Sex Differences in Waiting Times for Cataract Surgery in Sweden, 2010–2022: Nationwide Analysis of 1.4 Million Patients

Sex Differences in Waiting Times for Cataract Surgery in Sweden, 2010–2022: Nationwide Analysis of 1.4 Million Patients | 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 Article Sex Differences in Waiting Times for Cataract Surgery in Sweden, 2010–2022: Nationwide Analysis of 1.4 Million Patients Gustav Stålhammar, Philip Jute This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5623387/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Mar, 2025 Read the published version in Communications Medicine → Version 1 posted You are reading this latest preprint version Abstract Background Sex-based disparities in healthcare access and outcomes remain a challenge. Understanding differences in waiting times for cataract surgery between males and females can reveal inequities in care delivery. Methods This nationwide retrospective cohort study used data from the Swedish National Cataract Register, which covers > 93% of all cataract surgeries in Sweden. A total of 1,413,652 patients aged > 40 years who underwent first-eye cataract surgery between 2010 and 2022 were included. Exclusions were made for patients with waiting times > 24 months, those residing outside Sweden, and those with missing sex data. The primary outcome was waiting time, defined as the interval between preoperative assessment and surgery. Secondary analyses included stratification by visual acuity, regional variations, and the influence of demographic and clinical factors. Results The mean waiting time was 64 days for females (SD 126) and 60 days for males (SD 102), with a significant difference (P < 0.001). This disparity persisted across all visual acuity strata and regions. Multivariate Cox regression identified female sex, older age, specific comorbidities, and residence region as significant predictors of longer waiting times. Differences in comorbidities, including higher rates of pseudoexfoliation syndrome in females and endophthalmitis in males, were observed. Despite fluctuations in overall waiting times, the sex-based disparity remained consistent over the study period. Conclusions Persistent sex-based differences in waiting times for cataract surgery were identified in Sweden over 13 years. While small and unlikely to affect clinical outcomes, these differences highlight systemic inequities that merit further investigation and intervention to ensure equitable access to care. Health sciences/Diseases/Eye diseases/Lens diseases Health sciences/Health care/Therapeutics/Surgery Figures Figure 1 Figure 2 Figure 3 PLAIN LANGUAGE SUMMARY This study looked at differences in waiting times for cataract surgery between males and females in Sweden, using data from over 1.4 million patients between 2010 and 2022. Cataracts cause clouding of the eye's lens and require surgery for treatment. Females waited an average of 64 days for surgery compared to 60 days for males. This small but consistent difference was seen across all levels of vision impairment and regions in Sweden. Even after accounting for factors like age, other eye conditions, and location, females still faced longer delays. While these differences are unlikely to affect health outcomes, they may point to inequities in the healthcare system. Efforts are needed to ensure fair and equal access to cataract surgery for everyone. INTRODUCTION Cataract remains the leading cause of blindness worldwide, posing a significant public health challenge. . 1 , 2 In Sweden, where healthcare is largely tax-financed, over 140,000 cataract surgeries are performed annually, representing approximately 1.4% of the country’s 10-million population. 3 Previous research has identified female sex as a risk factor for cataract, even after adjusting for age and accounting for mortality as a competing risk. 4 Globally, a greater proportion of women than men experience blindness or visual impairment, and this disparity is projected to increase in the future. 5 Paradoxically, female sex has also been recognized as a barrier to accessing cataract surgery in Asia and Africa, and sex-based differences in ocular comorbidities, surgical complications, and preoperative best-corrected visual acuity (BCVA) have been documented in American and European cohorts. 6 – 10 In Sweden, we recently demonstrated that BCVA at the time of surgery is comparable between sexes, suggesting that differences in cataract surgery rates are unlikely to reflect disparities in healthcare-seeking behavior or surgical admission criteria. 11 A previous study of 102,532 Swedish patients undergoing cataract surgery between 2010 and 2011 found that women experienced longer waiting times than men, even when stratified by similar levels of visual acuity. 12 Despite ongoing efforts to provide equitable healthcare access, persistent sex-based disparities have been reported in several medical fields. 13 , 14 Here, we examine how sex-based differences in waiting times for cataract surgery in Sweden have evolved from 2010 to 2022. This study analyzes a cohort of over 1.4 million patients, covering approximately 93% of all cataract surgeries performed nationwide during this period, to provide a comprehensive understanding of trends in waiting times. METHODS Inclusion and Exclusion Criteria Data for this study were retrieved from the Swedish National Cataract Register (NCR), established in 1992 to document all cataract surgeries performed nationwide. 3 The register is governed by a steering committee comprising physicians representing both public and private healthcare sectors, academia, one nurse, and one patient representative. The NCR captures approximately 93% of all cataract surgeries conducted in Sweden, with data reliability continuously monitored and validated. 15 – 17 Patients eligible for inclusion were those over 40 years old undergoing a first-eye cataract operation between January 1, 2010, and December 31, 2022, following methods outlined in a previous study on cataract surgeries conducted in 2010 ( n = 1,482,725). 12 Patients aged 40 years or younger ( n = 66,495) were excluded, as cataracts in this group are typically congenital, juvenile, or secondary to other diseases or trauma, meaning standard waiting time rules do not apply. Additionally, patients with waiting times over 24 months ( n = 687) were excluded, as such extended delays are uncommon. These long waiting periods in the Swedish National Cataract Register (NCR) are likely due to registration errors or specific circumstances, such as a patient request for surgery by a particular surgeon. Thirdly, 1816 patients residing outside Sweden were excluded, as clinicopathological data may be less reliable for these, and their waiting time for surgery may be influenced by factors non-typical to the standard situation in the Swedish healthcare system. Lastly, 75 patients without a recorded sex was excluded, leaving 1,413,652 patients for analysis. The study was approved by the Swedish Ethical Review Authority (reference 2022-00930-02) and adhered to the tenets of the Declaration of Helsinki. The requirement for informed consent was waived due to the study's retrospective nature, relying solely on previously collected data. This research did not involve any new treatments, interventions, tests, analysis of biological samples, or collection of additional sensitive information. Additionally, we followed the The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Guidelines, details of which are provided in a supplementary file . Admission Visit In Sweden, the typical pathway for patients experiencing diminished visual acuity and other symptoms of cataracts often begins at a local optician. If cataracts are suspected, patients are referred to an ophthalmologist for further evaluation. Alternatively, patients may be referred by ophthalmologists who diagnose the condition during routine examinations for other eye-related issues. During the initial assessment, the patient's best-corrected visual acuity (BCVA) is measured by either an optometrist or an ophthalmic nurse. This is done using a KM-chart in a well-lit light box at a distance of three meters, where the BCVA is recorded on a decimal scale. 