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- Original Article Relationship between long-term PM2.5 exposure and myopia prevalence in adults: analysis of the Korea National Health and Nutrition Examination Survey–Air Pollution Linked Data, 2020
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Minju Jung1
, Hyungdoo Kim1, Ji Hoon Kim1, Dong-Jae Seo1, Jong-Han Leem1,2, Shin-Goo Park1,2, Dong-Wook Lee1,2, Hwan-Cheol Kim1,2,* -
Annals of Occupational and Environmental Medicine 2025;37:e8.
DOI: https://doi.org/10.35371/aoem.2025.37.e8
Published online: April 16, 2025
1Department of Occupational and Environmental Medicine, Inha University Hospital, Incheon, Korea
2Department of Social and Preventive Medicine, Inha University College of Medicine, Incheon, Korea
- *Corresponding author: Hwan-Cheol Kim Department of Occupational and Environmental Medicine, Inha University Hospital, Inha University College of Medicine, 27 Inhang-ro, Jung-gu, Incheon 22332, Korea E-mail: cheol17@hanmail.net, carpediem@inha.ac.kr
• Received: December 26, 2024 • Revised: April 8, 2025 • Accepted: April 8, 2025
© 2025 Korean Society of Occupational & Environmental Medicine
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Abstract
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Background PM2.5 (particulate matter less than 2.5 μm) causes various health problems. Recent studies suggest that long-term exposure to PM2.5 may have a negative impact on vision. This study examined the effects of long-term exposure to concentrations of PM2.5 exceeding Korean standards on myopia prevalence.
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Methods This study was conducted on adults aged 40–69 years. The PM2.5 concentrations were calculated as the 1–5-year moving averages based on the participants' residential areas. The relationships between the PM2.5 levels, categorized by the annual average concentration standard in Korea, and the prevalence of myopia were analyzed using binary logistic regression. The results were evaluated using the 95% confidence interval.
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Results PM2.5 concentrations averaged over 1–3 years were not significantly associated with the prevalence of myopia. On the other hand, the prevalence of myopia was significantly higher in areas where the 4–5-year moving average PM2.5 levels exceeded the Korean standards. These findings suggest that long-term exposure to PM2.5 may have a detrimental effect on vision.
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Conclusions This study revealed the impact of long-term PM2.5 exposure on the prevalence of myopia, highlighting the importance of managing PM2.5 levels. Nevertheless, further cohort studies focusing on adults and in-depth research into the effects of long-term exposure will be necessary.
BACKGROUND
Vision deterioration has a particularly severe impact on the elderly. Previous studies have shown that myopia is associated with depressive symptoms in individuals over 60 years old,1 and one study reported a significant increase in the likelihood of falls or fractures within 5 years among older adults who recently developed visual impairments.2 In addition, vision loss or myopia adversely affects various health aspects, such as cognitive decline.3
Exposure to fine particulate matter (PM2.5, particulate matter less than 2.5 μm) can influence the development of myopia through various biological mechanisms. PM2.5 can induce inflammatory responses on the surface and conjunctiva of the eyes, potentially disrupting normal eye growth and development.4 Moreover, PM2.5 can increase oxidative stress, causing damage to the ocular tissues, which may contribute to the onset of various eye diseases that can lead to myopia, such as dry eye syndrome, corneal and conjunctival diseases, cataracts, glaucoma, age-related macular degeneration, retinitis pigmentosa, and diabetic retinopathy.5 In addition, recent experimental evidence suggests that PM2.5 can disrupt the inner blood-retinal barrier by inducing inflammation and ferroptosis in retinal vascular endothelial cells, leading to retinal damage and dysfunction.6 These findings support the biological plausibility of PM2.5 exposure contributing to ocular pathologies, including myopia.
Myopia is caused primarily by genetic and environmental factors during childhood. On the other hand, recent studies suggest that air pollution, particularly PM2.5 exposure, can influence the prevalence of myopia, even in adults. Ruan et al. (2019)7 reported a J-shaped relationship between PM2.5 and ozone exposure and the prevalence of myopia in a study of adults aged 50 years and older, indicating that high concentrations of air pollution exposure may have a cumulative impact on adult eye health.
The World Health Organization (WHO) annual standard for PM2.5 is 5 µg/m3, whereas Korea's annual standard is three times higher than the WHO guideline, 15 µg/m3. This threshold was established based on Korea’s historical air quality trends and the feasibility of regulatory implementation. In Korea, when PM2.5 concentrations exceed 15 µg/m3, the government issues nationwide public alerts and implements air pollution reduction policies to mitigate exposure. Despite its regulatory importance, few studies have evaluated air pollution-related health outcomes using Korea’s national air pollution standards. Therefore, this study conducted a cross-sectional analysis using 1- to 5-year moving averages of the PM2.5 concentrations to determine how the prevalence of myopia in adults changes when the exposure exceeds or falls below Korea's annual PM2.5 standard of 15 μg/m3, aligning with the current conditions in Korea.
