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Full-Text PDF: 647-654.pdf
Impact of Heat Wave in 2005 on Mortality in Guangzhou, China
 

Original Article

Impact of Heat Wave in 2005 on Mortality in Guangzhou, China*

YANG Jun1,△, LIU Hua Zhang2,△, OU Chun Quan1,#, LIN Guo Zhen2, DING Yan3, ZHOU Qin2, SHEN Ji Chuan2, and CHEN Ping Yan1

1. Department of Biostatistics, School of Public Health and Tropical Medicine, Southern Medical University, Guangzhou 510515, Guangdong, China; 2. Guangzhou Center for Disease Control and Prevention, Guangzhou 510440, Guangdong, China; 3. Faculty of Medicine, Southern Medical University, Guangzhou 510515, Guangdong, China

*This work was supported by National Nature Science Foundation of China (81102207) and Guangdong Nature Science Foundation (S2011040005355).

YANG Jun and LIU Hua Zhang contributed equally to this study.

#Correspondence should be addressed to OU Chun Quan, Ph.D., Tel: 86-20-61360456; Fax: 86-20-61648319. E-mail:ouchunquan@hotmail.com

Biographical note of the first author: YANG Jun, male, born in 1987, master candidate, majoring in environmental epidemiology.

Received: August 11, 2012;             Accepted: November 02, 2012

 

*This work was supported by National Nature Science Foundation of China (81102207) and Guangdong Nature Science Foundation (S2011040005355).

YANG Jun and LIU Hua Zhang contributed equally to this study.

#Correspondence should be addressed to OU Chun Quan, Ph.D., Tel: 86-20-61360456; Fax: 86-20-61648319. E-mail:ouchunquan@hotmail.com

Biographical note of the first author: YANG Jun, male, born in 1987, master candidate, majoring in environmental epidemiology.

Received: August 11, 2012;             Accepted: November 02, 2012

 

Abstract

Objective  To assess the impact of the heat wave in 2005 on mortality among the residents in Guangzhou and to identify susceptible subpopulations in Guangzhou, China.

Methods  The data of daily number of deaths and meteorological measures from 2003 to 2006 in Guangzhou were used in this study. Heat wave was defined as ≥7 consecutive days with daily maximum temperature above 35.0 °C and daily mean temperature above the 97th percentile during the study period. The excess deaths and rate ratio (RR) of mortality in the case period compared with the reference period in the same summer were calculated.

Results  During the study period, only one heat wave in 2005 was identified and the total number of excess deaths was 145 with an average of 12 deaths per day. The effect of the heat wave on non-accidental mortality (RR=1.23, 95% CI: 1.11-1.37) was found with statistically significant difference. Also, greater effects were observed for cardiovascular mortality (RR=1.34, 95% CI: 1.13-1.59) and respiratory mortality (RR=1.31, 95% CI: 1.02-1.69). Females, the elderly and people with lower socioeconomic status were at significantly higher risk of heat wave-associated mortality.

Conclusion  The 2005 heat wave had a substantial impact on mortality among the residents in Guangzhou, particularly among some susceptible subpopulations. The findings from the present study may provide scientific evidences to develop relevant public health policies and prevention measures aimed at reduction of preventable mortality from heat waves.

Key words: Heat wave; Mortality; Temperature; China

Biomed Environ Sci, 2013; 26(8):647-654    doi: 10.3967/0895-3988.2013.08.003     ISSN:0895-3988

www.besjournal.com(full text)            CN: 11-2816/Q          Copyright ©2013 by China CDC

 


INTRODUCTION

T

he average surface temperature of earth has increased by about 0.8 °C since 1900 and is projected to rise by 1.1 to 6.4 °C during the next hundred years[1]. Global warming contributed to the increased frequency, intensity and duration of extreme weather events. The health impact of these extreme events has therefore become an increasing public health concern. Heat waves, a period of persistent extreme high temperatures, are the most lethal type of weather events. And an astonishing number of excess deaths and hospitalizations occurred during several devastating heat waves in the last decades. For example, the 1987 Athens heat wave caused around 2000 deaths[2]. The 1995 Chicago heat wave led to approximately 700 deaths[3], and the 2003 heat wave in Europe was estimated to cause about 15 000 deaths in France alone during 1-20 August[4]. Furthermore, relatively mild heat waves occur more and more frequently worldwide but their impacts have not yet been well documented.

