Interactive Effects between Temperature and Humidity on Outpatient Visits of Respiratory Diseases in Lanzhou, China

Hua He aSchool of Management, Lanzhou University, Lanzhou, China
bHospital Management Research Center, Lanzhou University, Lanzhou, China

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Guorong Chai aSchool of Management, Lanzhou University, Lanzhou, China
bHospital Management Research Center, Lanzhou University, Lanzhou, China

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Yana Su aSchool of Management, Lanzhou University, Lanzhou, China
bHospital Management Research Center, Lanzhou University, Lanzhou, China
cCollege of Economics and Management, Lanzhou Institute of Technology, Lanzhou, China

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Yongzhong Sha aSchool of Management, Lanzhou University, Lanzhou, China
bHospital Management Research Center, Lanzhou University, Lanzhou, China

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Shengliang Zong aSchool of Management, Lanzhou University, Lanzhou, China
bHospital Management Research Center, Lanzhou University, Lanzhou, China

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Hairong Bao dDepartment of Gerontal Respiratory Medicine, the First Hospital of Lanzhou University, Lanzhou, China

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Abstract

This study assessing the lag and interactive effects between the daily average temperature and relative humidity on respiratory disease (RD) morbidity in Lanzhou, China, using data from daily outpatient visits for RD between 2014 and 2017 and meteorological and pollutant data during the same period analyzed with Poisson generalized linear model and distributed lag nonlinear models; the effects are further explored by classifying the RD by gender, age, and disease type. The results showed that the effect of temperature and relative humidity on outpatient visits of different populations and types of RD is nonlinear, with a significant lag effect. Relative to 11°C, every 1°C decrease in temperature is associated with 10.98% [95% confidence interval (CI): 9.87%–12.11%] increase for total RD. Chronic obstructive pulmonary disease is affected only by low temperature, upper respiratory tract infection is affected by both low and high temperatures, and asthma is influenced by high temperature. When the relative humidity is less than 32%, every 1% decrease in relative humidity is associated with 6.00% (95% CI: 3.00%–9.11%) increase for total RD; relative humidity has different effects on the outpatient risk of different types of RD. Temperature and relative humidity have an obvious interactive effect on different types and populations of RD: when both temperature and humidity are at low levels, the number of outpatient visits for RD is higher. When the relative humidity is ≤50% and the temperature is ≤11°C, total RD outpatient visits increase by 4.502% for every 1°C drop in temperature; that is, a dry environment with low temperature has the most significant impact on RD.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Guorong Chai, chaigr@lzu.edu.cn

Abstract

This study assessing the lag and interactive effects between the daily average temperature and relative humidity on respiratory disease (RD) morbidity in Lanzhou, China, using data from daily outpatient visits for RD between 2014 and 2017 and meteorological and pollutant data during the same period analyzed with Poisson generalized linear model and distributed lag nonlinear models; the effects are further explored by classifying the RD by gender, age, and disease type. The results showed that the effect of temperature and relative humidity on outpatient visits of different populations and types of RD is nonlinear, with a significant lag effect. Relative to 11°C, every 1°C decrease in temperature is associated with 10.98% [95% confidence interval (CI): 9.87%–12.11%] increase for total RD. Chronic obstructive pulmonary disease is affected only by low temperature, upper respiratory tract infection is affected by both low and high temperatures, and asthma is influenced by high temperature. When the relative humidity is less than 32%, every 1% decrease in relative humidity is associated with 6.00% (95% CI: 3.00%–9.11%) increase for total RD; relative humidity has different effects on the outpatient risk of different types of RD. Temperature and relative humidity have an obvious interactive effect on different types and populations of RD: when both temperature and humidity are at low levels, the number of outpatient visits for RD is higher. When the relative humidity is ≤50% and the temperature is ≤11°C, total RD outpatient visits increase by 4.502% for every 1°C drop in temperature; that is, a dry environment with low temperature has the most significant impact on RD.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Guorong Chai, chaigr@lzu.edu.cn

1. Introduction

Respiratory diseases (RD; the appendix lists all acronyms used in this paper and their definitions) are common in winter and spring and are generally caused by viruses or bacteria, including upper respiratory tract infection (URTI), chronic obstructive pulmonary disease (COPD), and asthma, among others. The occurrence of RD often shows seasonality and an association with meteorological factors (such as temperature and relative humidity). The health of the human body is closely related to external meteorological conditions, and temperature is an important factor affecting human health. At a favorable temperature, the body can metabolize well and regulate normally to maintain a sound physical condition. At a low or high temperature, or at a sharply fluctuating temperature, the body needs additional heat generation and heat dissipation to maintain body temperature. When the humidity is low, the throat and respiratory tract become dry, whereas when the humidity is too high, it is difficult for the human body to dissipate heat. Once the body cannot adapt well to changes in temperature and humidity, it faces the risk of reduced immunity and inclination of paroxysms or even death. Studies have shown that the spread and prevalence of RD are also affected by temperature and humidity conditions. For example, Wang et al. (2016) studied the impact of the interaction between temperature and humidity in Beijing, China, from 2009 to 2011 on the number of emergency department visits for RD, and their results showed that low temperature and low humidity were important environmental factors for the occurrence of RD. Research by Mäkinen et al. (2009) also showed that low temperature and low humidity were related to the increase in the incidence of respiratory infections.

Currently, few studies on the influence of temperature and humidity on the incidence of RD have thoroughly investigated the threshold and nonlinear effects, and discussions on the interaction between temperature and humidity are rare. In addition, in both domestic and foreign studies on the effects of changes in temperature and humidity on health, the outcome indicators have primarily been presentation to the emergency department, hospitalization, or death, and few studies have examined the impact of changes in temperature and humidity on outpatient visits (Lin et al. 2009; M. Z. Wang 2013). In China, the sick typically go to the outpatient clinic first, and those who are not seriously ill are rarely hospitalized. Therefore, the outpatient data may be correlated more closely with meteorological factors, which calls for in-depth analysis and research. Lanzhou, which is situated in the inland semiarid area of northwest China, has a dry climate, large temperature differences, and rare precipitation. In addition, Lanzhou is controlled by cold and anticyclone weather systems in winter and spring and is frequently hit by cold air masses. In winter, the use of indoor heating increases the temperature difference between the indoors and the outdoors, which can easily trigger respiratory diseases. RD are common, with a high incidence in Lanzhou (Li et al. 2002; Jing and Ma 2011), but there have been no reports to date on research of the interactions between temperature and relative humidity on different groups of people and different types of RD in Lanzhou. Therefore, in the present study, we applied time series research methods, combined with the Poisson generalized linear regression model (PGLM) and distributed lag nonlinear models (DLNM), to investigate the impact of the daily average temperature and relative humidity and the interaction between temperature and relative humidity on the number of outpatient visits for different types of RD. We also use these methods to conduct stratification analysis of different groups of people based on their attributes.

