Analyzing the impact of climate and air pollution on cataract surgery volume using fuzzy cognitive maps

Analyzing the impact of climate and air pollution on cataract surgery volume using fuzzy cognitive maps | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Analyzing the impact of climate and air pollution on cataract surgery volume using fuzzy cognitive maps Yaxin Miao, Yi Xiang, Haixiang Jiang, Xu Wang, Xinyuan Huang, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7257236/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The burden of cataracts was substantial in the current aging world. We aimed to investigate the association of climate and air pollutants with surgical volumes for senile cataracts. We analyzed cohort data comprising 14086 patients (mean age: 70.25 ± 9.4 years) from Wuhan Centra Hospital, spanning January 1, 2019 to July 22, 2024. The environmental factors include temperature (minimum/ maximum), atmospheric pressure, humidity, wind speed, visibility, dew point, precipitation, PM 2.5 , PM 10 , CO and O 3 . The Fuzzy Cognitive Maps (FCMs) were employed as an analytical tool to evaluate potential environmental influences on cataract surgery timing decisions. The environmental parameters of temperature, atmospheric pressure, visibility, humidity, minimum temperature, wind speed, dew point, precipitation, CO and NO 2 showed positive correlation, while maximum temperature, SO 2 , PM 2.5 , PM 10 and O 3 showed negative correlation with cataract surgical volumes. Our findings provide empirical evidence for potential environmental influences on surgical timing decision in cataract management. Cataract surgeries Climate Air pollutants Fuzzy Cognitive Maps (FCMs) Figures Figure 1 Figure 2 Introduction Cataracts are a common ocular disorder characterized by the lens opacification, which can lead to severe vision impairment and, in some cases, blindness [ 1 ]. Globally, cataracts affect millions of individuals, significantly impairing emotional well-being and overall quality of life [ 2 – 4 ]. Cataract prevalence is projected to rise due to an aging population and longer life expectancy. Substantially, concurrently escalating the economic burden on healthcare systems due to high treatment demand. Currently, surgical intervention remains the only definitive treatment for cataracts [ 5 ]. Despite ongoing advancements in surgical techniques, challenges such as postoperative complications [ 6 – 8 ], high surgical costs [ 9 ], and limited healthcare resources persist [ 10 ]. Therefore, exploring factors influencing surgical timing is crucial for improving both therapeutic efficacy and quality-of-life outcomes in cataract patients. As one of the most commonly performed elective procedures, cataract surgery timing is determined by multifactorial considerations. Previous studies have indicated that significant correlations between surgical timing decisions and patients' visual function, overall health status, and quality of life [ 3 , 11 ]. Additionally, weather conditions may play a role in patients' decisions regarding the timing of surgery, as changes in weather can impact the severity of symptoms and patients' willingness to seek medical care. Although existing research suggests that environmental factors can influence eye health [ 12 – 14 ], their direct effects on cataract surgical timing remain poorly characterized, highlighting a significant gap in the literature that requires further exploration. Fuzzy Cognitive Maps (FCMs) [ 15 ] are a mathematical tool that are highly-appropriate for modeling system in which precise quantitative data might be scarce or difficult to obtain, and they can effectively depict the causal relationships among system components, capturing both positive and negative impacts [ 16 ]. FCMs provide unique advantages for examining quantitative relationships between multiple influencing factors and final choice, enabling effective management of complex, multidimensional datasets [ 17 ]. As demonstrated in recent research, FCMs serve as potent instrument for comprehensive health risk analysis and assessment [ 18 ]. The visual decision-support capabilities of FCMs enhance both intuitive understanding and analytical flexibility[ 19 ]. Our study will utilize FCMs as an analytical tool to explore the potential influence of environmental factors on cataract surgery timing decisions. It is our first time to employ FCMs to assess how environmental factors impact the decision-making process surrounding the timing of cataract surgery. By identifying the potential environmental determinants influencing surgical timing, this study will develop data-driven recommendations for optimizing the timing of interventions for patients. Ultimately, this research aspires to advance our understanding of environmental factors impact on medical decision-making and provide actionable guidance for clinical resource allocation, such as hospital resource planning, allowing for optimized staffing, surgical scheduling, and equipment allocation and so on. Material and methods Data collection and characteristics The study data were derived from inpatient records of cataract patients who underwent phacoemulsification with intraocular lens implantation at The Central Hospital of Wuhan between January 1, 2019and July 22, 2024. A total of 14,086 patients were included in the analysis, with 2,910 patients enrolled in 2019, 1,394 in 2020, 1,655 in 2021, 1,801 in 2022, 4,359 in 2023, and 1,967 in 2024. Among these patients, there were 5,856 males and 8,230 females, with an average age of 70.25 ± 9.4 years. Moreover, the weather data and air quality data for Wuhan during the corresponding period were obtained from two validated online sources ( https://rp5.ru/ and https://www.aqistudy.cn/ ) The statistical summary of the dataset is shown in Table 1 . The definition of temperature, dew point, minimum temperature, maximum temperature and wind speed are the air temperature at 2 meters above the ground, dew point temperature at 2 meters above the ground, the lowest temperature recorded in the past hours (up to 12 hours), the highest temperature recorded in the past hours (up to 12 hours), the maximum gust wind speed at 10–12 meters above ground level (AGL) within 10 minutes, respectively. Moreover, with over ten measurements per day, the daily value is calculated as the average of all measurements for that day. O 3 refers to 8-hour running average ozone concentration. All factors were preprocessed with min-max normalization before modeling. Each model was repeated constructing ten times and the model with best fitness were determined as the final model. Table 1 The statistical result of dataset. Factor Mean value Standard deviation Maximum value Minimum value PM 2.5 (µg/m³) 38.3365 26.5732 206 0 PM 10 (µg/m³) 60.3004 35.0651 410 0 SO 2 (µg/m³) 14.3553 20.1997 109 0 CO (mg/m³) 16.7522 18.800 99 0 NO 2 (µg/m³) 15.2931 18.4802 98 0.4000 O 3 (µg/m³) 99.6473 47.2967 258 0 Temperaturen (℃) 17.7446 9.3341 34.7250 -2.9875 Atmospheric pressure (mmHg) 759.6615 7.5687 779.9500 670.3500 Humidity (%) 76.1419 11.1800 99.2500 29.8750 Wind speed (m/s) 1.5895 0.8881 6.2500 0 Minimum temperature (℃) 11.1138 9.6613 31.6125 -7.3750 Maximum temperature (℃) 17.1276 10.9357 39.6625 -1.3000 Visibility (km) 10.9406 6.5545 30 0.4625 Dew point (℃) 12.8458 9.2560 27.2000 -16.1625 Precipitation (mm) 1.6709 5.3634 59.7500 0 Mount of surgeries (number of cases) 6.9492 7.6980 39 0 Fuzzy cognitive maps (FCMs) FCMs describe a set of concepts (nodes) and the causal relationships between them, as a directed cyclic graph \(\:G=(C,\:E,\:U,\:f)\) , where, \(\:\text{C}=({\text{c}}_{1},{\text{c}}_{2},...,\:{\text{c}}_{\text{n}})\) denotes the set of concepts that make up the nodes of the directed cyclic graph, n is the number of variables during the modeling process. E denotes the set of weight \(\:{e}_{ij}\) on the directed edge from the conceptual node \(\:{c}_{i}\) to conceptual node \(\:{c}_{j}\) ( \(\:1\le\:i,\:j\:\le\:\:n\) ), namely E is an adjacent matrix. The modeling process of the FCM is transformed into an optimization problem on its adjacent matrix E to fit the observational values along the time axis (time series prediction involved multiple variables). U , \(\:\text{U}\left(\text{t}\right)\) =( \(\:{u}_{1}\) ( t 0 ), \(\:{u}_{2}\) ( t 0 ),..., \(\:{u}_{c}\) ( t 0 )), is the observational values of all concepts (nodes) at the initial time t 0 . f denotes the transition function of the prediction value of a conceptual node i at time stamp t+1 from the observational values of all conceptual nodes at time stamp t , so that the prediction values of all nodes (concepts) at time stamp t + 1 is shown as Eq. (1), here \(\:f\left(x\right)=\frac{1}{1+{e}^{-\lambda\:x}},\lambda\:=5\) . $$\:{u}_{i}(\text{t}\hspace{0.17em}+\hspace{0.17em}1)=f\left(\sum\:_{j=1}^{c}{e}_{ji}{u}_{j}\right(\text{t}\left)\right)\:\left(1\right)$$ Similar to the common time series prediction, the order of FCM means the latency of all variables (concepts), namely 1st -order FCM means the values of all nodes at time stamp t depends on the values of all nodes at time stamp t-1 ; the 2nd -order FCM means the values of all nodes at time stamp t is related with the values of all nodes at time stamps t-1 and t-2 , and so on. In current research, we tired 1st -, 2nd -, 3rd -order FCMs to construct the complicated relationship between all variables and selected the model with the best fitting capacity (the best fitness in differential evolution) as the final FCM. Differential evolution (DE) algorithm We adopted the Differential Evolution (DE) [ 20 ] algorithm, which has been proven effective in numerical optimization problems to optimize the FCMs. DE applies a virtual population formed of some individuals that simulate evolutionary processes to iteratively refine the solution towards the optimal. Each individual is represented as a vector corresponding to the values in E of the FCMs, specifically the adjacency matrix of the FCM (graph). In each iteration, individuals (analogous to chromosomes in DE) undergo crossover and mutation operations to generate mutated individuals. Then the original individual and mutated individual will be compared via greedy selection to form the new population, namely the individual whose value from objective function is better is retained. The objective function is to obtain the adjacent matrix E to fit the historical data (observational data). Here, we aim to model the heterogeneity of each monitoring sites in different years, therefore, the objective function is shown as Eq. ( 2 ), where \(\:{u}_{i}\left(\text{t}\right)\) and \(\:{u}_{i}\left(\text{t}\right){\prime\:}\) are the predictive value and observational value of the i th node at the time stamp t . T is the total number of time stamps. The cross and mutation operation are shown as Eq. ( 3 ) and Eq. ( 4 ), where \(\:{\text{v}}_{\text{i}}\) is the i th individual, \(\:{\text{v}}_{1}\) , \(\:{\text{v}}_{2}\) and \(\:\text{F}\in\:\left[\text{0,1}\right]\) are two randomly selected individuals and cross factor (an algorithm parameter), respectively. \(\:{\text{v}}_{\text{i}.\text{j}}\) is the j th element in i th individual, \(\:rand\left(\right)\) , \(\:{\text{v}}_{\text{r},\text{j}}\) and CR are a random scalar in [0, 1], the j th element in a randomly selected individual \(\:{\text{v}}_{\text{r}}\) and mutation rate (an algorithm parameter), respectively. Eq. ( 4 ) will ensure the at least one element in original individual will be mutated, namely if all elements in \(\:{\text{v}}_{\text{i}}\) is not mutated, the selected j th element will be mutated. n is the number of indicators in present study that is consistent with the n in FCMs section. DE will stop the iterative computation when the maximum epoch is reached or the algorithm converged. All the algorithm parameters are shown in Table 1 . In current research, the individual in DE represents the E , and the predictive values after time stamp t 0 , \(\:{u}_{i}(\text{t}>\text{t}0)\) , could be figured out via Eq. (1) for optimization. Because the objective of this study is to explore the effect of climate and air pollutants on the number of cataract surgeries, we neglect the effect of the number of cataract surgeries on climate and air pollutants in the computation process of DE. $$\:\text{L}=\sum\:_{\text{i}=1}^{\text{n}}{\sum\:}_{\text{t}=1}^{\text{T}}{\left({u}_{i}\right(\text{t})-{u}_{i}(\text{t}\left){\prime\:}\right)}^{2}$$ 2 $$\:{v}_{i}={v}_{i}+F({v}_{1}-{v}_{2})$$ 3 $$\:{v}_{i.j}={v}_{r,j},\:if\:\:rand\left(\right)\:\le\:CR\:or\:j=rand(1,{n}^{2})$$ 4 Results and discussion Table 1 showed all statistical results for original dataset, including the mount of surgeries, air quality and climate. Figure. 