Original Article Yonsei Med J 2015 Nov;56(6):1663-1670 http://dx.doi.org/10.3349/ymj.2015.56.6.1663
pISSN: 0513-5796 · eISSN: 1976-2437
Factors Associated with Cataract in Korea: A Community Health Survey 2008–2012 Tyler Hyungtaek Rim1, Dong Wook Kim2, Sung Eun Kim1, and Sung Soo Kim1,3 Institute of Vision Research, Department of Ophthalmology, Severance Hospital, Yonsei University College of Medicine, Seoul; Department of Policy Research Affairs, National Health Insurance Corporation Ilsan Hospital, Goyang; 3 Yonsei Healthcare Big Data Based Knowledge Integration System Research Center, Institute of Convergence Science, Yonsei University College of Medicine, Seoul, Korea. 1 2
Purpose: To investigate sociodemographic factors, health behaviors, and comorbidities associated with cataracts in a large, nationally representative Korean sample. Materials and Methods: This cross-sectional study included 715554 adults aged 40 years or older who participated in the 2008– 2012 Community Health Survey. Significant risk factors were identified using multivariate logistic regression analysis for self-reported cataract, and a nomogram for analysis of cataract risk was generated. Results: Roughly 11% of participants (n=88464) reported being diagnosed with cataracts by a doctor. Age was the most important independent risk factor [adjusted odds ratio (aOR)=1.11, 99% confidence interval (CI), 1.11–1.11 for each increasing year]. Significant comorbidities with descending order of effect size (aOR, 99% CI), included diabetes mellitus (1.78, 1.71–1.85), osteoporosis (1.62, 1.56–1.69), arthritis (1.54, 1.48–1.59), hepatitis B infection (1.46, 1.31–1.63), atopic dermatitis (1.50, 1.33–1.69), angina (1.46, 1.35–1.57), allergic rhinitis (1.45, 1.36–1.55), dyslipidemia (1.38, 1.31–1.45), asthma (1.35, 1.26–1.44), and hypertension (1.23, 1.19–1.28). Subjects who sleep less than 6 hours/day were more likely to have cataract than subjects who sleep more than 9 hours/day as a reference group (aOR=1.22, 99% CI, 1.11–1.34). Conclusion: While the most important cataract risk factor was age, the ten comorbidities mentioned above were also significant risk factors. Interestingly, longer duration of sleep was associated with a protective effect against cataract development. Key Words: Cataract, a community health survey, risk factor, nomogram
INTRODUCTION Age-related cataracts are the leading cause of blindness worldwide, including America and Asia.1-7 Identifying risk factors associated with cataract development will facilitate the identification of new prevention and treatment options. Previous studies have evaluated risk factors associated with cataReceived: October 13, 2014 Revised: December 19, 2014 Accepted: February 3, 2015 Corresponding author: Prof. Sung Soo Kim, Institute of Vision Research, Department of Ophthalmology, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea. Tel: 82-2-2228-3570, Fax: 82-2-312-0541, E-mail:
[email protected]
ract, including sociodemographic factors and comorbidities.3,6,8-13 These findings offer insight into the pathophysiology of this multifactorial disease. In this study, we investigated the association between cataracts and sociodemographic factors, behavioral risk factors, and comorbidities in 715554 adults, aged 40 years and older, who participated in the Community Health Survey (CHS) 2008– 2012. This is a nationally representative survey conducted by the Korea Centers for Disease Control and Prevention (KCDCP) that provides data on cataract diagnosis, health status, and sociodemographic factors.
•The authors have no financial conflicts of interest. © Copyright: Yonsei University College of Medicine 2015 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/ licenses/ by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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MATERIALS AND METHODS Statement of ethics The study adhered to the tenets of the Declaration of Helsinki.
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The survey was reviewed and approved by the Institutional Review Board of the KCDCP, and all participants provided written informed consent. The study was approved by the Institutional Review Board of the Yonsei University College of Medicine, Seoul, Korea.
