Emergency Cardiovascular Hospitalization Risk Attributable to Cold Temperatures in Hong Kong
Associations between ambient temperature and cardiovascular morbidity have been well studied worldwide; however, few studies determined the cardiovascular disease burden attributable to temperature. We aimed to assess the risk attributed to temperature based on the exposure–lag–response relationship between temperature and circulatory diseases.
Methods and Results—
We collected daily time series data of emergency hospital admissions, mean temperature, and air pollution concentrations from January 2005 to December 2012 in Hong Kong. The association with temperature was modeled using a distributed lag nonlinear model integrated in quasi-Poisson regression. The cumulated effects of cold/hot temperature were abstracted. Attributable risk measures because of below optimal temperature (OT) were calculated to summarize the disease burden, and further separated into contributions from moderate and extreme cold temperatures. We observed significant nonlinear and delayed cold effect but no apparent hot effect lasting for 3 weeks on emergency circulatory hospitalizations. Compared with the identified OT at 23.0°C, the cumulative relative risk during 0 to 21 lag days was 1.69 (95% confidence interval, 1.56–1.82) for extreme cold (first percentile) and 1.22 (95% confidence interval, 1.15–1.29) for moderate cold temperature (10th percentile). Cold temperatures were responsible for temperature-related circulatory emergency hospitalizations, with attributable fraction of 6.33% for moderate cold and 0.82% for extreme cold while inducing 33 030 and 4257 cases, respectively. Several specific causes of cardiovascular diseases showed higher vulnerability.
Moderate cold weather was responsible for a considerable attributable risk for cardiovascular diseases. The temperature-related hospitalizations risk found in this study may provide evidence for guiding the public health policies and preventions for cardiovascular diseases.
WHAT IS KNOWN
Associations between ambient temperature and cardiovascular mortality and morbidity have been studied.
However, the risk of cardiovascular disease burden attributable to temperature is not known.
WHAT THE STUDY ADDS
We assessed the risk attributed to temperature based on the distributed lag–nonlinear relationship between temperature and circulatory diseases.
No effect was detected for hot temperature, but a nonlinear and delayed effect was noted for cold weather.
Cold weather was associated with a substantial attributable risk for cardiovascular diseases because cold temperatures were responsible for temperature-related circulatory emergency hospitalizations, with attributable fraction of 6.33% for moderate cold and 0.82% for extreme cold.
Associations between ambient temperature and mortality or morbidity have been studied worldwide.1,2 Both cold and hot weather are associated with increased risk of mortality and morbidity with the minimal risk at an OT. In general, cold effects are delayed and last for longer days, whereas heat effects seem more acute and followed by harvesting, that is, heat affects mainly a pool of frail individuals whose disease onset or death is brought forward by a brief period of time.3 Besides the whole range of temperature being examined, more specific attention has been paid to the effects of extreme temperature events, such as cold spells and heat waves.4–8
Although most studies have quantified the association with ambient temperature in terms of relative risk (RR), only a few studies have assessed the disease burden attributed to cold/hot temperatures.9–13 The attributable fraction (AF) representing the fraction of cases or deaths from a specific disease that would be avoided in the absence of exposure to extreme weather either in the exposed population or the population as a whole. AFs multiplied by the total number of cases of a given disease would obtain the absolute number (AN) of preventable cases because of extreme weather. AF and AN quantify the public health burden because of specific risk factors for specific diseases,14 which has important policy implications for future interventions and prevention.
The approaches to assess the disease burden attributable to extreme weather were diverse without considering the possible nonlinear and delayed effect of temperature.11–13 Until recently, Gasparrini et al9,10 introduced an updated approach to estimate the attributable risk (AR) based on the distributed lag nonlinear model (DLNM) framework with consideration of the complex pattern of potentially nonlinear and delayed associations described through exposure–lag–response associations for time series study. Furthermore, no study has examined the AR of temperature on the morbidity end points, such as emergency hospitalizations, which was considered to better catch the effect of temperature change than mortality did.
In this time series ecological study, we aimed to examine the nonlinear and delayed association between temperature and emergency hospital admissions for circulatory diseases on population level, and quantify the risk of emergency hospitalizations attribute to temperature by calculating the AF and AN with extended definition of AR measures based on the DLNM framework.