As an important industrial and commercial city in northwest China, Lanzhou has a large population of high density, with frequent interpersonal contacts and flows. Thus, there is a risk of a seasonal epidemic and the spread of RD. The prevalence and spread of respiratory diseases pose a serious threat to the smooth production order of society and is a huge test for the medical system. Therefore, understanding the impact of temperature and relative humidity on RD can help people take corresponding measures to control its prevalence and reduce the risk of its spread. Understanding the seasonal incidence of RD would help us to better estimate and predict the time and scale of the high RD morbidity and provide a scientific basis for future medical weather forecasts and for the prevention and treatment of RD.

2. Data and methods

a. Data source

On the basis of the number of outpatient visits and the geographical location of Lanzhou General Hospital, three of the largest comprehensive hospitals with complete electronic medical record systems were selected as the data source. These three hospitals are located in the east and west of Lanzhou City, with convenient transportation and surrounded by densely arranged residential areas. They are the largest general hospitals in Lanzhou, with advanced equipment, complete medical departments, and strong business and technical forces. They have a good reputation for treating RD, with more than 80% of local residents indicating that they would choose them for treatment (Ma et al. 2016, 2017; Bao et al. 2020a). Therefore, the sample data of outpatient visits in this paper are representative and remarkably reflect the incidence of common RD in Lanzhou. Daily consultation data on RD from these three comprehensive hospitals between 1 January 2014 and 31 December 2017 were collected, including patients’ residential address, final diagnosis, gender, age, and consultation date. From the 10th edition of the International Classification of Diseases (ICD-10), total RD (J00-J99), URTI (J06), COPD (J44), and asthma (J45) were screened in the present study. Patients whose residential address was not in Lanzhou were excluded from the study.

The daily report data on the air quality in Lanzhou from 1 January 2014 to 31 December 2017 were obtained from the national urban Real-Time Air Quality Index platform, and the daily monitoring data of the main urban area of Lanzhou were screened, including inhalable particulate matter (PM10), sulfur dioxide (SO2), and nitrogen dioxide (NO2). Air pollutant data were derived from three state-controlled monitoring stations, namely, the Provincial Workers’ Hospital, Railway Design Institute, and Biological Product Institute. First, the 24-h arithmetic average concentration of pollutants at each monitoring site was calculated; then, the data of all monitoring sites were averaged as the pollutant concentration value of Lanzhou on that day. The completeness and consistency of the data collected from the three state-controlled monitoring stations met the requirements stated in the state ambient air quality–monitoring specifications, and the data collected showed no abnormally large or small values, with remarkable accuracy. Because the three sites are concentrated in the main urban area of Lanzhou (see Fig. 1), the air pollution situation in Lanzhou was reflected comprehensively as a whole. In the present study, the daily average concentration of pollutants at the three monitoring sites was regarded as the daily average exposure concentration of pollutants in Lanzhou. Linear interpolation was used for missing values in the air pollution data, and the fill ratio of the missing values was 0.21%. The missing proportion of air pollution is small, there was no effect on the outcomes of the analysis presented in Table 1.

Fig. 1.
Fig. 1.

Location of study area (Lanzhou), hospitals, and air pollution monitoring stations The pictures are from the following two articles, and we have obtained the permission of the authors: Chai et al. (2020) and Bao et al. (2020a).

Citation: Weather, Climate, and Society 13, 3; 10.1175/WCAS-D-20-0040.1

Table 1.

Descriptive statistics of hospital outpatient visits for RD cases, meteorological variables, and air pollutants in Lanzhou during 2014–17.

Table 1.

The Gansu Meteorological Bureau provided daily meteorological data from January 2014 to December 2017, including daily average values of both temperature and relative humidity. Meteorological data were obtained from the nationally certified meteorological observation system and checked by professional technicians of the Gansu Meteorological Bureau, thereby ensuring the authenticity of the data used in this paper.

b. Statistical analysis

Daily outpatient visits for RD are assumed to be a small probability event and obey a Poisson distribution. Therefore, both PGLM and DLNM were adopted for data modeling and quantitative analysis. First, the Spearman correlation coefficient for ranked data was used to analyze the correlation between meteorological factors and the daily outpatient visits for total RD, and the degree of correlation among meteorological factors in Lanzhou, and meteorological factors related to the daily outpatient visits for the total RD were included in the DLNM, with a test level of 0.05. Results of the correlation analysis showed that the daily outpatient visits for total RD was significantly negatively correlated with temperature and relative humidity, and temperature had the most prominent effect for total RD (correlation coefficient r = −0.219; significance level P < 0.01); air pressure and wind speed had the least effect, with no statistical significance. Therefore, air pressure and wind speed were excluded from the model as confounding factors. As there may be collinearity among air pollutants, to reduce the impact of multicollinearity on the research results, the Spearman correlation test was performed on PM10, SO2, and NO2. Based on the advice of Mukaka (2012), the two variables showed low correlation when the correlation coefficient was less than 0.5, and medium or high correlation when the correlation coefficient was greater than 0.5. After testing, the correlation coefficients of PM10, SO2, and NO2 with the daily average temperature and relative humidity were all smaller than 0.5 and that between pollutants was also smaller than 0.5. Therefore, these three pollutants were included in the DLNM.

