Long-Term Exposure to Non-Optimal Ambient Temperatures associated with Dialysis Incidence and Mortality in Advanced CKD Patients: A Population-Based Cohort Study in Taiwan

Long-Term Exposure to Non-Optimal Ambient Temperatures associated with Dialysis Incidence and Mortality in Advanced CKD Patients: A Population-Based Cohort Study in Taiwan | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Long-Term Exposure to Non-Optimal Ambient Temperatures associated with Dialysis Incidence and Mortality in Advanced CKD Patients: A Population-Based Cohort Study in Taiwan Shih-Feng Chen, Yu-Huei Chien, Chu-Hao Weng, Yu-Chin Huang, Pau-Chung Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8691969/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 9 You are reading this latest preprint version Abstract Background Chronic kidney disease (CKD) of unknown etiology, potentially related to heat stress, has been reported among agricultural workers in tropical low- and middle-income countries. While high-income countries have greater heat-adaptation capacity, evidence on CKD risks from heat exposure in warm high-income settings is limited. Moreover, the effects of prolonged exposure to non-optimal cold or heat on kidney health remain unclear. This study investigates the associations between long-term non-optimal daytime temperature exposure and the risks of dialysis progression and mortality in advanced CKD patients in Taiwan. Methods We conducted a nationwide retrospective cohort study to examine associations between long-term exposure to non-optimal daytime temperatures and risks of dialysis progression and mortality among patients with advanced CKD in Taiwan. Data from 86,928 advanced CKD patients enrolled between 2008 and 2021 were analyzed, with follow-up through December 31, 2022. Non-optimal temperature days were defined as mean daytime temperatures ≥ 30°C (hot) or ≤ 15°C (cold). Time-weighted percentages and mean temparatures for hot and cold days were calculated for each patient. Additonally, mean daytime temperature across all days during the follow-up period was also used as an exposure index. Cox proportional hazards models estimated hazard ratios (HRs) for dialysis progression and mortality per 1% increase in exposure. Cumulative incidence curves assessed outcome differences across exposure quartiles, and restricted cubic spline analyses evaluated dose–response relationships. Results Each 1% increase in time-weighted cold-day exposure was associated with a 14% higher risk of dialysis progression and a 9% higher risk of mortality. Conversely, a 1% increase in hot-day exposure was associated with 5% and 3% lower risks of dialysis progression and mortality, respectively. Spline analyses demonstrated a dose-dependent protective effect of higher mean temperatures. Higher mean daytime temperatures during follow-up were associated with lower risks of dialysis progression and mortality, whereas lower temperatures increased risks (P for both linearity and non-linearity < 0.001). Conclusions Long-term cold exposure increases dialysis and mortality risks in advanced CKD, whereas prolonged heat exposure may be protective for both outcomes, reflecting population-level heat adaptation. Our findings emphasize the need for climate-sensitive policies to mitigate the health impacts of non-optimal temperatures in vulnerable populations. Non-optimal temperatures advanced CKD Dialysis Mortality Heat adaptation Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Climate change poses a major threat to human health, with the 2015 Lancet Commission identifying mitigation of its impacts as the greatest health opportunity of the 21st century [ 1 ]. The kidney, essential for coping with heat stress, is highly vulnerable to thermal injury [ 2 ]. Heat-related renal injury often presents as acute kidney injury (AKI) triggered by hyperthermia, volume depletion, and rhabdomyolysis [ 3 – 5 ]. Recurrent AKI may progress to chronic kidney disease (CKD) and end-stage kidney disease (ESKD) [ 2 , 3 , 5 ]. CKD affects over 10% of the global population and contributes significantly to the global disease burden [ 6 ]. Progression to ESKD imposes substantial morbidity, mortality, and socioeconomic burden due to renal replacement therapy [ 7 , 8 ]. Geographic variations in CKD prevalence cannot be fully explained by traditional risk factors, such as diabetes and hypertension [ 9 ], suggesting a potential role for environmental exposures, including extreme temperatures [ 4 , 5 , 9 , 10 ]. Over the past decade, CKD of unknown etiology has emerged among agricultural workers in tropical low- and middle-income countries (LMICs), likely driven by recurrent occupational heat stress [ 3 , 5 , 11 ]. This pattern suggests heat-induced nephropathy as a potential climate-driven epidemic [ 4 ]. Population-level vulnerability and adaptive capacity, however, vary, with high-income countries (HICs) generally better equipped to implement heat-mitigation strategies [ 12 , 13 ]. Nevertheless, evidence on the effects of environmental heat on kidney health in warm or hot HIC settings remain limited. Most studies on temperature and kidney health have emphasized summer heat, while relatively few have examined the effects of cold exposure on kidney disease onset, progression, or mortality. In South Korea, Park et al. (2024) reported that kidney function decline was associated not only with temperatures above 25°C but also with those below − 10°C [ 14 ], and in Japan, Htay et al. (2024) observed higher mortality at lower temperatures across all kidney disease categories [ 15 ]. Existing research has largely focused on short-term extreme temperatures and acute kidney injury, leaving the long-term effects of sustained exposure to non-optimal temperatures underexplored. Taiwan, ranked 14th globally in GDP per capita by Forbes in 2024 [ 16 ], spans subtropical and tropical zones and faces rising surface temperatures (1.0–1.4°C over the past century), seasonal variability, and increasing extreme weather [ 17 ]. It also has the world’s highest incidence and prevalence of treated ESKD [ 18 ], with a growing dialysis population that imposes substantial medical and socioeconomic burden [ 19 ]. In 2007, the government launched a multidisciplinary Pre-ESKD Care Program for CKD stages 3b–5 [ 20 ]. However, preventive strategies remain focused on diabetes and hypertension, while emerging risk factors such as climate-related temperature abnormalities require greater attention. In Taiwan, limited research has addressed short-term extreme temperatures and acute kidney-related events, including hospitalizations, emergency visits, and mortality [ 21 , 22 ]. The impact of long-term non-optimal hot and cold exposure on CKD outcomes, however, is unclear. This study therefore investigated the association between prolonged daytime (08:00–19:00) exposure to non-optimal ambient temperatures and dialysis incidence or mortality among advanced CKD patients (stage 3b–5; eGFR < 45 ml/min/1.73 m²) in Taiwan. Methods This retrospective nationwide cohort study linked the National Health Insurance Research Database (NHIRD) and the Taiwan Air Quality Monitoring Database (TAQMD) to evaluate the risks of progression to ESKD requiring dialysis and mortality among advanced CKD patients exposed to prolonged non-optimal ambient daytime temperatures. It was approved by the Research Ethics Review Committee of New Taipei City Hospital (protocol No. NTPC 113001-N). Data Sources The NHIRD contains de-identified claims data from Taiwan’s mandatory single-payer National Health Insurance program, established in 1995 and now covering 99.9% of the population. Prior to 2016, researchers accessed random or disease-specific samples; since then, full-population data have been available through the Health and Welfare Data Science Center (HWDC), enabling linkage with other registries (e.g., Taiwan Death Registry, Taiwan Cancer Registry) but requiring onsite analysis, which increases research time and cost [ 23 ]. Diagnoses were coded using ICD-9-CM before 2016 and both ICD-9-CM and ICD-10-CM thereafter. Detailed descriptions of the NHIRD are available elsewhere [ 24 ]. The TAQMD, established in 1993 by the Ministry of Environment (MOENV), has expanded over time and currently comprises 85 nationwide monitoring stations, mainly in urban and industrial areas with some rural coverage. These stations record real-time hourly meteorological data (temperature, humidity, rainfall, wind speed and direction) and air pollutants (PM₂.₅, PM₁₀, NO₂, SO₂, O₃, and CO). Data are publicly available via MOENV’s Air Quality Network ( https://wot.moenv.gov.tw/ ). Quality control is ensured through annual calibration, third-party validation, and adherence to international standards (e.g., ISO 9001), guaranteeing reliable and consistent measurements [ 25 ]. Study Population This nationwide retrospective cohort study included adults with advanced CKD (stages 3b–5; eGFR < 45 ml/min/1.73 m²) enrolled in Taiwan’s National Health Insurance Pre-ESKD Care Program (reimbursement code: P3402C). Daily average daytime (08:00–19:00) temperature and air pollutant data from the TAQMD were linked to patients’ residential areas using zip codes. Eligible participants were those aged ≥ 18 years who entered the Pre-ESKD Care Program between January 1, 2008, and December 31, 2021, and were followed until progression to maintenance dialysis, death (before or after maintenance dialysis), or December 31, 2022, whichever occurred first. In this study, patients’ residential areas were defined as the locations of clinics or hospitals where they received care for acute upper respiratory infections (ICD-9-CM: 460; ICD-10-CM: J00) or allergic rhinitis/sinusitis (ICD-9-CM: 472, 473, 477; ICD-10-CM: J30–J32), and those without such outpatient records, residential location was inferred using workplace information from the “beneficiary registry” dataset [ 26 ]. During participant selection, we excluded individuals who met any of the following criteria: (1) incomplete demographic information; (2) age below 18 or above 99; (3) prior enrollment in the pre-ESKD care program; (4) follow-up duration of less than 365 days; (5) initiation of dialysis within 365 days of follow-up; (6) residence in areas without air quality monitoring stations; or (7) missing data on ambient temperature, humidity, or PM 2.5 . After these exclusions, 86,928 patients with advanced CKD were included in the final analysis ( Fig. 1 ) . Measurements of Exposure Long-term exposure to non-optimal ambient temperatures was assessed by defining hot days as those with an average daytime temperature (08:00–19:00) ≥ 30°C and cold days as those ≤ 15°C [ 27 ]. Two indices were used: (1) the time-weighted percentage of exposure, calculated as the cumulative proportion of hot or cold days relative to total follow-up days, and (2) the mean daytime temperature across all hot days or, separately, across all cold days during follow-up. Regarding sensitivity analysis, for hot days, we also applied alternative thresholds of 32°C and 34°C; for cold days, in addition to the 15°C definition, we further examined results using a 13°C threshold. Furthermore, the long-term mean daytime temperature across all days during the follow-up period was also used as an exposure index. Outcome Ascertainment The primary outcome of this study is the initiation of maintenance dialysis. We defined the initiation date as the point at which advanced CKD patients applied for the catastrophic illness certificate (CIC) card for maintenance dialysis and initiated dialysis at the time around CIC application. The secondary outcome is death occurring more than 365 days after the index date. Death-related information, including the date, place, and causes, was linked to the Taiwan Death Registry database within the HWDC. For patients who experienced either outcome, follow-up was terminated, while for those who did not experience dialysis or death, follow-up continued until December 31, 2022. Baseline Characteristics and Covariates Baseline characteristics included both individual- and zip-code–level covariates. Individual-level factors were age, sex, diabetes mellitus (DM), hypertension, coronary artery disease, heart failure, stroke, liver cirrhosis, chronic obstructive pulmonary disease (COPD), malignancy, Charlson Comorbidity Index (CCI), and monthly insurance salary (≤ 19,047; 19,047–25,000; ≥25,000). Comorbidities (i.e., DM, hypertension, coronary artery disease, liver cirrhosis, and COPD) were defined if patients had at least two outpatient diagnoses or one inpatient diagnosis within the year before entering the pre-ESKD program. History of events (including heart failure and stroke) was defined as any prior hospitalization traceable back to the year 2000. Malignancy was confirmed by the possession of a CIC card. All comorbidities were identified using both the Ninth and Tenth Revisions of the International Classification of Diseases (ICD-9-CM and ICD-10-CM) ( Supplemental Table 1 ). Additionally, at the zip code level, baseline long-term PM 2.5 concentrations and relative humidity to which participants exposed were calculated as the mean values over the 365 days prior to the index date. Furthermore, we categorized the degree of urbanization of the residential area into four levels—the lowest, low, high, and the highest—based on the criteria proposed by Liu [ 28 ], corresponding to the city, town, suburban, and rural categories defined by Morrish and Florio [ 29 ]. Statistical Analysis Baseline characteristics and the time-weighted