Science of the Total Environment 505 (2015) 508–513

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Greater temperature variation within a day associated with increased emergency hospital admissions for asthma☆,☆☆ Hong Qiu, Ignatius Tak-sun Yu, Lap Ah Tse, Emily Y.Y. Chan, Tze Wai Wong, Linwei Tian ⁎ School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong Special Administrative Region

H I G H L I G H T S • • • • •

We examined the association between diurnal temperature range (DTR) and asthma. We observed greater DTR associated with increased emergency asthma hospitalizations. The effect of DTR was independent of daily mean/minimum temperature and air pollution. DTR exhibited significantly greater effect in cool season, in males and children. Great DTR was an environmental risk factor for asthma exacerbation.

a r t i c l e

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Article history: Received 13 June 2014 Received in revised form 19 September 2014 Accepted 3 October 2014 Available online xxxx Editor: Lidia Morawska Keywords: Asthma Diurnal temperature range Emergency hospital admission Temperature variation Time series study

a b s t r a c t Asthma is one of the most common chronic conditions affecting both children and adults. Examining the health effects of environmental triggers such as temperature variation may have implications for maintenance of asthma control and prevention. We hypothesized that large diurnal temperature range (DTR) might be a source of additional environmental stress and therefore a risk factor for asthma exacerbation. Daily meteorological data, air pollution concentrations and emergency hospital admissions for asthma from 2004 to 2011 in Hong Kong were collected. Poisson regression models were used to fit the relationship between daily DTR and asthma, after adjusting for the time trend, seasonality, mean temperature, humidity, and levels of outdoor air pollution. Acute adverse effect of DTR on asthma was observed. An increment of 1 °C in DTR over lag0 to lag4 days was associated with a 2.49% (95% CI: 1.86%, 3.14%) increase in daily emergency asthma hospitalizations. The association between DTR and asthma was robust on the adjustment for daily absolute temperature and air pollution. DTR exhibited significantly greater effect in cool season. Males and female children appeared to be more vulnerable to DTR. Results supported that greater temperature variation within a day was an environmental risk factor for asthma exacerbation. © 2014 Elsevier B.V. All rights reserved.

1. Introduction Asthma is one of the most common chronic conditions affecting both children and adults. It is characterized by airway hyperresponsiveness to physiologic or environmental triggers. This hyper-responsiveness results in pronounced constriction of airway

Abbreviations: DTR, diurnal temperature range; ERR, excess relative risk; ICD-9, international statistical classification of diseases, 9th revision; lagn, lag/previous n days; PACF, partial autocorrelation function; PM10, particles with an aerodynamic diameter less than 10 micron; NO2, nitrogen dioxide; O3, ozone. ☆ Financial support of the research: None. ☆☆ Conflict of interest: The authors declare they have no actual or potential competing financial interests. ⁎ Corresponding author at: 4/F, The Jockey Club School of Public Health and Primary Care, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin-NT, Hong Kong Special Administrative Region. Tel.: +852 2252 8879; fax: +852 2606 3500. E-mail address: [email protected] (L. Tian).

http://dx.doi.org/10.1016/j.scitotenv.2014.10.003 0048-9697/© 2014 Elsevier B.V. All rights reserved.

muscles, inflammation, swelling, mucus production, and subsequent respiratory distress (Subbarao et al., 2009). Risk factors for incident asthma among children included male sex, atopic sensitization, parental history of asthma, early-life stressors and infections, obesity, and exposure to indoor allergens, tobacco smoke and outdoor pollutants. Risk factors for adult-onset asthma included female sex, airway hyper-responsiveness, lifestyle factors, and work-related exposures (King et al., 2004). Although a family history of asthma is common, it is neither sufficient nor necessary for the development of asthma. The substantial increase in the incidence of asthma over the past few decades and the geographic variation in both base prevalence rates and the magnitude of the increases support that environmental factors play a large role in the current asthma epidemic (Subbarao et al., 2009). Although the etiology of asthma has not been fully elucidated, there is evidence that the environmental risk factors such as prenatal cigarette smoking (Neuman et al., 2012), air pollution (Lee et al., 2006; Ko et al.,