Considering the lag effect of meteorological factors on disease sensitivity and referring to related research on the impact of meteorological factors on RD (Wang and Lin 2015; H. X. Wang 2013; Geng et al. 2015), the maximum number of lag days in present study was set to 7. In addition, when analyzing the relationship between the daily average temperature and disease incidence, long-term trends, seasonality, and day of the week (DOW) may have confounding effects. Thus, all these confounding factors were included in the DLNM in present study (Gasparrini et al. 2010; Gasparrini 2011; Gasparrini and Armstrong 2013; Gasparrini 2014). The basic model is
log(Yt)=α+βTemt,l+NS(PM10t,4)+NS(SO2t,4)+NS(NO2t,4)+NS(RHt,4)+NS(Timet,7)+γDowt+μ,
where t is the day of the observation, Yt is the outpatient visits for total RD on day t, α is the intercept, Temt,l is the two-dimensional matrix for temperature derived using the cross-basis function in DLNM, β is the coefficient of the explanatory variable in the regression model, l is the number of lag days, PM10t is the concentration of inhalable particulate matter on day t, SO2t is the concentration of SO2 on day t, NO2t is the concentration of NO2 on day t, RHt is the relative humidity on day t, Timet is the time variable, Dowt is the day-of-week effect variable, and μ is the residual error; NS is a natural cubic spline function used to control PM10, SO2, NO2, long-term trends, seasonality, and day of the week. In the cross-base matrix, the degree of freedom of temperature and its lag day was 4; that of PM10, SO2, NO2 and relative humidity was 4; and that of the time variable Timet was 7 (Gasparrini et al. 2010; Gasparrini 2011; Gasparrini and Armstrong 2013; Gasparrini 2014).
Second, the relationship between the daily average relative humidity and the daily outpatient visits for total RD was analyzed. Similar to establishment method for the model in Eq. (1), the basic model is
log(Yt)=α+βRHt,l+NS(PM10t,4)+NS(SO2t,4)+NS(NO2t,4)+NS(Temt,4)+NS(Timet,7)+γDowt+μ

To examine the interactive effects between the air pollutants, daily average temperature, and relative humidity, the interaction terms were added to the PGLM; the natural cubic spline function degrees of freedom df = 4 was adopted to express the effect for the daily average temperature and relative humidity; the linear function was used to express the effect of PM10, SO2, and NO2; and the F test was used to judge the statistical significance of interactive effects, with a test level of P < 0.05 (Li et al. 2015).

There were three components to our analysis of the impact of the interaction between the daily average temperature and relative humidity on total RD. First, the generalized additive model (GAM) was utilized to introduce the daily average temperature and relative humidity into the model and to establish a nonstratification model to analyze the influence of the interaction between the daily average temperature and relative humidity on the daily outpatient visits for total RD. Based on this model, the Z and P values were calculated to test the effectiveness of the interaction, and the test level was P < 0.05. Second, if an interaction existed, a nonparametric bivariate response model was established to fit a three-dimensional graph of the interactive effects between the daily average temperature and relative humidity on total RD, thereby visually displaying the spatial distribution characteristics of the effects of the two on total RD. Third, a relative humidity stratification model was established to explore whether the influence of temperature on total RD differs under different relative humidity stratification conditions. Temt,l and RHt,l were placed into the basic model; based on the model, the exposure–response surfaces of the daily outpatient visits for total RD were obtained. By observing the characteristics of the curve, the critical value was detected, which is the value at which the daily average temperature or relative humidity had the least impact on the daily outpatient visits, and the corresponding daily average temperature and relative humidity thresholds were 11°C and 50%, respectively. That is, less than or equal to the threshold is 0, and greater than the threshold is 1, and thus the corresponding binary variables were constructed. To evaluate the interaction between the average temperature and relative humidity, the product term of the daily average temperature and relative humidity was introduced into the model (Gasparrini et al. 2010; Gasparrini 2011; Gasparrini and Armstrong 2013; Gasparrini 2014).

In addition, to evaluate the lag and interactive effects of the daily average temperature and relative humidity, total RD were stratified by gender (male and female), age (0–5, 6–14, 15–59, and ≥60 years), and disease types (URTI, COPD, and asthma). The “splines,” “mgcv,” and “dlnm” software packages in the R software package (version 3.4.4) were used for statistical analyses of the data.

3. Results

a. General situation

During the study period, the ratio of male to female patients with RD in the three large comprehensive hospitals in Lanzhou was 1.18:1; among patients with RD of different ages, the daily outpatient visits of the group aged 0–5 years was much higher than that of the other groups, with a total of 438 734, accounting for 47% of the daily outpatient visits for total RD. Among patients with different types of diseases, the daily average outpatient visits for total RD, URTI, COPD, and asthma were 634, 440, 37, and 21, respectively. During the same period, the daily average temperature was 11.28°C and the daily average relative humidity was 50.83%. During the study period, the daily average concentrations of PM10, SO2, and NO2 were 120.37, 21.48, and 46.26 μg m−3, respectively (see Table 1).

Figure 2 shows that the daily outpatient visits for total RD displayed obvious seasonal fluctuations, with most patients presenting in December each year, followed by November and January. That is, there are more outpatient visits for RD when the temperature is low, and fewer visits in summer and autumn. The low of relative humidity occurs in March and the peak in October, with no evident periodic changes. When the daily average relative humidity was low, the daily outpatient visits for total RD was higher.

Fig. 2.
Fig. 2.

The daily distribution of RD cases, mean temperature, and relative humidity in Lanzhou during 2014–17.

Citation: Weather, Climate, and Society 13, 3; 10.1175/WCAS-D-20-0040.1

b. Influence of daily average temperature and relative humidity on RD among different groups

Table 2 displays the cumulative lag effect of every 1°C drop in the daily average temperature on patients of different genders and ages for RD in Lanzhou. The impact of temperature on female was similar to that on male, the lag effect reached its maximum in lag 0–7 days, during this time, for every 1°C drop in daily average temperature, there was an increase of 0.76% [95% confidence interval (CI): 0.62%–0.91%] and 0.93% (95% CI: 0.78%–1.09%) in the daily outpatient visits of men and women, respectively. When grouping patients with RD by age, the aged 0–5 years had the maximum relative risk (RR) in lag 0–4 days, with RR value of 1.0146 (95% CI: 1.0125–1.0166). As compared with other groups, individuals aged 6–14 years were the most affected by the change in the daily average temperature, and the lag effect reached the maximum at lag 0–7 days, for every 1°C drop in temperature, the risk of outpatient visits of the 6–14-year-old group increased by 3.08% (95% CI: 2.80%–3.36%). The daily average temperature was statistically significant for the risk of outpatient visits only with the lag 0–7 days for the group aged 15–59 and ≥60 years.

Table 2.

RR (95% CI in parentheses) of RD hospital outpatient visits by gender and age groups per 1°C decrease in temperature in Lanzhou for 2014–17. Boldface numbers indicate statistical significance at P < 0.05.

