Weather fluctuations: predictive factors in the prevalence of acute coronary syndrome

Introduction

Acute coronary syndrome (ACS) is one of the most common health problems in the world, which can increase mental health problems, result in considerable disability, increase morbidity, and mortality.^1,2 Studies during several decades have focused on different pathophysiological mechanisms and predisposing factors for the incidence of ACS.^3-5 One of these studies focused on the effects of weather conditions on incidence of ACS that has been discussed in different areas of the world for more than 50 years.⁶ A number of investigators found that meteorological parameters and seasonal changes can play an important role in the occurrence of ACS.^7,8 The role of temperature is both direct and fast. Time series studies have revealed that there is a minimum lag time of about 24 hours between decrease in temperature and increase in mortality.⁹ The study reported that approximately 4% of ACS onsets are associated with different kinds of meteorological parameters.¹⁰ In another study, it was found that weather conditions like atmospheric air temperature, humidity, wind speed, and wind pressure effects the incidence of ACS.^11-13 Although several studies suggest that ACS occurs most often in the winter months,^14-17 other studies report that the occurrence rate of ACS increases in spring.^6,18 Yet another study shows that ACS occurs in summer when the temperature and humidity are high and atmospheric pressure is low.¹⁹ It has also been shown that there is an association between temperature, high relative humidity, and strong winds and ACS.⁹ It seems that physiologic stressors such as sympathetic activation, hypercoagulability and infection in cold weather condition (such as influenza epidemics and air pollution) are linked to the incidence of ACS.²⁰ Additionally, hemodynamic changes are exacerbated during winter, as are elevated immune reactions, uncontrolled hypertension, immobility, and risk for respiratory infections,²¹ that can affect the incidence of ACS. Some studies state that cold temperature effects platelet numbers, arterial blood pressure, and thrombus formation.¹⁹ Also, variation of incidence of ACS in different seasons is attributed to variability in ultraviolet-B exposure during the seasons and vitamin D deficiency that may increase the cardiovascular risk.²² Nevertheless, a 1°C decrease in temperature caused a 1%–2% rise in the number of deaths.⁹

Although‏ numerous‏ studies‏ have‏ been‏ conducted‏ on‏ the impact of climate variables and seasonal changes on the risk of ACS in different areas of the world; many of these studies lack national level data. However, there is almost no evidence to suggest an association between climate variables and the risk of ACS in Iran. So, due to the importance of reducing the incidence of ACS and its effects on quality of life, it is important to study the impact of meteorological parameters and seasonal changes on the incidence of ACS in a region with different weather conditions and great geographic diversity. In other words, a better understanding of the seasonal changes may provide novel pathways to prevent ACS.²³ Therefore, the present study aims to determine the relationship between meteorological parameters with the incidence of ACS.

Materials and Methods

This retrospective cross-sectional study design was used. This study is based on medical records of the Heart Center of Mazandaran province, Iran. This center provides the most comprehensive data in northern Iran on all patients with a diagnosis of ACS.

Results

Weather fluctuations

Figures 1 and 2 show the number of ACS events by gender and by age in quartiles for each month, respectively.

Figure 1. Number of AMI events by gender over month.

Figure 2. Number of AMI events by age quartiles over month.

Figures 1 and 2 show that the third to the fifth months had statistically higher (P < 0.001) occurrences of ACS events during the year. Overall the other months except for month 5 over month 1 (P = 0.581) adjusting for demographical and meteorological variables with Bonferroni correction (see Table 2).