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2007; Mar and Koenig, 2009), climate factors (Chen et al., 2006; D'Amato et al., 2010; Harju et al., 2010; Xu et al., 2013a), may increase the risk of asthma attacks. The diurnal temperature range (DTR), defined as the difference between maximal and minimal temperatures within one day, is a meteorological indicator which may be related to a variety of health outcomes (Chen et al., 2007; Liang et al., 2008; Cao et al., 2009; Tam et al., 2009; Lim et al., 2012). It is of interest whether the temperature variation within one day, i.e. DTR, is the risk factor for asthma independent of the corresponding absolute temperature. Only a few studies examined the associations between outdoor short-term temperature changes and asthma morbidity (Lim et al., 2012; Wasilevich et al., 2012; Xu et al., 2013b), and the results were conflicting with each other and inconclusive. The study conducted in four metropolitan areas in Korea (Lim et al., 2012) and the study conducted in Brisbane, Australia (Xu et al., 2013b) supported a positive association between DTR and asthma, while the other study conducted in Detroit, Michigan suggested a negligible association between short-term temperature change and emergency department visits for asthma among children (Wasilevich et al., 2012). To examine the health effects of environmental triggers such as temperature variation may have implications for patient education and maintenance of asthma control and prevention. In this study, we hypothesized that large diurnal temperature change might be a source of additional environmental stress and therefore a risk factor for asthma exacerbation. We aimed to examine the associations between DTR and asthma, and to test the effect differences by season, age group and gender to identify the vulnerable subgroups.

2. Materials and methods

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2.2. Statistical modeling This was a longitudinal time series study. Generalized additive Poisson regression models were used to fit the relationship between the daily DTR and the emergency asthma hospitalizations. We used the smoothing spline, s(.), to filter out seasonal patterns and long-term trends in daily hospitalizations, as well as the daily mean temperature and relative humidity (Peng et al., 2006). We also included an adjustment for the day of the week and dichotomous variables such as public holidays and influenza epidemic. To reduce the problems associated with multiple testing and model selection strategies, we followed previous studies to select a priori model specifications including the degree of freedom (df) for the time trend and other meteorological variables (Dominici et al., 2006; Peng et al., 2006; Qiu et al., 2013). We used a df of 8 per year for the time trend, a df of 6 for the mean temperature of the current day (Temp0) and the previous 3 days' moving average (Temp1–3) and a df of 3 for the current day relative humidity (Humid0). We included the day of the week (DOW) and public holidays (Holiday) in the model as dummy variables (Schwartz et al., 1996). To adjust for the confounding effect of an influenza epidemic on emergency hospital admissions, we entered a dummy variable for the weeks with a number of influenza hospital admissions exceeding the 75 percentile in a year into the core model (Wong et al., 2002). Briefly, we set up a core model to remove the long term trends, seasonal variations, and adjust for time varying confounders as follows: logðEðY ÞÞ ¼ α þ þ þ

þ sðt; d f ¼ 8=year  no: of yearsÞ sðTemp0 ; df ¼ 6Þ þ sðTemp1–3 ; d f ¼ 6Þ sðHumid0 ; df ¼ 3Þ þ β1 DOW þ β2 Holiday β3 influenza

ð1Þ

2.1. Data collection A daily count of emergency hospital admissions for asthma (ICD-9: 493) as the principal diagnosis from year 2004 to 2011 was obtained from the Hospital Authority Corporate Data Warehouse. Hospital Authority is the statutory body running all public hospitals in Hong Kong. The records of admission were taken from the publicly funded hospitals providing 24 hour accident and emergency services and covering 90% of hospital beds in Hong Kong for local residents (Wong et al., 1999). The patient data included age, gender, date of admission, source of admissions, and principal diagnosis on discharge. We abstracted the overall daily asthma admissions, asthma admissions by gender and by two age groups (age b 15, and ≥15 years old) as the health outcomes. It is the Hospital policy that all patients under 15 years of age are admitted to the pediatric wards (Wong et al., 2001). Daily admissions for influenza (ICD-9:487) were used to identify influenza epidemics, which were then treated as a potential confounder in the data analysis (Thach et al., 2010). Ethics approval and consent from individual subjects were not required by our institute as we just used aggregated data but not any individualized data in this study. We collected the meteorological information including the daily maximum, minimum, mean temperature and relative humidity for the same period from the Hong Kong Observatory. DTR was calculated by the maximum temperature minus the minimum temperature within the same day. As air pollutants (including particles with an aerodynamic diameter less than 10 micron, PM10; nitrogen dioxide, NO2; and ozone, O3) are associated with emergency asthma hospitalizations in Hong Kong (Lee et al., 2006; Ko et al., 2007), we also collected the air pollution data from the Environmental Protection Department and included them in the regression models for adjustment. Hourly concentrations of PM10, NO2 and O3 monitored in 10 general stations were used to generate daily mean air pollution concentrations in Hong Kong (Qiu et al., 2013).