18 The test involves identifying the smallest line in which six out of seven letters are read correctly after subjective refraction. Patients may use their own spectacles if they prefer.The procedure for measuring BCVA has remained consistent throughout the study period. In addition, intraocular pressure is measured, and a detailed examination of the anterior segment, including the lens, is conducted using a slit-lamp biomicroscope. Biometry assessments, including keratometry and either optical or ultrasound biometry, are performed to calculate the precise power of the intraocular lens (IOL) to be implanted to achieve the desired refraction. Once the admission visit is completed, surgery is scheduled as soon as reasonably possible. Waiting times for the procedure can vary based on several factors, including the availability of surgical staff and operating rooms, patient travel constraints, personal preferences, and any coexisting conditions that might delay surgical intervention. In this study, the period from the admission visit to the day of surgery is defined as the waiting time. Statistical analyses Statistical significance was defined as a two-sided P < 0.05 unless otherwise specified. Continuous variables were assessed for normality using the Shapiro-Wilk test. If the data deviated from a normal distribution ( P < 0.05), the Mann-Whitney U test was used for group comparisons; otherwise, Student’s t -test was applied. Categorical baseline characteristics were compared using Pearson’s chi-square test. To control for type I errors due to multiple comparisons, the two-stage step-up False Discovery Rate (FDR) method by Benjamini, Krieger, and Yekutieli was employed, with additional Bonferroni correction applied by multiplying P -values by the total number of statistical tests ( n = 38). Yearly trends in waiting times were analyzed using linear regression models, including interaction terms to assess sex-based differences over time. A Kaplan-Meier survival curve for time to cataract surgery was generated, with differences assessed using the log-rank test. A multivariate Cox regression model was constructed to identify predictors of waiting times, with independent variables including sex, age, ocular comorbidities (pseudoexfoliation syndrome, cornea guttata, macular disease, diabetes, and glaucoma), and regional differences. Supplementary analyses included stratification by visual acuity groups, categorizing waiting times by decimal visual acuity equivalents. For each stratum, mean waiting times were compared between sexes using unpaired t -tests with Welch correction. All statistical analyses were conducted using IBM SPSS Statistics (version 29, Armonk, NY), GraphPad Prism (version 10.0.2, San Diego, CA, USA), and R (R Core Team, Vienna, Austria, 2022), with relevant packages including dplyr, ggplot2, tidyr, knitr, survminer, and survival. RESULTS A total of 1,413,652 patients were included in the study, of whom 828,515 (59%) were female. Males had slightly better BCVA in the non-operated eye and were more likely to receive multifocal intraocular lenses (IOLs). Capsular tension rings were more commonly used, and postoperative endophthalmitis occurred more frequently in male patients, while pseudoexfoliations were more common among females. Detailed baseline characteristics are presented in Table 1 . The average waiting time from preoperative assessment to surgery was 64 days for females (standard deviation [SD] 126) and 60 days for males (SD 102). A Shapiro-Wilk test confirmed non-normal distribution in both groups ( P < 0.001 for both males and females); thus, a Mann-Whitney U test was conducted, indicating a significant difference in waiting times between males and females ( W = 2.51×10¹¹, P < 0.001). Differences in waiting times between females and males were observed across all visual acuity groups of the surgery eye, with females consistently experiencing longer average waiting times. These groups, categorized by visual acuity (≤ 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, and ≥ 1.0 on the decimal scale, which is equivalent to ≤ 20/200, 20/100, 20/66, 20/50, 20/40, 20/33, 20/28, 20/25, 20/22, and ≥ 20/20 on the Snellen scale, and 1.0, 0.7, 0.52, 0.40, 0.30, 0.22, 0.15, 0.10, 0.05, and 0.0 on the LogMAR scale.), demonstrated statistically significant disparities. For instance, in the ≤ 0.1 group, females had an average waiting time of 63 days (SD 71), compared to 57 days (SD 66 days) for males, a difference of 7 days (SE < 1 day, P < 0.001). Similar differences were evident across all other groups, with statistical significance consistently retained after adjusting for multiple comparisons using FDR. Unpaired t-tests with Welch correction confirmed statistically significant differences between males and females in all groups, with all P values < 0.001. The magnitude of differences ranged from 2 days (SE 18, P < 0.001) in the BCVA 0.7 group, to 7 days (SE 18, P < 0.001) in the BCVA ≤ 0.1 group (Fig. 1 ). These findings highlight a consistent trend of longer waiting times for females across all levels of preoperative visual acuity. Similarly, regional differences in waiting times were also evident, with the largest discrepancy observed in Region 17 (mean difference: 9 days) and the smallest in Region 13 (mean difference: 1 day, Fig. 2 ). Females had significantly longer waiting times in all regions (unpaired t -tests with Welch correction, P < 0.001 for all comparisons, adjusted for multiple comparisons using FDR). A full list of regional designations is available in Supplementary Table 1 . For regression analyses, regions were categorized into four tiers based on the magnitude of waiting time differences between genders, with Tier 1 representing regions with the smallest gender differences and Tier 4 those with the largest. When treating waiting time for cataract surgery as a time-to-event analysis, females experienced longer cataract-surgery-free periods than males, indicating a longer delay in undergoing surgery. At 30 days after admission, the estimated Kaplan-Meier cataract-surgery-free survival was 64.9% (95% CI 64.8–65.0) for females and 62.2% (95% CI 62.1–62.4) for males. At 60 days, the survival was 39.7% (95% CI 39.6–39.9) for females and 37.1% (95% CI 36.9–37.2) for males. By 90 days, the survival was 23.4% (95% CI 23.4–23.5) for females and 21.3% (95% CI 21.2–21.4) for males, and at 120 days, it declined to 13.7% (95% CI 13.6–13.7) for females and 12.1% (95% CI 12.1–12.2) for males (Fig. 3 A). Multivariate Cox Regression We performed a multivariate Cox regression analysis to investigate the effect of various factors on waiting time for cataract surgery (Table 2 ). The analyzed variables included sex (male vs. female), age, baseline visual acuity of the operated eye (BCVA), specific health conditions (presence of pseudoexfoliation, cornea guttata, diabetes, macular disease, and glaucoma), and region-specific waiting time tiers. All covariates, including patient sex, were found to be independent predictors of waiting time after applying Bonferroni adjustment to P values. Notably, diabetes (type I or II) emerged as a significant predictor of shorter waiting time (Hazard ratio: 0.88, 95% CI: 0.87–0.89). Time Trends An analysis of time trends in cataract surgery waiting times from 2010 to 2022 revealed fluctuating overall waiting times throughout the study period, forming a U-shaped trend. The period from 2020 to 2022 may have been influenced by the COVID-19 pandemic. In 2010, the average waiting time was approximately 74 days for females and 69 days for males. A linear regression model, including an interaction term to assess gender differences over time, showed a significant positive association between year and waiting time, with an estimated annual increase of < 1 day/0.01 months ( P < 0.001). However, the interaction term between year and sex did not reach statistical significance (Estimate < 1 day, P = 0.29), indicating that the trend in waiting times was consistent across genders. Thus, while waiting times for cataract surgery varied over the study period, no significant change was observed in the relative difference between males and females (Fig. 3 B, 3 C). DISCUSSION Main Findings This study confirms persistent, albeit small, sex-based differences in waiting times for cataract surgery in Sweden over a 13-year period, with female patients consistently experiencing longer delays than their male counterparts. Despite efforts to improve healthcare equity, this disparity has remained stable and statistically significant across the study period. Importantly, these differences are unlikely to be explained by clinical factors, such as visual acuity at the time of admission for surgery, age, or comorbidities, which were comparable between sexes or adjusted for in the analyses. Cataract is typically a slowly progressive condition, and although the absolute differences of a few days in waiting times are small and unlikely to have a major impact on health or long-term well-being, they may reflect underlying systemic inequalities or biases that warrant further investigation. In 2004, the National Board of Health and Welfare (NBHW) identified differences in access to care for females and elderly patients as examples of discrimination. 19 The persistent disparity observed suggests the need for targeted interventions to achieve more equitable access to surgery. Contextualizing with Previous Studies Our findings align with earlier research indicating sex-based disparities in waiting times for cataract surgery. A 2010–2011 study of Swedish patients reported similar patterns of longer waiting times for women. 12 Smirthwaite and colleagues reported, based on focus interviews with ophthalmologists, that females and males were regarded differently with respect to ascribed traits such as assertiveness and care-seeking behavior, and that their need for visual acuity in working life was perceived as distinct. 20 These disparities are not unique to Sweden; studies from other regions have reported sex-based differences in access to ophthalmic care, with women facing barriers to timely treatment even in high-resource settings. 