percentage of exposure to non-optimal temperatures were compared between event (patients experiencing outcomes) and non-event groups using independent-samples t tests for continuous variables and chi-square tests for categorical variables. Associations between the time-weighted percentage of exposure (modeled continuously) and outcomes were assessed using Cox proportional hazards models adjusted for all covariates. In addition, participants were stratified into quartiles based on the percentage of cumulative hot (≥ 30°C) or cold (≤ 15°C) daytime days relative to total follow-up, with equal sample sizes in each quartile. Cumulative incidence curves, estimated using Cox proportional hazards models, were used to compare the associations between non-optimal temperature exposure and outcomes across quartiles. Furthermore, we used restricted cubic spline (RCS) analysis to examine the dose–response relationship between the mean daytime temperature during follow-up and the risk of progression to maintenance dialysis or death. We also specifically focused on the associations between mean daytime non-optimal temperatures—either hot or cold—during follow-up and the risks of both outcomes. The mean daytime temperature and mean daytime non-optimal temperatures were modeled as flexible RCS variables with knots placed at the 10th, 50th, and 95th percentiles. RCS modeling was conducted using R version 4.4.2 (R Foundation for Statistical Computing) with the “rms” package. All other statistical analyses were performed using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA). A two-sided P value < 0.05 was considered statistically significant. All statistical analyses were carried out within the HWDC. Results Baseline Characteristics of Study Population This study included 86,928 adult advanced CKD patients residing in areas with available environmental data on temperature, relative humidity, and PM 2.5 levels ( Fig. 1 ) . The average age of the participants was 69.1 years, with 56,812 patients (65.4%) aged over 65 (Table 1 ). The mean follow-up period was 4.1 years. A majority of the participants were male, totaling 49,139 individuals (56.5%). Regarding comorbidities, 71,472 patients (82.2%) had hypertension, 49,798 (57.3%) had diabetes, 20,015 (23.0%) had coronary artery disease, and 9,901 (11.4%) had malignancies. Other conditions, such as COPD, liver cirrhosis, heart failure, and stroke, each accounted for less than 10% of the study population. Regarding the level of urbanization in residential areas, the majority of advanced CKD patients lived in high-urbanization areas (i.e., towns; 42,326 individuals, 48.7%), followed by those in the highest-urbanization areas (i.e., cities; 23,357, 26.9%) and low-urbanization areas (i.e., suburbs; 18,637, 21.4%). The smallest proportion resided in the lowest-urbanization areas (i.e., rural areas; 2,608, 3.0%). Table 1 Baseline characteristics of patients according to status of subsequent outcomes Progression to maintenance dialysis All-cause death Variable Total ( n = 86 928) Yes ( n = 19 494) No ( n = 67 434) P value Yes ( n = 27 524) No ( n = 59 404) P value Age, year 69.1 ± 13.4 64.0 ± 13.0 70.6 ± 13.1 < 0.001 75.2 ± 11.2 66.2 ± 13.3 < 0.001 Age ≥ 65 years 56 812 (65.4) 9 605 (49.3) 47 207 (70.0) < 0.001 22 512 (81.8) 34 300 (57.7) < 0.001 Male sex 49 139 (56.5) 10 698 (54.9) 38 441 (57.0) < 0.001 15 887 (57.7) 33 252 (56.0) < 0.001 Urbanization level of the residence < 0.001 < 0.001 The lowest level 2 608 (3.0) 512 (2.6) 2 096 (3.1) 935 (3.4) 1 673 (2.8) Low level 18 637 (21.4) 4 394 (22.5) 14 243 (21.1) 5 991 (21.8) 12 646 (21.3) High level 42 326 (48.7) 9 307 (47.7) 33 019 (49.0) 13 572 (49.3) 28 754 (48.4) The highest level 23 357 (26.9) 5 281 (27.1) 18 076 (26.8) 7 026 (25.5) 16 331 (27.5) Monthly income, NTD < 0.001 < 0.001 ≤ 19 047 25 475 (29.3) 5 350 (27.4) 20 125 (29.8) 9 461 (34.4) 16 014 (27.0) 19 047 − 25 200 31 531 (36.3) 7 315 (37.5) 24 216 (35.9) 10 181 (37.0) 21 350 (35.9) ≥ 25 200 29 922 (34.4) 6 829 (35.0) 23 093 (34.3) 7 882 (28.6) 22 040 (37.1) Comorbidity Diabetes mellitus 49 798 (57.3) 12 871 (66.0) 36 927 (54.8) < 0.001 16 766 (60.9) 33 032 (55.6) < 0.001 Hypertension 71 472 (82.2) 16 555 (84.9) 54 917 (81.4) < 0.001 23 130 (84.0) 48 342 (81.4) < 0.001 Coronary artery disease 20 015 (23.0) 4 012 (20.6) 16 003 (23.7) < 0.001 7 707 (28.0) 12 308 (20.7) < 0.001 COPD 6 728 (7.7) 962 (4.9) 5 766 (8.6) < 0.001 3 210 (11.7) 3 518 (5.9) < 0.001 Liver cirrhosis 1 862 (2.1) 351 (1.8) 1 511 (2.2) < 0.001 856 (3.1) 1 006 (1.7) < 0.001 Heart failure 4 201 (4.8) 1 078 (5.5) 3 123 (4.6) < 0.001 2 154 (7.8) 2 047 (3.5) < 0.001 Stroke 2 214 (2.6) 538 (2.8) 1 676 (2.5) 0.032 992 (3.6) 1 222 (2.1) < 0.001 Malignancy 9 901 (11.4) 1 643 (8.4) 8 258 (12.3) < 0.001 4 001 (14.5) 5 900 (9.9) < 0.001 CCI 4.9 ± 2.5 4.6 ± 2.3 4.9 ± 2.5 < 0.001 5.5 ± 2.5 4.6 ± 2.4 < 0.001 Baseline PM 2.5 , µg/m 3 22.9 ± 7.3 25.0 ± 7.4 22.3 ± 7.1 < 0.001 25.6 ± 7.5 21.6 ± 6.8 < 0.001 Baseline relative humidity, g/m 3 68.9 ± 3.4 69.2 ± 3.4 68.8 ± 3.4 < 0.001 69.3 ± 3.3 68.8 ± 3.4 < 0.001 Follow up year 4.1 ± 2.5 3.3 ± 2.0 4.4 ± 2.6 < 0.001 4.1 ± 2.3 5.1 ± 2.8 < 0.001 Abbreviation: ESRD, end stage renal disease; PM, particulate matter; CKD, chronic kidney disease; NTD, New Taiwan Dollar; COPD, chronic obstructive pulmonary disease; CCI, Charlson Comorbidity Index Data are presented as frequency (%) or mean ± standard deviation. Long-Term Exposure to Non-Optimal Daytime Temperatures by Outcomes In this cohort of patients with advanced CKD, exposure was measured as the time-weighted percentage of non-optimal daytime temperatures. Among dialysis progressors, exposure to hot days with mean temperatures ≥ 30°C accounted for 26.7 ± 8.6% of follow-up, versus 28.2 ± 7.8% in non-progressors. For cold days (≤ 15°C), exposures were 3.8 ± 4.7% versus 3.3 ± 4.7%, respectively (all P < 0.001). Sensitivity analyses using alternative thresholds (hot days ≥ 32°C and ≥ 34°C; cold days ≤ 13°C) showed similar patterns: hot-day exposures among progressors were 11.1 ± 6.3% and 0.8 ± 1.1%, compared with 12.2 ± 6.0% and 0.9 ± 1.2% in non-progressors; cold-day exposures were 1.5 ± 3.1% versus 1.4 ± 3.2%. All differences remained statistically significant ( P < 0.001). For the mortality outcome, the results were entirely consistent with those for progression to dialysis (Table 2 ). Table 2 The descriptive statistics of time-weighted percentage of exposure to non-optimal daytime temperatures among patients according to status of subsequent outcomes Outcome / statistics Event Non-event P value Progression to maintenance dialysis Number of patients 19 494 67 434 - Total number of hot days (≥ 30°C) / Total follow-up days (x 100%) 26.7 ± 8.6 (%) 28.2 ± 7.8 (%) < 0.001 Total number of hot days (≥ 32°C) / Total follow-up days (x 100%) 11.1 ± 6.3 (%) 12.2 ± 6.0 (%) < 0.001 Total number of hot days (≥ 34°C) / Total follow-up days (x 100%) 0.8 ± 1.1 (%) 0.9 ± 1.2 (%) < 0.001 Total number of cold days (≤ 15°C) / Total follow-up days (x 100%) 3.8 ± 4.7 (%) 3.3 ± 4.7 (%) < 0.001 Total number of cold days (≤ 13°C) / Total follow-up days (x 100%) 1.5 ± 3.1 (%) 1.4 ± 3.2 < 0.001 All-cause death Number of patients 27,524 59,404 - Total number of hot days (≥ 30°C) / Total follow-up days (x 100%) 26.8 ± 8.7 (%) 28.7 ± 7.2 (%) < 0.001 Total number of hot days (≥ 32°C) / Total follow-up days (x 100%) 11.0 ± 6.1 (%) 12.7 ± 5.8 (%) < 0.001 Total number of hot days (≥ 34°C) / Total follow-up days (x 100%) 0.7 ± 1.0 (%) 1.0 ± 1.3 (%) < 0.001 Total number of cold days (≤ 15°C) / Total follow-up days (x 100%) 3.7 ± 4.9 (%) 3.1 ± 4.5 (%) < 0.001 Total number of cold days (≤ 13°C) / Total follow-up days (x 100%) 1.6 ± 3.3 (%) 1.3 ± 3.1 (%) < 0.001 Abbreviation: PM, particulate matter; Data are presented as mean ± standard deviation. Long-Term Exposure to Non-Optimal Daytime Temperatures and the Risk of Outcomes Each 1% increase in time-weighted exposure to hot days (≥ 30°C) was associated with a 5% lower risk of dialysis progression (HR = 0.95; 95% CI: 0.95–0.96), whereas each 1% increase in cold-day exposure (≤ 15°C) corresponded to a 14% higher risk (HR = 1.14; 95% CI: 1.13–1.14). Sensitivity analyses using alternative thresholds for hot days (≥ 32°C and ≥ 34°C) indicated 3% (HR = 0.97; 95% CI: 0.96–0.97) and 11% (HR = 0.89; 95% CI: 0.87–0.91) reductions in progression risk, respectively, while cold-day exposure ≤ 13°C was associated with a 28% higher risk (HR = 1.28; 95% CI: 1.27–1.30). Mortality outcomes mirrored these patterns, with higher hot-day exposure linked to lower risk and higher cold-day exposure linked to higher risk ( Table 3 ). Table 3 The association between time-weighted percentage of exposure to non-optimal daytime temperatures and the risk of progression to maintenance dialysis and all-cause death Univariate analysis Multivariable analysis* Outcome / parameter HR/SHR (95% CI) P value HR/SHR (95% CI) P value Progression to maintenance dialysis (competing risk model) Total number of hot days (≥ 30°C) / Total follow-up days (x 100%) 0.99 (0.99–0.99) < 0.001 0.96 (0.96–0.96) < 0.001 Total number of hot days (≥ 32°C) / Total follow-up days (x 100%) 0.98 (0.98–0.99) < 0.001 0.97 (0.97–0.98) < 0.001 Total number of hot days (≥ 34°C) / Total follow-up days (x 100%) 0.96 (0.95–0.97) < 0.001 0.92 (0.90–0.94) < 0.001 Total number of cold days (≤ 15°C) / Total follow-up days (x 100%) 1.02 (1.01–1.02) < 0.001 1.12 (1.11–1.13) < 0.001 Total number of cold days (≤ 13°C) / Total follow-up days (x 100%) 1.01 (1.01–1.02) < 0.001 1.25 (1.23–1.27) < 0.001 Progression to maintenance dialysis (Cox model) Total number of hot days (≥ 30°C) / Total follow-up days (x 100%) 0.99 (0.98–0.99) < 0.001 0.95 (0.95–0.96) < 0.001 Total number of hot days (≥ 32°C) / Total follow-up days (x 100%) 0.98 (0.98–0.98) < 0.001 0.97 (0.96–0.97) < 0.001 Total number of hot days (≥ 34°C) / Total follow-up days (x 100%) 0.94 (0.93–0.96) < 0.001 0.89 (0.87–0.91) < 0.001 Total number of cold days (≤ 15°C) / Total follow-up days (x 100%) 1.02 (1.02–1.02) < 0.001 1.14 (1.13–1.14) < 0.001 Total number of cold days (≤ 13°C) / Total follow-up days (x 100%) 1.02 (1.01–1.02) < 0.001 1.28 (1.27–1.30) < 0.001 All-cause death Total number of hot days (≥ 30°C) / Total follow-up days (x 100%) 0.99 (0.98–0.99) < 0.001 0.97 (0.97–0.98) < 0.001 Total number of hot days (≥ 32°C) / Total follow-up days (x 100%) 0.98 (0.98–0.98) < 0.001 0.98 (0.97–0.98) < 0.001 Total number of hot days (≥ 34°C) / Total follow-up days (x 100%) 0.90 (0.89–0.91) < 0.001 0.90 (0.88–0.92) < 0.001 Total number of cold days (≤ 15°C) / Total follow-up days (x 100%) 1.02 (1.01–1.02) < 0.001 1.09 (1.09–1.10) < 0.001 Total number of cold days (≤ 13°C) / Total follow-up days (x 100%) 1.02 (1.01–1.02) < 0.001 1.20 (1.18–1.21) < 0.001 Abbreviation: CI, confidence interval; HR, hazard ratio; *Adjusted for age, sex, urbanization level of the residence, monthly income, all comorbidities as well as the Charlson Comorbidity Index score, baseline PM 2.5 and relative humidity at baseline. Outcomes by Quartile Groups with Varying Cumulative Exposure to Non-Optimal Daytime Temperatures Over the 10-year follow-up, those in the highest exposure group (33–52%) had a significantly lower risk of dialysis compared to the reference group (0–26%) (18.5% vs. 22.7%; HR = 0.88; 95% CI: 0.84–0.92) ( Fig. 2 A ) . A similar approach was used with time-weighted cumulative cold days (≤ 15°C ) to evaluate dialysis incidence across groups with varying levels of cold day exposure (0–1%, 1–3%, 3–4%, and 4–42%). The highest (4–42%) exposure groups showed significantly higher risks of dialysis progression compared to the lowest exposure group (0–1%) (25.8% vs. 21.7%; HR = 1.21; 95% CI: 1.17–1.26) ( Fig. 2 B ) . For the mortality outcome, relatively consistent results were observed: patients in the highest and second-highest exposure groups for hot days had a lower risk of death, whereas those in the highest exposure group for cold days had a higher risk of death ( Fig. 2 C- 2 D ) . Relationship of Mean Daytime Temperature During Follow-Up With Outcomes Using RCS analysis, we found that higher mean daytime temperatures during follow-up were associated with a lower risk of dialysis progression in patients with advanced CKD, while lower temperatures were linked to increased risk ( P for both linearity and non-linearity < 0.001). This protective effect of increase in temperatures on dialysis progression, however, it was attenuated at mean daytime temperatures above 26°C ( Fig. 3 A ) . Similarly, higher mean daytime temperatures were associated with reduced mortality risk ( P for both linearity and non-linearity < 0.001), but this benefit plateaued beyond 26°C ( Fig. 3 B ) . Relationship of Mean Non-Optimal Daytime Temperature During Follow-Up with Outcomes RCS analysis indicated that exposure to mean non-optimal hot daytime temperatures (≥ 30°C) was associated with reduced risks of dialysis progression and mortality in patients with advanced CKD, with the protective effect plateauing between 31.5 and 32°C. A stronger inverse association between temperature and both dialysis progression and mortality was observed above 32°C (P for both linearity and non-linearity < 0.001) ( Fig. 4 A; Fig. 4 B ). For non-optimal cold temperatures (≤ 15°C), a V-shaped relationship was observed, with the lowest risk of dialysis progression and mortality at approximately 13.5°C ( P for both linearity and non-linearity < 0.001) ( Fig. 4 C; Fig. 4 D ). Discussion This study examined long-term associations between non-optimal daytime temperatures and