Materials and Methods
Emergency Hospital Admission Data
The daily count of emergency hospital admissions for circulatory diseases as the principal diagnosis 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.15 For the study period of 2005 to 2012, the Hospital Authority provided daily counts of emergency hospital admissions aggregated for age, sex, date of admission, and principal diagnosis on discharge. We abstracted the overall daily circulatory emergency hospitalizations (International Classification of Diseases Ninth Revision [ICD-9]: 390–459), and some specific causes of circulatory diseases, such as ischemic heart disease (IHD, ICD-9: 410–414), acute myocardial infarction (AMI, ICD-9: 410), heart failure (HF, ICD-9: 428), and cerebrovascular diseases (stroke, ICD-9: 430–438). Because no studies in the literature showed the significant association between the onset of overall digestive diseases and ambient temperature, we also abstracted the diseases of digestive system (ICD-9: 520–579) as a negative control. Daily admissions for influenza (ICD-9:487) were used to identify influenza epidemics, which were then treated as a potential confounder in the analysis.16 Ethics approval and consent from individual subjects were not required by our institute because we used only aggregated data but not any individualized data. Mortality of the same disease categories collected from the Census and Statistics Department of Hong Kong was also examined as a secondary outcome measure.
Temperature and Air Pollution Data
The daily mean temperature and relative humidity from 2005 to 2012 was obtained from the Hong Kong Observatory. The extreme cold was defined as those days with daily mean temperature at or lower than the first percentile of its distribution in study period, whereas the moderate cold was those with daily mean temperature between the first percentile and OT that corresponds to a minimum circulatory hospitalization risk. The extreme hot and moderate hot was defined using the 99th percentile as the cut-off point.17
Air pollution concentrations in the same period were obtained from the Environmental Protection Department of Hong Kong. We calculated the daily 24-hour mean concentrations of nitrogen dioxide (NO2) and particulate matter with aerodynamic diameter <10 μm (PM10) and daytime 8-hour (10:00–17:00) mean concentrations of O3 for each general monitoring, and then averaged them across the 10 stations.18 Air pollutants would be acted as potential time varying confounders in the regression models.
This is a time series ecological study on population level. A standard time series quasi-Poisson regression was applied to derive estimates of associations between temperature and emergency hospital admissions for circulatory hospitalizations and each specific cause, reported as RR.
The association with temperature was estimated using a DLNM. The temperature was included in the model as cross-basis function, which can describe complex nonlinear and lagged dependencies through the combination of 2 functions that define the conventional exposure–response relationship and the additional lag–response relationship, respectively.19 The DLNM has the advantage of estimating cumulative effects of temperature over multiple days while adjusting for the collinearity of temperature on neighboring days.20
Specifically, we modeled the exposure–response curve with a natural cubic spline with 4 internal knots placed at equal spaces in the temperature range to allow for enough flexibility in the 2 ends of temperature distribution, and the lag–response curve with a natural cubic spline with an intercept and 3 internal knots placed at equally spaced values in the log scale to allow more flexible lag effects at shorter delays.21 The lag period is extended to 21 days to capture the long delay in the effects of cold and adequately assess the hot effects after excluding emergency hospitalizations advanced only by a few days (harvesting effect). The short lags cannot adequately assess the hot effects, as the harvesting effects are ignored.21 We included a natural cubic spline of time (t) with 8 degrees of freedom per year to control for seasonality and long-term trends, a natural cubic spline of relative humidity with 3 degrees of freedom, and dummy variables for day of the week, public holidays (Holiday), and influenza epidemics (Influenza) to control for these time varying confounders.22 Air pollutants (PM10, NO2, and O3) were controlled by including third-degree constrained polynomial distributed lags with a maximum lag of 3 days.23 Autocorrelation of the residuals of the model was checked by partial autocorrelation function, which showed no serial autocorrelations along the lags.24 The model can be specified as follows:
Here, E(Yt) means the expected daily counts of emergency hospital admission for circulatory diseases on day t; Tempt,l, matrix obtained by applying the DLNM to temperature; β, vector of coefficients for Tempt,l; l, the lag days; ns(.), the natural cubic spline function for nonlinear variables; df, degrees of freedom; RH, relative humidity; DOW, day of the week; and COVs, all time varying covariates.
The OT, corresponding to a minimum circulatory hospitalization risk, was derived from the best linear unbiased prediction of the overall cumulative exposure–response association, using a constrained segmented distributed lag approach.25,26 The minimum Bayesian information criterion was suggested to select the threshold, which is more probable to meet for segmented models.25 This threshold value was used as the reference temperature (centering value) for fitting the exposure–lag–response relationship, both in the overall analysis and in the stratified analyses in cool and warm season separately. The association was then reduced to the overall temperature–circulatory hospitalizations relationship, cumulating the risk during the lag period. As previous studies indicated that the effects of hot temperatures were generally more short term than those from cold temperatures while including some harvesting a few days later, besides the cumulative effect during 0 to 21 lag days, we also estimated temperature effects during the following lag periods: 0 to 1, 2 to 6, and 7 to 21 to represent the acute, delayed, and long-lasting effects, respectively.27 Because the temperature–mortality or morbidity relationship was generally “U” or “J” shaped, we calculated the RRs for cold and hot temperatures, respectively. Specifically, we abstracted the RRs for circulatory hospitalizations at the first percentile and 10th percentile of temperature distribution relative to the OT to represent the effect of extreme and moderate cold, and the RRs at the 99th percentile and 90th percentile relative to the OT to represent the effect of extreme and moderate hot, respectively.