9 Such differences are often attributed to social, economic, or systemic factors rather than biological differences in disease burden or progression. 21 , 22 Factors Influencing Waiting Times In addition to sex, other predictors of waiting time included age, home region, and specific comorbidities. Older patients tended to wait longer, potentially reflecting the prioritization of working-age individuals or the need to address comorbidities before surgery. Regional variations were notable, with differences likely driven by healthcare resource allocation, availability of surgeons, and logistical factors such as travel distance. Previous studies have also highlighted variations in complication rates of cataract surgery at regional, clinic, and even individual surgeon levels. 23 Although differences in surgical technique and complications between sexes were observed, these were minor and unlikely to account for the disparity in waiting times. For example, capsular tension rings and postoperative endophthalmitis were slightly more common in male patients, while pseudoexfoliation syndrome was more frequent among females. While statistically significant due to the large sample size, these differences are of limited clinical relevance. Strengths and Limitations A major strength of this study is its comprehensive dataset, encompassing over 1.4 million patients and covering approximately 93% of all cataract surgeries performed in Sweden during the study period. This extensive coverage ensures a robust representation of real-world clinical practice and reduces the risk of selection bias. However, the study's retrospective registry-based design limits causal inferences. While we identified significant predictors of waiting time, the underlying reasons for sex-based disparities remain unclear. Additionally, the accuracy of the findings relies on the quality of data entered into the Swedish National Cataract Register. Although previous audits have validated the register’s reliability, occasional inaccuracies in reporting cannot be excluded. 15 Implications and Future Directions The persistence of sex-based differences in waiting times for cataract surgery highlights the need for targeted interventions to address healthcare inequities. Potential strategies include improving referral practices, standardizing prioritization criteria, and ensuring adequate resource allocation across regions. Further research is needed to elucidate the root causes of these disparities, including qualitative studies to explore potential biases in clinical decision-making and patient preferences. Finally, while the observed differences may not have a significant impact on clinical outcomes, they could contribute to perceptions of inequity and undermine trust in the healthcare system. Addressing even small disparities is essential for ensuring that healthcare delivery is both equitable and perceived as fair by all patients. CONCLUSIONS In conclusion, females in Sweden experienced slightly longer waiting times for cataract surgery than males during the period 2010–2022. This disparity was consistent across the study period and remained significant after adjusting for clinical and demographic factors. While the differences are unlikely to have major clinical implications, they highlight the need for continued efforts to achieve equitable access to ophthalmic care. Declarations Data availability statement Patient-level data analyzed in this study are available from the Swedish National Cataract Register (https://rcsyd.se/anslutna-register/nationella-kataraktregistret). Access to these data requires approval from the Swedish Ethical Review Authority and the Swedish National Cataract Register's record keeper. Requests for data can be submitted through the register’s website and must comply with Swedish regulations governing the use of healthcare data for research purposes. Funding Support for this study was provided to Gustav Stålhammar from: Region Stockholm (RS-2019-1138) The funding organization had no role in the design or conduct of this study. Author contributions Philip Jute: Conceptualization, Writing – Review & Editing. Gustav Stålhammar: Conceptualization, Methodology, Software, Formal Analysis, Investigation, Data curation, Writing – Original Draft, Visualization, Project administration, Funding acquisition. References Cicinelli, M. V., Buchan, J. C., Nicholson, M., Varadaraj, V. & Khanna, R. C. Cataracts. Lancet 401, 377–389 (2023). https://doi.org:10.1016/S0140-6736(22)01839-6 Bourne, R. R. A. et al. Prevalence and causes of vision loss in high-income countries and in Eastern and Central Europe in 2015: magnitude, temporal trends and projections. 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Doctors Doing Gender at Eye Clinics—Gender Constructions in Relation to Waiting Times for Cataract Extractions in Sweden. NORA - Nordic Journal of Feminist and Gender Research 25, 107–125 (2017). https://doi.org:https://doi.org/10.1080/08038740.2017.1345006 Cameron, K. A., Song, J., Manheim, L. M. & Dunlop, D. D. Gender disparities in health and healthcare use among older adults. J Womens Health (Larchmt) 19, 1643–1650 (2010). https://doi.org:10.1089/jwh.2009.1701 Mauvais-Jarvis, F. et al. Sex and gender: modifiers of health, disease, and medicine. Lancet 396, 565–582 (2020). https://doi.org:10.1016/S0140-6736(20)31561-0 Zetterberg, M. et al. Cataract Surgery Volumes and Complications per Surgeon and Clinical Unit: Data from the Swedish National Cataract Register 2007 to 2016. Ophthalmology 127, 305–314 (2020). https://doi.org:10.1016/j.ophtha.2019.10.007 Tables Table 1. Baseline Patient Characteristics Variable Females, n =828,515 Males, n =585,137 P * Age, mean (SD) 74.5 (8.6) 74.3 (9.0) <0.001 BCVA operated eye † , mean (SD) <0.001 Decimal scale 0.46 (0.22) 0.45 (0.23) LogMAR 0.34 (0.21) 0.35 (0.22) Snellen 20/44 20/45 BCVA non-operated eye ‡ , mean (SD) <0.001 Decimal scale 0.86 (0.34) 0.89 (0.31) LogMAR 0.07 (0.04) 0.05 (0.04) Snellen 20/23 20/23 Surgery type, n ( % ) <0.001 Phaco. and IOL 822,193 (99.24) 579,420 (99.02) Phaco. and ACL 532 (0.06) 284 (0.05) Trab., phaco., and IOL 273 (0.03) 242 (0.04) Other** 5517 (0.67) 5191 (0.89) Lens material, n (%) <0.001 Hydrophobic acrylic 791,681 (95.55) 559,208 (95.57) Hydrophilic acrylic 30,920 (3.73) 21,662 (3.70) Patient left aphakic 1,670 (0.20) 1,323 (0.23) Silicone 1,335 (0.16) 897 (0.15) PMMA 219 (0.03) 136 (0.02) Multifocal, material unspecified 131 (0.02) 125 (0.02) PMMA HSM 26 (0.00) 12 (0.00) Other 2,520 (0.30) 1,772 (0.30) Not specified 13 (0.00) 2 (0.00) Lens shape, n (%) Aspherical 65,805 (7.94) 43,203 (7.38) <0.001 Multifocal 16,337 (1.97) 13,670 (2.34) <0.001 Use of capsular tension rings, n (%) 15,207 (1.84) 15,925 (2.07) <0.001 Postoperative endophthalmitis, n (%) 131 (0.02) 154 (0.03) <0.001 Other systemic and ocular conditions, n (%) Glaucoma, any type 70,398 (8.50) 53,113 (9.08) <0.001 Disease of the macula, any type 129,126 (15.59) 88,747 (15.17) <0.001 Pseudoexfoliations 86,697 (10.46) 47,472 (8.12) <0.001 Cornea Guttata 25,294 (3.05) 13,419 (2.29) <0.001 Diabetes, type I or II 26,713 (3.23) 32,663 (5.58) <0.001 *Mann-Whitney U test was used for continuous variables, and chi-square tests were used for categorical variables. Bonferroni correction was used to adjust P -values for multiple comparisons. † Eye planned for surgery, or the first eye operated if both were treated in the same session. ‡ Eye not planned for surgery, or the second eye operated if both were treated in the same session. **e.g., phacoemulsification (Phaco), intraocular lens (IOL), and simultaneous corneal surgery. ACL, anterior chamber lens; BCVA, best corrected visual acuity; HSM, heparin surface modified; IOL, intraocular lens; PMMA, polymethyl methacrylate; SD, standard deviation; Trab, trabeculectomy. Table 2. Multivariate Cox Regression: Predictors of Time to Cataract Surgery Variable B S.E. Wald Test P * Exp(B) 95% Confidence Interval Sex (Male) -0.064 0.003 608.7 <0.001 0.938 0.934–0.943 Age † -0.007 <0.001 2,708.2 <0.001 0.993 0.992–0.993 BCVA operated eye 0.172 0.006 838.2 <0.001 1.188 1.174–1.202 Pseudoexfoliations 0.077 0.005 257.5 <0.001 1.080 1.070–1.091 Cornea Guttata -0.032 0.008 14.9 <0.001 0.968 0.952–0.984 Diabetes, type I or II -0.127 0.006 407.0 <0.001 0.881 0.870–0.892 Macular Disease, any type 0.02 0.004 29.5 <0.001 1.020 1.013–1.027 Glaucoma, any type -0.052 0.005 130.2 <0.001 0.949 0.941–0.958 Region tier ‡ -0.125 0.001 10,144.3 <0.001 0.883 0.881–0.885 *Bonferroni correction was applied to the P values to adjust for multiple comparisons. † Patient age at the time of admission for cataract surgery. ‡ Regions were classified into four tiers based on average waiting time for cataract surgery, with Tier 1 containing regions with the longest waiting time, and Tier 4 those with the shortest. BCVA, best corrected visual acuity. S.E., standard error. Additional Declarations There is NO Competing Interest. Supplementary Files Supplementarymaterial.pdf Cite Share Download PDF Status: Published Journal Publication published 04 Mar, 2025 Read the published version in Communications Medicine → 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-5623387","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":392425501,"identity":"c183d39b-c714-4593-a9d4-ae74e60d24c2","order_by":0,"name":"Gustav