1 shows the produced FCMs, the elements in it signify the levels of direct association among cataract surgery volumes and various of climatic / air quality factors. The simple paths in the FCM are summarized (Fig. 2 ) and the weights of top ranked (whose abstract value ranked 20% percentile or 80% percentile) simple paths are showed in Fig. 2 a. Because the weight of a simple path decreases dramatically as its length increases, we set the length limit of the simple paths as 5. Moreover, the total amount of simple paths is pretty huge with the increase of the number of nodes, we only summarize the simple paths whose weights ranked 20% and 80% percentile. The statistical values of the weights on all simple paths are summarized in Table 2 . CO and NO 2 exert positive impact on number of surgeries (mean value = 0.007763 for CO, mean value = 0.001842 for NO 2 , Table 2 ). However other air quality factors exert negative influence (Table 2 ). For climatic factors, except maximum temperature exert negative impact on the mount of surgeries (mean value = -0.012142, Table 2 ), other climatic factors exert positive impact on the mount of surgeries. The dew point exerts positively the largest impact on the mount of surgery (mean value = 0.024834, Table 2 ). We also summarized the paths with maximum weights (whose abstract value ranked 20% percentile or 80% percentile). Temperature and PM 10 exert much more influence on the mount of surgeries (Fig. 2 b). Visibility plays the role of hub to transmit the influence from other climatic factors to the mount of surgery (Fig. 2 c). The number of simple paths with different starting and ending points are shown in Fig. 2 d, which shows that PM 10 , SO 2 , CO and visibility influence the mount of surgeries most. Table 2 The statistical result of the weights of all simple paths in Fig. 1 Factor Mean value Maximum value Minimum value PM 2.5 -0.005805 0.8252465 -0.642063 PM 10 -0.002843 0.975919 -0.759290 SO 2 -0.009209 0.404479 -0.452114 CO 0.007763 0.578658 -0.522336 NO 2 0.001842 0.796425 -0.619639 O 3 -0.003685 0.693014 -0.612005 Temperature 0.001050 0.729933 -0.557823 Atmospheric pressure 0.001272 0.533749 -0.703312 Humidity 0.000996 0.551902 -0.550886 Wind speed 0.006129 0.500712 -0.451977 Minimum temperature 0.013096 0.548757 -0.340277 Maximum temperature -0.012142 0.306425 -0.492703 Visibility 0.000057 0.581900 -0.360829 Dew point 0.024834 0.855436 -0.298341 Precipitation 0.009625 0.590565 -0.515893 This study implemented Fuzzy Cognitive Maps (FCMs) modeling approach to examine the relationship between climatic factors/air pollutants and cataract surgery incidence. Our analysis revealed significant associations between environmental factors and cataract surgery incidence in Wuhan. Specifically, positive correlations were observed between exposure to CO and NO₂ with increased cataract surgery cases. Negative correlations were identified for PM₂.₅, O₃, PM₁₀, and SO₂. Regarding climatic factors: maximum temperature demonstrated an inverse relationship with surgical volumes, and all other climatic variables showed positive associations. There has been an increasing number of investigations assessing the impact of climate change and air pollution on ocular health [ 13 , 14 , 21 ]. One of the most common eye diseases is cataracts, which can result from metabolic, nutritional or environmental insults, or they may be secondary to other ocular or systemic diseases [ 22 ]. In recent decades, accumulating scientific evidence has established the significant contribution of environmental factors to both cataractogenesis and progression [ 23 ]. Our FCMs analysis revealed divergent temperature impacts in Wuhan. We demonstrate that temperature, minimum temperature and dew point were positively related to number of cataract surgeries, while the maximum temperature showed an inverse relationship. This dichotomy aligns with existing literature. Research reported that high temperature serves as a risk factor for the incidence of cataract, the cataract prevalence increases with increasing temperature [ 24 – 27 ], leading to increasing in the number of operations for cataract. As one of the most common electively surgical procedure worldwide, previous studies indicated that the existence of a significant seasonal pattern in aged-related cataract hospitalizations in Canada, which phacoemulsification surgeries reach their highest frequency during spring and autumn, while experiencing a decline in summer and winter [ 28 ]. Located in a subtropical monsoon climate zone, Wuhan is identified as a primary thermal hotspot in southern China, colloquially referred to as the “Furnace City” due to its extreme summer temperatures [ 29 ]. Jingui Xie et al. investigated the association between extreme heat and hospital admissions for cataract in Hefei, China. They showed that the cataract hospitalizations will reduce with the increase of mean temperature and there is a negative relationship between extreme heat and hospital admissions for cataract [ 30 ]. Cataract patients usually mind that the treatment effect on acutely hot days may be worse than that during comfortable weather since extreme heat could increase the risk of infection for them after surgical treatment [ 31 , 32 ]. Thus, cataract patients may not prefer to choose to undergo surgery in hot weather, causing fewer surgeries. On the other hand, researchers have noticed that patients were less likely to visit hospitals during bad weather [ 33 – 35 ]. So, as a kind of severe weather condition, maximum temperature in Wuhan, could also prevent cataract patients from receiving surgeries since their condition is usually not exigent, thus leading to fewer surgical operations. Recent Chinese research has revealed that annual average humidity is inversely related with cataract prevalence [ 36 ], which appears inconsistent with our surgical volume results. This discrepancy may stem from selection bias, as only a subset of cataract patients ultimately undergo surgical intervention. Several studies revealed that relative humidity was negatively relevant to allergic conjunctivitis [ 37 , 38 ] and dry eye [ 39 , 40 ], may mean less postoperative adverse effects, which may constitutes a determinant in surgical timing selection and then increase the amount of surgeries. Furthermore, other studies have identified that the exposure of tear film to low relative humidity has adverse effect on the rate of evaporation, the thickness, and stability of the lipid layer, and the production of tears, this resulted in significant postoperative discomfort, especially in older people [ 41 ]. On the other hand, the annual volume of rainfall appears to be a protective factor in cataracts aged over 60 years old in southern Spain [ 42 ], which is consistent with our research for precipitation in Wuhan. In addition, literature have suggested that low-oxygen saturation caused by the hypobaric hypoxia can be harmful [ 43 , 44 ]. Studies on rheumatic diseases show that high atmospheric pressure has a positive effect on joint pain and low pressure promotes worsening of pain [ 45 , 46 ]. While hypobaric hypoxia did not induce structural changes in the lens, it may have an impact on human color recognition, dark vision and contrast sensitivity [ 44 ]. These visual functional impairments may contribute to increased postoperative visual disturbances, potentially reducing patient willingness to undergo elective cataract procedure during periods of low atmospheric pressure. Patients undergoing elective ocular surgery demonstrate particular sensitivity to environmental conditions. Clinical evidence indicates that moderate to high wind speed is related to more appropriate postoperative spherical equivalent (SE) of LASIK [ 47 ]. This correlation may lead to increased patient preference for surgical scheduling during periods of higher wind velocity, as they seek optimal visual outcomes. This might be attributed to the fact that high wind speed is usually correlated with uncomfortable weather conditions, which might compel postoperative patients to remain indoors and thereby facilitate postoperative recovery. What’s more, wind-induced particulate clearance may reduce airborne irritants that could otherwise compromise ocular surface healing. More studies are needed to confirm the relationship between visibility and eye health. While air pollution has been implicated in various ocular pathologies, its relationship with cataract development remains poorly characterized. Existing literature regarding air pollution and cataracts have yielded inconsistent findings. For PM 10 and SO 2 , the findings remain unclear. Choi et al. [ 48 ] observed no change in cataract incidence with increased exposure to these pollutants, whereas Shin et al. [ 49 ] noted an elevated risk of cataracts in Korea. Similarly, the relationship between NO 2 and cataracts is inconsistent. Choi et al. [ 48 ] identified a protective effect for specific subtypes of cataracts, Shin et al. [ 49 ] demonstrated that higher exposure to NO 2 was associated with an increased risk of cataract. Chua et al. [ 50 ] found greater NO 2 exposure was associated with higher risk of future cataract surgery, whereas, Grant et al. [ 51 ] found no association. The association between PM 2.5 exposure and cataract are ambiguous, too. While Shin et al. [ 49 ] estimated no statistically significant association, Chua et al. [ 50 ] showed that elevated PM2.5 levels were related to increased likelihood of cataract surgery. Meanwhile, Shin et al. [ 49 ] found no association between CO exposure and cataract. In contrast, our investigation demonstrates a consistent negative relationship between O 3 levels and cataract surgery volumes, which aligns with three published studies [ 48 , 49 , 51 ]. This coherent evidence suggests that elevated O 3 concentrations may exert a protective role against cataract, potentially explaining the observed reduction in cataract surgery volumes. Otherwise, a study from Taiwan reported that visiting an ophthalmologic outpatient clinic was associated with an increased chance of visiting an ophthalmology clinic for conjunctivitis due to increased exposure to PM 10 , PM 2.5 and O 3 [ 37 ]. In addition, airborne pollutants may contribute to dry eye syndrome and exacerbate pre-existing ocular surface conditions [ 13 ]. Such ocular discomfort frequently prompts cataract patients to address these surface symptoms before proceeding with elective cataract extraction. They will choose to undergo cataract surgery after the symptoms are alleviated, thereby minimizing postoperative discomfort and attaining a superior postoperative outcome. This may result in a decrease in the number of cataract surgeries. In our research, the number of female patients undergoing cataract surgery was 1.4 times that of male patients. Exposure to biomass fuels over adult lifetime was related to nuclear cataract for women in the India Eye Study [ 52 ]. Differences in rates of cataract by sex were shown in previous studies. Females had higher rates of cataract than males of same age [ 53 ]. Additionally, temperature, humidity, wind speed, and atmospheric pressure can directly or indirectly influence the concentration, distribution and composition of air pollutants [ 54 , 55 ] . There are several limitations that should be acknowledged when interpreting this research. First, the heterogeneity and sensitivity of all groups of patients towards climate and air quality is different, we only modeled the overall relationship without detailed subgroup information (e.g., older patients, students, Mental and physical laborers, sex, household income, smoking), similar with the first point, subgroup information may be some confounders affecting the modeling process so that the true causal and effect could not be easily found. Second, the cross-sectional design could not reveal direct causality between climatic factor, air pollution and the cataract surgery volumes. The observed associations likely represent composite effects stemming from diverse contributing elements. Temporal remains ambiguous that whether acute exposure peaks or chronic exposure drives surgical demand. Further longitudinal cohort studies tracking individual patients’ exposure histories and cataract progression, combined with clinical and experimental studies are needed to establish mechanistic causality. Third, the modeling process could not be finer grain with the data from all strict in Wuhan as the finer grain data source is scarce and the population mobility is general in China. Therefore, a global landscape should be modeled with the population mobility data within a national level (the data from all cities or provinces). Forth, the model did not adjust for comorbidities (e.g., diabetes, hypertension) that may accelerate cataract formation or influence surgery timing, so the wider spectrum is needed to be included to construct the spatial and temporal model to character diversity relationship. Fifth, during data retrieval, it was found that part of patients who had cataract surgery, were non-local residents, and we only assess the influence of climate and pollutants in Wuhan. This mismatch could bias association if patients’ exposure histories originated elsewhere. At last, Wuhan’s subtropical climate and air pollution profile may not represent other regions, and the limited samples of cataract surgery patients from Wuhan Central Hospital may not be representative of the general population, these potentially leading to biased results. Conclusions To our knowledge, this represents the first investigation employing FCMs to analyze the association between climatic factors, air pollutants and cataract surgery volumes at Wuhan Central Hospital. Our analysis demonstrates that temperature, atmospheric pressure, visibility, humidity, minimum temperature, wind speed, dew point, precipitation, CO and NO 2 are positively correlated with increased cataract surgery volumes, whereas maximum temperature, SO 2 , PM 2.5 , O 3 and PM 10 exhibit negative association. These results suggest that FCMs can serve as a valuable predictive tool for estimating future cataract surgery demand, offering quantitative insights to optimize hospital resource allocation and inform evidence-based environmental health policies. By implementing proactive strategies guided by these predictive analytics, healthcare systems can optimize operational efficiency, minimize surgical wait times, and ultimately improve patient outcomes amid growing environmental challenges. Declarations Author’s contribution M.