Association between Ambient Temperature and Nonaccidental Mortality in Liuzhou, China: A TimeSeries Study, 2014–2024

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This study aims to evaluate the effects of temperature on total non-accidental mortality, as well as mortality from cardiovascular and respiratory diseases, in Liuzhou City, China. Daily meteorological data and mortality records from 2014 to 2024 were collected for analysis. The cumulative relative risks (RRs) associated with non-optimal and extreme temperatures were estimated using a distributed lag nonlinear model (DLNM) combined with quasi-Poisson regression. Additionally, the attributable fractions (AFs) and attributable numbers (ANs) were calculated to assess the mortality burden attributable to non-optimal temperatures. Results indicated a significant U-shaped association between temperature and mortality, except in the 0–64 age group. The cumulative relative risk of extreme cold was 1.48 (95% CI: 1.32–1.66), while that of extreme heat was 1.22 (95% CI: 1.07–1.39). Non-optimal temperatures accounted for 9.69% (95% CI: 7.01–12.36) of total non-accidental deaths, with cold exposure contributing 7.19% (95% CI: 4.67–9.83) and heat exposure accounting for 2.49% (95% CI: 0.82–4.10). The findings indicate that exposure to non-optimal temperatures is significantly associated with increased mortality risk. Individuals aged 65 years and older demonstrated higher vulnerability to both cold and heat, highlighting the need for enhanced protective measures and targeted interventions for the elderly population. Earth and environmental sciences/Climate sciences Health sciences/Diseases Earth and environmental sciences/Environmental sciences Health sciences/Health care Health sciences/Medical research Health sciences/Risk factors Temperature mortality rate distributed lag nonlinear model time series analysis Figures Figure 1 Figure 2 Figure 3 Introduction With the intensification of global climate change, environmental temperature has emerged as a critical public health concern and an increasingly prominent environmental risk factor [1–3] . Projections by the Intergovernmental Panel on Climate Change (IPCC) indicate that rising temperatures due to climate change will significantly increase temperature-related mortality worldwide [4] . Consequently, localized epidemiological investigations into the health effects of heat and cold exposure are essential for informing timely and evidence-based climate adaptation policies. The association between ambient temperature and mortality remains a central topic in public health research, reflecting the profound influence of climatic conditions on human health and survival. A substantial body of evidence has demonstrated that both extreme heat and extreme cold are associated with adverse health outcomes, including elevated non-accidental mortality rates among the general population [5–7] . Numerous studies have consistently identified nonlinear relationships—typically characterized as U-shaped, J-shaped, or V-shaped—between temperature and mortality risk [8–10] . However, the nature and magnitude of this association vary considerably across regions due to differences in geography, demographic characteristics, and local climate patterns [11] . Therefore, region-specific environmental epidemiological studies are indispensable for accurately assessing temperature-related health risks and supporting locally relevant public health decision-making. Existing research suggests that populations in developing countries may be more vulnerable to extreme temperature events than those in developed nations, largely due to limited adaptive capacity and infrastructural constraints, resulting in higher susceptibility [12] . Nevertheless, the majority of current evidence on temperature-mortality associations originates from high-income countries, while data from low- and middle-income countries remain relatively scarce [13] . In China, growing attention has been paid to evaluating the health impacts of extreme temperatures in recent years. However, most existing studies have focused on economically advanced regions, such as coastal cities and provincial capitals [5–7] . Research on medium and small cities with lower levels of economic development remains limited. Moreover, certain studies have indicated that higher air conditioning penetration rates in economically developed areas may attenuate heat-related health risks, potentially leading to underestimation of the true impact of high temperatures [14–15] . Conversely, findings related to cold effects may be more applicable to southern regions with inadequate winter heating and lower access to air conditioning. Thus, there is a pressing need to conduct comprehensive environmental epidemiological studies in less developed urban areas within China. Given the considerable heterogeneity in climatic conditions and population health profiles across regions, locally tailored research is crucial for understanding context-specific risks. Such studies provide foundational insights for designing targeted public health interventions that address the unique challenges of each locality, thereby enhancing the effectiveness and relevance of preventive strategies. As a major city in southwest China, Liuzhou exhibits a subtropical monsoon climate characterized by long, hot summers; short, mild winters; and high humidity with concurrent rainfall and heat. To support the development of a localized early warning and prevention system aimed at mitigating temperature-related health burdens, this study investigates the relative risks and mortality attributable to non-optimal ambient temperatures in Liuzhou from 2014 to 2024. We further assess the respective contributions of extreme heat, extreme cold, moderate heat, and moderate cold to overall mortality. Additionally, we identify vulnerable subpopulations through stratified analyses by cause-specific diseases, age groups, and gender, with the goal of providing scientific evidence for implementing precise and effective public health interventions. Methods Research area and data source Liuzhou is a prefecture-level city located in the Guangxi Zhuang Autonomous Region in southwestern China. Designated by the State Council as a sub-provincial central city, it serves as a key urban center in central Guangxi. Situated between 23°54′13″ and 26°03′13″ north latitude and 108°35′12″ and 110°10′20″ east longitude, the city covers a total area of 18,596 square kilometers. It features a subtropical monsoon climate characterized by long summers, short winters, concurrent rainfall and heat, and a warm, humid environment. Liuzhou exhibits relatively low levels of air pollution and maintains a high forest coverage rate. Mortality data were obtained from the Liuzhou Center for Disease Control and Prevention (CDC), extracted from the national disease surveillance point system managed by the Chinese Center for Disease Control and Prevention, covering the period from January 1, 2014, to December 31, 2024. The dataset includes causes of death classified according to the International Classification of Diseases, 10th Revision (ICD-10), specifically non-accidental causes (A00–R99), respiratory diseases (J00–J99), and cardiovascular diseases (I00–I99). Data from December 18, 2022, to January 23, 2023, were excluded due to the disruptive impact of the COVID-19 outbreak on mortality patterns, which resulted in an anomalous surge in deaths that could introduce substantial bias into the analysis. The data were further stratified by cause-specific mortality (overall, respiratory, and cardiovascular), age group (0–64 years and ≥65 years), and gender (male and female). Daily meteorological data, including temperature and relative humidity, were provided by the Liuzhou Meteorological Bureau for the same time period. To account for potential confounding effects of air pollution, daily concentrations of sulfur dioxide (SO₂) and ozone (O₃) were obtained from the Liuzhou Municipal Ecological Environment Bureau. Daily pollutant levels were calculated as the arithmetic mean across all monitoring stations, consistent with standard practices in time-series studies. Missing values accounted for less than 1% at each monitoring site and were excluded from the analysis. The dataset contains no personally identifiable information. This study does not involve human subjects or sensitive personal data, and poses no risk to human health; therefore, ethical approval was not required. Statistical Analysis Given the nonlinear and delayed associations between temperature and mortality, a distributed lag nonlinear model (DLNM) was employed to assess the impact of temperature on non-accidental mortality. The core model incorporated natural cubic splines for calendar time with 8 degrees of freedom per year to adjust for long-term trends and seasonal variations [5,16] . Natural cubic splines with 3 degrees of freedom were also applied to control for daily concentrations of ambient pollutants and relative humidity. Additionally, indicator variables for day of the week were included. A cross-basis function was constructed using DLNM to model both exposure-response and lag-response relationships. The temperature cross-basis was specified using natural cubic splines with three internal knots placed at the 20th, 50th, and 70th percentiles of the temperature distribution. The lag structure was modeled using a natural cubic B-spline with logarithmically spaced knots, including the intercept and three internal nodes, and a maximum lag duration of 30 days, in line with previous studies [6] . The general model structure is expressed as follows: Let t denote the observation day, Yt represent the observed daily death count on day t, and α denote the intercept. The term cb(temp_t) represents the cross-basis function generated by DLNM, capturing the joint exposure-lag-response relationship [17] . The function ns() denotes a natural cubic spline. RH refers to relative humidity, Time represents the temporal trend variable, and SO₂ and O₃ indicate daily average concentrations of sulfur dioxide and ozone, respectively. The overall cumulative relative risk (RR) and corresponding 95% confidence interval (CI) were estimated. To quantify the mortality burden attributable to temperature, we computed the attributable fraction (AF) and the number of attributable deaths (AN) [18] . Temperature-related effects were categorized into cold and heat exposure based on deviations from the minimum mortality temperature (MMT). Extreme cold and extreme heat were defined as temperatures below the 2.5th percentile and above the 97.5th percentile of the temperature distribution, respectively. To estimate empirical confidence intervals (ECIs) for the attributable fractions and counts, Monte Carlo simulations were conducted, assuming multivariate normal distributions of the estimated coefficients under optimal linear unbiased estimation. Where N is the annual total counts of deaths, i is the lag day, L is the maximum lag day. Sensitivity Analysis A sensitivity analysis was performed to evaluate the robustness of the results by varying the degrees of freedom in the model: for long-term trends (df = 7–9), and for air pollutants and relative humidity (df = 2–5). All statistical analyses were conducted using R software (version 4.4.3), with the "dlnm" package used to implement the distributed lag nonlinear models. For all hypothesis tests, a two-tailed p-value of less than 0.05 was considered statistically significant. Results Descriptive analysis During the study period (January 1, 2014 to December 31, 2024), a total of 232,432 non-accidental deaths were recorded, of which 100,287 (43.2%) were attributed to cardiovascular diseases and 25,842 (11.1%) to respiratory diseases. The average daily counts of non-accidental, cardiovascular, and respiratory disease deaths were 56, 24, and 6, respectively. The mean daily temperature was 20.87 °C (range: 0.5 °C to 33.5 °C), with an average relative humidity of 76.84% (range: 28.0%–100.0%). Daily average concentrations