risks of dialysis progression and all-cause mortality in advanced CKD patients using nationwide data from a subtropical–tropical HIC, Taiwan. A dose–response relationship was observed, with higher mean temperatures during follow-up showing protective effects and lower temperatures increasing risk. These results are consistent with the findings of Zafirah et al. (2022), who reported elevated CKD-related mortality among older Taiwanese adults when mean ambient temperatures fell below 26°C [ 22 ]. Importantly, we found that prolonged exposure to both non-optimal cold (≤ 15°C) and hot (≥ 30°C) temperatures was significantly associated with both outcomes. Our study indicated that higher time-weighted percentage of long-term daytime cold exposure (≤ 15°C) was significantly associated with increased risk of dialysis progression in advanced CKD patients. This aligns with previous research linking cold exposure to AKI and CKD development [ 30 , 31 ]. Cold-induced vasoconstriction may reduce renal perfusion, elevate blood pressure, and exacerbate kidney damage [ 31 , 32 ], while also worsening cardiovascular disease in patients with hypertension or diabetes—key risk factors for CKD progression [ 33 , 34 ]. Additionally, cold exposure can trigger persistent inflammatory responses, further impairing renal function, particularly in those with pre-existing kidney disease [ 35 , 36 ]. To our knowledge, this is the first study examining the impact of cumulative long-term cold exposure on dialysis progression, highlighting the need to consider cold stress in CKD management. Our study found that higher time-weighted percentage of long-term daytime cold exposure (≤ 15°C) was significantly associated with increased mortality in advanced CKD patients. This aligns with prior research indicating that cold contributes more to temperature-related mortality than heat [ 15 , 22 , 37 , 38 ]. Globally, He et al. (2022) reported that CKD-related mortality attributable to low temperatures exceeded that from high temperatures [ 38 ], while Htay et al. (2024) observed a reversed J-shaped relationship between ambient temperature and renal disease mortality in Japan, wherein lower temperatures were associated with increased mortality [ 15 ]. In Taiwan, Zafirah et al. (2022) found that non-optimal cold temperatures were associated with a higher risk of CKD mortality for the elderly, particularly during cold spells (< 14°C) [ 22 ]. Mechanistically, cold-induced vasoconstriction may aggravate pre-existing cardiovascular conditions, heightening the risk of fatal events (e.g., stroke and myocardial infarction), particularly in CKD patients who are already vulnerable to cardiovascular complications [ 39 – 42 ]. Consistently, our results show that participants exposed to the highest proportion of cold days (4–42%) had greater risks of both dialysis progression and mortality compared to those with minimal exposure (0–1%), emphasizing the importance of mitigating cold exposure for CKD patients, especially in colder regions. However, a V-shaped association between cumulative mean daytime temperatures of cold days (≤ 15°C) and risks of dialysis progression and mortality was observed in this study, with the lowest risk at 13.5°C rather than 15°C. This pattern suggests that hypermetabolic or insulative adaptations to prolonged cold exposure may confer protection at 13.5–15°C [ 42 ], while sustained temperatures below 13.5°C may exceed adaptive capacity for Taiwanese, leading to adverse outcomes. These findings align with Zafirah et al. (2022), who reported heightened CKD-related mortality during cold spells below 14°C [ 22 ]. Our study identified an inverse association between time-weighted percentage of long-term daytime hot exposure (≥ 30°C) and dialysis progression in advanced CKD patients, with risk reductions observed at 30°C, 32°C, and 34°C. Quartile analyses further showed a decreasing trend in dialysis incidence with increasing proportion of hot-day exposure, and a inverse dose–response relationship was observed between mean ambient hot temperatures and dialysis risk. These findings align with recent studies reporting protective effects of higher temperatures on kidney function [ 30 , 43 , 44 ]. For instance, Kim et al. (2018) found elevated temperatures were associated with reduced CKD risk in Korea, particularly among females [ 30 ], while Chen et al. (2024) and Su et al. (2024) reported similar protective effects among females and residents of Central and Eastern Taiwan, respectively [ 43 , 44 ]. In contrast, previous reports documented CKD epidemics of uncertain etiology in tropical LMICs, primarily affecting rural manual laborers exposed to heat [ 4 , 5 , 11 ]. Recent studies in China have also shown that higher ambient heat exposure was associated with increased CKD prevalence in the general population and more rapid declines in renal function among patients with CKD [ 45 , 46 ]. This discrepancy may reflect differing exposure–response relationships, with HICs such as Korea and Taiwan exhibiting lower sensitivity to heat-related CKD risk than that in LMICs, consistent with He et al. (2022) [ 38 ], who reported regional variation in CKD burden based on sociodemographic indices, with higher indices associated with better health outcomes. Our study found an inverse association between time-weighted percentage of long-term daytime hot exposure (≥ 30°C) and mortality risk in advanced CKD patients. Quartile analyses revealed decreasing mortality rates with higher proportion of hot-day exposure, and an inverse dose–response relationship was observed for mean ambient hot temperatures. These findings align with prior studies linking higher temperatures to lower CKD mortality [ 15 , 22 ]. In Taiwan, Zafirah et al. (2022) reported a continuous decline in mortality risk with increasing temperatures among males [ 22 ]. Notably, Htay et al. (2024) observed that the inverse temperature–mortality relationship emerged only in the recent period (1999–2018) in Japan, contrasting earlier decades (1979–1998), suggesting population-level adaptation to heat over time [ 15 ]. This pattern mirrors historical trends in New York City, where heat-related mortality declined from the 1970s onward, largely due to increased access to air conditioning [ 47 ]. Recent studies offer potential explanations for our findings on adaptation to non-optimal hot temperatures. Vicedo-Cabrera et al. (2018) reported declining heat-related mortality across multiple countries, driven primarily by changes in exposure–response relationships rather than human physiological acclimatization, reflecting reduced population vulnerability due to improved infrastructure and healthcare capacity to cope with heat stress [ 48 ]. Despite growing interest in temperature–mortality relationships, few studies have examined population-level heat adaptation; a 2024 scoping review by Navas-Martín highlighted disparities in adaptive capacity influenced by individual, demographic, geographic, and social factors [ 13 ]. In Taiwan, heat adaptation may be supported by individual-level factors—including widespread heat-avoidance behaviors, near-universal tap water access (94.9% in 2023) [ 49 ], and extensive air conditioning use (96.7% of households in 2021) [ 50 ]—as well as societal-level factors such as low agricultural labor participation (4.3% of the workforce) and environmental regulations (e.g., Greenhouse Gas Reduction Act, Wetland Conservation Act, National Park Law) [ 51 ]. Compared with LMICs in tropical regions, where CKD of unknown etiology is increasingly recognized [ 11 ], populations in HICs such as Taiwan, Korea, and Japan appear to benefit from these individual- and societal-level adaptations to heat. Our findings provide guidance for targeted, climate-responsive policies in Taiwan and HICs with similar climates—particularly during cold waves (e.g., < 13.5°C in Taiwan)—through improved access to heating and healthcare to reduce dialysis progression and mortality among advanced CKD patients. Sustaining adaptive behaviors and infrastructure for heat resilience is also crucial, with particular attention to long-term health risks faced by outdoor workers exposed to non-optimal hot temperatures. Our study has several strengths. This nationwide, population-based cohort examined the association between long-term non-optimal daytime temperature exposure and the risk of progression from advanced CKD to dialysis or death. The large sample size enabled robust assessment of how baseline patient characteristics influence these outcomes, enhancing both reliability and generalizability. Our analysis also accounted for ambient humidity and PM 2.5 levels, addressing potential environmental confounders and strengthening the robustness of the findings. Notably, this is the first study to evaluate cumulative long-term daytime temperature exposure in relation to dialysis incidence and mortality in advanced CKD, providing a foundation for climate-responsive kidney health strategies. This study has several limitations. First, ambient temperatures were measured using fixed monitoring stations, which do not capture indoor conditions that may influence health; in Taiwan, 96.7% of households have air conditioning, potentially introducing exposure misclassification, especially in summer. Second, time-weighted cumulative temperature indices were used to quantify exposure to hot and cold days, which may not fully reflect daily variations or individual exposure patterns. Third, the NHIRD lacks information on personal health behaviors, physical measurements, and biochemical markers relevant to dialysis adequacy, possibly biasing the assessment of temperature exposure effects on CKD progression or mortality. Fourth, the study population comprised only patients with advanced CKD, limiting generalizability to those already on dialysis or in earlier disease stages. Fifth, residual confounding may persist despite adjustment for key covariates, as unmeasured factors—such as individual heat and cold tolerance, access to heating or cooling devices, and specific behaviors—could affect outcomes. Finally, given its observational design, causal inferences cannot be drawn. These limitations should be considered when interpreting the associations between non-optimal temperature exposure and CKD outcomes. In conclusion, long-term exposure to non-optimal daytime temperatures is significantly associated with dialysis progression and mortality in patients with advanced CKD in Taiwan. Cumulative cold exposure increased risks, whereas cumulative hot exposure was protective, suggesting population-level heat adaptation in this subtropical–tropical HIC. These findings support targeted, climate-responsive policies to mitigate the health impacts of non-optimal temperatures on vulnerable populations, particularly those with advanced CKD in HICs with climates similar to Taiwan. Abbreviations CKD: chronic kidney disease; AKI: acut kidney injury; ESKD: end-stage kidney disease; LMIC: low- and medium- income country; HIC: high income country; NHIRD: the National Health Insurance Research Database; TAQMD: the Taiwan Air Quality Monitoring Database; HWDC: the Health and Welfare Data Science Center; MOENV: the Ministry of Environment; CIC: catastrophic illness certificate; ICD-9-CM: the International Classification of Diseases, Ninth Revision, Clinical Modification; ICD-10-CM: the International Classification of Diseases, Tenth Revision, Clinical Modification; DM: diabetes mellitus; COPD: chronic obstructive pulmonary disease; CCI: the Charlson Comorbidity Index; RCS: restricted cubic spline; HR: hazard ratio; CI: confidence interval; Declarations Ethics approval This study was approved by the Research Ethics Review Committee of New Taipei City Hospital (protocol No. NTPC113001-N) on January 24, 2024. The Institutional Review Board operates in accordance with the Declaration of Helsinki and the International Council for Harmonisation Good Clinical Practice (ICH-GCP) guidelines.. Competing interests The authors declare the following potential competing interests: Shih-Feng Chen has received financial support from and is employed by New Taipei City Hospital, Sanchung Branch. The remaining authors declare no known financial or personal relationships that could have influenced the work reported in this study. Conflict of interest None Clinical trial number Not applicable Consent to participate Not applicable (This retrospective cohort study utilized de-identified data from the NHIRD, which is protected under regulations governing beneficiary privacy established by the Taiwanese government.) Supplementary material Supplemental Table 1. ICD diagnostic codes used in the study Funding This study was funded by New Taipei City Hospital. The funding source had no role in the study design, data collection, analysis, interpretation of data, decision to publish, or preparation of the manuscript. Author Contribution S.F.C. conceived and designed the study, developed the methodology, performed data analysis, drafted the original manuscript, and manuscript revision. Y.H.C contributed to project administration, data analysis, and investigation. C.H.W was responsible for data curation, formal analysis, and investigation. Y.C.H assisted with methodological development, investigation, data interpretation, and manuscript revision. P.C.C supervised the study, validated the findings, and contributed to study conceptualization, methodological design, and critical manuscript review. All authors read and approved the final version of the manuscript. Acknowledgement The authors thank Mr. Yen-Chang Chen (B.A. in Physics and Accounting, University of California, Los Angeles, USA) for assistance with English language editing and acknowledge financial support from New Taipei City Hospital. Data Availability The first dataset used in this study was obtained from the National Health Insurance Research Database (NHIRD), maintained by the Health and Welfare Data Science Center (HWDC). Because the NHIRD is not publicly accessible, researchers must submit a formal application to the HWDC, Department of Statistics, Ministry of Health and Welfare, Taiwan, to obtain access. The second dataset was derived from the Taiwan Air Quality Monitoring Database (TAQMD), managed by the Ministry of Environment, Taiwan. In contrast to the NHIRD, the TAQMD is publicly available, and historical ambient temperature and air quality data can be downloaded from the Air Quality Index (AQI) platform: Air Quality Index (AQI) | 環境部環境資料開放平臺. 