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Association of Meteorological Data to Incident of Cardiovascular Disease

ความสัมพันธ์ระหว่างข้อมูลทางอุตุนิยมวิทยากับอุบัติการณ์การเกิดโรคหลอดเลือดหัวใจ

Songsak Kiatchoosakun (ทรงศักดิ์ เกียรติชูสกุล) 1, Udomlack Peansukwech (อุดมลักษณ์ เพียรสุขเวช) 2, Sirirat Anutrakulchai (ศิริรัตน์ อนุตระกูลชัย) 3, Supatcha Prasertcharoensuk (สุภัชชา ประเสริฐเจริญสุข) 4




ของการเกิดโรคเรื้อรังนั้นมีสาเหตุมาจากปัจจัยทางพันธุกรรมและสิ่งแวดล้อม การวิจัยครั้งนี้มีวัตถุประสงค์เพื่อประเมินและรายงานความสัมพันธ์ระหว่างโรคหลอดเลือดหัวใจและสิ่งแวดล้อม

วิธีการศึกษา: เป็นการศึกษาย้อนหลังจากข้อมูลอุตุนิยมวิทยาจาก the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2)  และจำนวนผู้ป่วยนอกรายใหม่ในประเทศไทยจากศูนย์ข้อมูลสนับสนุนการจัดบริการสุขภาพที่ได้รับอนุมัติจากกระทรวงสาธารณสุขระหว่างวันที่ 1 มกราคม 2556 ถึงวันที่ 31 ธันวาคม 2560

ผลการศึกษา: จำนวนผู้ป่วยโรคหัวใจและหลอดเลือดรายใหม่ในประเทศไทยตั้งแต่วันที่ 1 มกราคม 2556 ถึงวันที่ 31 ธันวาคม 2560 แสดงแนวโน้มที่เพิ่มสูงขึ้นอย่างต่อเนื่อง โดยมีค่าเฉลี่ย 1,990 รายต่อเดือน จากโมเดลอนุกรมเวลามีเพียง 3 ปัจจัยที่เกี่ยวข้องกับการมีผู้ป่วยโรคหัวใจและหลอดเลือดรายใหม่อย่างมีนัยสำคัญ คือ อุณหภูมิ ความดันพื้นผิว และความชื้น

สรุป: ข้อมูลอุตุนิยมวิทยาได้แก่ อุณหภูมิ ความดันบรรยากาศ และความชื้นที่มีผลกับการมีผู้ป่วยโรคหัวใจและหลอดเลือดรายใหม่ 

 

Background and Objectives: Cardiovascular disease (CVDs) is the leading cause of death worldwide. The risks of developing chronic diseases are attributed to both genetic and environmental factors. The aim of this research was to assess and report on correlation between cardiovascular disease and environment.

Methods: This retrospective study evaluated meteorological data from the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) along with monthly new CVDs cases in Thailand from Health Data Center (HDC) v 4.0 approved by the Ministry of Public Health (MOPH) during January 1, 2013 to December 31,2017.

Results: Monthly mean values of new CVDs cases in Thailand from January 1, 2013 through December 31, 2017 display increasing trends over time with mean 1990 cases/month. By the time series model, only 3 factors related with new CVDs cases significantly including temperature, surface pressure, and humidity.

Conclusion: Meteorological data like temperature, atmosphere pressure and humidity has effect to CVDs event.

 

Introduction

Cardiovascular disease (CVDs) is the leading cause of death worldwide. It is estimated that 17.9 million people died from CVDs in 2016, representing 31% of all global death 1. The causes of CVDs are multifactorial such as: age, gender, ethnicity, family history of heart disease, type 2 diabetes, hypertension, dyslipidemias, obesity, tobacco smoking, alcohol abuse, lack of physical activity and dietary habits 2,3. To prevent and regression CVDs, it is essential to identify the risk factors of atherosclerotic lesions and need to be managed and treated 4. The risks of developing chronic diseases are attributed to both genetic and environmental factors, 70 to 90% of disease risks are probably due to differences in environments 5.

Accumulating evidence supports that ecological features are important determinants of cardiovascular health. As has previously been reported, Seasonal variation in sudden cardiac death (SCD) has been documented by several epidemiological studies with controversial outcome6,7. Variations of outdoor temperature were associated with variations in the majority of CVDs risk factors such as lower outdoor temperatures were significantly associated with higher levels of systolic blood pressure (SBP) and increased total cholesterol and LDL-cholesterol 8,9. The synergistic effect between low temperature and high humidity had the greatest impact on the CVDs death burden over a lag of 0–21 days 10.

The aim of this research was to assess and report on correlation between cardiovascular disease and environment such as temperature and air pollution in Thailand, potentially leading to improved understanding of climate vulnerability in the health sector, and more informed risk management and adaptation decisions.

