Int J Biometeorol DOI 10.1007/s00484-015-1106-7
ORIGINAL PAPER
Daily ambient temperature and renal colic incidence in Guangzhou, China: a time-series analysis Changyuan Yang 1 & Xinyu Chen 2 & Renjie Chen 1 & Jing Cai 1 & Xia Meng 1 & Yue Wan 3 & Haidong Kan 1
Received: 22 April 2015 / Revised: 21 October 2015 / Accepted: 28 October 2015 # ISB 2015
Abstract Few previous studies have examined the association between temperature and renal colic in developing regions, especially in China, the largest developing country in the world. We collected daily emergency ambulance dispatches (EADs) for renal colic from Guangzhou Emergency Center from 1 January 2008 to 31 December 2012. We used a distributed-lag nonlinear model in addition to the overdispersed generalized additive model to investigate the association between daily ambient temperature and renal colic incidence after controlling for seasonality, humidity, public holidays, and day of the week. We identified 3158 EADs for renal colic during the study period. This exposure-response curve was almost flat when the temperature was low and moderate and elevated when the temperature increased over 21 °C. For heat-related effects, the significant risk occurred on
the concurrent day and diminished until lag day 7. The cumulative relative risk of hot temperatures (90th percentile) and extremely hot temperatures (99th percentile) over lag days 0– 7 was 1.92 (95 % confidence interval, 1.21, 3.05) and 2.45 (95 % confidence interval, 1.50, 3.99) compared with the reference temperature of 21 °C. This time-series analysis in Guangzhou, China, suggested a nonlinear and lagged association between high outdoor temperatures and daily EADs for renal colic. Our findings might have important public health significance to prevent renal colic. Keywords Temperature . Renalcolic . Emergencyambulance dispatches . Time series . Epidemiology
Introduction Changyuan Yang and Xinyu Chen contributed equally to this work. Electronic supplementary material The online version of this article (doi:10.1007/s00484-015-1106-7) contains supplementary material, which is available to authorized users. * Renjie Chen [email protected] * Haidong Kan [email protected] 1
School of Public Health, Key Lab of Public Health Safety of the Ministry of Education, and Key Lab of Health Technology Assessment of the Ministry of Health, Fudan University, P.O. Box 249, 130 Dong-An Road, Shanghai 200032, China
2
State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Disease, The First Affiliated Hospital, Guangzhou Medical University, Guangzhou 510182, China
3
Division of Environment and Health Management, Department of Science, Technology and Standards, Ministry of Environmental Protection of PRC, Beijing 100035, China
Renal colic is an acute and severe clinical syndrome, with symptoms of sudden flank pain arising from obstruction of the ureter and complications including hematuria, nausea, and vomiting (Bultitude and Rees 2012; Cupisti et al. 2008). Nephrolithiasis is the most common cause of renal colic (Noble and Brown 2004). It was estimated that the prevalence of nephrolithiasis worldwide varied according to geographical, climatic, and socioeconomic conditions (Amato et al. 2004). The muggy and humid climate and favorable socioeconomic conditions are associated with a higher prevalence of nephrolithiasis (Amato et al. 2004). Previous studies have suggested that some risk factors can increase the risk of renal colic, including past or family history of nephrolithiasis; increased consumption of animal protein, calcium, and oxalate; high intake of calories; and prevalence of comorbidities (Bartoletti et al. 2007). It has been observed that renal colic occurs more frequently in warm seasons than in cool seasons, and its positive correlation with outdoor
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temperature has also been reported (Chauhan et al. 2004; Pincus et al. 2010; Tasian et al. 2014; Parks et al. 2003). However, the specific exposure-response relationship between all-year daily temperature and the incidence of renal colic has not been fully explored, especially in considering the potential nonlinear and lagged effects of temperature (Tasian et al. 2014). This topic has become increasingly important because of global warming. For example, an unanticipated result of global warming is the likely northward expansion of the presentday southeastern U.S. kidney stone Bbelt,^ and a climaterelated increase of 1.6–2.2 million lifetime cases of nephrolithiasis was estimated to occur by 2050 (Brikowski et al. 2008). The United Nations Intergovernmental Panel on Climate Change has concluded that developing countries were more vulnerable to climate change than developed countries (Solomon et al. 2007). However, few previous studies have examined the association between temperature and nephrolithiasis or renal colic in developing regions, especially in China, the largest developing country in the world. Therefore, we conducted a time-series study to investigate the short-term association between daily outdoor temperature and emergency ambulance dispatches (EADs) for renal colic in Guangzhou, a subtropical city of China.