Stålhammar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABB0lEQVRIie2PsUoEMRCGJwi3TcQ2yyr3ChcsbE7zGpYbFnabfYArcwhzzR62EX2O1BFhbe4BDtLsNtqccJYHK7p6VkJW7CzyFROYmY9/AhAI/G8OcP9GCPaXVaI+KwXyrdD6zwrLh/fHl8Vzs0MQIrrHZlea8dnNk7TQTb0KX5d8vkSQFZULvjSO37ncWoL+KK5LougKUgoS2aFxRCeFskQ9DChFO+9WIOhRi/GbcULHj6o/7N3/F5byKzoDUjGJSZ8iNRtZCyPrVSZ0w2+PZ0xW6xaTE+MyTfPUSsz8KYuied1MpiK6zur4xbhzHdWn22134U/ZH8B+tFOv0KeogWEgEAgEvvgAWFpZlVxPzcIAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-9401-8911","institution":"St. Erik Eye Hospital and Karolinska Institutet","correspondingAuthor":true,"prefix":"","firstName":"Gustav","middleName":"","lastName":"Stålhammar","suffix":""},{"id":392425502,"identity":"4dcb089b-6114-44a1-b102-1e7ae4feeefc","order_by":1,"name":"Philip Jute","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Philip","middleName":"","lastName":"Jute","suffix":""}],"badges":[],"createdAt":"2024-12-11 10:25:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5623387/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5623387/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s43856-025-00782-1","type":"published","date":"2025-03-04T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":72375176,"identity":"a3f25849-6458-459d-a829-0ec0f41116b2","added_by":"auto","created_at":"2024-12-26 08:18:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":122364,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of mean waiting times for females (orange) and males (green) across best-corrected visual acuity (BCVA) strata. BCVA is reported on a decimal scale (e.g., Snellen 20/20 and LogMAR 0.00 correspond to 1.0, while Snellen 20/200 and LogMAR 1.00 correspond to 0.1). \u003cstrong\u003eA)\u003c/strong\u003e In all visual acuity groups, females had significantly longer mean waiting times than males (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001, unpaired \u003cem\u003et\u003c/em\u003e-tests with Welch correction). Asterisks denote statistically significant findings (q\u0026lt;Q after False Discovery Rate adjustment). \u003cstrong\u003eB)\u003c/strong\u003e Volcano plot illustrating q values (-log10 scale) against the magnitude of differences in mean waiting times between females and males. Points further from the origin indicate greater differences and stronger statistical significance. \u003cstrong\u003eC)\u003c/strong\u003e Line plot visualizing the mean waiting times for females and males across BCVA groups. BCVA refers to the best-corrected visual acuity recorded at the time of admission for cataract surgery. Error bars represent one standard deviation.\u003c/p\u003e","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5623387/v1/79dd6345c6705c8b203e006b.png"},{"id":72374352,"identity":"cceff8d3-4a49-461c-b3de-ff630569a3a6","added_by":"auto","created_at":"2024-12-26 08:10:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":136552,"visible":true,"origin":"","legend":"\u003cp\u003eAverage waiting time for females (orange) and males (green) across Swedish regions.\u003cstrong\u003e \u003c/strong\u003eThe greatest discrepancy in waiting times was observed in Region 17 (9 days), while the smallest was in Region 13 (1 day). Asterisks denote statistically significant findings (q\u0026lt;Q after False Discovery Rate adjustment). The regions are highlighted on a map of Sweden, with shading representing the population in approximately corresponding areas.\u003c/p\u003e","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5623387/v1/715f1f968ba504aee95a178a.png"},{"id":72374353,"identity":"a68bc0aa-aad9-4d00-aa15-9db552b446a2","added_by":"auto","created_at":"2024-12-26 08:10:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":241519,"visible":true,"origin":"","legend":"\u003cp\u003eCataract-Surgery-Free Survival and Trends in Cataract Waiting Times (2010–2022).\u003cbr\u003e\n \u003cstrong\u003eA)\u003c/strong\u003e Kaplan-Meier curve of cataract-surgery-free survival stratified by sex, showing a significant difference (Log-rank \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001). The median cataract-surgery-free survival was 43 days for males (95% confidence interval [CI]: 43–43) and 47 days for females (95% CI: 47–47). \u003cstrong\u003eB)\u003c/strong\u003e Annual mean waiting times from preoperative assessment to cataract surgery for females and males from 2010 to 2022, presented in months. Shaded bands indicate 95% CIs. Waiting times followed a U-shaped trend over the study period. Dashed lines represent linear regression fits, with a significant positive association between year and waiting time (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001; estimate: 0.3 days/year). The interaction term between year and sex was not significant (\u003cem\u003eP\u003c/em\u003e=0.29; estimate: 0.05 days/year), indicating no sex-related differences in the rate of change over time. Relative differences in waiting times between sexes remained stable throughout the study period. \u003cstrong\u003eC)\u003c/strong\u003e Annual mean difference in waiting times (in days) between females and males, calculated as the mean waiting time for females minus the mean waiting time for males. Error bars represent the standard deviation of the mean difference.\u003c/p\u003e","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5623387/v1/299b78ba1cd96488a867cd98.png"},{"id":77754405,"identity":"523000a6-f78e-480a-a0b4-c876ed42c216","added_by":"auto","created_at":"2025-03-05 08:09:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1590725,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5623387/v1/0f3981bf-3030-4244-bb85-412725e79a4a.pdf"},{"id":72374347,"identity":"d9b425e1-88fc-4bb5-aa07-439cdc745dd0","added_by":"auto","created_at":"2024-12-26 08:10:43","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":58633,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5623387/v1/ca69be0d85985ab6fa963756.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Sex Differences in Waiting Times for Cataract Surgery in Sweden, 2010–2022: Nationwide Analysis of 1.4 Million Patients","fulltext":[{"header":"PLAIN LANGUAGE SUMMARY","content":"\u003cp\u003eThis study looked at differences in waiting times for cataract surgery between males and females in Sweden, using data from over 1.4 million patients between 2010 and 2022. Cataracts cause clouding of the eye's lens and require surgery for treatment. Females waited an average of 64 days for surgery compared to 60 days for males. This small but consistent difference was seen across all levels of vision impairment and regions in Sweden. Even after accounting for factors like age, other eye conditions, and location, females still faced longer delays. While these differences are unlikely to affect health outcomes, they may point to inequities in the healthcare system. Efforts are needed to ensure fair and equal access to cataract surgery for everyone.\u003c/p\u003e"},{"header":"INTRODUCTION","content":"\u003cp\u003eCataract remains the leading cause of blindness worldwide, posing a significant public health challenge. .\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e In Sweden, where healthcare is largely tax-financed, over 140,000 cataract surgeries are performed annually, representing approximately 1.4% of the country\u0026rsquo;s 10-million population.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003ePrevious research has identified female sex as a risk factor for cataract, even after adjusting for age and accounting for mortality as a competing risk.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e Globally, a greater proportion of women than men experience blindness or visual impairment, and this disparity is projected to increase in the future.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e Paradoxically, female sex has also been recognized as a barrier to accessing cataract surgery in Asia and Africa, and sex-based differences in ocular comorbidities, surgical complications, and preoperative best-corrected visual acuity (BCVA) have been documented in American and European cohorts.\u003csup\u003e\u003cspan additionalcitationids=\"CR7 CR8 CR9\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e In Sweden, we recently demonstrated that BCVA at the time of surgery is comparable between sexes, suggesting that differences in cataract surgery rates are unlikely to reflect disparities in healthcare-seeking behavior or surgical admission criteria.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eA previous study of 102,532 Swedish patients undergoing cataract surgery between 2010 and 2011 found that women experienced longer waiting times than men, even when stratified by similar levels of visual acuity.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e Despite ongoing efforts to provide equitable healthcare access, persistent sex-based disparities have been reported in several medical fields.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eHere, we examine how sex-based differences in waiting times for cataract surgery in Sweden have evolved from 2010 to 2022. This study analyzes a cohort of over 1.4\u0026nbsp;million patients, covering approximately 93% of all cataract surgeries performed nationwide during this period, to provide a comprehensive understanding of trends in waiting times.