of air pollutants are presented in Table 1. Temperature–mortality relationship Figure 1 illustrates the estimated cumulative association between mean temperature and total non-accidental, cardiovascular, and respiratory mortality over a 0–30 day lag period. The relationships were nonlinear, with elevated relative risks observed at both low and high temperature extremes. The minimum mortality temperatures (MMTs) for total non-accidental, cardiovascular, and respiratory deaths were identified as 23.5 °C, 24.5 °C, and 22.5 °C, respectively. Figures 2 and 3 highlight the temporal patterns of mortality risk associated with extreme temperatures. For extreme cold (6 °C, corresponding to the 2.5th percentile of daily mean temperature), the risk of all-cause mortality typically emerged on lag days 2–3, peaked on day 3, and gradually declined thereafter, remaining observable up to lag day 20. In contrast, for extreme heat (31 °C, the 97.5th percentile), the highest mortality risk occurred on the day of exposure (lag day 0), except among individuals aged 0–64 years. Subsequently, significant mortality displacement was observed across most causes after a 2–4 day lag. Table 2 summarizes the minimum mortality temperatures (MMTs) and corresponding relative risks (RRs) associated with extreme cold and heat across subgroups. MMTs ranged from 22 °C to 24.5 °C. With the exception of the 0–64 age group, trends were consistent across all subgroups. Under extreme cold conditions, the relative risks (with 95% confidence intervals) for non-accidental death, cardiovascular disease, respiratory disease, males, females, and individuals aged ≥65 were 1.481 (1.324–1.656), 1.694 (1.439–1.994), 1.442 (1.081–1.923), 1.550 (1.344–1.789), 1.400 (1.206–1.626), and 1.525 (1.346–1.728), respectively. For the 0–64 age group, the RR was 1.671 (1.138–2.452). These findings indicate that extreme cold is significantly associated with increased mortality risk across all subgroups, particularly among individuals with cardiovascular disease and those aged 0–64. Under extreme heat, the relative risks for non-accidental death, cardiovascular disease, respiratory disease, males, females, and individuals aged ≥65 were 1.219 (1.066–1.394), 1.228 (1.020–1.480), 1.510 (1.038–2.195), 1.185 (1.012–1.387), 1.290 (1.053–1.581), and 1.397 (1.192–1.638), respectively. Attributable fraction and attributable number As shown in Table 3 , the population attributable fraction (PAF) of non-accidental deaths attributable to non-optimal temperatures was 9.67% (95% CI: 7.01%–12.36%). Subgroup analyses revealed the highest PAF for cardiovascular diseases (13.15%, 95% CI: 6.97%–18.39%), followed by respiratory diseases (10.51%, 95% CI: −1.83% to 21.17%). By gender, the attributable fractions were 11.37% (95% CI: 6.18%–16.26%) for males and 4.06% (95% CI: −3.75%–10.85%) for females. Among age groups, the PAF was 8.93% (95% CI: 2.68%–14.95%) for those aged ≥65 years and 14.88% (95% CI: −2.87%–27.65%) for those aged 0–64 years. In Liuzhou, cold-related effects constitute the primary contributor to the mortality burden, although this effect was not statistically significant within the respiratory disease subgroup. Attributable death counts are detailed in Table 4 . In further analysis, temperature was categorized into four ranges: extreme cold (31 °C). As presented in Supplementary table 1 , mild cold accounted for the largest proportion of attributable deaths, ranging from 5.18% to 9.72%. Mild heat contributed 2.15% (95% CI: 0.65%–3.62%), extreme cold 0.81% (95% CI: 0.58%–1.02%), and extreme heat 0.23% (95% CI: 0.12%–0.34%). Notably, the respiratory disease subgroup did not exhibit significant attributable risks for either mild or extreme cold. Similarly, the female subgroup showed no significant risk for mild cold or mild heat, and the 0–64 age group showed no significant risk for mild cold, mild heat, or extreme heat. Compared to extreme heat, extreme cold exhibited a higher attribution ratio. Overall, mild cold was the leading contributor to temperature-related mortality. Attributable death counts are provided in Supplementary table 2. Sensitivity analysis A sensitivity analysis was conducted to evaluate the robustness of the findings. Specifically, we varied the degrees of freedom for time (7–9), as well as for humidity and air pollutants (2–5), to assess their influence on the estimated temperature–mortality associations and the minimum mortality temperature. None of these adjustments substantially altered the overall results, confirming the stability and reliability of the findings. Detailed results are presented in Supplementary table 3. Discussions The study employed a Distributed Lag Non-linear Model (DLNM) within a time-series framework to examine the exposure-response relationship between ambient temperature and non-accidental mortality from 2014 to 2024, stratified by gender, age group, and specific etiological categories. Additionally, we estimated the attributable fraction and attributable deaths associated with non-optimal temperatures to quantify their contribution to the overall mortality burden. The findings indicate that both extremely low and high temperatures are significant environmental risk factors contributing to increased mortality from non-accidental, cardiovascular, and respiratory causes. The exposure-response relationship between daily mean temperature and mortality due to cardiovascular and respiratory diseases exhibits a distinct "U"-shaped nonlinear pattern, consistent with several recent studies conducted in China [5–7] , thereby reinforcing the established association between temperature fluctuations and cardiorespiratory health outcomes. Subgroup analyses revealed that the risk associated with cold exposure systematically increases as temperatures decrease, aligning with prior evidence [19–20] . Notably, while heat-related risks generally intensified with rising temperatures across all age groups except individuals aged 0–64, this younger cohort demonstrated a comparatively weaker association, suggesting greater physiological resilience or adaptive capacity to high temperatures relative to those aged 65 and above. This may reflect age-related declines in thermoregulatory function, particularly diminished sweating efficiency, which impairs heat dissipation and increases susceptibility to heat accumulation among older adults [21–23] . Furthermore, the higher prevalence of comorbidities and compromised physiological regulation in the elderly population exacerbates vulnerability to extreme heat, especially among patients with pre-existing cardiovascular or respiratory conditions [24–25] . In contrast, individuals aged 0–64 appear more susceptible to extreme cold, potentially due to socioeconomic and behavioral factors—such as higher proportions of students and working populations—who engage in prolonged outdoor activities, thereby increasing exposure during cold periods. Given China’s rapidly aging population—where the number of individuals aged 65 and above reached 190 million in 2020 (13.5% of the total population) and is projected to exceed 25% by 2050—the health implications of extreme weather events for this demographic warrant urgent attention. Impaired ability to adapt to thermal stress significantly elevates mortality risk among the elderly. Therefore, considering the heightened sensitivity of older adults to high temperatures and the accelerating pace of demographic aging in China, targeted adaptive strategies and medical interventions are critically needed to safeguard their health and well-being. Temporal patterns in temperature-mortality associations were also identified: the effects of extreme cold typically exhibit a lag of 2–3 days and persist for approximately 20 days, whereas the impact of extreme heat is more immediate but shorter in duration—a finding consistent with existing literature [26–27] . With regard to gender differences, no statistically significant disparities were observed in this study, contrasting with previous reports suggesting higher vulnerability among women [28–29] or men [30] . These discrepancies may arise from variations in geographical context, population characteristics, and exposure profiles across studies [13,31] . Regarding cause-specific mortality, the influence of both cold and heat on cardiovascular disease deaths was found to be substantially greater than on respiratory disease mortality, corroborating findings from Suzhou, China [13] . In Liuzhou, cardiovascular diseases constituted the leading cause of death among elderly residents, accounting for 43.2% (100,287/232,432) of all registered deaths during the study period, underscoring the pronounced threat posed by extreme temperatures to cardiovascular health in this population. However, heterogeneity exists across regions: while multiple studies confirm significant impacts of both cold and heat on cardiovascular mortality, research in Jinan, China, reported only cold-related increases without significant heat effects [32] ; conversely, a study in Thailand identified a clear association between high temperatures and elevated respiratory mortality [33] . Such regional variation likely reflects complex interactions among climatic conditions, population structure, disease burden, and temporal exposure patterns. This study further quantified the mortality burden attributable to non-optimal temperatures and decomposed contributions by temperature category. Overall, 9.67% of non-accidental deaths were attributable to suboptimal thermal conditions, a proportion slightly lower than the 11.00% reported for China in a global analysis [34] . When distinguishing between cold and heat effects, cold-related exposures accounted for the majority of the attributable burden, consistent with prior findings [34] . Upon categorizing temperatures into four intervals—extremely cold, mildly cold, mildly hot, and extremely hot—we observed that mortality burdens from extremely cold conditions exceeded those from extremely hot ones, aligning with several existing reports [35–37] . This highlights the severity of extreme cold events, attributable to their prolonged lag effects and sustained risks. Notably, although some studies suggest that mild heat may contribute to higher attributable mortality due to its frequent occurrence [7] , our results indicate that mild cold is the primary contributor—a conclusion supported by multiple previous investigations [19,34,39] . Despite lower per-event risk, the high frequency of mild cold and mild heat exposures results in a disproportionately large cumulative mortality burden compared to rarer extreme events. For specific causes, non-optimal temperatures accounted for 13.15% of cardiovascular disease mortality and 10.51% of respiratory disease mortality, reflecting the stronger pathophysiological impact of cold on the cardiovascular system, as documented in earlier research [10,40,41] . Biologically, cold-induced cardiovascular risks are linked to autonomic nervous system dysregulation, elevated blood pressure, increased metabolic demand, inflammatory activation, and oxidative stress. In terms of demographic distribution, although the attributable mortality burden in the 0–64 age group did not reach statistical significance, both male individuals and those aged 65 and above exhibited higher attributable risks. Model robustness was assessed through sensitivity analyses, incorporating alternative specifications for time trends (7–9 degrees of freedom), air pollutants, and relative humidity (2–5 degrees of freedom). Estimated relative risks remained stable across different model configurations, indicating reliable and robust results. These findings underscore the pressing need for climate adaptation policies in Liuzhou City, particularly the development of localized and targeted interventions to mitigate the impacts of heatwaves and cold spells. Priority should be given to protecting vulnerable populations, including older adults and individuals with chronic illnesses. Implementing precise early warning systems and strengthening community-based