Low ambient temperature and air pollution are associated with hospitalization incidence of coronary artery disease: Insights from a cross-sectional study in Northeast China

Rui Jiang; Lingling Xu; Yue Liu; Guangna Zhao; Chun Xing; Youyuan Li; Yongchen Wang

doi:10.2478/fzm-2023-0030

Low ambient temperature and air pollution are associated with hospitalization incidence of coronary artery disease: Insights from a cross-sectional study in Northeast China

doi: 10.2478/fzm-2023-0030

Rui Jiang^{1, †},
Lingling Xu^{1, †},
Yue Liu¹,
Guangna Zhao²,
Chun Xing¹,
Youyuan Li³,
Yongchen Wang^{1
, ,}

1.
Department of General Practice, the Second Affiliated Hospital of Harbin Medical University, Harbin 150001, China
2.
Harbin Meteorological Bureau, Harbin Meteorological Administration, Harbin 150023, China
3.
Heilongjiang Meteorological Bureau, Heilongjiang Meteorological Administration, Harbin 150030, China

Funds:

the National Natural Science Foundation of China 72074065

the Harbin Medical University Innovative Scientific Research Funding Project 0202-31041220023

More Information

Corresponding author: Yongchen Wang, E-mail: yongchenwang@hrbmu.edu.cn

摘要

Abstract: Background Previous studies have established a link between fluctuations in climate and increased mortality due to coronary artery disease (CAD). However, there remains a need to explore and clarify the evidence for associations between meteorological changes and hospitalization incidences related to CAD and its subtypes, especially in cold regions. This study aimed to systematically investigate the relationship between exposure to meteorological changes, air pollutants, and hospitalization for CAD in cold regions. Methods We conducted a cross-sectional study using hospitalization records of 86, 483 CAD patients between January 1, 2009, and December 31, 2019. Poisson regression analysis, based on generalized additive models, was applied to estimating the influence of hospitalization for CAD. Results Significant associations were found between low ambient temperature [-10℃, RR= 1.65; 95% CI: (1.28–2.13)] and the incidence of hospitalization for CAD within a lag of 0–14 days. Furthermore, O₃ [95.50 μg/m³, RR = 12; 95% CI: (1.03–1.21)] and NO₂ [48.70 μg/m³, RR = 1.0895% CI: (1.01–1.15)] levels were identified as primary air pollutants affecting the incidence of CAD, ST-segment-elevation myocardial infarction (STEMI), and non-STEMI (NSTEMI) within the same lag period. Furthermore, O₃ [95.50 μg/m³, RR = 1.12; 95% CI: (1.03–1.21)] and NO₂ [48.70 μg/m³, RR = 1.0895% CI: (1.01–1.15)] levels were identified as primary air pollutants affecting the incidence of CAD, ST-segment-elevation myocardial infarction (STEMI), and non-STEMI (NSTEMI) within the same lag period. The effect curve of CAD hospitalization incidence significantly increased at lag days 2 and 4 when NO₂ and O₃ concentrations were higher, with a pronounced effect at 7 days, dissipating by lag 14 days. No significant associations were observed between exposure to PM, SO₂, air pressure, humidity, or wind speed and hospitalization incidences due to CAD and its subtypes. Conclusion Our findings suggest a positive correlation between short-term exposure to low ambient temperatures or air pollutants (O₃ and NO₂) and hospitalizations for CAD, STEMI, and NSTEMI. These results could aid the development of effective preparedness strategies for frequent extreme weather events and support clinical and public health practices aimed at reducing the disease burden associated with current and future abnormal weather events.
- meteorological changes /
- ambient temperature /
- air pollution /
- coronary heart disease /
- Poisson regression analysis

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Figure 1. Cumulative relative risk (RR) over lag 0–14 days and 95% empirical CI (shaded grey) of NO₂, O₃, and air temperature on hospitalization incidences of CAD, STEMI, and NSTEMI. Smooth red lines (A–C) represent RRs for nitrogen dioxide (NO₂); (D–F) denote O₃; (G–I) depict air temperature. CAD: coronary heart disease; STEMI: ST-segment-elevation myocardial infarction; NSTEMI: non-ST-segment-elevation myocardial infarction; NO₂: nitrogen dioxide.

