Association between Extreme Ambient Temperature and Incident Vision Impairment: A National Longitudinal Cohort Study in China (2011–2018)

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This study aimed to examine the association between ETE exposure and the risk of visual impairment (VI) in a national cohort of middle-aged and older adults in China. Methods A total of 13,419 participants without VI at baseline from the China Health and Retirement Longitudinal Study (CHARLS) from 2011 to 2018 were included. Meteorological data were obtained from the National Oceanic and Atmospheric Administration Global Summary of the Day database. ETEs were identified based on relative temperature thresholds combined with duration criteria, allowing the definitions to reflect local climatic conditions across study regions. The relationship between ETE exposure and new-onset VI was analyzed using time-varying Cox proportional hazards regression. Effect sizes were quantified as hazard ratios (HRs) alongside 95% confidence intervals (CIs). Population attributable fractions (PAFs) were calculated, and stratified analyses were conducted by geographic region. Results CS exposure was associated with an increased risk of incident VI. Each additional high-intensity cold spell (CS_P5_3d) was associated with a 16.0% higher risk (HR: 1.160; 95% CI: 1.133–1.187). A clear dose–response relationship was observed, with stronger effects at longer durations and greater exposure intensity. Approximately 10.66% of incident VI cases were attributable to CSs under the CS_P75_4d definition. In contrast, HW exposure showed a modest inverse association with VI risk (HR: 0.965; 95% CI: 0.956–0.974). Geographic heterogeneity was evident, with populations in Southern China showing greater vulnerability to cold exposure. Conclusions CSs were associated with an increased risk of VI among middle-aged and older adults in China, whereas HWs exposure showed a modest inverse association. Regional inequalities in vulnerability highlight the need to raise awareness of cold-related risks and implement targeted, region-specific adjustment strategies to protect visual health. Climate change Cold spells Heat waves Vision impairment Public health Longitudinal study China Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Extreme temperature events (ETEs), particularly heat waves (HW) and cold spells (CS), have become increasingly frequent due to climate change and represent a growing global public health challenge[ 1 ]. A robust body of evidence, synthesized by authoritative assessments such as the Lancet Countdown [ 2 ], has conclusively established the detrimental impact of extreme temperatures on human health. These environmental stressors act as potent drivers of morbidity and mortality, significantly exacerbating risks associated with cardiovascular, respiratory, and other temperature-sensitive diseases [ 3 , 4 ]. Despite growing investigations into the impact of temperatures on systemic health, the effects of ETEs on visual health remain largely overlooked. Unlike most internal organs, the eyes are continuously and directly exposed to the external environment. The ocular surface serves as a primary biological barrier against environmental stressors[ 5 ], rendering it particularly susceptible to meteorological variations. Vision impairment (VI) is frequently driven by major ocular diseases such as cataracts and age-related macular degeneration, and can also result from ocular surface disorders such as dry eye syndrome [ 6 , 7 ]. Globally, an estimated 2.2 billion individuals live with some form of VI. This widespread prevalence imposes profound health and socio-economic burdens, resulting in massive losses in global productivity and escalating healthcare expenditures. Furthermore, the impact of VI exhibits significant disparities across diverse populations and geographic regions [ 8 ]. In the context of accelerating population aging and climate change, understanding environmental determinants of visual health has become an increasingly important public health priority. Among these determinants, extreme temperature exposure has emerged as a potential yet insufficiently studied risk factor for VI. Current evidence regarding the relationship between ETEs and visual health remains inconsistent. While extreme heat and cold are known to influence physiological processes, their specific roles in ocular health are contentious. Some studies have suggested that cold exposure may aggravate VI by inducing physiological stress and exacerbating dry eye conditions [ 9 , 10 ]. Simultaneously, investigations have also reported that heat exposure may increase the risk of ocular diseases such as cataract and retinal detachment, potentially through mechanisms involving thermal stress and increased ultraviolet radiation [ 11 – 13 ]. Conversely, several studies have indicated that warmer conditions can have neutral or even beneficial effects on ocular surface stability and visual comfort [ 9 ]. These heterogeneous findings highlight substantial uncertainty regarding the overall impact of extreme temperature exposure on visual outcomes. To address these gaps, we leveraged data from the China Health and Retirement Longitudinal Study (CHARLS) to conduct the first national longitudinal analysis clarifying the independent and temporal associations between ETE exposure and VI among middle-aged and older adults across diverse environmental and socio-demographic contexts in China. By elucidating population vulnerability patterns under varying climatic contexts, this study aims to provide evidence to develop targeted climate adaptation strategies and optimize public health resource allocation, ultimately mitigating the impending burden of temperature-related VI in an aging society. Materials and methods Study design and study population This study utilized data from the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative survey of people aged 45 years and older. The survey covered 150 counties or districts and 450 villages or communities across 28 provinces in mainland China. Participants were classified into the Northern, Central, and Southern regions based on climatic zones. Baseline data (Wave 1) were collected in 2011–2012. Follow-up waves were conducted in 2013, 2015, and 2018 (Waves 2–4). Data collection was conducted through face-to-face interviews with standardized questionnaires, administered every 2 years. Further details on the CHARLS design and implementation have been reported elsewhere.[ 14 ] This study utilized a longitudinal cohort design between 2011 and 2018. The specific selection criteria were as follows: (1) individuals aged 45 years or older at baseline; (2) participants with normal vision status at the baseline survey (Wave 1); (3) availability of complete follow-up data regarding vision status and key independent variables; and (4) participants with identifiable city-level geolocation information for meteorological matching. After applying the selection criteria, 13,419 participants formed the final study population. This substantial cohort ensures robust statistical power for linking meteorological extremes to incident VI. Our confidence in these estimates is further reinforced by the prospective tracking and extensive covariate adjustments. Additionally, the broad spatial footprint of the survey captures populations across highly varied Chinese climates. Such geographic diversity naturally enhances the external validity of our conclusions. As detailed residential addresses were unavailable, environmental exposure was assigned at the city level. Residential stability was assessed by examining consistency in location information across survey waves based on standardized administrative codes in China. It was assumed that individuals residing within the same city were exposed to similar environmental conditions. This approach is consistent with the CHARLS geolocation framework and reflects the relatively stable residential patterns among older adults in China. All participants provided informed consent. The study protocol was approved by the Ethical Review Committee of Peking University. The data used in this study are publicly available. Assessment of VI Vision function was assessed based on self-reported responses to the CHARLS questionnaire regarding distance and near vision. Participants were asked: "How is your eyesight for seeing things at a distance (like recognizing a friend from across the street)?" and "How is your eyesight for seeing things up close (like reading ordinary newspaper print)?" Responses were graded on a 5-point scale: 1 (Excellent), 2 (Very good), 3 (Good), 4 (Fair), and 5 (Poor). In this study, the outcome of interest was new-onset VI, defined using a "Strict Logic" approach. A participant was classified as having VI if either distance or near eyesight was reported as "Poor" (score = 5). Conversely, participants were defined as having normal vision only if both distance and near eyesight were reported as non-poor (scores 1–4). Participants with existing VI at baseline (2011) were excluded to ensure the cohort nature of the study. The event date was defined as the mid-point between the last visit with normal vision and the first visit with reported impairment. Assessment of extreme temperature (HWs and CSs) Meteorological data were obtained from the National Oceanic and Atmospheric Administration (NOAA) Global Summary of the Day (GSOD) database, which provides standardized daily observations from meteorological stations worldwide. Daily maximum temperature (Tmax), minimum temperature (Tmin), and mean temperature were extracted from more than 370 meteorological stations across China covering the study period from 2011 to 2018. These meteorological records were spatially matched to participants in the China Health and Retirement Longitudinal Study (CHARLS) according to the city-level geographic location of their residence. Given the substantial climatic heterogeneity across China—from subarctic regions in the north to tropical climates in the south, using fixed absolute temperature thresholds may inadequately capture locally relevant temperature extremes. Therefore, we adopted a relative threshold approach based on the daily temperature range to define ETEs, including HWs and CSs. To capture varying intensities of extreme temperature exposure, three levels of event intensity were defined according to the value of the Ratio parameter: low intensity (Ratio = 7.5%, corresponding to the 92.5th percentile for HWs and the 7.5th percentile for CSs), moderate intensity (Ratio = 5.0%, corresponding to the 95th and 5th percentiles), and high intensity (Ratio = 2.5%, corresponding to the 97.5th and 2.5th percentiles). In addition to temperature thresholds, duration criteria were incorporated to define sustained extreme events. Consecutive periods of ≥ 2 days, ≥ 3 days, and ≥ 4 days were applied. By combining the three intensity levels with the three duration criteria, a total of 18 definitions were generated for HWs and CSs, respectively (detailed in Table 1 ). Table 1 Definitions and descriptive characteristics of heatwave and CS exposure variables employed in the present study. Heatwave/Coldspell definitions Threshold percentile (°C) for temperature Duration (Day) Intensity categories CS_P25_2d Falling below 2.5th percentiles of temperature during 7-year exposure window preceding cohort entry ≥ 2 High intensity CS_P25_3d Falling below 2.5th percentiles of temperature during 7-year exposure window preceding cohort entry ≥ 3 High intensity CS_P25_4d Falling below 2.5th percentiles of temperature during 7-year exposure window preceding cohort entry ≥ 4 High intensity CS_P5_2d Falling below 5th percentiles of temperature during 7-year exposure window preceding cohort entry ≥ 2 Middle intensity CS_P5_3d Falling below 5th percentiles of temperature during 7-year exposure window preceding cohort entry ≥ 3 Middle intensity CS_P5_4d Falling below 5th percentiles of temperature during 7-year exposure window preceding cohort entry ≥ 4 Middle intensity CS_P75_2d Falling below 7.5th percentiles of temperature during 7-year exposure window preceding cohort entry ≥ 2 Low intensity CS_P75_3d Falling below 7.5th percentiles of temperature during 7-year exposure window preceding cohort entry ≥ 3 Low intensity CS_P75_4d Falling below 7.5th percentiles of temperature during 7-year exposure window preceding cohort entry ≥ 4 Low intensity HW_P975_2d Exceeding 97.5th percentiles of temperature during 7-year exposure window preceding cohort entry ≥ 2 High intensity HW_P975_3d Exceeding 97.5th percentiles of temperature during 7-year exposure window preceding cohort entry ≥ 3 High intensity HW_P975_4d Exceeding 97.5th percentiles of temperature during 7-year exposure window preceding cohort entry ≥ 4 High intensity HW_P95_2d Exceeding 95th percentiles of temperature during 7-year exposure window preceding cohort entry ≥ 2 Middle intensity HW_P95_3d Exceeding 95th percentiles of temperature during 7-year exposure