Table 2.

Table 3 shows the cumulative lag effect of every 1% decrease in the daily average relative humidity on the daily outpatient visits for RD among the different groups in different lag days. As shown in this table, among individuals with RD of different genders and ages, the risk of outpatient visits of those aged 15–59 and ≥60 years was not statistically significant throughout the lag period, and the change patterns of the other groups were similar. The relative risk increased continuously in association with the accumulation of lag time and reached a maximum at lag 0–7 days. During this time period, every 1% drop in relative humidity increased the daily outpatient visits for male, female, and the groups aged 0–5 and 6–14 years by 2.65% (95% CI: 2.17%–3.13%), 3.03% (95% CI: 2.52%–3.55%), 6.70% (95% CI: 6.16%–7.26%), and 1.53% (95% CI: 0.61%–2.47%), respectively.

Table 3.

RR (95% CI) of gender- and age-specific RD per 1% in decrease in relative humidity in Lanzhou for 2014–17. Boldface numbers indicate statistical significance at P < 0.05.

Table 3.

c. Influence of daily average temperature and relative humidity on different types of RD

Figure 3 presents the exposure–response relationship surfaces, which shows the correlation between the daily average temperature and relative humidity with daily outpatient visits for total RD, URTI, COPD, and asthma. As shown in the figure, there was a nonlinear relationship between the daily average temperature and the daily outpatient visits for total RD. When the daily average temperature was lower than 11°C, the daily outpatient visits increased gradually as the temperature declined, and the effect was the greatest at −12°C, with RR of 1.168 (95% CI: 1.140–1.197); no harmful effect emerged at high temperatures. When threshold at 11°C, every 1°C decrease in daily average temperature was associated with 10.98% (95% CI: 9.87%–12.11%) increase for total RD outpatient visits. The exposure–response relationship between temperature and URTI was basically the same as that of total RD, but harmful effect appeared at both low and high temperature, with the former having greater harmful effect. The daily outpatient visits for COPD first increased with the increase of temperature and then decreased after reaching the peak, but there was no statistical significance in the harm effect. The risk of outpatient visits for patients with asthma was statistically significant only when the temperature was within the range of 11°–22°C, and the RR reached a maximum when the daily average temperature was 19°C, with RR value of 1.087 (95% CI: 1.039–1.136).

Fig. 3.
Fig. 3.

The exposure–response relationship between (left) mean temperature or (right) relative humidity and cause-specific RD in Lanzhou for 2014–17.

Citation: Weather, Climate, and Society 13, 3; 10.1175/WCAS-D-20-0040.1

Figure 3 shows that there is also a nonlinear relationship between the daily average relative humidity and the daily outpatient visits for total RD. When the relative humidity was lower than 32%, the daily outpatient visits gradually rose as the relative humidity declined, relative to 32%, for every 1% decrease in the daily average relative humidity, the daily outpatient visits for total RD increased by 6.00% (95% CI: 3.00%–9.11%). Moreover, the effect was the strongest when the relative humidity was 16%, with RR value of 1.395 (95% CI: 1.332–1.460), which was statistically significant. When the relative humidity was higher than 32%, the daily outpatient visits presented a trend of first rising and then falling in association with the rising relative humidity, but this was not statistically significant. When the relative humidity was relatively low, at 36% and 18%, respectively, the harmful effects on URTI and COPD were the most significant, with RR values of 1.047 (95% CI: 1.031–1.064) and 1.457 (95% CI: 1.240–1.711), respectively. The risk of outpatient visits for asthma showed a continuous fluctuating trend as the relative humidity increased; when the daily average relative humidity was within the ranges of 18%–20%, 44%–46%, and 50%–60%, its harmful effect was statistically significant.

Table 4 shows the relative risk and 95% Confidence interval of daily outpatient visits for the different types of RD for every 1°C drop in the daily average temperature in Lanzhou. The total RD and URTI showed harmful effect on the current day, and the harmful effect accumulated steadily with an extension of the lag time. However, COPD was not affected by the daily average temperature change on the current day, and harmful effect emerged at lag 0–6 days, which was similar to the case with total RD and URTI. Total RD, URTI, and COPD had the largest effect values at lag 0–7 days, with RR values of 1.0084 (95% CI: 1.0074–1.0095), 1.0085 (95% CI: 1.0071–1.0099), and 1.0065 (95% CI: 1.0020–1.0110). The risk of outpatient visits for asthma during the entire lag period was not statistically significant.

Table 4.

RR (95% CI) of RD hospital outpatient visits by cause-specific per 1°C in decrease in temperature in Lanzhou for 2014–17. Boldface numbers indicate statistical significance at P < 0.05.

Table 4.

Table 5 shows the relative risk of daily outpatient visits for total RD, URTI, COPD and asthma for every 1% decrease in the daily average relative humidity under different lag days. The impact of relative humidity on total RD showed harmful effect on the current day, the effect increased steadily as the lag time increased, and the RR reached a maximum at lag 0–7 days, every 1% drop in the daily average relative humidity was associated with a 2.83% (95% CI: 2.48%–3.18%) increase in the risk of outpatient visits for total RD. Under different lag days, the effect of relative humidity on URTI and COPD was similar to that of total RD. Patients with asthma were not affected by changes in the relative humidity from lag 0 to 0–7 days.

Table 5.

RR (95% CI) of cause-specific RD per 1% in decrease in relative humidity in Lanzhou for 2014–17. Boldface numbers indicate statistical significance at P < 0.05.

Table 5.

d. Interactive effects of air pollution, daily average temperature and relative humidity on RD

Figure 4 presents a three-dimensional effect diagram of the interaction among PM10, SO2, NO2, daily average temperature and relative humidity on total RD. The interactive effects between PM10 and the daily average temperature on total RD (F = 0.785; P = 0.554) was not statistically significant; furthermore, results no significant interaction between SO2 and the temperature on total RD (F = 1.413; P = 0.174) or between NO2 and the temperature on total RD (F = 1.621; P = 0.154). The result of interaction between PM10 and the daily average relative humidity on total RD (F = 1.642; P = 0.354) was also not statistically significant; neither was the interaction significant between SO2 and the relative humidity on total RD (F = 2.413; P = 0.874) nor between NO2 and the relative humidity on total RD (F = 1.821; P = 0.254). During the study period, the daily average concentrations of PM10, SO2, and NO2 did not exceed the national secondary ambient air quality standard in China (GB3095–2012), and the interaction between air pollutants, daily average temperature, and relative humidity on the risk of outpatient visits for total RD had little effect.