Computation of AR
The OT value corresponding to the minimum circulatory hospitalizations was also considered as the reference to compute the AR by centering the natural cubic spline that models the exposure–response association. For a specific disease category, the overall cumulative RR corresponding to each day’s temperature was used to compute the AF and number in the next 21 days, using a backward approach described by Gasparrini et al,9 which summarized the current burden because of the past exposures. These extended definitions of AR account for the additional temporal dimension in exposure–response associations, providing more appropriate attributable measures for the cold exposure with potentially complex temporal patterns.
The total attributable number of hospitalizations because of non-OT is given by the sum of the contributions from all the days of the series, and its ratio with the total number of hospitalizations provides the total AF. Because the overall hot effect on circulatory hospitalizations was not detected in Hong Kong, we computed the component attributable to cold by summing the subsets corresponding to days with temperatures lower than the optimum temperature. It was further separated into moderate cold and extreme cold contributions.9,10
Empirical confidence intervals were obtained by Monte Carlo simulations assuming a multivariate normal distribution of the reduced coefficients.9 We derived empirical confidence intervals for total attributable numbers and fractions, computed overall and separated components, by simulating 5000 samples from the assumed distribution.
All analyses were conducted in R statistical environment version 3.1.3 (R Development Core Team, 2014), with its dlnm package20 to fit DLNM to estimate the temperature effect, and modTempEff package28 to fit constrained segmented distributed lag model to identify the threshold temperature where hospitalizations reached minimum. The AR (AF and AN) was calculated by function attrdl provided by Gasparrini et al.29
During the study period, a total of 521 575 emergency hospital admissions for circulatory diseases were recorded in Hong Kong. On an average, there were 179 emergency circulatory hospitalizations per day, of which 35 were IHD, 13 were AMI, 37 were HF, and 48 were stroke. The daily mean temperature was 23.4°C with range between 8.7°C and 31.8°C, and the relative humidity was 78%. The daily 24-hour mean concentration was 50.5 μg/m3 for PM10 and 55.9 μg/m3 for NO2, whereas the daytime 8-hour mean concentration of O3 was 47.1 μg/m3 (Table 1). The time series plot shows the variation of daily mean temperature and emergency hospital admissions for circulatory diseases and each specific cause: both ambient temperature and cardiovascular diseases follow apparent seasonal patterns and long-term trends (Figure 1).
|Daily emergency hospital admissions|
|Ischemic heart diseases||34.8||8.6||12||29||34||40||79|
|Acute myocardial infarction||13.1||5.4||1||9||12||16||38|
|Total digestive diseases||125.0||16.5||72||113||124||136||202|
|Relative humidity, %||78.4||10.5||31.0||74.0||79.0||85.8||99.0|
|Pollution concentration, μg/m3|
Figure 2 shows the distributed lag–nonlinear relationship between temperature and total circulatory diseases, and the cumulative effect of temperature >21 lag days, using a natural cubic spline DLNM with 4 equal-spaced knots for temperature and 3 knots for lag. The exposure–lag–response surface revealed that the significant cold effect seemed on lag 1 day and lasted for 2 to 3 weeks, whereas acute hot effect occurred on the current day, followed by harvesting on lag 2 to 4 days and approached to null after 1 week. The overall temperature–hospitalization relationship was reversed ‘J’ shape with significant higher risk at low temperature and no apparent risk at high temperature.
The OT corresponding to the minimum circulatory hospitalizations in Hong Kong was identified as 23.0°C, close to the mean temperature in the study period. As indicated in Table 2, the temperature’s effects varied by lag periods. The overall RRs for extreme and moderate cold temperatures were nonsignificant on lag 0 to 1 days; the effects began on lag 2 day and generally increased with more lagged days. Compared with the OT, the RR during 0 to 21 lag days was 1.69 (95% confidence interval, 1.56–1.82) for extreme cold (first percentile) and 1.22 (95% confidence interval: 1.15–1.29) for moderate cold (10th percentile). The cold effects were stronger for IHD, AMI, and especially for HF, whereas weaker for stroke. In contrast, the hot temperature showed some acute effects on total circulatory diseases and followed by harvesting. The overall effects of hot temperatures during 0 to 21 lag days for circulatory diseases and each subcategory were found to be nonsignificant (Table 2). The exposure–response curves show the consistent associations between cold and hospitalizations for all circulatory diseases and each specific cause (Figure 3). Mortality as a secondary outcome generated consistent results with the emergency hospitalizations (Figure I in the Data Supplement). Further stratified analyses in cool and warm seasons separately, which is an alternative approach to control for seasonality, also shows consistent significant associations in cool seasons but no apparent associations in warm seasons (Figures II and III in the Data Supplement). We did not observe any significant associations between all digestive diseases and both cold and hot temperatures. At the same time, the significant associations with air pollution were found in modified models, as the standard time series air pollution studies have been done previously.30 (Table I in the Data Supplement).