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Figure 2. Spearman correlations between variables. PM_2.5: particulate matter with aerodynamic diameter ≤ 2.5 μm; PM₁₀: particulate matter with aerodynamic diameter ≤ 10 μm; SO₂: sulfur dioxide; NO₂: nitrogen dioxide.

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Figure 3. Lag response for associations with hospitalizations for CAD and its subtypes with meteorological variables and air pollution concentrations. CAD: coronary artery disease; STEMI: ST-segment-elevation myocardial infarction; NSTEMI: non-ST-segment-elevation myocardial infarction; PM_2.5: particulate matter with aerodynamic diameter ≤ 2.5 μm; PM10: particulate matter with aerodynamic diameter ≤ 10 μm; SO₂: sulfur dioxide; NO₂: nitrogen dioxide.

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Figure 4. Exposure-response curve for t hospitalizations due to CAD, STEMI, and NSTEMI in relation to NO₂, O₃, and air temperature at lag 2, 4, 7, 14 days. (A–C) Nitrogen dioxide (NO₂); (D–F) O₃; (G–I) O₃. RR: Relative Risk; CAD: coronary heart disease; STEMI: ST-segment-elevation myocardial infarction; NSTEMI: non-ST-segment-elevation myocardial infarction; NO₂: nitrogen dioxide.

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Table 1. Descriptive summary statistics of meteorological variables and air pollutants, with relative risk (RR) of hospitalizations for CAD, STEMI, NSTEMI, Unstable angina, Stable angina from 2010-2020.