window preceding cohort entry ≥ 3 Middle intensity HW_P95_4d Exceeding 95th percentiles of temperature during 7-year exposure window preceding cohort entry ≥ 4 Middle intensity HW_P90_2d Exceeding 90th percentiles of temperature during 7-year exposure window preceding cohort entry ≥ 2 Low intensity HW_P90_3d Exceeding 90th percentiles of temperature during 7-year exposure window preceding cohort entry ≥ 3 Low intensity HW_P90_4d Exceeding 90th percentiles of temperature during 7-year exposure window preceding cohort entry ≥ 4 Low intensity For the primary exposure metric, we calculated the annual frequency of these ETEs for each participant during the study period. Covariates Baseline characteristics were obtained during the 2011 enrollment wave. We recorded socio-demographic profiles, namely age, sex, marital status, educational attainment, and residential setting (urban versus rural). Additionally, the survey captured behavioral factors, including nightly sleep duration, smoking habits, and alcohol intake. The analysis also accounted for clinical history, defined by self-reported, physician-diagnosed chronic conditions (specifically hypertension, diabetes, dyslipidemia, alongside pulmonary, cardiac, hepatic, and renal diseases). Statistical analysis Data processing and statistical modeling were executed using R software (version 4.3.1). We first handled missing covariate information via the MICE algorithm (Multiple Imputation by Chained Equations), creating five complete datasets to maintain analytic rigor. Next, we summarized continuous baseline parameters using means and standard deviations, whereas categorical traits were expressed as percentages and counts. To assess inter-group variance, we applied Student's t-tests or Pearson's chi-square statistics where suitable. To evaluate our primary objective, time-dependent Cox proportional hazards models were constructed to estimate the relationship between meteorological exposures (extreme temperatures) and VI. Results were expressed as hazard ratios (HRs) with corresponding 95% confidence intervals (CIs). Potential confounding was addressed through a stepwise modeling strategy: an unadjusted model (Model 1), followed by progressive control for demographics (Model 2), behavioral variables (Model 3), and baseline comorbidities (Model 4). Furthermore, the population attributable fraction (PAF) was computed to assess the excess burden of VI driven by ETEs. We also performed subgroup analyses stratified by age, sex, and geographic location (North, Central, and South) to explore potential effect modification. A two-sided p-value below 0.05 defined statistical significance across all tests. Results Characteristics of the study participants The final analytical sample comprised 13,419 individuals. At baseline, the cohort had a mean age of 59.1 years (SD: 9.3) and a slightly higher proportion of females (52.8%, n = 7,091) than males (47.2%, n = 6,328). Over an average follow-up period of 6.4 years (SD: 1.4), we identified 980 incident cases of VI. Baseline characteristics stratified by incident VI status are detailed in Table 1 . Individuals who developed VI exhibited demographic profiles distinctly different from those who remained impairment-free. Specifically, the incident VI group was significantly younger (57.9 vs. 59.2 years, p < 0.001) and predominantly male (54.5% vs. 46.6%, p < 0.001). They also possessed slightly higher educational attainment, a distribution that may reflect increased occupational visual strain among the active working-age population. Summaries of the time-varying meteorological exposures are provided in Table S1 . The annual frequency of ETEs varied considerably across the study period. Mean HW exposures ranged from 3.68 to 24.28 events per year, while CS exposures spanned from 3.91 to 24.94 annual events (Table 2 ). Table 2 Descriptive characteristics of participants at baseline by VI Status Characteristics Total N = 13419 1 No Vision Impairment N = 12,439 1 Incident Vision Impairment N = 980 1 p -value 2 Age, years 59.08 (9.27) 59.17 (9.30) 57.89 (8.82) < 0.001 Gender < 0.001 Female 7,091 (53%) 6,645 (53%) 446 (46%) Male 6,328 (47%) 5,794 (47%) 534 (54%) Residency 0.149 Rural 8,259 (62%) 7,677 (62%) 582 (59%) Urban 5,160 (38%) 4,762 (38%) 398 (41%) Marital Status 0.417 Married 11,818 (88%) 10,947 (88%) 871 (89%) Unmarried/Other 1,601 (12%) 1,492 (12%) 109 (11%) Education Level < 0.001 Illiterate 6,141 (46%) 5,750 (46%) 391 (40%) Primary School 2,893 (22%) 2,693 (22%) 200 (20%) Middle School 2,767 (21%) 2,527 (20%) 240 (25%) High School+ 1,606 (12%) 1,459 (12%) 147 (15%) Sleep Duration, h/day 6.34 (1.88) 6.32 (1.90) 6.54 (1.72) 0.001 Smoking Status 0.002 Smoker 5,219 (39%) 4,792 (39%) 427 (44%) Non-smoker 8,195 (61%) 7,642 (61%) 553 (56%) Drinking Status < 0.001 Drinker 4,389 (33%) 4,021 (32%) 368 (38%) Non-drinker 9,024 (67%) 8,412 (68%) 612 (62%) Hypertension 3,419 (26%) 3,224 (26%) 195 (20%) < 0.001 Diabetes 806 (6.1%) 759 (6.2%) 47 (4.8%) 0.092 Chronic Lung Disease 1,281 (9.6%) 1,203 (9.7%) 78 (8.0%) 0.075 Heart Disease 1,618 (12%) 1,518 (12%) 100 (10%) 0.058 Liver Disease 461 (3.5%) 436 (3.5%) 25 (2.6%) 0.109 Kidney Disease 758 (5.7%) 712 (5.8%) 46 (4.7%) 0.174 1 Mean (SD); n (%) 2 Wilcoxon rank sum test; Pearson's Chi-squared test Associations between ETEs and incident VI The relationship between ETEs and new-onset VI revealed a clear divergence depending on the temperature direction (Table 2 , Fig. 2 ). Most notably, cold spell exposure consistently correlated with an elevated hazard of VI across the majority of defined event thresholds. After multivariable adjustment, the data demonstrated a robust adverse effect for cold stress. For example, experiencing high-intensity cold snaps (e.g., CS_P5_3d) raised the risk of VI by 16.0% (HR: 1.160; 95% CI: 1.133–1.187). This hazard escalated considerably under prolonged, extreme cold conditions; the strictest criteria (CS_P2.5_4d) yielded an HR of 1.379 (95% CI: 1.304–1.459). Overall, lower temperature thresholds combined with extended event durations reliably predicted steeper risk trajectories. In contrast, HW exposure predominantly exerted a null or marginally protective effect. Moderate heat events (e.g., HW_P95_3d), for instance, were associated with a slight risk reduction, generating an estimated HR of 0.965 (95% CI: 0.956–0.974). PAF of VI attributable to CS exposure The PAF was further calculated to quantify the public health burden of incident VI attributable to CS exposure. The analysis revealed that a substantial proportion of incident VI cases were attributable to CS exposure. Specifically, under the CS_P7.5_4d definition, the PAF reached 10.66%, indicating that approximately 10.66% of new-onset VI cases in this cohort could be attributed to CS exposure. Dose–response relationships Figure 2 also illustrates the exposure–response relationships between cumulative duration and intensity of ETEs and the risk of incident VI. A clear dose–response pattern was observed for CSs. Across different percentile thresholds and durations, the HRs for VI increased progressively with greater cold exposure. This upward trend became more pronounced under more extreme definitions, with the highest risks observed at lower temperature percentiles and longer durations (e.g., CS P2.5 with ≥ 3–4 days), indicating a robust, graded association ( p < 0.001). In contrast, HWs exhibited a modest inverse association with VI risk. Although the magnitude of change was relatively small, a gradual decline in HRs was observed with increasing heat exposure intensity and duration. This downward trend was more evident at higher percentile thresholds (e.g., HW P97.5), where longer-lasting heat events were associated with slightly lower risks of VI. Stratified analyses Subgroup analyses were conducted to explore potential differences in susceptibility to ETE exposure across age, sex, residence, and geographic region. (Figure. 3 and Figure. 4). A geographic distribution map was generated to depict the spatial patterns of VI incidence and the frequency of ETEs across provinces in China (Fig. 5 ). Generally, the detrimental impact of CSs was observed across all major demographic groups, with no statistically significant effect modification by gender or age ( p > 0.05). However, significant geographic heterogeneity was observed across different climatic zones. Stratified analyses revealed a clear geographic gradient in vulnerability thresholds: participants residing in the Southern and Central regions demonstrated elevated vulnerability even to mild or moderate CSs (e.g., CS_P75_3d). In contrast, participants in the Northern region exhibited a significantly increased risk of VI primarily under extreme cold conditions (e.g., CS_P5_4d). Sensitivity analysis To evaluate the robustness of our findings, three sensitivity analyses were performed. The results were consistent with the primary analyses, indicating stable associations between ETE exposure and the risk of VI. First, to isolate the independent long-term effects of extreme cold and heat, we constructed mutual adjustment models that simultaneously incorporated the CS and HW indices. The significant associations between CSs and VI risk remained robust after adjusting for corresponding HWs. For instance, in the mutually adjusted model, exposure to CSs (CS_P5_3d) was significantly associated with a higher risk of VI (HR = 1.18, 95% CI 1.15–1.22, p < 0.001), while corresponding HWs (HW_P95_3d) showed no increased risk (HR = 0.94, 95% CI 0.92–0.95) (Table S1 in Supplementary Material). Similar independent effects were observed for other thresholds, such as CS_P25_3d (HR = 1.48, 95% CI 1.37–1.61, p < 0.001). (Table 3 in Supplementary Materials) Second, to minimize the potential for reverse causality, we excluded participants who developed VI or were censored within the first 2 years of follow-up. Consistent with the primary analyses, the elevated risk of VI associated with CS exposure remained statistically significant. The HR for CS_P5_3d was 1.25 (95% CI 1.19–1.30), indicating that the temporal relationship between extreme temperature exposure and incident VI is reliable (Table 4 in Supplementary Material). Finally, we evaluated the stability of our results across different clinical definitions by analyzing incident distance VI only, near VI only, and any VI (defined as the onset of either distance or near VI). The adverse effects of extreme cold were highly consistent across all outcome definitions. Specifically, exposure to CS_P5_3d was significantly associated with an increased risk of distance VI (HR = 1.18, 95% CI 1.14–1.23), near VI (HR = 1.15, 95% CI 1.12–1.18), and any VI (HR = 1.18, 95% CI 1.12–1.24). Even stronger associations were observed for extreme CSs (CS_P25_3d) across distance VI (HR = 1.62, 95% CI 1.49–1.76), near VI (HR = 1.42, 95% CI 1.28–1.58), and any VI (HR = 1.59, 95% CI 1.48–1.70) (Table 5 in Supplementary Material). Discussion To the best of our knowledge, this is the first nationwide longitudinal cohort study to investigate the association between exposure to ETEs and the risk of VI among middle-aged and older Chinese adults. In this study, a distinct divergent pattern regarding the visual impacts of different ETEs was demonstrated, with CS exposure emerging as a significant risk factor for VI. This adverse effect was particularly amplified by the intensity and duration of the CSs, exhibiting a clear dose-response relationship. Notably, the PAF analysis further highlighted the substantial public health burden driven by cold exposure, revealing that up to 10.66% of VI incidence could be attributed to CSs. Stratified analyses revealed remarkably consistent associations across demographic characteristics such as age and gender. However, a clear geographic shift in vulnerability thresholds was observed, indicating that participants in southern regions were susceptible even to mild CSs. Conversely, HW exposure exhibited an inverse association with the development of VI, suggesting that moderate ambient warming may confer unexpected physiological benefits to ocular health. The significant positive association between CS exposure and the increased incidence of VI can be explained by several underlying ocular mechanisms. Unlike other body parts, the ocular surface serves as the eye's primary barrier in direct contact with the external environment, rendering it highly susceptible to damage from ambient temperature fluctuations. First, cold environments destabilize the tear film lipid layer and accelerate evaporation. subsequently provoking epithelial stress and symptomatic dry eye, clinically manifesting as fluctuating vision due to an irregular optical surface [ 10 ]. Ultimately, this persistent instability fosters chronic surface disorders that progressively degrade visual quality. Following tear film breakdown, the exposed corneal epithelium is more vulnerable to stress and injury. Cold-induced tear evaporation and hyperosmolarity increase epithelial cell stress and may promote subclinical keratitis, which