|Diseases||Lag Days||Extreme Cold*||Moderate Cold†||Moderate Hot‡||Extreme Hot§|
|All CIR||0–1||0.98 (0.95–1.01)||0.97 (0.95–0.99)||1.04 (1.01–1.06)||1.04 (1.01–1.07)|
|2–6||1.25 (1.21–1.29)||1.10 (1.08–1.13)||0.93 (0.91–0.96)||0.92 (0.89–0.94)|
|7–21||1.38 (1.30–1.47)||1.14 (1.09–1.18)||0.98 (0.94–1.03)||0.97 (0.92–1.02)|
|0–21||1.69 (1.56–1.82)||1.22 (1.15–1.29)||0.95 (0.89–1.01)||0.92 (0.86–0.99)|
|IHD||0–1||0.92 (0.87–0.98)||0.96 (0.92–1.00)||1.05 (1.00–1.11)||1.05 (0.99–1.12)|
|2–6||1.39 (1.30–1.48)||1.17 (1.12–1.23)||0.92 (0.87–0.97)||0.90 (0.85–0.96)|
|7–21||1.63 (1.44–1.83)||1.24 (1.14–1.35)||0.96 (0.87–1.06)||0.96 (0.86–1.06)|
|0–21||2.08 (1.76–2.45)||1.39 (1.24–1.56)||0.93 (0.82–1.06)||0.90 (0.78–1.05)|
|AMI||0–1||0.99 (0.91–1.09)||0.97 (0.90–1.03)||1.02 (0.94–1.11)||1.04 (0.94–1.14)|
|2–6||1.48 (1.35–1.64)||1.22 (1.13–1.31)||0.93 (0.85–1.01)||0.92 (0.83–1.01)|
|7–21||1.62 (1.33–1.97)||1.27 (1.11–1.46)||1.04 (0.89–1.21)||1.03 (0.86–1.23)|
|0–21||2.38 (1.83–3.10)||1.49 (1.24–1.80)||0.99 (0.80–1.22)||0.98 (0.77–1.25)|
|HF||0–1||0.96 (0.91–1.01)||0.91 (0.88–0.95)||1.04 (0.98–1.09)||1.01 (0.95–1.08)|
|2–6||1.39 (1.31–1.48)||1.18 (1.12–1.23)||0.87 (0.82–0.92)||0.85 (0.80–0.91)|
|7–21||1.95 (1.75–2.19)||1.33 (1.23–1.45)||0.99 (0.90–1.09)||1.03 (0.92–1.15)|
|0–21||2.61 (2.24–3.04)||1.43 (1.28–1.60)||0.89 (0.78–1.02)||0.89 (0.76–1.03)|
|Stroke||0–1||1.00 (0.95–1.05)||0.99 (0.95–1.02)||1.04 (1.00–1.09)||1.07 (1.01–1.12)|
|2–6||1.17 (1.11–1.24)||1.05 (1.01–1.09)||0.97 (0.92–1.01)||0.95 (0.90–1.00)|
|7–21||1.13 (1.02–1.26)||1.05 (0.98–1.13)||0.95 (0.88–1.03)||0.90 (0.82–0.98)|
|0–21||1.33 (1.15–1.53)||1.09 (0.99–1.20)||0.96 (0.86–1.07)||0.91 (0.80–1.02)|
|Digestive||0–1||0.93 (0.90–0.96)||0.98 (0.95–1.00)||1.04 (1.01–1.07)||1.04 (1.01–1.08)|
|2–6||1.09 (1.05–1.13)||1.04 (1.01–1.07)||0.98 (0.95–1.01)||0.98 (0.95–1.01)|
|7–21||1.06 (0.99–1.14)||1.01 (0.96–1.06)||1.02 (0.97–1.07)||1.02 (0.97–1.08)|
|0–21||1.08 (0.98–1.18)||1.03 (0.96–1.10)||1.04 (0.97–1.11)||1.05 (0.97–1.13)|
The OT was also used as the reference point for the computation of the AR measures. Table 3 provides estimates of the AF and AN for total circulatory diseases and specific subcategories that attributed to cold temperature, with the components attributed to extreme cold and moderate cold separately. The comparison of the 2 contributions clearly indicates that moderate cold is responsible for the major part of the temperature associated circulatory hospitalizations, with the AF=6.33% for moderate cold and 0.82% for extreme cold while inducing 33 030 and 4257 cases during the 8 years’ study period, respectively. Among the specific causes of circulatory diseases, AMI, HF, and IHD showed the higher risk of hospitalization attributed to cold temperature, with AF of 10.1% to 12.6% for moderate cold and 1.1% to 1.7% for extreme cold. The risk attributed to cold for stroke was less pronounced (Table 3).
