The Association Between Sleep Health and a History of Cataract Surgery in the United States Based on the National Health and Nutrition Examination Survey (NHANES) 2005–2008
The Second Clinical College, Anhui Medical University, Hefei 230601, China
*
Authors to whom correspondence should be addressed.
Healthcare 2025, 13(10), 1136; https://doi.org/10.3390/healthcare13101136
Submission received: 19 March 2025
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Revised: 8 May 2025
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Accepted: 11 May 2025
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Published: 13 May 2025
(This article belongs to the Special Issue Update on Cataract Surgery)
Abstract
:Background: The aim of this study was to assess the relationship between sleep-related variables (sleep duration, sleep trouble, and sleep disorder), comprehensive sleep patterns, and the reported history of cataract surgery in the U.S. population aged 20 years and older. Methods: We utilized data from the National Health and Nutrition Examination Survey (NHANES) 2005–2008 database. First, we analyzed the association between covariates and the reported history of cataract surgery using univariable Poisson regression. Subsequently, we constructed three models to evaluate the association between sleep-related variables and the reported history of cataract surgery using multivariable Poisson regression. Subgroup analyses were conducted to determine whether the association between sleep and the reported history of cataract surgery exhibited heterogeneity. Finally, we performed a sensitivity analysis to assess the stability of the results. Results: A total of 8591 participants were included in this study, among whom 774 had a history of cataract surgery. After adjusting for all covariates, participants experiencing sleep trouble had a higher prevalence of reported history of cataract surgery than participants without sleep trouble [PR = 1.40; 95%CI = (1.22, 1.62)]. Regarding combined sleep, participants with poor sleep patterns had a 36% higher prevalence of reported history of cataract surgery than those with healthy sleep patterns [PR = 1.36; 95%CI = (1.13, 1.64)]. The results of the sensitivity analysis indicate that the relationship between sleep patterns and the reported history of cataract surgery is robust. Conclusions: Sleep trouble and poor sleep patterns are positively linked to the high prevalence of a reported history of cataract surgery. Further research is needed to explore the underlying mechanisms.
1. Introduction
Cataracts are the primary reason for blindness in developing countries, impacting approximately 20 million people worldwide [1]. There are primarily characterized by the opacity of the crystalline lens [2]. Epidemiological studies have identified several risk factors for cataracts, including age, obesity, diabetes, hypertension, smoking, and sun exposure [3,4,5]. Among these, age is the most significant risk factor, and there is extensive research indicating that the prevalence of cataracts increases with age [6]. Cataract surgery remains the treatment of choice, with the annual cost of cataract surgery in the United States estimated at USD 3.4 billion [7]. The aging population will cause a rise in cataract surgery, increasing socio-economic and public health burdens [8].
Healthy sleep is essential for learning, memory, and brain development [9,10] and is integral to a healthy lifestyle. To achieve optimal health, the Sleep Research Organization recommends that adults obtain at least 7 h of sleep per night [11]. Insufficient sleep time and poor sleep quality can adversely affect cardiovascular, reproductive, and mental health [12]. A cohort study showed an increased risk of cataracts in patients with sleep apnea [13]. A cross-sectional study in Korea found that both long and short sleep duration were associated with a high prevalence of diabetic retinopathy [14]. Sleep is closely linked to eye health. However, the relationship between sleep and cataracts remains unclear. Elucidating this relationship may be beneficial for the prevention and treatment of cataracts.
In our study, we analyzed data from the National Health and Nutrition Examination Survey (NHANES) database for 2005–2008 to research the connection between sleep-related variables (sleep duration, sleep disorder, and sleep trouble) and integrated sleep behaviors (defined as ‘sleep patterns’) and a reported history of cataract surgery.
2. Methods
2.1. Data and Study Sample
NHANES is a population-based cross-sectional survey conducted by the National Center for Health Statistics (NCHS) at the Centers for Disease Control and Prevention (CDC). Its purpose is to gather information on the health and nutrition of Americans in their households. This study included data from two NHANES cycles (2005–2006 and 2007–2008) with 20,497 participants. The exclusions for this study are as follows: (1) missing cataract surgery data; (2) missing self-reported data on sleep duration, sleep disorder, and sleep trouble; and (3) missing complete covariate data. Figure 1 illustrates the filtering process.
2.2. Cataract Surgery Assessment
The investigators assessed the history of cataract surgery by asking participants, “Have you ever had cataract surgery?” (VIQ071). If the answer was ‘YES’, the participant was diagnosed with a reported history of cataract surgery.