Here E(Y) means the expected daily emergency asthma hospital admission counts on day t; s(.) is the smoothing spline function for nonlinear variables. We examined the residuals of the core model to check whether there were discernable patterns and autocorrelation by means of residual plot and partial autocorrelation function (PACF) plot. The PACF of residuals of the core model (1) was less than 0.1 for all lags, showing no serial autocorrelations in the residuals and sufficient confounder control (Wong et al., 2008). No discernible patterns and no autocorrelation in the residuals are the criteria for an adequate core model setup which is intended to remove all potential confounders in the daily variations of health outcome. The linear effects of DTR were then estimated for the same day and up to four days before the outcome (single-lag effect from lag0 to lag4). The overall cumulative effects of DTR due to the exposure over the period of lag0 through lag4 were estimated by constrained distributed lag models and denoted as ‘dlm04’ (Gasparrini et al., 2010). Exposure–response relationship between DTR and asthma hospitalizations was graphically examined by distributed lag model as well (Gasparrini et al., 2010; Xu et al., 2013b). Sensitivity analyses were conducted to test such association by further adjusting for the confounding effects from air pollution. Sensitivity analysis was also conducted to test whether the DTR effect is stable by replacing the mean temperature with the minimum temperature in core model (1) or by including the adjustment for the temperature with longer lags up to two weeks. In addition to the whole period analysis, we examined the effect of DTR for the warm season (from May to October) and the cool season (November to April) separately, using half the df of 4 per year for the time trend (Kan et al., 2008). Effect differences by gender under different age groups were also examined by using the subgroups of asthma hospitalizations as the health outcomes (Kan et al., 2008). We tested the statistical significance of differences by season, gender or age qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi group by calculating ðβ1 −β2 Þ= SE1 2 þ SE2 2 , where β1 and β2 are the estimates for the two categories (e.g., warm and cool season, or females

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Table 1 Distribution of emergency asthma hospital admissions, meteorological factors and air pollution concentrations in Hong Kong, 2004–2011 (n = 2922). Mean Emergency hospital admissions (counts/day) All respiratory diseases 238.7 Asthma 15.7 In cool season 17.2 In warm season 14.2 Age b 15 Female 1.9 Male 3.8 Age ≥ 15 Female 6.2 Male 3.8 Meteorological factors Mean temperature (°C) 23.4 Max temp (°C) 25.9 Min temp (°C) 21.5 DTR (°C) 4.4 In cool season 4.4 In warm season 4.5 Humidity (%) 77.9 Air pollution concentrations (μg/m3) PM10 52.8 55.9 NO2 36.2 O3

SD

Min.

P25

P50

P75

Max.

47.7 5.6 5.7 5

133 3 4 3

205 12 13 11

230 15 17 14

263 19 21 17

495 41 41 37

1.7 2.6

0 0

1 2

2 3

3 5

14 19

2.9 2.1

0 0

4 2

6 4

8 5

19 13

5.2 5.4 5.2 1.5 1.7 1.4 10.5

8.8 10.3 5.8 0.7 0.7 0.9 31

19.3 21.7 17.6 3.3 3.1 3.5 73

24.6 27 22.7 4.4 4.3 4.5 79

27.8 30.3 25.8 5.4 5.5 5.3 85

31.8 35.4 29.4 12.6 12.6 9.8 98

30.3 21.5 21.6

6.7 6.7 3

29 40.6 18.1

47.5 53.1 32.2

71 68.3 49.7

573 153.2 143.9

Abbreviation: SD, standard deviation; Px, xth percentiles; Min., minimum; Max., maximum.