 

Materials and methods

This retrospective study evaluated meteorological data along with monthly new CVDs cases in Thailand during January 1, 2013 to December 31,2017, were collected from Health Data Center (HDC) v 4.0 approved by the Ministry of Public Health (MOPH). The classification of the disease in new case, hospital admission and death cases were coded according to the International Classification of Diseases (ICD-10). For the present study, ICD-10 codes associated with cardiovascular disease (I00-I09, I20-I28, and I30-I52) were included. We obtained monthly mean temperature, surface pressure, humidity and amount of rain from January 2013 to December 2017 by the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), that is the latest atmospheric reanalysis of the modern satellite era produced by NASA’s Global Modeling and Assimilation Office (GMAO). The study was approved by Khon Kaen University Ethics Committee (KKU-EC) (Project identification code HE606166).

Data analysis was performed using Stata version 10.1. Meteorological data and CVDs cases were presented as frequency, median with standard deviation with percentile, respectively. A Spearman correlation was used to explore the relationship between CVDs and weather variables with its 95% confidence interval (CI).This study used a time-stratified case-crossover design to conventional time series regression for analysing associations between time series of environmental exposures (air pollution, weather) and counts of health outcomes and later in the same month in the same year. Monthly CVDs counts approximately followed Poison distribution.

 

Results

Monthly mean values of new CVDs cases in Thailand were plotted for the entire period from January 1, 2013 through December 31, 2017 to graphically display increasing trends over time with mean 1990 cases/month. Highest peak of new case is on October 2015 with 3372 cases and Lowest is on April 2013 (Graph 1). As expected, a distinctive seasonal pattern was seen, with highest new cases occurring in the winter period (Graph 2).

 

Graph 1 New CVDs cases each month from 2013-2017.

 

Graph 2 New CVDs cases distributed follow seasonal.

 

The distribution of monthly meteorological data is shown in wax and wane pattern of all meteorological factors as seasonal throughout study period (Graph 3). Rain and humidity exhibited the same patterns.

Graph 3 Meteorological variables in Thailand from 2013 to 2017 including temperature (A), surface pressure (B), rain (C) and humidity (D).

 

Monthly mean values of meteorological data in Thailand describe with average temperature 26.023 C, surface pressure 979.069 hPa, rain 0.193 mm/hr and humidity 0.016 % (Table1).

Table 1 Meteorological characteristics in Thailand during 2013-2017.

Meteorological data

mean values

SD

p25

p50

p75

Temperature (C)

26.023

2.239

24.686

25.898

27.414

Surface pressure (hPa)

979.069

2.367

977.113

978.86

980.809

Rain (mm/hr)

0.193

0.135

0.06

0.157

0.326

Humidity (%)

0.016

0.003

0.014

0.017

0.018

 

Correlation between meteorological data and new CVDs cases by Spearman method found positive correlation in pressure, rain and humidity, with the highest coefficient for surface pressure (0.2876) as shown in Table 2. While, the temperature had negative coefficient with new CVDs cases at -0.2825.

 

Table 2 Correlations between meteorological data and new CVDs cases by Spearman correlation.

 

CVDs

Temperature

Surface pressure

Rain

Humidity

CVDs

1

- 

 -

 -

- 

Temperature

-0.2825*

1

 -

 -

- 

Surface pressure

0.2876*

-0.6096**

1

- 

- 

Rain

0.0243

0.1544

-0.7911**

1

- 

Humidity

0.0356

0.3722**

-0.843**

0.8977**

1

* p < 0.05.

** p < 0.01

 

By the time series model, only 3 factors related with new CVDs cases significantly including temperature, surface pressure, and humidity (Table 3). The highest incidence rate ratio (IRR) was found in temperature at 0.97 (95%CI 0.97,0.98) and lowest in humidity.

 

 Table 3 Analysis of time-stratified case-crossover studies in environmental epidemiology

 

IRR

[95% Conf.

Interval]

P>z

Temperature

0.97

0.97

0.98

<0.01

Surface pressure

1.07

1.06

1.08

<0.01

Rain

0.96

0.75

1.21

0.71

Humidity

7.78E+25

6.10E+20

9.91E+30

<0.01

Note: Incidence Rate Ratio (IRR)

 

Discussion

This present study showed that in a population exposed to wide fluctuations in meteorology, was associated with a markedly resemble to wax and wane pattern of new CVDs cases during study period and also increase numbers of this disease, which is similar to the results of previous studies. In Hiroshima, Okayama, Yamaguchi and Matsue City, daily average events of acute myocardial infarction were 30- 40% higher in winter than in summer (p < 0.05). Daily average events increased as atmospheric temperature decreased 11. Early ST occurred with seasonal variation; in 31 (36%) in winter, in 29 (34%) in spring, in 17 (20%) in autumn, in 9 (10%) in the summer (P = 0.002), was more likely to occur in the winter months 12. Compatible with our study that higher new cases in winter period with lower temperature associated with higher incidence rate ration (IRR 0.97) after adjusting other meteorology.