Variable	CAD		STEMI		NSTEMI		Unstable angina		Stable angina
	Median (IQR)	RR	Median (IQR)	RR	Median (IQR)	RR	Median (IQR)	RR	Median (IQR)	RR
PM_2.5	31.330 (16.830, 64.920)	0.960 (0.930-1.000) 1.070 (0.990-1.160)	31.000 (16.790, 64.530)	1.010 (0.960-1.050) 0.970 (0.890-1.060)	54.300 (35.170, 89.790)	0.960 (0.890-1.020) 1.110 (0.950-1.290)	31.125 (16.690, 64.580)	0.950 (0.910-1.000) 1.100 (1.000-1.220)	31.290 (16.830, 64.810)	0.980 (0.920-1.030) 1.040 (0.930-1.150)
PM₁₀	58.120 (37.350, 69.200)	0.970 (0.930-1.000) 1.050 (0.980-1.130)	57.950 (37.230, 96.550)	1.010 (0.960-1.060) 0.970 (0.890-1.050)	27.080 (14.680, 54.920)	0.990 (0.930-1.050) 1.080 (0.890-1.330)	58.000 (37.330, 96.440)	0.950 (0.900-1.000) 1.100 (1.000-1.020)	58.120 (37.350, 96.600)	0.950 (0.890-1.010) 1.070 (0.960-1.180)
SO₂	37.040 (6.600, 23.200)	0.990 (0.920-1.070) 0.950 (0.830-1.090)	11.280 (6.540, 22.730)	0.900 (0.830-0.980) 1.190 (1.030-1.380)	9.890 (6.000, 18.300)	11.000 (0.990-1.230) 0.850 (0.710-1.010)	11.310 (6.540, 23.130)	1.070 (0.980-1.170) 0.820 (0.700-0.960)	11.330 (6.580, 23.150)	1.060 (0.990-1.150) 0.910 (0.800-1.030)
NO₂	11.330 (27.900, 48.700)	0.920 (0.870-0.970) 1.080 (1.010-1.150)	37.440 (27.830, 49.000)	0.910 (0.830-0.990) 1.160 (1.030-1.320)	36.380 (27.710, 47.820)	0.820 (0.690-0.980) 1.480 (1.220-1.810)	37.105 (27.920, 48.740)	0.940 (0.880-1.000)	37.040 (27.880, 48.710)	0.920 (0.850-0.990) 1.030 (0.950-1.120)
O₃	68.250 (46.600, 95.500)	0.960 (0.910-1.000) 1.120 (1.030-1.210)	68.500 (49.630, 96.060)	0.900 (0.830-0.990) 1.240 (1.060-1.450)	71.260 (48.000, 100.130)	0.790 (0.650-0.970) 1.010 (0.790-1.290)	68.250 (46.600, 95.620)	0.910(0.850-0.970) 1.190 (1.060-1.340)	68.250 (46.600, 95.510)	0.960 (0.900-1.040) 1.220 (1.050-1.420)
Air pressure	1014.200 (1007.000, 1022.000)	0.930 (0.850-1.020) 1.080 (0.970-1.200)	1014.300 (1007.100, 1022.700)	1.010 (0.900-1.120) 1.050 (0.920-1.200)	1013.500 (1006.800, 1022.200)	0.850 (0.710-1.010) 1.250 (1.030-1.510)	1014.300 (1007.000, 1022.700)	0.970 (0.860-1.090) 1.040 (0.900-1.210)	1014.200 (1007.000, 1022.700)	1.080 (0.960-1.220) 0.920 (0.790-1.080)
Air temperature	6.800 (-10.000, 19.000)	1.650 (1.280-2.130) 0.650 (0.570-0.750)	6.800 (-10.000, 18.000)	1.070 (0.810-1.420) 0.780 (0.630-0.950)	8.500 (-7.850, 18.900)	1.080 (0.660-1.780) 0.680 (0.520-0.890)	6.800 (-10.000, 18.800)	0.950 (0.710-1.260) 0.680 (0.560-0.830)	6.800 (-10.000, 18.900)	0.840 (0.700-1.010) 0.970 (0.860-1.100)
Humidness	66.000 (55.000, 75.000)	1.010 (0.940-1.080) 1.020 (0.960-1.090)	66.000 (54.000, 75.000)	1.070 (0.960-1.180) 1.040 (0.970-1.180)	65.000 (52.000, 75.000)	0.910 (0.790-1.060) 1.020 (0.900-1.150)	66.000 (54.000, 75.000)	1.110 (1.000-1.230) 0.950 (0.880-1.030)	66.000 (54.000, 75.000)	0.960 (0.840-1.090) 0.980 (0.900-1.080)
Wind speed	2.600 (1.900, 3.600)	1.010 (0.930-1.080) 1.020 (1.000-1.050)	2.600 (1.900, 3.600)	1.000 (0.900-1.100) 1.040 (0.980-1.100)	2.700 (2.000, 3.600)	1.170 (1.010-1.360) 0.880 (0.820-0.960)	2.600 (1.900, 3.600)	0.860 (0.780-0.950) 0.940 (0.880-1.000)	2.600 (1.900, 3.600)	0.850 (0.750-0.970) 0.990 (0.910-1.070)
CAD: coronary artery disease; STEMI: ST-segment-elevation myocardial infarction; NSTEMI: non-ST-segment-elevation myocardial infarction; IQR: inter-quartile range; RR: Relative risk; PM_2.5: particulate matter with aerodynamic diameter ≤ 2.5μm; PM₁₀: particulate matter with aerodynamic diameter ≤ 10μm; SO₂: sulfur dioxide; NO₂: nitrogen dioxide.

