1.469) 0.965(0.660–1.410) 1.016(0.694–1.489) Female 1.489(1.009–2.199) 1.519(1.027–2.245) 1.489(1.008-2.200) 1.497(1.013–2.211) 1.476(0.997–2.186) 1.471(0.992–2.179) 1.461(0.986–2.166) 1.491(1.003–2.216) 1.483(0.996–2.209) 1.532(1.026–2.290) Old 1.214(0.908–1.624) 1.232(0.920–1.650) 1.218(0.911–1.630) 1.216(0.908–1.627) 1.221(0.911–1.638) 1.213(0.904–1.628) 1.193(0.889–1.599) 1.224(0.911–1.644) 1.193(0.887–1.606) 1.277(0.947–1.721) Extremely high (99th) Total 1.309(0.985–1.740) 1.317(0.992–1.749) 1.260(0.940–1.688) 1.308(0.983–1.741) 1.314(0.987–1.751) 1.313(0.985–1.749) 1.302(0.978–1.734) 1.279(0.957–1.708) 1.269(0.945–1.704) 1.369(1.006–1.863) Male 1.306(0.892–1.911) 1.312(0.898–1.916) 1.226(0.829–1.814) 1.324(0.904–1.940) 1.350(0.920–1.981) 1.345(0.916–1.975) 1.330(0.906–1.951) 1.255(0.851–1.850) 1.228(0.828–1.821) 1.316(0.871–1.987) Female 1.290(0.857–1.942) 1.298(0.865–1.949) 1.271(0.835–1.936) 1.271(0.844–1.915) 1.258(0.833–1.898) 1.258(0.833-1.900) 1.252(0.829–1.891) 1.283(0.847–1.946) 1.290(0.844–1.971) 1.405(0.902–2.188) Old 1.380(1.021–1.865) 1.388(1.028–1.874) 1.323(0.971–1.804) 1.381(1.021–1.868) 1.391(1.026–1.884) 1.388(1.024–1.881) 1.376(1.016–1.864) 1.326(0.976–1.802) 1.326(0.971–1.812) 1.417(1.022–1.964) Table 4 cont. Variables Characteristics Baisc model Adjusting df of long-term and seasonal trend Adjusting df of relative humidity Introducing other air pollution factors 3 5 4 5 6 7 PM 2.5 NO 2 SO 2 Temperature Extremely cold (1th) Total 2.089(1.854–2.352) 2.089(1.855–2.353) 2.043(1.810–2.306) 2.088(1.854–2.352) 2.087(1.853–2.352) 2.097(1.861–2.363) 2.111(1.874–2.379) 2.089(1.842–2.368) 2.110(1.856–2.399) 2.026(1.787–2.297) Male 1.970(1.674–2.320) 1.974(1.677–2.322) 1.920(1.625–2.268) 1.970(1.673–2.320) 1.965(1.669–2.314) 1.975(1.677–2.327) 1.997(1.696–2.353) 1.990(1.674–2.364) 2.004(1.682–2.388) 1.916(1.614–2.276) Female 2.216(1.876–2.617) 2.208(1.870–2.606) 2.173(1.835–2.574) 2.214(1.875–2.615) 2.219(1.878–2.622) 2.228(1.885–2.633) 2.232(1.888–2.639) 2.192(1.838–2.614) 2.225(1.859–2.665) 2.141(1.796–2.553) Old 2.132(1.880–2.418) 2.132(1.881–2.417) 2.083(1.833–2.367) 2.132(1.880–2.417) 2.130(1.878–2.416) 2.140(1.887–2.428) 2.157(1.902–2.447) 2.123(1.859–2.425) 2.152(1.879–2.464) 2.045(1.791–2.334) Extremely hot (99th) Total 1.233(1.042–1.459) 1.218(1.029–1.441) 1.224(1.033–1.450) 1.233(1.042–1.459) 1.235(1.043–1.462) 1.237(1.045–1.465) 1.227(1.037–1.453) 1.174(0.974–1.415) 1.217(1.007–1.470) 1.255(1.047–1.505) Male 1.289(1.027–1.616) 1.279(1.020–1.603) 1.265(1.006–1.590) 1.299(1.035–1.631) 1.306(1.040–1.640) 1.310(1.043–1.645) 1.294(1.031–1.625) 1.246(0.969–1.602) 1.282(0.993–1.654) 1.299(1.017–1.659) Female 1.168(0.919–1.483) 1.148(0.903–1.460) 1.170(0.919–1.490) 1.156(0.909–1.469) 1.152(0.906–1.465) 1.153(0.907–1.467) 1.148(0.903–1.461) 1.093(0.837–1.427) 1.141(0.872–1.493) 1.202(0.927–1.557) Old 1.243(1.040–1.485) 1.226(1.026–1.466) 1.232(1.029–1.475) 1.243(1.040–1.487) 1.246(1.042–1.491) 1.249(1.044–1.493) 1.237(1.035–1.480) 1.177(0.966–1.435) 1.218(0.997–1.488) 1.265(1.043–1.533) 4. Discussions This study investigated the association between DTR and temperature changes and IHD mortality in Zhejiang, China, from 2014 to 2016. Generally, the correlation between DTR and IHD deaths shows a non-linear (J-shaped) trend, while a reverse J-shaped trend is observed in the effect of temperatures. We then examined the lag effects on IHD deaths. For DTRs, we found that low DTR had almost no influence on RR , while high DTR led to a cumulative effect over the lag period. Regarding temperatures, we found that low temperature also accumulated RR over 7 days, while only a limited cumulative effect was observed for high temperature, which ended on the third lag day. Furthermore, no significant differences were found between gender and age, and the introduction of different pollutants, including PM2.5, NO 2 , and SO 2 , did not significantly confound the results. Currently, extensive attention has been paid to the association of DTR with mortality 25,26 . However, to our best knowledge, few studies have examined how extreme DTR affects mortality risk from IHD. Our study provides more generalized evidence to support the effects of DTR on IHD. Specifically, we found a non-linear (J-shaped) association between DTR and IHD deaths. Additionally, our study identified the cumulative effect of extremely low DTR, which persisted throughout the lag period, while the effect of high DTR was immediate and limited. This result is consistent with a previous study in Jiuquan, which found that the effect of low DTR was significantly more deleterious than that of high DTR on hospital admissions 27 . A study in Guangdong also examined the cumulative effects of extreme DTRs, which were greater for extreme high DTRs than those of extreme low DTRs 28 . Generally, the prior conclusions regarding the association between DTR and daily mortality were not coherent. Some reported a linear relationship between DTR and mortality 29 , which was different from our results. However, some observed a non-linear (J-shaped) relationship between DTR and cardiovascular-specific mortality 30,31 ,which agrees with our findings. The results of these studies support that extreme DTR is a risk factor for IHD, and the different effects might be explained by the area characteristics and differences in analytic approaches 32 . In recent years, the study on the relationship between temperature and disease has received widespread attention from researchers. In general, correlations between daily main temperature and death present non-linear trends, mostly showing "U", "V" or "J" shapes 33 . A study in Taiwan found a U-shaped relation with temperature for CAD, and cold climates resulted in a reduction of the least temperature range for elderly death 34 . Another study in Hubei found that low temperature increased the risk of IHD deaths, while no effect of high temperature on IHD death was observed 33 . Overall, previous studies have suggested that extremely cold and hot temperatures should be considered to affect IHD mortality, but many ignored the lag effects. Consistent with the majority of existing evidence, we detected a non-linear (reversely J-shaped) association between temperature and IHD mortality. Besides, the effects of low temperature could last for more than 7 days, while high temperature only maintained effects for 3 days. This was similar to a study in Guangzhou, which detected a cold effect on IHB that persisted for approximately 12 days, while the hot effect was limited to the first 5 days 35 . On the one hand, this may be attributed to the hypothesis of acclimatization to local climatic conditions 36,37 . And Zhejiang Province, as a southern city, its citizens are not sensitive to thermal effects. On the other hand, our findings are biologically plausible. When repeatedly exposed to heat stress, the failure of thermoregulation and the physiological changes in the circulatory system may lead to an increase in mortality 38 . However, considering the exact mechanism for the temperature-mortality relationship is uncertain to date, further investigation is needed. Regarding influencing factors, some evidence has found that the magnitude of DTR and temperature effects might vary by gender and age. Several studies reported that males and the elderly showed more vulnerability to the adverse effects of DTR than females and the young for IHD mortality 27,31,39 , while some agreed that women and the elderly are more susceptible 40 . Besides, a previous study in Yuxi suggested no evidence for effect modification by gender 30 . Similarly, despite the slightly increased risk of IHD among females and the elderly, our research identified no significant discrepancies between males and females, as well as between the elderly and youth. The differences in extreme temperature and DTR effects on gender might rely on the research location and population 41 , and the susceptibility of elderly people may be due to their poor physiological adaptability to changes in ambient temperature. Further study in this field is needed to investigate these potential modifiers. Many previous studies considered the potential confounding role of air pollutants on the effects of temperature variability on mortalities 40,42,43 . Accordingly, we introduced the confounding effects of various air pollutants, including PM2.5, NO 2 , and SO 2 , to our base models. But no significant changes were found in the results, indicating that air pollutants were not confounders in this study. There were several strengths in this study. To our best knowledge, limited research simultaneously evaluated the impact of both extreme DTR and temperature on IHD-specific mortality. Additionally, we conducted modification analysis based on gender and age and incorporated humidity and ambient pollutants for subgroup analysis. This indicates that our study of the effects of extreme DTR and temperature on IHD mortality is systematic and comprehensive. This research, however, is subject to several limitations. Firstly, the design of the current study is ecological study. The lack of individual data might result in factual deviations and insufficient evidence of causality. Besides, other potential confounders such as individual habits or medical history might affect the accuracy of results. Secondly, our data only collect from one single city, which might affect the extrapolation of the results, especially for regions with different geographic situations and climates. Thirdly, insufficient sample size for statistical measurement limited us from performing further subgroup analysis, such as socioeconomic status. Thus, in order to validate the above findings and elucidate their potential mechanisms, it is necessary to further explore diverse groups and more detailed factors, and establish sophisticated models based on more comprehensive data. 5. Conclusions In summary, this study unveils a non-linear association between