-Y.X., Z.-L., and Z.-K.,. initiated the study., W.-X., W.-M.J., and Z.-K. designed and performed the experiments. W.-X., Z.-K., H.-X.Y., and Z.-W.J. wrote the code. Z.-L., and M.-Y.X. collected experimental data. Z.-L., Y.-D.Y., and Z.-K. reviewed the experiment results. M.-Y.X., Y.-D.Y., W.-X., W.-M.J., Z.-L., and Z.-K. critically reviewed and commended the manuscript. All authors contributed to the preparation of the manuscript. Code and data availability The code for training and testing the models in current research, the datasets generated or analyzed during this study are available from the corresponding author upon reasonable request. Declaration of interests All authors declare that the research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. Acknowledgements NA. Funding The Project Supported by Open Research Funds of the State Key Laboratory of Ophthalmology (2020KF05), Wuhan Municipal Health Commission Medical Research (WX19Q29), and Hubei Municipal Health Commission Medical Research (WJ2019H376), Natural Science Basic Research Plan in Shaanxi Province of China (2022JQ-175), Scientific Research Program Funded by Shaanxi Provincial Education Department (22JK0303). The sponsors of the study played no role in study design, data collection, analysis, or interpretation, manuscript preparation, or the decision to submit the manuscript for publication. Ethics approval and consent to participate The clinical data were enrolled from the department of Ophthalmology in the Central Hospital of Wuhan. The data of weather and air quality were obtained from two validated online sources. This study was approved by the Ethics Committee of Central Hospital of Wuhan (Ethics Number：WHZXKYL2025-129) and was conducted in accordance with the tenets of the Declaration of Helsinki. Informed consent was obtained from the participants. References Asbell PA, Dualan I, Mindel J, Brocks D, Ahmad M, Epstein S: Age-related cataract. Lancet (London, England) 2005, 365(9459):599-609. Cicinelli MV, Buchan JC, Nicholson M, Varadaraj V, Khanna RC: Cataracts. Lancet (London, England) 2023, 401(10374):377-389. Assi L, Chamseddine F, Ibrahim P, Sabbagh H, Rosman L, Congdon N, Evans J, Ramke J, Kuper H, Burton MJ et al: A Global Assessment of Eye Health and Quality of Life: A Systematic Review of Systematic Reviews. JAMA ophthalmology 2021, 139(5):526-541. Bohman E, Wyon M, Lundström M, Dafgård Kopp E: A comparison between patients with epiphora and cataract of the activity limitations they experience in daily life due to their visual disability. Acta ophthalmologica 2018, 96(1):77-80. Thompson J, Lakhani N: Cataracts. Primary care 2015, 42(3):409-423. Pathak M, Odayappan A, Nath M, Raman R, Bhandari S, Nachiappan S: Comparison of the outcomes of phacoemulsification and manual small-incision cataract surgery in posterior polar cataract - A retrospective study. Indian journal of ophthalmology 2022, 70(11):3977-3981. Llop SM, Papaliodis GN: Cataract Surgery Complications in Uveitis Patients: A Review Article. Seminars in ophthalmology 2018, 33(1):64-69. Zhang K, Liu X, Jiang J, Li W, Wang S, Liu L, Zhou X, Wang L: Prediction of postoperative complications of pediatric cataract patients using data mining. Journal of Translational Medicine 2019, 17(1):2. Bali J, Bali O, Sahu A, Boramani J, Deori N: Health economics and manual small-incision cataract surgery: An illustrative mini review. Indian journal of ophthalmology 2022, 70(11):3765-3770. Flessa S: Cataract Surgery in Low-Income Countries: A Good Deal! Healthcare (Basel, Switzerland) 2022, 10(12). Assi L, Rosman L, Chamseddine F, Ibrahim P, Sabbagh H, Congdon N, Evans J, Ramke J, Kuper H, Burton MJ et al: Eye health and quality of life: an umbrella review protocol. BMJ open 2020, 10(8):e037648. Cao F, Liu ZR, Ni QY, Zha CK, Zhang SJ, Lu JM, Xu YY, Tao LM, Jiang ZX, Pan HF: Emerging roles of air pollution and meteorological factors in autoimmune eye diseases. Environmental research 2023, 231(Pt 1):116116. Lin CC, Chiu CC, Lee PY, Chen KJ, He CX, Hsu SK, Cheng KC: The Adverse Effects of Air Pollution on the Eye: A Review. International journal of environmental research and public health 2022, 19(3). Muruganandam N, Mahalingam S, Narayanan R, Rajadurai E: Meandered and muddled: a systematic review on the impact of air pollution on ocular health. Environmental science and pollution research international 2023, 30(24):64872-64890. Zhang K, Pan Q, Yu D, Wang L, Liu Z, Li X, Liu X: Systemically modeling the relationship between climate change and wheat aphid abundance. Science of The Total Environment 2019, 674:392-400. Bakhtavar E, Valipour M, Yousefi S, Sadiq R, Hewage K: Fuzzy cognitive maps in systems risk analysis: a comprehensive review. Complex & Intelligent Systems 2021, 7(2):621-637. Amirkhani A, Papageorgiou EI, Mohseni A, Mosavi MR: A review of fuzzy cognitive maps in medicine: Taxonomy, methods, and applications. Computer methods and programs in biomedicine 2017, 142:129-145. Apostolopoulos ID, Papandrianos NI, Papathanasiou ND, Papageorgiou EI: Fuzzy Cognitive Map Applications in Medicine over the Last Two Decades: A Review Study. Bioengineering (Basel, Switzerland) 2024, 11(2). Mahmoodi SA, Mirzaie K, Mahmoodi MS, Mahmoudi SM: A Medical Decision Support System to Assess Risk Factors for Gastric Cancer Based on Fuzzy Cognitive Map. Computational and mathematical methods in medicine 2020, 2020:1016284. Hu Z, Gong W, Pedrycz W, Li Y: Deep reinforcement learning assisted co-evolutionary differential evolution for constrained optimization. Swarm and Evolutionary Computation 2023, 83:101387. Echevarría-Lucas L, Senciales-González JM, Medialdea-Hurtado ME, Rodrigo-Comino J: Impact of Climate Change on Eye Diseases and Associated Economical Costs. International journal of environmental research and public health 2021, 18(13). Periyasamy P, Shinohara T: Age-related cataracts: Role of unfolded protein response, Ca(2+) mobilization, epigenetic DNA modifications, and loss of Nrf2/Keap1 dependent cytoprotection. Progress in retinal and eye research 2017, 60:1-19. Raju P, George R, Ve Ramesh S, Arvind H, Baskaran M, Vijaya L: Influence of tobacco use on cataract development. The British journal of ophthalmology 2006, 90(11):1374-1377. Prokofyeva E, Wegener A, Zrenner E: Cataract prevalence and prevention in Europe: a literature review. Acta ophthalmologica 2013, 91(5):395-405. Heys KR, Friedrich MG, Truscott RJ: Presbyopia and heat: changes associated with aging of the human lens suggest a functional role for the small heat shock protein, alpha-crystallin, in maintaining lens flexibility. Aging cell 2007, 6(6):807-815. Fuller-Thomson E, Deng Z, Fuller-Thomson EG: Association Between Area Temperature and Severe Vision Impairment in a Nationally Representative Sample of Older Americans. Ophthalmic epidemiology 2024, 31(2):119-126. Miranda MN: Environmental temperature and senile cataract. Transactions of the American Ophthalmological Society 1980, 78:255-264. Leong AM, Crighton EJ, Moineddin R, Mamdani M, Upshur RE: Time series analysis of age related cataract hospitalizations and phacoemulsification. BMC ophthalmology 2006, 6:2. Chen S, Zhao J, Lee SB, Kim SW: Estimation of Relative Risk of Mortality and Economic Burden Attributable to High Temperature in Wuhan, China. Frontiers in public health 2022, 10:839204. Xie J, Zhu Y, Fan Y, Xie L, Xie R, Huang F, Cao L: Association between extreme heat and hospital admissions for cataract patients in Hefei, China. Environmental science and pollution research international 2020, 27(36):45381-45389. Anthony CA, Peterson RA, Polgreen LA, Sewell DK, Polgreen PM: The Seasonal Variability in Surgical Site Infections and the Association With Warmer Weather: A Population-Based Investigation. Infection control and hospital epidemiology 2017, 38(7):809-816. Rubio EF: Climatic influence on conjunctival bacteria of patients undergoing cataract surgery. Eye (London, England) 2004, 18(8):778-784. Lee HJ, Jin MH, Lee JH: The association of weather on pediatric emergency department visits in Changwon, Korea (2005-2014). The Science of the total environment 2016, 551-552:699-705. Xie J, Zhu Y, Fan Y, Xin L, Liu J: Association between rainfall and readmissions of rheumatoid arthritis patients: a time-stratified case-crossover analysis. International journal of biometeorology 2020, 64(1):145-153. Ou DK, To TP, Taylor DM: Weather patients will come? The Medical journal of Australia 2005, 183(11-12):675-677. Lv X, Gao X, Hu K, Yao Y, Zeng Y, Chen H: Associations of Humidity and Temperature With Cataracts Among Older Adults in China. Frontiers in public health 2022, 10:872030. Zhong J-Y, Lee Y-C, Hsieh C-J, Tseng C-C, Yiin L-M: Association between the first occurrence of allergic conjunctivitis, air pollution and weather changes in Taiwan. Atmospheric Environment 2019, 212:90-95. Das AV, Basu S: Environmental and Air Pollution Factors Affecting Allergic Eye Disease in Children and Adolescents in India. International journal of environmental research and public health 2021, 18(11). Hwang SH, Choi YH, Paik HJ, Wee WR, Kim MK, Kim DH: Potential Importance of Ozone in the Association Between Outdoor Air Pollution and Dry Eye Disease in South Korea. JAMA ophthalmology 2016, 134(5):503-510. Zhong JY, Lee YC, Hsieh CJ, Tseng CC, Yiin LM: Association between Dry Eye Disease, Air Pollution and Weather Changes in Taiwan. International journal of environmental research and public health 2018, 15(10). Tabernero J, Garcia-Porta N, Artal P, Pardhan S: Intraocular Scattering, Blinking Rate, and Tear Film Osmolarity After Exposure to Environmental Stress. Translational vision science & technology 2021, 10(9):12. Echevarría-Lucas L, Senciales-González JM, Rodrigo-Comino J: Analysing the Evidence of the Effects of Climate Change, Air Pollutants, and Occupational Factors in the Appearance of Cataracts. Environments 2024, 11(5):87. Burtscher J, Mallet RT, Burtscher M, Millet GP: Hypoxia and brain aging: Neurodegeneration or neuroprotection? Ageing research reviews 2021, 68:101343. Wang Y, Yu X, Liu Z, Lv Z, Xia H, Wang Y, Li J, Li X: Influence of hypobaric hypoxic conditions on ocular structure and biological function at high attitudes: a narrative review. Frontiers in neuroscience 2023, 17:1149664. McAlindon T, Formica M, Schmid CH, Fletcher J: Changes in barometric pressure and ambient temperature influence osteoarthritis pain. The American journal of medicine 2007, 120(5):429-434. Wilder FV, Hall BJ, Barrett JP: Osteoarthritis pain and weather. Rheumatology (Oxford, England) 2003, 42(8):955-958. Neuhaus-Richard I, Frings A, Ament F, Görsch IC, Druchkiv V, Katz T, Linke SJ, Richard G: Do air pressure and wind speed influence the outcome of myopic laser refractive surgery? Results from the Hamburg Weather Study. International ophthalmology 2014, 34(6):1249-1258. Choi YH, Park SJ, Paik HJ, Kim MK, Wee WR, Kim DH: Unexpected potential protective associations between outdoor air pollution and cataracts. Environmental science and pollution research international 2018, 25(11):10636-10643. Shin J, Lee H, Kim H: Association between Exposure to Ambient Air Pollution and Age-Related Cataract: A Nationwide Population-Based Retrospective Cohort Study. International journal of environmental research and public health 2020, 17(24). Chua SYL, Khawaja AP, Desai P, Rahi JS, Day AC, Hammond CJ, Khaw PT, Foster PJ: The Association of Ambient Air Pollution With Cataract Surgery in UK Biobank Participants: Prospective Cohort Study. Investigative ophthalmology & visual science 2021, 62(15):7. Grant A, Leung G, Freeman EE: Ambient Air Pollution and Age-Related Eye Disease: A Systematic Review and Meta-Analysis. Investigative ophthalmology & visual science 2022, 63(9):17. Ravilla TD, Gupta S, Ravindran RD, Vashist P, Krishnan T, Maraini G, Chakravarthy U, Fletcher AE: Use of Cooking Fuels and Cataract in a Population-Based Study: The India Eye Disease Study. Environmental health perspectives 2016, 124(12):1857-1862. Lou L, Ye X, Xu P, Wang J, Xu Y, Jin K, Ye J: Association of Sex With the Global Burden of Cataract. JAMA ophthalmology 2018, 136(2):116-121. Vithanage M, Bandara PC, Novo LAB, Kumar A, Ambade B, Naveendrakumar G, Ranagalage M, Magana-Arachchi DN: Deposition of trace metals associated with atmospheric particulate matter: Environmental fate and health risk assessment. Chemosphere 2022, 303(Pt 3):135051. Fujishima H, Satake Y, Okada N, Kawashima S, Matsumoto K, Saito H: Effects of diesel exhaust particles on primary cultured healthy human conjunctival epithelium. Annals of allergy, asthma & immunology : official publication of the American College of Allergy, Asthma, & Immunology 2013, 110(1):39-43. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7257236","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":498011213,"identity":"d501fbac-bc78-4c56-9d71-90090d260b67","order_by":0,"name":"Yaxin Miao","email":"","orcid":"","institution":"The Central Hospital of Wuhan, Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Yaxin","middleName":"","lastName":"Miao","suffix":""},{"id":498011214,"identity":"b80cbeea-1bd6-49d3-ab5b-3fc1c03fd4d0","order_by":1,"name":"Yi Xiang","email":"","orcid":"","institution":"The Central Hospital of Wuhan, Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Xiang","suffix":""},{"id":498011215,"identity":"cad91733-7f88-489a-9fb7-9c0aa6993aff","order_by":2,"name":"Haixiang Jiang","email":"","orcid":"","institution":"Haixiang eye Hospital","correspondingAuthor":false,"prefix":"","firstName":"Haixiang","middleName":"","lastName":"Jiang","suffix":""},{"id":498011219,"identity":"397f3834-2efc-4184-9e3c-b2f341b09e5e","order_by":3,"name":"Xu Wang","email":"","orcid":"","institution":"Shanxi Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Xu","middleName":"","lastName":"Wang","suffix":""},{"id":498011220,"identity":"e0bd1cec-42b9-4b26-9ff8-7c42843ce9f2","order_by":4,"name":"Xinyuan Huang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Xinyuan","middleName":"","lastName":"Huang","suffix":""},{"id":498011222,"identity":"eb1771c5-b2ef-4e5e-b18b-bb670d139d8d","order_by":5,"name":"Deying Yu","email":"","orcid":"","institution":"University Malaya Medical Centre","correspondingAuthor":false,"prefix":"","firstName":"Deying","middleName":"","lastName":"Yu","suffix":""},{"id":498011226,"identity":"8df04897-b5b9-4367-a8fa-047aa3d4835b","order_by":6,"name":"Meijia Wang","email":"","orcid":"","institution":"Shannxi University of Science \u0026 Technology, Xi'an Weiyang University Park","correspondingAuthor":false,"prefix":"","firstName":"Meijia","middleName":"","lastName":"Wang","suffix":""},{"id":498011227,"identity":"5d89c179-f62a-4002-82cb-e9d189627e61","order_by":7,"name":"Wenjin Zhang","email":"","orcid":"","institution":"Gyenno Science Co. Ltd","correspondingAuthor":false,"prefix":"","firstName":"Wenjin","middleName":"","lastName":"Zhang","suffix":""},{"id":498011229,"identity":"d624f19e-cfa3-4aaa-a73a-031ec75da942","order_by":8,"name":"Kai Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYBACgwNA4kNFDY99e2Pjww/EamGcceaYjAHP4WZjCWK1MPO2MdsYSKS3CfAQpeVG+sWHM86w8ZhLPmxjkGCwk9NtIKDF/kZOscGHChkey9mJbQ8KGJKNzQ4QtCUnTRJkC8PtxHYDCYYDiduI0SIN9AsPw82DbRI8xGlJPwbWYnCDkVgtZ94wGwIDmUeyJxEYyAbE+OV4+sMHwKi052c//vDhhwo7OYJaGBh4DJBNIKgcBNgfEKVsFIyCUTAKRjAAAFJ+SNlfc7vKAAAAAElFTkSuQmCC","orcid":"","institution":"Gyenno Science Co. Ltd","correspondingAuthor":true,"prefix":"","firstName":"Kai","middleName":"","lastName":"Zhang","suffix":""},{"id":498011230,"identity":"59e46a29-7796-47b3-bc8c-577d0f11ea5f","order_by":9,"name":"Li Zhang","email":"","orcid":"","institution":"The Central Hospital of Wuhan, Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-07-31 02:23:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7257236/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7257236/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89232395,"identity":"fce62285-08c6-4a31-bc58-15adbb1b718c","added_by":"auto","created_at":"2025-08-17 14:24:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":64246,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe final graph (FCM) produced by DE. Green filling indicates\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7257236/v1/e76781b57de6ef096dedc87a.png"},{"id":89232394,"identity":"8bfc3356-25c6-41b2-825f-3233adf6801e","added_by":"auto","created_at":"2025-08-17 14:24:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":136931,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe simple paths in final graph. (a) The distribution of the weights of simple paths started with different factors; (b) The word cloud image for the start points of simple paths; (c) The word cloud image of the interim points of simple paths; (d) The number of all simple paths with different starting and ending points in Fig. 1.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7257236/v1/ceede14644dc5db97ad2d3c4.png"},{"id":101205608,"identity":"9e94d080-365d-42c1-ae70-56167b5cc1ef","added_by":"auto","created_at":"2026-01-27 09:49:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":957203,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7257236/v1/8ade50d3-a08c-45c9-b3a3-6569a46c72bb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analyzing the impact of climate and air pollution on cataract surgery volume using fuzzy cognitive maps","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCataracts are a common ocular disorder characterized by the lens opacification, which can lead to severe vision impairment and, in some cases, blindness [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Globally, cataracts affect millions of individuals, significantly impairing emotional well-being and overall quality of life [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Cataract prevalence is projected to rise due to an aging population and longer life expectancy. Substantially, concurrently escalating the economic burden on healthcare systems due to high treatment demand. Currently, surgical intervention remains the only definitive treatment for cataracts [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Despite ongoing advancements in surgical techniques, challenges such as postoperative complications [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], high surgical costs [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], and limited healthcare resources persist [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Therefore, exploring factors influencing surgical timing is crucial for improving both therapeutic efficacy and quality-of-life outcomes in cataract patients.\u003c/p\u003e\u003cp\u003eAs one of the most commonly performed elective procedures, cataract surgery timing is determined by multifactorial considerations. Previous studies have indicated that significant correlations between surgical timing decisions and patients' visual function, overall health status, and quality of life [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Additionally, weather conditions may play a role in patients' decisions regarding the timing of surgery, as changes in weather can impact the severity of symptoms and patients' willingness to seek medical care. Although existing research suggests that environmental factors can influence eye health [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], their direct effects on cataract surgical timing remain poorly characterized, highlighting a significant gap in the literature that requires further exploration.\u003c/p\u003e\u003cp\u003eFuzzy Cognitive Maps (FCMs) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] are a mathematical tool that are highly-appropriate for modeling system in which precise quantitative data might be scarce or difficult to obtain, and they can effectively depict the causal relationships among system components, capturing both positive and negative impacts [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. FCMs provide unique advantages for examining quantitative relationships between multiple influencing factors and final choice, enabling effective management of complex, multidimensional datasets [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. As demonstrated in recent research, FCMs serve as potent instrument for comprehensive health risk analysis and assessment [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The visual decision-support capabilities of FCMs enhance both intuitive understanding and analytical flexibility[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Our study will utilize FCMs as an analytical tool to explore the potential influence of environmental factors on cataract surgery timing decisions. It is our first time to employ FCMs to assess how environmental factors impact the decision-making process surrounding the timing of cataract surgery. By identifying the potential environmental determinants influencing surgical timing, this study will develop data-driven recommendations for optimizing the timing of interventions for patients. Ultimately, this research aspires to advance our understanding of environmental factors impact on medical decision-making and provide actionable guidance for clinical resource allocation, such as hospital resource planning, allowing for optimized staffing, surgical scheduling, and equipment allocation and so on.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cp\u003e\u003cb\u003eData collection and characteristics\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe study data were derived from inpatient records of cataract patients who underwent phacoemulsification with intraocular lens implantation at The Central Hospital of Wuhan between January 1, 2019and July 22, 2024. A total of 14,086 patients were included in the analysis, with 2,910 patients enrolled in 2019, 1,394 in 2020, 1,655 in 2021, 1,801 in 2022, 4,359 in 2023, and 1,967 in 2024. Among these patients, there were 5,856 males and 8,230 females, with an average age of 70.25\u0026thinsp;\u0026plusmn;\u0026thinsp;9.4 years. Moreover, the weather data and air quality data for Wuhan during the corresponding period were obtained from two validated online sources (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://rp5.ru/\u003c/span\u003e\u003cspan address=\"https://rp5.ru/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.aqistudy.cn/\u003c/span\u003e\u003cspan address=\"https://www.aqistudy.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) The statistical summary of the dataset is shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The definition of temperature, dew point, minimum temperature, maximum temperature and wind speed are the air temperature at 2 meters above the ground, dew point temperature at 2 meters above the ground, the lowest temperature recorded in the past hours (up to 12 hours), the highest temperature recorded in the past hours (up to 12 hours), the maximum gust wind speed at 10\u0026ndash;12 meters above ground level (AGL) within 10 minutes, respectively. Moreover, with over ten measurements per day, the daily value is calculated as the average of all measurements for that day. O\u003csub\u003e3\u003c/sub\u003e refers to 8-hour running average ozone concentration. All factors were preprocessed with min-max normalization before modeling. Each model was repeated constructing ten times and the model with best fitness were determined as the final model.\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\u003eThe statistical result of dataset.