alters corneal curvature and smoothness, thereby impairing the eye's refractive surface and reducing visual acuity [ 9 ]. On the other hand, direct freezing injury in extreme cold has been documented to damage both the corneal epithelium and endothelium, leading to edema and surface irregularity that degrade optical clarity and cause visual blurring [ 15 ]. Beyond the surface, temperature changes may influence lens optics and refractive properties. The crystalline lens is a key refractive element for focusing light onto the retina, and its transparency is critical for clear vision. Exposure to cold stress can induce structural and functional impairments in the lens by promoting the conformational instability of lens proteins, rendering them susceptible to misfolding and aggregation. Furthermore, hypothermia disrupts metabolic homeostasis and exacerbates oxidative stress, which accelerates lens opacification and further compromises visual transparency. [ 16 ]. These alterations in lens optics can reduce visual sharpness and increase scattering of light, especially if lens opacities occur along the visual axis, which ultimately causes the VI. Moreover, the posterior segment and retinal perfusion are susceptible to cold-induced vascular changes. Evidence indicates that cold exposure reduces ocular blood flow, including the central retinal artery, which may impair oxygen and nutrient delivery to the retina and potentially disrupt retinal function [ 17 ]. Impaired perfusion can lead to hypoxic stress on retinal neurons and photoreceptors, ultimately reducing signal quality and visual function. While the aforementioned literature supports our findings, the precise thresholds and durations of cold exposure required to induce these ocular changes, as well as the exact underlying mechanisms, warrant further investigation. A contrasting pattern was observed for HWs, with exposure showing a modest inverse association with the risk of incident VI. This finding may be interpreted in light of the climatic characteristics of the study regions and the relative definition of heat exposure adopted in the present study. Most study areas were located within temperate and subtropical climate zones, where historical maximum temperatures generally remained below 36°C. Under such conditions, the heatwaves identified likely represent moderate increases in ambient temperature rather than absolute thermal extremes capable of inducing direct tissue injury. Consequently, the observed inverse association between HW exposure and VI risk may reflect the physiological effects of moderate warming rather than heat-related damage. Several biological mechanisms may help explain this hypothesis. Moderate increases in ambient temperature can enhance tear film stability by promoting the melting and uniform distribution of the meibomian lipid layer, thereby reducing tear evaporation and stabilizing the optical surface[ 18 ]. In addition, elevated temperature may induce ocular vasodilation and improve ocular hemodynamics, which could support retinal function and visual performance. Experimental studies have shown that warming temperature increases blood-flow velocities in both the anterior ciliary artery and the central retinal artery, indicating enhanced ocular perfusion under warm conditions [ 17 , 19 ]. Improved perfusion may facilitate the delivery of oxygen and nutrients to ocular tissues, thereby helping maintain visual function. Together, these effects on tear film stability and ocular circulation may create a more favorable ocular microenvironment, which could contribute to the reduced risk of VI. Moreover, recent studies suggest that temperature elevation may reverse lens opacity induced by cold exposure in animals [ 20 , 21 ]. However, the underlying biological mechanisms and the relevant temperature thresholds remain unclear. This evidence may partly support the hypothesis that moderate warming could be beneficial for maintaining visual function under certain environmental conditions. Given the multifactorial nature of VI, further investigation is needed to explore the complex pathways linking heat exposure to visual outcomes. Our analysis indicated that the adverse effects of CSs were largely consistent across age, sex, and residential subgroups. Previous studies on VI have reported heterogeneity by age and gender, and differences between urban and rural populations often reflect variations in environmental exposure and living conditions[ 22 ]. In contrast, the absence of significant effect modification in our study suggests that susceptibility to cold-related ocular damage may be broadly shared across populations. We speculate common physiological responses to low temperature possibly drive this consequence. When stratified by geographic region, a distinct spatial pattern emerged. In northern regions, an increased risk of VI was observed primarily under more extreme cold conditions, suggesting that sufficiently low temperatures may exceed the physiological tolerance threshold required to induce measurable ocular damage. This pattern is consistent with general expectations that severe cold exposure in these regions may impose a considerable burden on visual health. In contrast, populations in central and southern regions exhibited elevated risks even at relatively mild cold levels. This increased vulnerability may be associated with lower levels of cold-protective awareness, longer durations of cold exposure, and insufficient behavioral adaptation to cold environments. Together, these factors may exacerbate cumulative cold stress, thereby increasing the risk of VI in these regions. These findings underscore the need for targeted public health and climate adaptation strategies. Priority should be given to protecting populations at higher risk during ETEs, particularly older adults and individuals residing in regions with insufficient cold-protection infrastructure. In Central and Southern China, where vulnerability to even moderate cold exposure is evident, interventions should focus on improving indoor thermal conditions, strengthening awareness of cold-related health risks, and promoting household-level adaptations, such as better insulation and adequate heating. In contrast, in Northern regions where health risks are primarily associated with extreme cold conditions, preventive strategies should emphasize behavioral modifications. These include reducing unnecessary outdoor exposure during severe cold events and adopting personal protective measures such as appropriate eye protection. At the same time, region-specific resource allocation and infrastructure planning should be aligned with local climatic conditions to enhance resilience against temperature-related health risks. The current study possesses several notable strengths. First, by leveraging a nationwide prospective cohort of middle-aged and older adults in China, it offers robust longitudinal evidence linking ETEs to the risk of incident VI. Second, rather than relying on static meteorological measures, we employed a time-varying approach with cumulative exposure definitions. This refined assessment better captures the dynamic reality of chronic environmental stress. Finally, the inclusion of population attributable fractions (PAF) alongside stratified analyses allowed us to comprehensively quantify the specific public health burden of cold exposure across diverse geographic and demographic subgroups. Several limitations should also be acknowledged. First, the assessment of vision relied on self-reported questionnaires. While standard survey protocols were strictly followed, this approach inherently carries a potential risk of recall bias. Second, residual confounding remains a possibility; unmeasured individual behaviors, such as precise daily outdoor activity duration or the availability of indoor heating, might influence the observed associations. Third, environmental exposure was estimated at the city level due to privacy constraints. This broader spatial resolution may miss micro-environmental variations, leading to potential exposure misclassification. Consequently, future multi-center studies are warranted to validate the impact of ETEs on visual health across broader age groups and different global climatic zones. Conclusion In conclusion, this is the first nationwide prospective cohort study to explore the significant impacts of ETEs on the risk of incident VI. CSs were identified as a major contributor to the burden of VI in China. These findings highlight the importance of incorporating ocular health into climate adaptation strategies and emphasize the need for targeted protective measures alongside strengthened public awareness of temperature-related risks. Abbreviations ETEs Extreme temperature events HW / HWs Heat wave(s) CS / CSs Cold spell(s) VI Visual impairment CHARLS China Health and Retirement Longitudinal Study NOAA National Oceanic and Atmospheric Administration GSOD Global Summary of the Day HR Hazard ratio CI Confidence interval PAF Population attributable fraction Declarations Ethical Statement The data were derived from the China Health and Retirement Longitudinal Study (CHARLS), a publicly available and de-identified dataset. As this study involved secondary analysis of anonymized data, additional ethical approval was not required. Consent for publication Not applicable Competing interests The authors have nothing to declare. Funding This work is supported by the funds from the National Natural Science Foundation of China (82471045, Cheng-wei Lu). Author Contribution Yuan-hao Li: methodology, software, visualization, validation and original draft preparation; Xiu-fen Liu: conceptualization, supervision, validation and review and editing; Song-tao Wang: data curation, visualization, formal analysis and original draft preparation; Yibo Wang: investigation and original draft preparation; Zi-han Tang: data curation and original draft preparation; Qian Li: data curation, visualization, formal analysis. Dan Li: data curation, visualization, formal analysis. Cheng-wei Lu: funding acquisition, project administration, supervision, conceptualization, and review and editing. All authors read and approved the final manuscript. Acknowledgement We would like to acknowledge the China Health and Retirement Longitudinal Study (CHARLS) team and the National Oceanic and Atmospheric Administration (NOAA) team, for providing high-quality, nationally representative data. Data Availability The baseline data is available from [https://charls.charlsdata.com/pages/Data](https:/charls.charlsdata.com/pages/Data) and [https://www.ncei.noaa.gov/maps/daily](https:/www.ncei.noaa.gov/maps/daily) . For more information on study protocols and for other datasets, please contact a corresponding author. References Sampath V, Aguilera J, Prunicki M, Nadeau KC. Mechanisms of climate change and related air pollution on the immune system leading to allergic disease and asthma. Semin Immunol. 2023;67:101765. Romanello M, Walawender M, Hsu SC, Moskeland A, Palmeiro-Silva Y, Scamman D, Smallcombe JW, Abdullah S, Ades M, Al-Maruf A, et al. The 2025 report of the Lancet Countdown on health and climate change: climate change action offers a lifeline. Lancet. 2025;406(10521):2804–57. Mou P, Qu H, Guan J, Yao Y, Zhang Z, Dong J. Extreme temperature events, functional dependency, and cardiometabolic multimorbidity: Insights from a national cohort study in China. Ecotoxicol Environ Saf. 2024;284:117013. Tang J, Gu W, Wang M, Liu J, Chen Y, Zhang X. Association between extreme temperature events and multimorbidity among older adults: evidence from the CHARLS. BMC Med. 2025;23(1):625. Lu CW, Fu J, Liu XF, Cui ZH, Chen WW, Guo L, Li XL, Ren Y, Shao F, Chen LN, et al. Impacts of air pollution and meteorological conditions on dry eye disease among residents in a northeastern Chinese metropolis: a six-year crossover study in a cold region. Light Sci Appl. 2023;12(1):186. Resnikoff S, Pascolini D, Etya'ale D, Kocur I, Pararajasegaram R, Pokharel GP, Mariotti SP. Global data on visual impairment in the year 2002. Bull World Health Organ. 2004;82(11):844–51. Tsubota K, Pflugfelder SC, Liu Z, Baudouin C, Kim HM, Messmer EM, Kruse F, Liang L, Carreno-Galeano JT, Rolando M et al. Defining Dry Eye from a Clinical Perspective. Int J Mol Sci 2020, 21(23). Forrest SL, Mercado CL, Engmann CM, Stacey AW, Hariharan L, Khan S, Cabrera MT. Does the Current Global Health Agenda Lack Vision? Glob Health Sci Pract 2023, 11(1). Abusharha AA, Pearce EI, Fagehi R. Effect of Ambient Temperature on the Human Tear Film. Eye Contact Lens. 2016;42(5):308–12. Ho WT, Chiu CY, Chang SW. Low ambient temperature correlates with the severity of dry eye symptoms. Taiwan J Ophthalmol. 