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Table of Contents
RESEARCH ARTICLE
Year : 2019  |  Volume : 4  |  Issue : 1  |  Page : 29-34

Associations of extremely hot weather and cardiovascular disease mortality: Results from 2011 to 2017, Jinan City, China


1 Department of Environmental Health, Jinan Municipal Center for Disease Control and Prevention, Beijing, China
2 Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing, China

Date of Web Publication28-Mar-2019

Correspondence Address:
Liangliang Cui
Department of Environmental Health, Jinan Municipal Center for Disease Control and Prevention, No. 2 Weiliu Road, Jinan 250021, Shandong
China
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/cp.cp_36_18

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  Abstract 

Objective: The main objective of this study is to investigate the acute effects of extremely high-temperature weather and heat waves on the risk of cardiovascular disease (CVD) resulting in the death in Jinan City, China. Methods: We collected the daily CVD death cases of permanent residents, daily weather data, and air pollution data (PM2.5, SO2, NO2, and O3) from Jinan, China, from 2011 to 2017 for May–September, when the temperatures are their highest. Three levels of extremely high-temperature weather were defined by the 90th percentile (29.5°C), the 95th percentile (30.6°C), or the 99th percentile (32.4°C) of daily mean temperature. They are represented by P90, P95, and P99, respectively. The definition of heat wave also included three levels, namely, Heat Wave I, Heat Wave II, and Heat Wave III for the P90, P95, and P99 of daily average temperatures that lasted for 3 days or longer. A time-stratified case-crossover study was used to assess the acute effects of extremely hot weather and heat waves on the risk of CVD death with a lag time of 6 days. Results: A total of 54,374 CVD deaths were detected during the study, with an average of 52 deaths/day. No Heat Wave III was present between 2011 and 2017. The maximum effect of P90, P95, and P99 on CVD death occurred in Lag 1 with odds ratio values of 1.21 (95% confidence interval [CI]: 1.16-1.66), 1.25 (95% CI: 1.18–1.32), and 1.28 (95% CI: 1.13-1.45). At Lag 1, Heat Wave I increased the risk of CVD death by 1.30 (95% CI: 1.23–1.37); at Lag 2, Heat Wave II increased the risk of CVD death by 1.47 (95% CI: 1.36–1.59). Conclusions: A 7-year analysis of Jinan City, China, found that extremely high-temperature weather and heat waves can lead to a significant increase in the risk of death from CVD, suggesting that a health-risk management and response mechanism should be established that include both extreme high temperatures (day effect) and heat waves (sustained effect).

Keywords: Cardiovascular disease, case cross-over study, death, extremely hot weather


How to cite this article:
Cao R, Zhou L, Xu J, Cui L. Associations of extremely hot weather and cardiovascular disease mortality: Results from 2011 to 2017, Jinan City, China. Cardiol Plus 2019;4:29-34

How to cite this URL:
Cao R, Zhou L, Xu J, Cui L. Associations of extremely hot weather and cardiovascular disease mortality: Results from 2011 to 2017, Jinan City, China. Cardiol Plus [serial online] 2019 [cited 2019 Jun 27];4:29-34. Available from: http://www.cardiologyplus.org/text.asp?2019/4/1/29/255075


  Introduction Top


As the global climate changes, the frequency of extreme weather events continues to rise.[1] Among them, high-temperature heat waves are a prominent manifestation of global warming, and their frequency, intensity, and duration are also increasing.[2],[3],[4] In most parts of China, there is a clear trend of climate warming, and extreme weather events occur frequently.[5]

Cardiovascular disease (CVD) is the leading cause of death and disability worldwide. It is estimated that by 2020, >80% of global CVD will occur in low- and middle-income countries. China and India are the countries with the greatest disease burden.[6] The prevalence of CVD is on a rising trend in China. According to the “Summary of report on CVDs in China (2017),” the number of CVD patients in China is about 290 million, and the mortality rate ranks first in the total cause of death, accounting for >42% of the total cause of death.[7] Some studies have shown that the rising temperatures can increase the risk of CVD death.[8],[9],[10] At present, studies in China have described the acute effects of extreme temperatures on CVD morbidity and mortality[9],[10],[11],[12],[13],[14] but have a short-coverage period, and the assessment of high-temperature weather and heat waves is a critical knowledge gap. The current study examines the effects of both extreme single-day temperature and long-lasting heat waves on the mortality of people with CVD.