2.3. Assessment of Sleep-Related Variables
Falling asleep time was obtained by asking participants, “How long does it usually take you to fall asleep at bedtime?” The length of sleep was determined by asking participants, “How long do you usually sleep on weekdays or Sunday nights?”; we then divided the patients’ answers into short sleep periods (<7 h per night), healthy sleep periods (7–8 h per night), and long sleep periods (>8 h per night), in which short sleep and long sleep were both classed as unhealthy sleep. Sleep trouble and sleep disorders were assessed by the patient’s response to the questions ‘Have you ever told your doctor about your sleep problems?’ and ‘Has your doctor ever told you that you have a sleep disorder?’ The answers to these two questions were recorded. Participants’ sleep patterns were assessed using the three sleep behaviors described above. Healthy sleep duration, no sleep disorder, and no sleep trouble were scored with 1 point each. On the other hand, unhealthy sleep duration, sleep disorder, and sleep trouble were scored with 0, respectively. The scores for the three behaviors were summed to generate a composite sleep score. Detailed information is presented in Table S3. Based on the composite score, participants were categorized into three groups: healthy sleep pattern (3 points), intermediate sleep pattern (2 points), and poor sleep pattern (0–1 points) [15,16].
2.4. Assessment of Covariates
In line with previous studies on cataracts [17,18], we included covariates, including sociodemographic factors (sex, age, race, education level, marital status, and poverty–income ratio). Race was categorized as Mexican American, other Hispanic, non-Hispanic white, non-Hispanic black, and other race. Educational level was categorized as less than 9th grade, 9th-11th grade, high school graduate or equivalent, some college or AA degree, and college graduate or above. Economic level was assessed by the poverty–income ratio (PIR: household income/U.S. Department of Health and Human Services Federal Poverty Level). Based on the PIR, we classified the economic level into three categories (<1, 1–3, >3). A higher PIR indicates a better economic status of the household. Marital status was classified as married or living with a partner and unmarried or other. Sociodemographic factors, body measurements (body mass index (BMI)), lifestyle factors (smoking and alcohol consumption), and comorbidities (hypertension and diabetes) were obtained from self-reported questionnaires. The BMI was calculated by multiplying weight (kg) by height square (m2) and dividing it into three categories (<25, 25–30, >30 kg/m²). Two self-reported questionnaires assessed smoking and alcohol consumption. Participants were diagnosed with diabetes or hypertension if a doctor had told them that they had diabetes or hypertension.
2.5. Data Analysis
Given that NHANES uses complex multistage sampling and that we chose two consecutive cycles, 2-year moving examination center (MEC) weights divided by two were used to ensure that subjects were representative of the national population. On baseline descriptions, continuous variables are represented by means (SD) and categorized by frequency (percentage). The comparison of the baseline characteristics of the participants was performed using Wilcoxon rank tests and Rao–Scott chi-square tests. We then tested the association between the covariates of interest and the history of cataract surgery using univariable Poisson regression. To minimize confounding due to multicollinearity, the relationship between each sleep construct and the reported history of cataract surgery was tested individually in each one of Models 1, 2, and 3 for a total of 15 models: Model 1: unadjusted; Model 2: adjusted for sex, age and race; Model 3: adjusted for all covariates. Subgroup analyses were stratified by age, gender, race, educational level, marital status, economic level, BMI, alcohol consumption, smoking status, hypertension, and diabetes. Multiplicative interactions were assessed by including product terms between sleep patterns and the stratification variables in the models. Interaction p-values were obtained by comparing models with and without interaction terms using likelihood ratio tests. Finally, we use sensitivity analysis to test the reliability of the results. We analyzed the data using R4.3.2 (including “survey”, “dplyr”, and “foreign” packages), with a significant p-value < 0.05.
3. Results
3.1. Characteristics of Participants
Baseline characteristics were analyzed based on whether the subjects had undergone cataract surgery (Table 1). This study included 8591 subjects with complete information, of whom 774 had previously undergone cataract surgery. The subjects had an average age of 46.26 (16.55), with 51.8% identifying as women, primarily of a non-Hispanic white ethnicity.
Participants with poor sleep patterns had a mean age (standard deviation) of 49.75 (15.41) and were more likely to be female and less educated, have a lower economic level, be unmarried and obese, smoke and drink alcohol, and have hypertension and diabetes mellitus (Table 2). Additionally, a higher prevalence of a reported history of cataract surgery was associated with sleep patterns deteriorating.
3.2. Univariable Poisson Regression Analysis of Cataract-Related Variables
Univariable Poisson regression was applied to analyze the association of covariables with a reported history of cataract surgery (Figure 2). We found that women had a 45% higher prevalence of a reported history of cataract surgery than men [PR = 1.45; 95%CI = 1.25, 1.68)]. Non-Hispanic whites had a higher prevalence of reported history of cataract surgery compared to Mexican Americans [PR = 3.43; 95%CI = (2.50, 4.72)]. Regarding education, a negative association was observed between participants’ education level and the prevalence of a reported history of cataract surgery. As for economic level, participants with an economic level between 1 and 3 had a 115% higher prevalence of a reported history of cataract surgery than those with an economic level < 1 [PR = 2.15; 95%CI = (1.62, 2.85)]. Regarding marital status, married participants had a lower prevalence of a reported history of cataract surgery than unmarried participants [PR = 0.63; 95%CI = (0.52, 0.76)]. Regarding lifestyle habits, the prevalence of a reported history of cataract surgery among smokers was higher compared to non-smokers [PR = 1.22; 95%CI = (1.00,1.49)]. Individuals who consumed more than 12 drinks per year had a higher prevalence of a reported history of cataract surgery than those who consumed less than 12 drinks per year [PR = 1.47; 95%CI = (1.43,1.64)]. Individuals with diabetes [PR = 3.35; 95%CI = (2.76,4.08)] or hypertension [PR = 3.44; 95%CI = (2.84,4.18)] had a higher prevalence of a reported history of cataract surgery than those without diabetes or hypertension.