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFactor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStandard deviation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMaximum value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMinimum value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e (\u0026micro;g/m\u0026sup3;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e38.3365\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26.5732\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e206\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e (\u0026micro;g/m\u0026sup3;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e60.3004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e35.0651\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e410\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSO\u003csub\u003e2\u003c/sub\u003e (\u0026micro;g/m\u0026sup3;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14.3553\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20.1997\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCO (mg/m\u0026sup3;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16.7522\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18.800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNO\u003csub\u003e2\u003c/sub\u003e (\u0026micro;g/m\u0026sup3;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15.2931\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18.4802\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.4000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eO\u003csub\u003e3\u003c/sub\u003e (\u0026micro;g/m\u0026sup3;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e99.6473\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e47.2967\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e258\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTemperaturen (℃)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e17.7446\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.3341\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34.7250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.9875\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAtmospheric pressure (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e759.6615\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.5687\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e779.9500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e670.3500\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHumidity (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e76.1419\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.1800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e99.2500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e29.8750\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWind speed (m/s)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.5895\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.8881\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.2500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMinimum temperature (℃)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11.1138\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.6613\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31.6125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-7.3750\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaximum temperature (℃)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e17.1276\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.9357\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39.6625\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.3000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVisibility (km)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10.9406\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.5545\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.4625\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDew point (℃)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12.8458\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.2560\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27.2000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-16.1625\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrecipitation (mm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.6709\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.3634\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e59.7500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMount of surgeries (number of cases)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.9492\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.6980\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\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\u003e\u003cb\u003eFuzzy cognitive maps (FCMs)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFCMs describe a set of concepts (nodes) and the causal relationships between them, as a directed cyclic graph \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:G=(C,\\:E,\\:U,\\:f)\\)\u003c/span\u003e\u003c/span\u003e, where, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{C}=({\\text{c}}_{1},{\\text{c}}_{2},...,\\:{\\text{c}}_{\\text{n}})\\)\u003c/span\u003e\u003c/span\u003e denotes the set of concepts that make up the nodes of the directed cyclic graph, \u003cem\u003en\u003c/em\u003e is the number of variables during the modeling process. \u003cem\u003eE\u003c/em\u003e denotes the set of weight \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{e}_{ij}\\)\u003c/span\u003e\u003c/span\u003e on the directed edge from the conceptual node \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{c}_{i}\\)\u003c/span\u003e\u003c/span\u003e to conceptual node \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{c}_{j}\\)\u003c/span\u003e\u003c/span\u003e (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:1\\le\\:i,\\:j\\:\\le\\:\\:n\\)\u003c/span\u003e\u003c/span\u003e), namely \u003cem\u003eE\u003c/em\u003e is an adjacent matrix. The modeling process of the FCM is transformed into an optimization problem on its adjacent matrix \u003cem\u003eE\u003c/em\u003e to fit the observational values along the time axis (time series prediction involved multiple variables). \u003cem\u003eU\u003c/em\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{U}\\left(\\text{t}\\right)\\)\u003c/span\u003e\u003c/span\u003e=(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{u}_{1}\\)\u003c/span\u003e\u003c/span\u003e(\u003cem\u003et\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e),\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{u}_{2}\\)\u003c/span\u003e\u003c/span\u003e(\u003cem\u003et\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e),...,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{u}_{c}\\)\u003c/span\u003e\u003c/span\u003e(\u003cem\u003et\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e)), is the observational values of all concepts (nodes) at the initial time \u003cem\u003et\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e. \u003cem\u003ef\u003c/em\u003e denotes the transition function of the prediction value of a conceptual node \u003cem\u003ei\u003c/em\u003e at time stamp \u003cem\u003et+1\u003c/em\u003e from the observational values of all conceptual nodes at time stamp \u003cem\u003et\u003c/em\u003e, so that the prediction values of all nodes (concepts) at time stamp \u003cem\u003et\u0026thinsp;+\u0026thinsp;1\u003c/em\u003e is shown as Eq.\u0026nbsp;(1), here \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:f\\left(x\\right)=\\frac{1}{1+{e}^{-\\lambda\\:x}},\\lambda\\:=5\\)\u003c/span\u003e\u003c/span\u003e.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{u}_{i}(\\text{t}\\hspace{0.17em}+\\hspace{0.17em}1)=f\\left(\\sum\\:_{j=1}^{c}{e}_{ji}{u}_{j}\\right(\\text{t}\\left)\\right)\\:\\left(1\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSimilar to the common time series prediction, the order of FCM means the latency of all variables (concepts), namely 1st -order FCM means the values of all nodes at time stamp \u003cem\u003et\u003c/em\u003e depends on the values of all nodes at time stamp \u003cem\u003et-1\u003c/em\u003e; the 2nd -order FCM means the values of all nodes at time stamp \u003cem\u003et\u003c/em\u003e is related with the values of all nodes at time stamps \u003cem\u003et-1\u003c/em\u003e and \u003cem\u003et-2\u003c/em\u003e, and so on. In current research, we tired 1st -, 2nd -, 3rd -order FCMs to construct the complicated relationship between all variables and selected the model with the best fitting capacity (the best fitness in differential evolution) as the final FCM.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDifferential evolution (DE) algorithm\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe adopted the Differential Evolution (DE) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] algorithm, which has been proven effective in numerical optimization problems to optimize the FCMs. DE applies a virtual population formed of some individuals that simulate evolutionary processes to iteratively refine the solution towards the optimal. Each individual is represented as a vector corresponding to the values in \u003cem\u003eE\u003c/em\u003e of the FCMs, specifically the adjacency matrix of the FCM (graph). In each iteration, individuals (analogous to chromosomes in DE) undergo crossover and mutation operations to generate mutated individuals. Then the original individual and mutated individual will be compared via greedy selection to form the new population, namely the individual whose value from objective function is better is retained. The objective function is to obtain the adjacent matrix \u003cem\u003eE\u003c/em\u003e to fit the historical data (observational data). Here, we aim to model the heterogeneity of each monitoring sites in different years, therefore, the objective function is shown as Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e2\u003c/span\u003e), where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{u}_{i}\\left(\\text{t}\\right)\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{u}_{i}\\left(\\text{t}\\right){\\prime\\:}\\)\u003c/span\u003e\u003c/span\u003e are the predictive value and observational value of the \u003cem\u003ei\u003c/em\u003eth node at the time stamp \u003cem\u003et\u003c/em\u003e. \u003cem\u003eT\u003c/em\u003e is the total number of time stamps. The cross and mutation operation are shown as Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and Eq.\u0026nbsp;(\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e4\u003c/span\u003e), where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{v}}_{\\text{i}}\\)\u003c/span\u003e\u003c/span\u003e is the \u003cem\u003ei\u003c/em\u003eth individual, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{v}}_{1}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{v}}_{2}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{F}\\in\\:\\left[\\text{0,1}\\right]\\)\u003c/span\u003e\u003c/span\u003e are two randomly selected individuals and cross factor (an algorithm parameter), respectively. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{v}}_{\\text{i}.\\text{j}}\\)\u003c/span\u003e\u003c/span\u003e is the \u003cem\u003ej\u003c/em\u003eth element in \u003cem\u003ei\u003c/em\u003eth individual, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:rand\\left(\\right)\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{v}}_{\\text{r},\\text{j}}\\)\u003c/span\u003e\u003c/span\u003e and CR are a random scalar in [0, 1], the \u003cem\u003ej\u003c/em\u003eth element in a randomly selected individual \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{v}}_{\\text{r}}\\)\u003c/span\u003e\u003c/span\u003e and mutation rate (an algorithm parameter), respectively. Eq.\u0026nbsp;(\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e4\u003c/span\u003e) will ensure the at least one element in original individual will be mutated, namely if all elements in \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{v}}_{\\text{i}}\\)\u003c/span\u003e\u003c/span\u003e is not mutated, the selected \u003cem\u003ej\u003c/em\u003eth element will be mutated. \u003cem\u003en\u003c/em\u003e is the number of indicators in present study that is consistent with the \u003cem\u003en\u003c/em\u003e in FCMs section. DE will stop the iterative computation when the maximum epoch is reached or the algorithm converged. All the algorithm parameters are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In current research, the individual in DE represents the \u003cem\u003eE\u003c/em\u003e, and the predictive values after time stamp \u003cem\u003et\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{u}_{i}(\\text{t}\u0026gt;\\text{t}0)\\)\u003c/span\u003e\u003c/span\u003e, could be figured out via Eq.\u0026nbsp;(1) for optimization. Because the objective of this study is to explore the effect of climate and air pollutants on the number of cataract surgeries, we neglect the effect of the number of cataract surgeries on climate and air pollutants in the computation process of DE.