Some subpopulations may be more vulnerable to extreme temperature than others due to greater exposure, underlying physiological factors (e.g. aging and pre-existing chronic diseases) or poor access to other amenities (e.g. nutritious food, air conditioner and heater and proper health care). Previous studies have indicated that the elderly[5-6], females[6-7], and the black[7] are at higher risk of mortality due to heat waves and elevated temperature. And these findings provided important information for the establishment of heat warning systems and development of other prevention measures targeting the susceptible subpopulations in the West. The magnitude of the effect of heat waves differed by region and time[8-11]. This spatial and temporal heterogeneity may be modified by the characteristics of heat wave (e.g. intensity, duration and timing in the season)[11-12] and previous winter mortality[13-14]. It is also suggested that area-level sociodemographic factors are potential modifiers[8,10]. However, there was no inconclusive evidence of effect modification by socioeconomic factors in the United States[15], Europe[16], and Australia[17]. Individual-level socioeconomic data have greater gradient than area-level data, and therefore may stimulate a more powerful study. However, the private information is generally unavailable in official mortality dataset in the West.

The literatures about the association between heat wave and mortality mainly came from Europe and the United States, but the impact of heat wave has not yet been well characterized in Asia, particularly in tropical or subtropical regions[11,18], where people are more frequently and intensively exposed to extreme high temperature and therefore greater impacts of heat waves are supposed. Therefore, more studies in these regions are needed to better understand the effects of global warming and also to guide local intervention programs. In this study, we used a simple method to calculate excess deaths from all natural causes, cardiovascular diseases and respiratory diseases during a heat wave in Guangzhou. We also identified susceptible subpopulations by gender, age, educational level and occupation class.

MATERIALS AND METHODS

Data Collection

Guangzhou, located at 23° 7north latitude, is the largest city in southern China with a typical subtropical climate. It has a population of about 12.7 million, and the population density is 1708 inhabitants/km2.

Daily non-accidental mortality from January 1, 2003 to December 31, 2006 in Guangzhou were collected from Guangzhou Municipal Health Bureau. The causes of death were coded according to International Classification of Diseases, Revision 10 (ICD-10). The data on mortality were classified into deaths due to all non-accidental causes (ICD-10: A00-R99), cardiovascular diseases (ICD-10: I00-I99) and respiratory diseases (ICD-10: J00-J98). The data were also stratified by gender, age (0-64, 65-74, ≥75 years), educational level and occupation class. Educational level, defined as the highest degree of schooling completed before death, included three degrees: illiterate or semiliterate, primary education and secondary or higher education. Three occupation classes were considered: the unemployed (including housewife), blue-collar and white-collar workers.

Temperature data (daily minimum, maximum, and mean temperature) were derived from China Meteorological Data Sharing Service System[19].

Definition of Heat Wave

A heat wave is a prolonged period of excessive heat. Although several methods exist to give the definition of a heat wave in terms of duration and threshold of heat, there is no universally accepted definition. In the present study, a definition of a “heat day” with daily maximum temperature exceeding   35 °C by Chinese Meteorological Administration was used. A heat wave was defined as ≥7 consecutive heat days and daily mean temperature above the 95th percentile. And this definition has been used in previous research[20]. Based on this definition, we identified a heat wave between July 13 and July 21, 2005 (9 days) in Guangzhou.

Statistical Analysis

To account for the possibility of delayed effects of the heat wave, we extend the case period by three subsequent days beyond the heat wave period[21-22]. Thus, the case period was from 13 to 24 July 2005 (12 days). To control potential time-varying confounding effects in assessing excess deaths associated with the heat wave, we chose a bidirectional near-term reference period of the same summer and with the same distribution of days of the week for the case period. And the periods from 22 to 27 June 2005 and from 9 to 14 August 2005 were defined as the reference period. Similar definitions of reference period by Huang et al. in Shanghai[18] and Knowlton et al. in California[23] were used to estimate the heat-related mortality or morbidity.