3.3. Association Between Sleep and History of Cataract Surgery
Three multivariable Poisson regression models were employed to analyze the relationship between sleep and the prevalence of a reported history of cataract surgery (Table 3). In Model 3, participants with self-reported sleep trouble had a 40% higher prevalence of a reported history of cataract surgery compared to those without sleep trouble [PR = 1.40; 95%CI (1.22,1.62)]. In terms of sleep patterns, after adjusting for all covariates, participants with intermediate and poor sleep patterns showed 24% [PR = 1.24; 95% CI (1.05–1.46)] and 36% [OR = 1.36, 95% CI (1.13–1.64)] higher prevalence of reporting a history of cataract surgery, respectively, compared to those with healthy sleep patterns.
3.4. Subgroup Analyses
We conducted a subgroup analysis based on covariates to further explore the relationship between sleep trouble, sleep patterns, and a reported history of cataract surgery across different populations. The results (Table 4 and Table 5) showed no statistically significant differences between the subgroups, suggesting that factors such as sociodemographics, body measurements, lifestyle factors, and comorbidities did not significantly affect this association (all p for interaction > 0.05).
3.5. Sensitivity Analyses
We conducted three sensitivity analyses: 1. To account for the effects of extreme samples, we excluded participants who slept less than 3 hours (0.48%) and equal to 12 h (0.50%). After adjusting for all covariates, intermediate and poor sleep patterns participants had a higher prevalence of a reported history of cataract surgery compared to healthy sleep pattern participants [intermediate: PR (95%CI) = 1.24 (1.05,1.46); poor: PR (95%CI) = 1.36 (1.13,1.65)]. 2. Unweighted data were used. The results indicated that when the healthy sleep pattern was considered as a control, the PR (95% CI) for the intermediate and poor sleep patterns were 1.14 (0.96, 1.35) and 1.34 (1.09, 1.63), respectively. 3. We also adjusted for cardiovascular diseases, including congestive heart failure, coronary heart disease, angina, and stroke (N = 8521). The results of the multivariable Poisson regression showed that individuals with intermediate and poor sleep patterns had 24% and 39% higher prevalence of a reported history of cataract surgery compared to those with a healthy sleep pattern, respectively [intermediate: PR (95%CI) = 1.24 (1.03,1.49); poor: PR (95%CI) = 1.39(1.15,1.68)] (Tables S4–S6).
4. Discussion
This is the first study to utilize the NHANES data to explore the association between sleep patterns and the reported history of cataract surgery. Using multivariable Poisson regression analyses, we found that sleep trouble correlated with a reported history of cataract surgery. We then assessed the correlation between sleep patterns and a history of cataract surgery; participants with poor sleep patterns had a higher prevalence of underdoing cataract surgery. Sensitivity analyses further confirmed our findings.
Previous research has indicated that a short sleep duration is a significant risk factor for cataracts [19], while a long sleep duration has been found to have an insignificant association with cataracts [20]. We note that a Korean study showed that a longer sleep duration was linked to protection against cataract development [21]. In contrast, our findings show that there is no relationship between sleep duration and cataracts. The possible reasons for the conflicting results may be (1) the fact that different databases used, leading to differences in the study populations; (2) inconsistencies in the nadir criteria; (3) differences in the covariates adjusted for in the studies; and (4) the fact that some scientists have argued that a long sleep duration is not indicative of good sleep quality and it may be that poor sleepers increase their sleep by proxy [22,23].
In addition to sleep duration, participants who self-reported sleep problems had an increased risk of cataracts. This association may be related to some metabolic factors. Previous research has demonstrated that sleep deprivation is linked to hyperglycemia and poor blood sugar control [24,25] and disrupts circadian rhythms, affecting morning cortisol levels and sympathetic vagal homeostasis [26], which can lead to decreased insulin sensitivity [27] and an increased likelihood of diabetes [28]. Furthermore, numerous studies have demonstrated that sleep problems are linked to a higher chance of hypertension [29,30]. Diabetes and hypertension are known to be risk factors for cataracts. Chronic sleep disturbance can lead to metabolic imbalance, disrupting the intraocular environment and causing the degeneration of lens proteins, which may trigger the development of cataracts.