Materials and methods Study region Guangzhou is the third largest Chinese city and the capital of Guangdong Province, which has a population of approximately 12.78 million in an area of 7434.4 km2 according to the 2010 national census. Located in the Pearl River Delta, Guangzhou serves as an important national trading port and a center of commerce and manufacturing in Southern China. The city has a typical humid subtropical climate influenced by East Asian monsoon, where summer days are hot and humid and winter days are mild and dry. Data collection The daily EADs for renal colic from 1 January 2008 to 31 December 2012 were collected from the Guangzhou Emergency Center. This center has an advanced dynamic ambulance dispatching system serving more than 7 million permanent residents in nine urban districts of Guangzhou. According to the regulations of Guangzhou Emergency Center, the ambulance cars must arrive at the rescue scene within 30 min after an emergency call is made. Afterwards, the physicians in the emergency departments of hospitals will make an admitting diagnosis based on the first presentation of patients transported by ambulance cars. The diagnosis for
renal colic was made on the basis of clinical history, physical examination, urinalysis, and imaging examination. Finally, the EADs for renal colic were aggregated on a daily basis. Daily meteorological data (including daily mean temperature, daily maximum temperature, daily minimum temperature, and relative humidity) during the study period were collected from the Central Weather Station affiliated with the Guangzhou Meteorological Bureau. We assigned the same temperature measurements to all cases on the same day. The Institutional Review Board at the School of Public Health, Fudan University, approved the study protocol (NO. 2012-03-0324) with a waiver of informed consent. Data were analyzed at the aggregate level, and no participants were contacted. Statistical analysis The onset risk of renal colic relies not only on exposure to the present day’s temperature but also on the previous days’ temperature (Boscolo-Berto et al. 2008). Specifically, there exists a lag effect in the relationship between renal colic and temperature. This effect of the temperature over days or even weeks after exposure is often treated by establishing distributed-lag models in statistics. Additionally, the relationship between renal colic and temperature is not simply linear but appears to be J-shaped (Boscolo-Berto et al. 2008). We applied the distributed-lag nonlinear model (DLNM) to explore the possible lagged and nonlinear association between temperature and daily EADs for renal colic. This model has the advantages of calculating cumulative effects of temperature on multiple days after adjusting for the collinearity of temperature on neighboring days and accounting for the nonlinear exposure-response relationship (Gasparrini 2011). To allow for entering the DLNM, we established a cross-basis function for temperature with three knots placed at equal intervals over the range of both temperatures and lag space in the natural cubic splines (Gasparrini et al. 2015; Guo et al. 2014). We decided to use natural cubic splines in our models because they were most commonly used in smooth functions and more conservative in smoothing the data. Furthermore, a previous study suggested that the association between temperature extremes and nephrolithiasis can be adequately captured by natural cubic splines (Tasian et al. 2014). For a DLNM, we adopted an over-dispersed generalized additive model to link the outcome (daily renal colic) and the exposure (daily mean temperature). We also incorporated several covariates to control their potential confounding effects (Ma et al. 2014): (1) a natural cubic smooth function of calendar time with seven degrees of freedom (df) per year, which can exclude unmeasured long-term and seasonal trends in renal colic incidence; (2) a cross-basis function for relative humidity as performed for temperature; (3) an indicator variable for Bday of the week^; and (4) a binary variable for holidays.
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We plotted the lag structure for the association between temperature and renal colic using a wide range of 30 lag days and thereby determined the cumulative lags used for estimating the risk of temperature. We used a lag of 3 days for humidity because few studies suggested a longer lagged effect. After the basic model was built, we flexibly plotted the exposureresponse relationship curves between temperature and renal colic. We further calculated the risks of temperature at a cutoff (percentile) of temperature relative to the reference temperature. To allow for quantitatively estimating the relative risks (RRs) of temperature, we used some specific cutoff points of temperature, including extremely cold (1st percentile of temperature), cold (10th percentile), hot (90th percentile), and extremely hot (99th percentile of temperature) temperatures. To evaluate the robustness of our results, we conducted two sensitivity analyses. First, we examined the associations of daily maximum and minimum temperatures substituting mean temperature in the basic model and compared the values of generalized cross validation in these models, a common measure of model fit. Second, we selected alternative df with 4–10 per year for the smoothness of time trends. The statistical tests were two-sided, and the effect estimates with lower 95 % confidence interval (CI) of RRs above 1 were considered statistically significant. All models were fitted with the R software (version 2.15.3): the mgcv package for GAMs and the dlnm package for DLNMs.
Results Descriptive statistics There were no missing values for either health or weather data. Table 1 provides a summary of the descriptive statistics in this study. During the study period (1827 days), there were 3158 EADs for renal colic, with an average of 1.8 cases per day. The average daily mean temperature and relative humidity were 22.9 °C and 73.8 %, respectively, reflecting the typical features of the subtropical climate in Guangzhou. Figure 1 graphically shows that daily EADs for renal colic followed an evident seasonal trend with peaks in summer and Table 1 Descriptive data of daily average EADs for renal colic and weather conditions in Guangzhou, China, from 2008 to 2012
Variables
EADs for renal colic Mean temperature(°C) Minimum temperature (°C) Maximum temperature (°C) Relative humidity (%)
Mean
1.8 22.9 18.5 27.2 73.8
troughs in winter, and there have been no significant changes of EADs for renal colic over time. Figure 1 also demonstrates apparent fluctuations of daily mean temperature with season. Regression results Figure 2 depicts the lag pattern of the temperature’s risks on renal colic using a reasonably long period (30 lag days). We did not find any significant risk estimates of cold and extremely cold temperatures with RRs around 1 in addition to the entire period. However, for heat-related effects, the significant risk occurred on the concurrent day and diminished until lag day 7. Therefore, a maximum lag of 7 days was used to calculate the risk estimates of temperature. As shown in Fig. 3, the exposure-response curve between daily mean temperature at lag days 0–7 and EADs for renal colic were nonlinear and almost J-shaped. Specifically, this curve was almost flat when the temperature was low and moderate and elevated when the temperature increased above 21 °C (see Fig. 3). We thus defined 21 °C as the reference temperature to calculate the risk estimates of some cutoff points in the distribution of temperature. For example, the RRs accumulated over a 7-day period comparing a temperature percentile with the reference were 1.10 (95 % CI, 0.67, 1.79) for extremely cold, 1.07 (95 % CI, 0.80, 1.44) for cold, 1.92 (95 % CI, 1.21, 3.05) for hot, and 2.45 (95 % CI, 1.50, 3.99) for extremely hot temperatures. The use of daily minimum and maximum temperatures in the basic models produced similar shapes of exposureresponse relationship curves and lag patterns with daily mean temperature, but the reference temperature differed. As shown in S-Fig. 1, the reference was 18 °C for the minimum temperature and 23 °C for the maximum temperature. The model with a mean temperature had a better model fit than that with a minimum temperature and maximum temperature in terms of the value of generalized cross-validation (1.2154 versus 1.2166 and 1.2199). As shown in Table 2, we still obtained similar risk estimates of hot and extremely hot temperatures when alternative indicators of temperature were used. In another sensitivity analysis, the risk estimates of daily mean temperature on renal colic did not vary substantially
SD
1.4 6.7 6.7 7.1 13.1
Min
0 4.8 1.8 7.0 25
Percentiles
Max
1
10
50
90
99
0 7.1 3.8 8.8 35
0 12.6 8.0 16.3 57
2 24.8 20.3 29.0 76
4 30.7 25.8 35.0 89
6 32.6 27.9 37.0 95
EADs emergency ambulance dispatches, SD standard deviation
10 33.9 29.7 40.0 99
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Fig. 1 Time series of daily emergency ambulance dispatches for renal colic and mean temperature in Guangzhou, China, 2008–2012
when df varied from 4 to 10 per year in the smoothness of calendar time (see S-Table 1 in the supplement).