diurnal temperature range (DTR), temperature variability, and mortality from ischemic heart disease (IHD) in Hangzhou, China, with extreme temperature and DTR exhibiting a cumulative effect over the lag period. Gender and age were found not to significantly modify these associations. These findings hold significant implications for the public health department in Hangzhou, suggesting the need to adopt prevention and intervention strategies aimed at reducing exposure to extreme temperature and DTR. Additionally, our study provides insights for research in other Asian cities facing similar climate challenges. In the broader context of global climate change, our findings indicate that by enhancing our understanding of the intricate relationship between temperature variability and health outcomes, we can better equip ourselves to address the multifaceted challenges posed by a changing climate. Declarations Source of Funding: This research was funded by the National Natural Science Foundation of China (82273749 and 81773468), the Natural Science Foundation of Zhejiang Province, China (LTGY23H240001), the Opening Foundation of NHC Key Laboratory of Etiology and Epidemiology (Harbin Medical University) (NHCKLEE20230908). Disclosures : The authors declare no conflict of interest. Data Availability Statement: The data supporting the findings of this study are available from the Mortality Surveillance System of the Zhejiang Provincial Center for Disease Prevention and Control. However, due to restrictions on data availability under license for this study, they are not publicly accessible. Access to the data may be granted upon reasonable request and with permission from the Mortality Surveillance System of the Zhejiang Provincial Center for Disease Prevention and Control. Due to privacy concerns regarding mortality data, it cannot be publicly disclosed at this time. For verification purposes, we can provide software screenshots. For inquiries regarding the data, please contact the corresponding author, Gaofeng Cai, at [email protected] . Author Contribution Manjin Xu and Yunfeng Xu wrote the main manuscript text and Zhe Mo prepared figures 1-3 and tables 1-4.All authors reviewed the manuscript. References Mozaffarian, D. et al. Heart Disease and Stroke Statistics-2016 Update A Report From the American Heart Association. CIRCULATION 133 , E38–E360 (2016). Zhao, D., Liu, J., Wang, M., Zhang, X. & Zhou, M. 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Extreme Weather and Climate Change: Population Health and Health System Implications. in ANNUAL REVIEW OF PUBLIC HEALTH, VOL 42, 2021 (ed. Fielding, J.) vol. 42 293–315 (2021). Kim, J. & Lee, J. Synoptic approach to evaluate the effect of temperature on pediatric respiratory disease-related hospitalization in Seoul, Korea. Environ. Res. 178 , (2019). Liu, J., Varghese, B. & Hansen, A. Heat exposure and cardiovascular health outcomes: a systematic review and meta-analysis (vol 6, pg e484, 2022). LANCET Planet. Health 6 , E644–E644 (2022). Braganza, K., Karoly, D. & Arblaster, J. Diurnal temperature range as an index of global climate change during the twentieth century. Geophys. Res. Lett. 31 , (2004). Davis, R., Hondula, D. & Sharif, H. Examining the diurnal temperature range enigma: why is human health related to the daily change in temperature? Int. J. Biometeorol. 64 , 397–407 (2020). Gallou, A. et al. Diurnal temperature range as a key predictor of plants’ elevation ranges globally. Nat. Commun. 14 , (2023). Cheng, J. et al. Impact of diurnal temperature range on human health: a systematic review. Int. J. Biometeorol. 58 , 2011–2024 (2014). Dai, L. et al. Pollution characteristics and source analysis of microplastics in the Qiantang River in southeastern China. CHEMOSPHERE 293 , (2022). Gu, C., Hu, L., Zhang, X., Wang, X. & Guo, J. Climate change and urbanization in the Yangtze River Delta. HABITAT Int. 35 , 544–552 (2011). Gasparrini, A., Armstrong, B. & Kenward, M. G. Distributed lag non‐linear models. Stat. Med. 29 , 2224–2234 (2010). Armstrong, B. Models for the Relationship Between Ambient Temperature and Daily Mortality. Epidemiology 17 , 624–631 (2006). Peng, R., Dominici, F. & Louis, T. Model choice in time series studies of air pollution and mortality. J. R. Stat. Soc. Ser. -Stat. Soc. 169 , 179–198 (2006). Alahmad, B., Shakarchi, A., Alseaidan, M. & Fox, M. The effects of temperature on short-term mortality risk in Kuwait: A time-series analysis. Environ. Res. 171 , 278–284 (2019). Gasparrini, A. et al. Mortality risk attributable to high and low ambient temperature: a multicountry observational study. LANCET 386 , 369–375 (2015). Schenker, N. & Gentleman, J. On judging the significance of differences by examining the overlap between confidence intervals. Am. Stat. 55 , 182–186 (2001). Guo, H. et al. Short-term exposure to nitrogen dioxide and outpatient visits for cause-specific conjunctivitis: A time-series study in Jinan, China. Atmos. Environ. 247 , (2021). Li, D., He, R., Liu, P. & Jiang, H. Di erential e ects of size-specific particulate matter on the number of visits to outpatient fever clinics: A time-series analysis in Zhuhai, China. Front. Public Health . Zhang, Y., Peng, M., Wang, L. & Yu, C. Association of diurnal temperature range with daily mortality in England and Wales: A nationwide time-series study. Sci. TOTAL Environ. 619 , 291–300 (2018). Kim, J. et al. Comprehensive approach to understand the association between diurnal temperature range and mortality in East Asia. Sci. Total Environ. 539 , 313–321 (2016). Zhai, G., Zhang, J., Zhang, K. & Chai, G. Impact of diurnal temperature range on hospital admissions for cerebrovascular disease among farmers in Northwest China. Sci. Rep. 12 , (2022). Luo, Y. et al. Lagged Effect of Diurnal Temperature Range on Mortality in a Subtropical Megacity of China. PLoS ONE 8 , e55280 (2013). Lim, Y.-H., Park, A. K. & Kim, H. Modifiers of diurnal temperature range and mortality association in six Korean cities. Int. J. Biometeorol. 56 , 33–42 (2012). Ding, Z. et al. Impact of diurnal temperature range on mortality in a high plateau area in southwest China: A time series analysis. Sci. TOTAL Environ. 526 , 358–365 (2015). Tang, J. et al. Effects of diurnal temperature range on mortality in Hefei city, China. Int. J. Biometeorol. 62 , 851–860 (2018). Bao, J., Wang, Z., Yu, C. & Li, X. The influence of temperature on mortality and its Lag effect: a study in four Chinese cities with different latitudes. BMC PUBLIC Health 16 , (2016). Zhang, Y. et al. The Short-Term Effect of Ambient Temperature on Mortality in Wuhan, China: A Time-Series Study Using a Distributed Lag Non-Linear Model. Int. J. Environ. Res. Public. Health 13 , 722 (2016). PAN, W., LI, L. & TSAI, M. TEMPERATURE EXTREMES AND MORTALITY FROM CORONARY HEART-DISEASE AND CEREBRAL INFARCTION IN ELDERLY CHINESE. LANCET 345 , 353–355 (1995). Yang, J., Ou, C.-Q., Ding, Y., Zhou, Y.-X. & Chen, P.-Y. Daily temperature and mortality: a study of distributed lag non-linear effect and effect modification in Guangzhou. Environ. Health 11 , 63 (2012). McMichael, A., Woodruff, R. & Hale, S. Climate change and human health: present and future risks (vol 367, pg 859, 2006). LANCET 368 , 842–842 (2006). Guo, Y. et al. Extremely cold and hot temperatures increase the risk of ischaemic heart disease mortality: epidemiological evidence from China. Heart 99 , 195–203 (2013). Nixdorf-Miller, A., Hunsaker, D. & Hunsaker, J. Hypothermia and hyperthermia medicolegal investigation of morbidity and mortality from exposure to environmental temperature extremes. Arch. Pathol. Lab. Med. 130 , 1297–1304 (2006). Zha, Q., Chai, G., Zhang, Z., Sha, Y. & Su, Y. Effects of diurnal temperature range on cardiovascular disease hospital admissions in farmers in China’s Western suburbs. Environ. Sci. Pollut. Res. 28 , 64693–64705 (2021). Xiao, Y. et al. Short-Term Effect of Temperature Change on Non-Accidental Mortality in Shenzhen, China. Int. J. Environ. Res. Public. Health 18 , (2021). Basu, R. High ambient temperature and mortality: a review of epidemiologic studies from 2001 to 2008. Environ. Health 8 , (2009). Hu, Y. et al. Season-stratified effects of meteorological factors on childhood asthma in Shanghai, China. Environ. Res. 191 , (2020). Zhang, T., Qin, W., Nie, T., Zhang, D. & Wu, X. Effects of meteorological factors on the incidence of varicella in Lu’an, Eastern China, 2015-2020. Environ. Sci. Pollut. Res. 30 , 10052–10062 (2023). Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4617516","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":330171134,"identity":"712291a8-4013-48d4-ad62-5aab6cfe5906","order_by":0,"name":"Zhe Mo","email":"","orcid":"","institution":"Zhejiang Provincial Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Zhe","middleName":"","lastName":"Mo","suffix":""},{"id":330171135,"identity":"9bd0c583-a94f-4ab7-b3ea-1b0f9ae650ec","order_by":1,"name":"Manjin Xu","email":"","orcid":"","institution":"Zhejiang Provincial Center for Disease Control and 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Disease Control and Prevention","correspondingAuthor":true,"prefix":"","firstName":"Gaofeng","middleName":"","lastName":"Cai","suffix":""}],"badges":[],"createdAt":"2024-06-21 12:50:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4617516/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4617516/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-78902-5","type":"published","date":"2024-12-04T15:58:16+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":60945300,"identity":"c3989c9f-71ca-4417-9e2e-2c9cbe6c023f","added_by":"auto","created_at":"2024-07-23 22:22:47","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":37589,"visible":true,"origin":"","legend":"\u003cp\u003eRelative risk of IHD death with DTR and temperature in lags (lag 0 to lag 7 day).