Fig. 4.
Fig. 4.

The interactive effects between air pollutants (PM10, SO2, and NO2), mean temperature, and relative humidity for RD in Lanzhou for 2014–17.

Citation: Weather, Climate, and Society 13, 3; 10.1175/WCAS-D-20-0040.1

e. Interactive effects between daily average temperature, relative humidity, and RD in different groups of people

To determine whether there was an interaction between the daily average temperature and relative humidity on RD in different groups of people, a nonstratification model was first used; the results showed that there was an interactive effect (P < 0.05). Subsequently, a nonparametric bivariate response model was used to evaluate the interaction between the daily average temperature and relative humidity on the daily outpatient visits in each group with RD. Figure 5 presents a three-dimensional effect diagram of the interaction between the daily average temperature and relative humidity on RD in different groups of people. As shown in the figure, the change patterns of male, female and the group aged 0–5 years with RD were similar in each group, and the most harmful effect appeared under conditions of low temperature and low humidity. The most significant impact on the 6–14-year-old group was from high-temperature and high-humidity environments and low-temperature and high-humidity environments. In addition, there was a small peak in outpatient visits for the 6–14-year-old population under environments of low temperature and low humidity and high temperature and low humidity. The group aged 15–59 years was the most significantly affected by the low-temperature and low-humidity environment and the high-temperature and low-humidity environment, whereas for the group aged ≥60 years the peak risks of outpatient visits for RD occurred under environments of low temperature and low humidity and of low temperature and high humidity.

Fig. 5.
Fig. 5.

The interactive effects between mean temperature and relative humidity for gender- and age-specific RD in Lanzhou for 2014–17.

Citation: Weather, Climate, and Society 13, 3; 10.1175/WCAS-D-20-0040.1

To analyze the impact of the daily average temperature on RD of different groups under different relative humidity levels, we divided the conditions into two layers on the basis of the threshold of the daily average relative humidity (50%) and temperature (11°C) (see Table 6). The interaction between the daily average temperature and relative humidity had different effects on the groups. Except for female and the group aged ≥60 years, other groups showed the highest risk of outpatient visits at a relative humidity ≤50% and a daily average temperature ≤11°C. During this time, every 1°C drop in temperature was associated with 4.502%, 5.749%, 4.413%, 5.260%, and 5.545% increase for outpatient visits of total RD, males, and ages 0–5, 6–14, and 15–59 years, respectively. The harmful effect in the female group was the most significant when the relative humidity was ≤50% and the daily average temperature was >11°C; every 1°C drop in temperature was associated with 4.653% increase in the daily outpatient visits. When the relative humidity was >50% and the daily average temperature was ≤11°C, the impact on the group aged ≥60 years was the greatest. For the group ≥60 years of age, every 1°C drop in temperature was associated with 7.350% increase in the risk of outpatient visits. However, for the groups aged 0–5 and 15–59 years, there was no statistically significant effect on the daily outpatient visits when the relative humidity was >50% and the daily average temperature was >11°C.

Table 6.

Percent change (%) in outpatient visits for gender- and age-specific RD per 1°C decrease in temperature by relative humidity level in Lanzhou for 2014–17. Boldface numbers indicate statistical significance at P < 0.05. Here, ER indicates excess risk.

Table 6.

f. Interactive effects between daily average temperature, relative humidity, and different types of RD

Figure 6 shows that, taking the total RD as a case, when both daily average temperature and relative humidity were at low levels, the more outpatient visits for total RD; that is, a low-temperature and low-humidity environment has the most significant impact on total RD. Among different groups of patients with RD, the impact of the temperature and relative humidity on URTI was similar to that of total RD. Outpatient visits for COPD will soar under low temperature and low humidity, and the risk of outpatient visits for asthma is affected not only by low temperature and low humidity but also by low temperature and high humidity and high temperature and low humidity.

Fig. 6.
Fig. 6.

The interactive effects between mean temperature and relative humidity for cause-specific RD in Lanzhou for 2014–17.

Citation: Weather, Climate, and Society 13, 3; 10.1175/WCAS-D-20-0040.1

4. Discussion

In the present study, both the PGLM and DLNM were used to quantitatively evaluate the impact of the daily average temperature on the daily outpatient visits for total RD. The daily average temperature was found to be nonlinearly related to the daily outpatient visits for total RD, using a threshold of 11°C, every 1°C drop in the daily average temperature was associated with 10.98% (95% CI: 9.87%–12.11%) increase of in the daily outpatient visits for total RD, but there was no harmful effect at high temperatures. Mo et al. (2012) used the GAM to investigate the relationship between the daily average temperature and the emergency department visits for RD in Beijing and found that when the daily average temperature was lower than the optimum temperature (4°C), for every 1°C drop in temperature, the excess risk for emergency department visits was 4.83% (95% CI of RR: 0.9383–0.9653) at lag 0 days. Zhang et al. (2014a) reported the impact of cold wave of weather (<−22°C) on the daily outpatient visits for RD in Harbin, the results showed that the cold wave weather increased daily outpatient visits for RD by 1.19% (95% CI: 1.12%–1.27%). Wang et al. (2016) showed that the average temperature in Beijing from 2009 to 2011 had an impact on the emergency RD cases and reported that when the average temperature was <12°C (the critical temperature), every 1°C decline in the daily average temperature was associated with an expected increase of 2.26% (95% CI: 2.09%–2.43%) in the emergency RD cases. When the average temperature was >12°C, for every 1°C increase in the daily average temperature, the emergency RD cases increased by 0.92% (95% CI: 0.72%–1.11%). Zhang et al. (2014b) discussed the relationship between the daily average temperature and the hospital admissions for RD in Shanghai, China, when the low temperature (<25°C) lagged by lag 30, the admissions for total RD increased by 3% (95% CI: 2.54%–3.45%) for every 1°C decrease in temperature; when the high temperature (>25°C) lagged by lag 30, the admissions for total RD increased by 2.15% (95% CI: 0.67%–3.66%) for every 1°C rise in temperature.