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eInclusion and Exclusion Criteria\u003c/h2\u003e \u003cp\u003eData for this study were retrieved from the Swedish National Cataract Register (NCR), established in 1992 to document all cataract surgeries performed nationwide.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e The register is governed by a steering committee comprising physicians representing both public and private healthcare sectors, academia, one nurse, and one patient representative. The NCR captures approximately 93% of all cataract surgeries conducted in Sweden, with data reliability continuously monitored and validated.\u003csup\u003e\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003ePatients eligible for inclusion were those over 40 years old undergoing a first-eye cataract operation between January 1, 2010, and December 31, 2022, following methods outlined in a previous study on cataract surgeries conducted in 2010 (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1,482,725).\u003csup\u003e12\u003c/sup\u003e\u003c/p\u003e \u003cp\u003ePatients aged 40 years or younger (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;66,495) were excluded, as cataracts in this group are typically congenital, juvenile, or secondary to other diseases or trauma, meaning standard waiting time rules do not apply. Additionally, patients with waiting times over 24 months (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;687) were excluded, as such extended delays are uncommon. These long waiting periods in the Swedish National Cataract Register (NCR) are likely due to registration errors or specific circumstances, such as a patient request for surgery by a particular surgeon.\u003c/p\u003e \u003cp\u003eThirdly, 1816 patients residing outside Sweden were excluded, as clinicopathological data may be less reliable for these, and their waiting time for surgery may be influenced by factors non-typical to the standard situation in the Swedish healthcare system. Lastly, 75 patients without a recorded sex was excluded, leaving 1,413,652 patients for analysis.\u003c/p\u003e \u003cp\u003e The study was approved by the Swedish Ethical Review Authority (reference 2022-00930-02) and adhered to the tenets of the Declaration of Helsinki. The requirement for informed consent was waived due to the study's retrospective nature, relying solely on previously collected data. This research did not involve any new treatments, interventions, tests, analysis of biological samples, or collection of additional sensitive information. Additionally, we followed the The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Guidelines, details of which are provided in a \u003cb\u003esupplementary file\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAdmission Visit\u003c/h3\u003e\n\u003cp\u003eIn Sweden, the typical pathway for patients experiencing diminished visual acuity and other symptoms of cataracts often begins at a local optician. If cataracts are suspected, patients are referred to an ophthalmologist for further evaluation. Alternatively, patients may be referred by ophthalmologists who diagnose the condition during routine examinations for other eye-related issues. During the initial assessment, the patient's best-corrected visual acuity (BCVA) is measured by either an optometrist or an ophthalmic nurse. This is done using a KM-chart in a well-lit light box at a distance of three meters, where the BCVA is recorded on a decimal scale.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e The test involves identifying the smallest line in which six out of seven letters are read correctly after subjective refraction. Patients may use their own spectacles if they prefer.The procedure for measuring BCVA has remained consistent throughout the study period. In addition, intraocular pressure is measured, and a detailed examination of the anterior segment, including the lens, is conducted using a slit-lamp biomicroscope. Biometry assessments, including keratometry and either optical or ultrasound biometry, are performed to calculate the precise power of the intraocular lens (IOL) to be implanted to achieve the desired refraction. Once the admission visit is completed, surgery is scheduled as soon as reasonably possible. Waiting times for the procedure can vary based on several factors, including the availability of surgical staff and operating rooms, patient travel constraints, personal preferences, and any coexisting conditions that might delay surgical intervention. In this study, the period from the admission visit to the day of surgery is defined as the waiting time.\u003c/p\u003e\n\u003ch3\u003eStatistical analyses\u003c/h3\u003e\n\u003cp\u003eStatistical significance was defined as a two-sided \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 unless otherwise specified. Continuous variables were assessed for normality using the Shapiro-Wilk test. If the data deviated from a normal distribution (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), the Mann-Whitney U test was used for group comparisons; otherwise, Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test was applied. Categorical baseline characteristics were compared using Pearson\u0026rsquo;s chi-square test. To control for type I errors due to multiple comparisons, the two-stage step-up False Discovery Rate (FDR) method by Benjamini, Krieger, and Yekutieli was employed, with additional Bonferroni correction applied by multiplying \u003cem\u003eP\u003c/em\u003e-values by the total number of statistical tests (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;38). Yearly trends in waiting times were analyzed using linear regression models, including interaction terms to assess sex-based differences over time. A Kaplan-Meier survival curve for time to cataract surgery was generated, with differences assessed using the log-rank test. A multivariate Cox regression model was constructed to identify predictors of waiting times, with independent variables including sex, age, ocular comorbidities (pseudoexfoliation syndrome, cornea guttata, macular disease, diabetes, and glaucoma), and regional differences. Supplementary analyses included stratification by visual acuity groups, categorizing waiting times by decimal visual acuity equivalents. For each stratum, mean waiting times were compared between sexes using unpaired \u003cem\u003et\u003c/em\u003e-tests with Welch correction. All statistical analyses were conducted using IBM SPSS Statistics (version 29, Armonk, NY), GraphPad Prism (version 10.0.2, San Diego, CA, USA), and R (R Core Team, Vienna, Austria, 2022), with relevant packages including dplyr, ggplot2, tidyr, knitr, survminer, and survival.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eA total of 1,413,652 patients were included in the study, of whom 828,515 (59%) were female. Males had slightly better BCVA in the non-operated eye and were more likely to receive multifocal intraocular lenses (IOLs). Capsular tension rings were more commonly used, and postoperative endophthalmitis occurred more frequently in male patients, while pseudoexfoliations were more common among females. Detailed baseline characteristics are presented in \u003cb\u003eTable\u0026nbsp;1\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eThe average waiting time from preoperative assessment to surgery was 64 days for females (standard deviation [SD] 126) and 60 days for males (SD 102). A Shapiro-Wilk test confirmed non-normal distribution in both groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for both males and females); thus, a Mann-Whitney U test was conducted, indicating a significant difference in waiting times between males and females (\u003cem\u003eW\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.51\u0026times;10\u0026sup1;\u0026sup1;, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eDifferences in waiting times between females and males were observed across all visual acuity groups of the surgery eye, with females consistently experiencing longer average waiting times. These groups, categorized by visual acuity (\u0026le;\u0026thinsp;0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, and \u0026ge;\u0026thinsp;1.0 on the decimal scale, which is equivalent to \u0026le;\u0026thinsp;20/200, 20/100, 20/66, 20/50, 20/40, 20/33, 20/28, 20/25, 20/22, and \u0026ge;\u0026thinsp;20/20 on the Snellen scale, and 1.0, 0.7, 0.52, 0.40, 0.30, 0.22, 0.15, 0.10, 0.05, and 0.0 on the LogMAR scale.), demonstrated statistically significant disparities. For instance, in the \u0026le;\u0026thinsp;0.1 group, females had an average waiting time of 63 days (SD 71), compared to 57 days (SD 66 days) for males, a difference of 7 days (SE\u0026thinsp;\u0026lt;\u0026thinsp;1 day, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similar differences were evident across all other groups, with statistical significance consistently retained after adjusting for multiple comparisons using FDR. Unpaired t-tests with Welch correction confirmed statistically significant differences between males and females in all groups, with all \u003cem\u003eP\u003c/em\u003e values\u0026thinsp;\u0026lt;\u0026thinsp;0.001. The magnitude of differences ranged from 2 days (SE 18, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in the BCVA 0.7 group, to 7 days (SE 18, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in the BCVA\u0026thinsp;\u0026le;\u0026thinsp;0.1 group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These findings highlight a consistent trend of longer waiting times for females across all levels of preoperative visual acuity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSimilarly, regional differences in waiting times were also evident, with the largest discrepancy observed in Region 17 (mean difference: 9 days) and the smallest in Region 13 (mean difference: 1 day, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Females had significantly longer waiting times in all regions (unpaired \u003cem\u003et\u003c/em\u003e-tests with Welch correction, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for all comparisons, adjusted for multiple comparisons using FDR). A full list of regional designations is available in \u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor regression analyses, regions were categorized into four tiers based on the magnitude of waiting time differences between genders, with Tier 1 representing regions with the smallest gender differences and Tier 4 those with the largest.