2022;12(2):191–7. Auger N, Rheaume MA, Bilodeau-Bertrand M, Tang T, Kosatsky T. Climate and the eye: Case-crossover analysis of retinal detachment after exposure to ambient heat. Environ Res. 2017;157:103–9. Roberts JE. Ultraviolet radiation as a risk factor for cataract and macular degeneration. Eye Contact Lens. 2011;37(4):246–9. Takayama Y, Hatsusaka N, Yamada Y, Sasaki H, Hirata A. Ambient heat exposure as a risk factor for cataracts: Evidence from a nationwide claims-based study in Japan. Environ Res. 2026;292:123680. Zhao Y, Hu Y, Smith JP, Strauss J, Yang G. Cohort profile: the China Health and Retirement Longitudinal Study (CHARLS). Int J Epidemiol. 2014;43(1):61–8. Yin Y, Gong L. The ocular frostbite involving cornea caused by low-temperature environment. BMC Ophthalmol. 2025;25(1):542. Gao T, Wang X, Jiang Z, Mu X, Luo B, Lei Y, Chen R. Molecular mechanisms of cold stress-induced lens opacity: An investigation through multi-omics integrative analysis. Exp Eye Res. 2026;262:110739. Shamshad MA, Amitava AK, Ahmad I, Wahab S. Changes in central retinal artery blood flow after ocular warming and cooling in healthy subjects. Indian J Ophthalmol. 2010;58(3):189–94. Olson MC, Korb DR, Greiner JV. Increase in tear film lipid layer thickness following treatment with warm compresses in patients with meibomian gland dysfunction. Eye Contact Lens. 2003;29(2):96–9. Li TT, Shao GB, Jiang YL, Wang JX, Zhou XR, Ren M, Li LQ. Ocular surface heat effects on ocular hemodynamics detected by real-time measuring device. Int J Ophthalmol. 2018;11(12):1902–8. Blackburn BJ, McPheeters MT, Jenkins MW, Dupps WJ Jr., Rollins AM. Phase-Decorrelation Optical Coherence Tomography Measurement of Cold-Induced Nuclear Cataract. Transl Vis Sci Technol. 2023;12(3):25. Yang H, Ping X, Zhou J, Ailifeire H, Wu J, Nadal-Nicolas FM, Miyagishima KJ, Bao J, Huang Y, Cui Y et al. Reversible cold-induced lens opacity in a hibernator reveals a molecular target for treating cataracts. J Clin Invest 2024, 134(18). Pan CW, Qian DJ, Sun HP, Ma Q, Xu Y, Song E. Visual Impairment among Older Adults in a Rural Community in Eastern China. J Ophthalmol. 2016;2016:9620542. Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterials.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 24 Apr, 2026 Editor assigned by journal 23 Apr, 2026 Editor invited by journal 02 Apr, 2026 Submission checks completed at journal 02 Apr, 2026 First submitted to journal 02 Apr, 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9213569","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":634325385,"identity":"2426299c-83ab-4193-8fca-65209ed458cb","order_by":0,"name":"Yuan-hao Li","email":"","orcid":"","institution":"First Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Yuan-hao","middleName":"","lastName":"Li","suffix":""},{"id":634325387,"identity":"1bf441fa-27e4-4b3c-a3c5-00367038b6c2","order_by":1,"name":"Xiu-fen Liu","email":"","orcid":"","institution":"First Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Xiu-fen","middleName":"","lastName":"Liu","suffix":""},{"id":634325390,"identity":"a38cfd65-c41a-47fe-a3f3-705ff3302d9b","order_by":2,"name":"Song-tao Wang","email":"","orcid":"","institution":"First Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Song-tao","middleName":"","lastName":"Wang","suffix":""},{"id":634325401,"identity":"7a81e2ce-dd72-455f-8793-0ae10faed0a7","order_by":3,"name":"Zi-han Tang","email":"","orcid":"","institution":"First Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Zi-han","middleName":"","lastName":"Tang","suffix":""},{"id":634325420,"identity":"c2b3c1bb-9902-475b-b8b6-16f4ef1d2f17","order_by":4,"name":"Yi-bo Wang","email":"","orcid":"","institution":"First Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Yi-bo","middleName":"","lastName":"Wang","suffix":""},{"id":634325423,"identity":"87a5b517-7e80-4af1-87f2-7b98bc553a63","order_by":5,"name":"Qian Li","email":"","orcid":"","institution":"First Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Qian","middleName":"","lastName":"Li","suffix":""},{"id":634325427,"identity":"69df7b40-e3c1-416b-a58e-5054d8ecb336","order_by":6,"name":"Dan Li","email":"","orcid":"","institution":"First Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Dan","middleName":"","lastName":"Li","suffix":""},{"id":634325437,"identity":"8b9974c4-36c0-4cc6-b144-732be3a20ab5","order_by":7,"name":"cheng-wei Lu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYBACPiBmZjBgYOBnZmx8IFEAEkvAr4UNpkWyvfmwgYQB0VqAwODMsTQJBqK0sJ8x/FxQcEdeckaOWYWFwWEGfvYcA4afO/Bo4ckxlp5h8MywXyLH7IYEUItkzxsDxt4zeLRI8G5j5jE4zDhzBlSLwY0cA2bGNsJa7DfcyDErAGmxJ1ZL4gag9xnAtkgQ0sKT/1kaqCV5JjCQJSQM0nkkzjwrONiLRws/+7HEzzx/Dtv2A6Pys0SFtRx/e/LGBz/xaEEBzBIMDDwgxgEiNTAwMH4gWukoGAWjYBSMJAAA22tJA8mrTp8AAAAASUVORK5CYII=","orcid":"","institution":"First Hospital of Jilin University","correspondingAuthor":true,"prefix":"","firstName":"cheng-wei","middleName":"","lastName":"Lu","suffix":""}],"badges":[],"createdAt":"2026-03-24 14:55:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9213569/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9213569/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108977160,"identity":"fc7257bd-e7c9-4c77-96a1-c9d0d7e00606","added_by":"auto","created_at":"2026-05-11 11:30:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":69472,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of participant selection from the CHARLS cohort (2011–2018). \u003c/strong\u003eThis Figure illustrates the selection process of study participants from the China Health and Retirement Longitudinal Study (CHARLS). Individuals aged ≥45 years at baseline (Wave 1, 2011–2012) were initially considered. Participants were excluded if they had pre-existing vision impairment at baseline, missing follow-up data, incomplete covariate information, or lacked valid city-level geographic data for environmental exposure assessment. A total of 13,419 participants were included in the final analysis.\u003c/p\u003e","description":"","filename":"floatimage121.png","url":"https://assets-eu.researchsquare.com/files/rs-9213569/v1/6615f33988b2a515c8799526.png"},{"id":109067469,"identity":"7e8a2e6e-b142-4cba-9779-f84ba8ae3b59","added_by":"auto","created_at":"2026-05-12 09:53:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":63065,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociations between extreme temperature events and incident vision impairment. \u003c/strong\u003eThis Figure presents HRs and 95% CIs for the association between exposure to ETEs, including CSs and HWs, and the risk of incident VI. Estimates are derived from time-dependent Cox proportional hazards models adjusted for demographic, lifestyle, and clinical covariates.\u003c/p\u003e\n\u003cp\u003eCS exposure consistently showed a positive association with VI risk across multiple definitions, with stronger effects observed for more intense and longer-duration events. In contrast, HW exposure demonstrated a modest inverse association or null effect across most exposure definitions.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9213569/v1/b05dc4430fdf377841785ef2.png"},{"id":108838405,"identity":"c30bd9cf-7688-4b96-b0cc-9dd172c7a884","added_by":"auto","created_at":"2026-05-09 00:35:19","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":15376,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSubgroup analyses of the association between extreme temperature exposure and incident vision impairment.\u003c/strong\u003e Forest plot showing HRs and 95% CIs for the association between extreme temperature exposure and incident VI, stratified by age (≥65 vs. 45–64 years), gender (male vs. female), and residency (urban vs. rural). Estimates were obtained from fully adjusted Cox proportional hazards models. The dashed vertical line represents the reference value (HR = 1.0).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9213569/v1/7e466d8c7dfb52835726d64a.png"},{"id":108838407,"identity":"68aa664b-e3c1-480d-a18b-b9e5172e76b6","added_by":"auto","created_at":"2026-05-09 00:35:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":18370,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePopulation attributable fraction of vision impairment associated with CSs by intensity and region. \u003c/strong\u003ePAF was calculated for each region (North, Central, and South) under different CS definitions. All estimates were derived from fully adjusted models controlling for age, gender, residence, education level, marital status, smoking status, drinking status, sleep duration, and chronic diseases.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9213569/v1/e74786abcf3424c1f6acef55.png"},{"id":108838406,"identity":"a85a5ab4-825e-438e-ba83-fff7de1bd296","added_by":"auto","created_at":"2026-05-09 00:35:19","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":161609,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGeographic distribution of vision impairment incidence and extreme temperature events in China. \u003c/strong\u003eGeographic distribution of (A) incidence rate of VI, (B) annual average frequency of CSs (extreme cold), and (C) annual average frequency of HWs (extreme heat) across provinces in China.\u003c/p\u003e\n\u003cp\u003eColor scales represent the magnitude of each indicator, with darker shades indicating higher values. All measures are aggregated at the provincial level.\u003c/p\u003e","description":"","filename":"floatimage53.png","url":"https://assets-eu.researchsquare.com/files/rs-9213569/v1/bd53529b2e7298bed7b6f1e2.png"},{"id":109069943,"identity":"f36ac11c-2dae-48d0-801f-a354d35653ba","added_by":"auto","created_at":"2026-05-12 10:28:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":674575,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9213569/v1/d1ab1d8a-d59a-423d-9adb-df9315b30fe9.pdf"},{"id":108838402,"identity":"ca81511f-1bb6-4f47-bfc8-aadd9e5037bc","added_by":"auto","created_at":"2026-05-09 00:35:19","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":810261,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-9213569/v1/9c257d20538286a0d520aa4b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between Extreme Ambient Temperature and Incident Vision Impairment: A National Longitudinal Cohort Study in China (2011–2018)","fulltext":[{"header":"Introduction","content":"\u003cp\u003eExtreme temperature events (ETEs), particularly heat waves (HW) and cold spells (CS), have become increasingly frequent due to climate change and represent a growing global public health challenge[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. A robust body of evidence, synthesized by authoritative assessments such as the Lancet Countdown [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], has conclusively established the detrimental impact of extreme temperatures on human health. These environmental stressors act as potent drivers of morbidity and mortality, significantly exacerbating risks associated with cardiovascular, respiratory, and other temperature-sensitive diseases [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Despite growing investigations into the impact of temperatures on systemic health, the effects of ETEs on visual health remain largely overlooked.\u003c/p\u003e \u003cp\u003eUnlike most internal organs, the eyes are continuously and directly exposed to the external environment. The ocular surface serves as a primary biological barrier against environmental stressors[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], rendering it particularly susceptible to meteorological variations. Vision impairment (VI) is frequently driven by major ocular diseases such as cataracts and age-related macular degeneration, and can also result from ocular surface disorders such as dry eye syndrome [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Globally, an estimated 2.2\u0026nbsp;billion individuals live with some form of VI. This widespread prevalence imposes profound health and socio-economic burdens, resulting in massive losses in global productivity and escalating healthcare expenditures. Furthermore, the impact of VI exhibits significant disparities across diverse populations and geographic regions [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In the context of accelerating population aging and climate change, understanding environmental determinants of visual health has become an increasingly important public health priority. Among these determinants, extreme temperature exposure has emerged as a potential yet insufficiently studied risk factor for VI.\u003c/p\u003e \u003cp\u003eCurrent evidence regarding the relationship between ETEs and visual health remains inconsistent. While extreme heat and cold are known to influence physiological processes, their specific roles in ocular health are contentious. Some studies have suggested that cold exposure may aggravate VI by inducing physiological stress and exacerbating dry eye conditions [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Simultaneously, investigations have also reported that heat exposure may increase the risk of ocular diseases such as cataract and retinal detachment, potentially through mechanisms involving thermal stress and increased ultraviolet radiation [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Conversely, several studies have indicated that warmer conditions can have neutral or even beneficial effects on ocular surface stability and visual comfort [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. These heterogeneous findings highlight substantial uncertainty regarding the overall impact of extreme temperature exposure on visual outcomes.