and males), and SE1 and SE2 are their respective standard errors (Schenker and Gentleman, 2001; Zeka et al., 2006; Kan et al., 2008). An absolute value larger than 1.96 was considered as a statistically significant difference at α = 0.05 level. The results were expressed in terms of the percentage increase (Excess Relative Risk, ERR (%)) in emergency asthma hospital admissions for 1 °C increase of DTR, and its respective 95% confidence intervals (CI). All analyses were conducted using ‘mgcv’ (Wood, 2006) and ‘dlnm’ (Gasparrini, 2011) packages in the statistical environment R3.0.3 (R Development Core Team, 2014: http://www.r-project.org).

accounting for about 6.6% of emergency hospitalizations due to total respiratory diseases. On average there were 16 emergency admissions per day for asthma, of which approximately 36% were children and 64% were adults. There was apparent gender difference for asthma hospitalizations, with more males (66.7%) in children and more females (62.0%) in adults. The daily mean emergency asthma admissions were significantly higher in the cool season than those in the warm season (17 vs. 14) (Table 1). The daily mean air temperature was 23.4 °C with a mean DTR of 4.4 °C. The daily mean concentration of air pollutants was 52.8, 55.9, and 36.2 μg/m3 for PM10, NO2, and O3, respectively.

3. Results

3.2. Regression results

3.1. Data description

Fig. 1 shows the time series of the daily count of observed asthma hospitalizations, and the predicted values fitted by the Poisson regression core model. There was a slight increase in the number of daily asthma admissions during the study period. Exposure–response

During our study period, a total of 45,896 emergency hospital admissions for asthma were recorded in our study population,

Fig. 1. Observed and predicted daily counts of emergency hospital admissions for asthma. (Fitted values in red.) (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

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4. Discussion

Fig. 2. Exposure–response curve between the logarithm of emergency asthma hospital admission and DTR at distributed lags of 0–4 days. (ERR, excess relative risk, % change of asthma hospitalizations.)

curve (Fig. 2) shows that the association between the risk of emergency asthma hospitalizations and DTR was essentially linear. Some acute adverse effects of DTR on emergency asthma admissions were observed in our study, after adjusting for the time trend, seasonality, absolute air temperature, calendar effect, and influenza epidemic. Every 1 °C increase of DTR at lag2–lag4 corresponded to around 1% increase of emergency hospital admissions for asthma. The cumulative effect of DTR per 1 °C increment distributing for lag 0–4 days was associated with 2.49% (95% CI: 1.86%, 3.14%) increase of emergency asthma hospitalizations. Further adjustment for the possible confounding effects from air pollutants at the same lags resulted in slightly decreased excess risks in general, showing that the association between DTR and asthma was stable and robust to the inclusion of air pollution (Table 2). Replacing the terms of mean temperature with the minimum temperature in core model (1) showed that the association between DTR and asthma hospitalizations was also robust to the adjustment for daily minimum temperature (Table 2). The effect estimates of DTR did not change significantly when adjusting for temperature at longer lags. The associations between DTR and emergency asthma hospitalizations were significantly greater in cool season than those in warm season (Fig. 3). Stratified analyses by gender under different age groups (Table 3) showed greater effects of DTR on males in both age groups and female asthmatic children, which associated with 3.47–4.31% increase of asthma hospitalizations per 1 °C increase of DTR over lag 0–4 days. Results revealed that DTR exposure exhibited higher effects on asthmatic children and males (Table 3).