\u003c/p\u003e \u003cp\u003eWhen treating waiting time for cataract surgery as a time-to-event analysis, females experienced longer cataract-surgery-free periods than males, indicating a longer delay in undergoing surgery. At 30 days after admission, the estimated Kaplan-Meier cataract-surgery-free survival was 64.9% (95% CI 64.8\u0026ndash;65.0) for females and 62.2% (95% CI 62.1\u0026ndash;62.4) for males. At 60 days, the survival was 39.7% (95% CI 39.6\u0026ndash;39.9) for females and 37.1% (95% CI 36.9\u0026ndash;37.2) for males. By 90 days, the survival was 23.4% (95% CI 23.4\u0026ndash;23.5) for females and 21.3% (95% CI 21.2\u0026ndash;21.4) for males, and at 120 days, it declined to 13.7% (95% CI 13.6\u0026ndash;13.7) for females and 12.1% (95% CI 12.1\u0026ndash;12.2) for males (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eMultivariate Cox Regression\u003c/h3\u003e\n\u003cp\u003eWe performed a multivariate Cox regression analysis to investigate the effect of various factors on waiting time for cataract surgery (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The analyzed variables included sex (male vs. female), age, baseline visual acuity of the operated eye (BCVA), specific health conditions (presence of pseudoexfoliation, cornea guttata, diabetes, macular disease, and glaucoma), and region-specific waiting time tiers. All covariates, including patient sex, were found to be independent predictors of waiting time after applying Bonferroni adjustment to \u003cem\u003eP\u003c/em\u003e values. Notably, diabetes (type I or II) emerged as a significant predictor of shorter waiting time (Hazard ratio: 0.88, 95% CI: 0.87\u0026ndash;0.89).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eTime Trends\u003c/h2\u003e \u003cp\u003eAn analysis of time trends in cataract surgery waiting times from 2010 to 2022 revealed fluctuating overall waiting times throughout the study period, forming a U-shaped trend. The period from 2020 to 2022 may have been influenced by the COVID-19 pandemic. In 2010, the average waiting time was approximately 74 days for females and 69 days for males. A linear regression model, including an interaction term to assess gender differences over time, showed a significant positive association between year and waiting time, with an estimated annual increase of \u0026lt;\u0026thinsp;1 day/0.01 months (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, the interaction term between year and sex did not reach statistical significance (Estimate\u0026thinsp;\u0026lt;\u0026thinsp;1 day, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.29), indicating that the trend in waiting times was consistent across genders. Thus, while waiting times for cataract surgery varied over the study period, no significant change was observed in the relative difference between males and females (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eMain Findings\u003c/h2\u003e \u003cp\u003eThis study confirms persistent, albeit small, sex-based differences in waiting times for cataract surgery in Sweden over a 13-year period, with female patients consistently experiencing longer delays than their male counterparts. Despite efforts to improve healthcare equity, this disparity has remained stable and statistically significant across the study period. Importantly, these differences are unlikely to be explained by clinical factors, such as visual acuity at the time of admission for surgery, age, or comorbidities, which were comparable between sexes or adjusted for in the analyses.\u003c/p\u003e \u003cp\u003eCataract is typically a slowly progressive condition, and although the absolute differences of a few days in waiting times are small and unlikely to have a major impact on health or long-term well-being, they may reflect underlying systemic inequalities or biases that warrant further investigation. In 2004, the National Board of Health and Welfare (NBHW) identified differences in access to care for females and elderly patients as examples of discrimination.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e The persistent disparity observed suggests the need for targeted interventions to achieve more equitable access to surgery.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eContextualizing with Previous Studies\u003c/h2\u003e \u003cp\u003eOur findings align with earlier research indicating sex-based disparities in waiting times for cataract surgery. A 2010\u0026ndash;2011 study of Swedish patients reported similar patterns of longer waiting times for women.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e Smirthwaite and colleagues reported, based on focus interviews with ophthalmologists, that females and males were regarded differently with respect to ascribed traits such as assertiveness and care-seeking behavior, and that their need for visual acuity in working life was perceived as distinct.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThese disparities are not unique to Sweden; studies from other regions have reported sex-based differences in access to ophthalmic care, with women facing barriers to timely treatment even in high-resource settings.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e Such differences are often attributed to social, economic, or systemic factors rather than biological differences in disease burden or progression.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eFactors Influencing Waiting Times\u003c/h2\u003e \u003cp\u003eIn addition to sex, other predictors of waiting time included age, home region, and specific comorbidities. Older patients tended to wait longer, potentially reflecting the prioritization of working-age individuals or the need to address comorbidities before surgery. Regional variations were notable, with differences likely driven by healthcare resource allocation, availability of surgeons, and logistical factors such as travel distance. Previous studies have also highlighted variations in complication rates of cataract surgery at regional, clinic, and even individual surgeon levels.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eAlthough differences in surgical technique and complications between sexes were observed, these were minor and unlikely to account for the disparity in waiting times. For example, capsular tension rings and postoperative endophthalmitis were slightly more common in male patients, while pseudoexfoliation syndrome was more frequent among females. While statistically significant due to the large sample size, these differences are of limited clinical relevance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and Limitations\u003c/h2\u003e \u003cp\u003eA major strength of this study is its comprehensive dataset, encompassing over 1.4\u0026nbsp;million patients and covering approximately 93% of all cataract surgeries performed in Sweden during the study period. This extensive coverage ensures a robust representation of real-world clinical practice and reduces the risk of selection bias.\u003c/p\u003e \u003cp\u003eHowever, the study's retrospective registry-based design limits causal inferences. While we identified significant predictors of waiting time, the underlying reasons for sex-based disparities remain unclear.\u003c/p\u003e \u003cp\u003eAdditionally, the accuracy of the findings relies on the quality of data entered into the Swedish National Cataract Register. Although previous audits have validated the register\u0026rsquo;s reliability, occasional inaccuracies in reporting cannot be excluded.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eImplications and Future Directions\u003c/h2\u003e \u003cp\u003eThe persistence of sex-based differences in waiting times for cataract surgery highlights the need for targeted interventions to address healthcare inequities. Potential strategies include improving referral practices, standardizing prioritization criteria, and ensuring adequate resource allocation across regions. Further research is needed to elucidate the root causes of these disparities, including qualitative studies to explore potential biases in clinical decision-making and patient preferences.