We estimated the impact of heat wave by comparing mortality rate during the case period with that during the reference period. Since the population changed little during one summer and the case period had identical length with the reference period, the person-time units in the denominators of the two mortality rates were equivalent for these two periods. This allowed us to calculate the rate ratio (RR) as the ratio between the number of deaths during the case period (C) and deaths during the reference period (R), and excess deaths were calculated as difference in the numbers of deaths during these two periods. RR is not generally asymptotically normal, while the natural logarithm of RR is usually normally distributed. We also calculated the 95% confidence interval (95% CI) of RRc using the following equation[24]:

Adobe Systems

We tested statistically a potential effect modifier by calculating the 95% CI of the relative difference of RRs between two subgroups using the following equation. The relative difference of RRs is actually the ratio of the two RRs, which can be called as relative risk ratio (RRR).

Adobe Systems

Where Q1 and Q2 are their respective ln(RR) for the two subgroups (e.g., females and males); SE1 and SE2 are the standard error of ln(RR)[25]. The effect modification was regarded as statistically significant if the 95% CI of RRR did not include 1.

To determine whether the effect estimate of the 2005 heat waves was robust to the statistical method, we used a time-stratified case-crossover design to re-estimate the effect of the heat wave. In this case-crossover design, each subject acts as their own controls. Control days were matched to the case day (i.e. the day of death) using day of the week within a stratum length of 28 days[26]. Conditional logistic regression was also performed to calculate odds ratio (OR) of mortality for the heat wave by comparing the case day with control days. In addition, a less strict definition of a heat wave was used to detect other potential heat waves during the study period from 2003 to 2006. A heat wave was defined alternatively as a period of at least 5 or 6 consecutive days with daily maximum temperature above 35.0 °C and with daily mean temperature above the 95th percentile of temperature distribution.

All analyses were conducted using R 2.13.1 statistical software package, Vienna, Australia.

RESULTS

From 2003 to 2006, the average daily mean temperature was 23.0 °C (6.3-34.2 °C) in Guangzhou (Table 1), reflecting the characteristic of subtropical climate. The 95th percentile of daily mean temperature was 30.7 °C. A 9-day heat wave was identified during the period from 13 to 21 July 2005. The average daily maximum temperature during the 2005 heat wave was 37.0 °C (35.6 to 39.0 °C). The average daily maximum temperature during the reference period was 31.0 °C (27.4 to 34.2 °C).

During the 2005 heat wave in Guangzhou, there were 145 excess deaths in the study population with an average of 12 deaths per day (Figure 1). Among these deaths, 109 (75.2%) were caused by cardiores- piratory diseases. The heat wave resulted in a 23%, short but intensive, increase of all non-accidental mortality compared with the reference period (RR=1.23, 95% CI: 1.11-1.37). The heat wave led to the obvious increase of deaths from cardiovascular disease (RR=1.34, 95% CI: 1.13-1.59) and respiratory disease (RR=1.31, 95% CI: 1.02-1.69) (Table 2).


Table 1. Summary Statistics of Daily Maximum, Mean, and Minimum Temperature in Guangzhou, 2003-2006

Temperature Measures

Mean

Min.

P5

Median

P95

Max.

SD

Mean temperature(°C)

22.9

6.3

11.7

24.5

30.7

34.2

6.2

Minimum temperature(°C)

19.7

2.1

8.0

21.1

27.3

30.4

6.2

Maximum temperature(°C)

27.6

7.1

15.3

28.8

35.6

39.1

6.3


Note. P5 is the 5th percentile and P95 is the 95th percentile.