\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4617516/v1/d3f4cd07eca9d6420939a90a.jpg"},{"id":60944758,"identity":"c79cc8f6-2947-4420-9d41-d88a05cdca88","added_by":"auto","created_at":"2024-07-23 22:14:47","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":21106,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative effects of DTR and temperature at lag 0–7 on IHD death.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4617516/v1/59a02f5626e2aef557f81372.jpg"},{"id":60944756,"identity":"d454624b-d397-4b6a-a5a4-d5b0e2f99a72","added_by":"auto","created_at":"2024-07-23 22:14:47","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":28779,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative effects of extremely DTR and temperature over 7 days at various lag structures.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4617516/v1/39a3f64955bff82094554cc0.jpg"},{"id":70965014,"identity":"56b2bdbc-2e05-4b5c-bfac-a015f2e649cc","added_by":"auto","created_at":"2024-12-09 16:17:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":934533,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4617516/v1/1d3f1831-3de7-41ab-8c29-cb5cc3ccb521.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Effects of Daily Mean Temperature and Diurnal Temperature Range on Ischemic Heart Disease Mortality in Hangzhou, China","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eCardiovascular disease (CVD) accounts for approximately one-third of global deaths\u003csup\u003e1\u003c/sup\u003e. CVD encompasses conditions affecting the vascular system supplying the heart, brain, and other vital organs\u003csup\u003e2\u003c/sup\u003e. Among these, ischemic heart disease (IHD) stands as the most prevalent cardiovascular disease\u003csup\u003e3\u003c/sup\u003e. IHD results from insufficient oxygen delivery to meet the heart's demands, often presenting as angina, acute myocardial infarction, and ischemic heart failure\u003csup\u003e4\u003c/sup\u003e. Merely 2%-7% of the general population exhibit no IHD risk factors, while over 70% of high-risk individuals have multiple risk factors\u003csup\u003e5\u003c/sup\u003e. Despite its global prominence and substantial impact on population health, the correlation between IHD mortality rates and environmental risk factors remains inadequately explored.\u003c/p\u003e \u003cp\u003eIn recent years, climate change has emerged as a pressing environmental public health concern, particularly with the escalating incidence of global warming\u003csup\u003e6\u003c/sup\u003e and extreme weather events\u003csup\u003e7\u003c/sup\u003e. Among meteorological factors, temperature has received widespread attention. It's widely acknowledged that rising temperatures escalate the risk of adverse health outcomes\u003csup\u003e8\u003c/sup\u003e, ranging from mild subclinical conditions to severe fatal events\u003csup\u003e9\u003c/sup\u003e. Heat exposure is an important but underappreciated risk factor contributing to cardiovascular disease\u003csup\u003e10\u003c/sup\u003e, particularly for those already afflicted by cardiovascular diseases. Increased temperatures can elevate blood pressure, augment blood viscosity, and amplify cardiac workload, consequently increasing the incidence of cardiovascular events.\u003c/p\u003e \u003cp\u003eDiurnal temperature range (DTR) denotes the disparity between the maximum and minimum temperatures observed within a specific location over a single day\u003csup\u003e11\u003c/sup\u003e. Studying DTR is pivotal for comprehending climate patterns and their effects on ecosystems, agriculture, human health\u003csup\u003e12\u003c/sup\u003e, and various environmental facets\u003csup\u003e13\u003c/sup\u003e. Alterations in DTR can substantially impact ecological environment, crop cultivation, energy consumption, and the occurrence of weather-related health issues. Increasing epidemiological studies have shown that a rapid temperature change within a day is an independent risk factor for human health\u003csup\u003e14\u003c/sup\u003e. Nevertheless, in China, scant studies have delved into the effects of temperature variability on IHD mortality.\u003c/p\u003e \u003cp\u003eHangzhou, situated in the southeastern coastal area of China's Zhejiang Province, serves as the provincial capital and a hub for politics, economics, culture, and transportation within the province. Nestled on the southern bank of the Qiantang River\u003csup\u003e15\u003c/sup\u003e, adjacent to the East China Sea, Hangzhou lies within the Yangtze River Delta Economic Zone. The city experiences a subtropical monsoon climate characterized by four distinct seasons: sultry and humid summers, mild and pleasant springs and autumns, and chilly and damp winters. The rapid economic expansion and burgeoning population exert profound influence on the trajectories of annual average and extreme temperature indices\u003csup\u003e16\u003c/sup\u003e. Therefore, studying temperature variability has become an urgent task. Consequently, this research endeavors to scrutinize the correlation between daily mean temperature and Diurnal Temperature Range (DTR) as representative indicators of temperature variation, and mortality from IHD. Using daily meteorological data and mortality statistics from Hangzhou, China to explore this association through time-series analysis.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Mortality data\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eDaily data on IHD deaths were retrieved from the mortality surveillance system of Zhejiang Provincial Center for Disease Prevention and Control from 1 January 2014 to 31 December 2016. IHD deaths were classified according to the International Classification of Diseases and Related Health Problems, 10th revision (ICD-10,120\u0026ndash;125). Moreover, demographic information (including age and gender) was also collected from the surveillance system.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Meteorological and air pollution data\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eDaily meteorological data including temperature and relative humidity were obtained from Zhejiang Meteorological Bureau during the study period. Daily mean temperature was the 24-hour average temperature. DTR can be calculated in each day from maximum temperature and minimum temperature by means of:\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDTR\u0026thinsp;=\u0026thinsp;T\u003csub\u003emax\u003c/sub\u003e \u0026minus; T\u003csub\u003emin\u003c/sub\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOver the same period, daily air pollution data on particulate matter less than 2.5 \u0026micro;m in aerodynamic diameter (PM2.5), nitrogen dioxide (NO\u003csub\u003e2\u003c/sub\u003e) and sulfur dioxide (SO\u003csub\u003e2\u003c/sub\u003e) were obtained from eight environmental monitoring stations in urban areas. The 24-hour average concentrations were applied for air pollutants.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Statistical analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFrist, a descriptive review of the distribution of IHD deaths, pollutant datasets, and meteorological were presented, then the data were summarized as the mean, standard deviation, and percentile for continuous variables, as well as the absolute and relative frequencies for categorical variables. Next, time series analysis is applied to estimate the relationship between temperature exposure and IHD deaths.\u003c/p\u003e \u003cp\u003eDaily IHD deaths are a small probability event for the general population, with a statistical distribution similar to the Poisson distribution. Thus, this study used a Poisson generalized linear model (GLM) for time series data, which incorporated a distributed lag nonlinear model (DLNM) with natural cubic spline function and a maximum lag of 7 days to account for the relationship between delay and non-linear effects\u003csup\u003e17,18\u003c/sup\u003e. Specifically, we modeled the cross-basis function for the lag effect on mortality or morbidity for temperature at different day.\u003c/p\u003e \u003cp\u003eThe quasi-Akaike information criterion (QAIC) was used for model selection to determine the number of degrees of freedom (df) for temperature and DTR\u003csup\u003e19\u003c/sup\u003e. The lowest QAIC determined the best model.\u003c/p\u003e \u003cp\u003eThereafter, 3 df for temperature and lag were adopted, and the df for DTR and lag were set to 3 as they would generate the best model, as well as df for long-term trend was 2 df per year. Besides, the moving average of lag 0\u0026ndash;3 and relative humidity were adjusted using a natural cubic spline with 3 degrees of freedom. We also adjusted for the control variables mentioned in previous studies, including the day of the week and public holidays\u003csup\u003e20\u003c/sup\u003e. In this study, the model used is represented by equation:\u003c/p\u003e \u003cp\u003eY\u003csub\u003et\u003c/sub\u003e \u0026sim; Poisson (\u0026micro;\u003csub\u003et\u003c/sub\u003e):\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elog(\u0026micro;\u003csub\u003et\u003c/sub\u003e) = α\u0026thinsp;+\u0026thinsp;β(DTR\u003csub\u003et\u003c/sub\u003e, Lag)+γ(temperature\u003csub\u003et\u003c/sub\u003e, Lag)\u0026thinsp;+\u0026thinsp;ns(humidity\u003csub\u003e03\u003c/sub\u003e, df)\u0026thinsp;+\u0026thinsp;ns(time\u003csub\u003et\u003c/sub\u003e, df)\u0026thinsp;+\u0026thinsp;DOW\u0026thinsp;+\u0026thinsp;Holiday\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ewhere, \u0026micro;\u003csub\u003et\u003c/sub\u003e is the daily death number of IHD on specific day t; α and β represents the intercept of the model and the vector of the coefficient for DTR, respectively; γ refers to the vector of the coefficient for temperature. The minimum mortality temperature with the lowest risk was defined as the reference value\u003csup\u003e21\u003c/sup\u003e, and the 99th percentile of the data distribution were treated as the extreme high temperature and extreme DTR. To evaluate cumulative the time lag and correlation between temperature change and IHD deaths, we calculated relative risks (\u003cem\u003eRRs\u003c/em\u003e) and 95% confidence intervals (\u003cem\u003eCIs\u003c/em\u003e) of the data, followed by conducting age stratification analysis with the purpose of investigating whether there is a potential differential association between subgroups of exposure\u003csup\u003e22\u003c/sup\u003e. Seasonal modification for DTR was also examined. Consistent with the previous literature, warm season was defined as April to September, and cold season was defined as October to March\u003csup\u003e23\u003c/sup\u003e. The 95% \u003cem\u003eCI\u003c/em\u003e was used to test the significant difference between subgroups.