Impact of Ambient Temperature on Respiratory Disease Hospitalization in Rural Wuwei, Northwest China

Impact of Ambient Temperature on Respiratory Disease Hospitalization in Rural Wuwei, Northwest China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Impact of Ambient Temperature on Respiratory Disease Hospitalization in Rural Wuwei, Northwest China Jirong Wu, Guorong Chai, Guangyu Zhai This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6113731/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Respiratory diseases (RD) pose a significant public health challenge, particularly in vulnerable populations. However, the impact of ambient temperature on RD hospitalizations in rural areas of Northwest China remains understudied. this study aimed to investigate the relationship between ambient temperature and RD hospitalization. In this research, meteorological information and hospitalization data of RD in Wuwei's rural area from 2011 to 2015 were integrated. Time series analysis was performed using distributed lag nonlinear model (DLNM). Furthermore, we conducted stratifed analysis based on gender and age. Relative risk and 95% confidence intervals were used to assess the relationship between temperature and admission risk. The results show that the damage caused by low temperature gradually increased with increasing lag days, and the damage effect reached a maximum at -20°C and 7 days after lag (RR = 1.313, 95% CI: 1.257–1.371). For the effect of low temperature, the cumulative risk of hospitalization was greater with a lag of 0–7 days (RR = 3.162, 95% CI: 2.918–3.427) than with other lag days. For the cold effect, the RR of extreme cold and moderate cold at 0–7 day lags were 1.822 (95% CI: 1.732–1.917) and 1.217 (95% CI: 1.200-1.233), respectively. Subgroup analysis demonstrated heightened vulnerability to cold temperatures among males and elderly individuals (≥ 65 years) compared to females and younger adults (< 65 years). Conversely, higher temperatures had a protective effect on RD hospitalization across all demographic groups. Extreme and moderate cold significantly increased RD admissions. male and older people being more susceptible to RD at low temperatures. These findings can inform the development of public health and meteorological policies aimed at safeguarding vulnerable populations and mitigating the burden of RD. respiratory diseases ambient temperature admission distributed lag nonlinear model cold effect heat effect Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Respiratory diseases (RD) are common and frequently occurring diseases that seriously endanger human health. Global data from 2017 revealed that chronic RD resulted in approximately 3.91 million deaths, representing a 19.0% increase since 1990 and highlighting its substantial impact on global health. The incidence of chronic RD among Chinese residents surged from 5.808 million in 1990 to 11.975 million in 2019, underscoring the increasing public health concern(sheng et al.2024). The prevention and treatment of RD has become an important public health problem. Various risk factors contribute to the development of RD, including bacterial and viral infections, genetic predispositions, tobacco and occupational exposures, socioeconomic disparities, air pollution, and meteorological conditions (Cohen et al.1999; D'Ovidio et al.2023; Saarentaus et al.2023). The undeniable effects of ongoing global climate change are increasingly evident in the form of frequent and intense extreme weather events, such as heat waves and cold spells, which impact overall health (Wang et al.2023). These shifts in weather patterns have a direct impact on human health, with ambient temperature emerging as a primary determinant of well-being. Research in China revealed that nonoptimal temperatures contribute significantly to mortality, accounting for 14.33% of nonaccidental deaths, with cold temperatures identified as the leading cause (Chen et al. 2018). The relationship between ambient temperature and RD has garnered increasing attention in recent years, with studies revealing complex associations between temperature extremes and RD incidence or mortality. Numerous studies have demonstrated that both extremely low and high temperatures increase the risk of RD-related deaths, often following "U," "V," or "J" shaped exposure-response curves (Guo et al.2022; Jacobson et al.2021; Shao et al.2021). Studies have also analyzed the relationship between ambient temperature and the number of outpatient or emergency visits from RD. Ma et al. ( 2019 ) and Feng et al. ( 2021 ) reported that both high and low temperatures could increase the number of emergency visits to RD. Chai et al. (2020)reported that ambient temperature was correlated with the number of outpatient visits to RD. Although mortality and outpatient and emergency department visits are frequently studied health outcomes, external temperature does not directly cause death, so it can only reflect a small part of the health effects of environmental exposure. RD is the most common cause of hospitalization worldwide, and reports show that the hospitalization rate of RD increased from 4.2‰ in 2003 to 13.3‰ in 2013 (Lu et al.2019). Therefore, studying the impact of ambient temperature on RD hospitalization can provide a theoretical basis for health policy formulation and the optimization of medical resource allocation. However, the current research evidence is mostly concentrated in developed countries or regions, and there is relatively little research evidence in low- and middle-income countries(Bergmann et al.2023;Gronlund et al.2014;Lim et al. 2012 ). In China, research is also concentrated mainly in economically developed areas (Jia et al.2022;Lei et al.2022;Zhu et al.2022). In addition, there are great differences in the natural environment, climate, social economy, population structure, living habits and health habits in different regions (Zhai et al.2022). The influence of ambient temperature on the RD hospitalization rate is different in different regions and different populations. Given these limited studies, further research is needed to clarify the relationship between ambient temperature and RD hospitalization. Compared with their urban counterparts, rural populations, particularly farmers, face unique health challenges. Limited access to essential amenities such as safe drinking water, central heating, and air conditioning, coupled with the occupational demands of outdoor labor, expose them to frequent and extreme temperature fluctuations (Bai et al.2016). Additionally, rural areas often suffer from limited healthcare infrastructure and access to medical services(Benmarhnia et al.2015). These factors make rural populations, including those in suburban areas, particularly vulnerable to the impacts of ambient temperature on their health. Therefore, it is necessary to explore the influence of ambient temperature on the RD hospitalization of rural residents in the China. This study investigated the association between average ambient temperature and RD hospitalizations among rural residents in Wuwei city, China, from 2011 to 2015. Employing a distributed lag nonlinear model (DLNM), we analyzed the nonlinear exposure‒response relationship and exposure lag effect of temperature on RD hospitalizations. Subgroup analysis further explored these relationships across different age and gender groups. The findings of this study are crucial for informing meteorological policies aimed at protecting the health of vulnerable populations in rural China. Data and methods Study area Wuwei (101°41'E-104°16'E, 36°29'N-39°27'N), which is situated in the Shiyang River basin of Gansu Province, lies at the convergence of the Loess Plateau, Qinghai-Tibet Plateau, and Mongolian-Xin Plateau (Fig. 1 ). The city boasts a temperate continental arid climate with distinct seasons, experiencing cold winters and hot summers, coupled with substantial day-to-night and annual temperature variations. Precipitation is low and unevenly distributed, contributing to a dry climate with high evaporation rates. This environment results in strong solar radiation and abundant sunlight. Wuwei, with a permanent population of 1.445 million (713,000 urban, 732,000 rural), is the most populous city in the Hexi Corridor. Despite this, Wuwei is an economically underdeveloped area, with a per capita disposable income of 14,859 yuan for rural residents. Data collection This study utilized data from the New Rural Cooperative Medical Insurance Database (NRCMS) of Gansu Province to analyze respiratory disease (RD) hospitalizations among rural residents in Wuwei. The NRCMS, one of China's primary medical insurance schemes, covered 98.84% of farmers in Wuwei City by December 2015. Due to data availability limitations, the study period was January 1, 2011, to December 31, 2015. The data included patient demographics (gender, age, residence) and hospital admission information, with disease classifications coded according to the International Classification of Diseases, 10th Revision (ICD-10). The ICD-10 code for RD in this study was J00-J99. To explore potential differences in temperature-related risk, the population was stratified by gender (male vs. female) and age (< 65 vs. ≥65 years). Meteorological data, encompassing average temperature, relative humidity, pressure, wind speed, precipitation, and sunshine duration, were obtained from the China Meteorological Science Data Sharing Service Network ( http://data.cma.gov.cn ). Statistical analysis Numerous studies have demonstrated that ambient temperature's impact on human health exhibits both non-linearity and a time lag (Yezli et al.2023). The DLNM effectively captures this complex relationship by integrating the traditional exposure-response relationship with a hysteresis response, allowing for a more comprehensive understanding of the effects. Given that daily hospitalizations for RD represent a low probability event with a distribution resembling a Poisson distribution; this study employed a DLNM model coupled with a quasi-Poisson regression model. This approach enabled us to evaluate the non-linear and hysteretic effects of temperature on daily RD hospitalizations. The model is defined as follows: Log[ E(Y t ) ] = ɑ + β (Temp t,l ) + ns (Humidity t , df ) + ns (Sun t, df ) + ns (Windspeed t, df ) + ns(Time, df ) + DOW + Holiday t where t is the number of days observed, E(Y t ) is the expected number of hospital admissions per day for RD on day t, log represents the logarithmic form of E(Yt), Alpha represents the intercept, Temp t, l is the DLNM "cross basis" matrix of average daily air temperature, l is the lag days, βis the Tempt coefficient vector of l, ns is the DLNM model that illustrates the natural cubic spline function of nonlinear variables, Time notifies long-term trends and seasonal trends, df is degrees of freedom, DOW t is a dummy variable indicating the day of the week, and Holiday t is a dummy variable indicating whether t days are public holidays (public holidays: Holiday = 1; Nonpublic holidays: Holiday = 0). Many studies have shown that long-term trends, seasonality and other meteorological variables (including relative humidity, sunshine duration and wind speed) are important risk factors for RD (Li,et al. 2022). Therefore, this study uses a natural cubic spline function to control these factors to clarify the independent influence of ambient temperature on RD. The degree of freedom (df) of the model was selected according to the Akaike information criterion (AIC) (Gasparrini ,et al.2014). A df of 7 per year was chosen as the time variable to account for long-term trends and seasonality. Meteorological variables such as relative humidity, sunshine hours, and wind speed were controlled using 3-df. Additionally, 3-df natural cubic splines were applied for both temperature and hysteresis, with a maximum lag of 7 days selected to analyze the influence of ambient temperature on RD admissions. Average daily temperature was selected as the temperature index, as it provides a more comprehensive representation of temperature-health relationships compared to maximum or minimum temperatures (Wang,et al. 2020 ). Relative risk and 95% confidence intervals were used to assess the relationship between temperature and admission risk. In this study, 10°C (10°C is close to the median temperature during the study period) was first used as the reference temperature variable, and the influence of different temperatures and different lag days on the number of RD inpatients was studied by drawing a three-dimensional graph. The cumulative lag effect of the average temperature on different lag days (lag0, lag0-3, lag0-5, and lag0-7) was also studied. The cumulative effect of extreme temperature on the number of RDs in patients with different lag days was also studied. The RRs and 95% CIs of admissions for extreme low temperature (defined as the 1st percentile of the temperature distribution), moderate low temperature (defined as the 10th percentile), extreme high temperature (defined as the 99th percentile) and moderate high temperature (defined as the 90th percentile) were reported, where extreme low and moderate low temperature were relative to the 25th percentile, and extreme and moderate high temperature were relative to the 75th percentile. To explore potential differences in temperature-related risks, hospitalized patients were stratified into two subgroups based on gender (men vs. women) and age (< 65 years vs. ≥65 years). The effects of low and high temperatures on each subgroup were analyzed to determine their susceptibility to cold and heat-related RD admissions. The "cold effect" was defined as the 95% CI and RR for RD admission at both extreme and moderate low temperature. Conversely, the "heat effect" was defined as the 95% CI and RR for RD admissions at extreme and moderate high temperature. Air pollutants were not included in the analysis to minimize the risk of model overfitting. Evidence indicates that air pollutant concentrations are generally low in non-industrial rural areas(Ebi et al.2004), suggesting a limited direct impact on respiratory diseases in this specific setting. This approach ensures unbiased effect estimation, focusing on the primary influence of temperature on respiratory health. To ensure the robustness of our findings, sensitivity analyses were conducted by adjusting the degrees of freedom for the time variables (6, 8, and 9) to evaluate the consistency of the heat and cold effects across the lag period (Table S1 ). Additionally, sensitivity analyses were performed by varying the maximum lag days and relative humidity (Figures S1 and S2) to assess the responsiveness of the model to these factors. Data analysis was performed using R software, employing the DLNM package for distributed lag nonlinear model analyses and Spearman correlation analysis to assess associations. Statistical significance was set at P < 0.05. Results Table 1 shows the distribution and main meteorological variables of RD patients in Wuwei rural from January 1, 2011, to December 31, 2015. A total of 58,391 patients with RD were collected in the rural of Wuwei city, with an average of 32 cases per day, including 31,478 male patients and 26,913 female patients. In addition, 25,624 adult(< 65 years) and 32,767 elderly patients(≥ 65 years) were included. During the study period, the average daily temperature was 9.62 ± 11.13°C, and the average daily temperature ranged from − 18.40°C to 28.90°C. The average relative humidity, wind speed and sunshine duration were 44.85 ± 16.99%, 1.74 ± 0.64% and 8.14 ± 3.42%, respectively. Table 1 Summary statistics of RD cases and meteorological variables in the rural of Wuwei from 2011–2015 Variable x̅ x̅ ± SD Min P25 Med P75 Max All 32.12 32.12 ± 16.14 1.00 20.00 28.00 42.00 95.00 Sex Male 17.36 17.36 ± 9.35 1.00 10.00 15.00 23.00 54.00 Female 14.76 14.76 ± 7.77 1.00 9.00 13.00 19.00 46.00 Age < 65 years 14.32 14.32 ± 8.88 0.00 8.00 12.00 18.00 52.00 ≥ 65 years 17.80 17.80 ± 10.40 0.00 10.00 15.00 24.00 62.00 Meteorology Temperature(℃) 9.62 9.62 ± 11.13 −18.40 0.00 11.25 19.40 28.90 Relative humidity(%) 44.85 44.85 ± 16.99 11.00 32.00 43.00 56.00 98.00 Windspeed(m/s) 1.74 1.74 ± 0.64 0.40 1.30 1.60 2.00 5.30 Sunshine hours(h) 8.14 8.14 ± 3.42 0.00 6.90 8.60 10.50 14.00 Figure 2 shows the Spearman correlation coefficients between the meteorological variables and RD inpatients. The correlation coefficients between RD inpatients and temperature, humidity, wind speed and sunshine hours were − 0.46, -0.16, -0.06, and − 0.02 ( P < 0.05), respectively, indicating a negative correlation. Figure 3 shows the time series and temperature series of daily RD hospitalizations from January 1, 2011, to December 31, 2015. The patients and RD temperature in the rural of Wuwei showed seasonal changes, with obvious seasonal trends. With increasing temperature, the number of RD hospitalized patients decreased, and with the change in temperature from warm to cold, the number of RD hospitalized patients increased. Figures 4 and 5 illustrate the effect of average temperature on daily RD hospitalizations over a 7-day lag. Figure 4 shows a nonlinear correlation between the average temperature and the number of hospitalizations. The damage caused by low temperature gradually increased with increasing lag days, and the damage effect reached a maximum at -20°C and 7 days after lag (RR = 1.313, 95% CI: 1.257–1.371). The effect of high temperature mainly manifested as a protective effect, which was most obvious on the same day (lag0), and the protective effect manifested within a 5-day lag. Figure 5 more intuitively shows the impact of average temperature on daily RD admissions. Figure S3 shows the effect of temperature on the RD admission day (lag0) and the cumulative effect at lag0-1, lag0-3, lag0-5, and lag0-7. For the effect of low temperature, the cumulative risk of hospitalization was greater with a lag of 0–7 days (RR = 3.162, 95% CI: 2.918–3.427) than with other lag days. The effect of high temperature was protective, and the cumulative effect was most obvious at 32°C, with a lag of 0–5 days (RR = 0.683, 95% CI: 0.596–0.783). Table 2 shows the cumulative effects of low temperature and high temperature on RD admissions with different lag days compared to their respective reference temperatures. For the cold effect, the RR of extreme cold and moderate cold at 0–7 day lags were 1.822 (95% CI: 1.732–1.917) and 1.217 (95% CI: 1.200-1.233), respectively. For the heat effect, the RR for extreme heat with a lag of 0–3 days was 0.869 (95% CI: 0.809–0.932), and the RR for moderate heat with a lag of 0–5 days was 0.928 (95% CI: 0.903–0.954). For the effects caused by low temperatures, the RR of RD admissions at a lag of 7 days was greater than that at other lag days. High temperature had a protective effect on RD at admission. Table 2 The relative risk with 95% Confidence Intervals of cold and heat effects of low and high temperatures on RD Hospitalizations along lag day Lag Extreme cold 1 Moderate cold 2 Moderate heat 3 Extreme heat 4 0 1.019(0.995, 1.044) 0.999(0.990, 1.009) 0.975(0.965, 0.986) 0.944(0.919, 0.971) 0–1 1.055(1.012, 1.101) 1.006(0.990, 1.022) 0.956(0939, 0.974) 0.905(0.862, 0.950) 0–3 1.187(1.118, 1.261) 1.041(1.019, 1.064) 0.933(0.908, 0.959) 0.869(0.809, 0.932) 0–5 1.424(1.346, 1.507) 1.109(1.088, 1.131) 0.928(0.903, 0.954) 0.884(0.821, 0.951) 0–7 1.822(1.732, 1.917) 1.217(1.200, 1.233) 0.942(0.916, 0.969) 0.954(0.883, 1.030) 1 The first percentile of temperature (-13.4℃) relative to the 25th percentile of temperature(0.3℃) 2 The 10th percentile of temperature (--5.7℃) relative to 25th percentile of temperature(0.3℃) 3 The 90th percentile of temperature (23.3℃) relative to 75th percentile of temperature(19.4℃) 4 The 99th percentile of temperature (27℃) relative to 75th percentile of temperature(19.4℃) Figure S4 shows the cold and heat effects of low and high temperatures on different lag days. During the whole lag period, the RR caused by extreme cold and moderate cold increased gradually and reached a maximum when the lag was 7 days, and the cold effect caused injury to RD admission. Moderate and extreme heat had a protective effect on RD admissions, and the RR decreased first and then increased. Table 3 shows the RR for high and low temperatures stratified by sex and age. Low temperature had a damaging effect, and the relative risk of damage in males was greater than that in females. The cumulative RRs (95% CIs) of extreme cold and moderate cold ranged from 1.023 (0.987, 1.060) to 1.860 (1.727, 2.004) and from 0.999 (0.986, 1.013) to 1.233 (1.208, 1.258), respectively, in the