support networks could effectively reduce excess mortality associated with extreme temperatures. This study has several limitations. First, it was conducted in a single city—Liuzhou—and thus geographic generalizability may be limited due to variations in topography, climate, and population characteristics. Second, cause-of-death data were derived from ICD-10 codes recorded on death certificates, which may introduce classification inaccuracies. Third, exposure assessments for air pollutants and meteorological variables relied on fixed monitoring stations rather than personal-level measurements, potentially introducing exposure misclassification. Fourth, certain socioeconomic confounders—such as income level and access to healthcare—were not adjusted for in the analysis. Future multi-city studies that account for these confounding factors are necessary to provide a more comprehensive understanding of the temperature-mortality relationship and enhance the accuracy of public health risk assessments. Conclusions This study revealed that in Liuzhou, exposure to non-optimal temperatures—encompassing both cold and hot extremes—is significantly associated with increased mortality. In comparison to extreme heat, extreme cold poses a relatively higher risk. Although the health impacts of cold temperatures may not manifest immediately, their prolonged duration often leads to underestimation, potentially resulting in a greater mortality burden. Notably, individuals aged 65 years and above demonstrate heightened vulnerability to temperature fluctuations. Declarations Acknowledgements We extend our sincere gratitude to all the researchers and participants for their invaluable support for this study. We would also like to thank all the staff of the Liuzhou Center for Disease Control and Prevention, Guangxi, China, for providing us with a good research environment. Funding This work was supported by Scientific research Project of the Health Commission of Guangxi (grant Z-B20241388). The findings and conclusions in this report are those of the author(s) and do not necessarily represent the official position of the funding agencies. Authors' Contributions YL - data collection, primary data analysis, and writing SY - data collection, analysis, revisions, and writing JL, JY, DH- conception and design LZ- supervision, revisions, and final review YL and SY have made equal contributions to this article and are all recognized as co-first authors. Data sharing statement The data used in this study are stored at the Institute of Environmental Hygiene and School Health Prevention and Control, Liuzhou Center for Disease Control and Prevention. The data are managed in strict accordance with the data security and confidentiality requirements imposed by the Liuzhou Center for Disease Control and Prevention and are not publicly available. Access to the data may be granted to institutions that meet the criteria and have a reasonable research purpose. Formal requests must first be submitted to the Institute of Environmental Hygiene and School Health Prevention and Control (contact via [email protected] ). Upon preliminary review, the request will be forwarded to the Liuzhou Center for Disease Control and Prevention for final approval. Competing Interests The authors have declared that no competing interests exist. References Guo, Yuming et al. “The impact of temperature on mortality in Tianjin, China: a case-crossover design with a distributed lag nonlinear model.” Environmental health perspectives vol. 119,12 (2011) Lee, Hyewon et al. “Association between ambient temperature and injury by intentions and mechanisms: A case-crossover design with a distributed lag nonlinear model.” The Science of the total environment vol. 746 (2020) Kouis, Panayiotis et al. “The effect of ambient air temperature on cardiovascular and respiratory mortality in Thessaloniki, Greece.” The Science of the total environment vol. 647 (2019) Change, Climate I: "The physical science basis." Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. 2013. Xia, Yizhang et al. “Effects of ambient temperature on mortality among elderly residents of Chengdu city in Southwest China, 2016-2020: a distributed-lag non-linear time series analysis.” BMC public health vol. 23,1 149. 21 Jan. 2023 Guo, Hongju et al. “Time series study on the effects of daily average temperature on the mortality from respiratory diseases and circulatory diseases: a case study in Mianyang City.” BMC public health vol. 22,1 1001. 17 May. 2022 Chen, Xuanhao et al. “Association between ambient temperature and non-accidental mortality in Guiyang, China: A time-series analysis (2013-2023).” PloS one vol. 20,4 e0319863. 1 Apr. 2025 Breitner, Susanne et al. “Short-term effects of air temperature on mortality and effect modification by air pollution in three cities of Bavaria, Germany: a time-series analysis.” The Science of the total environment vol. 485-486 (2014) Anderson, Brooke G, and Michelle L Bell. “Weather-related mortality: how heat, cold, and heat waves affect mortality in the United States.” Epidemiology (Cambridge, Mass.) vol. 20,2 (2009) Ma, Wenjuan et al. “Temperature-related mortality in 17 large Chinese cities: how heat and cold affect mortality in China.” Environmental research vol. 134 (2014) Guo, Yuming et al. “Global variation in the effects of ambient temperature on mortality: a systematic evaluation.” Epidemiology (Cambridge, Mass.) vol. 25,6 (2014) Costello, Anthony et al. “Managing the health effects of climate change: Lancet and University College London Institute for Global Health Commission.” Lancet (London, England) vol. 373,9676 (2009) Wang, Cuicui et al. “Temperature and daily mortality in Suzhou, China: a time series analysis.” The Science of the total environment vol. 466-467 (2014) Ma WJ. A study on temperature-related mortality in 16 Chinese cities [D] Fudan University; 2014. Ma XM, Li RK, Luo K, et al. Association between temperature and mortality in three cities in China [J] Basic and clinic medicine. 2016;36(6):805–810. Achebak, Hicham et al. “Trends in temperature-related age-specific and sex-specific mortality from cardiovascular diseases in Spain: a national time-series analysis.” The Lancet. Planetary health vol. 3,7 (2019) Gasparrini, A et al. “Distributed lag non-linear models.” Statistics in medicine vol. 29,21 (2010) Gasparrini, Antonio, and Michela Leone. “Attributable risk from distributed lag models.” BMC medical research methodology vol. 14 55. 23 Apr. 2014 Petkova, Elisaveta P et al. “Mortality attributable to heat and cold among the elderly in Sofia, Bulgaria.” International journal of biometeorology vol. 65,6 (2021) Zhao, Zhonghui et al. “Association between ambient cold exposure and mortality risk in Shandong Province, China: Modification effect of particulate matter size.” Frontiers in public health vol. 10 1093588. 5 Jan. 2023 Herbst, Jonathon et al. “Heat-related deaths in Adelaide, South Australia: review of the literature and case findings - an Australian perspective.” Journal of forensic and legal medicine vol. 22 (2014) Falchetta, Giacomo et al. “Global projections of heat exposure of older adults.” Nature communications vol. 15,1 3678. 14 May. 2024 Yuan, Lei et al. “Non-optimal temperature-attributable mortality and morbidity burden by cause, age and sex under climate and population change scenarios: a nationwide modelling study in Japan.” The Lancet regional health. Western Pacific vol. 52 101214. 8 Oct. 2024, Fouillet, A et al. “Excess mortality related to the August 2003 heat wave in France.” International archives of occupational and environmental health vol. 80,1 (2006) Vaneckova, Pavla et al. “Spatial analysis of heat-related mortality among the elderly between 1993 and 2004 in Sydney, Australia.” Social science & medicine (1982) vol. 70,2 (2010) Li, Guoxing et al. “Temporal variation in associations between temperature and years of life lost in a southern China city with typical subtropical climate.” Scientific reports vol. 7,1 4650. 5 Jul. 2017 Zhang, Yunquan et al. “The burden of ambient temperature on years of life lost: A multi-community analysis in Hubei, China.” The Science of the total environment vol. 621 (2018) Deng, Jixiang et al. “Ambient temperature and non-accidental mortality: a time series study.” Environmental science and pollution research international vol. 27,4 (2020) Seposo, Xerxes T et al. “Evaluating the Effects of Temperature on Mortality in Manila City (Philippines) from 2006-2010 Using a Distributed Lag Nonlinear Model.” International journal of environmental research and public health vol. 12,6 6842-57. 16 Jun. 2015 Son, Ji-Young et al. “Vulnerability to temperature-related mortality in Seoul, Korea.” Environmental research letters : ERL [Web site] vol. 6,3 (2011) Ma, Wenjuan et al. “Modifiers of the temperature-mortality association in Shanghai, China.” International journal of biometeorology vol. 56,1 (2012) Han, Jing et al. “The impact of temperature extremes on mortality: a time-series study in Jinan, China.” BMJ open vol. 7,4 e014741. 2 May. 2017 Guo, Yuming et al. “Effects of temperature on mortality in Chiang Mai city, Thailand: a time series study.” Environmental health : a global access science source vol. 11 36. 9 Jul. 2012 Gasparrini, Antonio et al. “Mortality risk attributable to high and low ambient temperature: a multicountry observational study.” Lancet (London, England) vol. 386,9991 (2015) Martínez-Solanas, Èrica et al. “Projections of temperature-attributable mortality in Europe: a time series analysis of 147 contiguous regions in 16 countries.” The Lancet. Planetary health vol. 5,7 (2021) Alahmad, Barrak et al. “Associations Between Extreme Temperatures and Cardiovascular Cause-Specific Mortality: Results From 27 Countries.” Circulation vol. 147,1 (2023) Masselot, Pierre et al. “Excess mortality attributed to heat and cold: a health impact assessment study in 854 cities in Europe.” The Lancet. Planetary health vol. 7,4 (2023) Chen, Renjie et al. “Association between ambient temperature and mortality risk and burden: time series study in 272 main Chinese cities.” BMJ (Clinical research ed.) vol. 363 k4306. 31 Oct. 2018 Petkova, Elisaveta P et al. “Mortality attributable to heat and cold among the elderly in Sofia, Bulgaria.” International journal of biometeorology vol. 65,6 (2021) Lee, Whan-Hee et al. “An Investigation on Attributes of Ambient Temperature and Diurnal Temperature Range on Mortality in Five East-Asian Countries.” Scientific reports vol. 7,1 10207. 31 Aug. 2017 Zeka, Ariana et al. “The association of cold weather and all-cause and cause-specific mortality in the island of Ireland between 1984 and 2007.” Environmental health : a global access science source vol. 13 104. 