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Supplementary Files SupplementalTable1.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 20 Apr, 2026 Reviews received at journal 10 Apr, 2026 Reviewers agreed at journal 31 Mar, 2026 Reviews received at journal 16 Mar, 2026 Reviewers agreed at journal 03 Mar, 2026 Reviewers invited by journal 27 Jan, 2026 Editor assigned by journal 26 Jan, 2026 Submission checks completed at journal 26 Jan, 2026 First submitted to journal 25 Jan, 2026 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. 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Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsElEQVRIiWNgGAWjYFACHiCugDAlSNByBqIaSBgQqYWxjRQt5uxnDz4unHenzuAA88HbPAx/EhsIabHsyUs2nrntmYTBAbZkax4GA8JaDG7wmEnzbjsM1AJkALXkEqPF/DfvHJAW/m9EazFj5m0A28JGnBbLnhxjaZ5jhyVnHmYztpxjYFxPUIs5+xnDzzw1h/n5jjc/vPGmQs6YkA6keGBG5RKjZRSMglEwCkYBLgAAvfI0cJFE8+oAAAAASUVORK5CYII=","orcid":"","institution":"National Taiwan University College of Public Health","correspondingAuthor":true,"prefix":"","firstName":"Pau-Chung","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2026-01-25 11:09:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8691969/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8691969/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101439221,"identity":"6f745ba3-9733-4b0c-b65e-14f6b54b8e1c","added_by":"auto","created_at":"2026-01-29 16:42:36","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":97839,"visible":true,"origin":"","legend":"\u003cp\u003eThe flowchart for demonstrating the inclusion and exclusion of the study participants.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8691969/v1/b9872a0dc1e8c67d8df277a4.jpg"},{"id":101439218,"identity":"859973f8-c3fe-45b9-a147-2da2f714a5b3","added_by":"auto","created_at":"2026-01-29 16:42:36","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":139366,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative event rates across quartiles of the time-weighted percentage of exposure: (A) progression to maintenance dialysis by hot days (≥30 °C), (B) progression to maintenance dialysis by cold days (≤15 °C), (C) mortality by hot days (≥30 °C), and (D) mortality by cold days (≤15 °C).\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8691969/v1/cdcb6e56316a98e9602aedcb.jpg"},{"id":101752056,"identity":"355d68ef-6097-481a-8199-4c27fbd559d4","added_by":"auto","created_at":"2026-02-03 10:25:08","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":75248,"visible":true,"origin":"","legend":"\u003cp\u003eAssociations between long-term mean daytime temperature and the risk of (A) progression to maintenance dialysis and (B) all-cause mortality.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8691969/v1/c1598aa41906c396fca89953.jpg"},{"id":101439220,"identity":"aa292a85-b120-458c-bfd5-f305e8edfa1d","added_by":"auto","created_at":"2026-01-29 16:42:36","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":62242,"visible":true,"origin":"","legend":"\u003cp\u003eAssociations between mean daytime non-optimal temperatures and clinical outcomes: (A) hot temperature (≥30 °C) and progression to maintenance dialysis, (B) hot temperature (≥30 °C) and all-cause mortality, (C) cold temperature (≤15 °C) and progression to maintenance dialysis, and (D) cold temperature (≤15 °C) and all-cause mortality.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8691969/v1/444c6b9c1fab2bc462ab7963.jpg"},{"id":101881030,"identity":"ba43927f-adcd-46ad-ae46-6aa73b7b848d","added_by":"auto","created_at":"2026-02-04 15:09:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1701847,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8691969/v1/4cd30d9c-c24b-4959-914a-2835642d563e.pdf"},{"id":101439217,"identity":"e88247fd-3cbe-4fb4-a2f6-8b654850b25d","added_by":"auto","created_at":"2026-01-29 16:42:36","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":15082,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8691969/v1/0e6e284433197e04d7ddaf74.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eLong-Term Exposure to Non-Optimal Ambient Temperatures associated with Dialysis Incidence and Mortality in Advanced CKD Patients: A Population-Based Cohort Study in Taiwan\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eClimate change poses a major threat to human health, with the 2015 Lancet Commission identifying mitigation of its impacts as the greatest health opportunity of the 21st century [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The kidney, essential for coping with heat stress, is highly vulnerable to thermal injury [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Heat-related renal injury often presents as acute kidney injury (AKI) triggered by hyperthermia, volume depletion, and rhabdomyolysis [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Recurrent AKI may progress to chronic kidney disease (CKD) and end-stage kidney disease (ESKD) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCKD affects over 10% of the global population and contributes significantly to the global disease burden [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Progression to ESKD imposes substantial morbidity, mortality, and socioeconomic burden due to renal replacement therapy [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Geographic variations in CKD prevalence cannot be fully explained by traditional risk factors, such as diabetes and hypertension [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], suggesting a potential role for environmental exposures, including extreme temperatures [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOver the past decade, CKD of unknown etiology has emerged among agricultural workers in tropical low- and middle-income countries (LMICs), likely driven by recurrent occupational heat stress [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This pattern suggests heat-induced nephropathy as a potential climate-driven epidemic [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Population-level vulnerability and adaptive capacity, however, vary, with high-income countries (HICs) generally better equipped to implement heat-mitigation strategies [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Nevertheless, evidence on the effects of environmental heat on kidney health in warm or hot HIC settings remain limited.\u003c/p\u003e \u003cp\u003eMost studies on temperature and kidney health have emphasized summer heat, while relatively few have examined the effects of cold exposure on kidney disease onset, progression, or mortality. In South Korea, Park et al. (2024) reported that kidney function decline was associated not only with temperatures above 25\u0026deg;C but also with those below \u0026minus;\u0026thinsp;10\u0026deg;C [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], and in Japan, Htay et al. (2024) observed higher mortality at lower temperatures across all kidney disease categories [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Existing research has largely focused on short-term extreme temperatures and acute kidney injury, leaving the long-term effects of sustained exposure to non-optimal temperatures underexplored.\u003c/p\u003e \u003cp\u003eTaiwan, ranked 14th globally in GDP per capita by \u003cem\u003eForbes\u003c/em\u003e in 2024 [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], spans subtropical and tropical zones and faces rising surface temperatures (1.0\u0026ndash;1.4\u0026deg;C over the past century), seasonal variability, and increasing extreme weather [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. It also has the world\u0026rsquo;s highest incidence and prevalence of treated ESKD [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], with a growing dialysis population that imposes substantial medical and socioeconomic burden [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In 2007, the government launched a multidisciplinary Pre-ESKD Care Program for CKD stages 3b\u0026ndash;5 [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, preventive strategies remain focused on diabetes and hypertension, while emerging risk factors such as climate-related temperature abnormalities require greater attention.\u003c/p\u003e \u003cp\u003eIn Taiwan, limited research has addressed short-term extreme temperatures and acute kidney-related events, including hospitalizations, emergency visits, and mortality [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The impact of long-term non-optimal hot and cold exposure on CKD outcomes, however, is unclear. This study therefore investigated the association between prolonged daytime (08:00\u0026ndash;19:00) exposure to non-optimal ambient temperatures and dialysis incidence or mortality among advanced CKD patients (stage 3b\u0026ndash;5; eGFR\u0026thinsp;\u0026lt;\u0026thinsp;45 ml/min/1.73 m\u0026sup2;) in Taiwan.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis retrospective nationwide cohort study linked the National Health Insurance Research Database (NHIRD) and the Taiwan Air Quality Monitoring Database (TAQMD) to evaluate the risks of progression to ESKD requiring dialysis and mortality among advanced CKD patients exposed to prolonged non-optimal ambient daytime temperatures. It was approved by the Research Ethics Review Committee of New Taipei City Hospital (protocol No. NTPC 113001-N).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Sources\u003c/h2\u003e \u003cp\u003eThe NHIRD contains de-identified claims data from Taiwan\u0026rsquo;s mandatory single-payer National Health Insurance program, established in 1995 and now covering 99.9% of the population. Prior to 2016, researchers accessed random or disease-specific samples; since then, full-population data have been available through the Health and Welfare Data Science Center (HWDC), enabling linkage with other registries (e.g., Taiwan Death Registry, Taiwan Cancer Registry) but requiring onsite analysis, which increases research time and cost [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Diagnoses were coded using ICD-9-CM before 2016 and both ICD-9-CM and ICD-10-CM thereafter. Detailed descriptions of the NHIRD are available elsewhere [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe TAQMD, established in 1993 by the Ministry of Environment (MOENV), has expanded over time and currently comprises 85 nationwide monitoring stations, mainly in urban and industrial areas with some rural coverage. These stations record real-time hourly meteorological data (temperature, humidity, rainfall, wind speed and direction) and air pollutants (PM₂.₅, PM₁₀, NO₂, SO₂, O₃, and CO). Data are publicly available via MOENV\u0026rsquo;s Air Quality Network (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://wot.moenv.gov.tw/\u003c/span\u003e\u003cspan address=\"https://wot.moenv.gov.tw/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Quality control is ensured through annual calibration, third-party validation, and adherence to international standards (e.g., ISO 9001), guaranteeing reliable and consistent measurements [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Population\u003c/h3\u003e\n\u003cp\u003eThis nationwide retrospective cohort study included adults with advanced CKD (stages 3b\u0026ndash;5; eGFR\u0026thinsp;\u0026lt;\u0026thinsp;45 ml/min/1.73 m\u0026sup2;) enrolled in Taiwan\u0026rsquo;s National Health Insurance Pre-ESKD Care Program (reimbursement code: P3402C). Daily average daytime (08:00\u0026ndash;19:00) temperature and air pollutant data from the TAQMD were linked to patients\u0026rsquo; residential areas using zip codes. Eligible participants were those aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years who entered the Pre-ESKD Care Program between January 1, 2008, and December 31, 2021, and were followed until progression to maintenance dialysis, death (before or after maintenance dialysis), or December 31, 2022, whichever occurred first.