\u003c/p\u003e \u003cp\u003eSeveral sensitivity analyses were performed to assess the robustness of our results. On the one hand, we varied the df of calendar time and relative humidity to examine the relative risks of temperatures. On the other hand, we added air pollutants in our analysis, including PM2.5, NO\u003csub\u003e2\u003c/sub\u003e, and SO\u003csub\u003e2\u003c/sub\u003e in the model, respectively.\u003c/p\u003e \u003cp\u003eAll the statistical analyses were performed using R software (4.1.0). Statistical significance was considered when a two-sided \u003cem\u003ep\u003c/em\u003e value was less than 0.05 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Descriptive analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents a concise overview of the study population, air pollutants, and meteorological conditions. Over the survey period spanning from January 1, 2014, to December 31, 2016, encompassing 1,096 days, a total of 7,423 deaths were documented within the study area, averaging 6.79 deaths per day (standard deviation, \u003cem\u003eSD\u003c/em\u003e: 3.03). Usually, individuals aged 65 and above, commonly categorized as elderly, comprised a substantial proportion of the deceased, accounting for 92.12% of all recorded deaths. Among the deceased, males constituted 52.46% of the total, slightly outnumbering females at 47.54%.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary statistics for daily weather and air pollution variables in Hangzhou during the study period (January 1, 2014 to December 31, 2016).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStandard Deviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c11\" namest=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003csub\u003e25\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP\u003csub\u003e50\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP\u003csub\u003e75\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eP\u003csub\u003e90\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIHD death\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7423(100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e8.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e11.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3894(52.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e6.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3529(47.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e6.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOld\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6838(92.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e8.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e10.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAir pollution\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePM25 (\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e24.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e34.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e50.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e72.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e97.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e228.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003csub\u003e2\u003c/sub\u003e (\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e34.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e45.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e58.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e72.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e118.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSO\u003csub\u003e2\u003c/sub\u003e (\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e14.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e20.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e28.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e81.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeteorological data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperature (℃)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e19.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e24.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e28.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e34.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDTR (℃)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e10.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e12.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e18.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelative humidity (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e74.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e54.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e65.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e76.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e85.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e93.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e98.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe daily mean concentrations of PM2.5, NO\u003csub\u003e2\u003c/sub\u003e, and SO\u003csub\u003e2\u003c/sub\u003e stood at 56.83 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e (ranging from 8.00 to 228.56 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e), 47.9 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e (ranging from 10.89 to 118.30 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e), and 16.29 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e (ranging from 4.33 to 81.20 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e), respectively, in Hangzhou, China. Furthermore, the daily mean temperature averaged at 17.77\u0026deg;C (ranging from \u0026minus;\u0026thinsp;5.00 to 34.40\u0026deg;C), with a diurnal temperature range (DTR) of 7.57\u0026deg;C (ranging from 0.90 to 18.50\u0026deg;C), and a relative humidity of 74.41% (ranging from 27.00 to 98.00%).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the Spearman correlation coefficients between weather conditions and key pollutants. The criteria for interpreting correlation coefficients were established based on previous research (low for |\u003cem\u003er\u003c/em\u003e| \u0026lt; 0.4; moderate for 0.4 \u0026le; |\u003cem\u003er\u003c/em\u003e| \u0026le; 0.6; high for |\u003cem\u003er\u003c/em\u003e| \u0026gt; 0.6)\u003csup\u003e24\u003c/sup\u003e. Notably, air pollutants exhibited strong intercorrelations, while demonstrating only slight correlations with IHD deaths. Daily mean temperature displayed negative correlations with both IHD deaths and air pollutants. Conversely, there were low correlations observed between DTR and both air pollutants and temperature. Relative humidity exhibited a slight negative correlation with both IHD deaths and air pollutants, yet demonstrated a notably high negative correlation with DTR.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSpearman correlation coefficients between weather conditions in Hangzhou during the study period.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIHD death\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePM25\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTemperature\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDTR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRelative humidity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIHD death\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePM25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.142**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.161**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.703**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.142**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.677**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.643**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.339**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.378**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.519**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.447**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.246**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.130**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.252**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.168**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelative humidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.097**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.225**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.069*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.487**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.085**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.642**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e* p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Time‑lag and cumulative effects\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e depicts a three-dimensional graph illustrating the \u003cem\u003eRRs\u003c/em\u003e for IHD deaths concerning DTR and temperature across various lag days. The results indicate a non-linear exposure-response relationship with increased \u003cem\u003eRRs\u003c/em\u003e observed at both high and low temperatures. Moreover, high DTR appears to mitigate adverse effects on IHD deaths, while substantial temperature increases exhibit delayed adverse effects on IHD deaths.