6 Dec. 2014 Tables Table 1 . Summary descriptive statistics of meteorological factors, air pollutants and daily non-accidental deaths in Liuzhou, 2014–2024. Variables Total Mean SD Min P 25 P 50 P 75 Max Non-accidental deaths 232432 56.33 16.69 4 45 56 66 122 Respiratory deaths 25842 6.16 3.07 1 4 6 8 21 Cardiovascular deaths 100287 24.22 9.13 2 18 23 30 62 Male 138835 33.62 10.34 2 26 33 40 78 Female 93589 22.71 7.99 2 17 22 28 60 Age 0-64 years 61189 15.08 4.60 1 12 15 18 34 Age ≥ 65 years 171243 41.24 14.24 3 31 41 50 100 Temperature (°C) - 20.87 7.32 -0.50 15.00 22.35 27.2 33.5 Humidity (%) - 76.84 14.05 28 68 78 87 100 SO 2 (μg/m 3 ) - 15.29 10.76 3.17 7.67 11.83 19.62 80.00 O 3 (μg/m 3 ) - 77.77 31.93 5.43 55.62 75.43 96.71 212.14 Table 2. Minimum mortality temperatures (MMTs) and relative risks (RRs) associated with extreme low and high temperatures with 95% empirical confidence interval (95% CI). Groups The minimum mortality temperature (°C) Extreme low temperature (%) Extreme high temperature (%) Overall 23.5 1.481(1.324,1.656) 1.219(1.066,1.394) Respiratory disease 22.5 1.442(1.081,1.923) 1.510(1.038,2.195) Cardiovascular disease 24.5 1.694(1.439,1.994) 1.228(1.020,1.480) male 24.5 1.550(1.344,1.789) 1.185(1.012,1.387) female 22 1.400(1.206,1.626) 1.290(1.053,1.581) ≥65 years 23 1.525(1.346,1.728) 1.397(1.192,1.638) 0-64 years 33.5 1.671(1.138,2.452) 1.059(0.922,1.218) Note: Extreme Low and extreme High were defined as temperatures below the 2.5th percentile and above the 97.5th percentile of the temperature distribution Table 3. The attributable fractions to non-optimal temperatures with 95% empirical confidence interval (95% CI) Groups Total (%) Cold (%) Heat (%) Overall 9.69(7.01,12.36) 7.19(4.67,9.83) 2.49(0.82,4.1) Respiratory disease 10.51('-1.83,21.17) 5.24('-6.69,14.75) 5.27(2.58,7.79) Cardiovascular disease 13.15(6.97,18.39) 10.82(4.93,15.80) 2.31(1.15,3.31) male 11.37(6.18,16.26) 9.27(4.17,13.78) 2.09(1.10,3.03) female 4.06('-3.75,10.85) 2.56(0.27,4.49) 1.51('-4.36,7.00) ≥65 years 8.93(2.68,14.95) 5.93(0.54,10.09) 3.01(1.75,4.15) 0-64 years 14.88('-2.87,27.65) 14.88('-2.87,27.65) 0(0,0) Note: Cold: temperatures 31 °C Table 4. The attributable numbers to non-optimal temperatures with 95% empirical confidence interval (95% CI). Groups Total Cold Heat Overall 21727(15706,27704) 16124(10464,22038) 5587 (1833,9183) Respiratory disease 2565('-448,5167) 1279('-1633,3600) 1286(630,1901) Cardiovascular disease 12677(6722,17720) 10433(4753,15232) 2225(1108,3185) male 15217(8266,21750) 12396(5574,18442) 2794(1476,4059) female 3669('-3390,9810) 2312(242,4056) 1368('-3941,6329) ≥65 years 14662(4397,24538) 9728(880,17893) 4934(2869,6816) 0-64 years 8935('-1723,16601) 8935('-1723,16601) 0(0,0) Note: Cold: temperatures 31 °C Additional Declarations No competing interests reported. Supplementary Files Supplementarytable1.docx Supplementary table 1. The attributable fractions to mild and extreme non-optimal temperature with 95% empirical confidence interval (95% CI). Supplementarytable2.docx Supplementary table 2. The attributable numbers to mild and extreme non-optimal temperature with 95% empirical confidence interval (95% CI). Supplementarytable3.docx Supplementary table 3. The influence of changing the covariate degree of freedom (df) on the research results. 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8346096","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":588053827,"identity":"35ece4f1-1c9e-47b1-8374-833fe508c5fd","order_by":0,"name":"Yingji Lan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYDACZiCWMDgAZDAffPChQkJOnngt7G3JhjPOWBgbNhBnF1ALzxkzad62ikQQGy8wOM57+IVFwQF5c4kEA2neeRIJjA3MDx/dwKflMF+aBdBhhjtnJCQYzt0mkcfOwGZsnINXC4+ZAVAL44YbCQcS3m6TKGZs4GGTJkaL/YYbiQ0HeOdIAEnCWowfALUkbjhzmLGRt4EILZJAW0CBnLzheBsz44xjEsaGzQT8wnf+jPFniT//bDcc5v/+40NNnZw8e/PDx/i0KBxgYJOWQBFixqMcBOQbGJg/fiCgaBSMglEwCkY4AABEgFHsL1kAagAAAABJRU5ErkJggg==","orcid":"","institution":"Liuzhou Center for Disease Control and Prevention","correspondingAuthor":true,"prefix":"","firstName":"Yingji","middleName":"","lastName":"Lan","suffix":""},{"id":588053828,"identity":"85ff28c1-6d63-4844-a81a-032150bd8239","order_by":1,"name":"Shaoyan Ye","email":"","orcid":"","institution":"Liuzhou Center for Disease Control and 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exposure-response curves from January 1, 2014 to December 31, 2024, illustrating the relative risk associated with the impact of daily temperature on non-accidental mortality.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8346096/v1/f29d403949d4771d93f6f7b6.png"},{"id":102385325,"identity":"6f6b7e61-4c44-4d68-a209-d1c943780785","added_by":"auto","created_at":"2026-02-11 07:41:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3741490,"visible":true,"origin":"","legend":"\u003cp\u003eThe lag structures in effects of extreme low temperature on non-accidental mortality.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8346096/v1/f9c96306fff80b7771416313.png"},{"id":102385328,"identity":"d77a8479-dd25-4f44-bfdc-f272c648d016","added_by":"auto","created_at":"2026-02-11 07:41:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2435662,"visible":true,"origin":"","legend":"\u003cp\u003eThe lag structures in effects of extreme high temperature on non-accidental mortality.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8346096/v1/f23a09eeb618f8a5849dac2c.png"},{"id":109444444,"identity":"f4bffb3a-ccec-488f-8af7-d3afe0596649","added_by":"auto","created_at":"2026-05-18 07:57:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9987491,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8346096/v1/f9c44a0b-7383-478c-ae53-255c5ff7e0e4.pdf"},{"id":102397853,"identity":"a5658913-722c-4739-b942-732d037e691d","added_by":"auto","created_at":"2026-02-11 10:19:58","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18373,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary table 1. \u003c/strong\u003eThe attributable fractions to mild and extreme non-optimal temperature with 95% empirical confidence interval (95% CI).\u003c/p\u003e","description":"","filename":"Supplementarytable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8346096/v1/7dd8a2c8595cd4d1b7fe9600.docx"},{"id":102398184,"identity":"2ac4d08e-eb6c-483d-b1ef-b076d7d3f077","added_by":"auto","created_at":"2026-02-11 10:21:41","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":18495,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary table 2. \u003c/strong\u003eThe attributable numbers to mild and extreme non-optimal temperature with 95% empirical confidence interval (95% CI).\u003c/p\u003e","description":"","filename":"Supplementarytable2.docx","url":"https://assets-eu.researchsquare.com/files/rs-8346096/v1/4eaf5afc386412fe1b40e88a.docx"},{"id":102398509,"identity":"493852e1-bbce-4dfe-8a0b-27f0d9dfe614","added_by":"auto","created_at":"2026-02-11 10:23:07","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":19201,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary table 3. \u003c/strong\u003eThe influence of changing the covariate degree of freedom (df) on the research results.\u003c/p\u003e","description":"","filename":"Supplementarytable3.docx","url":"https://assets-eu.researchsquare.com/files/rs-8346096/v1/b28f393841b4f4976c667d4b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between Ambient Temperature and Nonaccidental Mortality in Liuzhou, China: A TimeSeries Study, 2014–2024","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWith the intensification of global climate change, environmental temperature has emerged as a critical public health concern and an increasingly prominent environmental risk factor \u003cstrong\u003e[1\u0026ndash;3]\u003c/strong\u003e. Projections by the Intergovernmental Panel on Climate Change (IPCC) indicate that rising temperatures due to climate change will significantly increase temperature-related mortality worldwide\u0026nbsp;\u003cstrong\u003e[4]\u003c/strong\u003e. Consequently, localized epidemiological investigations into the health effects of heat and cold exposure are essential for informing timely and evidence-based climate adaptation policies.\u003cbr\u003eThe association between ambient temperature and mortality remains a central topic in public health research, reflecting the profound influence of climatic conditions on human health and survival. A substantial body of evidence has demonstrated that both extreme heat and extreme cold are associated with adverse health outcomes, including elevated non-accidental mortality rates among the general population \u003cstrong\u003e[5\u0026ndash;7]\u003c/strong\u003e. Numerous studies have consistently identified nonlinear relationships\u0026mdash;typically characterized as U-shaped, J-shaped, or V-shaped\u0026mdash;between temperature and mortality risk \u003cstrong\u003e[8\u0026ndash;10]\u003c/strong\u003e. However, the nature and magnitude of this association vary considerably across regions due to differences in geography, demographic characteristics, and local climate patterns\u0026nbsp;\u003cstrong\u003e[11]\u003c/strong\u003e. Therefore, region-specific environmental epidemiological studies are indispensable for accurately assessing temperature-related health risks and supporting locally relevant public health decision-making.\u003cbr\u003eExisting research suggests that populations in developing countries may be more vulnerable to extreme temperature events than those in developed nations, largely due to limited adaptive capacity and infrastructural constraints, resulting in higher susceptibility \u003cstrong\u003e[12]\u003c/strong\u003e. Nevertheless, the majority of current evidence on temperature-mortality associations originates from high-income countries, while data from low- and middle-income countries remain relatively scarce \u003cstrong\u003e[13]\u003c/strong\u003e. In China, growing attention has been paid to evaluating the health impacts of extreme temperatures in recent years. However, most existing studies have focused on economically advanced regions, such as coastal cities and provincial capitals \u003cstrong\u003e[5\u0026ndash;7]\u003c/strong\u003e. Research on medium and small cities with lower levels of economic development remains limited. Moreover, certain studies have indicated that higher air conditioning penetration rates in economically developed areas may attenuate heat-related health risks, potentially leading to underestimation of the true impact of high temperatures\u0026nbsp;\u003cstrong\u003e[14\u0026ndash;15]\u003c/strong\u003e. Conversely, findings related to cold effects may be more applicable to southern regions with inadequate winter heating and lower access to air conditioning. Thus, there is a pressing need to conduct comprehensive environmental epidemiological studies in less developed urban areas within China.\u003cbr\u003e\u0026nbsp;Given the considerable heterogeneity in climatic conditions and population health profiles across regions, locally tailored research is crucial for understanding context-specific risks. Such studies provide foundational insights for designing targeted public health interventions that address the unique challenges of each locality, thereby enhancing the effectiveness and relevance of preventive strategies.\u003cbr\u003e\u0026nbsp;As a major city in southwest China, Liuzhou exhibits a subtropical monsoon climate characterized by long, hot summers; short, mild winters; and high humidity with concurrent rainfall and heat. To support the development of a localized early warning and prevention system aimed at mitigating temperature-related health burdens, this study investigates the relative risks and mortality attributable to non-optimal ambient temperatures in Liuzhou from 2014 to 2024. We further assess the respective contributions of extreme heat, extreme cold, moderate heat, and moderate cold to overall mortality. Additionally, we identify vulnerable subpopulations through stratified analyses by cause-specific diseases, age groups, and gender, with the goal of providing scientific evidence for implementing precise and effective public health interventions.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eResearch area and data source\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLiuzhou is a prefecture-level city located in the Guangxi Zhuang Autonomous Region in southwestern China. Designated by the State Council as a sub-provincial central city, it serves as a key urban center in central Guangxi. Situated between 23°54′13″ and 26°03′13″ north latitude and 108°35′12″ and 110°10′20″ east longitude, the city covers a total area of 18,596 square kilometers. It features a subtropical monsoon climate characterized by long summers, short winters, concurrent rainfall and heat, and a warm, humid environment. Liuzhou exhibits relatively low levels of air pollution and maintains a high forest coverage rate.