\u003c/p\u003e \u003cp\u003eTo address these gaps, we leveraged data from the China Health and Retirement Longitudinal Study (CHARLS) to conduct the first national longitudinal analysis clarifying the independent and temporal associations between ETE exposure and VI among middle-aged and older adults across diverse environmental and socio-demographic contexts in China. By elucidating population vulnerability patterns under varying climatic contexts, this study aims to provide evidence to develop targeted climate adaptation strategies and optimize public health resource allocation, ultimately mitigating the impending burden of temperature-related VI in an aging society.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and study population\u003c/h2\u003e \u003cp\u003eThis study utilized data from the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative survey of people aged 45 years and older. The survey covered 150 counties or districts and 450 villages or communities across 28 provinces in mainland China. Participants were classified into the Northern, Central, and Southern regions based on climatic zones. Baseline data (Wave 1) were collected in 2011\u0026ndash;2012. Follow-up waves were conducted in 2013, 2015, and 2018 (Waves 2\u0026ndash;4). Data collection was conducted through face-to-face interviews with standardized questionnaires, administered every 2 years. Further details on the CHARLS design and implementation have been reported elsewhere.[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/p\u003e \u003cp\u003e This study utilized a longitudinal cohort design between 2011 and 2018. The specific selection criteria were as follows: (1) individuals aged 45 years or older at baseline; (2) participants with normal vision status at the baseline survey (Wave 1); (3) availability of complete follow-up data regarding vision status and key independent variables; and (4) participants with identifiable city-level geolocation information for meteorological matching.\u003c/p\u003e \u003cp\u003eAfter applying the selection criteria, 13,419 participants formed the final study population. This substantial cohort ensures robust statistical power for linking meteorological extremes to incident VI. Our confidence in these estimates is further reinforced by the prospective tracking and extensive covariate adjustments. Additionally, the broad spatial footprint of the survey captures populations across highly varied Chinese climates. Such geographic diversity naturally enhances the external validity of our conclusions.\u003c/p\u003e \u003cp\u003eAs detailed residential addresses were unavailable, environmental exposure was assigned at the city level. Residential stability was assessed by examining consistency in location information across survey waves based on standardized administrative codes in China. It was assumed that individuals residing within the same city were exposed to similar environmental conditions. This approach is consistent with the CHARLS geolocation framework and reflects the relatively stable residential patterns among older adults in China.\u003c/p\u003e \u003cp\u003eAll participants provided informed consent. The study protocol was approved by the Ethical Review Committee of Peking University. The data used in this study are publicly available.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAssessment of VI\u003c/h3\u003e\n\u003cp\u003eVision function was assessed based on self-reported responses to the CHARLS questionnaire regarding distance and near vision. Participants were asked: \"How is your eyesight for seeing things at a distance (like recognizing a friend from across the street)?\" and \"How is your eyesight for seeing things up close (like reading ordinary newspaper print)?\" Responses were graded on a 5-point scale: 1 (Excellent), 2 (Very good), 3 (Good), 4 (Fair), and 5 (Poor).\u003c/p\u003e \u003cp\u003eIn this study, the outcome of interest was new-onset VI, defined using a \"Strict Logic\" approach. A participant was classified as having VI if either distance or near eyesight was reported as \"Poor\" (score\u0026thinsp;=\u0026thinsp;5). Conversely, participants were defined as having normal vision only if both distance and near eyesight were reported as non-poor (scores 1\u0026ndash;4). Participants with existing VI at baseline (2011) were excluded to ensure the cohort nature of the study. The event date was defined as the mid-point between the last visit with normal vision and the first visit with reported impairment.\u003c/p\u003e\n\u003ch3\u003eAssessment of extreme temperature (HWs and CSs)\u003c/h3\u003e\n\u003cp\u003eMeteorological data were obtained from the National Oceanic and Atmospheric Administration (NOAA) Global Summary of the Day (GSOD) database, which provides standardized daily observations from meteorological stations worldwide. Daily maximum temperature (Tmax), minimum temperature (Tmin), and mean temperature were extracted from more than 370 meteorological stations across China covering the study period from 2011 to 2018. These meteorological records were spatially matched to participants in the China Health and Retirement Longitudinal Study (CHARLS) according to the city-level geographic location of their residence.\u003c/p\u003e \u003cp\u003eGiven the substantial climatic heterogeneity across China\u0026mdash;from subarctic regions in the north to tropical climates in the south, using fixed absolute temperature thresholds may inadequately capture locally relevant temperature extremes. Therefore, we adopted a relative threshold approach based on the daily temperature range to define ETEs, including HWs and CSs.\u003c/p\u003e \u003cp\u003eTo capture varying intensities of extreme temperature exposure, three levels of event intensity were defined according to the value of the Ratio parameter: low intensity (Ratio\u0026thinsp;=\u0026thinsp;7.5%, corresponding to the 92.5th percentile for HWs and the 7.5th percentile for CSs), moderate intensity (Ratio\u0026thinsp;=\u0026thinsp;5.0%, corresponding to the 95th and 5th percentiles), and high intensity (Ratio\u0026thinsp;=\u0026thinsp;2.5%, corresponding to the 97.5th and 2.5th percentiles).\u003c/p\u003e \u003cp\u003eIn addition to temperature thresholds, duration criteria were incorporated to define sustained extreme events. Consecutive periods of \u0026ge;\u0026thinsp;2 days, \u0026ge; 3 days, and \u0026ge;\u0026thinsp;4 days were applied. By combining the three intensity levels with the three duration criteria, a total of 18 definitions were generated for HWs and CSs, respectively (detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\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\u003eDefinitions and descriptive characteristics of heatwave and CS exposure variables employed in the present study.\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\u003eHeatwave/Coldspell definitions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThreshold percentile (\u0026deg;C) for temperature\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDuration (Day)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIntensity categories\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_P25_2d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFalling below 2.5th percentiles of temperature during 7-year exposure window preceding cohort entry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh intensity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_P25_3d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFalling below 2.5th percentiles of temperature during 7-year exposure window preceding cohort entry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh intensity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_P25_4d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFalling below 2.5th percentiles of temperature during 7-year exposure window preceding cohort entry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh intensity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_P5_2d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFalling below 5th percentiles of temperature during 7-year exposure window preceding cohort entry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMiddle intensity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_P5_3d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFalling below 5th percentiles of temperature during 7-year exposure window preceding cohort entry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMiddle intensity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_P5_4d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFalling below 5th percentiles of temperature during 7-year exposure window preceding cohort entry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMiddle intensity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_P75_2d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFalling below 7.5th percentiles of temperature during 7-year exposure window preceding cohort entry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow intensity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_P75_3d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFalling below 7.5th percentiles of temperature during 7-year exposure window preceding cohort entry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow intensity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_P75_4d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFalling below 7.5th percentiles of temperature during 7-year exposure window preceding cohort entry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow intensity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHW_P975_2d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExceeding 97.5th percentiles of temperature during 7-year exposure window preceding cohort entry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh intensity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHW_P975_3d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExceeding 97.5th percentiles of temperature during 7-year exposure window preceding cohort entry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh intensity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHW_P975_4d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExceeding 97.5th percentiles of temperature during 7-year exposure window preceding cohort entry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh intensity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHW_P95_2d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExceeding 95th percentiles of temperature during 7-year exposure window preceding cohort entry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMiddle intensity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHW_P95_3d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExceeding 95th percentiles of temperature during 7-year exposure window preceding cohort entry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMiddle intensity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHW_P95_4d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExceeding 95th percentiles of temperature during 7-year exposure window preceding cohort entry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMiddle intensity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHW_P90_2d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExceeding 90th percentiles of temperature during 7-year exposure window preceding cohort entry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow intensity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHW_P90_3d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExceeding 90th percentiles of temperature during 7-year exposure window preceding cohort entry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow intensity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHW_P90_4d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExceeding 90th percentiles of temperature during 7-year exposure window preceding cohort entry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow intensity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFor the primary exposure metric, we calculated the annual frequency of these ETEs for each participant during the study period.\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eBaseline characteristics were obtained during the 2011 enrollment wave. We recorded socio-demographic profiles, namely age, sex, marital status, educational attainment, and residential setting (urban versus rural). Additionally, the survey captured behavioral factors, including nightly sleep duration, smoking habits, and alcohol intake. The analysis also accounted for clinical history, defined by self-reported, physician-diagnosed chronic conditions (specifically hypertension, diabetes, dyslipidemia, alongside pulmonary, cardiac, hepatic, and renal diseases).