male group. The cumulative RRs (95% CIs) for extreme cold and moderate cold ranged from 1.009 (0.976, 1.043) to 1.773 (1.654, 1.901) and from 0.996 (0.984, 1.009) to 1.201 (1.179, 1.224), respectively, in the female group. For high temperatures, the relative risk for females is lower than that for males. The cumulative RRs (95% CIs) of extreme and moderate heat ranged from 0.838 (0.759, 0.924) to 0.935 (0.900, 0.972) and from 0.906 (0.872, 0.942) to 0.971 (0.960, 0.986), respectively, in the female group. The cumulative RRs (95% CIs) for extreme and moderate heat ranged from 0.900 (0.812, 0.995) to 0.995 (0.890, 1.111) and from 0.948 (0.911, 0.986) to 0.980 (0.965, 0.995), respectively, in the male group. Male were more susceptible to RD than female at low temperature. The protective effect of high temperature on the incidence of RD in females was more obvious at admission. Table 3 Cumulative effect of different temperatures for age-specific and gender-specific patient of RD in Wuwei Variables Temperature Lag0 lag0-1 Lag0−3 Lag0−5 Lag0−7 male −13.4 1.023(0.987, 1.060) 1.063(0.999, 1.131) 1.204(1.102, 1.314) 1.450(1.335, 1.575) 1.860(1.727, 2.004) −5.7 0.999(0.986, 1.013) 1.006(0.983, 1.030) 1.044(1.010, 1.078) 1.117(1.086, 1.149) 1.233(1.208, 1.258) 23.3 0.980(0.965, 0.995) 0.965(0.939, 0.991) 0.948(0.911, 0.986) 0.948(0.911, 0.986) 0.965(0.927, 1.006) 27 0.955(0.918, 0.994) 0.924(0.862, 0.990) 0.900(0.812, 0.995) 0.921(0.829, 1.024) 0.995(0.890, 1.111) Female −13.4 1.009(0.976, 1.043) 1.036(0.978, 1.097) 1.153(1.062, 1.252) 1.379(1.276, 1.491) 1.773(1.654, 1.901) −5.7 0.996(0.984, 1.009) 1.000(0.979, 1.022) 1.032(1.001, 1.063) 1.096(1.068, 1.125) 1.201(1.179, 1.224) 23.3 0.971(0.960, 0.986) 0.948(0.924, 0.973) 0.918(0.883, 0.953) 0.906(0.872, 0.942) 0.913(0.878, 0.950) 27 0.935(0.900, 0.972) 0.888(0.830, 0.950) 0.838(0.759, 0.924) 0.840(0.758, 0.930) 0.895(0.804, 0.996) adult −13.4 1.163(1.090, 1.240) 1.329(1.188, 1.488) 1.651(1.403, 1.943) 1.915(1.623, 2.261) 2.076(1.745, 2.470) −5.7 1.045(1.024, 1.065) 1.085(1.049, 1.123) 1.153(1.098, 1.210) 1.198(1.144, 1.254) 1.219(1.167, 1.273) 23.3 0.984(0.967, 1.002) 0.973(0.943, 1.003) 0.959(0.918, 1.002) 0.959(0.918, 1.003) 0.973(0.932, 1.016) 27 0.953(0.913, 0.996) 0.923(0.854, 0.996) 0.903(0.807, 1.011) 0.939(0.836, 1.053) 1.035(0.920, 1.164) older −13.4 1.211(1.094, 1.341) 1.447(1.210, 1.729) 1.983(1.529, 2.571) 2.574(1.973, 3.358) 3.166(2.399, 4.179) −5.7 1.045(1.012, 1.078) 1.088(1.030, 1.150) 1.174(1.086, 1.268) 1.254(1.165, 1.350) 1.327(1.237, 1.423) 23.3 0.976(0.948, 1.004) 0.955(0.908, 1.004) 0.925(0.860, 0.994) 0.907(0.843, 0.975) 0.902(0.838, 0.970) 27 0.947(0.881, 1.018) 0.907(0.799, 1.029) 0.861(0.716, 1.036) 0.857(0.708, 1.037) 0.893(0.732, 1.090) For low temperature, the RR of elderly patients was greater than that of adult patients. The cumulative RRs (95% CIs) of extreme and moderate cold ranged from 1.163 (1.090, 1.240) to 2.076 (1.745, 2.470) and from 1.045 (1.024, 1.065) to 1.219 (1.167, 1.273), respectively, in the adult group. The cumulative RRs (95% CIs) for extreme cold and moderate cold ranged from 1.211 (1.094, 1.341) to 3.166 (2.399, 4.179) and from 1.045 (1.012, 1.078) to 1.327 (1.237, 1.423), respectively, in the elderly group. For high temperature, extreme heat and moderate heat, the cumulative RRs (95% CIs) ranged from 0.903 (0.807, 1.011) to 1.035 (0.920, 1.164) and from 0.959 (0.918, 1.002) to 0.984 (0.967, 1.002), respectively, in the adult group. The cumulative RRs (95% CIs) of extreme and moderate heat ranged from 0.857 (0.708, 1.037) to 0.947 (0.881, 1.018) and from 0.902 (0.838, 0.970) to 0.976 (0.948, 1.004), respectively, in the elderly group. Figure S5 - S8 shows a subgroup analysis of the cold-heat effect, with male and older people being more susceptible to RD at low temperatures. Discussion This study, conducted in rural Wuwei, China, delves into the intricate relationship between ambient temperature and RD hospitalizations using a quasi-Poisson regression model combined with a DLNM. Our findings highlight the significant impact of temperature on RD admissions, revealing both damaging and protective effects. The study confirms that low temperatures significantly increase RD hospitalizations, with the maximum effect observed at -20°C with a 7-day lag (RR = 1.313, 95% CI: 1.257–1.371). Li et al. (2022) also reported that low temperature has the greatest damaging effect on the maximum lag day (lag21). A study by Tran et al. ( 2022 )showed that the damage effect of low temperature was greatest when the lag time was 1 day (RR = 1.39, 95% CI: 1.26–1.54), after which the RR decreased with increasing lag time. This study also revealed that high temperature had a protective effect within 5 days of lag and a weak damaging effect after 7 days of lag (RR = 1.030, 95% CI = 0.977–1.086). Nhung et al.(2023)reported that heat waves had a damaging effect on the incidence of RD in the Ninh Thuan area, and the damaging effect was greatest when the delay was 2 days (RR = 1.083, 95% CI: 1.006–1.166). This suggests that there are regional differences in the delayed effect of hypothermia on RD admission. For exposure to low temperatures, the cumulative RR for RD admission with a lag of 0–7 days for extreme cold was 1.822 (95% CI: 1.732–1.917). Feng et al. (2023)reported that the cumulative RR of cold waves for RD admissions in Shanxi Province during the whole lag period was 1.232 (95% CI: 1.090–1.394). Similar results were also reported for Shanghai (RR: 1.320; 95% CI: 1.240, 1.400), Beijing (RR: 1.394; 95% CI: 1.193, 1.630), and Hefei (RR: 1.413; 95% CI: 1.131, 1.764) (Liu et al.2021a;Liu et al.2021b;Ma et al.2011). This study also revealed that the cumulative RR of moderate cold with a lag of 0–7 days was 1.217 (95% CI: 1.200-1.233), which was consistent with the report by Zhang et al.(2023). Many studies have explored the mechanism underlying the influence of low environmental temperature on RD, in which inflammation and oxidative stress may play important roles. Low temperature can stimulate the infiltration of inflammatory cells such as white blood cells, neutrophils and lymphocytes in lung tissue and the secretion of various cytokines, thus causing lung tissue inflammation, mucus secretion and small airway obstruction (Davis et al.2007; Geng et al.2019; Sabnis et al.2008). Low temperature can also stimulate the production of reactive oxygen species (ROS) and nitric oxide (NO) in lung tissue and disturb redox homeostasis in lung tissue by inhibiting the activation of the nuclear factor 2-related factor 2 (Nrf2) signaling pathway (Sun et al.2014;Sun et al.2016;Zhang et al.2018). Recent studies have also shown that short-term low-temperature exposure can cause inflammation, lipid and DNA oxidative damage, and promote the release of tetraiodothyronine (T4). Oxidative damage to DNA and some hormones may be involved in the effects of temperature on lung function (Qiu et al.2024). This study revealed that heat effect had a protective effect on RD at admission. A study by Iniguez et al. (Iñiguez et al.2021) on the Spanish population revealed that high temperature had no effect on the incidence of RD, which was consistent with the findings of the present study. However, a study by Ragettli et al. (Ragettli et al. 2019) in Switzerland revealed that heat waves were significantly associated with an increased risk of hospitalization for cardiopulmonary diseases, and the number of days of heat waves increased the risk of hospitalization for pneumonia by 20.5% (6.9% ~ 35.9%). In addition, studies have reported that high temperatures in Shanghai (Ma et al.2011), Jiangsu (Ma et al. 2020 ), Guangdong (Yang et al.2015) and other places in China are also positively correlated with the incidence or death of RD. The reason for this regional difference may be related to the local climate type, local economic development, population age distribution, air pollution and other factors. In addition, the stratified analysis of gender and age in this study revealed that both age and gender could affect the temperature-related admission risk of RD patients. At low temperature, males are more susceptible to RD than females are. Feng et al. ( 2021 ) conducted a study in Lanzhou and reported that low temperature was associated with a greater risk of RD admission in males than in females. However, Zhou et al. ( 2017 ) and Tran et al. ( 2022 ) reported that females were more susceptible to RD under low-temperature conditions. Other studies have also shown that age and sex are not associated with temperature-related RD admission risk (Requia et al.2023). Therefore, the risk of admission to the hospital for temperature-related RD differs by sex. On the one hand, this may be related to the different physical characteristics and activity habits of men and women. On the other hand, this may also be related to the fact that men are more sensitive to temperature changes and are more exposed to cold weather due to their social roles and personalities (Teng et al.2021). This study also revealed that the RR in older patients was greater than that in adult patients at low temperatures. This is consistent with what Zhang et al. ( 2023 ) reported. These results may also be related to differences in the health status of people in different age groups. With the aging of the body, body temperature regulation and physiological function, such as a decline in sweat gland function and a decrease in alveolar elasticity, decline. Moreover, elderly individuals may suffer from multiple underlying respiratory diseases at the same time. Thus, the risk of hospital admission due to RD is increased in elderly patients. Conclusion This study, using a DLNM, revealed a significant impact of ambient temperature on RD hospital admissions in rural Wuwei, China. Extreme and moderate cold significantly increased RD admissions, particularly within 0–7 day lags of exposure. Notably, males and older adults (≥ 65 years) displayed heightened vulnerability to low temperatures compared to females and adults (< 65 years). Conversely, high temperatures exhibited a protective effect on RD hospitalizations effect across all demographic groups. These findings highlight the need for targeted interventions, particularly for vulnerable populations, to mitigate the burden of RDs. By promoting awareness of temperature-related health risks, implementing preventive measures, and ensuring accessible healthcare, we can safeguard public health in regions with similar climatic conditions. Abbreviations RD, Respiratory Disease; CI, confidence interval; DLNM, distributed lag nonlinear model;RR, relative risk Declarations Author contribution Guorong Chai contributed to the conception or design of the work. What's more, Jirong Wu and Guangyu Zhai contributed to the acquisition, analysis, or interpretation of data for the work. In addition, Jirong Wu drafted the manuscript and critically revised the manuscript. Funding This work was supported by the Natural Science Foundation of Gansu Province(No.17JR5RA260) Availability of data and materials The datasets generated and/or analyzed during the current study are not publicly available due to confidentiality agreements but are available from the corresponding author upon reasonable request. Ethical approval This study involves human participants but is not a survey or observationof human behavior. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. For this type of study, formal consent is not required. This study complies with the current laws in China. Consent for publication All authors consent to publish this article in International Journal of Biometeorology. 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Proceedings of the 34th Annual Meeting of Chinese Meteorological Society S15 Sub-meeting on Climate and Environmental Change and Human Health, Zhengzhou, pp 400-402 Supplementary Files FigureS1.tif Figure S1 Three-Dimension plot for relative risks of RD hospitalizations and temperature by different lag period FigureS2.tif FigureS2 Association between temperature and hospitalizations with adjustment of the df (3,5,6) for relative humidity FigureS3.tif Figure S3 Cumulative effect of temperature on hospitalizations along lags 0, 0-1, 0-3, 0-5, 0-7 days respectively FigureS4.tif Figure S4 The cold and hot effect of low and high temperature along lag days FigureS5.tif Figure S5 The cold and hot effect of low and high temperature on male along lag days FigureS6.tif Figure S6 The cold and hot effect of low and high temperature on female along lag days FigureS7.tif Figure S7 The cold and hot effect of low and high temperature on elderly along lag days FigureS8.tif Figure S8 The cold and hot effect of low and high temperature on adult along lag days TableS1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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humidity\u003c/p\u003e","description":"","filename":"FigureS2.tif","url":"https://assets-eu.researchsquare.com/files/rs-6113731/v1/e60ef712cfe0bc8ddeb049af.tif"},{"id":78817496,"identity":"4e90e2b9-de01-4544-80d6-8ed4445bff27","added_by":"auto","created_at":"2025-03-19 10:49:44","extension":"tif","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":8533064,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S3\u003c/strong\u003e Cumulative effect of temperature on hospitalizations along lags 0, 0-1, 0-3, 0-5, 0-7 days respectively\u003c/p\u003e","description":"","filename":"FigureS3.tif","url":"https://assets-eu.researchsquare.com/files/rs-6113731/v1/60e342bd200b07082edbf746.tif"},{"id":78817495,"identity":"454e8c64-7ec4-4b54-98c0-3b9a273c376a","added_by":"auto","created_at":"2025-03-19 10:49:44","extension":"tif","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":6600816,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S4\u003c/strong\u003e The cold and hot effect of low and high temperature along lag days\u003c/p\u003e","description":"","filename":"FigureS4.tif","url":"https://assets-eu.researchsquare.com/files/rs-6113731/v1/ebaa8f8f7c12be85a714a658.tif"},{"id":78817273,"identity":"70cf509f-bbef-449a-ae45-e8abc470d65b","added_by":"auto","created_at":"2025-03-19 10:41:44","extension":"tif","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":6202612,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S5\u003c/strong\u003e The cold and hot effect of low and high temperature on male along lag days\u003c/p\u003e","description":"","filename":"FigureS5.tif","url":"https://assets-eu.researchsquare.com/files/rs-6113731/v1/28d62f9381ed0596dd1ed555.tif"},{"id":78817287,"identity":"49c0d11b-a1d6-47c2-a5dd-d9f2b8e6639a","added_by":"auto","created_at":"2025-03-19 10:41:45","extension":"tif","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":6255008,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S6\u003c/strong\u003e The cold and hot effect of low and high temperature on female along lag days\u003c/p\u003e","description":"","filename":"FigureS6.tif","url":"https://assets-eu.researchsquare.com/files/rs-6113731/v1/1a87b7334c9a21994084574d.tif"},{"id":78817502,"identity":"568f976a-01e0-4307-a325-64ed9051410e","added_by":"auto","created_at":"2025-03-19 10:49:44","extension":"tif","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":6053020,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S7\u003c/strong\u003e The cold and hot effect of low and high temperature on elderly along lag days\u003c/p\u003e","description":"","filename":"FigureS7.tif","url":"https://assets-eu.researchsquare.com/files/rs-6113731/v1/143ac76945f8fc96749a8dd9.tif"},{"id":78817280,"identity":"73f1c35f-3ec7-448e-8581-0759e7ff3c31","added_by":"auto","created_at":"2025-03-19 10:41:44","extension":"tif","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":6134688,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S8\u003c/strong\u003e The cold and hot effect of low and high temperature on adult along lag days\u003c/p\u003e","description":"","filename":"FigureS8.tif","url":"https://assets-eu.researchsquare.com/files/rs-6113731/v1/9bda2e5086c3cfe6f7d71788.tif"},{"id":78818146,"identity":"dd88c686-52ff-45cc-be67-f34da48ab3f5","added_by":"auto","created_at":"2025-03-19 10:57:44","extension":"docx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":18254,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6113731/v1/3973069bc886cbf9afe5962c.docx"}],"financialInterests":"","formattedTitle":"Impact of Ambient Temperature on Respiratory Disease Hospitalization in Rural Wuwei, Northwest China","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRespiratory diseases (RD) are common and frequently occurring diseases that seriously endanger human health. Global data from 2017 revealed that chronic RD resulted in approximately 3.91\u0026nbsp;million deaths, representing a 19.0% increase since 1990 and highlighting its substantial impact on global health. The incidence of chronic RD among Chinese residents surged from 5.808\u0026nbsp;million in 1990 to 11.975\u0026nbsp;million in 2019, underscoring the increasing public health concern(sheng et al.2024). The prevention and treatment of RD has become an important public health problem. Various risk factors contribute to the development of RD, including bacterial and viral infections, genetic predispositions, tobacco and occupational exposures, socioeconomic disparities, air pollution, and meteorological conditions (Cohen et al.1999; D'Ovidio et al.2023; Saarentaus et al.2023). The undeniable effects of ongoing global climate change are increasingly evident in the form of frequent and intense extreme weather events, such as heat waves and cold spells, which impact overall health (Wang et al.2023). These shifts in weather patterns have a direct impact on human health, with ambient temperature emerging as a primary determinant of well-being. Research in China revealed that nonoptimal temperatures contribute significantly to mortality, accounting for 14.33% of nonaccidental deaths, with cold temperatures identified as the leading cause (Chen et al. 2018).\u003c/p\u003e \u003cp\u003eThe relationship between ambient temperature and RD has garnered increasing attention in recent years, with studies revealing complex associations between temperature extremes and RD incidence or mortality. Numerous studies have demonstrated that both extremely low and high temperatures increase the risk of RD-related deaths, often following \"U,\" \"V,\" or \"J\" shaped exposure-response curves (Guo et al.2022; Jacobson et al.2021; Shao et al.2021). Studies have also analyzed the relationship between ambient temperature and the number of outpatient or emergency visits from RD. Ma et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and Feng et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) reported that both high and low temperatures could increase the number of emergency visits to RD. Chai et al. (2020)reported that ambient temperature was correlated with the number of outpatient visits to RD. Although mortality and outpatient and emergency department visits are frequently studied health outcomes, external temperature does not directly cause death, so it can only reflect a small part of the health effects of environmental exposure. RD is the most common cause of hospitalization worldwide, and reports show that the hospitalization rate of RD increased from 4.2\u0026permil; in 2003 to 13.3\u0026permil; in 2013 (Lu et al.2019). Therefore, studying the impact of ambient temperature on RD hospitalization can provide a theoretical basis for health policy formulation and the optimization of medical resource allocation. However, the current research evidence is mostly concentrated in developed countries or regions, and there is relatively little research evidence in low- and middle-income countries(Bergmann et al.2023;Gronlund et al.2014;Lim et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In China, research is also concentrated mainly in economically developed areas (Jia et al.2022;Lei et al.2022;Zhu et al.2022). In addition, there are great differences in the natural environment, climate, social economy, population structure, living habits and health habits in different regions (Zhai et al.2022). The influence of ambient temperature on the RD hospitalization rate is different in different regions and different populations. Given these limited studies, further research is needed to clarify the relationship between ambient temperature and RD hospitalization.