\u003cbr\u003e\u0026nbsp;Mortality data were obtained from the Liuzhou Center for Disease Control and Prevention (CDC), extracted from the national disease surveillance point system managed by the Chinese Center for Disease Control and Prevention, covering the period from January 1, 2014, to December 31, 2024. The dataset includes causes of death classified according to the International Classification of Diseases, 10th Revision (ICD-10), specifically non-accidental causes (A00–R99), respiratory diseases (J00–J99), and cardiovascular diseases (I00–I99). Data from December 18, 2022, to January 23, 2023, were excluded due to the disruptive impact of the COVID-19 outbreak on mortality patterns, which resulted in an anomalous surge in deaths that could introduce substantial bias into the analysis. The data were further stratified by cause-specific mortality (overall, respiratory, and cardiovascular), age group (0–64 years and ≥65 years), and gender (male and female). Daily meteorological data, including temperature and relative humidity, were provided by the Liuzhou Meteorological Bureau for the same time period.\u003cbr\u003e\u0026nbsp;To account for potential confounding effects of air pollution, daily concentrations of sulfur dioxide (SO₂) and ozone (O₃) were obtained from the Liuzhou Municipal Ecological Environment Bureau. Daily pollutant levels were calculated as the arithmetic mean across all monitoring stations, consistent with standard practices in time-series studies. Missing values accounted for less than 1% at each monitoring site and were excluded from the analysis.\u003cbr\u003e\u0026nbsp;The dataset contains no personally identifiable information. This study does not involve human subjects or sensitive personal data, and poses no risk to human health; therefore, ethical approval was not required.\u003cbr\u003e\u0026nbsp;\u003cbr\u003e\u0026nbsp;\u003cstrong\u003eStatistical Analysis\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003eGiven the nonlinear and delayed associations between temperature and mortality, a distributed lag nonlinear model (DLNM) was employed to assess the impact of temperature on non-accidental mortality. The core model incorporated natural cubic splines for calendar time with 8 degrees of freedom per year to adjust for long-term trends and seasonal variations \u003cstrong\u003e[5,16]\u003c/strong\u003e. Natural cubic splines with 3 degrees of freedom were also applied to control for daily concentrations of ambient pollutants and relative humidity. Additionally, indicator variables for day of the week were included. A cross-basis function was constructed using DLNM to model both exposure-response and lag-response relationships. The temperature cross-basis was specified using natural cubic splines with three internal knots placed at the 20th, 50th, and 70th percentiles of the temperature distribution. The lag structure was modeled using a natural cubic B-spline with logarithmically spaced knots, including the intercept and three internal nodes, and a maximum lag duration of 30 days, in line with previous studies \u003cstrong\u003e[6]\u003c/strong\u003e. The general model structure is expressed as follows:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"591\" height=\"20\" 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\" alt=\"image\"\u003eLet t denote the observation day, Yt represent the observed daily death count on day t, and α denote the intercept. The term cb(temp_t) represents the cross-basis function generated by DLNM, capturing the joint exposure-lag-response relationship\u0026nbsp;\u003cstrong\u003e[17]\u003c/strong\u003e. The function ns() denotes a natural cubic spline. RH refers to relative humidity, Time represents the temporal trend variable, and SO₂ and O₃ indicate daily average concentrations of sulfur dioxide and ozone, respectively. The overall cumulative relative risk (RR) and corresponding 95% confidence interval (CI) were estimated.\u003cbr\u003eTo quantify the mortality burden attributable to temperature, we computed the attributable fraction (AF) and the number of attributable deaths (AN) \u003cstrong\u003e[18]\u003c/strong\u003e. Temperature-related effects were categorized into cold and heat exposure based on deviations from the minimum mortality temperature (MMT). Extreme cold and extreme heat were defined as temperatures below the 2.5th percentile and above the 97.5th percentile of the temperature distribution, respectively. To estimate empirical confidence intervals (ECIs) for the attributable fractions and counts, Monte Carlo simulations were conducted, assuming multivariate normal distributions of the estimated coefficients under optimal linear unbiased estimation.\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"209\" height=\"67\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u003c/p\u003e\n\u003cp\u003eWhere N is the annual total counts of deaths, i is the lag day, L is the maximum lag day.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003cstrong\u003eSensitivity Analysis\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003eA sensitivity analysis was performed to evaluate the robustness of the results by varying the degrees of freedom in the model: for long-term trends (df = 7–9), and for air pollutants and relative humidity (df = 2–5). All statistical analyses were conducted using R software (version 4.4.3), with the \"dlnm\" package used to implement the distributed lag nonlinear models. For all hypothesis tests, a two-tailed p-value of less than 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eDescriptive analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the study period (January 1, 2014 to December 31, 2024), a total of 232,432 non-accidental deaths were recorded, of which 100,287 (43.2%) were attributed to cardiovascular diseases and 25,842 (11.1%) to respiratory diseases. The average daily counts of non-accidental, cardiovascular, and respiratory disease deaths were 56, 24, and 6, respectively. The mean daily temperature was 20.87 °C (range: 0.5 °C to 33.5 °C), with an average relative humidity of 76.84% (range: 28.0%–100.0%). Daily average concentrations of air pollutants are presented in \u003cstrong\u003eTable 1.\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;\u003cstrong\u003eTemperature–mortality relationship\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;Figure 1\u003c/strong\u003e illustrates the estimated cumulative association between mean temperature and total non-accidental, cardiovascular, and respiratory mortality over a 0–30 day lag period. The relationships were nonlinear, with elevated relative risks observed at both low and high temperature extremes. The minimum mortality temperatures (MMTs) for total non-accidental, cardiovascular, and respiratory deaths were identified as 23.5 °C, 24.5 °C, and 22.5 °C, respectively.\u003cbr\u003e\u003cstrong\u003eFigures 2 and 3\u003c/strong\u003e highlight the temporal patterns of mortality risk associated with extreme temperatures. For extreme cold (6 °C, corresponding to the 2.5th percentile of daily mean temperature), the risk of all-cause mortality typically emerged on lag days 2–3, peaked on day 3, and gradually declined thereafter, remaining observable up to lag day 20. In contrast, for extreme heat (31 °C, the 97.5th percentile), the highest mortality risk occurred on the day of exposure (lag day 0), except among individuals aged 0–64 years. Subsequently, significant mortality displacement was observed across most causes after a 2–4 day lag.\u003cbr\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e summarizes the minimum mortality temperatures (MMTs) and corresponding relative risks (RRs) associated with extreme cold and heat across subgroups. MMTs ranged from 22 °C to 24.5 °C. With the exception of the 0–64 age group, trends were consistent across all subgroups. Under extreme cold conditions, the relative risks (with 95% confidence intervals) for non-accidental death, cardiovascular disease, respiratory disease, males, females, and individuals aged\u0026nbsp;≥65 were 1.481 (1.324–1.656), 1.694 (1.439–1.994), 1.442 (1.081–1.923), 1.550 (1.344–1.789), 1.400 (1.206–1.626), and 1.525 (1.346–1.728), respectively. For the 0–64 age group, the RR was 1.671 (1.138–2.452). These findings indicate that extreme cold is significantly associated with increased mortality risk across all subgroups, particularly among individuals with cardiovascular disease and those aged 0–64. Under extreme heat, the relative risks for non-accidental death, cardiovascular disease, respiratory disease, males, females, and individuals aged ≥65 were 1.219 (1.066–1.394), 1.228 (1.020–1.480), 1.510 (1.038–2.195), 1.185 (1.012–1.387), 1.290 (1.053–1.581), and 1.397 (1.192–1.638), respectively.\u003cbr\u003e\u0026nbsp;\u003cbr\u003e\u0026nbsp;\u003cstrong\u003eAttributable fraction and attributable number\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003eAs shown in \u003cstrong\u003eTable 3\u003c/strong\u003e, the population attributable fraction (PAF) of non-accidental deaths attributable to non-optimal temperatures was 9.67% (95% CI: 7.01%–12.36%). Subgroup analyses revealed the highest PAF for cardiovascular diseases (13.15%, 95% CI: 6.97%–18.39%), followed by respiratory diseases (10.51%, 95% CI: −1.83% to 21.17%). By gender, the attributable fractions were 11.37% (95% CI: 6.18%–16.26%) for males and 4.06% (95% CI: −3.75%–10.85%) for females. Among age groups, the PAF was 8.93% (95% CI: 2.68%–14.95%) for those aged ≥65 years and 14.88% (95% CI: −2.87%–27.65%) for those aged 0–64 years. In Liuzhou, cold-related effects constitute the primary contributor to the mortality burden, although this effect was not statistically significant within the respiratory disease subgroup. Attributable death counts are detailed in\u003cstrong\u003e\u0026nbsp;Table 4\u003c/strong\u003e.\u003cbr\u003e\u0026nbsp;\u003cbr\u003e\u0026nbsp;In further analysis, temperature was categorized into four ranges: extreme cold (\u0026lt;6 °C), mild cold (6 °C to MMT), mild heat (MMT to 31 °C), and extreme heat (\u0026gt;31 °C). As presented in \u003cstrong\u003eSupplementary table 1\u003c/strong\u003e, mild cold accounted for the largest proportion of attributable deaths, ranging from 5.18% to 9.72%. Mild heat contributed 2.15% (95% CI: 0.65%–3.62%), extreme cold 0.81% (95% CI: 0.58%–1.02%), and extreme heat 0.23% (95% CI: 0.12%–0.34%). Notably, the respiratory disease subgroup did not exhibit significant attributable risks for either mild or extreme cold. Similarly, the female subgroup showed no significant risk for mild cold or mild heat, and the 0–64 age group showed no significant risk for mild cold, mild heat, or extreme heat. Compared to extreme heat, extreme cold exhibited a higher attribution ratio. Overall, mild cold was the leading contributor to temperature-related mortality. Attributable death counts are provided in \u003cstrong\u003eSupplementary table 2.\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;\u003cstrong\u003eSensitivity analysis\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003eA sensitivity analysis was conducted to evaluate the robustness of the findings. Specifically, we varied the degrees of freedom for time (7–9), as well as for humidity and air pollutants (2–5), to assess their influence on the estimated temperature–mortality associations and the minimum mortality temperature. None of these adjustments substantially altered the overall results, confirming the stability and reliability of the findings. Detailed results are presented in \u003cstrong\u003eSupplementary table 3.\u003c/strong\u003e\u003c/p\u003e"},{"header":"Discussions","content":"\u003cp\u003eThe study employed a Distributed Lag Non-linear Model (DLNM) within a time-series framework to examine the exposure-response relationship between ambient temperature and non-accidental mortality from 2014 to 2024, stratified by gender, age group, and specific etiological categories. Additionally, we estimated the attributable fraction and attributable deaths associated with non-optimal temperatures to quantify their contribution to the overall mortality burden.