Discussion This time-series study demonstrated that the short-term exposure to high ambient temperatures was associated with increased EADs for renal colic in Guangzhou, China. This risk increased when the daily mean temperature was above 21 °C. The heat-mediated risk on renal colic incidence may occur immediately and last up to 7 days thereafter. To our
knowledge, this epidemiological study was the first to examine the potential impact of ambient temperature on renal colic incidence in China. Consistent with several studies, our results indicated increased incidence of nephrolithiasis or renal colic when the outdoor temperature was high. For example, Chauhan et al. (2004) found a significant difference in renal colic visits between seasons, with a higher incidence in the warm season in New Jersey, USA. Pincus et al. (2010) also found that the incidence of renal colic in the summer was significantly higher than that in the winter, and there were significantly positive correlations between monthly mean maximum temperature
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Fig. 2 The lag patterns for the risks of extremely cold (a), cold (b), hot (c), and extremely hot (d) temperatures on daily emergency ambulance dispatches for renal colic. The x-axis is the lag period, and the y-axis is the relative risk estimate. Extreme cold risk refers to the relative risk comparing the 1st percentile of temperature distribution with the reference temperature (21 °C). Cold risk refers to the relative risk
comparing the 10th percentile of temperature distribution with the reference. Hot risk refers to the relative risk comparing the 90th percentile of temperature distribution with the reference. Extreme hot risk refers to the relative risk comparing the 99th percentile of temperature distribution with the reference. The thick lines are the mean relative risks, and the gray areas are the 95 % confidence intervals
and renal colic. However, these previous studies failed to evaluate the exposure-response relationship between daily temperature and renal colic after adjusting for confounders (such as
inherent seasonality) and did not explore the potential lagged and nonlinear effects associated with an increase in temperature.
Fig. 3 The cumulative risks of mean temperature (°C) on daily emergency ambulance dispatches for renal colic at lags 0–7 days. The x-axis is the mean temperature (°C), and the y-axis is the relative risk (RR)
at each temperature compared with the reference temperature (21 °C). The thick line is the mean relative risk, and the gray area is the 95 % confidence interval
Int J Biometeorol Table 2 The risks (mean and 95 % confidence interval) of extremely cold, cold, hot, and extremely hot temperatures on daily EADs for renal colic in Guangzhou, China, from 2008 to 2012 Extremely colda
Coldb
Hotc
Extremely hotd
Mean temperature
1.10 (0.67, 1.79)
1.07 (0.80, 1.44)
1.92 (1.21, 3.05)*
2.45 (1.50, 3.99)*
Minimum temperature Maximum temperature
1.14 (0.65, 2.00) 1.12 (0.68, 1.85)
1.21 (0.84, 1.74) 1.00 (0.84, 1.19)
1.76 (1.16, 2.67)* 1.70 (1.12, 2.58)*
2.46 (1.50, 4.03)* 2.09 (1.32, 3.32)*
*p < 0.05 a
Extremely cold risk refers to the relative risk comparing the 1st percentile of temperature distribution with the reference temperature (21 °C)
b
Cold risk refers to the relative risk comparing the 10th percentile of temperature distribution with the reference (21 °C)
c
Hot risk refers to the relative risk comparing the 90th percentile of temperature distribution with the reference (21 °C)
d
Extremely hot risk refers to the relative risk comparing the 99th percentile of temperature distribution with the reference (21 °C)
The underlying pathophysiological mechanisms for the associations between hot temperature and renal colic are still unclear. Renal colic has multiple causes, including nephrolithiasis, inflammation, and tumor in the upper urinary tract. As the predominant cause of renal colic, nephrolithiasis (but not the remaining causes) has been proposed to be associated with ambient temperature in previous studies (BoscoloBerto et al. 2008; Chen et al. 2008; Tasian et al. 2014). Higher temperatures may lead to a reduction in urine volume and consequently an increased risk of concentrated urine and crystalluria due to the excessive transdermal fluid loss (Chen et al. 2008; Ma et al. 2014; Fakheri and Goldfarb 2011). The resulting supersaturation of calcium and uric acid may promote nucleation, growth, and aggregation of lithogenic minerals in urine and further induce renal colic after the upper urinary tract is obstructed (Parks et al. 2003; Tasian et al. 2014). We further identified a reference daily mean temperature of approximately 21 °C, above which we may expect a significant increase in renal colic incidence in Guangzhou, China. Another study in Padova, Italy, demonstrated an association between the onset of renal colic and exposure to hot and dry weather, particularly when temperatures rose above 27 °C (Boscolo-Berto et al. 2008). A time-series study in five US cities also found an adverse effect of high ambient temperature on nephrolithiasis with diverse exposure-response relationship curves in various cities (Tasian et al. 2014). A comparison of the results between our study and the two other studies was very difficult because multiple complicated factors contributed to the differences, including methodology, geographical location (latitude, etc.), climatic conditions, age, sex, diet, water hardness, race, and occupation (Shoag et al. 2015). Our results differed from a previous multicity study mainly in the reference temperature (21 versus 10 °C). The lower latitude in Guangzhou (23° N) than in the five other cities studied (32– 40° N) may reflect the fact that Guangzhou residents have a stronger ability to adapt to heat exposure (Tasian et al. 2014). We did not observe that increases in the RRs of renal colic were immediately followed by decreases in RR. The