\u003c/p\u003e \u003cp\u003eCompared with their urban counterparts, rural populations, particularly farmers, face unique health challenges. Limited access to essential amenities such as safe drinking water, central heating, and air conditioning, coupled with the occupational demands of outdoor labor, expose them to frequent and extreme temperature fluctuations (Bai et al.2016). Additionally, rural areas often suffer from limited healthcare infrastructure and access to medical services(Benmarhnia et al.2015). These factors make rural populations, including those in suburban areas, particularly vulnerable to the impacts of ambient temperature on their health. Therefore, it is necessary to explore the influence of ambient temperature on the RD hospitalization of rural residents in the China. This study investigated the association between average ambient temperature and RD hospitalizations among rural residents in Wuwei city, China, from 2011 to 2015. Employing a distributed lag nonlinear model (DLNM), we analyzed the nonlinear exposure‒response relationship and exposure lag effect of temperature on RD hospitalizations. Subgroup analysis further explored these relationships across different age and gender groups. The findings of this study are crucial for informing meteorological policies aimed at protecting the health of vulnerable populations in rural China.\u003c/p\u003e"},{"header":"Data and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy area\u003c/h2\u003e \u003cp\u003eWuwei (101\u0026deg;41'E-104\u0026deg;16'E, 36\u0026deg;29'N-39\u0026deg;27'N), which is situated in the Shiyang River basin of Gansu Province, lies at the convergence of the Loess Plateau, Qinghai-Tibet Plateau, and Mongolian-Xin Plateau (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The city boasts a temperate continental arid climate with distinct seasons, experiencing cold winters and hot summers, coupled with substantial day-to-night and annual temperature variations. Precipitation is low and unevenly distributed, contributing to a dry climate with high evaporation rates. This environment results in strong solar radiation and abundant sunlight. Wuwei, with a permanent population of 1.445\u0026nbsp;million (713,000 urban, 732,000 rural), is the most populous city in the Hexi Corridor. Despite this, Wuwei is an economically underdeveloped area, with a per capita disposable income of 14,859 yuan for rural residents.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eThis study utilized data from the New Rural Cooperative Medical Insurance Database (NRCMS) of Gansu Province to analyze respiratory disease (RD) hospitalizations among rural residents in Wuwei. The NRCMS, one of China's primary medical insurance schemes, covered 98.84% of farmers in Wuwei City by December 2015. Due to data availability limitations, the study period was January 1, 2011, to December 31, 2015. The data included patient demographics (gender, age, residence) and hospital admission information, with disease classifications coded according to the International Classification of Diseases, 10th Revision (ICD-10). The ICD-10 code for RD in this study was J00-J99. To explore potential differences in temperature-related risk, the population was stratified by gender (male vs. female) and age (\u0026lt;\u0026thinsp;65 vs. \u0026ge;65 years).\u003c/p\u003e \u003cp\u003eMeteorological data, encompassing average temperature, relative humidity, pressure, wind speed, precipitation, and sunshine duration, were obtained from the China Meteorological Science Data Sharing Service Network (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://data.cma.gov.cn\u003c/span\u003e\u003cspan address=\"http://data.cma.gov.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eNumerous studies have demonstrated that ambient temperature's impact on human health exhibits both non-linearity and a time lag (Yezli et al.2023). The DLNM effectively captures this complex relationship by integrating the traditional exposure-response relationship with a hysteresis response, allowing for a more comprehensive understanding of the effects. Given that daily hospitalizations for RD represent a low probability event with a distribution resembling a Poisson distribution; this study employed a DLNM model coupled with a quasi-Poisson regression model. This approach enabled us to evaluate the non-linear and hysteretic effects of temperature on daily RD hospitalizations. The model is defined as follows:\u003c/p\u003e \u003cp\u003eLog[\u003cem\u003eE(Y\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e)\u003c/em\u003e] = \u003cem\u003eɑ\u003c/em\u003e + \u003cem\u003eβ\u003c/em\u003e(Temp\u003csub\u003et,l\u003c/sub\u003e)\u0026thinsp;+\u0026thinsp;\u003cem\u003ens\u003c/em\u003e(Humidity\u003csub\u003et\u003c/sub\u003e, \u003cem\u003edf\u003c/em\u003e)\u0026thinsp;+\u0026thinsp;\u003cem\u003ens\u003c/em\u003e(Sun\u003csub\u003et,\u003c/sub\u003e\u003cem\u003edf\u003c/em\u003e)\u0026thinsp;+\u0026thinsp;\u003cem\u003ens\u003c/em\u003e(Windspeed\u003csub\u003et,\u003c/sub\u003e\u003cem\u003edf\u003c/em\u003e)\u0026thinsp;+\u0026thinsp;ns(Time, \u003cem\u003edf\u003c/em\u003e)\u0026thinsp;+\u0026thinsp;DOW\u0026thinsp;+\u0026thinsp;Holiday\u003csub\u003et\u003c/sub\u003e\u003c/p\u003e \u003cp\u003ewhere t is the number of days observed, E(Y\u003csub\u003et\u003c/sub\u003e) is the expected number of hospital admissions per day for RD on day t, log represents the logarithmic form of E(Yt), Alpha represents the intercept, Temp\u003csub\u003et, l\u003c/sub\u003e is the DLNM \"cross basis\" matrix of average daily air temperature, l is the lag days, βis the Tempt coefficient vector of l, ns is the DLNM model that illustrates the natural cubic spline function of nonlinear variables, Time notifies long-term trends and seasonal trends, df is degrees of freedom, DOW\u003csub\u003et\u003c/sub\u003e is a dummy variable indicating the day of the week, and Holiday\u003csub\u003et\u003c/sub\u003e is a dummy variable indicating whether t days are public holidays (public holidays: Holiday\u0026thinsp;=\u0026thinsp;1; Nonpublic holidays: Holiday\u0026thinsp;=\u0026thinsp;0).\u003c/p\u003e \u003cp\u003eMany studies have shown that long-term trends, seasonality and other meteorological variables (including relative humidity, sunshine duration and wind speed) are important risk factors for RD (Li,et al. 2022). Therefore, this study uses a natural cubic spline function to control these factors to clarify the independent influence of ambient temperature on RD. The degree of freedom (df) of the model was selected according to the Akaike information criterion (AIC) (Gasparrini ,et al.2014). A df of 7 per year was chosen as the time variable to account for long-term trends and seasonality. Meteorological variables such as relative humidity, sunshine hours, and wind speed were controlled using 3-df. Additionally, 3-df natural cubic splines were applied for both temperature and hysteresis, with a maximum lag of 7 days selected to analyze the influence of ambient temperature on RD admissions. Average daily temperature was selected as the temperature index, as it provides a more comprehensive representation of temperature-health relationships compared to maximum or minimum temperatures (Wang,et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Relative risk and 95% confidence intervals were used to assess the relationship between temperature and admission risk.\u003c/p\u003e \u003cp\u003eIn this study, 10\u0026deg;C (10\u0026deg;C is close to the median temperature during the study period) was first used as the reference temperature variable, and the influence of different temperatures and different lag days on the number of RD inpatients was studied by drawing a three-dimensional graph. The cumulative lag effect of the average temperature on different lag days (lag0, lag0-3, lag0-5, and lag0-7) was also studied. The cumulative effect of extreme temperature on the number of RDs in patients with different lag days was also studied. The RRs and 95% CIs of admissions for extreme low temperature (defined as the 1st percentile of the temperature distribution), moderate low temperature (defined as the 10th percentile), extreme high temperature (defined as the 99th percentile) and moderate high temperature (defined as the 90th percentile) were reported, where extreme low and moderate low temperature were relative to the 25th percentile, and extreme and moderate high temperature were relative to the 75th percentile.\u003c/p\u003e \u003cp\u003eTo explore potential differences in temperature-related risks, hospitalized patients were stratified into two subgroups based on gender (men vs. women) and age (\u0026lt;\u0026thinsp;65 years vs. \u0026ge;65 years). The effects of low and high temperatures on each subgroup were analyzed to determine their susceptibility to cold and heat-related RD admissions. The \"cold effect\" was defined as the 95% CI and RR for RD admission at both extreme and moderate low temperature. Conversely, the \"heat effect\" was defined as the 95% CI and RR for RD admissions at extreme and moderate high temperature.\u003c/p\u003e \u003cp\u003eAir pollutants were not included in the analysis to minimize the risk of model overfitting. Evidence indicates that air pollutant concentrations are generally low in non-industrial rural areas(Ebi et al.2004), suggesting a limited direct impact on respiratory diseases in this specific setting. This approach ensures unbiased effect estimation, focusing on the primary influence of temperature on respiratory health.\u003c/p\u003e \u003cp\u003eTo ensure the robustness of our findings, sensitivity analyses were conducted by adjusting the degrees of freedom for the time variables (6, 8, and 9) to evaluate the consistency of the heat and cold effects across the lag period (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Additionally, sensitivity analyses were performed by varying the maximum lag days and relative humidity (Figures \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and S2) to assess the responsiveness of the model to these factors. Data analysis was performed using R software, employing the DLNM package for distributed lag nonlinear model analyses and Spearman correlation analysis to assess associations. Statistical significance was set at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the distribution and main meteorological variables of RD patients in Wuwei rural from January 1, 2011, to December 31, 2015. A total of 58,391 patients with RD were collected in the rural of Wuwei city, with an average of 32 cases per day, including 31,478 male patients and 26,913 female patients. In addition, 25,624 adult(\u0026lt;\u0026thinsp;65 years) and 32,767 elderly patients(\u0026ge;\u0026thinsp;65 years) were included. During the study period, the average daily temperature was 9.62\u0026thinsp;\u0026plusmn;\u0026thinsp;11.13\u0026deg;C, and the average daily temperature ranged from \u0026minus;\u0026thinsp;18.40\u0026deg;C to 28.90\u0026deg;C. The average relative humidity, wind speed and sunshine duration were 44.85\u0026thinsp;\u0026plusmn;\u0026thinsp;16.99%, 1.