\u003cbr\u003eThe findings indicate that both extremely low and high temperatures are significant environmental risk factors contributing to increased mortality from non-accidental, cardiovascular, and respiratory causes. The exposure-response relationship between daily mean temperature and mortality due to cardiovascular and respiratory diseases exhibits a distinct \u0026quot;U\u0026quot;-shaped nonlinear pattern, consistent with several recent studies conducted in China \u003cstrong\u003e[5\u0026ndash;7]\u003c/strong\u003e, thereby reinforcing the established association between temperature fluctuations and cardiorespiratory health outcomes. Subgroup analyses revealed that the risk associated with cold exposure systematically increases as temperatures decrease, aligning with prior evidence \u003cstrong\u003e[19\u0026ndash;20]\u003c/strong\u003e. Notably, while heat-related risks generally intensified with rising temperatures across all age groups except individuals aged 0\u0026ndash;64, this younger cohort demonstrated a comparatively weaker association, suggesting greater physiological resilience or adaptive capacity to high temperatures relative to those aged 65 and above. This may reflect age-related declines in thermoregulatory function, particularly diminished sweating efficiency, which impairs heat dissipation and increases susceptibility to heat accumulation among older adults \u003cstrong\u003e[21\u0026ndash;23]\u003c/strong\u003e. Furthermore, the higher prevalence of comorbidities and compromised physiological regulation in the elderly population exacerbates vulnerability to extreme heat, especially among patients with pre-existing cardiovascular or respiratory conditions\u0026nbsp;\u003cstrong\u003e[24\u0026ndash;25]\u003c/strong\u003e. In contrast, individuals aged 0\u0026ndash;64 appear more susceptible to extreme cold, potentially due to socioeconomic and behavioral factors\u0026mdash;such as higher proportions of students and working populations\u0026mdash;who engage in prolonged outdoor activities, thereby increasing exposure during cold periods.\u003cbr\u003e\u0026nbsp;Given China\u0026rsquo;s rapidly aging population\u0026mdash;where the number of individuals aged 65 and above reached 190 million in 2020 (13.5% of the total population) and is projected to exceed 25% by 2050\u0026mdash;the health implications of extreme weather events for this demographic warrant urgent attention. Impaired ability to adapt to thermal stress significantly elevates mortality risk among the elderly. Therefore, considering the heightened sensitivity of older adults to high temperatures and the accelerating pace of demographic aging in China, targeted adaptive strategies and medical interventions are critically needed to safeguard their health and well-being.\u003cbr\u003eTemporal patterns in temperature-mortality associations were also identified: the effects of extreme cold typically exhibit a lag of 2\u0026ndash;3 days and persist for approximately 20 days, whereas the impact of extreme heat is more immediate but shorter in duration\u0026mdash;a finding consistent with existing literature \u003cstrong\u003e[26\u0026ndash;27]\u003c/strong\u003e. With regard to gender differences, no statistically significant disparities were observed in this study, contrasting with previous reports suggesting higher vulnerability among women \u003cstrong\u003e[28\u0026ndash;29]\u003c/strong\u003e or men \u003cstrong\u003e[30]\u003c/strong\u003e. These discrepancies may arise from variations in geographical context, population characteristics, and exposure profiles across studies \u003cstrong\u003e[13,31]\u003c/strong\u003e. Regarding cause-specific mortality, the influence of both cold and heat on cardiovascular disease deaths was found to be substantially greater than on respiratory disease mortality, corroborating findings from Suzhou, China \u003cstrong\u003e[13]\u003c/strong\u003e. In Liuzhou, cardiovascular diseases constituted the leading cause of death among elderly residents, accounting for 43.2% (100,287/232,432) of all registered deaths during the study period, underscoring the pronounced threat posed by extreme temperatures to cardiovascular health in this population. However, heterogeneity exists across regions: while multiple studies confirm significant impacts of both cold and heat on cardiovascular mortality, research in Jinan, China, reported only cold-related increases without significant heat effects \u003cstrong\u003e[32]\u003c/strong\u003e; conversely, a study in Thailand identified a clear association between high temperatures and elevated respiratory mortality\u0026nbsp;\u003cstrong\u003e[33]\u003c/strong\u003e. Such regional variation likely reflects complex interactions among climatic conditions, population structure, disease burden, and temporal exposure patterns.\u003cbr\u003eThis study further quantified the mortality burden attributable to non-optimal temperatures and decomposed contributions by temperature category. Overall, 9.67% of non-accidental deaths were attributable to suboptimal thermal conditions, a proportion slightly lower than the 11.00% reported for China in a global analysis \u003cstrong\u003e[34]\u003c/strong\u003e. When distinguishing between cold and heat effects, cold-related exposures accounted for the majority of the attributable burden, consistent with prior findings \u003cstrong\u003e[34]\u003c/strong\u003e. Upon categorizing temperatures into four intervals\u0026mdash;extremely cold, mildly cold, mildly hot, and extremely hot\u0026mdash;we observed that mortality burdens from extremely cold conditions exceeded those from extremely hot ones, aligning with several existing reports \u003cstrong\u003e[35\u0026ndash;37]\u003c/strong\u003e. This highlights the severity of extreme cold events, attributable to their prolonged lag effects and sustained risks. Notably, although some studies suggest that mild heat may contribute to higher attributable mortality due to its frequent occurrence \u003cstrong\u003e[7]\u003c/strong\u003e, our results indicate that mild cold is the primary contributor\u0026mdash;a conclusion supported by multiple previous investigations \u003cstrong\u003e[19,34,39]\u003c/strong\u003e. Despite lower per-event risk, the high frequency of mild cold and mild heat exposures results in a disproportionately large cumulative mortality burden compared to rarer extreme events. For specific causes, non-optimal temperatures accounted for 13.15% of cardiovascular disease mortality and 10.51% of respiratory disease mortality, reflecting the stronger pathophysiological impact of cold on the cardiovascular system, as documented in earlier research\u0026nbsp;\u003cstrong\u003e[10,40,41]\u003c/strong\u003e. Biologically, cold-induced cardiovascular risks are linked to autonomic nervous system dysregulation, elevated blood pressure, increased metabolic demand, inflammatory activation, and oxidative stress. In terms of demographic distribution, although the attributable mortality burden in the 0\u0026ndash;64 age group did not reach statistical significance, both male individuals and those aged 65 and above exhibited higher attributable risks.\u003cbr\u003e\u0026nbsp;Model robustness was assessed through sensitivity analyses, incorporating alternative specifications for time trends (7\u0026ndash;9 degrees of freedom), air pollutants, and relative humidity (2\u0026ndash;5 degrees of freedom). Estimated relative risks remained stable across different model configurations, indicating reliable and robust results.\u003cbr\u003e\u0026nbsp;These findings underscore the pressing need for climate adaptation policies in Liuzhou City, particularly the development of localized and targeted interventions to mitigate the impacts of heatwaves and cold spells. Priority should be given to protecting vulnerable populations, including older adults and individuals with chronic illnesses. Implementing precise early warning systems and strengthening community-based support networks could effectively reduce excess mortality associated with extreme temperatures.\u003cbr\u003e\u0026nbsp;This study has several limitations. First, it was conducted in a single city\u0026mdash;Liuzhou\u0026mdash;and thus geographic generalizability may be limited due to variations in topography, climate, and population characteristics. Second, cause-of-death data were derived from ICD-10 codes recorded on death certificates, which may introduce classification inaccuracies. Third, exposure assessments for air pollutants and meteorological variables relied on fixed monitoring stations rather than personal-level measurements, potentially introducing exposure misclassification. Fourth, certain socioeconomic confounders\u0026mdash;such as income level and access to healthcare\u0026mdash;were not adjusted for in the analysis. Future multi-city studies that account for these confounding factors are necessary to provide a more comprehensive understanding of the temperature-mortality relationship and enhance the accuracy of public health risk assessments.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study revealed that in Liuzhou, exposure to non-optimal temperatures\u0026mdash;encompassing both cold and hot extremes\u0026mdash;is significantly associated with increased mortality. In comparison to extreme heat, extreme cold poses a relatively higher risk. Although the health impacts of cold temperatures may not manifest immediately, their prolonged duration often leads to underestimation, potentially resulting in a greater mortality burden. Notably, individuals aged 65 years and above demonstrate heightened vulnerability to temperature fluctuations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe extend our sincere gratitude to all the researchers and participants for their invaluable support for this study. We would also like to thank all the staff of the Liuzhou Center for Disease Control and Prevention, Guangxi, China, for providing us with a good research environment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Scientific research Project of the Health Commission of Guangxi (grant Z-B20241388). The findings and conclusions in this report are those of the author(s) and do not necessarily represent the official position of the funding agencies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYL - data collection, primary data analysis, and writing\u003c/p\u003e\n\u003cp\u003eSY - data collection, analysis, revisions, and writing\u003c/p\u003e\n\u003cp\u003eJL, JY, DH- conception and design\u003c/p\u003e\n\u003cp\u003eLZ- supervision, revisions, and final review\u003c/p\u003e\n\u003cp\u003eYL and SY have made equal contributions to this article and are all recognized as co-first authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData sharing statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this study are stored at the Institute of Environmental Hygiene and School Health Prevention and Control, Liuzhou Center for Disease Control and Prevention. The data are managed in strict accordance with the data security and confidentiality requirements imposed by the Liuzhou Center for Disease Control and Prevention and are not publicly available. Access to the data may be granted to institutions that meet the criteria and have a reasonable research purpose. Formal requests must first be submitted to the Institute of Environmental Hygiene and School Health Prevention and Control (contact via [email protected]). Upon preliminary review, the request will be forwarded to the Liuzhou Center for Disease Control and Prevention for final approval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have declared that no competing interests exist.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eGuo, Yuming et al. \u0026ldquo;The impact of temperature on mortality in Tianjin, China: a case-crossover design with a distributed lag nonlinear model.\u0026rdquo; \u003cem\u003eEnvironmental health perspectives\u003c/em\u003e vol. 119,12 (2011)\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eLee, Hyewon et al. \u0026ldquo;Association between ambient temperature and injury by intentions and mechanisms: A case-crossover design with a distributed lag nonlinear model.\u0026rdquo; \u003cem\u003eThe Science of the total environment\u003c/em\u003e vol. 746 (2020)\u003c/li\u003e\n \u003cli\u003eKouis, Panayiotis et al. \u0026ldquo;The effect of ambient air temperature on cardiovascular and respiratory mortality in Thessaloniki, Greece.