As seen from the different types of diseases, COPD is affected only by low temperature, URTI is affected by both low temperature and high temperature, and asthma is affected by high temperature. At low temperatures, threshold as 11°C, every 1°C drop in temperature was associated with an expected increase of 0.81% (95% CI: 0.11%–1.51%) and 11.81% (95% CI: 10.27%–13.38%), respectively, in daily outpatient visits for COPD and URTI. At high temperatures, relative to 11°C, for every 1°C increase in temperature, the risk of outpatient visits for URTI increased by 4.18% (95% CI: 3.26%–5.11%), and for asthma, it increased by 6.05% (95% CI: 2.89%–9.33%). A study of temperature and both COPD and asthma in Taiwan showed that the RR of low temperature (18°C) to outpatient visits of the two diseases were 1.08 (95% CI: 1.01–1.14) and 1.09 (95% CI: 1.01–1.17), respectively (Wang and Lin 2015). In the study by Yue et al. (2018) on the impact of temperature changes on URTI in Zunyi, China, the authors reported that the risk of outpatient visits under low temperature (1.1°C) at lag 0–9 days was 1.829 (95% CI: 1.272–2.631) and that under high temperature (28.8°C) at lag 0 days had a RR value of 1.106 (95% CI: 1.009–1.212).

The results of this study are basically consistent with the findings of the aforementioned studies; that is, when the daily average temperature is relatively low, the impact on RD is more significant. At low temperatures, the number of patients with RD in Lanzhou is relatively large, which could be attributed to the higher rate of respiratory viral infections in Lanzhou in winter (Jing and Ma 2011). In addition, the invasion of cold air during the cold season increases the secretion of bronchial mucus, weakens the movement of bronchial cilia, and reduces local resistance, leaving the body vulnerable to bacterial invasion, with symptoms of RD, such as repeated coughing, expectoration, and shortness of breath (Zhou et al. 2015; Bao et al. 2020b).

This study showed that the daily average temperature exerts different effects on patients with RD of different genders and different ages. Temperature has slightly greater harmful effects on females than on males, and temperature changes have a more harmful effect on individuals aged 6–14 years. This may be because children aged 6–14 spend more time in learning and activities and have been studying and living in school for a long time; the dense populations and poor ventilation conditions in schools are associated with a high incidence of many common respiratory disease (National Health Commission of the People’s Republic of China 2020). The incidence of various RD among people aged 6–14 years is increased because the body can hardly adapt to the ambient condition when the temperature changes sharply.

In addition, we found a nonlinear influence of relative humidity on the daily outpatient visits for total RD. When the relative humidity is lower than 32%, the daily outpatient visits increase steadily as the relative humidity decreases. For every 1% decrease in the daily average relative humidity, the daily outpatient visits for total RD increases by 6.00% (95% CI: 3.00%–9.11%). It is generally believed that the human body feels the most comfortable when the relative humidity is maintained between 40% and 60%, when the relative humidity is too high or low, an adverse effect will occur to human health. The study by Wang et al. (2016) on the impact of relative humidity on emergency outpatient visits for RD in Beijing showed that when the relative humidity was ≤51%, every 10% increase in relative humidity reduced the emergency visits for RD by 3.43% (95% CI: −3.47% to −3.38%); when the relative humidity was >51%, every 10% increase in relative humidity was associated with an expected increase of 1.80% (95% CI: 1.76%–1.85%) in the emergency outpatient visits for RD. In their study, Ma et al. (2018) found that low or high relative humidity significantly increased the risk of outpatient visits for RD, during the entire lag period (0–14 days), when the relative humidity was about 40%, the RR value was low; when the relative humidity was <30% and >60%, the harmful effect was fierce, and it could be maintained for more than 10 days.

As seen from the perspective of different types of diseases, relative humidity has different effects on the risk of outpatient visits for various RD. Relative to 50%, the daily outpatient visits for URTI increases by 1.21% (95% CI: 1.01%–1.40%) for every 1% drop in humidity. When the daily average relative humidity is within the range of 16%–24% and 38%–50%, there is a statistically significant influence of relative humidity on the daily outpatient visits for COPD; relative to 50%, every 1% drop in humidity results in an increase of 6.24% (95% CI: 4.30%–8.22%) in the daily outpatient visits for COPD. Relative to 50%, every 1% drop in humidity increases the risk of outpatient visits for asthma by 2.28% (95% CI: −0.24% to 4.85%) for asthma. A study in Zunyi City showed that when the relative humidity is 56%, the RR of URTI is 1.091 (95% CI: 0.963–1.235) for every 1% reduction in relative humidity at lag 0–3 days (Yue et al. 2018). Bao et al. (2020b) reported that when the daily average relative humidity is 16%, the daily outpatient visits for COPD reaches a peak, with RR value of 1.730 (95% CI: 1.450–2.050). In the present study, we found that the influence of relative humidity on the daily outpatient visits for various RD differs from the above research, which is mainly related to the local climate of Lanzhou, a region that is dry and cold in winter and accompanied by a heating supply that reduces indoor air humidity. Low humidity and dry air facilitate the reproduction of influenza viruses and highly pathogenic gram-positive bacteria, the diffusion of dust and the spread of epidemics. If the humidity is too low, there is a general decline in the function of human respiratory tract mucosa and cilia motor ability, which reduces the ability of the respiratory tract to resist diseases and increases the potential for respiratory infections.

This study showed that relative humidity has differentiated effects among individuals of different genders and ages. In general, the harmful effect of low humidity on females is slightly greater than that on males, indicating that different genders have different degrees of adaptation to changes in humidity, this may be because of the different physiological characteristics and activity habits of males and females (Mo et al. 2012; Zhang et al. 2014a), and females might be more sensitive to changes in humidity. Among individuals of different ages, humidity has a greater effect on the group aged 0–5 years than other groups. This is mainly because the immunity level of children aged 0–5 years is lower than that of adults, and they are susceptible to external conditions; thus, they have a lower tolerance for low relative humidity and may be infected by various RD under this condition (Wang 2018).