In this time series study, we compared the day-to-day variations of DTR and the day-to-day variations of emergency asthma hospitalizations, and estimated the short-term effects of DTR on asthma. We found significantly acute adverse effects of DTR on asthma admissions. The associations between DTR and asthma were robust to the adjustment for absolute temperature (mean or minimum) and air pollution concentrations, and were significantly greater in cool season, in children and males. The temperature variation within a day is a novel environmental risk factor which asthma patients and caretakers should be aware of. It has been found that cold temperature is related to an increased risk of significant exacerbation of asthma (Abe et al., 2009; Guo et al., 2012). Cold-related respiratory symptoms were more often reported by people with chronic respiratory diseases such as asthma (men 69%/ women 78%) (Harju et al., 2010). The underlying biological mechanisms have been studied. Cold temperature is associated with increased occurrence of respiratory tract infections, and a decrease in temperature often precedes the onset of the infection (Mäkinen et al., 2009). Cold temperature can induce contraction of the tracheal smooth muscle and decrease pulmonary circulation and lung perfusion (Khadadah et al., 2011). Cold air also induced the bronchial hyper-reactivity which has long been recognized as a hallmark of chronic asthma (Grootendorst and Rabe, 2004). Only a few studies examined the associations between outdoor short-term temperature changes and asthma morbidity (Lim et al., 2012; Wasilevich et al., 2012; Xu et al., 2013b), and the results were conflicting with each other and inconclusive. The study conducted in four metropolitan areas in Korea supported a positive association between DTR and asthma hospital admissions (Lim et al., 2012) and the study conducted in Brisbane, Australia suggested large DTR trigger children asthma (Xu et al., 2013b), which were consistent with our findings. Another study conducted in Detroit, Michigan determined the impact of maximum temperature change and change rate measured during 4-, 8-, 12-, and 24-hour periods (Wasilevich et al., 2012). Researchers found that a greater 24-hour temperature change decreased the risk of asthma-related emergency department visits among children; however, this association disappeared after controlling for environmental air pollutants. Authors suggested a negligible association between other short-term temperature change indicators and asthma. In this Detroit study, authors excluded visits that resulted in a hospitalization which might be the main reason for the differences from our findings. We used the emergency asthma hospitalizations as the health endpoints which were more severe and might be more sensitive to the short-term temperature change. Plausible biological mechanisms of DTR on respiratory diseases have been hypothesized. It was postulated that short-term temperature variation causes a mild inflammatory reaction and thereby causes airway narrowing which increases the risk of susceptibility to respiratory diseases. Bull suggested that changes in weather might affect either

Table 2 Sensitivity analyses for the effects of DTR on emergency hospital admissions for asthma by lags (lag0–lag4 and dlm04), 2004–2011 (ERR% (95%CI) for 1 °C increment of DTR). Lag days

Effect estimatea

lag0 lag1 lag2 lag3 lag4 dlm04c

0.44 (−0.29, 1.16) 0.27 (−0.42, 0.96) 1.02 (0.35, 1.69) 1.05 (0.40, 1.71) 0.97 (0.32, 1.63) 2.49 (1.86, 3.14)

a b c

Further adjusted for air pollutants at the same lagsa

Effect estimateb

PM10

NO2

O3

0.45 (−0.27, 1.18) 0.20 (−0.48, 0.89) 0.95 (0.28, 1.63) 0.98 (0.32, 1.64) 0.90 (0.24, 1.56) 2.33 (1.69, 2.98)

0.23 (−0.50, 0.97) 0.00 (−0.70, 0.71) 0.82 (0.13, 1.51) 0.81 (0.13, 1.49) 0.87 (0.19, 1.54) 1.94 (1.31, 2.58)

0.58 (−0.14, 1.31) 0.28 (−0.40, 0.97) 0.95 (0.28, 1.62) 0.99 (0.34, 1.66) 0.95 (0.29, 1.60) 2.16 (1.52, 2.80)

0.23 (−0.47, 0.93) −0.04 (−0.72, 0.65) 0.68 (0.02, 1.35) 0.76 (0.10, 1.42) 0.89 (0.24, 1.54) 1.66 (1.04, 2.28)

Effects were estimated from core model (1). Effects were estimated by replacing the terms of mean temperature with the minimum temperature in core model (1). Statistically significant effect estimates were in bold. Overall cumulative effects of DTR lasting for lag 0–4 days (dlm04) were estimated by constrained distributed lag models.

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Fig. 3. Differences of DTR effects on asthma hospitalizations in Hong Kong by season (ERR% (95%CI) for 1 °C increment of DTR). Seasonal differences were statistically significant at lag3 and dlm04, p b 0.05.