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64% and 8.14\u0026thinsp;\u0026plusmn;\u0026thinsp;3.42%, respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary statistics of RD cases and meteorological variables in the rural of Wuwei from 2011\u0026ndash;2015\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ex̅\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ex̅\u003c/em\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP25\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMed\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP75\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e32.12\u0026thinsp;\u0026plusmn;\u0026thinsp;16.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e28.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e42.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e95.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e17.36\u0026thinsp;\u0026plusmn;\u0026thinsp;9.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e23.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e54.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e14.76\u0026thinsp;\u0026plusmn;\u0026thinsp;7.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e19.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e46.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;65 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e14.32\u0026thinsp;\u0026plusmn;\u0026thinsp;8.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e18.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e52.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;65 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e17.80\u0026thinsp;\u0026plusmn;\u0026thinsp;10.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e62.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeteorology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperature(℃)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e9.62\u0026thinsp;\u0026plusmn;\u0026thinsp;11.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;18.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e19.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e28.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelative humidity(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e44.85\u0026thinsp;\u0026plusmn;\u0026thinsp;16.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e43.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e56.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e98.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWindspeed(m/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSunshine hours(h)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e8.14\u0026thinsp;\u0026plusmn;\u0026thinsp;3.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e14.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the Spearman correlation coefficients between the meteorological variables and RD inpatients. The correlation coefficients between RD inpatients and temperature, humidity, wind speed and sunshine hours were \u0026minus;\u0026thinsp;0.46, -0.16, -0.06, and \u0026minus;\u0026thinsp;0.02 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), respectively, indicating a negative correlation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the time series and temperature series of daily RD hospitalizations from January 1, 2011, to December 31, 2015. The patients and RD temperature in the rural of Wuwei showed seasonal changes, with obvious seasonal trends. With increasing temperature, the number of RD hospitalized patients decreased, and with the change in temperature from warm to cold, the number of RD hospitalized patients increased.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigures \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003e illustrate the effect of average temperature on daily RD hospitalizations over a 7-day lag. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows a nonlinear correlation between the average temperature and the number of hospitalizations. The damage caused by low temperature gradually increased with increasing lag days, and the damage effect reached a maximum at -20\u0026deg;C and 7 days after lag (RR\u0026thinsp;=\u0026thinsp;1.313, 95% CI: 1.257\u0026ndash;1.371). The effect of high temperature mainly manifested as a protective effect, which was most obvious on the same day (lag0), and the protective effect manifested within a 5-day lag. Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003e more intuitively shows the impact of average temperature on daily RD admissions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e shows the effect of temperature on the RD admission day (lag0) and the cumulative effect at lag0-1, lag0-3, lag0-5, and lag0-7. For the effect of low temperature, the cumulative risk of hospitalization was greater with a lag of 0\u0026ndash;7 days (RR\u0026thinsp;=\u0026thinsp;3.162, 95% CI: 2.918\u0026ndash;3.427) than with other lag days. The effect of high temperature was protective, and the cumulative effect was most obvious at 32\u0026deg;C, with a lag of 0\u0026ndash;5 days (RR\u0026thinsp;=\u0026thinsp;0.683, 95% CI: 0.596\u0026ndash;0.783).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the cumulative effects of low temperature and high temperature on RD admissions with different lag days compared to their respective reference temperatures. For the cold effect, the RR of extreme cold and moderate cold at 0\u0026ndash;7 day lags were 1.822 (95% CI: 1.732\u0026ndash;1.917) and 1.217 (95% CI: 1.200-1.233), respectively. For the heat effect, the RR for extreme heat with a lag of 0\u0026ndash;3 days was 0.869 (95% CI: 0.809\u0026ndash;0.932), and the RR for moderate heat with a lag of 0\u0026ndash;5 days was 0.928 (95% CI: 0.903\u0026ndash;0.954). For the effects caused by low temperatures, the RR of RD admissions at a lag of 7 days was greater than that at other lag days. High temperature had a protective effect on RD at admission.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe relative risk with 95% Confidence Intervals of cold and heat effects of low and high temperatures on RD Hospitalizations along lag day\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLag\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtreme cold\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate cold\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerate heat\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eExtreme heat\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.019(0.995, 1.044)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.999(0.990, 1.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.975(0.965, 0.986)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.944(0.919, 0.971)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026ndash;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.055(1.012, 1.101)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.006(0.990, 1.022)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.956(0939, 0.974)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.905(0.862, 0.950)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.187(1.118, 1.261)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.041(1.019, 1.064)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.933(0.908, 0.959)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.869(0.809, 0.932)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.424(1.346, 1.507)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.109(1.088, 1.131)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.928(0.903, 0.954)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.884(0.821, 0.951)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026ndash;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.822(1.732, 1.917)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.217(1.200, 1.233)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.942(0.916, 0.969)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.954(0.883, 1.030)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003e1\u003c/sup\u003e The first percentile of temperature (-13.4℃) relative to the 25th percentile of temperature(0.3℃)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003e2\u003c/sup\u003e The 10th percentile of temperature (--5.7℃) relative to 25th percentile of temperature(0.3℃)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003e3\u003c/sup\u003eThe 90th percentile of temperature (23.3℃) relative to 75th percentile of temperature(19.4℃)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003e4\u003c/sup\u003e The 99th percentile of temperature (27℃) relative to 75th percentile of temperature(19.4℃)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e shows the cold and heat effects of low and high temperatures on different lag days. During the whole lag period, the RR caused by extreme cold and moderate cold increased gradually and reached a maximum when the lag was 7 days, and the cold effect caused injury to RD admission. Moderate and extreme heat had a protective effect on RD admissions, and the RR decreased first and then increased.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the RR for high and low temperatures stratified by sex and age. Low temperature had a damaging effect, and the relative risk of damage in males was greater than that in females. The cumulative RRs (95% CIs) of extreme cold and moderate cold ranged from 1.023 (0.987, 1.060) to 1.860 (1.727, 2.004) and from 0.999 (0.986, 1.013) to 1.233 (1.208, 1.258), respectively, in the male group. The cumulative RRs (95% CIs) for extreme cold and moderate cold ranged from 1.009 (0.976, 1.043) to 1.773 (1.654, 1.901) and from 0.996 (0.984, 1.009) to 1.201 (1.179, 1.224), respectively, in the female group. For high temperatures, the relative risk for females is lower than that for males. The cumulative RRs (95% CIs) of extreme and moderate heat ranged from 0.838 (0.759, 0.924) to 0.935 (0.900, 0.972) and from 0.906 (0.872, 0.942) to 0.971 (0.960, 0.986), respectively, in the female group. The cumulative RRs (95% CIs) for extreme and moderate heat ranged from 0.900 (0.812, 0.995) to 0.995 (0.890, 1.111) and from 0.948 (0.911, 0.986) to 0.980 (0.965, 0.995), respectively, in the male group. Male were more susceptible to RD than female at low temperature. The protective effect of high temperature on the incidence of RD in females was more obvious at admission.