Jinan City is located at 36°40′ north latitude and 117°00′ east longitude. It is located at the junction of the low hills in South-central Shandong and the alluvial plains in Northwestern Shandong. The terrain is high in the south and low in the north. It has a warm, temperate, and semi-humid continental monsoon climate. In the context of global climate change, climate disasters in Jinan City are happening more frequently and with greater intensity, especially high-temperature events and record-setting extreme temperatures.[15] For these reasons, this study selected Jinan City to evaluate the acute effects of high-temperature heat waves on CVD death. This study provides a basis for the health-risk management in high temperatures and heat waves.


  Methods Top


Mortality data

During the period of 2011–2017, the analysis of the cause of death in Jinan showed that CVD was the leading cause of death in Jinan City (data from the cause of death monitoring system of Jinan City Center for Disease Control and Prevention, unpublished data). The hot weather in Jinan mainly occurs in the warm season from May to September,[11] and hence the time range of this study was set to be from May 1 to September 30 of each year. We collected the information on daily CVD deaths of permanent residents in Jinan City during the study. The indicators included death time, resident address code, and coding of underlying causes. The underlying cause of death coding CVD was I00–I99, based on the International Classification of Diseases, 10th Revision, ICD-10.

Environmental data

We collected daily weather indicators of Jinan during the study, including daily maximum temperature (Tmax, °C), daily minimum temperature (Tmin, °C), daily average temperature (Taverage, °C), daily average relative humidity (RH, %), daily average pressure (Pressure, kPa), and daily average wind speed (Wind, m/s), data from China meteorological science data sharing service network (http://cdc.cma.gov.cn/home.do), monitoring site number is 54823. We considered the impact of air pollution on the death of CVD and collected data on atmospheric pollutants at the same time. Measured air pollutants included PM2.5 (μg/m3), SO2 (μg/m3), NO2(μg/m3), and O3 (μg/m3). Data from the Jinan Environmental Protection Bureau were collected. There are 14 air quality monitoring stations in Jinan, eight of which are national monitoring stations and six are provincial monitoring stations covering all jurisdictions of the city. The average of the daily air pollutant concentrations of all 14 air quality monitoring stations was included in the analysis of the city's atmospheric pollutant concentration in this study.

Definitions of extremely hot weather and heat wave

At present, there is no uniform definition standard for an extreme high-temperature days and heat waves in the world. This study refers to previous studies[13],[14],[16],[17],[18] and defines extreme high-temperature days at three levels when the daily average temperature is higher than the mean temperature of the study. The 90th percentile (29.5°C), the 95th percentile (30.6°C), and the 99th percentile (32.4°C) of the daily average temperature of the period are represented by P90, P95, and P99, respectively. To further analyze the continuous effect of high-temperature weather, heat waves were defined as daily average temperatures in the 90th, 95th, or 99th percentile lasted for 3 days or longer. They are represented by Heat Wave I, Heat Wave II, and Heat Wave III, respectively.

Statistical analysis

Time-stratified case-crossover study

This study, according to the principle of strict control selection, considered the long-term trend, seasonal fluctuations and the effects of the day of the week, the dates of the same year, the same month, and the same week as the death case as controls. The generalized linear model was used to fit the effects of extremely hot weather and heat waves on the daily death toll of CVD. The model also controlled for the daily mean humidity (RH), daily mean pressure (pressure), and average daily wind speed (wind). The model formula is as follows:

Log (E [Yt]) = α + βX + RH + Pressure + Wind + strata

Where, t is the observation day/observation period; Yt is the number of deaths of CVD that occurred on the observation day; α is the intercept term; X is the extreme high-temperature weather (P90, P95, and P99; Heat Wave I, Heat Wave II, and Heat Wave III) “1” and “0 the categorical variable; β is the correlation coefficient of X; strata is the categorical variable, which is the matching variable with the case year, month, and week. According to the significance of the model analysis results, the hysteresis effect results are shown as Lag 1d–to–Lag 6d (Lag 1-Lag 6).

Sensitive analysis

Sensitivity analysis selected the effects of extreme high-temperature day and heat wave on CVD death risk at maximum hysteresis effect day (Lag 1 or Lag 2), implemented pollutant model (PM2.5, SO2, NO2, and O3), and daily maximum temperature impact model (Tmax) to analysis and evaluate the stability of the main model.

Statistical analysis

Data cleaning and statistical analysis were performed using the R software (version 3.3.2; R Development Core Team 2012, http://www.R-project.org/). The meteorological indicators and atmospheric pollutant concentrations during the study were approximately obeying the normal distribution. The daily population CVD deaths were subject to the semi-Poisson regression. For the convenience of expression, the mean ± standard deviation was used. We calculated the number of extreme high-temperature days, heat wave days, composition ratios in each year, the average number of deaths from CVD during extreme high-temperature days and heat waves, and the average number of deaths in nonextreme weather.