Table 3 Differences of DTR effects on emergency asthma admissions in Hong Kong by gender under different age groups (ERR% (95%CI) for 1 °C increment of DTR). Lag days

lag0 lag1 lag2 lag3 lag4 dlm04

Age b 15

Age ≥ 15

Female

Male

Female

Male

0.00 (−2.03, 2.07) 0.68 (−1.24, 2.65) 2.42 (0.53, 4.34)⁎# 1.25 (−0.60, 3.13) 1.16 (−0.68, 3.03) 3.47 (1.65, 5.32)#

1.72 (0.21, 3.25)⁎ 1.56 (0.12, 3.02) 0.77 (−0.61, 2.17) 0.83 (−0.53, 2.20) 1.05 (−0.30, 2.42) 4.31 (2.96, 5.67)

−0.38 (−1.46, 0.71) −0.75 (−1.77, 0.28) 0.55 (−0.45, 1.55) 0.91 (−0.07, 1.91) 0.18 (−0.79, 1.16) 0.13 (−0.81, 1.08)

0.80 (−0.57, 2.18) 0.47 (−0.82, 1.78) 1.27 (0.02, 2.55) 1.31 (0.07, 2.56) 2.07 (0.84, 3.32)⁎ 4.09 (2.87, 5.33)⁎

Overall cumulative effects of DTR lasting for lag 0–4 days (dlm04) were estimated by constrained distributed lag models. Statistically significant effect estimates were in bold. ⁎ Differences between females and males in the same age group, p b 0.05. # Differences between age b 15 and age ≥ 15 in the same gender group, p b 0.05.

humoral or cellular immunity (Bull, 1980). Sudden temperature changes caused more inflammatory nasal responses in an allergic rhinitis group (Graudenz et al., 2006), which may explain some of the mechanisms associated with the effects of DTR on asthma admissions. Children were identified as a more vulnerable subgroup which may be partly attributed to less self-care ability, under-developed immune system and thermoregulation capacity of them. While facing a large temperature change within a day, they may not dress appropriately and their thermoregulation may not offset the thermal stress. Males appeared to be more sensitive to large DTR exposure which may probably be explained by differential growth rates of lung/airway size, along with immunological differences between genders (Becklake and Kauffmann, 1999). Further studies are needed to explore the biological mechanisms for this gender difference in the adults. A better understanding of vulnerable subgroups would give important clues to asthma patients and the caregivers for early protection. Hong Kong has a moderate cool winter and indoor heating system is very uncommon; hence, a decrease in outdoor temperature can affect indoor temperature rather quickly in cool season and affect the patients at risk. In contrast, during the hot and humid summer in Hong Kong, people usually use air-conditioning indoors and engage in less outdoor activities, thus reducing the risks of temperature change. This might be the reason for the significantly greater association between DTR and asthma hospitalizations in cool season. Our findings provide some insight into the prevention of temperature change related emergency asthma hospitalizations. Early warning system for impending large temperature change may reduce the impact of DTR on population health. Asthma patients should be closely monitored and offered access to heated indoor environments to reduce the great DTR exposure in the cool season. A randomized controlled trial conducted in households in five communities in New Zealand has found

that installing non-polluting, more effective heating in the homes of children with asthma did significantly reduce symptoms of asthma, days off school, healthcare utilization, and visits to a pharmacist (Howden-chapman et al., 2008). The strength of this study relied on the reliable and comprehensive hospital admission data, which were central-computerized source of patient data covering over 90% of the population in Hong Kong. We included about 46 thousands asthma emergency admissions over the 8 years' study period, which is the largest single-city study to estimate the association between DTR and asthma hospitalization up to date. Some limitations should be noted. As in other time-series studies, we used available outdoor monitoring data to represent the population exposure to ambient temperature, temperature change and air pollution. Indoor temperature and personal exposure data were not available. Exposure misclassification should not be ignored due to the widely used air-conditioning in summer, which may also underestimate the DTR effect in warm season in Hong Kong. Another limitation was that we could not identify the re-admissions for patients with asthma according to the available data. We have to put the first onset of asthma and the re-admission patients together, and observed the increase of DTR associated with the increase of the total hospital admissions for asthma. Furthermore, studies in other settings with different climate and larger DTR are recommended in order to provide a better understanding of the effects of temperature change on health. 5. Conclusion We observed the short-term adverse effects of DTR on asthma admissions. The effects of DTR were robust to the adjustment for absolute mean or minimum temperature and air pollution concentrations, and were significantly greater in the cool season, in children and

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