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

doi:10.21203/rs.3.rs-4617516/v1

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

https://doi.org/10.21203/rs.3.rs-4617516/v1

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Background: Ischemic 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.

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 RRs 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 RRs at lag 0–2 days were 1.559 (95% CI: 1.250, 1.943); for extremely cold temperatures, the RRs 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

Cardiovascular disease (CVD) accounts for approximately one-third of global deaths¹. CVD encompasses conditions affecting the vascular system supplying the heart, brain, and other vital organs². Among these, ischemic heart disease (IHD) stands as the most prevalent cardiovascular disease³. IHD results from insufficient oxygen delivery to meet the heart's demands, often presenting as angina, acute myocardial infarction, and ischemic heart failure⁴. Merely 2%-7% of the general population exhibit no IHD risk factors, while over 70% of high-risk individuals have multiple risk factors⁵. 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 warming⁶ and extreme weather events⁷. Among meteorological factors, temperature has received widespread attention. It's widely acknowledged that rising temperatures escalate the risk of adverse health outcomes⁸, ranging from mild subclinical conditions to severe fatal events⁹. Heat exposure is an important but underappreciated risk factor contributing to cardiovascular disease¹⁰, 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 day¹¹. Studying DTR is pivotal for comprehending climate patterns and their effects on ecosystems, agriculture, human health¹², and various environmental facets¹³. 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 health¹⁴. 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 River¹⁵, 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 indices¹⁶. 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.

2.1. 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 (NO₂) and sulfur dioxide (SO₂) 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 DTR¹⁹. 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 holidays²⁰. 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(humidity₀₃, 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 value²¹, 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 exposure²². 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 March²³. 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, NO₂, and SO₂ 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).

3.1. 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
Variables	N (%)	Mean	Standard Deviation	Minimum	P₁₀	P₂₅	P₅₀	P₇₅	P₉₀	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/m³)	-	56.83	31.45	8.00	24.70	34.08	50.20	72.24	97.55	228.56
NO₂ (µg/m³)	-	47.90	17.63	10.89	27.20	34.76	45.85	58.03	72.40	118.30
SO₂ (µg/m³)	-	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, NO₂, and SO₂ stood at 56.83 µg/m³ (ranging from 8.00 to 228.56 µg/m³), 47.9 µg/m³ (ranging from 10.89 to 118.30 µg/m³), and 16.29 µg/m³ (ranging from 4.33 to 81.20 µg/m³), 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| < 0.4; moderate for 0.4 ≤ |r| ≤ 0.6; high 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	NO₂	SO₂	Temperature	DTR	Relative humidity
IHD death	1
PM25	0.142**	1
NO₂	0.161**	0.703**	1
SO₂	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 NO₂ were added to the model, the RRs slightly decreased, whereas the inclusion of SO₂ 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
Variables	Characteristics	Baisc model	3	5	4	5	6	7	PM_2.5	NO₂	SO₂
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
Variables	Characteristics	Baisc model	3	5	4	5	6	7	PM_2.5	NO₂	SO₂
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)

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, NO₂, and SO₂, 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 admissions²⁷. A study in Guangdong also examined the cumulative effects of extreme DTRs, which were greater for extreme high DTRs than those of extreme low DTRs²⁸. Generally, the prior conclusions regarding the association between DTR and daily mortality were not coherent. Some reported a linear relationship between DTR and mortality²⁹, 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 approaches³².

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" shapes³³. 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 death³⁴. Another study in Hubei found that low temperature increased the risk of IHD deaths, while no effect of high temperature on IHD death was observed³³. 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 days³⁵. 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 mortality³⁸. 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 susceptible⁴⁰. Besides, a previous study in Yuxi suggested no evidence for effect modification by gender³⁰. 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 population⁴¹, 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, NO₂, and SO₂, 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.

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.

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.

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No competing interests reported.

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

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Abstract

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1. Introduction

2. Methods

2.1. Mortality data

2.2. Meteorological and air pollution data

2.3. Statistical analysis

3. Results

3.1. Descriptive analysis

3.2. Time‑lag and cumulative effects

3.3. Sensitivity analysis

4. Discussions

5. Conclusions

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Author Contribution

References

Additional Declarations

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