Meanwhile, the results of this study also show that the daily average temperature and relative humidity have an interactive effect on total RD. Our analysis of different types of RD indicated that the daily average temperature and relative humidity have an interactive effect on URTI, COPD, and asthma, both low-temperature and dry-air environments and low-temperature and high-humidity environments increase the risk of outpatient visits for the above three RD, but the effects of low temperature and low humidity are more significant. This study demonstrated that the harmful effect of temperature and humidity on the incidence of RD emerge primarily in winter, November to January of the following year is a time of high incidence of RD, meteorological factors, such as low temperature and low humidity, significantly increase the incidence of RD, and they are influencing factors. In addition, respiratory disease itself has an incubation period, the differences in factors, such as age and the immune mechanism, also result in differences of the body’s response time to RD. At present, the meteorological monitoring and early warning system is relatively complete. Based on the relationship between RD and meteorological factors, we can establish early warning and forecast models for diseases as well as forecast the impact of changes in weather factors, such as the daily temperature, on human health in a few days, provide timely and accurate early warnings and responses to the onset of RD, and actively take preventive measures to greatly reduce the incidence of RD.

5. Conclusions

The results of this study show that the effects of the daily average temperature and relative humidity on outpatient visits for total RD, URTI, COPD, asthma, and gender- and age-specific RD are nonlinearly distributed, with an obvious lag. The daily average temperature and relative humidity have an interactive effect on different groups of people and different types of RD. A low temperature and dry environment have the most significant impact on RD.

Acknowledgments

This work was supported by the National Natural Science Foundation of China (71472079 and 71861026), the Key Project of China Ministry of Education for Philosophy and Social Science (16JZD023), the Fundamental Research Funds for the Central Universities (18LZUJBWZD07 and 18LZUJBWZY011), Belt and Road Special Project of Lanzhou University (2018ldbryb026), China Postdoctoral Science Foundation (2016M600827), and Science Foundation of Gansu Province, China (Grant 18JR3RA354). The authors declare no conflicts of interest. The authors thank Zhe George Zhang for his constructive suggestions on the paper.

APPENDIX

List of Abbreviations

CI

Confidence interval

COPD

Chronic obstructive pulmonary diseases

DF

Degree of freedom

DLNM

Distributed lag nonlinear model

GAM

Generalized additive model

PGLM

Poisson generalized linear regression model

RD

Respiratory disease

RR

Relative risk

URTI

Upper respiratory tract infection

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  • Wang, M. Z., and Coauthors, 2016: Interaction of temperature and relative humidity on emergency room visits for respiratory diseases. Chin. Environ. Sci., 36, 581588.

    • Search Google Scholar
    • Export Citation
  • Wang, Y. C., and Y. K. Lin, 2015: Temperature effects on outpatient visits of respiratory diseases, asthma, and chronic airway obstruction in Taiwan. Int. J. Biometeor., 59, 815825, https://doi.org/10.1007/s00484-014-0899-0.

    • Search Google Scholar
    • Export Citation
  • Wang, Y. J., 2018: Correlation between meteorological factors and daily respiratory emergency in Urumqi City. M.S. thesis, Xinjiang Medical University, 65 pp.

  • Yue, M., S. G. Wang, J. J. Xie, P. Ma, and K. Z. Shang, 2018: Study about the impact of environmental conditions on respiratory diseases and prediction in Zunyi City. Chin. Environ. Sci., 38, 43344347.

    • Search Google Scholar
    • Export Citation
  • Zhang, Y., Y. L. Jin, C. Guoquan, Y. Chao, L. Li, L. Chengcheng, X. Dongqun, 2014a: Impact of cold wave on respiratory diseases in Harbin in 2009–2011. J. Environ. Hyg., 4, 125127.

    • Search Google Scholar
    • Export Citation
  • Zhang, Y., H. Kan, L. Peng, Y. Liu, and W. Wang, 2014b: Effects of daily mean temperature on respiratory hospital admissions in Shanghai: Time-series analysis. Chin. J. Prev. Med., 48, 795799.

    • Search Google Scholar
    • Export Citation
  • Zhou, X.-F., and Coauthors, 2015: Effect of meteorological factors on outpatient visits in patients with chronic obstructive pulmonary disease. Environ. Occup. Med., 32, 711716.

    • Search Google Scholar
    • Export Citation
Save
  • Bao, H., J. Dong, X. Liu, E. Tan, J. Shu, and S. Li, 2020a: Association between ambient particulate matter and hospital outpatient visits for chronic obstructive pulmonary disease in Lanzhou, China. Environ. Sci. Pollut. Res. Int., 27, 22 84322 854, https://doi.org/10.1007/s11356-020-08797-y.

    • Search Google Scholar
    • Export Citation
  • Bao, H., X. Liu, L. Tanen, J. Shu, J. Dong, and S. Li, 2020b: Effects of temperature and relative humidity on the number of outpatients with chronic obstructive pulmonary disease and their interaction effect in Lanzhou, China. J. Peking Univ., 52, 308316, https://doi.org/10.19723/j.issn.1671-167X.2020.02.019.

    • Search Google Scholar
    • Export Citation
  • Chai, G., H. He., Y. Su, Y. Sha, and S. Zong, 2020: Lag effect of air temperature on the incidence of respiratory diseases in Lanzhou, China. Int. J. Biometeor., 64, 8393, https://doi.org/10.1007/s00484-019-01795-x.

    • Search Google Scholar
    • Export Citation
  • Gasparrini, A., 2011: Distributed lag linear and non-linear models in R: The package dlnm. J. Stat. Software, 43 (8), 120, https://doi.org/10.18637/jss.v043.i08.

    • Search Google Scholar
    • Export Citation
  • Gasparrini, A., 2014: Modeling exposure-lag-response associations with distributed lag non-linear models. Stat. Med., 33, 881899, https://doi.org/10.1002/sim.5963.

    • Search Google Scholar
    • Export Citation
  • Gasparrini, A., and B. Armstrong, 2013: Reducing and meta-analyzing estimates from distributed lag non-linear models. BMC Med. Res. Methodol., 13, 1, https://doi.org/10.1186/1471-2288-13-1.

    • Search Google Scholar
    • Export Citation
  • Gasparrini, A., B. Armstrong, and M. G. Kenward, 2010: Distributed lag non-linear models. Stat. Med., 29, 22242234, https://doi.org/10.1002/sim.3940.

    • Search Google Scholar
    • Export Citation
  • Geng, D., H. Sun, W. Jiang, S.-G. Wang, K.-Z. Shang, Y. Zhang, and X.-P. Ma, 2015: The relationship between respiratory disease death toll and meteorological factors in Nanjing City. J. Lanzhou Univ., 51, 9397.

    • Search Google Scholar
    • Export Citation
  • Jing, W. C., and Y. X. Ma, 2011: Analysis on the relationship between the respiratory diseases and meteorological factors in Lanzhou. 28th Annual Meeting of the Chinese Meteorological Society, Xiamen, China, China Meteorological Administration, 51–65.