\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eData processing and statistical modeling were executed using R software (version 4.3.1). We first handled missing covariate information via the MICE algorithm (Multiple Imputation by Chained Equations), creating five complete datasets to maintain analytic rigor. Next, we summarized continuous baseline parameters using means and standard deviations, whereas categorical traits were expressed as percentages and counts. To assess inter-group variance, we applied Student's t-tests or Pearson's chi-square statistics where suitable.\u003c/p\u003e \u003cp\u003eTo evaluate our primary objective, time-dependent Cox proportional hazards models were constructed to estimate the relationship between meteorological exposures (extreme temperatures) and VI. Results were expressed as hazard ratios (HRs) with corresponding 95% confidence intervals (CIs). Potential confounding was addressed through a stepwise modeling strategy: an unadjusted model (Model 1), followed by progressive control for demographics (Model 2), behavioral variables (Model 3), and baseline comorbidities (Model 4).\u003c/p\u003e \u003cp\u003eFurthermore, the population attributable fraction (PAF) was computed to assess the excess burden of VI driven by ETEs. We also performed subgroup analyses stratified by age, sex, and geographic location (North, Central, and South) to explore potential effect modification. A two-sided p-value below 0.05 defined statistical significance across all tests.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of the study participants\u003c/h2\u003e \u003cp\u003eThe final analytical sample comprised 13,419 individuals. At baseline, the cohort had a mean age of 59.1 years (SD: 9.3) and a slightly higher proportion of females (52.8%, n\u0026thinsp;=\u0026thinsp;7,091) than males (47.2%, n\u0026thinsp;=\u0026thinsp;6,328). Over an average follow-up period of 6.4 years (SD: 1.4), we identified 980 incident cases of VI.\u003c/p\u003e \u003cp\u003eBaseline characteristics stratified by incident VI status are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Individuals who developed VI exhibited demographic profiles distinctly different from those who remained impairment-free. Specifically, the incident VI group was significantly younger (57.9 vs. 59.2 years, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and predominantly male (54.5% vs. 46.6%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). They also possessed slightly higher educational attainment, a distribution that may reflect increased occupational visual strain among the active working-age population.\u003c/p\u003e \u003cp\u003eSummaries of the time-varying meteorological exposures are provided in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. The annual frequency of ETEs varied considerably across the study period. Mean HW exposures ranged from 3.68 to 24.28 events per year, while CS exposures spanned from 3.91 to 24.94 annual events (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\u003eDescriptive characteristics of participants at baseline by VI Status\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 \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;13419\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eNo Vision Impairment \u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;12,439\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIncident Vision Impairment \u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;980\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge, years\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59.08 (9.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e59.17 (9.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57.89 (8.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7,091 (53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e6,645 (53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e446 (46%)\u003c/p\u003e \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\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,328 (47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e5,794 (47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e534 (54%)\u003c/p\u003e \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\u003e\u003cb\u003eResidency\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8,259 (62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e7,677 (62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e582 (59%)\u003c/p\u003e \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\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,160 (38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e4,762 (38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e398 (41%)\u003c/p\u003e \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\u003e\u003cb\u003eMarital Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.417\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11,818 (88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e10,947 (88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e871 (89%)\u003c/p\u003e \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\u003eUnmarried/Other\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,601 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1,492 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e109 (11%)\u003c/p\u003e \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\u003e\u003cb\u003eEducation Level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIlliterate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,141 (46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e5,750 (46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e391 (40%)\u003c/p\u003e \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\u003ePrimary School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,893 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e2,693 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e200 (20%)\u003c/p\u003e \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\u003eMiddle School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,767 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e2,527 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e240 (25%)\u003c/p\u003e \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\u003eHigh School+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,606 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1,459 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e147 (15%)\u003c/p\u003e \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\u003e\u003cb\u003eSleep Duration, h/day\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.34 (1.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e6.32 (1.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.54 (1.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,219 (39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e4,792 (39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e427 (44%)\u003c/p\u003e \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\u003eNon-smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8,195 (61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e7,642 (61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e553 (56%)\u003c/p\u003e \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\u003e\u003cb\u003eDrinking Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,389 (33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e4,021 (32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e368 (38%)\u003c/p\u003e \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\u003eNon-drinker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9,024 (67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e8,412 (68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e612 (62%)\u003c/p\u003e \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\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,419 (26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e3,224 (26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e195 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e806 (6.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e759 (6.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47 (4.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChronic Lung Disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,281 (9.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1,203 (9.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78 (8.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHeart Disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,618 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1,518 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100 (10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLiver Disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e461 (3.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e436 (3.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25 (2.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eKidney Disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e758 (5.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e712 (5.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46 (4.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e1\u003c/sup\u003eMean (SD); n (%)\u003c/p\u003e \u003cp\u003e\u003csup\u003e2\u003c/sup\u003eWilcoxon rank sum test; Pearson's Chi-squared test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAssociations between ETEs and incident VI\u003c/h3\u003e\n\u003cp\u003eThe relationship between ETEs and new-onset VI revealed a clear divergence depending on the temperature direction (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Most notably, cold spell exposure consistently correlated with an elevated hazard of VI across the majority of defined event thresholds.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAfter multivariable adjustment, the data demonstrated a robust adverse effect for cold stress. For example, experiencing high-intensity cold snaps (e.g., CS_P5_3d) raised the risk of VI by 16.0% (HR: 1.160; 95% CI: 1.133\u0026ndash;1.187). This hazard escalated considerably under prolonged, extreme cold conditions; the strictest criteria (CS_P2.5_4d) yielded an HR of 1.379 (95% CI: 1.304\u0026ndash;1.459). Overall, lower temperature thresholds combined with extended event durations reliably predicted steeper risk trajectories.\u003c/p\u003e \u003cp\u003eIn contrast, HW exposure predominantly exerted a null or marginally protective effect. Moderate heat events (e.g., HW_P95_3d), for instance, were associated with a slight risk reduction, generating an estimated HR of 0.965 (95% CI: 0.956\u0026ndash;0.974).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePAF of VI attributable to CS exposure\u003c/h2\u003e \u003cp\u003eThe PAF was further calculated to quantify the public health burden of incident VI attributable to CS exposure. The analysis revealed that a substantial proportion of incident VI cases were attributable to CS exposure. Specifically, under the CS_P7.5_4d definition, the PAF reached 10.66%, indicating that approximately 10.66% of new-onset VI cases in this cohort could be attributed to CS exposure.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDose\u0026ndash;response relationships\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e also illustrates the exposure\u0026ndash;response relationships between cumulative duration and intensity of ETEs and the risk of incident VI. A clear dose\u0026ndash;response pattern was observed for CSs. Across different percentile thresholds and durations, the HRs for VI increased progressively with greater cold exposure. This upward trend became more pronounced under more extreme definitions, with the highest risks observed at lower temperature percentiles and longer durations (e.g., CS P2.5 with \u0026ge;\u0026thinsp;3\u0026ndash;4 days), indicating a robust, graded association (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In contrast, HWs exhibited a modest inverse association with VI risk. Although the magnitude of change was relatively small, a gradual decline in HRs was observed with increasing heat exposure intensity and duration. This downward trend was more evident at higher percentile thresholds (e.g., HW P97.5), where longer-lasting heat events were associated with slightly lower risks of VI.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStratified analyses\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSubgroup analyses were conducted to explore potential differences in susceptibility to ETE exposure across age, sex, residence, and geographic region. (Figure. 3 and Figure. 