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents exposure-response curves detailing the cumulative effects over 7 days of both DTR and temperature on IHD deaths. The association between DTR and IHD deaths exhibits a J-shaped pattern, while the association with temperature demonstrates a reverse J-shaped pattern, with no clear threshold identified. Using median values as reference points, the minimum mortality temperature for DTR and temperature were found to be 6.5\u0026deg;C and 22.0\u0026deg;C, respectively. Subsequently, these values were used as references for further analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the cumulative effects of extremely DTR and temperature over a 7-days period. Regarding DTR, a steadily increasing association was observed between extreme high DTR (99th percentile: 16.0\u0026deg;C) and IHD deaths throughout the lag period, with a significant association noted in lag 0\u0026ndash;7 days (\u003cem\u003eRR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.309, 95% \u003cem\u003eCI\u003c/em\u003e: 0.985, 1.740). Conversely, the greatest \u003cem\u003eRR\u003c/em\u003e was observed between extreme low DTR (1st percentile: 1.4\u0026deg;C) and IHD deaths in lag 0\u0026ndash;2 days (\u003cem\u003eRR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.234, 95% \u003cem\u003eCI\u003c/em\u003e: 1.043, 1.460). Similarly, temperature (1st percentile: 1.86\u0026deg;C; 99th percentile: 33.0\u0026deg;C) displayed significant associations with IHD deaths across the entire lag period, with the highest \u003cem\u003eRR\u003c/em\u003e observed at lag 0\u0026ndash;2 days (\u003cem\u003eRR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.559, 95% \u003cem\u003eCI\u003c/em\u003e: 1.250, 1.943) for extremely hot temperatures. Conversely, for extremely cold temperatures, the \u003cem\u003eRR\u003c/em\u003e increased from 1.049 (95% \u003cem\u003eCI\u003c/em\u003e: 0.930, 1.183) to 2.089 (95% \u003cem\u003eCI\u003c/em\u003e: 1.854, 2.352).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents an analysis of the overall cumulative effect among various sub-groups based on gender and age. Notably, females exhibited greater susceptibility than males across different lag days, except for extreme high DTR, although no significant associations were found when comparing both extreme DTR and temperature with IHD deaths. In terms of age, the elderly demonstrated stronger sensitivity compared to the younger population.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCumulative effect of extremely DTR and Temperature on IHD death by different population characteristics in various lag structures.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLag 0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLag 0\u0026ndash;2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLag 0\u0026ndash;4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLag 0\u0026ndash;7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003eDTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eExtremely low (1th)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.158(1.064\u0026ndash;1.261)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.234(1.043\u0026ndash;1.460)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.113(0.891\u0026ndash;1.389)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.209(0.918\u0026ndash;1.592)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.095(0.977\u0026ndash;1.229)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.116(0.889-1.400)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.006(0.746\u0026ndash;1.357)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.997(0.687\u0026ndash;1.447)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.232(1.091\u0026ndash;1.391)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.377(1.084\u0026ndash;1.749)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.240(0.904\u0026ndash;1.701)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.489(1.009\u0026ndash;2.199)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOld\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.153(1.053\u0026ndash;1.262)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.223(1.024\u0026ndash;1.462)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.106(0.874\u0026ndash;1.399)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.214(0.908\u0026ndash;1.624)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eExtremely high (99th)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.044(0.945\u0026ndash;1.154)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.138(0.932\u0026ndash;1.390)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.230(0.952\u0026ndash;1.589)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.309(0.985\u0026ndash;1.740)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.110(0.973\u0026ndash;1.265)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.251(0.962\u0026ndash;1.627)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.293(0.920\u0026ndash;1.818)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.306(0.892\u0026ndash;1.911)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.965\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.969(0.837\u0026ndash;1.122)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.011(0.755\u0026ndash;1.354)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.147(0.791\u0026ndash;1.662)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.290(0.857\u0026ndash;1.942)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOld\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.067(0.960\u0026ndash;1.185)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.189(0.964\u0026ndash;1.468)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.291(0.984\u0026ndash;1.692)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.380(1.021\u0026ndash;1.865)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003eTemperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eExtremely cold (1th)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.049(0.930\u0026ndash;1.183)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.219(1.011\u0026ndash;1.470)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.494(1.230\u0026ndash;1.815)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.089(1.854\u0026ndash;2.352)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.004(0.853\u0026ndash;1.181)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.133(0.880\u0026ndash;1.460)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.417(1.090\u0026ndash;1.842)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.970(1.674\u0026ndash;2.320)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.661\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.096(0.924\u0026ndash;1.299)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.311(1.005\u0026ndash;1.710)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.580(1.197\u0026ndash;2.084)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.216(1.876\u0026ndash;2.617)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOld\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.053(0.927\u0026ndash;1.195)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.268(1.040\u0026ndash;1.545)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.618(1.318\u0026ndash;1.987)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.132(1.880\u0026ndash;2.418)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eExtremely hot (99th)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.250(1.074\u0026ndash;1.456)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.559(1.250\u0026ndash;1.943)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.533(1.222\u0026ndash;1.923)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.233(1.042\u0026ndash;1.459)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.165(0.948\u0026ndash;1.430)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.401(1.039\u0026ndash;1.891)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.464(1.076\u0026ndash;1.994)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.289(1.027\u0026ndash;1.616)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.558\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.348(1.086\u0026ndash;1.674)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.743(1.276\u0026ndash;2.381)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.604(1.166\u0026ndash;2.207)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.168(0.919\u0026ndash;1.483)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOld\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.290(1.098\u0026ndash;1.515)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.644(1.302\u0026ndash;2.075)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.592(1.253\u0026ndash;2.023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.243(1.040\u0026ndash;1.485)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Sensitivity analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo test the robustness of our results, several sensitivity analyses were conducted and presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Initially, we adjusted the df from 3 to 5 for long-term and seasonal trends, 4 to 7 for relative humidity. The results indicated minimal changes in the estimation values, suggesting that our findings remained stable. Furthermore, we conducted sensitivity analyses by incorporating additional air pollutants into the model. Overall, when PM2.5 and NO\u003csub\u003e2\u003c/sub\u003e were added to the model, the \u003cem\u003eRRs\u003c/em\u003e slightly decreased, whereas the inclusion of SO\u003csub\u003e2\u003c/sub\u003e led to a moderate increase in \u003cem\u003eRRs\u003c/em\u003e. However, these adjustments did not significantly alter the results. Consequently, we have confidence that the model specifications employed in our study accurately reflect the main effects of DTR and temperature on IHD deaths.