In recent years, researchers have increasingly recognized the importance of overall sleep quality. According to two studies, individuals who have poor overall sleep quality are at higher risk of cardiovascular disease and depression compared to those who have healthy sleep quality [31,32]. Consistently with their findings, our study found that participants with poor sleep patterns were more likely to have cataracts than those with healthy sleep patterns. This may be because unhealthy sleep patterns increase systemic oxidative stress and inflammation [33,34], which are critical pathological processes in cataract formation. Oxidative stress products impair the body’s antioxidant defense system, leading to the damage of lens proteins and the apoptosis of lens epithelial cells, which in turn contributes to the development of cataracts [35,36,37]. Secondly, poor sleep quality can disrupt normal cortisol rhythms [38], leading to the overactivation of the hypothalamic–pituitary–adrenal (HPA) axis and elevated serum cortisol levels. Increased cortisol levels may affect lens metabolism and accelerate the denaturation and aggregation of lens proteins, ultimately contributing to the development of cataracts [39,40].
We must acknowledge the limitations of this study. Firstly, because it was a cross-sectional study, we cannot establish a causal relationship between sleep and a history of cataract surgery. Secondly, obtaining sleep variables and assessing combined sleep patterns by self-report may result in recall bias and misclassification. Objective sleep testing tools are needed to assess this in the future. And more than 50% of participants were indeed excluded from the analysis due to missing data. However, this missingness was not random, and given the large overall sample size, we chose not to impute the missing values in order to preserve the integrity of the data. However, such an approach could introduce bias in the data. Thirdly, there was no adjustment for residuals and other confounding variables, which may have introduced bias. Fourth, since the study utilized public databases in the United States, the results may not accurately reflect the prevalence of cataract surgery in different regions. Finally, the timing of the cataract surgery is not stated in the history. And it is not known whether the sleep problem developed before or after the surgery.
5. Conclusions
Sleep trouble and poorer sleep patterns were associated with a higher prevalence of reporting a history of cataract surgery. This finding provides new ideas for the treatment and prevention of cataracts. However, the causal relationship between sleep and a reported history of cataract surgery must be investigated experimentally.
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/healthcare13101136/s1, Table S1: The unadjusted rates of history of cataract surgery and no history of cataract surgery in different subgroups; Table S2: The unadjusted rates of different sleep pattern in different subgroups; Table S3: The overall sleep quality according to each sleep behavior; Table S4: The relationship between sleep and cataract surgery after excluding extreme values; Table S5: The relationship between sleep and cataract surgery after using unweighted data; Table S6: The relationship between sleep and cataract surgery after including cardiovascular diseases.
Author Contributions
C.W.: conceptualization, methodology, data curation, and writing—original draft. N.B.: data curation, visualization, and writing—review and editing; Z.J.: Writing–review and editing, funding acquisition, and supervision. All authors have reviewed the manuscript. All authors have read and agreed to the published version of the manuscript.
Funding
The research was funded by the National Natural Science Foundation of China (Grant No. 82371080) and the Natural Science Funds for Distinguished Young Scholars of Anhui Province (Grant No. 2308085J29).
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