deficiency of Bharvesting effects^ in the present study was not unreasonable because such a harvesting phenomenon for heat effects was not always observed in previous temperaturerelated epidemiological studies (Guo et al. 2014; Ma et al. 2014). The existing evidence on the possible harvesting effects of temperature on renal colic was limited. In a previous multicity study, Tasian et al. observed a significant harvesting phenomenon in the association between high ambient temperature and nephrolithiasis in some cities but not in other cities (Tasian et al. 2014). Therefore, further studies are needed to confirm this phenomenon in more cities. Our study may have important public health significance. First, this time-series study addressed the short-term association between temperature increase and the onset of renal colic, so patients with nephrolithiasis should be taught to avoid exposure to high ambient temperatures. Second, heat-associated adverse risks are potentially modifiable via air cooling or lifestyle changes such as drinking more water. Third, the present study found that the risks associated with hot temperature occurred immediately and lasted up to 7 days, suggesting precautionary actions may be continued for at least 7 days after exposure to heat. Fourth, global warming may make heat-related renal colic more prevalent and severe. For example, Brikowski and his colleagues found that the incidence and prevalence of nephrolithiasis were obviously higher in the Southeast than in the other regions of the USA, and the high-risk zones of nephrolithiasis in the USA have likely expanded northward due to global warming (Brikowski et al. 2008). They also predicted a 10 % increase in the prevalence rate and a 25 % increase in health-care expenditures in the next half century due to global warming (Fakheri and Goldfarb 2011). Climate warming is likely to have a greater impact on renal colic morbidity in developing countries; therefore, more stringent public health interventions are needed in these countries including China. Our study had several strengths. First, this study was one of few studies based on emergency data, which might be more sensitive to reflect the acute risks of temperature on renal colic than hospital admissions. Second, in comparison with
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previous studies based on emergency department visits at one or several hospitals (Boscolo-Berto et al. 2008; Pincus et al. 2010), this study collected all emergency data via EADs in the entire city and thus might be more representative of the incidence of renal colic in the general population. Third, we used up-to-date methodology (DLNM) to evaluate the potential lagged and nonlinear acute risks of temperature on renal colic with adjustment of seasonality and day of the week effects. Nevertheless, some limitations of our study should also be mentioned. First, we failed to evaluate the potential modification on the association between temperature and renal colic by individual characteristics, such as gender, age, comorbid conditions, and socioeconomic conditions in this retrospective analysis. Furthermore, stratification analyses to particular patient characteristics were not reasonable in the present study because of the reduction in power that would result from the much smaller sample size. Second, as in most previous timeseries studies, exposure misclassification of temperature cannot be eliminated, as it is inherited in the time-series design that uses ambient measurements as proxies for population exposure. The magnitude and direction of the misclassification are complicated to quantify, although it tends to bias the risk estimates downwards (Zeger et al. 2000), and accordingly, it does not limit the public health importance of our findings. We also believed that this issue was not important because temperature generally does not vary substantially within a city. Third, we did not have cause-specific data on renal colic, and we may expect a larger risk of nephrolithiasis simply because the other causes of renal colic were less common and not affected biologically by ambient temperature. Fourth, because the medical documents for all renal colic patients were spread out among various hospitals in Guangzhou and the retrieval of these documents was almost impractical, we did not validate these diagnoses in this analysis. However, we do not believe the potential diagnosis bias was substantial because the uniform clinical standard was adopted in admitting diagnoses. In conclusion, this time-series analysis in Guangzhou, China, suggested a nonlinear and lagged association between high outdoor temperature and daily EADs for renal colic. Our findings might have important public health significance to prevent renal colic.
Acknowledgments The study was supported by the Public Welfare Research Program of National Health and Family Planning Commission of China (201502003), National Natural Science Foundation of China (81222036), China Medical Board Collaborating Program (13-152), and Cyrus Tang Foundation (CTF-FD2014001). Compliance with ethical standards Conflict of interest The authors declare that they have no competing interests.