\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:\\text{L}=\\sum\\:_{\\text{i}=1}^{\\text{n}}{\\sum\\:}_{\\text{t}=1}^{\\text{T}}{\\left({u}_{i}\\right(\\text{t})-{u}_{i}(\\text{t}\\left){\\prime\\:}\\right)}^{2}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:{v}_{i}={v}_{i}+F({v}_{1}-{v}_{2})$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:{v}_{i.j}={v}_{r,j},\\:if\\:\\:rand\\left(\\right)\\:\\le\\:CR\\:or\\:j=rand(1,{n}^{2})$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Results and discussion","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e showed all statistical results for original dataset, including the mount of surgeries, air quality and climate. Figure. 1 shows the produced FCMs, the elements in it signify the levels of direct association among cataract surgery volumes and various of climatic / air quality factors. The simple paths in the FCM are summarized (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) and the weights of top ranked (whose abstract value ranked 20% percentile or 80% percentile) simple paths are showed in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea. Because the weight of a simple path decreases dramatically as its length increases, we set the length limit of the simple paths as 5. Moreover, the total amount of simple paths is pretty huge with the increase of the number of nodes, we only summarize the simple paths whose weights ranked 20% and 80% percentile. The statistical values of the weights on all simple paths are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. CO and NO\u003csub\u003e2\u003c/sub\u003e exert positive impact on number of surgeries (mean value\u0026thinsp;=\u0026thinsp;0.007763 for CO, mean value\u0026thinsp;=\u0026thinsp;0.001842 for NO\u003csub\u003e2\u003c/sub\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). However other air quality factors exert negative influence (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For climatic factors, except maximum temperature exert negative impact on the mount of surgeries (mean value = -0.012142, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), other climatic factors exert positive impact on the mount of surgeries. The dew point exerts positively the largest impact on the mount of surgery (mean value\u0026thinsp;=\u0026thinsp;0.024834, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). We also summarized the paths with maximum weights (whose abstract value ranked 20% percentile or 80% percentile). Temperature and PM\u003csub\u003e10\u003c/sub\u003e exert much more influence on the mount of surgeries (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). Visibility plays the role of hub to transmit the influence from other climatic factors to the mount of surgery (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). The number of simple paths with different starting and ending points are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed, which shows that PM\u003csub\u003e10\u003c/sub\u003e, SO\u003csub\u003e2\u003c/sub\u003e, CO and visibility influence the mount of surgeries most.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe statistical result of the weights of all simple paths in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFactor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMaximum value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMinimum value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.005805\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.8252465\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.642063\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.002843\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.975919\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.759290\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.009209\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.404479\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.452114\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.007763\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.578658\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.522336\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.001842\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.796425\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.619639\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.003685\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.693014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.612005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTemperature\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.001050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.729933\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.557823\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAtmospheric pressure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.001272\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.533749\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.703312\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHumidity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.000996\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.551902\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.550886\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWind speed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.006129\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.500712\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.451977\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMinimum temperature\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.013096\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.548757\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.340277\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaximum temperature\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.012142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.306425\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.492703\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVisibility\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.000057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.581900\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.360829\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDew point\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.024834\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.855436\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.298341\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrecipitation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.009625\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.590565\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.515893\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\u003eThis study implemented Fuzzy Cognitive Maps (FCMs) modeling approach to examine the relationship between climatic factors/air pollutants and cataract surgery incidence. Our analysis revealed significant associations between environmental factors and cataract surgery incidence in Wuhan. Specifically, positive correlations were observed between exposure to CO and NO₂ with increased cataract surgery cases. Negative correlations were identified for PM₂.₅, O₃, PM₁₀, and SO₂. Regarding climatic factors: maximum temperature demonstrated an inverse relationship with surgical volumes, and all other climatic variables showed positive associations.\u003c/p\u003e\u003cp\u003eThere has been an increasing number of investigations assessing the impact of climate change and air pollution on ocular health [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. One of the most common eye diseases is cataracts, which can result from metabolic, nutritional or environmental insults, or they may be secondary to other ocular or systemic diseases [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In recent decades, accumulating scientific evidence has established the significant contribution of environmental factors to both cataractogenesis and progression [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Our FCMs analysis revealed divergent temperature impacts in Wuhan. We demonstrate that temperature, minimum temperature and dew point were positively related to number of cataract surgeries, while the maximum temperature showed an inverse relationship. This dichotomy aligns with existing literature. Research reported that high temperature serves as a risk factor for the incidence of cataract, the cataract prevalence increases with increasing temperature [\u003cspan additionalcitationids=\"CR25 CR26\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], leading to increasing in the number of operations for cataract. As one of the most common electively surgical procedure worldwide, previous studies indicated that the existence of a significant seasonal pattern in aged-related cataract hospitalizations in Canada, which phacoemulsification surgeries reach their highest frequency during spring and autumn, while experiencing a decline in summer and winter [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Located in a subtropical monsoon climate zone, Wuhan is identified as a primary thermal hotspot in southern China, colloquially referred to as the \u0026ldquo;Furnace City\u0026rdquo; due to its extreme summer temperatures [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Jingui Xie et al. investigated the association between extreme heat and hospital admissions for cataract in Hefei, China. They showed that the cataract hospitalizations will reduce with the increase of mean temperature and there is a negative relationship between extreme heat and hospital admissions for cataract [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Cataract patients usually mind that the treatment effect on acutely hot days may be worse than that during comfortable weather since extreme heat could increase the risk of infection for them after surgical treatment [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Thus, cataract patients may not prefer to choose to undergo surgery in hot weather, causing fewer surgeries. On the other hand, researchers have noticed that patients were less likely to visit hospitals during bad weather [\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. So, as a kind of severe weather condition, maximum temperature in Wuhan, could also prevent cataract patients from receiving surgeries since their condition is usually not exigent, thus leading to fewer surgical operations.\u003c/p\u003e\u003cp\u003eRecent Chinese research has revealed that annual average humidity is inversely related with cataract prevalence [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], which appears inconsistent with our surgical volume results. This discrepancy may stem from selection bias, as only a subset of cataract patients ultimately undergo surgical intervention. Several studies revealed that relative humidity was negatively relevant to allergic conjunctivitis [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] and dry eye [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], may mean less postoperative adverse effects, which may constitutes a determinant in surgical timing selection and then increase the amount of surgeries. Furthermore, other studies have identified that the exposure of tear film to low relative humidity has adverse effect on the rate of evaporation, the thickness, and stability of the lipid layer, and the production of tears, this resulted in significant postoperative discomfort, especially in older people [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. On the other hand, the annual volume of rainfall appears to be a protective factor in cataracts aged over 60 years old in southern Spain [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], which is consistent with our research for precipitation in Wuhan. In addition, literature have suggested that low-oxygen saturation caused by the hypobaric hypoxia can be harmful [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Studies on rheumatic diseases show that high atmospheric pressure has a positive effect on joint pain and low pressure promotes worsening of pain [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. While hypobaric hypoxia did not induce structural changes in the lens, it may have an impact on human color recognition, dark vision and contrast sensitivity [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. These visual functional impairments may contribute to increased postoperative visual disturbances, potentially reducing patient willingness to undergo elective cataract procedure during periods of low atmospheric pressure. Patients undergoing elective ocular surgery demonstrate particular sensitivity to environmental conditions. Clinical evidence indicates that moderate to high wind speed is related to more appropriate postoperative spherical equivalent (SE) of LASIK [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. This correlation may lead to increased patient preference for surgical scheduling during periods of higher wind velocity, as they seek optimal visual outcomes. This might be attributed to the fact that high wind speed is usually correlated with uncomfortable weather conditions, which might compel postoperative patients to remain indoors and thereby facilitate postoperative recovery. What\u0026rsquo;s more, wind-induced particulate clearance may reduce airborne irritants that could otherwise compromise ocular surface healing. More studies are needed to confirm the relationship between visibility and eye health.