Table 2. Comparison of ACS by month adjusted for age, meteorological variables and sex
Reference	Month	Mean Difference (Reference-Month)	95% Confidence Interval for Difference	Bonferroni corrected P value
			Lower	Upper
Month 1	2	58	-6.1	122.1	0.152
	3	-188	-276.4	-99.6	< 0.001
	4	-325	-428.1	-221.9	< 0.001
	5	-87	-198.3	24.3	0.561
	6	67	-29.3	163.3	1.000
	7	66	-34.7	166.7	1.000
	8	58	-44.8	160.8	1.000
	9	65	-29.7	159.7	1.000
	10	74	-6.0	154.0	0.122
	11	54	-13.1	121.1	0.445
	12	60	-3.3	123.3	0.092
Month 2	3	-246	-333.0	-159.0	< 0.001
	4	-383	-477.9	-288.1	< 0.001
	5	-145	-235.3	-54.7	< 0.001
	6	9	-64.7	82.7	1.000
	7	8	-70.2	86.2	1.000
	8	0	-80.2	80.2	1.000
	9	7	-65.9	79.9	1.000
	10	16	-43.0	75.0	1.000
	11	-4	-56.4	48.4	1.000
	12	2	-47.8	51.8	1.000
Month 3	4	-137	-257.1	-16.9	0.008
	5	101	-31.9	233.9	0.690
	6	255	133.6	376.4	< 0.001
	7	254	128.1	379.9	< 0.001
	8	246	117.1	374.9	< 0.001
	9	253	131.7	374.3	< 0.001
	10	262	154.8	369.2	< 0.001
	11	242	148.2	335.8	< 0.001
	12	248	159.5	336.5	< 0.001
Month 4	5	238	116.7	359.3	< 0.001
	6	392	281.4	502.6	< 0.001
	7	391	277.9	504.1	< 0.001
	8	383	269.9	496.1	< 0.001
	9	390	281.9	498.1	< 0.001
	10	399	299.9	498.1	< 0.001
	11	379	282.8	475.2	< 0.001
	12	385	290.1	479.9	< 0.001
Month 5	6	154	87.2	220.8	< 0.001
	7	153	84.0	222.0	< 0.001
	8	145	75.9	214.1	< 0.001
	9	152	81.7	222.3	< 0.001
	10	161	87.5	234.5	< 0.001
	11	141	53.4	228.6	< 0.001
	12	147	56.4	237.6	< 0.001
Month 6	7	-1	-50.8	48.8	1.000
	8	-9	-59.5	41.5	1.000
	9	-2	-52.2	48.2	1.000
	10	7	-46.8	60.8	1.000
	11	-13	-82.4	56.4	1.000
	12	-7	-80.3	66.3	1.000
Month 7	8	-8	-58.8	42.8	1.000
	9	-1	-51.5	49.5	1.000
	10	8	-48.1	64.1	1.000
	11	-12	-85.4	61.4	1.000
	12	-6	-83.4	71.4	1.000
Month 8	9	7	-43.2	57.2	1.000
	10	16	-39.9	71.9	1.000
	11	-4	-79.5	71.5	1.000
	12	2	-77.4	81.4	1.000
Month 9	10	9	-42.3	60.3	1.000
	11	-11	-78.7	56.7	1.000
	12	-5	-76.5	66.5	1.000
Month 10	11	-20	-76.1	36.1	1.000
	12	-14	-72.0	44.0	1.000
Month 11	12	6	-44.8	56.8	1.000
General Linear Model performed.

Discussion

The study aimed to evaluate the meteorological parameters and seasonal changes in relationship with incidence of ACS; as well as to identify gender differences. The results indicated that ACS admissions were higher March through May. Similarly March was reported as the month with the highest incidence of ACS in Germany.²⁵ However, January was reported to have the highest incidence in the United Sates.¹⁷ Other studies found that the incidence and fatality risk of ACS were higher in the winter and spring.^{4,16,18,20,26-30} However, two additional studies reported that ACS was more common in summer.^31,32 There are several mechanisms of climate changes that are suggested for the two pathological exogenous and endogenous responses. Lipid serum level, coagulation systems, and hormonal changes are among these features.²³ Also, behavioral pattern variations following seasonal changes like changes in diet, physical activity, and psychosocial factors such as mood are considered as the emerging explanations for the high incidence of ACS in these seasons.³³ Besides, variation in temperature,³⁴ seasonal pattern occur such as infections like influenza epidemics,³⁵ elevated concentration of fine element air pollution,¹⁰ seasonality phenomenon (i.e. winter depression, anxiety, sadness, social withdrawal, sleep disturbances, irritability, etc),¹⁰ respiratory tract infections,³⁶ and reduction in the number of solar light hours³⁷ are other proposed factors of the incidence of ACS. Therefore, differences in the patterns of ACS prevalence according to the time of year and changes in ambient climate within the same location may be reasonable explanations. This concept appears to be especially applicable to the regions of the world subjected to four distinct seasons and significantly different winter-to-summer weather conditions.³⁸