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ORIGINAL PAPER
Daily ambient temperature and renal colic incidence in Guangzhou, China: a time-series analysis Changyuan Yang 1 & Xinyu Chen 2 & Renjie Chen 1 & Jing Cai 1 & Xia Meng 1 & Yue Wan 3 & Haidong Kan 1
Received: 22 April 2015 / Revised: 21 October 2015 / Accepted: 28 October 2015 # ISB 2015
Abstract Few previous studies have examined the association between temperature and renal colic in developing regions, especially in China, the largest developing country in the world. We collected daily emergency ambulance dispatches (EADs) for renal colic from Guangzhou Emergency Center from 1 January 2008 to 31 December 2012. We used a distributed-lag nonlinear model in addition to the overdispersed generalized additive model to investigate the association between daily ambient temperature and renal colic incidence after controlling for seasonality, humidity, public holidays, and day of the week. We identified 3158 EADs for renal colic during the study period. This exposure-response curve was almost flat when the temperature was low and moderate and elevated when the temperature increased over 21 °C. For heat-related effects, the significant risk occurred on
the concurrent day and diminished until lag day 7. The cumulative relative risk of hot temperatures (90th percentile) and extremely hot temperatures (99th percentile) over lag days 0– 7 was 1.92 (95 % confidence interval, 1.21, 3.05) and 2.45 (95 % confidence interval, 1.50, 3.99) compared with the reference temperature of 21 °C. This time-series analysis in Guangzhou, China, suggested a nonlinear and lagged association between high outdoor temperatures and daily EADs for renal colic. Our findings might have important public health significance to prevent renal colic. Keywords Temperature . Renalcolic . Emergencyambulance dispatches . Time series . Epidemiology
Introduction Changyuan Yang and Xinyu Chen contributed equally to this work. Electronic supplementary material The online version of this article (doi:10.1007/s00484-015-1106-7) contains supplementary material, which is available to authorized users. * Renjie Chen [email protected] * Haidong Kan [email protected] 1
School of Public Health, Key Lab of Public Health Safety of the Ministry of Education, and Key Lab of Health Technology Assessment of the Ministry of Health, Fudan University, P.O. Box 249, 130 Dong-An Road, Shanghai 200032, China
2
State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Disease, The First Affiliated Hospital, Guangzhou Medical University, Guangzhou 510182, China
3
Division of Environment and Health Management, Department of Science, Technology and Standards, Ministry of Environmental Protection of PRC, Beijing 100035, China
Renal colic is an acute and severe clinical syndrome, with symptoms of sudden flank pain arising from obstruction of the ureter and complications including hematuria, nausea, and vomiting (Bultitude and Rees 2012; Cupisti et al. 2008). Nephrolithiasis is the most common cause of renal colic (Noble and Brown 2004). It was estimated that the prevalence of nephrolithiasis worldwide varied according to geographical, climatic, and socioeconomic conditions (Amato et al. 2004). The muggy and humid climate and favorable socioeconomic conditions are associated with a higher prevalence of nephrolithiasis (Amato et al. 2004). Previous studies have suggested that some risk factors can increase the risk of renal colic, including past or family history of nephrolithiasis; increased consumption of animal protein, calcium, and oxalate; high intake of calories; and prevalence of comorbidities (Bartoletti et al. 2007). It has been observed that renal colic occurs more frequently in warm seasons than in cool seasons, and its positive correlation with outdoor
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temperature has also been reported (Chauhan et al. 2004; Pincus et al. 2010; Tasian et al. 2014; Parks et al. 2003). However, the specific exposure-response relationship between all-year daily temperature and the incidence of renal colic has not been fully explored, especially in considering the potential nonlinear and lagged effects of temperature (Tasian et al. 2014). This topic has become increasingly important because of global warming. For example, an unanticipated result of global warming is the likely northward expansion of the presentday southeastern U.S. kidney stone Bbelt,^ and a climaterelated increase of 1.6–2.2 million lifetime cases of nephrolithiasis was estimated to occur by 2050 (Brikowski et al. 2008). The United Nations Intergovernmental Panel on Climate Change has concluded that developing countries were more vulnerable to climate change than developed countries (Solomon et al. 2007). However, few previous studies have examined the association between temperature and nephrolithiasis or renal colic in developing regions, especially in China, the largest developing country in the world. Therefore, we conducted a time-series study to investigate the short-term association between daily outdoor temperature and emergency ambulance dispatches (EADs) for renal colic in Guangzhou, a subtropical city of China.