  • Li, L., C. Guoa, P.-Y. Chena, C.-Q. Ou, and Y. Guo, 2015: Particulate matter modifies the magnitude and time course of the non-linear temperature-mortality association. Environ. Pollut., 196, 423430, https://doi.org/10.1016/j.envpol.2014.11.005.

    • Search Google Scholar
    • Export Citation
  • Li, X. X., and Coauthors, 2002: Analysis on relationship between respiratory tract disease and meteorological conditions in Lanzhou region during autumn and winter. Gansu Qixiang, 20, 3133.

    • Search Google Scholar
    • Export Citation
  • Lin, S., M. Luo, R. J. Walker, X. Liu, S.-A. Hwang, and R. Chinery, 2009: Extreme high temperatures and hospital admissions for respiratory and cardiovascular diseases. Epidemiology, 20, 738746, https://doi.org/10.1097/EDE.0b013e3181ad5522.

    • Search Google Scholar
    • Export Citation
  • Ma, P., S.-G. Wang, K.-Z. Shang, T.-S. Li, and L. Yin, 2018: The impact of meteorological comfort conditions on respiratory disease. Chin. Environ. Sci., 38, 374382.

    • Search Google Scholar
    • Export Citation
  • Ma, Y., B. Xiao, C. Liu, Y. Zhao, and X. Zheng, 2016: Association between ambient air pollution and emergency room visits for respiratory diseases in spring dust storm season in Lanzhou, China. Int. J. Environ. Res. Public Health, 13, 613, https://doi.org/10.3390/ijerph13060613.

    • Search Google Scholar
    • Export Citation
  • Ma, Y., H. Zhang, Y. Zhao, J. Zhou, S. Yang, X. Zheng, and S. Wang, 2017: Short-term effects of air pollution on daily hospital admissions for cardiovascular diseases in western China. Environ. Sci. Pollut. Res., 24, 1407114079, https://doi.org/10.1007/s11356-017-8971-z.

    • Search Google Scholar
    • Export Citation
  • Mäkinen, T. M., and Coauthors, 2009: Cold temperature and low humidity are associated with increased occurrence of respiratory tract infections. Respir. Med., 103, 456462, https://doi.org/10.1016/j.rmed.2008.09.011.

    • Search Google Scholar
    • Export Citation
  • Mo, Y.-Z., Y.-A. Zheng, H. Tao, M.-M. Xu, G.-X. Li, F.-M. Dong, J.-H. Liu, and X.-C. Pan, 2012: Relationship between daily mean temperature and emergency department visits for respiratory diseases: A time-series analysis. J. Peking Univ., 44, 416420.

    • Search Google Scholar
    • Export Citation
  • Mukaka, M., 2012: Statistics corner: A guide to appropriate use of Correlation coefficient in medical research. Malawi Med. J., 24, 6971.

    • Search Google Scholar
    • Export Citation
  • National Health Commission of the People’s Republic of China, 2020: Press Conference on the Joint Prevention and Control Mechanism of the State Council (in Chinese). http://www.nhc.gov.cn/xwzb/webcontroller.do?titleSeq=11325&gecstype=1.

  • Wang, H. X., 2013: Analysis of the effects of meteorological factors on public health in Lanzhou. M.S. thesis, Lanzhou University, 133 pp.

  • Wang, M. Z., 2013: Research on the response of respiratory disease to meteorological elements and prediction in three representative cities of China. Ph.D. dissertation, Lanzhou University, 143 pp.

  • Wang, M. Z., and Coauthors, 2016: Interaction of temperature and relative humidity on emergency room visits for respiratory diseases. Chin. Environ. Sci., 36, 581588.

    • Search Google Scholar
    • Export Citation
  • Wang, Y. C., and Y. K. Lin, 2015: Temperature effects on outpatient visits of respiratory diseases, asthma, and chronic airway obstruction in Taiwan. Int. J. Biometeor., 59, 815825, https://doi.org/10.1007/s00484-014-0899-0.

    • Search Google Scholar
    • Export Citation
  • Wang, Y. J., 2018: Correlation between meteorological factors and daily respiratory emergency in Urumqi City. M.S. thesis, Xinjiang Medical University, 65 pp.

  • Yue, M., S. G. Wang, J. J. Xie, P. Ma, and K. Z. Shang, 2018: Study about the impact of environmental conditions on respiratory diseases and prediction in Zunyi City. Chin. Environ. Sci., 38, 43344347.

    • Search Google Scholar
    • Export Citation
  • Zhang, Y., Y. L. Jin, C. Guoquan, Y. Chao, L. Li, L. Chengcheng, X. Dongqun, 2014a: Impact of cold wave on respiratory diseases in Harbin in 2009–2011. J. Environ. Hyg., 4, 125127.

    • Search Google Scholar
    • Export Citation
  • Zhang, Y., H. Kan, L. Peng, Y. Liu, and W. Wang, 2014b: Effects of daily mean temperature on respiratory hospital admissions in Shanghai: Time-series analysis. Chin. J. Prev. Med., 48, 795799.

    • Search Google Scholar
    • Export Citation
  • Zhou, X.-F., and Coauthors, 2015: Effect of meteorological factors on outpatient visits in patients with chronic obstructive pulmonary disease. Environ. Occup. Med., 32, 711716.

    • Search Google Scholar
    • Export Citation
  • Fig. 1.

    Location of study area (Lanzhou), hospitals, and air pollution monitoring stations The pictures are from the following two articles, and we have obtained the permission of the authors: Chai et al. (2020) and Bao et al. (2020a).

  • Fig. 2.

    The daily distribution of RD cases, mean temperature, and relative humidity in Lanzhou during 2014–17.

  • Fig. 3.

    The exposure–response relationship between (left) mean temperature or (right) relative humidity and cause-specific RD in Lanzhou for 2014–17.

  • Fig. 4.

    The interactive effects between air pollutants (PM10, SO2, and NO2), mean temperature, and relative humidity for RD in Lanzhou for 2014–17.

  • Fig. 5.

    The interactive effects between mean temperature and relative humidity for gender- and age-specific RD in Lanzhou for 2014–17.

  • Fig. 6.

    The interactive effects between mean temperature and relative humidity for cause-specific RD in Lanzhou for 2014–17.

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