|Extreme Cold†||Moderate Cold‡|
|Subgroup||Total No.*||AF (%)||AN||AF (%)||AN|
|Total circulatory||521 575||0.82 (0.69–0.94)||4257 (3604–4922)||6.33 (4.72–7.75)||33 030 (24 465–40 510)|
|IHD||101 548||1.14 (0.87–1.39)||1157 (898–1429)||10.11 (7.27–12.76)||10 264 (7076–13 136)|
|AMI||38 292||1.44 (0.99–1.83)||552 (387–705)||12.56 (8.1–16.81)||4809 (2966–6424)|
|HF||108 961||1.74 (1.44–2.04)||1899 (1578–2199)||11.47 (7.81–14.51)||12 501 (8903–15 727)|
|Stroke||140 358||0.45 (0.24–0.64)||628 (307–905)||3.3 (0.42–5.93)||4638 (373–8092)|
In this study, we examined the nonlinear delayed association between ambient temperature and emergency hospital admissions for circulatory diseases and assessed the risk attributed to cold temperature. We observed significant positive effect of cold started on lag 2 day and lasted for 3 weeks. The hot effect was acute on lag 0 day, followed by harvesting and led to a null cumulative effect during 2 to 3 weeks. Hospitalization risk for cardiovascular diseases attributed to temperature was main because of the cold, and moderate cold was responsible for the major part of the AF and number of hospitalizations. Specific diseases such as AMI, HF, and IHD showed higher susceptibility to cold temperature exposure.
The association between cold temperature and CVD morbidity has been well documented in the literature.2 Although the effect estimates were heterogeneous across geographical locations, the main finding was consistent as delayed and longer cold effects for ≤2 to 3 weeks. In this study, we observed similar exposure-lag–response relationship between cold and emergency CVD hospitalizations, and added to the literature the higher RRs for AMI, HF, and IHD. The associations with air pollutants were also estimated with the standard model30 and presented in the in the Data Supplement for reference.
Biological processes underlying the cold-related morbidity has been explored. Exposure to cold would associate with cardiovascular stress by affecting blood pressure and plasma fibrinogen, increasing thrombosis, vasoconstriction and blood viscosity, and inflammatory responses.31,32 These physiological responses can persist for longer than those effects from heat,32 and seem to produce hospitalization risk after a persistent response, so that the most of the AR occurred on moderately cold days.
The effect of hot temperatures on emergency hospital admissions for CVD was reported widely in regions2,4,12; however, we did not observe the positive associations between hot temperature and CVD in this study. The results were consistent with a previous study in Vietnam, which reported the nonsignificant association between hot temperature and CVD hospital admissions.33 Even though some acute hot effect on lag 0 to 1 days was significant for stroke, the cumulative effect of hot temperature >1 week and longer period disappeared because of the harvesting. Hong Kong is a subtropical city located on the southern coast of China with hot and humid summer, whereas the mean temperature in summer (May to October) was 27.8°C, ranging between 18.7°C and 31.8°C. Although the mean temperature in summer was higher than the optimum for 98% of days, we did not find significant hot temperature effect for CVD and stroke hospitalizations, which may probably because of the population adaptation, high prevalence of air conditioner usage, and fewer outdoor activities. A previous study revealed that heat-related mortality declined and the population had become resilient to heat over time, especially in the elderly subjects >75 years of age.34 High prevalence of air conditioner usage was also linked to the decreased extreme hot–related mortality or morbidity.35
Harvesting is the phenomenon that arises when a stressor affects mainly a pool of frail individuals, whose events are only brought forward by a brief period of time by the exposure.36 Evidence of harvesting effect of hot temperature on mortality displacement has been well documented37–39; however, the impact of harvesting on morbidity has not been fully investigated. Schwartz et al3 reported similar evidence of a short-term advance in emergency hospital admissions for heart diseases and myocardial infarction among people >65 years of age within a few days after hot temperature exposure, with a positive association on the day of admission followed by a period of lower-than-average admissions, returning to the baseline after a week. The temporal pattern for the effect of high temperatures suggested harvesting effects, whereas no evidence of harvesting effect was observed for cold weather for heart diseases, which was consistent with the findings in this study.