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCumulative effect of different temperatures for age-specific and gender-specific patient of RD in Wuwei\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTemperature\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLag0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003elag0-1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLag0\u0026minus;3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLag0\u0026minus;5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLag0\u0026minus;7\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;13.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.023(0.987, 1.060)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.063(0.999, 1.131)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.204(1.102, 1.314)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.450(1.335, 1.575)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.860(1.727, 2.004)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.999(0.986, 1.013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.006(0.983, 1.030)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.044(1.010, 1.078)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.117(1.086, 1.149)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.233(1.208, 1.258)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.980(0.965, 0.995)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.965(0.939, 0.991)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.948(0.911, 0.986)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.948(0.911, 0.986)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.965(0.927, 1.006)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.955(0.918, 0.994)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.924(0.862, 0.990)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.900(0.812, 0.995)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.921(0.829, 1.024)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.995(0.890, 1.111)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;13.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.009(0.976, 1.043)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.036(0.978, 1.097)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.153(1.062, 1.252)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.379(1.276, 1.491)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.773(1.654, 1.901)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.996(0.984, 1.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000(0.979, 1.022)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.032(1.001, 1.063)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.096(1.068, 1.125)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.201(1.179, 1.224)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.971(0.960, 0.986)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.948(0.924, 0.973)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.918(0.883, 0.953)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.906(0.872, 0.942)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.913(0.878, 0.950)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.935(0.900, 0.972)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.888(0.830, 0.950)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.838(0.759, 0.924)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.840(0.758, 0.930)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.895(0.804, 0.996)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eadult\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;13.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.163(1.090, 1.240)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.329(1.188, 1.488)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.651(1.403, 1.943)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.915(1.623, 2.261)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.076(1.745, 2.470)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.045(1.024, 1.065)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.085(1.049, 1.123)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.153(1.098, 1.210)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.198(1.144, 1.254)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.219(1.167, 1.273)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.984(0.967, 1.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.973(0.943, 1.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.959(0.918, 1.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.959(0.918, 1.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.973(0.932, 1.016)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.953(0.913, 0.996)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.923(0.854, 0.996)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.903(0.807, 1.011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.939(0.836, 1.053)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.035(0.920, 1.164)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eolder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;13.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.211(1.094, 1.341)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.447(1.210, 1.729)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.983(1.529, 2.571)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.574(1.973, 3.358)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.166(2.399, 4.179)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.045(1.012, 1.078)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.088(1.030, 1.150)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.174(1.086, 1.268)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.254(1.165, 1.350)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.327(1.237, 1.423)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.976(0.948, 1.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.955(0.908, 1.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.925(0.860, 0.994)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.907(0.843, 0.975)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.902(0.838, 0.970)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.947(0.881, 1.018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.907(0.799, 1.029)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.861(0.716, 1.036)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.857(0.708, 1.037)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.893(0.732, 1.090)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFor low temperature, the RR of elderly patients was greater than that of adult patients. The cumulative RRs (95% CIs) of extreme and moderate cold ranged from 1.163 (1.090, 1.240) to 2.076 (1.745, 2.470) and from 1.045 (1.024, 1.065) to 1.219 (1.167, 1.273), respectively, in the adult group. The cumulative RRs (95% CIs) for extreme cold and moderate cold ranged from 1.211 (1.094, 1.341) to 3.166 (2.399, 4.179) and from 1.045 (1.012, 1.078) to 1.327 (1.237, 1.423), respectively, in the elderly group. For high temperature, extreme heat and moderate heat, the cumulative RRs (95% CIs) ranged from 0.903 (0.807, 1.011) to 1.035 (0.920, 1.164) and from 0.959 (0.918, 1.002) to 0.984 (0.967, 1.002), respectively, in the adult group. The cumulative RRs (95% CIs) of extreme and moderate heat ranged from 0.857 (0.708, 1.037) to 0.947 (0.881, 1.018) and from 0.902 (0.838, 0.970) to 0.976 (0.948, 1.004), respectively, in the elderly group. Figure \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e-\u003cspan refid=\"MOESM8\" class=\"InternalRef\"\u003eS8\u003c/span\u003e shows a subgroup analysis of the cold-heat effect, with male and older people being more susceptible to RD at low temperatures.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study, conducted in rural Wuwei, China, delves into the intricate relationship between ambient temperature and RD hospitalizations using a quasi-Poisson regression model combined with a DLNM. Our findings highlight the significant impact of temperature on RD admissions, revealing both damaging and protective effects. The study confirms that low temperatures significantly increase RD hospitalizations, with the maximum effect observed at -20\u0026deg;C with a 7-day lag (RR\u0026thinsp;=\u0026thinsp;1.313, 95% CI: 1.257\u0026ndash;1.371). Li et al. (2022) also reported that low temperature has the greatest damaging effect on the maximum lag day (lag21). A study by Tran et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)showed that the damage effect of low temperature was greatest when the lag time was 1 day (RR\u0026thinsp;=\u0026thinsp;1.39, 95% CI: 1.26\u0026ndash;1.54), after which the RR decreased with increasing lag time. This study also revealed that high temperature had a protective effect within 5 days of lag and a weak damaging effect after 7 days of lag (RR\u0026thinsp;=\u0026thinsp;1.030, 95% CI\u0026thinsp;=\u0026thinsp;0.977\u0026ndash;1.086). Nhung et al.(2023)reported that heat waves had a damaging effect on the incidence of RD in the Ninh Thuan area, and the damaging effect was greatest when the delay was 2 days (RR\u0026thinsp;=\u0026thinsp;1.083, 95% CI: 1.006\u0026ndash;1.166). This suggests that there are regional differences in the delayed effect of hypothermia on RD admission.