Table 2. Rate Ratio (RR) of Mortality by Cause, Gender, Age, Educational Level, and Occupation Class during Case Period and Reference Period

Variables

Number of Deaths

RR (95% CI)a

Case Period

Reference Period

Excess Deaths

Cause

 

 

 

 

All non-accidental

767

622

145

1.23 (1.11, 1.37)b

Cardiovascular

301

225

76

1.34 (1.13, 1.59)b

Respiratory

138

105

33

1.31 (1.02, 1.69)b

Gender

 

 

 

 

Male

390

364

26

1.07 (0.93, 1.24)

Female

377

258

119

1.46 (1.25, 1.71)b

Age (years)

 

 

 

 

0-64

161

169

-8

0.95 (0.77, 1.18)

65-74

175

164

11

1.07 (0.86, 1.32)

75+

431

289

142

1.49 (1.28, 1.73)b

Education

 

 

 

 

Illiterate/semiliterate

175

115

60

1.52 (1.20, 1.93)b

Primary

333

270

63

1.23 (1.05, 1.45)b

Secondary or higher

234

213

21

1.10 (0.91, 1.32)

Occupation class

 

 

 

 

White-collar

84

86

-2

0.98 (0.72, 1.33)

Blue-collar

339

244

95

1.39 (1.18, 1.64)b

Unemployed

166

147

19

1.13 (0.90, 1.41)

Note. The case period was during 13-24 July 2005 and the reference period was during 22-27, June and 9-14 August 2005. a Rate ratios (RRs) were calculated as ratios between the death numbers during the case period and the reference period. bSignificant at the 0.05 level.

Adobe Systems

Figure 1. Daily death number, maximum and mean temperature during case period (13-24 July 2005, shown in the central grey quadrangle) and during reference period (22-27 June and 9-14 August 2005, shown in the light grey quadrangle) in Guangzhou.


Table 2 shows the effect estimates of the 2005 heat wave by subpopulations and table 3 presents the differences between strata. Those aged ≥75 years were more likely to be affected by the heat wave for non-accidental mortality compared with those aged <65 years, the difference was statistically significant (RRR=1.565, 95% CI: 1.204-2.034). The risk of heat wave-related mortality in females was significantly greater than that in males (RRR=1.363, 95% CI: 1.101-1.689). A trend of decreased heat-related mortality risk with increased education level was observed. The people receiving no education suffered more from the heat wave than those receiving secondary or higher education (RRR=0.722, 95% CI: 0.534-0.974). We also observed significantly greater effect of the heat wave to the blue-collar workers than to the white-collar workers (RRR =1.422, 95% CI: 1.010-2.004). And the effect differences between subpopulations for cardiovascular and respiratory mortality were generally similar with the differences for non-accidental mortality (Table 3), though some differences were not statistically significant.

We re-estimated the effects of the 2005 heat wave using a case-crossover design. The estimates by case-crossover analyses were similar with those obtained by the primary method. And the estimates were only slightly changed after adjusting for potential confounding variables, such as humidity, barometric pressure and air pollution (Table 4). We also performed sensitivity analysis based on less strict definition of a heat wave. By defining heat wave as ≥6 days with daily maximum temperature above 35 °C and daily mean temperature above the 95th percentile, we identified a 6-day heat wave during 28 June-3 July 2004, during which the average daily maximum temperature was 37.6 °C (36.2-39.1 °C). Given a duration of 5 or more days considered, we identified another 5-day heat wave from 28 July to 1 August 2003, which had an average daily maximum temperature of 36.0 °C (35.2-37.6 °C). We observed a similar effect of the 2004 heat wave and smaller effect of the 2003 heat wave compared with the 2005 heat wave (Table 5).

DISCUSSION

Generalized additive model was widely used to assess health impact of ambient temperatures. However, statistical modeling may be very complicated, and the effect estimates are likely to be biased and undetected due to lack of statistical power in the case of rare events, such as heat waves.