\u003c/p\u003e \u003cp\u003eIn this study, patients\u0026rsquo; residential areas were defined as the locations of clinics or hospitals where they received care for acute upper respiratory infections (ICD-9-CM: 460; ICD-10-CM: J00) or allergic rhinitis/sinusitis (ICD-9-CM: 472, 473, 477; ICD-10-CM: J30\u0026ndash;J32), and those without such outpatient records, residential location was inferred using workplace information from the \u0026ldquo;beneficiary registry\u0026rdquo; dataset [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. During participant selection, we excluded individuals who met any of the following criteria: (1) incomplete demographic information; (2) age below 18 or above 99; (3) prior enrollment in the pre-ESKD care program; (4) follow-up duration of less than 365 days; (5) initiation of dialysis within 365 days of follow-up; (6) residence in areas without air quality monitoring stations; or (7) missing data on ambient temperature, humidity, or PM\u003csub\u003e2.5\u003c/sub\u003e. After these exclusions, 86,928 patients with advanced CKD were included in the final analysis \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e\n\u003ch3\u003eMeasurements of Exposure\u003c/h3\u003e\n\u003cp\u003eLong-term exposure to non-optimal ambient temperatures was assessed by defining hot days as those with an average daytime temperature (08:00\u0026ndash;19:00)\u0026thinsp;\u0026ge;\u0026thinsp;30\u0026deg;C and cold days as those\u0026thinsp;\u0026le;\u0026thinsp;15\u0026deg;C [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Two indices were used: (1) the time-weighted percentage of exposure, calculated as the cumulative proportion of hot or cold days relative to total follow-up days, and (2) the mean daytime temperature across all hot days or, separately, across all cold days during follow-up. Regarding sensitivity analysis, for hot days, we also applied alternative thresholds of 32\u0026deg;C and 34\u0026deg;C; for cold days, in addition to the 15\u0026deg;C definition, we further examined results using a 13\u0026deg;C threshold. Furthermore, the long-term mean daytime temperature across all days during the follow-up period was also used as an exposure index.\u003c/p\u003e\n\u003ch3\u003eOutcome Ascertainment\u003c/h3\u003e\n\u003cp\u003eThe primary outcome of this study is the initiation of maintenance dialysis. We defined the initiation date as the point at which advanced CKD patients applied for the catastrophic illness certificate (CIC) card for maintenance dialysis and initiated dialysis at the time around CIC application. The secondary outcome is death occurring more than 365 days after the index date. Death-related information, including the date, place, and causes, was linked to the Taiwan Death Registry database within the HWDC. For patients who experienced either outcome, follow-up was terminated, while for those who did not experience dialysis or death, follow-up continued until December 31, 2022.\u003c/p\u003e\n\u003ch3\u003eBaseline Characteristics and Covariates\u003c/h3\u003e\n\u003cp\u003eBaseline characteristics included both individual- and zip-code\u0026ndash;level covariates. Individual-level factors were age, sex, diabetes mellitus (DM), hypertension, coronary artery disease, heart failure, stroke, liver cirrhosis, chronic obstructive pulmonary disease (COPD), malignancy, Charlson Comorbidity Index (CCI), and monthly insurance salary (\u0026le;\u0026thinsp;19,047; 19,047\u0026ndash;25,000; \u0026ge;25,000). Comorbidities (i.e., DM, hypertension, coronary artery disease, liver cirrhosis, and COPD) were defined if patients had at least two outpatient diagnoses or one inpatient diagnosis within the year before entering the pre-ESKD program. History of events (including heart failure and stroke) was defined as any prior hospitalization traceable back to the year 2000. Malignancy was confirmed by the possession of a CIC card. All comorbidities were identified using both the Ninth and Tenth Revisions of the International Classification of Diseases (ICD-9-CM and ICD-10-CM) (\u003cb\u003eSupplemental Table\u0026nbsp;1\u003c/b\u003e). Additionally, at the zip code level, baseline long-term PM\u003csub\u003e2.5\u003c/sub\u003e concentrations and relative humidity to which participants exposed were calculated as the mean values over the 365 days prior to the index date. Furthermore, we categorized the degree of urbanization of the residential area into four levels\u0026mdash;the lowest, low, high, and the highest\u0026mdash;based on the criteria proposed by Liu [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], corresponding to the city, town, suburban, and rural categories defined by Morrish and Florio [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eBaseline characteristics and the time-weighted percentage of exposure to non-optimal temperatures were compared between event (patients experiencing outcomes) and non-event groups using independent-samples t tests for continuous variables and chi-square tests for categorical variables. Associations between the time-weighted percentage of exposure (modeled continuously) and outcomes were assessed using Cox proportional hazards models adjusted for all covariates. In addition, participants were stratified into quartiles based on the percentage of cumulative hot (\u0026ge;\u0026thinsp;30\u0026deg;C) or cold (\u0026le;\u0026thinsp;15\u0026deg;C) daytime days relative to total follow-up, with equal sample sizes in each quartile. Cumulative incidence curves, estimated using Cox proportional hazards models, were used to compare the associations between non-optimal temperature exposure and outcomes across quartiles.\u003c/p\u003e \u003cp\u003eFurthermore, we used restricted cubic spline (RCS) analysis to examine the dose\u0026ndash;response relationship between the mean daytime temperature during follow-up and the risk of progression to maintenance dialysis or death. We also specifically focused on the associations between mean daytime non-optimal temperatures\u0026mdash;either hot or cold\u0026mdash;during follow-up and the risks of both outcomes. The mean daytime temperature and mean daytime non-optimal temperatures were modeled as flexible RCS variables with knots placed at the 10th, 50th, and 95th percentiles. RCS modeling was conducted using R version 4.4.2 (R Foundation for Statistical Computing) with the \u0026ldquo;rms\u0026rdquo; package. All other statistical analyses were performed using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA). A two-sided \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. All statistical analyses were carried out within the HWDC.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eBaseline Characteristics of Study Population\u003c/h2\u003e \u003cp\u003eThis study included 86,928 adult advanced CKD patients residing in areas with available environmental data on temperature, relative humidity, and PM\u003csub\u003e2.5\u003c/sub\u003e levels \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. The average age of the participants was 69.1 years, with 56,812 patients (65.4%) aged over 65 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The mean follow-up period was 4.1 years. A majority of the participants were male, totaling 49,139 individuals (56.5%). Regarding comorbidities, 71,472 patients (82.2%) had hypertension, 49,798 (57.3%) had diabetes, 20,015 (23.0%) had coronary artery disease, and 9,901 (11.4%) had malignancies. Other conditions, such as COPD, liver cirrhosis, heart failure, and stroke, each accounted for less than 10% of the study population. Regarding the level of urbanization in residential areas, the majority of advanced CKD patients lived in high-urbanization areas (i.e., towns; 42,326 individuals, 48.7%), followed by those in the highest-urbanization areas (i.e., cities; 23,357, 26.9%) and low-urbanization areas (i.e., suburbs; 18,637, 21.4%). The smallest proportion resided in the lowest-urbanization areas (i.e., rural areas; 2,608, 3.0%).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of patients according to status of subsequent outcomes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eProgression to maintenance dialysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003eAll-cause death\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;86 928)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;19 494)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;67 434)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;27 524)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;59 404)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69.1\u0026thinsp;\u0026plusmn;\u0026thinsp;13.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.0\u0026thinsp;\u0026plusmn;\u0026thinsp;13.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70.6\u0026thinsp;\u0026plusmn;\u0026thinsp;13.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e75.2\u0026thinsp;\u0026plusmn;\u0026thinsp;11.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e66.2\u0026thinsp;\u0026plusmn;\u0026thinsp;13.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u0026thinsp;\u0026ge;\u0026thinsp;65 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56 812 (65.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 605 (49.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47 207 (70.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22 512 (81.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e34 300 (57.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49 139 (56.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 698 (54.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38 441 (57.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15 887 (57.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e33 252 (56.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrbanization level of the residence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe lowest level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 608 (3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e512 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 096 (3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e935 (3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 673 (2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 637 (21.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 394 (22.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 243 (21.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5 991 (21.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12 646 (21.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42 326 (48.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 307 (47.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33 019 (49.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13 572 (49.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e28 754 (48.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe highest level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 357 (26.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 281 (27.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 076 (26.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7 026 (25.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e16 331 (27.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonthly income, NTD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;19 047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 475 (29.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 350 (27.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 125 (29.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9 461 (34.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e16 014 (27.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19 047\u0026thinsp;\u0026minus;\u0026thinsp;25 200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 531 (36.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 315 (37.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 216 (35.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10 181 (37.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e21 350 (35.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;25 200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 922 (34.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 829 (35.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23 093 (34.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7 882 (28.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e22 040 (37.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49 798 (57.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 871 (66.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36 927 (54.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16 766 (60.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e33 032 (55.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71 472 (82.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 555 (84.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54 917 (81.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23 130 (84.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e48 342 (81.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoronary artery disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 015 (23.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 012 (20.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 003 (23.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7 707 (28.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12 308 (20.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 728 (7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e962 (4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 766 (8.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3 210 (11.