\u003c/p\u003e \u003cp\u003eFinally, while the observed differences may not have a significant impact on clinical outcomes, they could contribute to perceptions of inequity and undermine trust in the healthcare system. Addressing even small disparities is essential for ensuring that healthcare delivery is both equitable and perceived as fair by all patients.\u003c/p\u003e \u003c/div\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eIn conclusion, females in Sweden experienced slightly longer waiting times for cataract surgery than males during the period 2010\u0026ndash;2022. This disparity was consistent across the study period and remained significant after adjusting for clinical and demographic factors. While the differences are unlikely to have major clinical implications, they highlight the need for continued efforts to achieve equitable access to ophthalmic care.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatient-level data analyzed in this study are available from the Swedish National Cataract Register (https://rcsyd.se/anslutna-register/nationella-kataraktregistret). Access to these data requires approval from the Swedish Ethical Review Authority and the Swedish National Cataract Register\u0026apos;s record keeper. Requests for data can be submitted through the register\u0026rsquo;s website and must comply with Swedish regulations governing the use of healthcare data for research purposes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupport for this study was provided to Gustav St\u0026aring;lhammar from:\u003c/p\u003e\n\u003cp\u003eRegion Stockholm (RS-2019-1138)\u003c/p\u003e\n\u003cp\u003eThe funding organization had no role in the design or conduct of this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhilip Jute:\u003c/strong\u003e Conceptualization, Writing \u0026ndash; Review \u0026amp; Editing. \u003cstrong\u003eGustav St\u0026aring;lhammar:\u003c/strong\u003e Conceptualization, Methodology, Software, Formal Analysis, Investigation, Data curation, Writing \u0026ndash; Original Draft, Visualization, Project administration, Funding acquisition.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCicinelli, M. 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Ophthalmology 127, 305\u0026ndash;314 (2020). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1016/j.ophtha.2019.10.007\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1016/j.ophtha.2019.10.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"612\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 403px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Baseline Patient Characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 210px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemales, \u003cem\u003en\u003c/em\u003e=828,515\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMales, \u003cem\u003en\u003c/em\u003e=585,137\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eAge, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 117px;\"\u003e\n \u003cp\u003e74.5 (8.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e74.3 (9.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eBCVA operated eye\u003csup\u003e\u0026dagger;\u003c/sup\u003e, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 117px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eDecimal scale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 117px;\"\u003e\n \u003cp\u003e0.46 (0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0.45 (0.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eLogMAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 117px;\"\u003e\n \u003cp\u003e0.34 (0.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0.35 (0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eSnellen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 117px;\"\u003e\n \u003cp\u003e20/44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e20/45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eBCVA non-operated eye\u003csup\u003e\u0026Dagger;\u003c/sup\u003e, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eDecimal scale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 117px;\"\u003e\n \u003cp\u003e0.86 (0.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0.89 (0.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eLogMAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 117px;\"\u003e\n \u003cp\u003e0.07 (0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0.05 (0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eSnellen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 117px;\"\u003e\n \u003cp\u003e20/23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e20/23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eSurgery type, \u003cem\u003en\u003c/em\u003e (\u003cem\u003e%\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003ePhaco. and IOL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 117px;\"\u003e\n \u003cp\u003e822,193 (99.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e579,420 (99.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003ePhaco. and ACL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 117px;\"\u003e\n \u003cp\u003e532 (0.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e284 (0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eTrab., phaco., and IOL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 117px;\"\u003e\n \u003cp\u003e273 (0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e242 (0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eOther**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 117px;\"\u003e\n \u003cp\u003e5517 (0.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e5191 (0.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eLens material, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eHydrophobic acrylic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 117px;\"\u003e\n \u003cp\u003e791,681 (95.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e559,208 (95.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eHydrophilic acrylic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 117px;\"\u003e\n \u003cp\u003e30,920 (3.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e21,662 (3.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003ePatient left aphakic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 117px;\"\u003e\n \u003cp\u003e1,670 (0.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e1,323 (0.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eSilicone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 117px;\"\u003e\n \u003cp\u003e1,335 (0.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e897 (0.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003ePMMA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 117px;\"\u003e\n \u003cp\u003e219 (0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e136 (0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eMultifocal, material unspecified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 117px;\"\u003e\n \u003cp\u003e131 (0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e125 (0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003ePMMA HSM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 117px;\"\u003e\n \u003cp\u003e26 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e12 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 117px;\"\u003e\n \u003cp\u003e2,520 (0.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e1,772 (0.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eNot specified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 117px;\"\u003e\n \u003cp\u003e13 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e2 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eLens shape, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eAspherical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 117px;\"\u003e\n \u003cp\u003e65,805 (7.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e43,203 (7.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eMultifocal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 117px;\"\u003e\n \u003cp\u003e16,337 (1.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e13,670 (2.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eUse of capsular tension rings, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 117px;\"\u003e\n \u003cp\u003e15,207 (1.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e15,925 (2.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003ePostoperative endophthalmitis, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 117px;\"\u003e\n \u003cp\u003e131 (0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e154 (0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eOther systemic and ocular conditions, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eGlaucoma, any type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 117px;\"\u003e\n \u003cp\u003e70,398 (8.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e53,113 (9.