Data available on request due to restrictions. NHANES is a public database and all researchers have access to the data from www.cdc.gov/nchs/nhanes, accessed on 15 March 2025.
Acknowledgments
The authors thank the participants and researchers for their dedication and efforts.
Conflicts of Interest
The authors declare no conflicts of interest.
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Figure 1.
Flowchart of inclusion and exclusion criteria for the study population.
Figure 2.
Univariable Poisson regression of the association between a history of cataract surgery and covariates. Abbreviations: PR: prevalence ratio; CI: confidence interval; BMI: body mass index.
Figure 2.
Univariable Poisson regression of the association between a history of cataract surgery and covariates. Abbreviations: PR: prevalence ratio; CI: confidence interval; BMI: body mass index.
Table 1.
Characteristics of participants stratified by cataract surgery from NHANES 2005–2008.
| All | Non-Cataract Surgery | Cataract Surgery | p-Value | |
|---|---|---|---|---|
| Number | 8591 | 7817 (93.4) | 774 (6.6) | |
| Gender (N, %) | <0.001 | |||
| Male | 4183 (48.2) | 3819 (48.9) | 364 (39.1) | |
| Female | 4408 (51.8) | 3998 (51.1) | 410 (60.9) | |
| Age [years, mean (SD)] | 46.26 (16.55) | 44.43 (15.28) | 72.22 (11.48) | <0.001 |
| Ethnicity (N, %) | <0.001 | |||
| Mexican American | 1556 (7.9) | 1497(8.2) | 59 (2.7) | |
| Other Hispanic | 602 (3.9) | 562(4.0) | 40 (2.3) | |
| Non-Hispanic white | 4277 (72.1) | 3730(71.2) | 547 (84.8) | |
| Non-Hispanic black | 1830 (10.9) | 1721(11.2) | 109 (6.2) | |
| Other | 326 (5.3) | 307(5.4) | 19 (3.9) | |
| Education (N, %) | <0.001 | |||
| Less than 9th grade | 997 (5.9) | 835 (5.4) | 162 (13.2) | |
| 9th–11th grade | 1417 (12.2) | 1290 (12.1) | 127 (14.6) | |
| High school graduate or equivalent | 2078 (24.8) | 1874 (24.4) | 204 (30.3) | |
| Some college or AA degree | 2366 (30.6) | 2200 (31.0) | 166 (24.4) | |
| College graduate or above | 1733 (26.5) | 1618 (27.2) | 115 (17.4) | |
| BMI (N, %) | 0.085 | |||
| <25 kg/m2 | 2554 (32.5) | 2318 (32.4) | 236 (32.8) | |
| 25–30 kg/m2 | 2981 (33.9) | 2689 (33.6) | 292 (37.6) | |
| >30 kg/m2 | 3056 (33.7) | 2810 (34.0) | 246 (29.6) | |
| Economic level (N, %) | <0.001 | |||
| <1 | 1596 (12.0) | 1481 (12.3) | 115 (9.0) | |
| 1–3 | 3640 (35.8) | 3189 (34.3) | 451 (57.6) | |
| >3 | 3355 (52.1) | 3147 (53.4) | 208 (33.4) | |
| Marital status (N, %) | <0.001 | |||
| Unmarried or other | 5350 (65.6) | 4941 (66.4) | 409 (54.5) | |
| Married or living with a partner | 3241 (34.4) | 2876 (33.6) | 365 (45.5) | |
| Alcohol consumption (N, %) | <0.001 | |||
| ≥12 drinks/year | 6028 (74.8) | 5567 (75.8) | 461 (61.1) | |
| <12 cups/year | 2563 (25.2) | 2250 (24.2) | 313 (38.9) | |
| Smoking status (N, %) | 0.047 | |||
| Yes | 4115 (48.2) | 3700 (47.8) | 415 (53.2) | |
| No | 4476 (51.8) | 4117 (52.2) | 359 (46.8) | |
| Hypertension (N, %) | <0.001 | |||
| Yes | 2876 (29.8) | 2407 (27.7) | 469 (59.4) | |
| No | 5715 (70.2) | 5410 (72.3) | 305 (40.6) | |
| Diabetes (N, %) | <0.001 | |||
| Yes | 963 (8.0) | 767 (6.9) | 196 (22.5) | |
| No | 7628 (92.0) | 7050 (93.1) | 578 (77.5) | |
| Sleep time (N, %) | <0.001 | |||
| <7 | 3330 (36.4) | 3060 (36.6) | 270 (33.4) | |
| 7–9 | 5030 (61.3) | 4570 (61.4) | 460 (61.1) | |
| >9 | 231 (2.2) | 187 (2.0) | 44 (5.4) | |
| Falling asleep time [min, mean (SD)] | 21.36 (19.12) | 21.21 (19.03) | 23.42 (20.21) | 0.050 |
| Sleep trouble (N, %) | <0.001 | |||
| Yes | 1945 (24.3) | 1698 (23.5) | 247 (34.6) | |
| No | 6646 (75.7) | 6119 (76.5) | 527 (65.4) | |
| Sleep disorder (N, %) | 0.515 | |||
| Yes | 619 (7.3) | 550 (7.3) | 69 (7.9) | |
| No | 7972 (92.7) | 7267 (92.7) | 705 (92.1) | |
| Sleep patterns (N, %) | <0.001 | |||
| Healthy sleep | 4121 (48.7) | 3780 (49.0) | 341 (43.9) | |
| Intermediate sleep | 3115 (35.5) | 2846 (35.6) | 269 (34.5) | |
| Poor sleep | 1355 (15.8) | 1191 (15.4) | 164 (21.6) |
Abbreviations: SD: standard deviation; BMI: body mass index.