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3 518 (5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver cirrhosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 862 (2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e351 (1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 511 (2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e856 (3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 006 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 201 (4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 078 (5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 123 (4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2 154 (7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2 047 (3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 214 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e538 (2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 676 (2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e992 (3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 222 (2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalignancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 901 (11.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 643 (8.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 258 (12.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4 001 (14.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5 900 (9.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline PM\u003csub\u003e2.5\u003c/sub\u003e, \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.9\u0026thinsp;\u0026plusmn;\u0026thinsp;7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.0\u0026thinsp;\u0026plusmn;\u0026thinsp;7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.3\u0026thinsp;\u0026plusmn;\u0026thinsp;7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25.6\u0026thinsp;\u0026plusmn;\u0026thinsp;7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e21.6\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline relative humidity, g/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e69.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e68.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFollow up year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eAbbreviation: ESRD, end stage renal disease; PM, particulate matter; CKD, chronic kidney disease; NTD, New Taiwan Dollar; COPD, chronic obstructive pulmonary disease; CCI, Charlson Comorbidity Index\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eData are presented as frequency (%) or mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eLong-Term Exposure to Non-Optimal Daytime Temperatures by Outcomes\u003c/h2\u003e \u003cp\u003eIn this cohort of patients with advanced CKD, exposure was measured as the time-weighted percentage of non-optimal daytime temperatures. Among dialysis progressors, exposure to hot days with mean temperatures\u0026thinsp;\u0026ge;\u0026thinsp;30\u0026deg;C accounted for 26.7\u0026thinsp;\u0026plusmn;\u0026thinsp;8.6% of follow-up, versus 28.2\u0026thinsp;\u0026plusmn;\u0026thinsp;7.8% in non-progressors. For cold days (\u0026le;\u0026thinsp;15\u0026deg;C), exposures were 3.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7% versus 3.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7%, respectively (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Sensitivity analyses using alternative thresholds (hot days\u0026thinsp;\u0026ge;\u0026thinsp;32\u0026deg;C and \u0026ge;\u0026thinsp;34\u0026deg;C; cold days\u0026thinsp;\u0026le;\u0026thinsp;13\u0026deg;C) showed similar patterns: hot-day exposures among progressors were 11.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.3% and 0.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1%, compared with 12.2\u0026thinsp;\u0026plusmn;\u0026thinsp;6.0% and 0.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2% in non-progressors; cold-day exposures were 1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1% versus 1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2%. All differences remained statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eFor the mortality outcome, the results were entirely consistent with those for progression to dialysis (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe descriptive statistics of time-weighted percentage of exposure to non-optimal daytime temperatures among patients according to status of subsequent outcomes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome / statistics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEvent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-event\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProgression to maintenance dialysis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of patients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67 434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal number of hot days (\u0026ge;\u0026thinsp;30\u0026deg;C) / Total follow-up days (x 100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.7\u0026thinsp;\u0026plusmn;\u0026thinsp;8.6 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.2\u0026thinsp;\u0026plusmn;\u0026thinsp;7.8 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal number of hot days (\u0026ge;\u0026thinsp;32\u0026deg;C) / Total follow-up days (x 100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.3 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.2\u0026thinsp;\u0026plusmn;\u0026thinsp;6.0 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal number of hot days (\u0026ge;\u0026thinsp;34\u0026deg;C) / Total follow-up days (x 100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal number of cold days (\u0026le;\u0026thinsp;15\u0026deg;C) / Total follow-up days (x 100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal number of cold days (\u0026le;\u0026thinsp;13\u0026deg;C) / Total follow-up days (x 100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAll-cause death\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of patients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27,524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59,404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal number of hot days (\u0026ge;\u0026thinsp;30\u0026deg;C) / Total follow-up days (x 100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.8\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.7\u0026thinsp;\u0026plusmn;\u0026thinsp;7.2 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal number of hot days (\u0026ge;\u0026thinsp;32\u0026deg;C) / Total follow-up days (x 100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.0\u0026thinsp;\u0026plusmn;\u0026thinsp;6.1 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.7\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal number of hot days (\u0026ge;\u0026thinsp;34\u0026deg;C) / Total follow-up days (x 100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal number of cold days (\u0026le;\u0026thinsp;15\u0026deg;C) / Total follow-up days (x 100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal number of cold days (\u0026le;\u0026thinsp;13\u0026deg;C) / Total follow-up days (x 100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eAbbreviation: PM, particulate matter;\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eData are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eLong-Term Exposure to Non-Optimal Daytime Temperatures and the Risk of Outcomes\u003c/h2\u003e \u003cp\u003eEach 1% increase in time-weighted exposure to hot days (\u0026ge;\u0026thinsp;30\u0026deg;C) was associated with a 5% lower risk of dialysis progression (HR\u0026thinsp;=\u0026thinsp;0.95; 95% CI: 0.95\u0026ndash;0.96), whereas each 1% increase in cold-day exposure (\u0026le;\u0026thinsp;15\u0026deg;C) corresponded to a 14% higher risk (HR\u0026thinsp;=\u0026thinsp;1.14; 95% CI: 1.13\u0026ndash;1.14). Sensitivity analyses using alternative thresholds for hot days (\u0026ge;\u0026thinsp;32\u0026deg;C and \u0026ge;\u0026thinsp;34\u0026deg;C) indicated 3% (HR\u0026thinsp;=\u0026thinsp;0.97; 95% CI: 0.96\u0026ndash;0.97) and 11% (HR\u0026thinsp;=\u0026thinsp;0.89; 95% CI: 0.87\u0026ndash;0.91) reductions in progression risk, respectively, while cold-day exposure\u0026thinsp;\u0026le;\u0026thinsp;13\u0026deg;C was associated with a 28% higher risk (HR\u0026thinsp;=\u0026thinsp;1.28; 95% CI: 1.27\u0026ndash;1.30).\u003c/p\u003e \u003cp\u003eMortality outcomes mirrored these patterns, with higher hot-day exposure linked to lower risk and higher cold-day exposure linked to higher risk \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe association between time-weighted percentage of exposure to non-optimal daytime temperatures and the risk of progression to maintenance dialysis and all-cause death\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMultivariable analysis*\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome / parameter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR/SHR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR/SHR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProgression to maintenance dialysis (competing risk model)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal number of hot days (\u0026ge;\u0026thinsp;30\u0026deg;C) / Total follow-up days\u003c/p\u003e \u003cp\u003e(x 100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.99\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.96 (0.96\u0026ndash;0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal number of hot days (\u0026ge;\u0026thinsp;32\u0026deg;C) / Total follow-up days\u003c/p\u003e \u003cp\u003e(x 100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.98 (0.98\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.97 (0.97\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal number of hot days (\u0026ge;\u0026thinsp;34\u0026deg;C) / Total follow-up days\u003c/p\u003e \u003cp\u003e(x 100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.96 (0.95\u0026ndash;0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.92 (0.90\u0026ndash;0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal number of cold days (\u0026le;\u0026thinsp;15\u0026deg;C) / Total follow-up days\u003c/p\u003e \u003cp\u003e(x 100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.02 (1.01\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.12 (1.11\u0026ndash;1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal number of cold days (\u0026le;\u0026thinsp;13\u0026deg;C) / Total follow-up days\u003c/p\u003e \u003cp\u003e(x 100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01 (1.01\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.25 (1.23\u0026ndash;1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProgression to maintenance dialysis (Cox model)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal number of hot days (\u0026ge;\u0026thinsp;30\u0026deg;C) / Total follow-up days\u003c/p\u003e \u003cp\u003e(x 100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.98\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.95 (0.95\u0026ndash;0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal number of hot days (\u0026ge;\u0026thinsp;32\u0026deg;C) / Total follow-up days\u003c/p\u003e \u003cp\u003e(x 100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.98 (0.98\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.97 (0.96\u0026ndash;0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal number of hot days (\u0026ge;\u0026thinsp;34\u0026deg;C) / Total follow-up days\u003c/p\u003e \u003cp\u003e(x 100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.94 (0.93\u0026ndash;0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.89 (0.87\u0026ndash;0.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal number of cold days (\u0026le;\u0026thinsp;15\u0026deg;C) / Total follow-up days\u003c/p\u003e \u003cp\u003e(x 100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.02 (1.02\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.14 (1.13\u0026ndash;1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal number of cold days (\u0026le;\u0026thinsp;13\u0026deg;C) / Total follow-up days\u003c/p\u003e \u003cp\u003e(x 100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.02 (1.01\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.28 (1.27\u0026ndash;1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAll-cause death\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal number of hot days (\u0026ge;\u0026thinsp;30\u0026deg;C) / Total follow-up days\u003c/p\u003e \u003cp\u003e(x 100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.98\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.97 (0.97\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal number of hot days (\u0026ge;\u0026thinsp;32\u0026deg;C) / Total follow-up days\u003c/p\u003e \u003cp\u003e(x 100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.98 (0.98\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.98 (0.97\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal number of hot days (\u0026ge;\u0026thinsp;34\u0026deg;C) / Total follow-up days\u003c/p\u003e \u003cp\u003e(x 100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.90 (0.89\u0026ndash;0.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.90 (0.88\u0026ndash;0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal number of cold days (\u0026le;\u0026thinsp;15\u0026deg;C) / Total follow-up days\u003c/p\u003e \u003cp\u003e(x 100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.02 (1.01\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.09 (1.09\u0026ndash;1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal number of cold days (\u0026le;\u0026thinsp;13\u0026deg;C) / Total follow-up days\u003c/p\u003e \u003cp\u003e(x 100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.02 (1.01\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.20 (1.18\u0026ndash;1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviation: CI, confidence interval; HR, hazard ratio;\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e*Adjusted for age, sex, urbanization level of the residence, monthly income, all comorbidities as well as the Charlson Comorbidity Index score, baseline PM\u003csub\u003e2.5\u003c/sub\u003e and relative humidity at baseline.