\u003c/p\u003e\u003cp\u003eWhile air pollution has been implicated in various ocular pathologies, its relationship with cataract development remains poorly characterized. Existing literature regarding air pollution and cataracts have yielded inconsistent findings. For PM\u003csub\u003e10\u003c/sub\u003e and SO\u003csub\u003e2\u003c/sub\u003e, the findings remain unclear. Choi et al. [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] observed no change in cataract incidence with increased exposure to these pollutants, whereas Shin et al. [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] noted an elevated risk of cataracts in Korea. Similarly, the relationship between NO\u003csub\u003e2\u003c/sub\u003e and cataracts is inconsistent. Choi et al. [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] identified a protective effect for specific subtypes of cataracts, Shin et al. [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] demonstrated that higher exposure to NO\u003csub\u003e2\u003c/sub\u003e was associated with an increased risk of cataract. Chua et al. [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] found greater NO\u003csub\u003e2\u003c/sub\u003e exposure was associated with higher risk of future cataract surgery, whereas, Grant et al. [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] found no association. The association between PM\u003csub\u003e2.5\u003c/sub\u003e exposure and cataract are ambiguous, too. While Shin et al. [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] estimated no statistically significant association, Chua et al. [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] showed that elevated PM2.5 levels were related to increased likelihood of cataract surgery. Meanwhile, Shin et al. [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] found no association between CO exposure and cataract. In contrast, our investigation demonstrates a consistent negative relationship between O\u003csub\u003e3\u003c/sub\u003e levels and cataract surgery volumes, which aligns with three published studies [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. This coherent evidence suggests that elevated O\u003csub\u003e3\u003c/sub\u003e concentrations may exert a protective role against cataract, potentially explaining the observed reduction in cataract surgery volumes. Otherwise, a study from Taiwan reported that visiting an ophthalmologic outpatient clinic was associated with an increased chance of visiting an ophthalmology clinic for conjunctivitis due to increased exposure to PM\u003csub\u003e10\u003c/sub\u003e, PM\u003csub\u003e2.5\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In addition, airborne pollutants may contribute to dry eye syndrome and exacerbate pre-existing ocular surface conditions [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Such ocular discomfort frequently prompts cataract patients to address these surface symptoms before proceeding with elective cataract extraction. They will choose to undergo cataract surgery after the symptoms are alleviated, thereby minimizing postoperative discomfort and attaining a superior postoperative outcome. This may result in a decrease in the number of cataract surgeries. In our research, the number of female patients undergoing cataract surgery was 1.4 times that of male patients. Exposure to biomass fuels over adult lifetime was related to nuclear cataract for women in the India Eye Study [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Differences in rates of cataract by sex were shown in previous studies. Females had higher rates of cataract than males of same age [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Additionally, temperature, humidity, wind speed, and atmospheric pressure can directly or indirectly influence the concentration, distribution and composition of air pollutants [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e] .\u003c/p\u003e\u003cp\u003eThere are several limitations that should be acknowledged when interpreting this research. First, the heterogeneity and sensitivity of all groups of patients towards climate and air quality is different, we only modeled the overall relationship without detailed subgroup information (e.g., older patients, students, Mental and physical laborers, sex, household income, smoking), similar with the first point, subgroup information may be some confounders affecting the modeling process so that the true causal and effect could not be easily found. Second, the cross-sectional design could not reveal direct causality between climatic factor, air pollution and the cataract surgery volumes. The observed associations likely represent composite effects stemming from diverse contributing elements. Temporal remains ambiguous that whether acute exposure peaks or chronic exposure drives surgical demand. Further longitudinal cohort studies tracking individual patients\u0026rsquo; exposure histories and cataract progression, combined with clinical and experimental studies are needed to establish mechanistic causality. Third, the modeling process could not be finer grain with the data from all strict in Wuhan as the finer grain data source is scarce and the population mobility is general in China. Therefore, a global landscape should be modeled with the population mobility data within a national level (the data from all cities or provinces). Forth, the model did not adjust for comorbidities (e.g., diabetes, hypertension) that may accelerate cataract formation or influence surgery timing, so the wider spectrum is needed to be included to construct the spatial and temporal model to character diversity relationship. Fifth, during data retrieval, it was found that part of patients who had cataract surgery, were non-local residents, and we only assess the influence of climate and pollutants in Wuhan. This mismatch could bias association if patients\u0026rsquo; exposure histories originated elsewhere. At last, Wuhan\u0026rsquo;s subtropical climate and air pollution profile may not represent other regions, and the limited samples of cataract surgery patients from Wuhan Central Hospital may not be representative of the general population, these potentially leading to biased results.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eTo our knowledge, this represents the first investigation employing FCMs to analyze the association between climatic factors, air pollutants and cataract surgery volumes at Wuhan Central Hospital. Our analysis demonstrates that temperature, atmospheric pressure, visibility, humidity, minimum temperature, wind speed, dew point, precipitation, CO and NO\u003csub\u003e2\u003c/sub\u003e are positively correlated with increased cataract surgery volumes, whereas maximum temperature, SO\u003csub\u003e2\u003c/sub\u003e, PM\u003csub\u003e2.5\u003c/sub\u003e, O\u003csub\u003e3\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e exhibit negative association. These results suggest that FCMs can serve as a valuable predictive tool for estimating future cataract surgery demand, offering quantitative insights to optimize hospital resource allocation and inform evidence-based environmental health policies. By implementing proactive strategies guided by these predictive analytics, healthcare systems can optimize operational efficiency, minimize surgical wait times, and ultimately improve patient outcomes amid growing environmental challenges.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch3\u003eAuthor\u0026rsquo;s contribution\u003c/h3\u003e\n\u003cp\u003eM.-Y.X., Z.-L., and Z.-K.,. initiated the study., W.-X., W.-M.J., and Z.-K. designed and performed the experiments. W.-X., Z.-K., H.-X.Y., and Z.-W.J. wrote the code. Z.-L., and M.-Y.X. collected experimental data. Z.-L., Y.-D.Y., and Z.-K. reviewed the experiment results. M.-Y.X., Y.-D.Y., W.-X., W.-M.J., Z.-L., and Z.-K. critically reviewed and commended the manuscript. All authors contributed to the preparation of the manuscript.\u003c/p\u003e\n\u003ch3\u003eCode\u0026nbsp;and data\u0026nbsp;availability\u003c/h3\u003e\n\u003cp\u003eThe code for training and testing the models in current research, the datasets generated or analyzed during this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003ch3\u003eDeclaration of interests\u003c/h3\u003e\n\u003cp\u003eAll authors declare that the research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003ch3\u003eAcknowledgements\u003c/h3\u003e\n\u003cp\u003eNA.\u003c/p\u003e\n\u003ch3\u003eFunding\u003c/h3\u003e\n\u003cp\u003eThe Project Supported by\u0026nbsp;Open Research Funds of the State Key Laboratory of Ophthalmology (2020KF05), Wuhan Municipal Health Commission Medical Research (WX19Q29), and Hubei Municipal Health Commission Medical Research (WJ2019H376), Natural Science Basic Research Plan in Shaanxi Province of China (2022JQ-175), Scientific Research Program Funded by Shaanxi Provincial Education Department (22JK0303). The sponsors of the study played no role in study design, data collection, analysis, or interpretation, manuscript preparation, or the decision to submit the manuscript for publication.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eEthics approval and consent to participate\u003c/h3\u003e\n\u003cp\u003eThe clinical data were enrolled from the department of Ophthalmology in the Central Hospital of Wuhan. The \u0026nbsp;data of weather and air quality were obtained from two validated online sources. This study was approved by the Ethics Committee of Central Hospital of Wuhan (Ethics Number：WHZXKYL2025-129) and was conducted in accordance with the tenets of the Declaration of Helsinki. Informed consent was obtained from the participants.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAsbell PA, Dualan I, Mindel J, Brocks D, Ahmad M, Epstein S: Age-related cataract. Lancet (London, England) 2005, 365(9459):599-609.\u003c/li\u003e\n\u003cli\u003eCicinelli MV, Buchan JC, Nicholson M, Varadaraj V, Khanna RC: Cataracts. Lancet (London, England) 2023, 401(10374):377-389.\u003c/li\u003e\n\u003cli\u003eAssi L, Chamseddine F, Ibrahim P, Sabbagh H, Rosman L, Congdon N, Evans J, Ramke J, Kuper H, Burton MJ et al: A Global Assessment of Eye Health and Quality of Life: A Systematic Review of Systematic Reviews. JAMA ophthalmology 2021, 139(5):526-541.\u003c/li\u003e\n\u003cli\u003eBohman E, Wyon M, Lundstr\u0026ouml;m M, Dafg\u0026aring;rd Kopp E: A comparison between patients with epiphora and cataract of the activity limitations they experience in daily life due to their visual disability. Acta ophthalmologica 2018, 96(1):77-80.\u003c/li\u003e\n\u003cli\u003eThompson J, Lakhani N: Cataracts. Primary care 2015, 42(3):409-423.\u003c/li\u003e\n\u003cli\u003ePathak M, Odayappan A, Nath M, Raman R, Bhandari S, Nachiappan S: Comparison of the outcomes of phacoemulsification and manual small-incision cataract surgery in posterior polar cataract - A retrospective study. Indian journal of ophthalmology 2022, 70(11):3977-3981.\u003c/li\u003e\n\u003cli\u003eLlop SM, Papaliodis GN: Cataract Surgery Complications in Uveitis Patients: A Review Article. Seminars in ophthalmology 2018, 33(1):64-69.\u003c/li\u003e\n\u003cli\u003eZhang K, Liu X, Jiang J, Li W, Wang S, Liu L, Zhou X, Wang L: Prediction of postoperative complications of pediatric cataract patients using data mining. Journal of Translational Medicine 2019, 17(1):2.\u003c/li\u003e\n\u003cli\u003eBali J, Bali O, Sahu A, Boramani J, Deori N: Health economics and manual small-incision cataract surgery: An illustrative mini review. Indian journal of ophthalmology 2022, 70(11):3765-3770.\u003c/li\u003e\n\u003cli\u003eFlessa S: Cataract Surgery in Low-Income Countries: A Good Deal! Healthcare (Basel, Switzerland) 2022, 10(12).