Table 2.
Characteristics of participants stratified by sleep pattern from NHANES 2005–2008.
| Sleep Pattern (Col%) | ||||
|---|---|---|---|---|
| Healthy | Intermediate | Poor | p-Value | |
| Number | 3791 (48.7) | 3387 (35.5) | 1413 (15.8) | |
| Gender (N, %) | 0.003 | |||
| Male | 1888 (49.6) | 1685 (49.5) | 610 (41.8) | |
| Female | 1903 (50.4) | 1702 (50.5) | 803 (58.2) | |
| Age [years, mean (SD)] | 45.47 (16.70) | 45.66 (16.67) | 49.75 (15.41) | <0.001 |
| Ethnicity (N, %) | <0.001 | |||
| Mexican American | 832 (9.5) | 574 (7.5) | 150 (4.2) | |
| Other Hispanic | 285 (4.3) | 214 (3.7) | 103 (3.4) | |
| Non-Hispanic White | 1917 (72.8) | 1569 (69.2) | 791 (76.7) | |
| Non-Hispanic Black | 614 (8.0) | 898 (14.2) | 318 (11.1) | |
| Other | 143 (5.4) | 132 (5.5) | 51 (4.6) | |
| Education (N, %) | <0.001 | |||
| Less than 9th grade | 482 (6.3) | 356 (5.3) | 159 (6.0) | |
| 9th–11th grade | 614 (11.5) | 588 (13.3) | 215 (11.8) | |
| High school graduate or equivalent | 868 (22.9) | 842 (25.7) | 368 (27.8) | |
| Some college or AA degree | 982 (28.7) | 952 (31.3) | 432 (33.9) | |
| College graduate or above | 845 (30.6) | 649 (24.3) | 239 (20.4) | |
| BMI (N, %) | <0.001 | |||
| <25 kg/m 2 | 1229 (35.9) | 996 (31.3) | 329 (25.7) | |
| 25–30 kg/m 2 | 1392 (34.7) | 1171 (35.1) | 418 (28.6) | |
| >30 kg/m 2 | 1170 (29.4) | 1220 (33.6) | 666 (45.6) | |
| Economic level (N, %) | 0.001 | |||
| <1 | 682 (11.2) | 593 (11.4) | 321 (15.9) | |
| 1–3 | 1556 (33.5) | 1500 (38.9) | 584 (35.1) | |
| >3 | 1553 (55.3) | 1294 (49.7) | 508 (49.0) | |
| Marital status (N, %) | <0.001 | |||
| Unmarried or other | 1278 (31.1) | 1340 (35.8) | 614 (39.9) | |
| Married or living with a partner | 2504 (68.9) | 2047 (64.2) | 799 (60.1) | |
| Alcohol consumption (N, %) | 0.009 | |||
| ≥12 drinks/year | 2684 (75.6) | 2385 (75.5) | 959 (71.3) | |
| <12 cups/year | 1107 (24.4) | 1002 (24.5) | 454 (28.7) | |
| Smoking status (N, %) | <0.001 | |||
| Yes | 1692 (44.7) | 1612 (48.6) | 811 (56.6) | |
| No | 2099 (55.3) | 1775 (51.4) | 602 (43.4) | |
| Hypertension (N, %) | <0.001 | |||
| Yes | 1024 (24.3) | 1119 (29.7) | 732 (44.9) | |
| No | 2767 (75.7) | 2268 (70.3) | 680 (55.1) | |
| Diabetes (N, %) | <0.001 | |||
| Yes | 340 (6.3) | 356 (7.0) | 267 (14.8) | |
| No | 3451 (93.7) | 3031 (93.0) | 1146 (85.1) | |
| Cataract surgery (N, %) | ||||
| Yes | 289 (5.3) | 310 (6.9) | 175 (9.1) | <0.001 |
| No | 3502 (94.7) | 3077 (93.1) | 1238 (90.9) | |
Abbreviations: SD: standard deviation; BMI: body mass index.
Table 3.
The correlation between sleep and a history of cataract surgery.
| Variable | Model 1 | Model 2 | Model 3 | |||
|---|---|---|---|---|---|---|
| PR (95%CI) | p-Value | PR (95%CI) | p-Value | PR (95%CI) | p-Value | |
| Sleep duration (continuous) | 1.15 (1.07, 1.24) | <0.001 | 0.99 (0.94, 1.05) | 0.834 | 1.00 (0.94, 1.06) | 0.996 |
| Sleep duration (multi-category) | ||||||
| 7–8 h | ref | ref | ref | ref | ||
| <7 h | 1.00 (0.78, 1.27) | 0.97 | 1.12 (0.91, 1.38) | 0.284 | 1.07 (0.87, 1.32) | 0.463 |
| >8 h | 2.26 (1.77, 2.90) | <0.001 | 1.21 (0.97, 1.51) | 0.083 | 1.17 (0.91, 1.49) | 0.196 |
| Sleep disorder | ||||||
| No | ref | ref | ref | |||
| Yes | 1.10 (0.83, 1.45) | 0.514 | 1.16 (0.88, 1.53) | 0.273 | 1.11 (0.82, 1.50) | 0.455 |
| Sleep trouble | ||||||
| No | ref | ref | ref | |||
| Yes | 1.65 (1.46, 1.86) | <0.001 | 1.46 (1.29, 1.66) | <0.001 | 1.40 (1.22, 1.62) | <0.001 |
| Sleep patterns | ||||||
| Healthy sleep | ref | ref | ref | |||
| Intermediate sleep | 1.29 (1.06, 1.57) | 0.0117 | 1.27 (1.09, 1.49) | 0.004 | 1.24 (1.05, 1.46) | 0.017 |
| Poor sleep | 1.70 (1.38, 2.10) | <0.001 | 1.46 (1.23, 1.74) | <0.001 | 1.36 (1.13, 1.64) | 0.004 |
Model 1: unadjusted. Model 2: Model 1 + sex, age, and race. Model 3: Model 2 + educational level, marital status, economic level, BMI, alcohol consumption, smoking status, hypertension, and diabetes. Abbreviations: BMI: body mass index; PR: prevalence ratio; CI: confidence interval.
Table 4.