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eOutcomes by Quartile Groups with Varying Cumulative Exposure to Non-Optimal Daytime Temperatures\u003c/h2\u003e \u003cp\u003eOver the 10-year follow-up, those in the highest exposure group (33\u0026ndash;52%) had a significantly lower risk of dialysis compared to the reference group (0\u0026ndash;26%) (18.5% vs. 22.7%; HR\u0026thinsp;=\u0026thinsp;0.88; 95% CI: 0.84\u0026ndash;0.92) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. A similar approach was used with time-weighted cumulative cold days (\u0026le;\u0026thinsp;15\u0026deg;C ) to evaluate dialysis incidence across groups with varying levels of cold day exposure (0\u0026ndash;1%, 1\u0026ndash;3%, 3\u0026ndash;4%, and 4\u0026ndash;42%). The highest (4\u0026ndash;42%) exposure groups showed significantly higher risks of dialysis progression compared to the lowest exposure group (0\u0026ndash;1%) (25.8% vs. 21.7%; HR\u0026thinsp;=\u0026thinsp;1.21; 95% CI: 1.17\u0026ndash;1.26) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. For the mortality outcome, relatively consistent results were observed: patients in the highest and second-highest exposure groups for hot days had a lower risk of death, whereas those in the highest exposure group for cold days had a higher risk of death \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eC-\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eRelationship of Mean Daytime Temperature During Follow-Up With Outcomes\u003c/h2\u003e \u003cp\u003eUsing RCS analysis, we found that higher mean daytime temperatures during follow-up were associated with a lower risk of dialysis progression in patients with advanced CKD, while lower temperatures were linked to increased risk (\u003cem\u003eP\u003c/em\u003e for both linearity and non-linearity\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This protective effect of increase in temperatures on dialysis progression, however, it was attenuated at mean daytime temperatures above 26\u0026deg;C \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. Similarly, higher mean daytime temperatures were associated with reduced mortality risk (\u003cem\u003eP\u003c/em\u003e for both linearity and non-linearity\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but this benefit plateaued beyond 26\u0026deg;C \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eRelationship of Mean Non-Optimal Daytime Temperature During Follow-Up with Outcomes\u003c/h2\u003e \u003cp\u003eRCS analysis indicated that exposure to mean non-optimal hot daytime temperatures (\u0026ge;\u0026thinsp;30\u0026deg;C) was associated with reduced risks of dialysis progression and mortality in patients with advanced CKD, with the protective effect plateauing between 31.5 and 32\u0026deg;C. A stronger inverse association between temperature and both dialysis progression and mortality was observed above 32\u0026deg;C \u003cem\u003e(P\u003c/em\u003e for both linearity and non-linearity\u0026thinsp;\u0026lt;\u0026thinsp;0.001) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e4\u003c/span\u003eA; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e4\u003c/span\u003eB\u003cb\u003e).\u003c/b\u003e For non-optimal cold temperatures (\u0026le;\u0026thinsp;15\u0026deg;C), a V-shaped relationship was observed, with the lowest risk of dialysis progression and mortality at approximately 13.5\u0026deg;C (\u003cem\u003eP\u003c/em\u003e for both linearity and non-linearity\u0026thinsp;\u0026lt;\u0026thinsp;0.001) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e4\u003c/span\u003eC; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e4\u003c/span\u003eD\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study examined long-term associations between non-optimal daytime temperatures and risks of dialysis progression and all-cause mortality in advanced CKD patients using nationwide data from a subtropical\u0026ndash;tropical HIC, Taiwan. A dose\u0026ndash;response relationship was observed, with higher mean temperatures during follow-up showing protective effects and lower temperatures increasing risk. These results are consistent with the findings of Zafirah et al. (2022), who reported elevated CKD-related mortality among older Taiwanese adults when mean ambient temperatures fell below 26\u0026deg;C [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Importantly, we found that prolonged exposure to both non-optimal cold (\u0026le;\u0026thinsp;15\u0026deg;C) and hot (\u0026ge;\u0026thinsp;30\u0026deg;C) temperatures was significantly associated with both outcomes.\u003c/p\u003e \u003cp\u003eOur study indicated that higher time-weighted percentage of long-term daytime cold exposure (\u0026le;\u0026thinsp;15\u0026deg;C) was significantly associated with increased risk of dialysis progression in advanced CKD patients. This aligns with previous research linking cold exposure to AKI and CKD development [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Cold-induced vasoconstriction may reduce renal perfusion, elevate blood pressure, and exacerbate kidney damage [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], while also worsening cardiovascular disease in patients with hypertension or diabetes\u0026mdash;key risk factors for CKD progression [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Additionally, cold exposure can trigger persistent inflammatory responses, further impairing renal function, particularly in those with pre-existing kidney disease [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. To our knowledge, this is the first study examining the impact of cumulative long-term cold exposure on dialysis progression, highlighting the need to consider cold stress in CKD management.\u003c/p\u003e \u003cp\u003eOur study found that higher time-weighted percentage of long-term daytime cold exposure (\u0026le;\u0026thinsp;15\u0026deg;C) was significantly associated with increased mortality in advanced CKD patients. This aligns with prior research indicating that cold contributes more to temperature-related mortality than heat [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Globally, He et al. (2022) reported that CKD-related mortality attributable to low temperatures exceeded that from high temperatures [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], while Htay et al. (2024) observed a reversed J-shaped relationship between ambient temperature and renal disease mortality in Japan, wherein lower temperatures were associated with increased mortality [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In Taiwan, Zafirah et al. (2022) found that non-optimal cold temperatures were associated with a higher risk of CKD mortality for the elderly, particularly during cold spells (\u0026lt;\u0026thinsp;14\u0026deg;C) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Mechanistically, cold-induced vasoconstriction may aggravate pre-existing cardiovascular conditions, heightening the risk of fatal events (e.g., stroke and myocardial infarction), particularly in CKD patients who are already vulnerable to cardiovascular complications [\u003cspan additionalcitationids=\"CR40 CR41\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Consistently, our results show that participants exposed to the highest proportion of cold days (4\u0026ndash;42%) had greater risks of both dialysis progression and mortality compared to those with minimal exposure (0\u0026ndash;1%), emphasizing the importance of mitigating cold exposure for CKD patients, especially in colder regions. However, a V-shaped association between cumulative mean daytime temperatures of cold days (\u0026le;\u0026thinsp;15\u0026deg;C) and risks of dialysis progression and mortality was observed in this study, with the lowest risk at 13.5\u0026deg;C rather than 15\u0026deg;C. This pattern suggests that hypermetabolic or insulative adaptations to prolonged cold exposure may confer protection at 13.5\u0026ndash;15\u0026deg;C [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], while sustained temperatures below 13.5\u0026deg;C may exceed adaptive capacity for Taiwanese, leading to adverse outcomes. These findings align with Zafirah et al. (2022), who reported heightened CKD-related mortality during cold spells below 14\u0026deg;C [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur study identified an inverse association between time-weighted percentage of long-term daytime hot exposure (\u0026ge;\u0026thinsp;30\u0026deg;C) and dialysis progression in advanced CKD patients, with risk reductions observed at 30\u0026deg;C, 32\u0026deg;C, and 34\u0026deg;C. Quartile analyses further showed a decreasing trend in dialysis incidence with increasing proportion of hot-day exposure, and a inverse dose\u0026ndash;response relationship was observed between mean ambient hot temperatures and dialysis risk. These findings align with recent studies reporting protective effects of higher temperatures on kidney function [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. For instance, Kim et al. (2018) found elevated temperatures were associated with reduced CKD risk in Korea, particularly among females [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], while Chen et al. (2024) and Su et al. (2024) reported similar protective effects among females and residents of Central and Eastern Taiwan, respectively [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. In contrast, previous reports documented CKD epidemics of uncertain etiology in tropical LMICs, primarily affecting rural manual laborers exposed to heat [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Recent studies in China have also shown that higher ambient heat exposure was associated with increased CKD prevalence in the general population and more rapid declines in renal function among patients with CKD [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. This discrepancy may reflect differing exposure\u0026ndash;response relationships, with HICs such as Korea and Taiwan exhibiting lower sensitivity to heat-related CKD risk than that in LMICs, consistent with He et al. (2022) [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], who reported regional variation in CKD burden based on sociodemographic indices, with higher indices associated with better health outcomes.\u003c/p\u003e \u003cp\u003eOur study found an inverse association between time-weighted percentage of long-term daytime hot exposure (\u0026ge;\u0026thinsp;30\u0026deg;C) and mortality risk in advanced CKD patients. Quartile analyses revealed decreasing mortality rates with higher proportion of hot-day exposure, and an inverse dose\u0026ndash;response relationship was observed for mean ambient hot temperatures. These findings align with prior studies linking higher temperatures to lower CKD mortality [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In Taiwan, Zafirah et al. (2022) reported a continuous decline in mortality risk with increasing temperatures among males [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Notably, Htay et al. (2024) observed that the inverse temperature\u0026ndash;mortality relationship emerged only in the recent period (1999\u0026ndash;2018) in Japan, contrasting earlier decades (1979\u0026ndash;1998), suggesting population-level adaptation to heat over time [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. This pattern mirrors historical trends in New York City, where heat-related mortality declined from the 1970s onward, largely due to increased access to air conditioning [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecent studies offer potential explanations for our findings on adaptation to non-optimal hot temperatures. Vicedo-Cabrera et al. (2018) reported declining heat-related mortality across multiple countries, driven primarily by changes in exposure\u0026ndash;response relationships rather than human physiological acclimatization, reflecting reduced population vulnerability due to improved infrastructure and healthcare capacity to cope with heat stress [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Despite growing interest in temperature\u0026ndash;mortality relationships, few studies have examined population-level heat adaptation; a 2024 scoping review by Navas-Mart\u0026iacute;n highlighted disparities in adaptive capacity influenced by individual, demographic, geographic, and social factors [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn Taiwan, heat adaptation may be supported by individual-level factors\u0026mdash;including widespread heat-avoidance behaviors, near-universal tap water access (94.9% in 2023) [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], and extensive air conditioning use (96.7% of households in 2021) [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]\u0026mdash;as well as societal-level factors such as low agricultural labor participation (4.3% of the workforce) and environmental regulations (e.g., Greenhouse Gas Reduction Act, Wetland Conservation Act, National Park Law) [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Compared with LMICs in tropical regions, where CKD of unknown etiology is increasingly recognized [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], populations in HICs such as Taiwan, Korea, and Japan appear to benefit from these individual- and societal-level adaptations to heat.