\u0026rdquo; \u003cem\u003eThe Science of the total environment\u003c/em\u003e vol. 647 (2019)\u003c/li\u003e\n \u003cli\u003eChange, Climate I: \u0026quot;The physical science basis.\u0026quot; Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. 2013.\u003c/li\u003e\n \u003cli\u003eXia, Yizhang et al. \u0026ldquo;Effects of ambient temperature on mortality among elderly residents of Chengdu city in Southwest China, 2016-2020: a distributed-lag non-linear time series analysis.\u0026rdquo;\u0026nbsp;BMC public health\u0026nbsp;vol. 23,1 149. 21 Jan. 2023\u003c/li\u003e\n \u003cli\u003eGuo, Hongju et al. \u0026ldquo;Time series study on the effects of daily average temperature on the mortality from respiratory diseases and circulatory diseases: a case study in Mianyang City.\u0026rdquo;\u0026nbsp;BMC public health\u0026nbsp;vol. 22,1 1001. 17 May. 2022\u003c/li\u003e\n \u003cli\u003eChen, Xuanhao et al. \u0026ldquo;Association between ambient temperature and non-accidental mortality in Guiyang, China: A time-series analysis (2013-2023).\u0026rdquo; \u003cem\u003ePloS one\u003c/em\u003e vol. 20,4 e0319863. 1 Apr. 2025\u003c/li\u003e\n \u003cli\u003eBreitner, Susanne et al. \u0026ldquo;Short-term effects of air temperature on mortality and effect modification by air pollution in three cities of Bavaria, Germany: a time-series analysis.\u0026rdquo; \u003cem\u003eThe Science of the total environment\u003c/em\u003e vol. 485-486 (2014)\u003c/li\u003e\n \u003cli\u003eAnderson, Brooke G, and Michelle L Bell. \u0026ldquo;Weather-related mortality: how heat, cold, and heat waves affect mortality in the United States.\u0026rdquo; \u003cem\u003eEpidemiology (Cambridge, Mass.)\u003c/em\u003e vol. 20,2 (2009)\u003c/li\u003e\n \u003cli\u003eMa, Wenjuan et al. \u0026ldquo;Temperature-related mortality in 17 large Chinese cities: how heat and cold affect mortality in China.\u0026rdquo; \u003cem\u003eEnvironmental research\u003c/em\u003e vol. 134 (2014)\u003c/li\u003e\n \u003cli\u003eGuo, Yuming et al. \u0026ldquo;Global variation in the effects of ambient temperature on mortality: a systematic evaluation.\u0026rdquo; \u003cem\u003eEpidemiology (Cambridge, Mass.)\u003c/em\u003e vol. 25,6 (2014)\u003c/li\u003e\n \u003cli\u003eCostello, Anthony et al. \u0026ldquo;Managing the health effects of climate change: Lancet and University College London Institute for Global Health Commission.\u0026rdquo; \u003cem\u003eLancet (London, England)\u003c/em\u003e vol. 373,9676 (2009)\u003c/li\u003e\n \u003cli\u003eWang, Cuicui et al. \u0026ldquo;Temperature and daily mortality in Suzhou, China: a time series analysis.\u0026rdquo; \u003cem\u003eThe Science of the total environment\u003c/em\u003e vol. 466-467 (2014)\u003c/li\u003e\n \u003cli\u003eMa WJ. A study on temperature-related mortality in 16 Chinese cities [D] Fudan University; 2014.\u003c/li\u003e\n \u003cli\u003eMa XM, Li RK, Luo K, et al. Association between temperature and mortality in three cities in China [J] Basic and clinic medicine. 2016;36(6):805\u0026ndash;810.\u003c/li\u003e\n \u003cli\u003eAchebak, Hicham et al. \u0026ldquo;Trends in temperature-related age-specific and sex-specific mortality from cardiovascular diseases in Spain: a national time-series analysis.\u0026rdquo; \u003cem\u003eThe Lancet. Planetary health\u003c/em\u003e vol. 3,7 (2019)\u003c/li\u003e\n \u003cli\u003eGasparrini, A et al. \u0026ldquo;Distributed lag non-linear models.\u0026rdquo; \u003cem\u003eStatistics in medicine\u003c/em\u003e vol. 29,21 (2010)\u003c/li\u003e\n \u003cli\u003eGasparrini, Antonio, and Michela Leone. \u0026ldquo;Attributable risk from distributed lag models.\u0026rdquo; \u003cem\u003eBMC medical research methodology\u003c/em\u003e vol. 14 55. 23 Apr. 2014\u003c/li\u003e\n \u003cli\u003ePetkova, Elisaveta P et al. \u0026ldquo;Mortality attributable to heat and cold among the elderly in Sofia, Bulgaria.\u0026rdquo; \u003cem\u003eInternational journal of biometeorology\u003c/em\u003e vol. 65,6 (2021)\u003c/li\u003e\n \u003cli\u003eZhao, Zhonghui et al. \u0026ldquo;Association between ambient cold exposure and mortality risk in Shandong Province, China: Modification effect of particulate matter size.\u0026rdquo; \u003cem\u003eFrontiers in public health\u003c/em\u003e vol. 10 1093588. 5 Jan. 2023\u003c/li\u003e\n \u003cli\u003eHerbst, Jonathon et al. \u0026ldquo;Heat-related deaths in Adelaide, South Australia: review of the literature and case findings - an Australian perspective.\u0026rdquo; \u003cem\u003eJournal of forensic and legal medicine\u003c/em\u003e vol. 22 (2014)\u003c/li\u003e\n \u003cli\u003eFalchetta, Giacomo et al. \u0026ldquo;Global projections of heat exposure of older adults.\u0026rdquo; \u003cem\u003eNature communications\u003c/em\u003e vol. 15,1 3678. 14 May. 2024\u003c/li\u003e\n \u003cli\u003eYuan, Lei et al. \u0026ldquo;Non-optimal temperature-attributable mortality and morbidity burden by cause, age and sex under climate and population change scenarios: a nationwide modelling study in Japan.\u0026rdquo; \u003cem\u003eThe Lancet regional health. Western Pacific\u003c/em\u003e vol. 52 101214. 8 Oct. 2024,\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eFouillet, A et al. \u0026ldquo;Excess mortality related to the August 2003 heat wave in France.\u0026rdquo; \u003cem\u003eInternational archives of occupational and environmental health\u003c/em\u003e vol. 80,1 (2006)\u003c/li\u003e\n \u003cli\u003eVaneckova, Pavla et al. \u0026ldquo;Spatial analysis of heat-related mortality among the elderly between 1993 and 2004 in Sydney, Australia.\u0026rdquo; \u003cem\u003eSocial science \u0026amp; medicine (1982)\u003c/em\u003e vol. 70,2 (2010)\u003c/li\u003e\n \u003cli\u003eLi, Guoxing et al. \u0026ldquo;Temporal variation in associations between temperature and years of life lost in a southern China city with typical subtropical climate.\u0026rdquo; \u003cem\u003eScientific reports\u003c/em\u003e vol. 7,1 4650. 5 Jul. 2017\u003c/li\u003e\n \u003cli\u003eZhang, Yunquan et al. \u0026ldquo;The burden of ambient temperature on years of life lost: A multi-community analysis in Hubei, China.\u0026rdquo; \u003cem\u003eThe Science of the total environment\u003c/em\u003e vol. 621 (2018)\u003c/li\u003e\n \u003cli\u003eDeng, Jixiang et al. \u0026ldquo;Ambient temperature and non-accidental mortality: a time series study.\u0026rdquo; \u003cem\u003eEnvironmental science and pollution research international\u003c/em\u003e vol. 27,4 (2020)\u003c/li\u003e\n \u003cli\u003eSeposo, Xerxes T et al. \u0026ldquo;Evaluating the Effects of Temperature on Mortality in Manila City (Philippines) from 2006-2010 Using a Distributed Lag Nonlinear Model.\u0026rdquo; \u003cem\u003eInternational journal of environmental research and public health\u003c/em\u003e vol. 12,6 6842-57. 16 Jun. 2015\u003c/li\u003e\n \u003cli\u003eSon, Ji-Young et al. \u0026ldquo;Vulnerability to temperature-related mortality in Seoul, Korea.\u0026rdquo; \u003cem\u003eEnvironmental research letters : ERL [Web site]\u003c/em\u003e vol. 6,3 (2011)\u003c/li\u003e\n \u003cli\u003eMa, Wenjuan et al.\u0026nbsp;\u0026ldquo;Modifiers of the temperature-mortality association in Shanghai, China.\u0026rdquo;\u0026nbsp;International journal of biometeorology vol. 56,1 (2012)\u003c/li\u003e\n \u003cli\u003eHan, Jing et al. \u0026ldquo;The impact of temperature extremes on mortality: a time-series study in Jinan, China.\u0026rdquo; \u003cem\u003eBMJ open\u003c/em\u003e vol. 7,4 e014741. 2 May. 2017\u003c/li\u003e\n \u003cli\u003eGuo, Yuming et al. \u0026ldquo;Effects of temperature on mortality in Chiang Mai city, Thailand: a time series study.\u0026rdquo; \u003cem\u003eEnvironmental health : a global access science source\u003c/em\u003e vol. 11 36. 9 Jul. 2012\u003c/li\u003e\n \u003cli\u003eGasparrini, Antonio et al. \u0026ldquo;Mortality risk attributable to high and low ambient temperature: a multicountry observational study.\u0026rdquo; \u003cem\u003eLancet (London, England)\u003c/em\u003e vol. 386,9991 (2015)\u003c/li\u003e\n \u003cli\u003eMart\u0026iacute;nez-Solanas, \u0026Egrave;rica et al. \u0026ldquo;Projections of temperature-attributable mortality in Europe: a time series analysis of 147 contiguous regions in 16 countries.\u0026rdquo; \u003cem\u003eThe Lancet. Planetary health\u003c/em\u003e vol. 5,7 (2021)\u003c/li\u003e\n \u003cli\u003eAlahmad, Barrak et al. \u0026ldquo;Associations Between Extreme Temperatures and Cardiovascular Cause-Specific Mortality: Results From 27 Countries.\u0026rdquo; \u003cem\u003eCirculation\u003c/em\u003e vol. 147,1 (2023)\u003c/li\u003e\n \u003cli\u003eMasselot, Pierre et al. \u0026ldquo;Excess mortality attributed to heat and cold: a health impact assessment study in 854 cities in Europe.\u0026rdquo; \u003cem\u003eThe Lancet. Planetary health\u003c/em\u003e vol. 7,4 (2023)\u003c/li\u003e\n \u003cli\u003eChen, Renjie et al. \u0026ldquo;Association between ambient temperature and mortality risk and burden: time series study in 272 main Chinese cities.\u0026rdquo; \u003cem\u003eBMJ (Clinical research ed.)\u003c/em\u003e vol. 363 k4306. 31 Oct. 2018\u003c/li\u003e\n \u003cli\u003ePetkova, Elisaveta P et al. \u0026ldquo;Mortality attributable to heat and cold among the elderly in Sofia, Bulgaria.\u0026rdquo; \u003cem\u003eInternational journal of biometeorology\u003c/em\u003e vol. 65,6 (2021)\u003c/li\u003e\n \u003cli\u003eLee, Whan-Hee et al. \u0026ldquo;An Investigation on Attributes of Ambient Temperature and Diurnal Temperature Range on Mortality in Five East-Asian Countries.\u0026rdquo; \u003cem\u003eScientific reports\u003c/em\u003e vol. 7,1 10207. 31 Aug. 2017\u003c/li\u003e\n \u003cli\u003eZeka, Ariana et al. \u0026ldquo;The association of cold weather and all-cause and cause-specific mortality in the island of Ireland between 1984 and 2007.\u0026rdquo; \u003cem\u003eEnvironmental health : a global access science source\u003c/em\u003e vol. 13 104. 6 Dec. 2014\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e. Summary descriptive statistics of meteorological factors, air pollutants and daily non-accidental deaths in Liuzhou, 2014\u0026ndash;2024.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"554\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003eP\u003csub\u003e25\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003eP\u003csub\u003e50\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003eP\u003csub\u003e75\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eNon-accidental deaths\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e232432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e56.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e16.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e122\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eRespiratory deaths\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e25842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e6.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e3.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eCardiovascular deaths\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e100287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e24.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e9.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e62\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e138835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e33.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e10.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e78\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e93589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e22.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e7.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eAge 0-64 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e61189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e15.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e4.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e34\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eAge \u0026ge; 65 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e171243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e41.