AF and AN quantify the disease burden because of specific risk factors for specific diseases, which has important public health implications pointing to the potential impact of an intervention or prevention.14 In this study, we calculated AF and AN with extended definition of AR measures based on the DLNM framework. Results revealed that cold is mainly responsible for the temperature-related circulatory hospitalizations in Hong Kong, with 7.15% of total emergency circulatory hospitalizations attributed to cold. The AFs for moderate cold for specific causes of circulatory diseases were 7× to 9× of those for extreme cold. Most of the hospitalizations were in fact attributable to moderate cold in spite of the relatively lower RR compared with that of extreme cold, which may be explained that moderate temperature range included the majority of the days in the series.
The weather-warning system has been implemented in Hong Kong to alert the public for the dangers of extreme weather conditions.40 The present assessment suggests that implemented public health policies and interventions specifically designed for extreme weather conditions should be extended to consider the effects associated with cold temperature, and focused on the susceptible subpopulations.
The strength of this study is the application of updated flexible statistical model to characterize the temperature–hospitalization association and identified the OT. This is the first study to assess the AR of cold temperature on emergency circulatory hospitalizations, based on its nonlinear delayed relationship, and separated the attributable components into contributions from extreme and moderate cold temperatures. Meanwhile, some limitations should be noted. As in all other time series studies, personal exposure data were not available. The outdoor monitoring data were used to represent the population exposure to ambient temperature, which may induce exposure misclassifications. A study conducted in Greater Boston reported that the relationship between indoor and outdoor ambient temperature was different, with strong correlation only at warmer outdoor temperatures but weak correlation at cooler temperatures.41 But for the subtropical city of Hong Kong with high prevalence of air conditioner usage in summer but low prevalence of house heating in winter, the situation may be opposite. Outdoor fixed site measurement for ambient temperature may represent the average population exposure better in cool season than in hot season in Hong Kong. Another limitation was that we only include emergency hospital admissions for circulatory diseases but did not capture the less severe cases. However, the emergency hospitalizations could be assumed as less being influenced by health-seeking behaviors and were considered to be more sensitive and better reflect the acute effect of temperature change than mortality did. Finally, caution is warranted when the findings of this single-city study are generalized to other places with different climates and population characteristics.
We observed significant nonlinear and delayed effect of cold temperature on emergency circulatory hospitalizations in this study. The OT corresponding to the minimum circulatory hospitalizations was 23.0°C in Hong Kong. Extreme cold weather showed a higher RR while moderate cold weather was responsible for a considerable fraction and number of emergency circulatory hospitalizations. Public health policies and preventions should consider the temperature-related hospitalizations risk on susceptible subpopulations.
We thank the Hospital Authority for providing hospital admissions data, the Hong Kong Observatory for providing the temperature and humidity data, and the Hong Kong Environmental Protection Department for providing the air pollution monitoring data that required in this study.
Basu R, Samet JM.Relation between elevated ambient temperature and mortality: a review of the epidemiologic evidence.Epidemiol Rev. 2002; 24:190–202.CrossrefMedlineGoogle Scholar
Ye X, Wolff R, Yu W, Vaneckova P, Pan X, Tong S.Ambient temperature and morbidity: a review of epidemiological evidence.Environ Health Perspect. 2012; 120:19–28. doi: 10.1289/ehp.1003198.CrossrefMedlineGoogle Scholar
Schwartz J, Samet JM, Patz JA.Hospital admissions for heart disease: the effects of temperature and humidity.Epidemiology. 2004; 15:755–761.CrossrefMedlineGoogle Scholar