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参考文献(35)

[1]	Bhaskaran K, Hajat S, Haines A, et al. Short term effects of temperature on risk of myocardial infarction in England and Wales: time series regression analysis of the Myocardial Ischaemia National Audit Project (MINAP) registry. BMJ, 2010; 341: c3823–c3823. doi: 10.1136/bmj.c3823
[2]	Bhaskaran K, Hajat S, Haines A, et al. Effects of ambient temperature on the incidence of myocardial infarction. Heart, 2009; 95(21): 1760-1769. doi: 10.1136/hrt.2009.175000
[3]	Sun Z, Chen C, Xu D, et al. Effects of ambient temperature on myocardial infarction: a systematic review and meta-analysis. Environ Pollut, 2018; 241: 1106–1114. doi: 10.1016/j.envpol.2018.06.045
[4]	Yang J, Zhou M, Ou C Q, et al. Seasonal variations of temperature-related mortality burden from cardiovascular disease and myocardial infarction in China. Environ Pollut, 2017; 224: 400–406. doi: 10.1016/j.envpol.2017.02.020
[5]	Chen R, Yin P, Wang L, et al. Association between ambient temperature and mortality risk and burden: time series study in 272 main Chinese cities. BMJ, 2018. 363: k4306.
[6]	Gasparrini A, Guo Y, Hashizume M, et al. Mortality risk attributable to high and low ambient temperature: a multicountry observational study. The Lancet, 2015. 386(9991): 369–375. doi: 10.1016/S0140-6736(14)62114-0
[7]	Marti-Soler H, Marques-Vidal P. Weather and cardiovascular mortality. Heart, 2015. 101(24): 1941–2. doi: 10.1136/heartjnl-2015-308613
[8]	Bowe B, Xie Y, Yan Y, et al. Burden of cause-specific mortality associated with PM_2.5 air pollution in the United States. JAMA Network Open, 2019. 2(11): e1915834 doi: 10.1001/jamanetworkopen.2019.15834
[9]	Wu Y, Li M, Tian Y, et al. Short-term effects of ambient fine particulate air pollution on inpatient visits for myocardial infarction in Beijing, China. Environmental Science and Pollution Research, 2019; 26(14): 14178-14183.
[10]	Tuan T S, Venâncio T S, Nascimento L F. Effects of air pollutant exposure on acute myocardial infarction, according to gender. Arq Bras Cardiol, 2016; 107(3): 216–222.
[11]	Mannucci P M, Harari S, Martinelli I, et al. Effects on health of air pollution: a narrative review. Intern Emerg Med, 2015; 10(6): 657–662. doi: 10.1007/s11739-015-1276-7
[12]	Shrey K, Suchit A, Deepika D, et al. Air pollutants: the key stages in the pathway towards the development of cardiovascular disorders. Environmental Toxicology and Pharmacology, 2011; 31(1): 1–9. doi: 10.1016/j.etap.2010.09.002
[13]	von Klot S, Peters A, Aalto P, et al. Ambient air pollution is associated with increased risk of hospital cardiac readmissions of myocardial infarction survivors in five european cities. Circulation, 2005; 112(20): 3073–3079. doi: 10.1161/CIRCULATIONAHA.105.548743
[14]	Ravindra K, Rattan P, Mor S, et al. Generalized additive models: Building evidence of air pollution, climate change and human health. Environ Int, 2019; 132: 104987. doi: 10.1016/j.envint.2019.104987
[15]	Samoli E, Aga E, Touloumi G, et al. Short-term effects of nitrogen dioxide on mortality: an analysis within the APHEA project. Eur Respir J, 2006; 27(6): 1129–1138. doi: 10.1183/09031936.06.00143905
[16]	Gasparrini A, Armstrong B, Kenward M G. Modeling exposure-lag-response associations with distributed lag non-linear models. Statistics in Medicine, 2013; 33(5): 881–899.
[17]	Gasparrini A, Armstrong B. Reducing and meta-analysing estimates from distributed lag non-linear models. Gasparrini and Armstrong BMCMedical Research Methodology 2013, 13: 1