4). A geographic distribution map was generated to depict the spatial patterns of VI incidence and the frequency of ETEs across provinces in China (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Generally, the detrimental impact of CSs was observed across all major demographic groups, with no statistically significant effect modification by gender or age (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, significant geographic heterogeneity was observed across different climatic zones. Stratified analyses revealed a clear geographic gradient in vulnerability thresholds: participants residing in the Southern and Central regions demonstrated elevated vulnerability even to mild or moderate CSs (e.g., CS_P75_3d). In contrast, participants in the Northern region exhibited a significantly increased risk of VI primarily under extreme cold conditions (e.g., CS_P5_4d).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity analysis\u003c/h2\u003e \u003cp\u003eTo evaluate the robustness of our findings, three sensitivity analyses were performed. The results were consistent with the primary analyses, indicating stable associations between ETE exposure and the risk of VI.\u003c/p\u003e \u003cp\u003eFirst, to isolate the independent long-term effects of extreme cold and heat, we constructed mutual adjustment models that simultaneously incorporated the CS and HW indices. The significant associations between CSs and VI risk remained robust after adjusting for corresponding HWs. For instance, in the mutually adjusted model, exposure to CSs (CS_P5_3d) was significantly associated with a higher risk of VI (HR\u0026thinsp;=\u0026thinsp;1.18, 95% CI 1.15\u0026ndash;1.22, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while corresponding HWs (HW_P95_3d) showed no increased risk (HR\u0026thinsp;=\u0026thinsp;0.94, 95% CI 0.92\u0026ndash;0.95) (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e in Supplementary Material). Similar independent effects were observed for other thresholds, such as CS_P25_3d (HR\u0026thinsp;=\u0026thinsp;1.48, 95% CI 1.37\u0026ndash;1.61, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). (Table\u0026nbsp;3 in Supplementary Materials)\u003c/p\u003e \u003cp\u003eSecond, to minimize the potential for reverse causality, we excluded participants who developed VI or were censored within the first 2 years of follow-up. Consistent with the primary analyses, the elevated risk of VI associated with CS exposure remained statistically significant. The HR for CS_P5_3d was 1.25 (95% CI 1.19\u0026ndash;1.30), indicating that the temporal relationship between extreme temperature exposure and incident VI is reliable (Table\u0026nbsp;4 in Supplementary Material).\u003c/p\u003e \u003cp\u003eFinally, we evaluated the stability of our results across different clinical definitions by analyzing incident distance VI only, near VI only, and any VI (defined as the onset of either distance or near VI). The adverse effects of extreme cold were highly consistent across all outcome definitions. Specifically, exposure to CS_P5_3d was significantly associated with an increased risk of distance VI (HR\u0026thinsp;=\u0026thinsp;1.18, 95% CI 1.14\u0026ndash;1.23), near VI (HR\u0026thinsp;=\u0026thinsp;1.15, 95% CI 1.12\u0026ndash;1.18), and any VI (HR\u0026thinsp;=\u0026thinsp;1.18, 95% CI 1.12\u0026ndash;1.24). Even stronger associations were observed for extreme CSs (CS_P25_3d) across distance VI (HR\u0026thinsp;=\u0026thinsp;1.62, 95% CI 1.49\u0026ndash;1.76), near VI (HR\u0026thinsp;=\u0026thinsp;1.42, 95% CI 1.28\u0026ndash;1.58), and any VI (HR\u0026thinsp;=\u0026thinsp;1.59, 95% CI 1.48\u0026ndash;1.70) (Table\u0026nbsp;5 in Supplementary Material).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo the best of our knowledge, this is the first nationwide longitudinal cohort study to investigate the association between exposure to ETEs and the risk of VI among middle-aged and older Chinese adults. In this study, a distinct divergent pattern regarding the visual impacts of different ETEs was demonstrated, with CS exposure emerging as a significant risk factor for VI. This adverse effect was particularly amplified by the intensity and duration of the CSs, exhibiting a clear dose-response relationship. Notably, the PAF analysis further highlighted the substantial public health burden driven by cold exposure, revealing that up to 10.66% of VI incidence could be attributed to CSs. Stratified analyses revealed remarkably consistent associations across demographic characteristics such as age and gender. However, a clear geographic shift in vulnerability thresholds was observed, indicating that participants in southern regions were susceptible even to mild CSs. Conversely, HW exposure exhibited an inverse association with the development of VI, suggesting that moderate ambient warming may confer unexpected physiological benefits to ocular health.\u003c/p\u003e \u003cp\u003eThe significant positive association between CS exposure and the increased incidence of VI can be explained by several underlying ocular mechanisms. Unlike other body parts, the ocular surface serves as the eye's primary barrier in direct contact with the external environment, rendering it highly susceptible to damage from ambient temperature fluctuations. First, cold environments destabilize the tear film lipid layer and accelerate evaporation. subsequently provoking epithelial stress and symptomatic dry eye, clinically manifesting as fluctuating vision due to an irregular optical surface [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Ultimately, this persistent instability fosters chronic surface disorders that progressively degrade visual quality. Following tear film breakdown, the exposed corneal epithelium is more vulnerable to stress and injury. Cold-induced tear evaporation and hyperosmolarity increase epithelial cell stress and may promote subclinical keratitis, which alters corneal curvature and smoothness, thereby impairing the eye's refractive surface and reducing visual acuity [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. On the other hand, direct freezing injury in extreme cold has been documented to damage both the corneal epithelium and endothelium, leading to edema and surface irregularity that degrade optical clarity and cause visual blurring [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBeyond the surface, temperature changes may influence lens optics and refractive properties. The crystalline lens is a key refractive element for focusing light onto the retina, and its transparency is critical for clear vision. Exposure to cold stress can induce structural and functional impairments in the lens by promoting the conformational instability of lens proteins, rendering them susceptible to misfolding and aggregation. Furthermore, hypothermia disrupts metabolic homeostasis and exacerbates oxidative stress, which accelerates lens opacification and further compromises visual transparency. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. These alterations in lens optics can reduce visual sharpness and increase scattering of light, especially if lens opacities occur along the visual axis, which ultimately causes the VI. Moreover, the posterior segment and retinal perfusion are susceptible to cold-induced vascular changes. Evidence indicates that cold exposure reduces ocular blood flow, including the central retinal artery, which may impair oxygen and nutrient delivery to the retina and potentially disrupt retinal function [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Impaired perfusion can lead to hypoxic stress on retinal neurons and photoreceptors, ultimately reducing signal quality and visual function. While the aforementioned literature supports our findings, the precise thresholds and durations of cold exposure required to induce these ocular changes, as well as the exact underlying mechanisms, warrant further investigation.\u003c/p\u003e \u003cp\u003eA contrasting pattern was observed for HWs, with exposure showing a modest inverse association with the risk of incident VI. This finding may be interpreted in light of the climatic characteristics of the study regions and the relative definition of heat exposure adopted in the present study. Most study areas were located within temperate and subtropical climate zones, where historical maximum temperatures generally remained below 36\u0026deg;C. Under such conditions, the heatwaves identified likely represent moderate increases in ambient temperature rather than absolute thermal extremes capable of inducing direct tissue injury. Consequently, the observed inverse association between HW exposure and VI risk may reflect the physiological effects of moderate warming rather than heat-related damage.\u003c/p\u003e \u003cp\u003eSeveral biological mechanisms may help explain this hypothesis. Moderate increases in ambient temperature can enhance tear film stability by promoting the melting and uniform distribution of the meibomian lipid layer, thereby reducing tear evaporation and stabilizing the optical surface[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In addition, elevated temperature may induce ocular vasodilation and improve ocular hemodynamics, which could support retinal function and visual performance. Experimental studies have shown that warming temperature increases blood-flow velocities in both the anterior ciliary artery and the central retinal artery, indicating enhanced ocular perfusion under warm conditions [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Improved perfusion may facilitate the delivery of oxygen and nutrients to ocular tissues, thereby helping maintain visual function. Together, these effects on tear film stability and ocular circulation may create a more favorable ocular microenvironment, which could contribute to the reduced risk of VI. Moreover, recent studies suggest that temperature elevation may reverse lens opacity induced by cold exposure in animals [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. However, the underlying biological mechanisms and the relevant temperature thresholds remain unclear. This evidence may partly support the hypothesis that moderate warming could be beneficial for maintaining visual function under certain environmental conditions. Given the multifactorial nature of VI, further investigation is needed to explore the complex pathways linking heat exposure to visual outcomes.\u003c/p\u003e \u003cp\u003eOur analysis indicated that the adverse effects of CSs were largely consistent across age, sex, and residential subgroups. Previous studies on VI have reported heterogeneity by age and gender, and differences between urban and rural populations often reflect variations in environmental exposure and living conditions[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In contrast, the absence of significant effect modification in our study suggests that susceptibility to cold-related ocular damage may be broadly shared across populations. We speculate common physiological responses to low temperature possibly drive this consequence. When stratified by geographic region, a distinct spatial pattern emerged. In northern regions, an increased risk of VI was observed primarily under more extreme cold conditions, suggesting that sufficiently low temperatures may exceed the physiological tolerance threshold required to induce measurable ocular damage. This pattern is consistent with general expectations that severe cold exposure in these regions may impose a considerable burden on visual health. In contrast, populations in central and southern regions exhibited elevated risks even at relatively mild cold levels. This increased vulnerability may be associated with lower levels of cold-protective awareness, longer durations of cold exposure, and insufficient behavioral adaptation to cold environments. Together, these factors may exacerbate cumulative cold stress, thereby increasing the risk of VI in these regions.\u003c/p\u003e \u003cp\u003eThese findings underscore the need for targeted public health and climate adaptation strategies. Priority should be given to protecting populations at higher risk during ETEs, particularly older adults and individuals residing in regions with insufficient cold-protection infrastructure. In Central and Southern China, where vulnerability to even moderate cold exposure is evident, interventions should focus on improving indoor thermal conditions, strengthening awareness of cold-related health risks, and promoting household-level adaptations, such as better insulation and adequate heating. In contrast, in Northern regions where health risks are primarily associated with extreme cold conditions, preventive strategies should emphasize behavioral modifications. These include reducing unnecessary outdoor exposure during severe cold events and adopting personal protective measures such as appropriate eye protection. At the same time, region-specific resource allocation and infrastructure planning should be aligned with local climatic conditions to enhance resilience against temperature-related health risks.