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSensitivity analysis of different df for long-term and seasonal trend, relative humidity and controlling air pollution factors.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBaisc model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eAdjusting df of long-term and seasonal trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eAdjusting df of relative humidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003eIntroducing other air pollution factors\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eSO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003eDTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eExtremely low (1th)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.209(0.918\u0026ndash;1.592)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.226(0.930\u0026ndash;1.616)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.213(0.921\u0026ndash;1.597)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.210(0.919\u0026ndash;1.595)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.213(0.919\u0026ndash;1.601)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.206(0.913\u0026ndash;1.592)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.187(0.899\u0026ndash;1.567)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.214(0.918\u0026ndash;1.605)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.186(0.896\u0026ndash;1.571)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.235(0.931\u0026ndash;1.639)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.997(0.687\u0026ndash;1.447)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.007(0.694\u0026ndash;1.462)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.003(0.691\u0026ndash;1.457)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.994(0.684\u0026ndash;1.445)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.011(0.694\u0026ndash;1.473)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.003(0.688\u0026ndash;1.461)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.979(0.672\u0026ndash;1.425)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.006(0.689\u0026ndash;1.469)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.965(0.660\u0026ndash;1.410)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.016(0.694\u0026ndash;1.489)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.489(1.009\u0026ndash;2.199)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.519(1.027\u0026ndash;2.245)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.489(1.008-2.200)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.497(1.013\u0026ndash;2.211)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.476(0.997\u0026ndash;2.186)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.471(0.992\u0026ndash;2.179)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.461(0.986\u0026ndash;2.166)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.491(1.003\u0026ndash;2.216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.483(0.996\u0026ndash;2.209)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.532(1.026\u0026ndash;2.290)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOld\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.214(0.908\u0026ndash;1.624)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.232(0.920\u0026ndash;1.650)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.218(0.911\u0026ndash;1.630)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.216(0.908\u0026ndash;1.627)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.221(0.911\u0026ndash;1.638)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.213(0.904\u0026ndash;1.628)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.193(0.889\u0026ndash;1.599)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.224(0.911\u0026ndash;1.644)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.193(0.887\u0026ndash;1.606)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.277(0.947\u0026ndash;1.721)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eExtremely high (99th)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.309(0.985\u0026ndash;1.740)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.317(0.992\u0026ndash;1.749)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.260(0.940\u0026ndash;1.688)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.308(0.983\u0026ndash;1.741)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.314(0.987\u0026ndash;1.751)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.313(0.985\u0026ndash;1.749)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.302(0.978\u0026ndash;1.734)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.279(0.957\u0026ndash;1.708)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.269(0.945\u0026ndash;1.704)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.369(1.006\u0026ndash;1.863)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.306(0.892\u0026ndash;1.911)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.312(0.898\u0026ndash;1.916)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.226(0.829\u0026ndash;1.814)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.324(0.904\u0026ndash;1.940)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.350(0.920\u0026ndash;1.981)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.345(0.916\u0026ndash;1.975)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.330(0.906\u0026ndash;1.951)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.255(0.851\u0026ndash;1.850)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.228(0.828\u0026ndash;1.821)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.316(0.871\u0026ndash;1.987)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.290(0.857\u0026ndash;1.942)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.298(0.865\u0026ndash;1.949)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.271(0.835\u0026ndash;1.936)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.271(0.844\u0026ndash;1.915)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.258(0.833\u0026ndash;1.898)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.258(0.833-1.900)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.252(0.829\u0026ndash;1.891)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.283(0.847\u0026ndash;1.946)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.290(0.844\u0026ndash;1.971)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.405(0.902\u0026ndash;2.188)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOld\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.380(1.021\u0026ndash;1.865)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.388(1.028\u0026ndash;1.874)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.323(0.971\u0026ndash;1.804)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.381(1.021\u0026ndash;1.868)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.391(1.026\u0026ndash;1.884)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.388(1.024\u0026ndash;1.881)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.376(1.016\u0026ndash;1.864)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.326(0.976\u0026ndash;1.802)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.326(0.971\u0026ndash;1.812)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.417(1.022\u0026ndash;1.964)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003econt.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBaisc model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eAdjusting df of long-term and seasonal trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eAdjusting df of relative humidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003eIntroducing other air pollution factors\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eSO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003eTemperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eExtremely cold (1th)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.089(1.854\u0026ndash;2.352)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.089(1.855\u0026ndash;2.353)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.043(1.810\u0026ndash;2.306)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.088(1.854\u0026ndash;2.352)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.087(1.853\u0026ndash;2.352)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.097(1.861\u0026ndash;2.363)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.111(1.874\u0026ndash;2.379)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.089(1.842\u0026ndash;2.368)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.110(1.856\u0026ndash;2.399)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.026(1.787\u0026ndash;2.297)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.970(1.674\u0026ndash;2.320)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.974(1.677\u0026ndash;2.322)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.920(1.625\u0026ndash;2.268)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.970(1.673\u0026ndash;2.320)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.965(1.669\u0026ndash;2.314)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.975(1.677\u0026ndash;2.327)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.997(1.696\u0026ndash;2.353)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.990(1.674\u0026ndash;2.364)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.004(1.682\u0026ndash;2.388)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.916(1.614\u0026ndash;2.276)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.216(1.876\u0026ndash;2.617)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.208(1.870\u0026ndash;2.606)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.173(1.835\u0026ndash;2.574)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.214(1.875\u0026ndash;2.615)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.219(1.878\u0026ndash;2.622)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.228(1.885\u0026ndash;2.633)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.232(1.888\u0026ndash;2.639)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.192(1.838\u0026ndash;2.614)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.225(1.859\u0026ndash;2.665)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.141(1.796\u0026ndash;2.553)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOld\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.132(1.880\u0026ndash;2.418)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.132(1.881\u0026ndash;2.417)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.083(1.833\u0026ndash;2.367)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.132(1.880\u0026ndash;2.417)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.130(1.878\u0026ndash;2.416)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.140(1.887\u0026ndash;2.428)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.157(1.902\u0026ndash;2.447)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.123(1.859\u0026ndash;2.425)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.152(1.879\u0026ndash;2.464)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.045(1.791\u0026ndash;2.334)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eExtremely