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e14.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eTemperature (\u0026deg;C)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e20.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e7.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e-0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e15.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e22.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e27.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e33.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eHumidity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e76.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e14.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eSO\u003csub\u003e2\u003c/sub\u003e(\u0026mu;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e15.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e10.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e3.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e7.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e11.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e19.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e80.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eO\u003csub\u003e3\u003c/sub\u003e (\u0026mu;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e77.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e31.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e5.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e55.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e75.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e96.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e212.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003eMinimum mortality temperatures (MMTs) and relative risks (RRs) associated with extreme low and high temperatures with 95% empirical confidence interval (95% CI).\u003c/p\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"659\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003eGroups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eThe minimum mortality temperature (\u0026deg;C)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExtreme low temperature (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExtreme high temperature (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e23.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e1.481(1.324,1.656)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e1.219(1.066,1.394)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 110px;\"\u003e\n \u003cp\u003eRespiratory disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e22.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e1.442(1.081,1.923)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e1.510(1.038,2.195)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 110px;\"\u003e\n \u003cp\u003eCardiovascular disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e24.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e1.694(1.439,1.994)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e1.228(1.020,1.480)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e24.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e1.550(1.344,1.789)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e1.185(1.012,1.387)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e1.400(1.206,1.626)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e1.290(1.053,1.581)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026ge;65 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e1.525(1.346,1.728)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e1.397(1.192,1.638)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0-64 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e33.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e1.671(1.138,2.452)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e1.059(0.922,1.218)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u0026nbsp;\u003c/strong\u003eExtreme Low and extreme High were defined as temperatures below the 2.5th percentile and above the 97.5th percentile of the temperature distribution\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003eThe attributable fractions to non-optimal temperatures with 95% empirical confidence interval (95% CI)\u003c/p\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"682\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003eGroups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCold (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHeat (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e9.69(7.01,12.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e7.19(4.67,9.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e2.49(0.82,4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 137px;\"\u003e\n \u003cp\u003eRespiratory disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e10.51(\u0026apos;-1.83,21.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e5.24(\u0026apos;-6.69,14.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e5.27(2.58,7.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 137px;\"\u003e\n \u003cp\u003eCardiovascular disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e13.15(6.97,18.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e10.82(4.93,15.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e2.31(1.15,3.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e11.37(6.18,16.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e9.27(4.17,13.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e2.09(1.10,3.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e4.06(\u0026apos;-3.75,10.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e2.56(0.27,4.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e1.51(\u0026apos;-4.36,7.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026ge;65 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e8.93(2.68,14.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e5.93(0.54,10.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e3.01(1.75,4.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 137px;\"\u003e\n \u003cp\u003e0-64 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e14.88(\u0026apos;-2.87,27.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e14.88(\u0026apos;-2.87,27.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e0(0,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u0026nbsp;\u003c/strong\u003eCold: temperatures \u0026lt;6 \u0026deg;C; Heat: temperatures \u0026gt;31 \u0026deg;C\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u0026nbsp;\u003c/strong\u003eThe attributable numbers to non-optimal temperatures with 95% empirical confidence interval (95% CI).\u003c/p\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"730\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003eGroups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCold\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHeat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e21727(15706,27704)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e16124(10464,22038)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e5587 (1833,9183)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 137px;\"\u003e\n \u003cp\u003eRespiratory disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e2565(\u0026apos;-448,5167)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e1279(\u0026apos;-1633,3600)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e1286(630,1901)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 137px;\"\u003e\n \u003cp\u003eCardiovascular disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e12677(6722,17720)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e10433(4753,15232)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e2225(1108,3185)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e15217(8266,21750)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e12396(5574,18442)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e2794(1476,4059)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e3669(\u0026apos;-3390,9810)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e2312(242,4056)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e1368(\u0026apos;-3941,6329)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026ge;65 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e14662(4397,24538)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e9728(880,17893)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e4934(2869,6816)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 137px;\"\u003e\n \u003cp\u003e0-64 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e8935(\u0026apos;-1723,16601)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e8935(\u0026apos;-1723,16601)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e0(0,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u0026nbsp;\u003c/strong\u003eCold: temperatures \u0026lt;6 \u0026deg;C; Heat: temperatures \u0026gt;31 \u0026deg;C\u003c/p\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":"Temperature, mortality rate, distributed lag nonlinear model, time series analysis","lastPublishedDoi":"10.21203/rs.3.rs-8346096/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8346096/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"With the intensification of global climate change, the impact of environmental temperature on human health has attracted growing attention. This study aims to evaluate the effects of temperature on total non-accidental mortality, as well as mortality from cardiovascular and respiratory diseases, in Liuzhou City, China. Daily meteorological data and mortality records from 2014 to 2024 were collected for analysis. The cumulative relative risks (RRs) associated with non-optimal and extreme temperatures were estimated using a distributed lag nonlinear model (DLNM) combined with quasi-Poisson regression. Additionally, the attributable fractions (AFs) and attributable numbers (ANs) were calculated to assess the mortality burden attributable to non-optimal temperatures. Results indicated a significant U-shaped association between temperature and mortality, except in the 0–64 age group. The cumulative relative risk of extreme cold was 1.48 (95% CI: 1.32–1.66), while that of extreme heat was 1.22 (95% CI: 1.07–1.39). Non-optimal temperatures accounted for 9.69% (95% CI: 7.01–12.36) of total non-accidental deaths, with cold exposure contributing 7.19% (95% CI: 4.67–9.83) and heat exposure accounting for 2.49% (95% CI: 0.82–4.10). The findings indicate that exposure to non-optimal temperatures is significantly associated with increased mortality risk. Individuals aged 65 years and older demonstrated higher vulnerability to both cold and heat, highlighting the need for enhanced protective measures and targeted interventions for the elderly population.","manuscriptTitle":"Association between Ambient Temperature and Nonaccidental Mortality in Liuzhou, China: A TimeSeries Study, 2014–2024","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-11 07:41:22","doi":"10.21203/rs.3.rs-8346096/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":"42077a6c-b6d6-4663-92d2-34997255228f","owner":[],"postedDate":"February 11th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-18T07:42:34+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-08T11:16:37+00:00","index":110,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":62555799,"name":"Earth and environmental sciences/Climate sciences"},{"id":62555800,"name":"Health sciences/Diseases"},{"id":62555801,"name":"Earth and environmental sciences/Environmental sciences"},{"id":62555802,"name":"Health sciences/Health care"},{"id":62555803,"name":"Health sciences/Medical research"},{"id":62555804,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-05-18T07:56:13+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-11 07:41:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8346096","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8346096","identity":"rs-8346096","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}