Abstract
Purpose: To investigate the association of visual impairment (VI) with socioeconomic status, including the highest educational level and household income when other confounding variables were adjusted. Methods: The Korean National Health and Nutrition Examination Survey 2010–2012 data were analyzed. The final analyses included a total of 16,905 subjects with their demographic, socioeconomic, and health data. The socioeconomic data included the household income and the highest educational level. Household income was categorized into quartiles. The educational attainment was categorized as ≤ elementary school, middle school, high school, and ≥ university. VI was defined when the distance-corrected VA was worse than 0.32 (20/63 Snellen) in the better-seeing eye according to the definition of the World Health Organization. Four multiple logistic regression models were used to determine the odds ratio (OR) and 95% confidence interval (CI) between VI and household income or between VI and the highest educational level, by adjusting for several confounding variables. Results: The prevalence of VI was 5.0% (844 subjects). Multiple logistic regression analyses indicated that the adjusted OR was 1.729 (95% CI: 1.108–2.697) for developing VI in subjects with ≤ elementary school as their highest education level compared to those with ≥ university education. Also, the adjusted OR of subjects in the first quartile of household income was 1.502 (95% CI: 1.061–2.127) for developing VI compared to those in the fourth quartile of household income. Conclusions: The present study showed that household income and education were independently associated with VI even after adjusting for significant confounding variables.
| Original language | English |
|---|---|
| Pages (from-to) | 131-136 |
| Number of pages | 6 |
| Journal | Seminars in Ophthalmology |
| Volume | 34 |
| Issue number | 3 |
| DOIs | |
| State | Published - 3 Apr 2019 |
Bibliographical note
Publisher Copyright:©, © Taylor & Francis.
Keywords
- Visual impairment
- education level
- household income
- multiple logistic regression analyses
- socioeconomic status
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