hot (99th)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.233(1.042\u0026ndash;1.459)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.218(1.029\u0026ndash;1.441)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.224(1.033\u0026ndash;1.450)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.233(1.042\u0026ndash;1.459)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.235(1.043\u0026ndash;1.462)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.237(1.045\u0026ndash;1.465)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.227(1.037\u0026ndash;1.453)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.174(0.974\u0026ndash;1.415)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.217(1.007\u0026ndash;1.470)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.255(1.047\u0026ndash;1.505)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.289(1.027\u0026ndash;1.616)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.279(1.020\u0026ndash;1.603)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.265(1.006\u0026ndash;1.590)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.299(1.035\u0026ndash;1.631)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.306(1.040\u0026ndash;1.640)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.310(1.043\u0026ndash;1.645)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.294(1.031\u0026ndash;1.625)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.246(0.969\u0026ndash;1.602)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.282(0.993\u0026ndash;1.654)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.299(1.017\u0026ndash;1.659)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.168(0.919\u0026ndash;1.483)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.148(0.903\u0026ndash;1.460)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.170(0.919\u0026ndash;1.490)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.156(0.909\u0026ndash;1.469)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.152(0.906\u0026ndash;1.465)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.153(0.907\u0026ndash;1.467)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.148(0.903\u0026ndash;1.461)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.093(0.837\u0026ndash;1.427)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.141(0.872\u0026ndash;1.493)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.202(0.927\u0026ndash;1.557)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOld\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.243(1.040\u0026ndash;1.485)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.226(1.026\u0026ndash;1.466)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.232(1.029\u0026ndash;1.475)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.243(1.040\u0026ndash;1.487)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.246(1.042\u0026ndash;1.491)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.249(1.044\u0026ndash;1.493)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.237(1.035\u0026ndash;1.480)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.177(0.966\u0026ndash;1.435)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.218(0.997\u0026ndash;1.488)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.265(1.043\u0026ndash;1.533)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussions","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis study investigated the association between DTR and temperature changes and IHD mortality in Zhejiang, China, from 2014 to 2016. Generally, the correlation between DTR and IHD deaths shows a non-linear (J-shaped) trend, while a reverse J-shaped trend is observed in the effect of temperatures. We then examined the lag effects on IHD deaths. For DTRs, we found that low DTR had almost no influence on \u003cem\u003eRR\u003c/em\u003e, while high DTR led to a cumulative effect over the lag period. Regarding temperatures, we found that low temperature also accumulated \u003cem\u003eRR\u003c/em\u003e over 7 days, while only a limited cumulative effect was observed for high temperature, which ended on the third lag day. Furthermore, no significant differences were found between gender and age, and the introduction of different pollutants, including PM2.5, NO\u003csub\u003e2\u003c/sub\u003e, and SO\u003csub\u003e2\u003c/sub\u003e, did not significantly confound the results.\u003c/p\u003e \u003cp\u003eCurrently, extensive attention has been paid to the association of DTR with mortality\u003csup\u003e25,26\u003c/sup\u003e. However, to our best knowledge, few studies have examined how extreme DTR affects mortality risk from IHD. Our study provides more generalized evidence to support the effects of DTR on IHD. Specifically, we found a non-linear (J-shaped) association between DTR and IHD deaths. Additionally, our study identified the cumulative effect of extremely low DTR, which persisted throughout the lag period, while the effect of high DTR was immediate and limited. This result is consistent with a previous study in Jiuquan, which found that the effect of low DTR was significantly more deleterious than that of high DTR on hospital admissions\u003csup\u003e27\u003c/sup\u003e. A study in Guangdong also examined the cumulative effects of extreme DTRs, which were greater for extreme high DTRs than those of extreme low DTRs\u003csup\u003e28\u003c/sup\u003e. Generally, the prior conclusions regarding the association between DTR and daily mortality were not coherent. Some reported a linear relationship between DTR and mortality\u003csup\u003e29\u003c/sup\u003e, which was different from our results. However, some observed a non-linear (J-shaped) relationship between DTR and cardiovascular-specific mortality\u003csup\u003e30,31\u003c/sup\u003e,which agrees with our findings. The results of these studies support that extreme DTR is a risk factor for IHD, and the different effects might be explained by the area characteristics and differences in analytic approaches\u003csup\u003e32\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn recent years, the study on the relationship between temperature and disease has received widespread attention from researchers. In general, correlations between daily main temperature and death present non-linear trends, mostly showing \"U\", \"V\" or \"J\" shapes\u003csup\u003e33\u003c/sup\u003e. A study in Taiwan found a U-shaped relation with temperature for CAD, and cold climates resulted in a reduction of the least temperature range for elderly death\u003csup\u003e34\u003c/sup\u003e. Another study in Hubei found that low temperature increased the risk of IHD deaths, while no effect of high temperature on IHD death was observed\u003csup\u003e33\u003c/sup\u003e. Overall, previous studies have suggested that extremely cold and hot temperatures should be considered to affect IHD mortality, but many ignored the lag effects. Consistent with the majority of existing evidence, we detected a non-linear (reversely J-shaped) association between temperature and IHD mortality. Besides, the effects of low temperature could last for more than 7 days, while high temperature only maintained effects for 3 days. This was similar to a study in Guangzhou, which detected a cold effect on IHB that persisted for approximately 12 days, while the hot effect was limited to the first 5 days\u003csup\u003e35\u003c/sup\u003e. On the one hand, this may be attributed to the hypothesis of acclimatization to local climatic conditions\u003csup\u003e36,37\u003c/sup\u003e. And Zhejiang Province, as a southern city, its citizens are not sensitive to thermal effects. On the other hand, our findings are biologically plausible. When repeatedly exposed to heat stress, the failure of thermoregulation and the physiological changes in the circulatory system may lead to an increase in mortality\u003csup\u003e38\u003c/sup\u003e. However, considering the exact mechanism for the temperature-mortality relationship is uncertain to date, further investigation is needed.\u003c/p\u003e \u003cp\u003eRegarding influencing factors, some evidence has found that the magnitude of DTR and temperature effects might vary by gender and age. Several studies reported that males and the elderly showed more vulnerability to the adverse effects of DTR than females and the young for IHD mortality\u003csup\u003e27,31,39\u003c/sup\u003e, while some agreed that women and the elderly are more susceptible\u003csup\u003e40\u003c/sup\u003e. Besides, a previous study in Yuxi suggested no evidence for effect modification by gender\u003csup\u003e30\u003c/sup\u003e. Similarly, despite the slightly increased risk of IHD among females and the elderly, our research identified no significant discrepancies between males and females, as well as between the elderly and youth. The differences in extreme temperature and DTR effects on gender might rely on the research location and population\u003csup\u003e41\u003c/sup\u003e, and the susceptibility of elderly people may be due to their poor physiological adaptability to changes in ambient temperature. Further study in this field is needed to investigate these potential modifiers.