In the regression model, the odds ratio (OR) and its 95% confidence interval (CI) were used as the estimated expression of CVD death effects in the extreme weather. The maximum value of the hysteresis analysis results was chosen as an estimate of the exposure risk of extremely high temperatures and heat waves to CVD deaths. Value of P < 0.05 was considered as statistically significant.


  Results Top


Descriptive analysis

Daily average distribution of deaths from meteorological, air pollutants and cardiovascular diseases in the warm season

[Table 1] describes meteorological indicators, air pollutants, and CVD deaths during the warm season (1071 days) of Jinan City from 2011 to 2017. A total of 54,374 CVD deaths were detected with an average of 52 deaths/day. The average daily death rate has been increasing slowly year by year, and the highest daily death rate was in 2017. Consistent with the trend of increasing the death rate, the daily average temperature, the lowest temperature of the day, and the highest temperature of the day also increased year by year. Only 2013 had a lower maximum temperature than 2017. Among the four atmospheric pollutants, the levels of PM2.5, SO2, and NO2 decreased year by year, and the concentration of O3 increased.
Table 1: Description (mean±standard deviation) of weather, air pollution, and cardiovascular disease mortality in the warm season months from 2011 through 2017, Jinan City

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The composition of extremely hot weather and heat waves

[Table 2] shows the composition of the extremely hot weather and the days of heat waves during the warm season each year. We found that there were different degrees of high-temperature days and heat waves every year, and the number of days and proportions appearing in different years were significantly different. The most frequent occurrences of hot weather were in 2013 and 2017. In the case of heat waves, there was no Heat Wave III during the study period. In 2011, there were no heat waves. In 2015, there was no Heat Wave II. In the rest of the year, the number of days of Heat Wave I was about 10 days.
Table 2: Number of days and proportion of extremely hot weather in warm season months during 2011 and 2017, Jinan City

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Daily average number of deaths from cardiovascular disease during extremely hot weather and nonextremely hot weather

[Table 3] shows daily CVD deaths during extremely hot weather and heat waves. The number of CVD deaths per day during hot weather and heat waves was generally higher than average, with the largest increase in CVD death rate occurring in 2016. The average daily deaths during P90, P95, and P99 on extremely hot days were increased by 26.9%, 42.3%, and 35.8%, respectively. The average daily deaths during Heat Wave I and Heat Wave II were increased by 33.3% and 69.8%, respectively.
Table 3: Mean values of deaths related to total cardiovascular disease in the extremely hot weather days and not during warm season months from 2011 to 2017, Jinan City

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Acute effects of extremely high-temperature days on cardiovascular disease death

[Table 4] shows the effects of extremely high-temperature days on CVD death during the study period. We found that P90, P95, and P99 all increased the risk of CVD death, with a significant increasing risk at Lag0 and the maximum effect observed at Lag 1. The ORs and 95% CIs of P90, P95, and P99 were 1.21 (95% CI: 1.16-1.66), 1.25 (95% CI: 1.18–1.32), and 1.28 (95% CI: 1.13–1.45), respectively.
Table 4: Associations (odds ratio and 95% confidence interval) of daily cardiovascular disease mortality and extremely hot temperature during warm season months from 2011 to 2017, Jinan City

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Acute effects of heat waves on cardiovascular disease death

[Table 5] shows the effects of heat waves on CVD death during the study period. Since the Heat Wave III was not detected during the study, it could not be analyzed. The results of Heat Wave I and Heat Wave II analysis showed that a significant increase in the risk of CVD death was observed on the days of the heat wave, which continued throughout the heat wave. Heat Wave I reached the highest value at Lag 1 with an OR of 1.30 (95% CI: 1.23-1.37) and Heat Wave II reached its highest value at Lag 2 with an OR of 1.47 (95% CI: 1.36–1.59). Heat Wave I and Heat Wave II had a long-lasting effect on CVD death effects even to 6 days after the end of the heat wave.
Table 5: Associations (odds ratio and 95% confidence interval) of daily cardiovascular disease mortality and heat wave during warm season months from 2011 to 2017, Jinan City