Materials and methods Study region Guangzhou is the third largest Chinese city and the capital of Guangdong Province, which has a population of approximately 12.78 million in an area of 7434.4 km2 according to the 2010 national census. Located in the Pearl River Delta, Guangzhou serves as an important national trading port and a center of commerce and manufacturing in Southern China. The city has a typical humid subtropical climate influenced by East Asian monsoon, where summer days are hot and humid and winter days are mild and dry. Data collection The daily EADs for renal colic from 1 January 2008 to 31 December 2012 were collected from the Guangzhou Emergency Center. This center has an advanced dynamic ambulance dispatching system serving more than 7 million permanent residents in nine urban districts of Guangzhou. According to the regulations of Guangzhou Emergency Center, the ambulance cars must arrive at the rescue scene within 30 min after an emergency call is made. Afterwards, the physicians in the emergency departments of hospitals will make an admitting diagnosis based on the first presentation of patients transported by ambulance cars. The diagnosis for
renal colic was made on the basis of clinical history, physical examination, urinalysis, and imaging examination. Finally, the EADs for renal colic were aggregated on a daily basis. Daily meteorological data (including daily mean temperature, daily maximum temperature, daily minimum temperature, and relative humidity) during the study period were collected from the Central Weather Station affiliated with the Guangzhou Meteorological Bureau. We assigned the same temperature measurements to all cases on the same day. The Institutional Review Board at the School of Public Health, Fudan University, approved the study protocol (NO. 2012-03-0324) with a waiver of informed consent. Data were analyzed at the aggregate level, and no participants were contacted. Statistical analysis The onset risk of renal colic relies not only on exposure to the present day’s temperature but also on the previous days’ temperature (Boscolo-Berto et al. 2008). Specifically, there exists a lag effect in the relationship between renal colic and temperature. This effect of the temperature over days or even weeks after exposure is often treated by establishing distributed-lag models in statistics. Additionally, the relationship between renal colic and temperature is not simply linear but appears to be J-shaped (Boscolo-Berto et al. 2008). We applied the distributed-lag nonlinear model (DLNM) to explore the possible lagged and nonlinear association between temperature and daily EADs for renal colic. This model has the advantages of calculating cumulative effects of temperature on multiple days after adjusting for the collinearity of temperature on neighboring days and accounting for the nonlinear exposure-response relationship (Gasparrini 2011). To allow for entering the DLNM, we established a cross-basis function for temperature with three knots placed at equal intervals over the range of both temperatures and lag space in the natural cubic splines (Gasparrini et al. 2015; Guo et al. 2014). We decided to use natural cubic splines in our models because they were most commonly used in smooth functions and more conservative in smoothing the data. Furthermore, a previous study suggested that the association between temperature extremes and nephrolithiasis can be adequately captured by natural cubic splines (Tasian et al. 2014). For a DLNM, we adopted an over-dispersed generalized additive model to link the outcome (daily renal colic) and the exposure (daily mean temperature). We also incorporated several covariates to control their potential confounding effects (Ma et al. 2014): (1) a natural cubic smooth function of calendar time with seven degrees of freedom (df) per year, which can exclude unmeasured long-term and seasonal trends in renal colic incidence; (2) a cross-basis function for relative humidity as performed for temperature; (3) an indicator variable for Bday of the week^; and (4) a binary variable for holidays.
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We plotted the lag structure for the association between temperature and renal colic using a wide range of 30 lag days and thereby determined the cumulative lags used for estimating the risk of temperature. We used a lag of 3 days for humidity because few studies suggested a longer lagged effect. After the basic model was built, we flexibly plotted the exposureresponse relationship curves between temperature and renal colic. We further calculated the risks of temperature at a cutoff (percentile) of temperature relative to the reference temperature. To allow for quantitatively estimating the relative risks (RRs) of temperature, we used some specific cutoff points of temperature, including extremely cold (1st percentile of temperature), cold (10th percentile), hot (90th percentile), and extremely hot (99th percentile of temperature) temperatures. To evaluate the robustness of our results, we conducted two sensitivity analyses. First, we examined the associations of daily maximum and minimum temperatures substituting mean temperature in the basic model and compared the values of generalized cross validation in these models, a common measure of model fit. Second, we selected alternative df with 4–10 per year for the smoothness of time trends. The statistical tests were two-sided, and the effect estimates with lower 95 % confidence interval (CI) of RRs above 1 were considered statistically significant. All models were fitted with the R software (version 2.15.3): the mgcv package for GAMs and the dlnm package for DLNMs.
Results Descriptive statistics There were no missing values for either health or weather data. Table 1 provides a summary of the descriptive statistics in this study. During the study period (1827 days), there were 3158 EADs for renal colic, with an average of 1.8 cases per day. The average daily mean temperature and relative humidity were 22.9 °C and 73.8 %, respectively, reflecting the typical features of the subtropical climate in Guangzhou. Figure 1 graphically shows that daily EADs for renal colic followed an evident seasonal trend with peaks in summer and Table 1 Descriptive data of daily average EADs for renal colic and weather conditions in Guangzhou, China, from 2008 to 2012
Variables
EADs for renal colic Mean temperature(°C) Minimum temperature (°C) Maximum temperature (°C) Relative humidity (%)
Mean
1.8 22.9 18.5 27.2 73.8
troughs in winter, and there have been no significant changes of EADs for renal colic over time. Figure 1 also demonstrates apparent fluctuations of daily mean temperature with season. Regression results Figure 2 depicts the lag pattern of the temperature’s risks on renal colic using a reasonably long period (30 lag days). We did not find any significant risk estimates of cold and extremely cold temperatures with RRs around 1 in addition to the entire period. However, for heat-related effects, the significant risk occurred on the concurrent day and diminished until lag day 7. Therefore, a maximum lag of 7 days was used to calculate the risk estimates of temperature. As shown in Fig. 3, the exposure-response curve between daily mean temperature at lag days 0–7 and EADs for renal colic were nonlinear and almost J-shaped. Specifically, this curve was almost flat when the temperature was low and moderate and elevated when the temperature increased above 21 °C (see Fig. 3). We thus defined 21 °C as the reference temperature to calculate the risk estimates of some cutoff points in the distribution of temperature. For example, the RRs accumulated over a 7-day period comparing a temperature percentile with the reference were 1.10 (95 % CI, 0.67, 1.79) for extremely cold, 1.07 (95 % CI, 0.80, 1.44) for cold, 1.92 (95 % CI, 1.21, 3.05) for hot, and 2.45 (95 % CI, 1.50, 3.99) for extremely hot temperatures. The use of daily minimum and maximum temperatures in the basic models produced similar shapes of exposureresponse relationship curves and lag patterns with daily mean temperature, but the reference temperature differed. As shown in S-Fig. 1, the reference was 18 °C for the minimum temperature and 23 °C for the maximum temperature. The model with a mean temperature had a better model fit than that with a minimum temperature and maximum temperature in terms of the value of generalized cross-validation (1.2154 versus 1.2166 and 1.2199). As shown in Table 2, we still obtained similar risk estimates of hot and extremely hot temperatures when alternative indicators of temperature were used. In another sensitivity analysis, the risk estimates of daily mean temperature on renal colic did not vary substantially
SD
1.4 6.7 6.7 7.1 13.1
Min
0 4.8 1.8 7.0 25
Percentiles
Max
1
10
50
90
99
0 7.1 3.8 8.8 35
0 12.6 8.0 16.3 57
2 24.8 20.3 29.0 76
4 30.7 25.8 35.0 89
6 32.6 27.9 37.0 95
EADs emergency ambulance dispatches, SD standard deviation
10 33.9 29.7 40.0 99
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Fig. 1 Time series of daily emergency ambulance dispatches for renal colic and mean temperature in Guangzhou, China, 2008–2012
when df varied from 4 to 10 per year in the smoothness of calendar time (see S-Table 1 in the supplement).