Design and study population The CHS was administered to roughly 900 subjects from 253 community health centers throughout South Korea; participants were selected among adults, aged 19 years or older, who resided in the catchment area of each South Korean community health center. A stratified multistage probability sampling design was employed, and each participant was surveyed only once. This survey was performed for 3 months of every year, from 2008–2012. One-on-one, protocol-based interviews were conducted by trained interviewers. Questionnaires included 253 questions in 11 fields, covering sociodemographic factors, behavioral factors, comorbidities, and quality of life. In order to ensure high quality survey data, the Ministry of Health and Welfare selected another agency to re-administer the survey to 10% of subjects via telephone. If the results were not consistent, the interviewer involved was re-trained by KCDCP. The survey was administered to 220258 individuals in 2008; 230715 in 2009; 229229 in 2010; 229226 in 2011; and 228921 in 2012. The response rate was not available, because the subjects who did not participate were not included in this survey. Overall, the study included 715554 participants who completed the questionnaires about cataracts and potential risk factors.
Presence of self-reported cataract Subjects were asked “Have you ever had a cataract before?” to ensure accurate self-reporting. They were also asked “Has a doctor ever diagnosed you with a cataract before (either eye)?”
Independent variables: potential risk factors Sociodemographic variables included: current age, gender, residential area (rural/urban), monthly household income (lowest quintile/2nd–4th quintiles/highest quintile), highest educational level attained (elementary school graduate or lower/ middle school graduate/high school graduate/university graduate or higher), Medicaid recipient, spouse, and occupation (administrator, management, professional occupations/business and financial operation occupations/sales and related occupations/farming, fishing and forestry occupations/installation, maintenance, and repair occupations or laborer/housewife/ others, including unemployed). Health status variables included hypertension, diabetes mellitus (DM; yes/no), dyslipidemia, cerebrovascular accident, myocardial infarction, angina, arthritis, osteoporosis, asthma, allergic rhinitis, atopic dermatitis, and hepatitis B infection. Health behavior variables included being a lifetime smoker, being a lifetime alcohol user, physical activity of moderate intensity (more than three times a week/no regular exercise), salt
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intake (low/normal/high), sleep duration (9 hours), and body mass index (BMI; quintile). Supplementary Table 1 (only online) lists detailed definitions of the independent variables. To assess co-morbidities, subjects were asked the question “Have you ever been diagnosed with [disease] by a doctor before?” with possible responses of “yes” or “no.” The trained interviewer emphasized “by a doctor” to ensure more accurate results.
Statistical analysis Basic characteristics of the study population were reported. We determined the age-standardized prevalence of cataracts in Korea using the weighting system recommended by the KCDCP. Statistical analyses were conducted using the Stata/SE version 13 (StataCorp, College Station, TX, USA) and survey procedures were used in all analyses to account for the stratified sampling design of CHS; this procedure is adjusted for oversampling, missing responses, and age structure of the Korean population. A two-step approach was used to identify cataract risk factors. First, we constructed multivariate logistic regression models to identify cataract risk factors using all variables, including sociodemographic factors, comorbidities, and health behavior. In order to compare the effect size [adjusted odds ratio (aOR)], we defined the group with the smallest values of effect size as a reference group. Second, a final model was constructed to include the ten most important variables in regards to comorbidities, based on aOR values (aOR, 1.24–1.87); the most important variable, sleep duration, in regards to health behavior (aOR=1.33); and age, according to 5-year intervals. Other variables related to health behavior had an aOR of less than1.10, and were not included in the final model due to small effect size, compared to comorbidities. A nomogram was constructed using the final model. We used receiver operating characteristics derived area under the curve to quantify the predictive accuracy of our model. We graphically investigated the performance characteristics of the nomogram using calibration plots. Models were internally validated via bootstrapping with 200 replicates. Nomograms were created using R software for Windows version 3.1.0 (R Foundation for Statistical Computing, Nashville, TN, USA). All statistical tests were performed in Stata/SE version 13. Because this study had such a large sample size, there was the potential to obtain results that were statistically significant according to their p-values but do not have practical significance. For this reason, a relatively strict cut-off of a twosided p-value9 hrs/day 6–9 hrs/day