Bai L, Ding G, Gu S, Bi P, Su B, Qin D, Xu G, Liu Q.The effects of summer temperature and heat waves on heat-related illness in a coastal city of China, 2011-2013.Environ Res. 2014; 132:212–219. doi: 10.1016/j.envres.2014.04.002.CrossrefMedlineGoogle Scholar
Huynen MM, Martens P, Schram D, Weijenberg MP, Kunst AE.The impact of heat waves and cold spells on mortality rates in the Dutch population.Environ Health Perspect. 2001; 109:463–470.CrossrefMedlineGoogle Scholar
Ma W, Zeng W, Zhou M, Wang L, Rutherford S, Lin H, Liu T, Zhang Y, Xiao J, Zhang Y, Wang X, Gu X, Chu C.The short-term effect of heat waves on mortality and its modifiers in China: an analysis from 66 communities.Environ Int. 2015; 75:103–109. doi: 10.1016/j.envint.2014.11.004.CrossrefMedlineGoogle Scholar
Peng RD, Bobb JF, Tebaldi C, McDaniel L, Bell ML, Dominici F.Toward a quantitative estimate of future heat wave mortality under global climate change.Environ Health Perspect. 2011; 119:701–706. doi: 10.1289/ehp.1002430.CrossrefMedlineGoogle Scholar
Ryti NR, Guo Y, Jaakkola JJ.Global association of cold spells and adverse health effects: a systematic review and meta-analysis.Environ Health Perspect. 2016; 124:12–22. doi: 10.1289/ehp.1408104.CrossrefMedlineGoogle Scholar
Gasparrini A, Leone M.Attributable risk from distributed lag models.BMC Med Res Methodol. 2014; 14:55. doi: 10.1186/1471-2288-14-55.CrossrefMedlineGoogle Scholar
Gasparrini A, Guo Y, Hashizume M, Lavigne E, Zanobetti A, Schwartz J, Tobias A, Tong S, Rocklöv J, Forsberg B, Leone M, De Sario M, Bell ML, Guo YL, Wu CF, Kan H, Yi SM, de Sousa Zanotti Stagliorio Coelho M, Saldiva PH, Honda Y, Kim H, Armstrong B.Mortality risk attributable to high and low ambient temperature: a multicountry observational study.Lancet. 2015; 386:369–375. doi: 10.1016/S0140-6736(14)62114-0.CrossrefMedlineGoogle Scholar
Honda Y, Kondo M, McGregor G, Kim H, Guo YL, Hijioka Y, Yoshikawa M, Oka K, Takano S, Hales S, Kovats RS.Heat-related mortality risk model for climate change impact projection.Environ Health Prev Med. 2014; 19:56–63. doi: 10.1007/s12199-013-0354-6.CrossrefMedlineGoogle Scholar
Kim CT, Lim YH, Woodward A, Kim H.Heat-attributable deaths between 1992 and 2009 in Seoul, South Korea.PLoS One. 2015; 10:e0118577. doi: 10.1371/journal.pone.0118577.MedlineGoogle Scholar
Baccini M, Kosatsky T, Analitis A, Anderson HR, D’Ovidio M, Menne B, Michelozzi P, Biggeri A; PHEWE Collaborative Group. Impact of heat on mortality in 15 European cities: attributable deaths under different weather scenarios.J Epidemiol Community Health. 2011; 65:64–70. doi: 10.1136/jech.2008.085639.CrossrefMedlineGoogle Scholar
Steenland K, Armstrong B.An overview of methods for calculating the burden of disease due to specific risk factors.Epidemiology. 2006; 17:512–519. doi: 10.1097/01.ede.0000229155.05644.43.CrossrefMedlineGoogle Scholar
Wong TW, Lau TS, Yu TS, Neller A, Wong SL, Tam W, Pang SW.Air pollution and hospital admissions for respiratory and cardiovascular diseases in Hong Kong.Occup Environ Med. 1999; 56:679–683.CrossrefMedlineGoogle Scholar
Wong CM, Atkinson RW, Anderson HR, Hedley AJ, Ma S, Chau PY, Lam TH.A tale of two cities: effects of air pollution on hospital admissions in Hong Kong and London compared.Environ Health Perspect. 2002; 110:67–77.CrossrefMedlineGoogle Scholar
Chen R, Wang C, Meng X, Chen H, Thach TQ, Wong CM, Kan H.Both low and high temperature may increase the risk of stroke mortality.Neurology. 2013; 81:1064–1070. doi: 10.1212/WNL.0b013e3182a4a43c.CrossrefMedlineGoogle Scholar
Qiu H, Yu IT, Wang X, Tian L, Tse LA, Wong TW.Cool and dry weather enhances the effects of air pollution on emergency IHD hospital admissions.Int J Cardiol. 2013; 168:500–505. doi: 10.1016/j.ijcard.2012.09.199.CrossrefMedlineGoogle Scholar
Gasparrini A, Armstrong B, Kenward MG.Distributed lag non-linear models.Stat Med. 2010; 29:2224–2234. doi: 10.1002/sim.3940.CrossrefMedlineGoogle Scholar
Gasparrini A.Distributed lag linear and non-linear models in R: the package DLNM.J Stat Softw. 2011; 43:1–20.CrossrefMedlineGoogle Scholar
Guo Y, Barnett AG, Pan X, Yu W, Tong S.The impact of temperature on mortality in Tianjin, China: a case-crossover design with a distributed lag nonlinear model.Environ Health Perspect. 2011; 119:1719–1725. doi: 10.1289/ehp.1103598.CrossrefMedlineGoogle Scholar