Table 3. Relative Differences of Rate Ratios (RRR) of Heat Wave-related Mortality by Gender, Age, Educational Level and Occupation Class

Effect Modifiers

Relative Difference of Rate Ratio (95% CI)

All Non-accidental

Cardiovascular

Respiratory

Gender

 

 

 

Male (reference group)

Female

1.363 (1.101, 1.689)b

1.327 (0.938, 1.879)

1.430 (0.859, 2.382)

Age

 

 

 

0-64 (reference group)

65-74

1.120 (0.828, 1.517)

0.909 (0.533, 1.551)

2.363 (0.915, 6.110)

75+

1.565 (1.204, 2.034)b

1.547 (0.972, 2.457)

2.266 (1.018, 5.038)b

Education

 

 

 

Illiterate/semiliterate (reference group)

Primary

0.811 (0.610, 1.078)

0.846 (0.544, 1.315)

0.851 (0.473, 1.533)

Secondary or higher

0.722 (0.534, 0.974)b

0.694 (0.433, 1.115)

0.792 (0.373, 1.684)

Occupation Class

 

 

 

White-collar(reference group)

Blue-collar

1.422 (1.010, 2.004)b

1.034 (0.557, 1.912)

1.334 (0.527, 3.374)

Unemployed

1.156 (0.795, 1.680)

0.934 (0.460, 1.898)

1.071 (0.388, 2.962)

Note. bSignificant at the 0.05 level.



Table 4. Odds Ratios of Mortality Associated with the 2005 Heat Wave by Cause, Gender, Age, Educational Level, and Occupation Class and by Case-crossover Design

Variables

OR (95% CI)a

OR (95% CI)b

Cause

 

 

All non-accidental

1.20 (1.09, 1.32)c

1.20 (1.08, 1.32)c

Cardiovascular

1.40 (1.20, 1.64)c

1.37 (1.17, 1.62)c

Respiratory

1.25 (1.02, 1.49)c

1.24 (1.03, 1.47)c

Gender

 

 

Male

1.13 (0.98, 1.29)

1.13 (0.98, 1.30)

Female

1.28 (1.11, 1.47)c

1.27 (1.10, 1.47)c

Age (years)

 

 

0-64

1.08 (0.88, 1.33)

1.08 (0.88,1.34)

65-74

1.11 (0.92, 1.36)

1.11 (0.90, 1.36)

≥75

1.29 (1.13, 1.47)c

1.29 (1.13,1.48)c

Education

 

 

Illiterate/semiliterate

1.40 (1.14, 1.72)

1.42 (1.14, 1.77)

Primary

1.17 (1.01, 1.35)c

1.17 (1.01, 1.36)c

Secondary or higher

1.18 (0,95, 1.47)

1.10 (0.89, 1.36)

Occupation Class

 

 

White-collar

1.22 (0.93, 1.61)

1.25 (0.94, 1.67)

Blue-collar

1.33 (1.12, 1.54)c

1.32 (1.10, 1.55)c

Unemployed

1.20 (0.95, 1.51)

1.19 (0.93, 1.51)

Note: aCase-crossover design; bCase-crossover design by adjusting for humidity, barometric pressure, PM10, NO2, and SO2; cSignificant at the 0.05 level.

 

In the present study, we used a simple method to assess the impact of the 2005 heat wave in Guangzhou by comparing the observed number of deaths during the case period with that during the reference period. By using a time-close reference period, all time-invariant variables (e.g. populations) are controlled. Furthermore, we found that the estimated effect values were very similar with those produced by the case-crossover design, indicating that this simple method is robust. Compared with the case-crossover analysis, this simple method does not need statistical modeling or a long study period, and the results are easy to be interpreted. We believe that this method can be therefore widely applied to assess health impact of any individual extreme weather event.

Table 5. Rate Ratios of Mortality Related to the 2003 and 2004 Heat Waves

Variables

Number of Deaths

RR

(95% CI)a

Case

Period

Reference

Period

Excess

Deaths

2003 heat wave

 

 

 

 

All non-accidental

448

413

35

1.08

(0.95. 1.24)

Cardiovascular

159

151

8

1.05

(0.84, 1.32)

Respiratory

81

76

5

1.07

(0.78, 1.46)

2004 heat wave

 

 

 

 

All non-accidental

703

549

154

1.28

(1.15,1.43)b

Cardiovascular

293

204

89

1.44

(1.20,1.72)b

Respiratory

146

105

41

1.39

(1.08,1.79)b

Note. bSignificant at the 0.05 level.