\u003c/p\u003e \u003cp\u003eMany previous studies considered the potential confounding role of air pollutants on the effects of temperature variability on mortalities\u003csup\u003e40,42,43\u003c/sup\u003e. Accordingly, we introduced the confounding effects of various air pollutants, including PM2.5, NO\u003csub\u003e2\u003c/sub\u003e, and SO\u003csub\u003e2\u003c/sub\u003e, to our base models. But no significant changes were found in the results, indicating that air pollutants were not confounders in this study.\u003c/p\u003e \u003cp\u003eThere were several strengths in this study. To our best knowledge, limited research simultaneously evaluated the impact of both extreme DTR and temperature on IHD-specific mortality. Additionally, we conducted modification analysis based on gender and age and incorporated humidity and ambient pollutants for subgroup analysis. This indicates that our study of the effects of extreme DTR and temperature on IHD mortality is systematic and comprehensive.\u003c/p\u003e \u003cp\u003eThis research, however, is subject to several limitations. Firstly, the design of the current study is ecological study. The lack of individual data might result in factual deviations and insufficient evidence of causality. Besides, other potential confounders such as individual habits or medical history might affect the accuracy of results. Secondly, our data only collect from one single city, which might affect the extrapolation of the results, especially for regions with different geographic situations and climates. Thirdly, insufficient sample size for statistical measurement limited us from performing further subgroup analysis, such as socioeconomic status. Thus, in order to validate the above findings and elucidate their potential mechanisms, it is necessary to further explore diverse groups and more detailed factors, and establish sophisticated models based on more comprehensive data.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eIn summary, this study unveils a non-linear association between diurnal temperature range (DTR), temperature variability, and mortality from ischemic heart disease (IHD) in Hangzhou, China, with extreme temperature and DTR exhibiting a cumulative effect over the lag period. Gender and age were found not to significantly modify these associations. These findings hold significant implications for the public health department in Hangzhou, suggesting the need to adopt prevention and intervention strategies aimed at reducing exposure to extreme temperature and DTR. Additionally, our study provides insights for research in other Asian cities facing similar climate challenges. In the broader context of global climate change, our findings indicate that by enhancing our understanding of the intricate relationship between temperature variability and health outcomes, we can better equip ourselves to address the multifaceted challenges posed by a changing climate.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eSource of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This research was funded by the National Natural Science Foundation of China (82273749 and 81773468), the Natural Science Foundation of Zhejiang Province, China (LTGY23H240001), the Opening Foundation of NHC Key Laboratory of Etiology and Epidemiology (Harbin Medical University) (NHCKLEE20230908).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosures\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e The authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u003c/strong\u003e The data supporting the findings of this study are available from the Mortality Surveillance System of the Zhejiang Provincial Center for Disease Prevention and Control. However, due to restrictions on data availability under license for this study, they are not publicly accessible. Access to the data may be granted upon reasonable request and with permission from the Mortality Surveillance System of the Zhejiang Provincial Center for Disease Prevention and Control. Due to privacy concerns regarding mortality data, it cannot be publicly disclosed at this time. For verification purposes, we can provide software screenshots. For inquiries regarding the data, please contact the corresponding author, Gaofeng Cai, at [email protected].\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eManjin Xu and Yunfeng Xu wrote the main manuscript text and Zhe Mo prepared figures 1-3 and tables 1-4.All authors reviewed the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMozaffarian, D. \u003cem\u003eet al.\u003c/em\u003e Heart Disease and Stroke Statistics-2016 Update A Report From the American Heart Association. \u003cem\u003eCIRCULATION\u003c/em\u003e \u003cstrong\u003e133\u003c/strong\u003e, E38\u0026ndash;E360 (2016).\u003c/li\u003e\n\u003cli\u003eZhao, D., Liu, J., Wang, M., Zhang, X. \u0026amp; Zhou, M. Epidemiology of cardiovascular disease in China: current features and implications. \u003cem\u003eNat. Rev. 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Res.\u003c/em\u003e\u003cstrong\u003e30\u003c/strong\u003e, 10052\u0026ndash;10062 (2023).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Temperature, Ischemic heart disease, Mortality, Distributed lag non-linear model","lastPublishedDoi":"10.21203/rs.3.rs-4617516/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4617516/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eIschemic heart disease (IHD) is a leading cause of death in cardiovascular patients. In China, the disease burden of IHD deaths has significantly increased. One of the main influencing factors of IHD is changing climates, and temperature and diurnal temperature range (DTR) are important indicators of climate change.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e To evaluate the effects of daily mean temperature and DTR on IHD mortality in Hangzhou, Zhejiang Province, China.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We obtained daily IHD mortality data and meteorological data from mortality surveillance system from 2014 to 2016. Quasi-Poisson generalized linear regression with a distributed lag non-linear model (DLNM) was applied to estimate the associations between temperature variability and IHD deaths. Potential confounders were controlled in the analysis, including relative humidity, day of the week, public holidays, and long-term trends.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e A total of 7423 IHD mortality data were included in this study. A J-shaped pattern of DTR and a reversely J-shaped pattern of temperature for IHD mortality were observed. Risk estimates showed that the relative risks (\u003cem\u003eRRs\u003c/em\u003e) of IHD mortality with extreme high DTR at lag 0–7 days were 1.309 (95% \u003cem\u003eCI\u003c/em\u003e: 0.985, 1.740) while \u003cem\u003eRR\u003c/em\u003es of IHD mortality with extreme low DTR at lag 0–2 days were 1.234 (95% \u003cem\u003eCI\u003c/em\u003e: 1.043, 1.460). For extreme hot temperature, the highest \u003cem\u003eRR\u003c/em\u003es at lag 0–2 days were 1.559 (95% \u003cem\u003eCI\u003c/em\u003e: 1.250, 1.943); for extremely cold temperatures, the \u003cem\u003eRR\u003c/em\u003es increased from 1.049 (95% \u003cem\u003eCI\u003c/em\u003e: 0.930, 1.183) to 2.089 (95% \u003cem\u003eCI\u003c/em\u003e: 1.854, 2.352). \u003cstrong\u003eConclusion:\u003c/strong\u003e In Hangzhou city, short-term exposure to extreme temperature was associated with mortality for IHD. These findings have implications for policy decision-making and targeted interventions.\u003c/p\u003e","manuscriptTitle":"The Effects of Daily Mean Temperature and Diurnal Temperature Range on Ischemic Heart Disease Mortality in Hangzhou, China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-23 22:14:42","doi":"10.21203/rs.3.rs-4617516/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-07-22T12:28:21+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-22T00:23:53+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-15T10:28:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"160473994294137273391277438238033195819","date":"2024-07-14T03:31:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"231873573599030723350360932232182409477","date":"2024-07-11T07:20:00+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-10T04:33:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-10T04:31:45+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-07-02T13:50:32+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-01T09:48:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-06-21T12:48:50+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"41511655-9e2f-47d0-85ab-e3f00c078b2b","owner":[],"postedDate":"July 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":34948424,"name":"Earth and environmental sciences/Climate sciences"},{"id":34948425,"name":"Health sciences/Cardiology"}],"tags":[],"updatedAt":"2024-12-09T16:08:17+00:00","versionOfRecord":{"articleIdentity":"rs-4617516","link":"https://doi.org/10.1038/s41598-024-78902-5","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-12-04 15:58:16","publishedOnDateReadable":"December 4th, 2024"},"versionCreatedAt":"2024-07-23 22:14:42","video":"","vorDoi":"10.1038/s41598-024-78902-5","vorDoiUrl":"https://doi.org/10.1038/s41598-024-78902-5","workflowStages":[]},"version":"v1","identity":"rs-4617516","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4617516","identity":"rs-4617516","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}