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Sensitive analysis results

[Figure 1] is a sensitivity analysis of the risk of CVD death in the extremely high-temperature weather. It can be observed that when Tmax was included in the main model, the effects of P90, P95, and P99 on the risk of CVD death were changed a little, suggesting that the main model fit well and the results were robust. When the sensitivity analysis of air pollutants model were carried out separately, the change of P90 was small, and the OR of P95 and P99 had an increasing trend, but the results of fitting the main model were the best estimates. [Figure 2] shows the sensitivity of the heat wave to the risk of CVD death. The results suggest that the changes and stability of the two types of sensitivity analysis results were very good.
Figure 1: The sensitivity analysis of extremely hot weather (P90, P95 and P99)

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Figure 2: The sensitivity analysis of heat Wave I and heat Wave II

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  Discussion Top


Through the analysis of meteorological factors and CVD death monitoring data for seven consecutive years in Jinan City from 2011 to 2017, we found that the frequency and extent of extremely high-temperature weather and heat waves have increased in recent years. Compared with normal temperatures, the average daily death of CVD was significantly increased during single extreme heat days and heat waves. Case-cross-sectional analysis of a time-stratified design found that extremely high temperature and heat waves significantly increased the acute risk of cardiovascular death and showed a significant time lag pattern.

The results suggest that extremely high-temperature weather and heat waves have a significant correlation with the risk of CVD death. At the same time, we found that the larger the increase of temperature, the higher the risk of death, suggesting the existence of a relationship between high-temperature intensity and exposure risk of CVD death risk. The results are consistent with to previous studies on the distribution lag effect of daily temperature and death risk,[19] and the results of the gradual increase in death risk as discussed in different definitions of heat wave levels.[14]

Furthermore, the results are consistent with the prior studies that conducted in other geographical locations: Lee et al.[8] conducted a study on the effects of heat waves on death in Japan and South Korea; Yang et al.[13] conducted a study on the effects of heat waves on death in Guangzhou, China; and Chen et al.[14] studied the heat wave risk of death under different definitions in Nanjing, China.

Han et al.[17] recently studied the effects of heat waves on death in Jinan City, China. This study only analyzed the acute effects of heat waves, while the current study not only quantitatively evaluated the acute effects of different degrees of heat waves but also investigated the single-day effects of extremely high-temperature weather. The results suggest that the health risks of the population need to be taken seriously even with a single day of extremely hot weather.

Previous time-series studies have found that the acute effects of temperature and population risk of CVD death show a significant time lag pattern.[9],[19],[20] The analysis of the lag time effect of extremely high-temperature weather and heat wave found that the single-day effect of extremely high-temperature weather had a lag time of 0–4 days, but when the duration of the heat wave effect lasts 5 or 6 days, the maximum effect appears in Lag 1 or Lag 2. In most cases, the lag time of most studies is usually between 0 and 4 days.[19],[20] Chen et al.[14] explored the effects of different heat wave definitions on daily mortality in Nanjing, China and found that the maximum risk of cardiovascular-related deaths during heat waves also appeared in Lag 2, which is the same as the time lag mode of the Heat Wave II. This suggests that when extremely high-temperature weather and heat waves occur, it is necessary to pay attention not only to the health risks of the population at the time of occurrence but also to the hysteresis effect. Pay attention to the timeliness and sustainability of the adoption and implementation of high-temperature measures.

Due to the lack of uniform standards and definitions for extreme high-temperature weather and heat wave research, the current widely used the time-stratified case cross-sectional analysis method was used in this study. Therefore, the following limitations are unavoidable. First, the research is based on the evaluation results of meteorological factors in Jinan City from 2011 to 2017, so it is of great significance for the localization applications. However, the standards for an extremely high-temperature and heat waves are different in different regions. Therefore, the results of this study can only be used as a reference for other regions. Second, the temperature changes have regional and urban heat island effects.[8] In this study, the city's mean value was roughly used as an estimate of the exposure of all CVD deaths, and hence there was a selection bias. In addition, Heat Wave III was not detected in this study and its risk was not analyzed. However, this study has certain representativeness and novelty as a study on the risk of extreme weather death in typical high-temperature cities in China. The strengths of the study include long-observation time, a large sample size, high-temperature characteristics in the city, the combined study of effects of extremely hot weather and heat waves, as well as the different categories of sensitivity analysis which resulted in good stability and high reliability.


  Conclusions Top


Through the analysis of records of Jinan City, China over 7 years, we found that extremely high-temperature weather and heat waves can lead to a significant increase in the risk of death from CVD in the local population. Both the severity of extreme heat and the risk of cardiovascular death in the population show increasing trends. It is suggested that Jinan City should establish a health-risk management and response mechanism that include plans for the both extreme high-temperature weather (day effect) and heat wave (sustained effect), especially focusing on the health risk of people suffering from CVD.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.

 
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