For atmospheric pressure, a V-shaped relationship, with a minimum of daily event rates at 1016 hPa. A 10-hPa decrease in atmospheric pressure < 1016 mbar was associated with a 12% increase in total coronary event rates, a 13% increase in coronary deaths, an 8% increase in incidence rates, and a 30% increase in recurrent event rates, but atmospheric pressure levels > 1016 hPa, a 10-hPa increase was associated with an 11% increase in total coronary event rates, an 18% increase in coronary deaths, a 7% increase in incidence rates, and a 30% increase in recurrent event rates 13. These effects were independent and influenced both coronary morbidity and mortality rates. The fall and winter seasons had the highest variability in atmospheric pressure, there was a significant correlation (p = 0.0083) between a decrease in atmospheric pressure and the occurrence of Acute Myocardial Infarction (AMI) the day after a pressure decrease, especially during the fall and winter seasons 14. Contrast to our study that demonstrate increase atmospheric pressure increase CVDs cases.

High humidity may lead to increased thrombotic risk, a positive correlation between daily CVD death and relative humidity (r = 0.035, p < 0.05). Similar to our study that humidity impact to new CVDs case even little effect.

Strength of this study lies in the data from a national registry approved by MOPH, and our limitation is that we used exposure data that are measured directly by the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), that is the latest atmospheric reanalysis of the modern satellite era produced by NASA’s Global Modeling and Assimilation Office (GMAO), but no correlation to ground station limited to developing process.

 

Conclusion

          Meteorology like temperature, atmosphere pressure and humidity has effect to CVDs event.

 

Acknowledgment

We acknowledge (a) the Chronic Kidney Disease prevention in the Northeast of Thailand project of Khon Kaen University (CDKNET) for assistance with data management and statistical analysis, and (b) Prof. Kittisak Sawanyawisuth for assistance with writing this manuscript. The term of this arrangement did not lead to any conflict of interests regarding the publication.

 

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

 

 

References

1.       Organization WH. Cardiovascular diseases (CVDs). [Accessed 12/09/2019]. https://www.who.int/en/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds)., 2019.

2.       Mazalin Protulipac J, Sonicki Z, Reiner Z. Cardiovascular disease (CVD) risk factors in older adults - Perception and reality. Arch Gerontol Geriatr 2015; 61(1): 88-92.

3.       Tzoulaki I, Elliott P, Kontis V, Ezzati M. Worldwide Exposures to Cardiovascular Risk Factors and Associated Health Effects: Current Knowledge and Data Gaps. Circulation 2016; 133(23): 2314-2333.

4.       Kinoshita M, Yokote K, Arai H, Lida M, Ishigaki Y, Ishibashi S, et al. Japan Atherosclerosis Society (JAS) Guidelines for Prevention of Atherosclerotic Cardiovascular Diseases 2017. J Atheroscler Thromb 2018; 25(9): 846-984.

5.       Rappaport SM, Smith MT. Epidemiology. Environment and disease risks. Science 2010; 330(6003): 460-461.

6.       Toro K, Bartholy J, Pongracz R, Kis Z, Keller E, Dunay G. Evaluation of meteorological factors on sudden cardiovascular death. J Forensic Leg Med 2010; 17(5): 236-242.

7.       Silverman RA, Ito K, Freese J, Kaufman BJ, Claro DD, Braun J, et al. Association of ambient fine particles with out-of-hospital cardiac arrests in New York City. Am J Epidemiol 2010; 172(8): 917-923.

8.       Sartini C, Barry SJ, Whincup PH, Wannamethee SG, Lowe GD, Jefferis BJ, et al. Relationship between outdoor temperature and cardiovascular disease risk factors in older people. Eur J Prev Cardiol 2017; 24(4): 349-356.

9.       Modesti PA, Rapi S, Rogolino A, Tosi B, Galanti G. Seasonal blood pressure variation: implications for cardiovascular risk stratification. Hypertens Res 2018; 41(7): 475-482.

10.     Zeng J, Zhang X, Yang J, Bao J, Xiang H, Dear K, et al. Humidity May Modify the Relationship between Temperature and Cardiovascular Mortality in Zhejiang Province, China. Int J Environ Res Public Health 2017; 14(11): 1383.

11.     Wang H, Kakehashi M, Matsumura M, Eboshida A. [Association between occurrence of acute myocardial infarction and meteorological factors]. J Cardiol 2007; 49(1): 31-40.

12.     Isik T, Ayhan E, Uyarel H, Akkaya E, Ergelen M, Cicek G, et al. Circadian, weekly, and seasonal variation in early stent thrombosis patients who previously underwent primary percutaneous intervention with ST elevation myocardial infarction. Clin Appl Thromb Hemost 2013; 19(6): 679-684.

13.     Danet S, Richard F, Montaye M, Beauchant S, Lemaire B, Graux C, et al. Unhealthy effects of atmospheric temperature and pressure on the occurrence of myocardial infarction and coronary deaths. A 10-year survey: the Lille-World Health Organization MONICA project (Monitoring trends and determinants in cardiovascular disease). Circulation 1999; 100(1): E1-7.

14.     Houck PD, Lethen JE, Riggs MW, Gantt DS, Dehmer GJ. Relation of atmospheric pressure changes and the occurrences of acute myocardial infarction and stroke. Am J Cardiol 2005; 96(1): 45-51.

 

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