Subgroup analyses of the association between sleep patterns and cataract surgery.
| Healthy Sleep | Intermediate Sleep | Poor Sleep | p for Interaction | ||||
|---|---|---|---|---|---|---|---|
| Subgroup | PR (95%CI) | p-Value | PR (95%CI) | p-Value | |||
| Age (years) | 0.249 | ||||||
| <60 | 51/5796 | ref | 2.13 (0.91, 4.99) | 0.076 | 2.46 (0.95, 6.35) | 0.061 | |
| ≥60 | 723/2795 | ref | 1.18 (0.95, 1.47) | 0.116 | 1.29 (1.04, 1.61) | 0.025 | |
| Gender | 0.867 | ||||||
| Male | 364/4183 | ref | 1.19 (0.85, 1.66) | 0.291 | 1.39 (0.93, 2.08) | 0.102 | |
| Female | 410/4408 | ref | 1.27 (1.06, 1.53) | 0.015 | 1.37 (1.12, 1.69) | 0.006 | |
| Ethnicity | 0.832 | ||||||
| Mexican American | 59/1556 | ref | 0.90 (0.53, 1.52) | 0.660 | 1.55 (1.02, 2.34) | 0.040 | |
| Other Hispanic | 40/602 | ref | 1.60 (0.60, 4.27) | 0.270 | 1.57 (0.25, 9.95) | 0.561 | |
| Non-Hispanic white | 547/4277 | ref | 1.26 (1.06, 1.49) | 0.011 | 1.39 (1.14, 1.70) | 0.003 | |
| Non-Hispanic black | 109/1830 | ref | 1.15 (0.82, 1.62) | 0.389 | 1.28 (0.88, 1.87) | 0.185 | |
| Other | 19/326 | ref | 0.92 (0.23, 3.73) | 0.896 | 1.21 (0.25, 5.72) | 0.798 | |
| Education | 0.939 | ||||||
| Less than 9th grade | 162/997 | ref | 1.16 (0.74, 1.82) | 0.486 | 1.05 (0.75, 1.47) | 0.761 | |
| 9th–11th grade | 127/1417 | ref | 1.41 (0.92, 2.17) | 0.109 | 1.71 (1.03, 2.84) | 0.040 | |
| High school graduate or equivalent | 204/2078 | ref | 1.29 (0.99, 1.68) | 0.062 | 1.55 (1.04, 2.31) | 0.033 | |
| Some college or AA degree | 166/2366 | ref | 1.15 (0.83, 1.60) | 0.379 | 1.31 (0.79, 2.18) | 0.275 | |
| College graduate or above | 155/1773 | ref | 1.30 (0.79, 2.15) | 0.281 | 1.37 (0.69, 2.71) | 0.338 | |
| BMI | 0.062 | ||||||
| <25 kg/m2 | 236/2554 | ref | 1.36 (1.05, 1.75) | 0.023 | 1.32 (0.91, 1.93) | 0.133 | |
| 25–30 kg/m2 | 292/2981 | ref | 1.37 (0.99, 1.89) | 0.058 | 1.13 (0.82, 1.55) | 0.432 | |
| >30 kg/m2 | 246/3056 | ref | 0.79 (0.54, 1.16) | 0.208 | 1.48 (1.02, 2.41) | 0.040 | |
| Economic level | 0.465 | ||||||
| <1 | 115/1596 | ref | 0.86 (0.48, 1.53) | 0.576 | 1.33 (0.68, 2.64) | 0.374 | |
| 1–3 | 451/3640 | ref | 1.21 (1.00, 1.47) | 0.052 | 1.32 (1.02, 1.71) | 0.040 | |
| >3 | 208/3355 | ref | 1.45 (1.03, 2.04) | 0.034 | 1.41 (0.90, 2.21) | 0.119 | |
| Marital status | 0.562 | ||||||
| Unmarried or other | 365/3241 | ref | 1.10 (0.88, 1.39) | 0.361 | 1.21 (0.89, 1.66) | 0.198 | |
| Married or living with a partner | 409/5350 | ref | 1.33 (1.08, 1.64) | 0.011 | 1.44 (1.02, 2.04) | 0.040 | |
| Alcohol consumption | 0.751 | ||||||
| ≥12 drinks/year | 461/6028 | ref | 1.17 (0.94, 1.44) | 0.142 | 1.31 (1.02, 1.69) | 0.034 | |
| <12 cups/year | 313/2563 | ref | 1.33 (1.00, 1.76) | 0.047 | 1.43 (1.04, 1.97) | 0.032 | |
| Smoking status | 0.551 | ||||||
| Yes | 415/4115 | ref | 1.20 (0.91, 1.56) | 0.173 | 1.45 (1.13, 1.87) | 0.007 | |
| No | 359/4476 | ref | 1.26 (1.04, 1.54) | 0.024 | 1.28 (0.93, 1.72) | 0.127 | |
| Hypertension | 0.785 | ||||||
| Yes | 469/2876 | ref | 1.24 (1.01, 1.52) | 0.044 | 1.29 (0.97, 1.70) | 0.072 | |
| No | 305/5715 | ref | 1.20 (0.90, 1.60) | 0.188 | 1.46 (0.96, 2.22) | 0.075 | |
| Diabetes | 0.965 | ||||||
| Yes | 196/963 | ref | 1.18 (0.86, 1.60) | 0.271 | 1.32 (0.91, 1.91) | 0.123 | |
| No | 578/7628 | ref | 1.24 (1.01, 1.52) | 0.043 | 1.36 (1.06, 1.73) | 0.020 | |
Abbreviations: BMI: body mass index; PR: prevalence ratio; CI: confidence interval. Each stratification is adjusted for all covariates.