Many previous studies have investigated the association between heat and mortality in the United States[27-28], Europe[9], Korea[11], and Australia[29], but few reported the situation in China. Findings from the present study were about non-accidental mortality[9,18,29]. We found that the 2005 heat wave led to an increase of 23% of non-accidental mortality and an increase of 34% of cardiovascular mortality. A multi-city study in Europe reported 8.5%-33.6% mortality risk for all natural deaths and 3.9%-92.5% for cardiovascular deaths caused by the 2003 heat wave[9].

Our study also found that estimated effect values were markedly higher for cardiovascular and respiratory deaths compared with all non-accidental deaths, consistent with previous studies[5,29-30]. Exposure to extreme heat may lead to direct cardiovascular stress including changes in blood pressure and vasoconstriction, and causes pathological responses of the respiratory epithelium. And this may contribute to increased cardiovascular mortality and respiratory mortality.

Consistent with reports from numerous studies[11,18,31-32], we found higher susceptibility to heat waves among the elderly. Aging induces physiological changes in thermoregulation and homeostasis, together with the increased prevalence of chronic conditions and use of medication, resulting in the susceptibility to heat stress. We observed significantly stronger heat wave-associated mortality in females than in males. This gender difference was also observed in previous epidemiological studies[8,32], and experimental evidence also showed that females were more heat intolerant than males due to potential gender-related physiological and thermoregulatory differences[33-34].

Furthermore, our present study indicated that heat wave-associated mortality was modified by educational level and occupation class. People with less education suffered more from heat waves, consistent with the results reported by cohort studies[6,11]. Notably, education level was generally used as a proxy of socioeconomic status in previous studies. However, there is lack of literature about the role of occupation class in modifying the effects of ambient temperatures. In the present study, we found significant greater mortality risk from the heat wave in blue-collar workers compared with that in white-collar workers. A preliminary explanation was the heterogeneity in exposure level. Blue-collar workers are engaged in manufacturing, mining, construction, maintenance and many other physical works, therefore they are more frequently and intensively exposed to outdoor extremely high temperatures than white-collar workers. In addition, the availability of air conditioning is relatively low for blue-collar workers at their homes and workplaces. Other possible explanation for the increased heat susceptibility due to disadvantaged socioeconomic status may be related to poor baseline health status, limited access to health care and high prevalence of health problems[10].

Some multi-city studies have shown that the impacts of heat waves are influenced by the characteristics of individual heat waves. Heat waves occurring earlier, lasting longer and being more intensive had larger health impacts[11-12]. This was also confirmed by the findings from our sensitivity analysis based on an alternative definition of heat wave. Compared with the 9-day 2005 heat wave occurring on 13 July, the 2004 heat wave occurred earlier (28 June) with a short duration (6 days) and more extremely high temperatures, the average daily maximum temperature was 37.6 °C (37.0 °C for the 2005 heat wave). As a result, these 2 heat waves had similar effects on mortality with the RR of 1.28 and 1.23, respectively. In contrast, the 2003 heat wave occurred later (July 28), lasted less days (5 days) and was milder (an average maximum temperature of 36.0 °C), therefore it yielded smaller impact on mortality with the RR of 1.08.

Some limitations should be noted in the present study. The impact of heat waves on mortality may vary with region and population due to adaptation facilities and acclimatization[10]. Our data are from one single city, it should be used with caution in other geographic areas and under other climatic environments. Moreover, we could not examine the lag distribution and mortality displacement of heat wave effect due to the inherent limitation of the method we applied. However, we considered potential lag effect by taking three lag days into the period of heat wave when comparing with the reference period. And future research is needed to explore the characteristics of health impacts of heat waves.

In conclusion, we observed the significant impact of a heat wave in the subtropical city, Guangzhou. Our findings highlight a need to increase the people’s awareness of heat wave risk and related protection behaviors. The elderly, females, people with low socioeconomic status and those with cardiovascular and respiratory diseases have been identified to be more vulnerable to heat waves. And these findings can be used in establishing heat warning systems and developing prevention programs in order to reduce heat wave-associated mortality.

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