The possible role of meteorological variables has been considered throughout the study. The present study confirmed the positive relationships between wind speed and the incidence of ACS. Goerre et al reported the same findings in their study in Switzerland.³⁹ An inverse relationship was noted in the 10-year ecological study in Great Britain⁴⁰ and a 12 years survey in Kaunas¹¹ reported also negative correlations. However, another study failed to observe any significant relationships.²³

It was investigated that relative humidity was among the factors that negatively correlated with the ACS incidences. Our findings concur with those by Abrignani et al,²³ Messner et al,⁴¹ and Lee et al.⁴² one findings stated no correlations⁴³ and two others reported positive relationships among these variables.^44,45 Moreover, it seems that the presence of high air humidity may hinder swelling and also make it difficult the automatic processes of internal temperature control. Therefore, the respiratory fatigue and heart rate will be increased.²³

Data on the role of environmental temperature are conflicting. One of the remarkable results of the present study was the negative association between daily minimum temperature and the hospital admission due to ACS; this suggests that the daily minimum temperature has a protective role in ACS. In other words, when the temperature is at lowest, it reduced ACS by 6 percent. Some studies indicated that the number of ACS are linked with the both colder and warmer temperatures.^44,46,47 A 9-year survey (i.e. 2000-2009) in Hong Kong and Taiwan indicated that the lower mean temperature was associated with lower ACS risk on the same day.⁷ However, Kysely et al⁴⁸ and Näyhä⁴⁹ believe that fatality related to cold or heat is certainly not affected by hypo or hyperthermia. Also, Stewart et al reported that there is some evidence showing cold adaptation through longer exposure to the cold weather may occur. However, this approach is debatable.³⁸ Also reduction in acute phase mortality, due to variations, such as earlier diagnosis of infarction, early and aggressive treatment, suitable reperfusion treatment, additional precise delineation of post ACS risk, as well as more suitable treatment of heart failure and mechanical complications after ACS are among the possible factors leading to reduction in morbidity and mortality following ACS.^50,51 It also can be caused by indirect effects including cardiovascular disease that is exacerbated by physiological reactions of the man`s body aimed to adapt to the thermal environment.^48,49 To the best of our knowledge, there is no study that reported a negative correlation between the minimum daily temperature and ACS incidence. Further research is needed to fully understand the individual conditions and cold exposure.

Given the fact that seasonal weather effects the prevalence, complications and outcomes of ACS, so that patients should modify their lifestyle particularly during the cold months with a diet rich in vitamins (e.g. vitamin D3), modifies activity level, suitable and warm clothes.⁵²

Although the data of the current study have been extracted from the patients referred to the Sari, capital of Mazandaran city, our findings will be generalized to predict the occurrence of ACS in order to take preventive and therapeutic across the southern Caspian Sea. This happening can be due to the two causes; at first, weather characteristics as well as seasonal changes in the southern parts of the Caspian Sea follow relatively similar pattern⁵³ and the next is that the residents of these areas are prone to vulnerability because of high population density.⁵⁴

Conclusion

Climate changes were found to be significantly associated with ACS. Especially from cold weather to hot weather in March, April and May. Therefore, emergency treatment service personnel should be more vigilant and fully prepared in March, April, and May for an increase in ACS patient admissions.

Ethical approval

The study was approved (Code: IR.MAZUMS.REC.96-10232) by the Ethics Committee of Mazandaran University of Medical Sciences, Sari, Iran, pursuant to its code of ethics, including assured confidentiality of all patient information.

Competing interests

The authors report no conflicts of interest. The authors alone are responsible for the content and writing of this article.

Authors’ contributions

HShN, YHC, RN, and AHG were on the management committee. HSHN, ESF, AHG, AKH, RP, and AY were on the scientific committee. RP, SPSh, HSHN, AKH, FA, and ESF were responsible for data interpretation and writing the report. RP, SPSh, and AKH did the statistical analysis. HShN, AHG, AY, RN, and RP were on the writing committee. HShN, AY, ESF, YHC, FA, AKH, and SPSh reviewed and revised the manuscript. All authors reviewed the manuscript.

Acknowledgments

This project was an inter-university collaboration and was supported by Mazandaran University of Medical Sciences, Taylor’s University and University of California San Francisco. We want to hereby extend our sincere gratitude to the all of the authorities of the heart center who helped to make this research possible.

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