Table 5.
Subgroup analyses of the association between sleep trouble and a history of cataract.
| Subgroup | PR (95%CI) | p-Value | p for Interaction | |
|---|---|---|---|---|
| Age (years) | 0.338 | |||
| <60 | 51/5796 | 1.84 (1.00, 3.41) | 0.049 | |
| ≥60 | 723/2795 | 1.30 (1.12, 1.51) | 0.002 | |
| Gender | 0.448 | |||
| Male | 364/4183 | 1.29 (0.98, 1.70) | 0.070 | |
| Female | 410/4408 | 1.50 (1.29, 1.74) | <0.001 | |
| Ethnicity | 0.617 | |||
| Mexican American | 59/1556 | 2.37 (1.39, 4.03) | 0.004 | |
| Other Hispanic | 40/602 | 1.41 (0.36, 5.54) | 0.564 | |
| Non-Hispanic white | 547/4277 | 1.43 (1.23, 1.66) | <0.001 | |
| Non-Hispanic black | 109/1830 | 1.02 (0.67, 1.47) | 0.969 | |
| Other | 19/326 | 2.63 (0.52, 13.19) | 0.218 | |
| Education | 0.611 | |||
| Less than 9th grade | 162/997 | 1.16 (0.86, 1.56) | 0.321 | |
| 9th–11th grade | 127/1417 | 1.63 (1.14, 2.33) | 0.010 | |
| High school graduate or equivalent | 204/2078 | 1.59 (1.16, 2.18) | 0.006 | |
| Some college or AA degree | 166/2366 | 1.55 (1.06, 2.27) | 0.027 | |
| College graduate or above | 155/1773 | 1.22 (0.79, 1.88) | 0.350 | |
| BMI | 0.495 | |||
| <25 kg/m 2 | 236/2554 | 1.26 (0.97, 1.63) | 0.075 | |
| 25–30 kg/m 2 | 292/2981 | 1.36 (1.02, 1.81) | 0.040 | |
| >30 kg/m 2 | 246/3056 | 1.63 (1.19, 2.24) | 0.005 | |
| Economic level | 0.948 | |||
| <1 | 115/1596 | 1.32 (0.81, 2.14) | 0.238 | |
| 1–3 | 451/3640 | 1.38 (1.13, 1.68) | 0.004 | |
| >3 | 208/3355 | 1.44 (1.10, 1.89) | 0.012 | |
| Marital status | 0.901 | |||
| Unmarried or other | 409/5350 | 1.43 (1.08, 1.87) | 0.015 | |
| Married or living with a partner | 365/3241 | 1.33 (1.05, 1.67) | 0.020 | |
| Alcohol consumption | 0.319 | |||
| ≥12 drinks/year | 461/6028 | 1.34 (1.11, 1.61) | 0.005 | |
| <12 cups/year | 313/2563 | 1.53 (1.20, 1.96) | 0.002 | |
| Smoking status | 0.443 | |||
| Yes | 415/4115 | 1.35 (1.10, 1.66) | 0.008 | |
| No | 359/4476 | 1.49 (1.18, 1.89) | 0.003 | |
| Hypertension | 0.599 | |||
| Yes | 469/2876 | 1.34 (1.13, 1.58) | 0.002 | |
| No | 305/5715 | 1.50 (1.11, 2.03) | 0.013 | |
| Diabetes | 0.433 | |||
| Yes | 196/963 | 1.54 (1.14, 2.07) | 0.008 | |
| No | 578/7628 | 1.36 (1.15, 1.60) | 0.002 |
Abbreviations: BMI: body mass index; PR: prevalence ratio; CI: confidence interval. Each stratification is adjusted for all covariates.
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MDPI and ACS Style
Wang, C.; Bao, N.; Jiang, Z. The Association Between Sleep Health and a History of Cataract Surgery in the United States Based on the National Health and Nutrition Examination Survey (NHANES) 2005–2008. Healthcare 2025, 13, 1136. https://doi.org/10.3390/healthcare13101136
AMA Style
Wang C, Bao N, Jiang Z. The Association Between Sleep Health and a History of Cataract Surgery in the United States Based on the National Health and Nutrition Examination Survey (NHANES) 2005–2008. Healthcare. 2025; 13(10):1136. https://doi.org/10.3390/healthcare13101136
Chicago/Turabian StyleWang, Chuanxi, Ning Bao, and Zhengxuan Jiang. 2025. "The Association Between Sleep Health and a History of Cataract Surgery in the United States Based on the National Health and Nutrition Examination Survey (NHANES) 2005–2008" Healthcare 13, no. 10: 1136. https://doi.org/10.3390/healthcare13101136
APA StyleWang, C., Bao, N., & Jiang, Z. (2025). The Association Between Sleep Health and a History of Cataract Surgery in the United States Based on the National Health and Nutrition Examination Survey (NHANES) 2005–2008. Healthcare, 13(10), 1136. https://doi.org/10.3390/healthcare13101136
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