Discussion This time-series study demonstrated that the short-term exposure to high ambient temperatures was associated with increased EADs for renal colic in Guangzhou, China. This risk increased when the daily mean temperature was above 21 °C. The heat-mediated risk on renal colic incidence may occur immediately and last up to 7 days thereafter. To our
knowledge, this epidemiological study was the first to examine the potential impact of ambient temperature on renal colic incidence in China. Consistent with several studies, our results indicated increased incidence of nephrolithiasis or renal colic when the outdoor temperature was high. For example, Chauhan et al. (2004) found a significant difference in renal colic visits between seasons, with a higher incidence in the warm season in New Jersey, USA. Pincus et al. (2010) also found that the incidence of renal colic in the summer was significantly higher than that in the winter, and there were significantly positive correlations between monthly mean maximum temperature
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Fig. 2 The lag patterns for the risks of extremely cold (a), cold (b), hot (c), and extremely hot (d) temperatures on daily emergency ambulance dispatches for renal colic. The x-axis is the lag period, and the y-axis is the relative risk estimate. Extreme cold risk refers to the relative risk comparing the 1st percentile of temperature distribution with the reference temperature (21 °C). Cold risk refers to the relative risk
comparing the 10th percentile of temperature distribution with the reference. Hot risk refers to the relative risk comparing the 90th percentile of temperature distribution with the reference. Extreme hot risk refers to the relative risk comparing the 99th percentile of temperature distribution with the reference. The thick lines are the mean relative risks, and the gray areas are the 95 % confidence intervals
and renal colic. However, these previous studies failed to evaluate the exposure-response relationship between daily temperature and renal colic after adjusting for confounders (such as
inherent seasonality) and did not explore the potential lagged and nonlinear effects associated with an increase in temperature.
Fig. 3 The cumulative risks of mean temperature (°C) on daily emergency ambulance dispatches for renal colic at lags 0–7 days. The x-axis is the mean temperature (°C), and the y-axis is the relative risk (RR)
at each temperature compared with the reference temperature (21 °C). The thick line is the mean relative risk, and the gray area is the 95 % confidence interval
Int J Biometeorol Table 2 The risks (mean and 95 % confidence interval) of extremely cold, cold, hot, and extremely hot temperatures on daily EADs for renal colic in Guangzhou, China, from 2008 to 2012 Extremely colda
Coldb
Hotc
Extremely hotd
Mean temperature
1.10 (0.67, 1.79)
1.07 (0.80, 1.44)
1.92 (1.21, 3.05)*
2.45 (1.50, 3.99)*
Minimum temperature Maximum temperature
1.14 (0.65, 2.00) 1.12 (0.68, 1.85)
1.21 (0.84, 1.74) 1.00 (0.84, 1.19)
1.76 (1.16, 2.67)* 1.70 (1.12, 2.58)*
2.46 (1.50, 4.03)* 2.09 (1.32, 3.32)*
*p < 0.05 a
Extremely cold risk refers to the relative risk comparing the 1st percentile of temperature distribution with the reference temperature (21 °C)
b
Cold risk refers to the relative risk comparing the 10th percentile of temperature distribution with the reference (21 °C)
c
Hot risk refers to the relative risk comparing the 90th percentile of temperature distribution with the reference (21 °C)
d
Extremely hot risk refers to the relative risk comparing the 99th percentile of temperature distribution with the reference (21 °C)
The underlying pathophysiological mechanisms for the associations between hot temperature and renal colic are still unclear. Renal colic has multiple causes, including nephrolithiasis, inflammation, and tumor in the upper urinary tract. As the predominant cause of renal colic, nephrolithiasis (but not the remaining causes) has been proposed to be associated with ambient temperature in previous studies (BoscoloBerto et al. 2008; Chen et al. 2008; Tasian et al. 2014). Higher temperatures may lead to a reduction in urine volume and consequently an increased risk of concentrated urine and crystalluria due to the excessive transdermal fluid loss (Chen et al. 2008; Ma et al. 2014; Fakheri and Goldfarb 2011). The resulting supersaturation of calcium and uric acid may promote nucleation, growth, and aggregation of lithogenic minerals in urine and further induce renal colic after the upper urinary tract is obstructed (Parks et al. 2003; Tasian et al. 2014). We further identified a reference daily mean temperature of approximately 21 °C, above which we may expect a significant increase in renal colic incidence in Guangzhou, China. Another study in Padova, Italy, demonstrated an association between the onset of renal colic and exposure to hot and dry weather, particularly when temperatures rose above 27 °C (Boscolo-Berto et al. 2008). A time-series study in five US cities also found an adverse effect of high ambient temperature on nephrolithiasis with diverse exposure-response relationship curves in various cities (Tasian et al. 2014). A comparison of the results between our study and the two other studies was very difficult because multiple complicated factors contributed to the differences, including methodology, geographical location (latitude, etc.), climatic conditions, age, sex, diet, water hardness, race, and occupation (Shoag et al. 2015). Our results differed from a previous multicity study mainly in the reference temperature (21 versus 10 °C). The lower latitude in Guangzhou (23° N) than in the five other cities studied (32– 40° N) may reflect the fact that Guangzhou residents have a stronger ability to adapt to heat exposure (Tasian et al. 2014). We did not observe that increases in the RRs of renal colic were immediately followed by decreases in RR. The