Qiu H, Tian LW, Pun VC, Ho KF, Wong TW, Yu IT.Coarse particulate matter associated with increased risk of emergency hospital admissions for pneumonia in Hong Kong.Thorax. 2014; 69:1027–1033. doi: 10.1136/thoraxjnl-2014-205429.CrossrefMedlineGoogle Scholar
Yi W, Chan AP.Effects of temperature on mortality in Hong Kong: a time series analysis.Int J Biometeorol. 2015; 59:927–936. doi: 10.1007/s00484-014-0895-4.CrossrefMedlineGoogle Scholar
Wong CM, Vichit-Vadakan N, Kan H, Qian Z.Public Health and Air Pollution in Asia (PAPA): a multicity study of short-term effects of air pollution on mortality.Environ Health Perspect. 2008; 116:1195–1202. doi: 10.1289/ehp.11257.CrossrefMedlineGoogle Scholar
Muggeo VM, Hajat S.Modelling the non-linear multiple-lag effects of ambient temperature on mortality in Santiago and Palermo: a constrained segmented distributed lag approach.Occup Environ Med. 2009; 66:584–591. doi: 10.1136/oem.2007.038653.CrossrefMedlineGoogle Scholar
Muggeo VM.Modeling temperature effects on mortality: multiple segmented relationships with common break points.Biostatistics. 2008; 9:613–620. doi: 10.1093/biostatistics/kxm057.CrossrefMedlineGoogle Scholar
Armstrong B.Models for the relationship between ambient temperature and daily mortality.Epidemiology. 2006; 17:624–631. doi: 10.1097/01.ede.0000239732.50999.8f.CrossrefMedlineGoogle Scholar
Muggeo VMR.Package “modTempEff”: Modelling temperature effects using time series data.http://cran.r-project.org/web/packages/modTempEff/modTempEff.pdf. 2015. Accessed on May 18, 2015).Google Scholar
Gasparrini A.R documentation of “attrdl.Rd”: Attributable Risk from a DLNM.http://www.biomedcentral.com/content/supplementary/1471-2288-14-55-S2.pdf. 2014. Accessed April 16, 2015.Google Scholar
Peng RD, Dominici F, Louis T a.Model choice in time series studies of air pollution and mortality.J R Stat Soc Ser A (Statistics Soc.)2006; 169:179–203.CrossrefGoogle Scholar
Woodhouse PR, Khaw KT, Plummer M, Foley A, Meade TW.Seasonal variations of plasma fibrinogen and factor VII activity in the elderly: winter infections and death from cardiovascular disease.Lancet. 1994; 343:435–439.CrossrefMedlineGoogle Scholar
Keatinge WR, Coleshaw SR, Cotter F, Mattock M, Murphy M, Chelliah R.Increases in platelet and red cell counts, blood viscosity, and arterial pressure during mild surface cooling: factors in mortality from coronary and cerebral thrombosis in winter.BMJ. (Clin Res Ed). 1984; 289:1405–1408.CrossrefMedlineGoogle Scholar
Giang PN, Dung do V, Bao Giang K, Vinhc HV, Rocklöv J.The effect of temperature on cardiovascular disease hospital admissions among elderly people in Thai Nguyen Province, Vietnam.Glob Health Action. 2014; 7:23649.CrossrefMedlineGoogle Scholar
Bobb JF, Peng RD, Bell ML, Dominici F.Heat-related mortality and adaptation to heat in the United States.Environ Health Perspect. 2014; 122:811–816. doi: 10.1289/ehp.1307392.CrossrefMedlineGoogle Scholar
Anderson BG, Bell ML.Weather-related mortality: how heat, cold, and heat waves affect mortality in the United States.Epidemiology. 2009; 20:205–213. doi: 10.1097/EDE.0b013e318190ee08.CrossrefMedlineGoogle Scholar
Schwartz J.Is there harvesting in the association of airborne particles with daily deaths and hospital admissions?Epidemiology. 2001; 12:55–61.CrossrefMedlineGoogle Scholar
Hajat S, Armstrong BG, Gouveia N, Wilkinson P.Mortality displacement of heat-related deaths: a comparison of Delhi, São Paulo, and London.Epidemiology. 2005; 16:613–620.CrossrefMedlineGoogle Scholar
Saha MV, Davis RE, Hondula DM.Mortality displacement as a function of heat event strength in 7 US cities.Am J Epidemiol. 2014; 179:467–474. doi: 10.1093/aje/kwt264.CrossrefMedlineGoogle Scholar
Basu R, Malig B.High ambient temperature and mortality in California: exploring the roles of age, disease, and mortality displacement.Environ Res. 2011; 111:1286–1292. doi: 10.1016/j.envres.2011.09.006.CrossrefMedlineGoogle Scholar
Len W, Chan SH, Chan JY.Parabrachial nucleus induces suppression of baroreflex bradycardia by the release of glutamate in the rostral ventrolateral medulla of the rat.J Biomed Sci. 2000; 7:401–411. doi: 25475.CrossrefMedlineGoogle Scholar
Nguyen JL, Schwartz J, Dockery DW.The relationship between indoor and outdoor temperature, apparent temperature, relative humidity, and absolute humidity.Indoor Air. 2014; 24:103–112. doi: 10.1111/ina.12052.CrossrefMedlineGoogle Scholar