\u003c/p\u003e \u003cp\u003eFor exposure to low temperatures, the cumulative RR for RD admission with a lag of 0\u0026ndash;7 days for extreme cold was 1.822 (95% CI: 1.732\u0026ndash;1.917). Feng et al. (2023)reported that the cumulative RR of cold waves for RD admissions in Shanxi Province during the whole lag period was 1.232 (95% CI: 1.090\u0026ndash;1.394). Similar results were also reported for Shanghai (RR: 1.320; 95% CI: 1.240, 1.400), Beijing (RR: 1.394; 95% CI: 1.193, 1.630), and Hefei (RR: 1.413; 95% CI: 1.131, 1.764) (Liu et al.2021a;Liu et al.2021b;Ma et al.2011). This study also revealed that the cumulative RR of moderate cold with a lag of 0\u0026ndash;7 days was 1.217 (95% CI: 1.200-1.233), which was consistent with the report by Zhang et al.(2023). Many studies have explored the mechanism underlying the influence of low environmental temperature on RD, in which inflammation and oxidative stress may play important roles. Low temperature can stimulate the infiltration of inflammatory cells such as white blood cells, neutrophils and lymphocytes in lung tissue and the secretion of various cytokines, thus causing lung tissue inflammation, mucus secretion and small airway obstruction (Davis et al.2007; Geng et al.2019; Sabnis et al.2008). Low temperature can also stimulate the production of reactive oxygen species (ROS) and nitric oxide (NO) in lung tissue and disturb redox homeostasis in lung tissue by inhibiting the activation of the nuclear factor 2-related factor 2 (Nrf2) signaling pathway (Sun et al.2014;Sun et al.2016;Zhang et al.2018). Recent studies have also shown that short-term low-temperature exposure can cause inflammation, lipid and DNA oxidative damage, and promote the release of tetraiodothyronine (T4). Oxidative damage to DNA and some hormones may be involved in the effects of temperature on lung function (Qiu et al.2024). This study revealed that heat effect had a protective effect on RD at admission. A study by Iniguez et al. (I\u0026ntilde;iguez et al.2021) on the Spanish population revealed that high temperature had no effect on the incidence of RD, which was consistent with the findings of the present study. However, a study by Ragettli et al. (Ragettli et al. 2019) in Switzerland revealed that heat waves were significantly associated with an increased risk of hospitalization for cardiopulmonary diseases, and the number of days of heat waves increased the risk of hospitalization for pneumonia by 20.5% (6.9% ~ 35.9%). In addition, studies have reported that high temperatures in Shanghai (Ma et al.2011), Jiangsu (Ma et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), Guangdong (Yang et al.2015) and other places in China are also positively correlated with the incidence or death of RD. The reason for this regional difference may be related to the local climate type, local economic development, population age distribution, air pollution and other factors.\u003c/p\u003e \u003cp\u003eIn addition, the stratified analysis of gender and age in this study revealed that both age and gender could affect the temperature-related admission risk of RD patients. At low temperature, males are more susceptible to RD than females are. Feng et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) conducted a study in Lanzhou and reported that low temperature was associated with a greater risk of RD admission in males than in females. However, Zhou et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and Tran et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) reported that females were more susceptible to RD under low-temperature conditions. Other studies have also shown that age and sex are not associated with temperature-related RD admission risk (Requia et al.2023). Therefore, the risk of admission to the hospital for temperature-related RD differs by sex. On the one hand, this may be related to the different physical characteristics and activity habits of men and women. On the other hand, this may also be related to the fact that men are more sensitive to temperature changes and are more exposed to cold weather due to their social roles and personalities (Teng et al.2021). This study also revealed that the RR in older patients was greater than that in adult patients at low temperatures. This is consistent with what Zhang et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) reported. These results may also be related to differences in the health status of people in different age groups. With the aging of the body, body temperature regulation and physiological function, such as a decline in sweat gland function and a decrease in alveolar elasticity, decline. Moreover, elderly individuals may suffer from multiple underlying respiratory diseases at the same time. Thus, the risk of hospital admission due to RD is increased in elderly patients.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study, using a DLNM, revealed a significant impact of ambient temperature on RD hospital admissions in rural Wuwei, China. Extreme and moderate cold significantly increased RD admissions, particularly within 0\u0026ndash;7 day lags of exposure. Notably, males and older adults (\u0026ge;\u0026thinsp;65 years) displayed heightened vulnerability to low temperatures compared to females and adults (\u0026lt;\u0026thinsp;65 years). Conversely, high temperatures exhibited a protective effect on RD hospitalizations effect across all demographic groups. These findings highlight the need for targeted interventions, particularly for vulnerable populations, to mitigate the burden of RDs. By promoting awareness of temperature-related health risks, implementing preventive measures, and ensuring accessible healthcare, we can safeguard public health in regions with similar climatic conditions.\u003c/p\u003e"},{"header":"Abbreviations ","content":"\u003cp\u003eRD, Respiratory Disease; CI, confidence interval; DLNM, distributed lag nonlinear model;RR, relative risk\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e Guorong Chai contributed to the conception or design of the work. What\u0026apos;s more, Jirong Wu and Guangyu Zhai contributed to the acquisition, analysis, or interpretation of data for the work. In addition, Jirong Wu drafted the manuscript and critically revised the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u0026nbsp; This work was supported by the Natural Science Foundation of Gansu Province(No.17JR5RA260)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e The datasets generated and/or analyzed during the current study are not publicly available due to confidentiality agreements but are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e This study involves human participants but is not a survey or observationof human behavior. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. For this type of study, formal consent is not required. This study complies with the current laws in China.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e All authors consent to publish this article in International Journal of Biometeorology.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBai L, Woodward A, Cirendunzhu,Liu Q (2016) County-level heat vulnerability of urban and rural residents in Tibet, China. Environ Health 15:3. doi: 10.1186/s12940-015-0081-0. \u003c/li\u003e\n\u003cli\u003eBenmarhnia T, Deguen S, Kaufman JS, Smargiassi A(2015) Review Article: Vulnerability to Heat-related Mortality: A Systematic Review, Meta-analysis, and Meta-regression Analysis. 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Proceedings of the 34th Annual Meeting of Chinese Meteorological Society S15 Sub-meeting on Climate and Environmental Change and Human Health, Zhengzhou, pp 400-402\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"respiratory diseases, ambient temperature, admission, distributed lag nonlinear model, cold effect, heat effect","lastPublishedDoi":"10.21203/rs.3.rs-6113731/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6113731/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRespiratory diseases (RD) pose a significant public health challenge, particularly in vulnerable populations. However, the impact of ambient temperature on RD hospitalizations in rural areas of Northwest China remains understudied. this study aimed to investigate the relationship between ambient temperature and RD hospitalization. In this research, meteorological information and hospitalization data of RD in Wuwei's rural area from 2011 to 2015 were integrated. Time series analysis was performed using distributed lag nonlinear model (DLNM). Furthermore, we conducted stratifed analysis based on gender and age. Relative risk and 95% confidence intervals were used to assess the relationship between temperature and admission risk. The results show that the damage caused by low temperature gradually increased with increasing lag days, and the damage effect reached a maximum at -20\u0026deg;C and 7 days after lag (RR\u0026thinsp;=\u0026thinsp;1.313, 95% CI: 1.257\u0026ndash;1.371). For the effect of low temperature, the cumulative risk of hospitalization was greater with a lag of 0\u0026ndash;7 days (RR\u0026thinsp;=\u0026thinsp;3.162, 95% CI: 2.918\u0026ndash;3.427) than with other lag days. For the cold effect, the RR of extreme cold and moderate cold at 0\u0026ndash;7 day lags were 1.822 (95% CI: 1.732\u0026ndash;1.917) and 1.217 (95% CI: 1.200-1.233), respectively. Subgroup analysis demonstrated heightened vulnerability to cold temperatures among males and elderly individuals (\u0026ge;\u0026thinsp;65 years) compared to females and younger adults (\u0026lt;\u0026thinsp;65 years). Conversely, higher temperatures had a protective effect on RD hospitalization across all demographic groups. Extreme and moderate cold significantly increased RD admissions. male and older people being more susceptible to RD at low temperatures. These findings can inform the development of public health and meteorological policies aimed at safeguarding vulnerable populations and mitigating the burden of RD.\u003c/p\u003e","manuscriptTitle":"Impact of Ambient Temperature on Respiratory Disease Hospitalization in Rural Wuwei, Northwest China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-19 10:41:39","doi":"10.21203/rs.3.rs-6113731/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dd106a7c-d5c8-477f-951e-7c39672741d0","owner":[],"postedDate":"March 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-30T08:44:57+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-19 10:41:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6113731","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6113731","identity":"rs-6113731","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}