\u003c/p\u003e \u003cp\u003eOur findings provide guidance for targeted, climate-responsive policies in Taiwan and HICs with similar climates\u0026mdash;particularly during cold waves (e.g., \u0026lt;\u0026thinsp;13.5\u0026deg;C in Taiwan)\u0026mdash;through improved access to heating and healthcare to reduce dialysis progression and mortality among advanced CKD patients. Sustaining adaptive behaviors and infrastructure for heat resilience is also crucial, with particular attention to long-term health risks faced by outdoor workers exposed to non-optimal hot temperatures.\u003c/p\u003e \u003cp\u003eOur study has several strengths. This nationwide, population-based cohort examined the association between long-term non-optimal daytime temperature exposure and the risk of progression from advanced CKD to dialysis or death. The large sample size enabled robust assessment of how baseline patient characteristics influence these outcomes, enhancing both reliability and generalizability. Our analysis also accounted for ambient humidity and PM\u003csub\u003e2.5\u003c/sub\u003e levels, addressing potential environmental confounders and strengthening the robustness of the findings. Notably, this is the first study to evaluate cumulative long-term daytime temperature exposure in relation to dialysis incidence and mortality in advanced CKD, providing a foundation for climate-responsive kidney health strategies.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, ambient temperatures were measured using fixed monitoring stations, which do not capture indoor conditions that may influence health; in Taiwan, 96.7% of households have air conditioning, potentially introducing exposure misclassification, especially in summer. Second, time-weighted cumulative temperature indices were used to quantify exposure to hot and cold days, which may not fully reflect daily variations or individual exposure patterns. Third, the NHIRD lacks information on personal health behaviors, physical measurements, and biochemical markers relevant to dialysis adequacy, possibly biasing the assessment of temperature exposure effects on CKD progression or mortality. Fourth, the study population comprised only patients with advanced CKD, limiting generalizability to those already on dialysis or in earlier disease stages. Fifth, residual confounding may persist despite adjustment for key covariates, as unmeasured factors\u0026mdash;such as individual heat and cold tolerance, access to heating or cooling devices, and specific behaviors\u0026mdash;could affect outcomes. Finally, given its observational design, causal inferences cannot be drawn. These limitations should be considered when interpreting the associations between non-optimal temperature exposure and CKD outcomes.\u003c/p\u003e \u003cp\u003eIn conclusion, long-term exposure to non-optimal daytime temperatures is significantly associated with dialysis progression and mortality in patients with advanced CKD in Taiwan. Cumulative cold exposure increased risks, whereas cumulative hot exposure was protective, suggesting population-level heat adaptation in this subtropical\u0026ndash;tropical HIC. These findings support targeted, climate-responsive policies to mitigate the health impacts of non-optimal temperatures on vulnerable populations, particularly those with advanced CKD in HICs with climates similar to Taiwan.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCKD: chronic kidney disease; AKI: acut kidney injury; ESKD: end-stage kidney disease; LMIC:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003elow- and medium- income country; HIC: high income country; NHIRD: the National Health\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInsurance Research Database; TAQMD: the Taiwan Air Quality Monitoring Database; HWDC: the Health and Welfare Data Science Center; MOENV: the Ministry of Environment; CIC: catastrophic illness certificate; ICD-9-CM: the International Classification of Diseases, Ninth Revision, Clinical Modification; ICD-10-CM: the International Classification of Diseases, Tenth Revision, Clinical Modification; DM: diabetes mellitus; COPD: chronic obstructive pulmonary disease; CCI: the Charlson Comorbidity Index; RCS: restricted cubic spline; HR: hazard ratio; CI: confidence interval;\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval\u003c/strong\u003e \u003cp\u003e This study was approved by the Research Ethics Review Committee of New Taipei City Hospital (protocol No. NTPC113001-N) on January 24, 2024. The Institutional Review Board operates in accordance with the Declaration of Helsinki and the International Council for Harmonisation Good Clinical Practice (ICH-GCP) guidelines..\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare the following potential competing interests: Shih-Feng Chen has received financial support from and is employed by New Taipei City Hospital, Sanchung Branch. The remaining authors declare no known financial or personal relationships that could have influenced the work reported in this study.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConflict of interest\u003c/strong\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eClinical trial number\u003c/h2\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eConsent to participate\u003c/h2\u003e \u003cp\u003eNot applicable (This retrospective cohort study utilized de-identified data from the NHIRD, which is protected under regulations governing beneficiary privacy established by the Taiwanese government.)\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eSupplementary material\u003c/h2\u003e \u003cp\u003eSupplemental Table\u0026nbsp;1. ICD diagnostic codes used in the study\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was funded by New Taipei City Hospital. The funding source had no role in the study design, data collection, analysis, interpretation of data, decision to publish, or preparation of the manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eS.F.C. conceived and designed the study, developed the methodology, performed data analysis, drafted the original manuscript, and manuscript revision. Y.H.C contributed to project administration, data analysis, and investigation. C.H.W was responsible for data curation, formal analysis, and investigation. Y.C.H assisted with methodological development, investigation, data interpretation, and manuscript revision. P.C.C supervised the study, validated the findings, and contributed to study conceptualization, methodological design, and critical manuscript review. All authors read and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors thank Mr. Yen-Chang Chen (B.A. in Physics and Accounting, University of California, Los Angeles, USA) for assistance with English language editing and acknowledge financial support from New Taipei City Hospital.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe first dataset used in this study was obtained from the National Health Insurance Research Database (NHIRD), maintained by the Health and Welfare Data Science Center (HWDC). Because the NHIRD is not publicly accessible, researchers must submit a formal application to the HWDC, Department of Statistics, Ministry of Health and Welfare, Taiwan, to obtain access. The second dataset was derived from the Taiwan Air Quality Monitoring Database (TAQMD), managed by the Ministry of Environment, Taiwan. In contrast to the NHIRD, the TAQMD is publicly available, and historical ambient temperature and air quality data can be downloaded from the Air Quality Index (AQI) platform: Air Quality Index (AQI) | 環境部環境資料開放平臺. [https://data.moenv.gov.tw/](https:/data.moenv.gov.tw)\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWatts N, Adger WN, Agnolucci P, et al. Health and climate change: policy responses to protect public health. Lancet. 2015;386(10006):1861\u0026ndash;914. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0140-6736(15)60854-6\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(15)60854-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChapman CL, Johnson BD, Parker MD, Hostler D, Pryor RR, Schlader Z. 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Climate Change Administration. Adaptation Communication. Accessed September 28. 2025. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://service.cca.gov.tw/File/Get/cca/zh-tw/ZmtVdXaa7XeDr98\u003c/span\u003e\u003cspan address=\"https://service.cca.gov.tw/File/Get/cca/zh-tw/ZmtVdXaa7XeDr98\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"environmental-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"enhe","sideBox":"Learn more about [Environmental Health](http://ehjournal.biomedcentral.com)","snPcode":"12940","submissionUrl":"https://submission.nature.com/new-submission/12940/3","title":"Environmental Health","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Non-optimal temperatures, advanced CKD, Dialysis, Mortality, Heat adaptation","lastPublishedDoi":"10.21203/rs.3.rs-8691969/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8691969/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eChronic kidney disease (CKD) of unknown etiology, potentially related to heat stress, has been reported among agricultural workers in tropical low- and middle-income countries. While high-income countries have greater heat-adaptation capacity, evidence on CKD risks from heat exposure in warm high-income settings is limited. Moreover, the effects of prolonged exposure to non-optimal cold or heat on kidney health remain unclear. This study investigates the associations between long-term non-optimal daytime temperature exposure and the risks of dialysis progression and mortality in advanced CKD patients in Taiwan.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a nationwide retrospective cohort study to examine associations between long-term exposure to non-optimal daytime temperatures and risks of dialysis progression and mortality among patients with advanced CKD in Taiwan. Data from 86,928 advanced CKD patients enrolled between 2008 and 2021 were analyzed, with follow-up through December 31, 2022. Non-optimal temperature days were defined as mean daytime temperatures\u0026thinsp;\u0026ge;\u0026thinsp;30\u0026deg;C (hot) or \u0026le;\u0026thinsp;15\u0026deg;C (cold). Time-weighted percentages and mean temparatures for hot and cold days were calculated for each patient. Additonally, mean daytime temperature across all days during the follow-up period was also used as an exposure index. Cox proportional hazards models estimated hazard ratios (HRs) for dialysis progression and mortality per 1% increase in exposure. Cumulative incidence curves assessed outcome differences across exposure quartiles, and restricted cubic spline analyses evaluated dose\u0026ndash;response relationships.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eEach 1% increase in time-weighted cold-day exposure was associated with a 14% higher risk of dialysis progression and a 9% higher risk of mortality. Conversely, a 1% increase in hot-day exposure was associated with 5% and 3% lower risks of dialysis progression and mortality, respectively. Spline analyses demonstrated a dose-dependent protective effect of higher mean temperatures. Higher mean daytime temperatures during follow-up were associated with lower risks of dialysis progression and mortality, whereas lower temperatures increased risks (P for both linearity and non-linearity\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eLong-term cold exposure increases dialysis and mortality risks in advanced CKD, whereas prolonged heat exposure may be protective for both outcomes, reflecting population-level heat adaptation. Our findings emphasize the need for climate-sensitive policies to mitigate the health impacts of non-optimal temperatures in vulnerable populations.\u003c/p\u003e","manuscriptTitle":"Long-Term Exposure to Non-Optimal Ambient Temperatures associated with Dialysis Incidence and Mortality in Advanced CKD Patients: A Population-Based Cohort Study in Taiwan","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-29 16:42:31","doi":"10.21203/rs.3.rs-8691969/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-20T16:11:24+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-10T11:22:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"58161322325192771043353215619640091128","date":"2026-03-31T18:54:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-16T04:22:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"98093023190539884380280306805105522842","date":"2026-03-03T22:30:28+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-28T00:14:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-26T07:18:15+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-26T07:14:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Health","date":"2026-01-25T11:00:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"enhe","sideBox":"Learn more about [Environmental Health](http://ehjournal.biomedcentral.com)","snPcode":"12940","submissionUrl":"https://submission.nature.com/new-submission/12940/3","title":"Environmental Health","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7e08e3a9-9b03-4bc0-b0da-bf0c9368b69c","owner":[],"postedDate":"January 29th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-04-20T16:25:46+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-29 16:42:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8691969","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8691969","identity":"rs-8691969","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}