\u003c/p\u003e \u003cp\u003eThe current study possesses several notable strengths. First, by leveraging a nationwide prospective cohort of middle-aged and older adults in China, it offers robust longitudinal evidence linking ETEs to the risk of incident VI. Second, rather than relying on static meteorological measures, we employed a time-varying approach with cumulative exposure definitions. This refined assessment better captures the dynamic reality of chronic environmental stress. Finally, the inclusion of population attributable fractions (PAF) alongside stratified analyses allowed us to comprehensively quantify the specific public health burden of cold exposure across diverse geographic and demographic subgroups.\u003c/p\u003e \u003cp\u003eSeveral limitations should also be acknowledged. First, the assessment of vision relied on self-reported questionnaires. While standard survey protocols were strictly followed, this approach inherently carries a potential risk of recall bias. Second, residual confounding remains a possibility; unmeasured individual behaviors, such as precise daily outdoor activity duration or the availability of indoor heating, might influence the observed associations. Third, environmental exposure was estimated at the city level due to privacy constraints. This broader spatial resolution may miss micro-environmental variations, leading to potential exposure misclassification. Consequently, future multi-center studies are warranted to validate the impact of ETEs on visual health across broader age groups and different global climatic zones.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this is the first nationwide prospective cohort study to explore the significant impacts of ETEs on the risk of incident VI. CSs were identified as a major contributor to the burden of VI in China. These findings highlight the importance of incorporating ocular health into climate adaptation strategies and emphasize the need for targeted protective measures alongside strengthened public awareness of temperature-related risks.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eETEs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eExtreme temperature events\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHW / HWs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHeat wave(s)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCS / CSs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCold spell(s)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVisual impairment\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCHARLS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChina Health and Retirement Longitudinal Study\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNOAA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Oceanic and Atmospheric Administration\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGSOD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlobal Summary of the Day\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHazard ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePAF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePopulation attributable fraction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthical Statement\u003c/h2\u003e \u003cp\u003eThe data were derived from the China Health and Retirement Longitudinal Study (CHARLS), a publicly available and de-identified dataset. As this study involved secondary analysis of anonymized data, additional ethical approval was not required.\u003c/p\u003e\u003ch2\u003eConsent for publication\u003c/h2\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors have nothing to declare.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work is supported by the funds from the National Natural Science Foundation of China (82471045, Cheng-wei Lu).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYuan-hao Li: methodology, software, visualization, validation and original draft preparation; Xiu-fen Liu: conceptualization, supervision, validation and review and editing; Song-tao Wang: data curation, visualization, formal analysis and original draft preparation; Yibo Wang: investigation and original draft preparation; Zi-han Tang: data curation and original draft preparation; Qian Li: data curation, visualization, formal analysis. Dan Li: data curation, visualization, formal analysis. Cheng-wei Lu: funding acquisition, project administration, supervision, conceptualization, and review and editing. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe would like to acknowledge the China Health and Retirement Longitudinal Study (CHARLS) team and the National Oceanic and Atmospheric Administration (NOAA) team, for providing high-quality, nationally representative data.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe baseline data is available from [https://charls.charlsdata.com/pages/Data](https:/charls.charlsdata.com/pages/Data) and [https://www.ncei.noaa.gov/maps/daily](https:/www.ncei.noaa.gov/maps/daily) . For more information on study protocols and for other datasets, please contact a corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSampath V, Aguilera J, Prunicki M, Nadeau KC. Mechanisms of climate change and related air pollution on the immune system leading to allergic disease and asthma. Semin Immunol. 2023;67:101765.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRomanello M, Walawender M, Hsu SC, Moskeland A, Palmeiro-Silva Y, Scamman D, Smallcombe JW, Abdullah S, Ades M, Al-Maruf A, et al. The 2025 report of the Lancet Countdown on health and climate change: climate change action offers a lifeline. Lancet. 2025;406(10521):2804\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMou P, Qu H, Guan J, Yao Y, Zhang Z, Dong J. Extreme temperature events, functional dependency, and cardiometabolic multimorbidity: Insights from a national cohort study in China. Ecotoxicol Environ Saf. 2024;284:117013.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang J, Gu W, Wang M, Liu J, Chen Y, Zhang X. Association between extreme temperature events and multimorbidity among older adults: evidence from the CHARLS. BMC Med. 2025;23(1):625.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu CW, Fu J, Liu XF, Cui ZH, Chen WW, Guo L, Li XL, Ren Y, Shao F, Chen LN, et al. Impacts of air pollution and meteorological conditions on dry eye disease among residents in a northeastern Chinese metropolis: a six-year crossover study in a cold region. Light Sci Appl. 2023;12(1):186.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eResnikoff S, Pascolini D, Etya'ale D, Kocur I, Pararajasegaram R, Pokharel GP, Mariotti SP. Global data on visual impairment in the year 2002. Bull World Health Organ. 2004;82(11):844\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsubota K, Pflugfelder SC, Liu Z, Baudouin C, Kim HM, Messmer EM, Kruse F, Liang L, Carreno-Galeano JT, Rolando M et al. Defining Dry Eye from a Clinical Perspective. Int J Mol Sci 2020, 21(23).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eForrest SL, Mercado CL, Engmann CM, Stacey AW, Hariharan L, Khan S, Cabrera MT. Does the Current Global Health Agenda Lack Vision? Glob Health Sci Pract 2023, 11(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbusharha AA, Pearce EI, Fagehi R. Effect of Ambient Temperature on the Human Tear Film. Eye Contact Lens. 2016;42(5):308\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHo WT, Chiu CY, Chang SW. Low ambient temperature correlates with the severity of dry eye symptoms. Taiwan J Ophthalmol. 2022;12(2):191\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAuger N, Rheaume MA, Bilodeau-Bertrand M, Tang T, Kosatsky T. Climate and the eye: Case-crossover analysis of retinal detachment after exposure to ambient heat. Environ Res. 2017;157:103\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoberts JE. Ultraviolet radiation as a risk factor for cataract and macular degeneration. Eye Contact Lens. 2011;37(4):246\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTakayama Y, Hatsusaka N, Yamada Y, Sasaki H, Hirata A. Ambient heat exposure as a risk factor for cataracts: Evidence from a nationwide claims-based study in Japan. Environ Res. 2026;292:123680.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao Y, Hu Y, Smith JP, Strauss J, Yang G. Cohort profile: the China Health and Retirement Longitudinal Study (CHARLS). 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Eye Contact Lens. 2003;29(2):96\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi TT, Shao GB, Jiang YL, Wang JX, Zhou XR, Ren M, Li LQ. Ocular surface heat effects on ocular hemodynamics detected by real-time measuring device. Int J Ophthalmol. 2018;11(12):1902\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlackburn BJ, McPheeters MT, Jenkins MW, Dupps WJ Jr., Rollins AM. Phase-Decorrelation Optical Coherence Tomography Measurement of Cold-Induced Nuclear Cataract. Transl Vis Sci Technol. 2023;12(3):25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang H, Ping X, Zhou J, Ailifeire H, Wu J, Nadal-Nicolas FM, Miyagishima KJ, Bao J, Huang Y, Cui Y et al. Reversible cold-induced lens opacity in a hibernator reveals a molecular target for treating cataracts. J Clin Invest 2024, 134(18).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePan CW, Qian DJ, Sun HP, Ma Q, Xu Y, Song E. Visual Impairment among Older Adults in a Rural Community in Eastern China. J Ophthalmol. 2016;2016:9620542.\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":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Climate change, Cold spells, Heat waves, Vision impairment, Public health, Longitudinal study, China","lastPublishedDoi":"10.21203/rs.3.rs-9213569/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9213569/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eExtreme temperature events (ETEs), including cold spells (CS) and heat waves (HW), are increasing in frequency and intensity under climate change, yet their effects on visual health remain unclear. This study aimed to examine the association between ETE exposure and the risk of visual impairment (VI) in a national cohort of middle-aged and older adults in China.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA total of 13,419 participants without VI at baseline from the China Health and Retirement Longitudinal Study (CHARLS) from 2011 to 2018 were included. Meteorological data were obtained from the National Oceanic and Atmospheric Administration Global Summary of the Day database. ETEs were identified based on relative temperature thresholds combined with duration criteria, allowing the definitions to reflect local climatic conditions across study regions. The relationship between ETE exposure and new-onset VI was analyzed using time-varying Cox proportional hazards regression. Effect sizes were quantified as hazard ratios (HRs) alongside 95% confidence intervals (CIs). Population attributable fractions (PAFs) were calculated, and stratified analyses were conducted by geographic region.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eCS exposure was associated with an increased risk of incident VI. Each additional high-intensity cold spell (CS_P5_3d) was associated with a 16.0% higher risk (HR: 1.160; 95% CI: 1.133\u0026ndash;1.187). A clear dose\u0026ndash;response relationship was observed, with stronger effects at longer durations and greater exposure intensity. Approximately 10.66% of incident VI cases were attributable to CSs under the CS_P75_4d definition. In contrast, HW exposure showed a modest inverse association with VI risk (HR: 0.965; 95% CI: 0.956\u0026ndash;0.974). Geographic heterogeneity was evident, with populations in Southern China showing greater vulnerability to cold exposure.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eCSs were associated with an increased risk of VI among middle-aged and older adults in China, whereas HWs exposure showed a modest inverse association. Regional inequalities in vulnerability highlight the need to raise awareness of cold-related risks and implement targeted, region-specific adjustment strategies to protect visual health.\u003c/p\u003e","manuscriptTitle":"Association between Extreme Ambient Temperature and Incident Vision Impairment: A National Longitudinal Cohort Study in China (2011–2018)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-09 00:35:11","doi":"10.21203/rs.3.rs-9213569/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-04-24T17:37:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-23T09:04:23+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-02T18:26:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-02T17:39:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2026-04-02T17:03:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"55bcca95-c747-4b56-b0f3-4011911f8cf9","owner":[],"postedDate":"May 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-09T00:35:11+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-09 00:35:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9213569","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9213569","identity":"rs-9213569","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}