[18]	McMichael A J, Wilkinson P, Kovats R S, et al. International study of temperature, heat and urban mortality: the 'ISOTHURM' project. International Journal of Epidemiology, 2008; 37(5): 1121–1131. doi: 10.1093/ije/dyn086
[19]	Sun, Z. Cardiovascular responses to cold exposure. Front Biosci (Elite Ed). 2010; 2(2): 495–503.
[20]	Medina-Ramón M, Schwartz J. Temperature, temperature extremes, and mortality: a study of acclimatisation and effect modification in 50 US cities. Occup Environ Med, 2007; 64(12): 827–833. doi: 10.1136/oem.2007.033175
[21]	Yu B, Jin S, Wang C, et al. The association of outdoor temperature with blood pressure, and its influence on future cardio-cerebrovascular disease risk in cold areas. J Hypertens, 2020; 38(6): 1080–1089. doi: 10.1097/HJH.0000000000002387
[22]	Elwood P C, Pickering J, Yarnell J, et al. Temperature and risk factors for ischaemic heart disease in the Caerphilly prospective study. Br HeartJ, 1993; 70: 520–523. doi: 10.1136/hrt.70.6.520
[23]	Mohammadi R, Soori H, Alipour A, et al. The impact of ambient temperature on acute myocardial infarction admissions in Tehran, Iran. Journal of Thermal Biology, 2018; 73: 24–31. doi: 10.1016/j.jtherbio.2018.02.008
[24]	Phung D, Thai P K, Guo Y, et al. Ambient temperature and risk of cardiovascular hospitalization: an updated systematic review and meta-analysis. Sci Total Environ, 2016; 550: 1084–1102. doi: 10.1016/j.scitotenv.2016.01.154
[25]	Rowland S T, Boehme A K, Rush J, et al. Can ultra short-term changes in ambient temperature trigger myocardial infarction? Environment International, 2020; 143: 105910. doi: 10.1016/j.envint.2020.105910
[26]	WHO. World Health Assembly closes, passing resolutions on air pollution and epilepsy. (2015-05-26) [2023-10-17] https://www.who.int/news/item/26-05-2015-world-health-assembly-closes-passing-resolutions-on-air-pollution-and-epilepsy.
[27]	Yang B Y, Qian Z, Howard S W, et al. Relationship between ambient air pollution and hospital admissions for cardiovascular diseases in Kaohsiung, Taiwan. J Toxicol Environ Health A, 2004; 67(6): 483–93. doi: 10.1080/15287390490276502
[28]	Chen R, Yin P, Meng X, et al. Fine particulate air pollution and daily mortality: a nationwide analysis in 272 Chinese Cities. AJRCCM Articles in Press, 2017; 196(1): 73–81.
[29]	Le Tertre A, Medina S, Samoli E, et al. Short-term effects of particulate air pollution on cardiovascular diseases in eight European cities. J Epidemiol Community Health, 2002; 56(10): 773–779. doi: 10.1136/jech.56.10.773
[30]	Argacha J F, Collart P, Wauters A, et al. Air pollution and ST-elevation myocardial infarction: a case-crossover study of the Belgian STEMI registry 2009-2013. Int J Cardiol, 2018; 223: 300–305.
[31]	Newby D E, Mannucci P M, Tell G S, et al. Expert position paper on air pollution and cardiovascular disease. European Heart Journal, 2015; 36(2): 83–93. doi: 10.1093/eurheartj/ehu458
[32]	GBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020; 396(10258): 1204–1222. doi: 10.1016/S0140-6736(20)30925-9
[33]	Wang W, Zhang M, Xu C D, et al. Hypertension prevalence, awareness, treatment, and control in northeast China: a population-based cross-sectional survey. J Hum Hypertens, 2017; 32(1): 54–65.
[34]	Whayne T F Jr. Altitude and cold weather: are they vascular risks? Curr Opin Cardiol, 2014; 29(4): 396–402. doi: 10.1097/HCO.0000000000000064
[35]	Gronlund C J, Zanobetti A, Wellenius G A, et al. Susceptibility to mortality in weather extremes effect modification by personal and small-area characteristics. Clim Change, 2016; 136(3): 631–645.