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eDisease of the macula, any type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 117px;\"\u003e\n \u003cp\u003e129,126 (15.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e88,747 (15.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003ePseudoexfoliations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 117px;\"\u003e\n \u003cp\u003e86,697 (10.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e47,472 (8.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eCornea Guttata\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 117px;\"\u003e\n \u003cp\u003e25,294 (3.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e13,419 (2.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eDiabetes, type I or II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 117px;\"\u003e\n \u003cp\u003e26,713 (3.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e32,663 (5.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Mann-Whitney \u003cem\u003eU\u003c/em\u003e test was used for continuous variables, and chi-square tests were used for categorical variables. Bonferroni correction was used to adjust \u003cem\u003eP\u003c/em\u003e-values for multiple comparisons. \u003csup\u003e\u0026dagger;\u003c/sup\u003eEye planned for surgery, or the first eye operated if both were treated in the same session. \u003csup\u003e\u0026Dagger;\u003c/sup\u003eEye not planned for surgery, or the second eye operated if both were treated in the same session. **e.g., phacoemulsification (Phaco), intraocular lens (IOL), and simultaneous corneal surgery. ACL, anterior chamber lens; BCVA, best corrected visual acuity; HSM, heparin surface modified; IOL, intraocular lens; PMMA, polymethyl methacrylate; SD, standard deviation; Trab, trabeculectomy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Multivariate Cox Regression: Predictors of Time to Cataract Surgery\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"573\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eS.E.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWald Test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExp(B)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% Confidence Interval\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eSex (Male)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e-0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e608.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.938\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0.934\u0026ndash;0.943\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eAge\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e-0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e2,708.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.993\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0.992\u0026ndash;0.993\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eBCVA operated eye\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e838.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e1.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e1.174\u0026ndash;1.202\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003ePseudoexfoliations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e257.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e1.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e1.070\u0026ndash;1.091\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eCornea Guttata\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e-0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e14.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0.952\u0026ndash;0.984\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eDiabetes, type I or II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e-0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e407.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.881\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0.870\u0026ndash;0.892\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eMacular Disease, any type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e29.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e1.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e1.013\u0026ndash;1.027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eGlaucoma, any type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e-0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e130.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.949\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0.941\u0026ndash;0.958\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eRegion tier\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e-0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e10,144.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.883\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0.881\u0026ndash;0.885\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Bonferroni correction was applied to the \u003cem\u003eP\u003c/em\u003e values to adjust for multiple comparisons. \u003csup\u003e\u0026dagger;\u003c/sup\u003ePatient age at the time of admission for cataract surgery. \u003cem\u003e\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/em\u003eRegions were classified into four tiers based on average waiting time for cataract surgery, with Tier 1 containing regions with the longest waiting time, and Tier 4 those with the shortest. BCVA, best corrected visual acuity. S.E., standard error.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5623387/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5623387/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSex-based disparities in healthcare access and outcomes remain a challenge. Understanding differences in waiting times for cataract surgery between males and females can reveal inequities in care delivery.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis nationwide retrospective cohort study used data from the Swedish National Cataract Register, which covers\u0026thinsp;\u0026gt;\u0026thinsp;93% of all cataract surgeries in Sweden. A total of 1,413,652 patients aged\u0026thinsp;\u0026gt;\u0026thinsp;40 years who underwent first-eye cataract surgery between 2010 and 2022 were included. Exclusions were made for patients with waiting times\u0026thinsp;\u0026gt;\u0026thinsp;24 months, those residing outside Sweden, and those with missing sex data. The primary outcome was waiting time, defined as the interval between preoperative assessment and surgery. Secondary analyses included stratification by visual acuity, regional variations, and the influence of demographic and clinical factors.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe mean waiting time was 64 days for females (SD 126) and 60 days for males (SD 102), with a significant difference (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This disparity persisted across all visual acuity strata and regions. Multivariate Cox regression identified female sex, older age, specific comorbidities, and residence region as significant predictors of longer waiting times. Differences in comorbidities, including higher rates of pseudoexfoliation syndrome in females and endophthalmitis in males, were observed. Despite fluctuations in overall waiting times, the sex-based disparity remained consistent over the study period.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003ePersistent sex-based differences in waiting times for cataract surgery were identified in Sweden over 13 years. While small and unlikely to affect clinical outcomes, these differences highlight systemic inequities that merit further investigation and intervention to ensure equitable access to care.\u003c/p\u003e","manuscriptTitle":"Sex Differences in Waiting Times for Cataract Surgery in Sweden, 2010–2022: Nationwide Analysis of 1.4 Million Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-26 08:10:39","doi":"10.21203/rs.3.rs-5623387/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-medicine","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commsmed","sideBox":"Learn more about [Communications Medicine](http://www.nature.com/commsmed)","snPcode":"43856","submissionUrl":"https://mts-commsmed.nature.com/cgi-bin/main.plex","title":"Communications Medicine","twitterHandle":"@commsmedicine","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"121b5ccb-7fea-4375-ae0e-afc2d9755572","owner":[],"postedDate":"December 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":41832203,"name":"Health sciences/Diseases/Eye diseases/Lens diseases"},{"id":41832204,"name":"Health sciences/Health care/Therapeutics/Surgery"}],"tags":[],"updatedAt":"2025-03-05T08:09:24+00:00","versionOfRecord":{"articleIdentity":"rs-5623387","link":"https://doi.org/10.1038/s43856-025-00782-1","journal":{"identity":"communications-medicine","isVorOnly":false,"title":"Communications Medicine"},"publishedOn":"2025-03-04 05:00:00","publishedOnDateReadable":"March 4th, 2025"},"versionCreatedAt":"2024-12-26 08:10:39","video":"","vorDoi":"10.1038/s43856-025-00782-1","vorDoiUrl":"https://doi.org/10.1038/s43856-025-00782-1","workflowStages":[]},"version":"v1","identity":"rs-5623387","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5623387","identity":"rs-5623387","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}