deficiency of Bharvesting effects^ in the present study was not unreasonable because such a harvesting phenomenon for heat effects was not always observed in previous temperaturerelated epidemiological studies (Guo et al. 2014; Ma et al. 2014). The existing evidence on the possible harvesting effects of temperature on renal colic was limited. In a previous multicity study, Tasian et al. observed a significant harvesting phenomenon in the association between high ambient temperature and nephrolithiasis in some cities but not in other cities (Tasian et al. 2014). Therefore, further studies are needed to confirm this phenomenon in more cities. Our study may have important public health significance. First, this time-series study addressed the short-term association between temperature increase and the onset of renal colic, so patients with nephrolithiasis should be taught to avoid exposure to high ambient temperatures. Second, heat-associated adverse risks are potentially modifiable via air cooling or lifestyle changes such as drinking more water. Third, the present study found that the risks associated with hot temperature occurred immediately and lasted up to 7 days, suggesting precautionary actions may be continued for at least 7 days after exposure to heat. Fourth, global warming may make heat-related renal colic more prevalent and severe. For example, Brikowski and his colleagues found that the incidence and prevalence of nephrolithiasis were obviously higher in the Southeast than in the other regions of the USA, and the high-risk zones of nephrolithiasis in the USA have likely expanded northward due to global warming (Brikowski et al. 2008). They also predicted a 10 % increase in the prevalence rate and a 25 % increase in health-care expenditures in the next half century due to global warming (Fakheri and Goldfarb 2011). Climate warming is likely to have a greater impact on renal colic morbidity in developing countries; therefore, more stringent public health interventions are needed in these countries including China. Our study had several strengths. First, this study was one of few studies based on emergency data, which might be more sensitive to reflect the acute risks of temperature on renal colic than hospital admissions. Second, in comparison with
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previous studies based on emergency department visits at one or several hospitals (Boscolo-Berto et al. 2008; Pincus et al. 2010), this study collected all emergency data via EADs in the entire city and thus might be more representative of the incidence of renal colic in the general population. Third, we used up-to-date methodology (DLNM) to evaluate the potential lagged and nonlinear acute risks of temperature on renal colic with adjustment of seasonality and day of the week effects. Nevertheless, some limitations of our study should also be mentioned. First, we failed to evaluate the potential modification on the association between temperature and renal colic by individual characteristics, such as gender, age, comorbid conditions, and socioeconomic conditions in this retrospective analysis. Furthermore, stratification analyses to particular patient characteristics were not reasonable in the present study because of the reduction in power that would result from the much smaller sample size. Second, as in most previous timeseries studies, exposure misclassification of temperature cannot be eliminated, as it is inherited in the time-series design that uses ambient measurements as proxies for population exposure. The magnitude and direction of the misclassification are complicated to quantify, although it tends to bias the risk estimates downwards (Zeger et al. 2000), and accordingly, it does not limit the public health importance of our findings. We also believed that this issue was not important because temperature generally does not vary substantially within a city. Third, we did not have cause-specific data on renal colic, and we may expect a larger risk of nephrolithiasis simply because the other causes of renal colic were less common and not affected biologically by ambient temperature. Fourth, because the medical documents for all renal colic patients were spread out among various hospitals in Guangzhou and the retrieval of these documents was almost impractical, we did not validate these diagnoses in this analysis. However, we do not believe the potential diagnosis bias was substantial because the uniform clinical standard was adopted in admitting diagnoses. In conclusion, this time-series analysis in Guangzhou, China, suggested a nonlinear and lagged association between high outdoor temperature and daily EADs for renal colic. Our findings might have important public health significance to prevent renal colic.
Acknowledgments The study was supported by the Public Welfare Research Program of National Health and Family Planning Commission of China (201502003), National Natural Science Foundation of China (81222036), China Medical Board Collaborating Program (13-152), and Cyrus Tang Foundation (CTF-FD2014001). Compliance with ethical standards Conflict of interest The authors declare that they have no competing interests.
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