\u003c/li\u003e\n\u003cli\u003eAssi L, Rosman L, Chamseddine F, Ibrahim P, Sabbagh H, Congdon N, Evans J, Ramke J, Kuper H, Burton MJ et al: Eye health and quality of life: an umbrella review protocol. BMJ open 2020, 10(8):e037648.\u003c/li\u003e\n\u003cli\u003eCao F, Liu ZR, Ni QY, Zha CK, Zhang SJ, Lu JM, Xu YY, Tao LM, Jiang ZX, Pan HF: Emerging roles of air pollution and meteorological factors in autoimmune eye diseases. Environmental research 2023, 231(Pt 1):116116.\u003c/li\u003e\n\u003cli\u003eLin CC, Chiu CC, Lee PY, Chen KJ, He CX, Hsu SK, Cheng KC: The Adverse Effects of Air Pollution on the Eye: A Review. International journal of environmental research and public health 2022, 19(3).\u003c/li\u003e\n\u003cli\u003eMuruganandam N, Mahalingam S, Narayanan R, Rajadurai E: Meandered and muddled: a systematic review on the impact of air pollution on ocular health. Environmental science and pollution research international 2023, 30(24):64872-64890.\u003c/li\u003e\n\u003cli\u003eZhang K, Pan Q, Yu D, Wang L, Liu Z, Li X, Liu X: Systemically modeling the relationship between climate change and wheat aphid abundance. Science of The Total Environment 2019, 674:392-400.\u003c/li\u003e\n\u003cli\u003eBakhtavar E, Valipour M, Yousefi S, Sadiq R, Hewage K: Fuzzy cognitive maps in systems risk analysis: a comprehensive review. Complex \u0026amp; Intelligent Systems 2021, 7(2):621-637.\u003c/li\u003e\n\u003cli\u003eAmirkhani A, Papageorgiou EI, Mohseni A, Mosavi MR: A review of fuzzy cognitive maps in medicine: Taxonomy, methods, and applications. Computer methods and programs in biomedicine 2017, 142:129-145.\u003c/li\u003e\n\u003cli\u003eApostolopoulos ID, Papandrianos NI, Papathanasiou ND, Papageorgiou EI: Fuzzy Cognitive Map Applications in Medicine over the Last Two Decades: A Review Study. Bioengineering (Basel, Switzerland) 2024, 11(2).\u003c/li\u003e\n\u003cli\u003eMahmoodi SA, Mirzaie K, Mahmoodi MS, Mahmoudi SM: A Medical Decision Support System to Assess Risk Factors for Gastric Cancer Based on Fuzzy Cognitive Map. Computational and mathematical methods in medicine 2020, 2020:1016284.\u003c/li\u003e\n\u003cli\u003eHu Z, Gong W, Pedrycz W, Li Y: Deep reinforcement learning assisted co-evolutionary differential evolution for constrained optimization. Swarm and Evolutionary Computation 2023, 83:101387.\u003c/li\u003e\n\u003cli\u003eEchevarr\u0026iacute;a-Lucas L, Senciales-Gonz\u0026aacute;lez JM, Medialdea-Hurtado ME, Rodrigo-Comino J: Impact of Climate Change on Eye Diseases and Associated Economical Costs. International journal of environmental research and public health 2021, 18(13).\u003c/li\u003e\n\u003cli\u003ePeriyasamy P, Shinohara T: Age-related cataracts: Role of unfolded protein response, Ca(2+) mobilization, epigenetic DNA modifications, and loss of Nrf2/Keap1 dependent cytoprotection. Progress in retinal and eye research 2017, 60:1-19.\u003c/li\u003e\n\u003cli\u003eRaju P, George R, Ve Ramesh S, Arvind H, Baskaran M, Vijaya L: Influence of tobacco use on cataract development. The British journal of ophthalmology 2006, 90(11):1374-1377.\u003c/li\u003e\n\u003cli\u003eProkofyeva E, Wegener A, Zrenner E: Cataract prevalence and prevention in Europe: a literature review. Acta ophthalmologica 2013, 91(5):395-405.\u003c/li\u003e\n\u003cli\u003eHeys KR, Friedrich MG, Truscott RJ: Presbyopia and heat: changes associated with aging of the human lens suggest a functional role for the small heat shock protein, alpha-crystallin, in maintaining lens flexibility. Aging cell 2007, 6(6):807-815.\u003c/li\u003e\n\u003cli\u003eFuller-Thomson E, Deng Z, Fuller-Thomson EG: Association Between Area Temperature and Severe Vision Impairment in a Nationally Representative Sample of Older Americans. Ophthalmic epidemiology 2024, 31(2):119-126.\u003c/li\u003e\n\u003cli\u003eMiranda MN: Environmental temperature and senile cataract. Transactions of the American Ophthalmological Society 1980, 78:255-264.\u003c/li\u003e\n\u003cli\u003eLeong AM, Crighton EJ, Moineddin R, Mamdani M, Upshur RE: Time series analysis of age related cataract hospitalizations and phacoemulsification. BMC ophthalmology 2006, 6:2.\u003c/li\u003e\n\u003cli\u003eChen S, Zhao J, Lee SB, Kim SW: Estimation of Relative Risk of Mortality and Economic Burden Attributable to High Temperature in Wuhan, China. Frontiers in public health 2022, 10:839204.\u003c/li\u003e\n\u003cli\u003eXie J, Zhu Y, Fan Y, Xie L, Xie R, Huang F, Cao L: Association between extreme heat and hospital admissions for cataract patients in Hefei, China. Environmental science and pollution research international 2020, 27(36):45381-45389.\u003c/li\u003e\n\u003cli\u003eAnthony CA, Peterson RA, Polgreen LA, Sewell DK, Polgreen PM: The Seasonal Variability in Surgical Site Infections and the Association With Warmer Weather: A Population-Based Investigation. Infection control and hospital epidemiology 2017, 38(7):809-816.\u003c/li\u003e\n\u003cli\u003eRubio EF: Climatic influence on conjunctival bacteria of patients undergoing cataract surgery. Eye (London, England) 2004, 18(8):778-784.\u003c/li\u003e\n\u003cli\u003eLee HJ, Jin MH, Lee JH: The association of weather on pediatric emergency department visits in Changwon, Korea (2005-2014). The Science of the total environment 2016, 551-552:699-705.\u003c/li\u003e\n\u003cli\u003eXie J, Zhu Y, Fan Y, Xin L, Liu J: Association between rainfall and readmissions of rheumatoid arthritis patients: a time-stratified case-crossover analysis. International journal of biometeorology 2020, 64(1):145-153.\u003c/li\u003e\n\u003cli\u003eOu DK, To TP, Taylor DM: Weather patients will come? The Medical journal of Australia 2005, 183(11-12):675-677.\u003c/li\u003e\n\u003cli\u003eLv X, Gao X, Hu K, Yao Y, Zeng Y, Chen H: Associations of Humidity and Temperature With Cataracts Among Older Adults in China. Frontiers in public health 2022, 10:872030.\u003c/li\u003e\n\u003cli\u003eZhong J-Y, Lee Y-C, Hsieh C-J, Tseng C-C, Yiin L-M: Association between the first occurrence of allergic conjunctivitis, air pollution and weather changes in Taiwan. Atmospheric Environment 2019, 212:90-95.\u003c/li\u003e\n\u003cli\u003eDas AV, Basu S: Environmental and Air Pollution Factors Affecting Allergic Eye Disease in Children and Adolescents in India. International journal of environmental research and public health 2021, 18(11).\u003c/li\u003e\n\u003cli\u003eHwang SH, Choi YH, Paik HJ, Wee WR, Kim MK, Kim DH: Potential Importance of Ozone in the Association Between Outdoor Air Pollution and Dry Eye Disease in South Korea. JAMA ophthalmology 2016, 134(5):503-510.\u003c/li\u003e\n\u003cli\u003eZhong JY, Lee YC, Hsieh CJ, Tseng CC, Yiin LM: Association between Dry Eye Disease, Air Pollution and Weather Changes in Taiwan. International journal of environmental research and public health 2018, 15(10).\u003c/li\u003e\n\u003cli\u003eTabernero J, Garcia-Porta N, Artal P, Pardhan S: Intraocular Scattering, Blinking Rate, and Tear Film Osmolarity After Exposure to Environmental Stress. Translational vision science \u0026amp; technology 2021, 10(9):12.\u003c/li\u003e\n\u003cli\u003eEchevarr\u0026iacute;a-Lucas L, Senciales-Gonz\u0026aacute;lez JM, Rodrigo-Comino J: Analysing the Evidence of the Effects of Climate Change, Air Pollutants, and Occupational Factors in the Appearance of Cataracts. Environments 2024, 11(5):87.\u003c/li\u003e\n\u003cli\u003eBurtscher J, Mallet RT, Burtscher M, Millet GP: Hypoxia and brain aging: Neurodegeneration or neuroprotection? Ageing research reviews 2021, 68:101343.\u003c/li\u003e\n\u003cli\u003eWang Y, Yu X, Liu Z, Lv Z, Xia H, Wang Y, Li J, Li X: Influence of hypobaric hypoxic conditions on ocular structure and biological function at high attitudes: a narrative review. Frontiers in neuroscience 2023, 17:1149664.\u003c/li\u003e\n\u003cli\u003eMcAlindon T, Formica M, Schmid CH, Fletcher J: Changes in barometric pressure and ambient temperature influence osteoarthritis pain. The American journal of medicine 2007, 120(5):429-434.\u003c/li\u003e\n\u003cli\u003eWilder FV, Hall BJ, Barrett JP: Osteoarthritis pain and weather. Rheumatology (Oxford, England) 2003, 42(8):955-958.\u003c/li\u003e\n\u003cli\u003eNeuhaus-Richard I, Frings A, Ament F, G\u0026ouml;rsch IC, Druchkiv V, Katz T, Linke SJ, Richard G: Do air pressure and wind speed influence the outcome of myopic laser refractive surgery? Results from the Hamburg Weather Study. International ophthalmology 2014, 34(6):1249-1258.\u003c/li\u003e\n\u003cli\u003eChoi YH, Park SJ, Paik HJ, Kim MK, Wee WR, Kim DH: Unexpected potential protective associations between outdoor air pollution and cataracts. Environmental science and pollution research international 2018, 25(11):10636-10643.\u003c/li\u003e\n\u003cli\u003eShin J, Lee H, Kim H: Association between Exposure to Ambient Air Pollution and Age-Related Cataract: A Nationwide Population-Based Retrospective Cohort Study. International journal of environmental research and public health 2020, 17(24).\u003c/li\u003e\n\u003cli\u003eChua SYL, Khawaja AP, Desai P, Rahi JS, Day AC, Hammond CJ, Khaw PT, Foster PJ: The Association of Ambient Air Pollution With Cataract Surgery in UK Biobank Participants: Prospective Cohort Study. Investigative ophthalmology \u0026amp; visual science 2021, 62(15):7.\u003c/li\u003e\n\u003cli\u003eGrant A, Leung G, Freeman EE: Ambient Air Pollution and Age-Related Eye Disease: A Systematic Review and Meta-Analysis. Investigative ophthalmology \u0026amp; visual science 2022, 63(9):17.\u003c/li\u003e\n\u003cli\u003eRavilla TD, Gupta S, Ravindran RD, Vashist P, Krishnan T, Maraini G, Chakravarthy U, Fletcher AE: Use of Cooking Fuels and Cataract in a Population-Based Study: The India Eye Disease Study. Environmental health perspectives 2016, 124(12):1857-1862.\u003c/li\u003e\n\u003cli\u003eLou L, Ye X, Xu P, Wang J, Xu Y, Jin K, Ye J: Association of Sex With the Global Burden of Cataract. JAMA ophthalmology 2018, 136(2):116-121.\u003c/li\u003e\n\u003cli\u003eVithanage M, Bandara PC, Novo LAB, Kumar A, Ambade B, Naveendrakumar G, Ranagalage M, Magana-Arachchi DN: Deposition of trace metals associated with atmospheric particulate matter: Environmental fate and health risk assessment. Chemosphere 2022, 303(Pt 3):135051.\u003c/li\u003e\n\u003cli\u003eFujishima H, Satake Y, Okada N, Kawashima S, Matsumoto K, Saito H: Effects of diesel exhaust particles on primary cultured healthy human conjunctival epithelium. Annals of allergy, asthma \u0026amp; immunology : official publication of the American College of Allergy, Asthma, \u0026amp; Immunology 2013, 110(1):39-43.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cataract surgeries, Climate, Air pollutants, Fuzzy Cognitive Maps (FCMs)","lastPublishedDoi":"10.21203/rs.3.rs-7257236/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7257236/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe burden of cataracts was substantial in the current aging world. We aimed to investigate the association of climate and air pollutants with surgical volumes for senile cataracts. We analyzed cohort data comprising 14086 patients (mean age: 70.25\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;9.4 years) from Wuhan Centra Hospital, spanning January 1, 2019 to July 22, 2024. The environmental factors include temperature (minimum/ maximum), atmospheric pressure, humidity, wind speed, visibility, dew point, precipitation, PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, CO and O\u003csub\u003e3\u003c/sub\u003e. The Fuzzy Cognitive Maps (FCMs) were employed as an analytical tool to evaluate potential environmental influences on cataract surgery timing decisions. The environmental parameters of temperature, atmospheric pressure, visibility, humidity, minimum temperature, wind speed, dew point, precipitation, CO and NO\u003csub\u003e2\u003c/sub\u003e showed positive correlation, while maximum temperature, SO\u003csub\u003e2\u003c/sub\u003e, PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e showed negative correlation with cataract surgical volumes. Our findings provide empirical evidence for potential environmental influences on surgical timing decision in cataract management.\u003c/p\u003e","manuscriptTitle":"Analyzing the impact of climate and air pollution on cataract surgery volume using fuzzy cognitive maps","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-17 14:24:46","doi":"10.21203/rs.3.rs-7257236/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"446d476d-e565-4738-b657-c0efe2004d8a","owner":[],"postedDate":"August 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-26T16:25:49+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-17 14:24:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7257236","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7257236","identity":"rs-7257236","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}