METHODS
This study is based on the 2020 Korea National Health and Nutrition Examination Survey–Air Pollution Linked Data, examining the relationship between the participants’ myopia status and the 1–5 year moving averages of PM2.5 concentrations in their residential areas.
The Korea National Health and Nutrition Examination Survey uses the most recent population and housing census as a sampling frame, allowing the extraction of a representative sample of the target population—Korean residents aged 1 year and older. The survey process includes household confirmation, health surveys, clinical examinations, and nutritional surveys.
The Korea National Health and Nutrition Examination Survey air pollution dataset was combined with the eighth cycle data from 2020 with air pollution records. The linkage was achieved using geographic information, including administrative districts, si-gun-gu (cities, counties, and districts), and geocodes based on the participants' residential addresses and survey dates.8 Using the study data, it was possible to access basic information about participants, such as sex, health status, occupation, and residence, and the moving average of the PM2.5 concentrations around their residential areas.
A total of 7,359 individuals participated in the 2020 Korea National Health and Nutrition Examination Survey–Air Pollution Linked Data. Among them, ophthalmic interviews and clinical ophthalmic examinations were conducted on 4,488 individuals aged 40 years and older. After excluding 1,897 individuals with hyperopia, 883 individuals with a prior ophthalmic diagnosis, 123 individuals with a history of ophthalmic surgery, and 353 individuals newly diagnosed with an ophthalmic condition at the time of the survey, 1,232 participants initially met the inclusion criteria.
However, participants aged 70 years and older were excluded from the analysis not only due to the small sample size (n = 50), which could lead to reduced statistical power and increased variability in the results, but also because natural aging-related changes in the lens, such as sclerosis or mild lens opacity, could act as a confounding factor in the association between PM2.5 exposure and myopia prevalence. As a result, a total of 1,182 participants were included in the final analysis (Fig. 1).
In this study, normal vision was defined as having a refractive error between −0.75D (diopter) and +1D in both eyes according to the automatic refraction measurements. Myopia was defined as having a refractive error of −0.75D or less in at least one eye.9
The PM2.5 concentrations and their moving averages were derived using the Community Multiscale Air Quality model, which integrates meteorological data and emission inventories. The initial PM2.5 concentration estimates were refined using data assimilation techniques to improve accuracy. The satellite data was used to calculate the PM2.5 concentrations on a 1 km grid, and the results were validated against data from air quality monitoring stations. These concentrations were matched to the participants' residential coordinates. This approach assumes that the participant’s long-term exposure to PM2.5 is best represented by the pollution level at their primary residence, given that residential location is a major determinant of chronic air pollution exposure.
Although all participants underwent health examinations in 2020, the exact examination dates varied among individuals. Based on each participant's examination date, we calculated the 1-year moving average PM2.5 concentration (average PM2.5 concentration over 0–365 days prior to the examination date), the 2-year moving average concentration (average over 0–730 days), the 3-year moving average concentration (average over 0–1,095 days), the 4-year moving average concentration (average over 0–1,460 days), and the 5-year moving average concentration (average over 0–1,826 days). A moving average is a statistical method used to smooth short-term fluctuations and highlight longer-term trends in data. In this study, it was used to reflect the cumulative exposure of participants to PM2.5 over multiple years rather than relying on a single-timepoint measurement. This approach is based on the hypothesis that prolonged exposure to air pollution may have a cumulative detrimental effect on ocular health, potentially contributing to the development of myopia.
Covariates included age (40–54 or 55–69), sex, monthly household income relative to the median (categorized as ‘<2,072,900 won’ or ‘≥2,072,900 won’), education level (categorized as ‘below middle school’ or ‘above high school’), marital status (‘married’ or ‘unmarried’), alcohol consumption (‘non-drinkers’ or ‘drinkers’), smoking status (ever-smoked or never-smoked), urbanity (urban or rural)7 and daily near-work hours (“<4 hours/day” or “≥4 hours/day”).10
A Pearson’s chi-square test was used to examine the general characteristics of the study participants and the statistical significance of the relationship with myopia. Binary logistic regression analysis was used to evaluate the association between the PM2.5 concentrations and myopia. The dependent variable was the myopia status (1: myopia, 0: normal vision), while the independent variables included the PM2.5 concentration and other potential confounders.
In addition, we also conducted an additional analysis using PM2.5 as a continuous variable. The rationale for using a continuous variable was to determine whether myopia prevalence increases linearly with PM2.5 concentration, rather than being influenced only when PM2.5 exceeds a specific threshold (15 μg/m3). The PM2.5 concentration was calculated as the 1–5-year moving average based on each participant’s residential area, which was assigned using a 1 km × 1 km grid system. Logistic regression analysis was performed using the continuous PM2.5 variable to estimate odds ratios (ORs) and 95% confidence intervals (CIs).
All statistical analyses were conducted using SPSS for Windows version 25.0 (IBM Corp., Armonk, NY, USA), with the statistical significance set at p < 0.05.
This study used data from the Analysis of the Korea National Health and Nutrition Examination Survey–Air Pollution Linked Data, 2020, carried out by the Korea Disease Control and Prevention Agency (KDCA) (2018-01-03-2C-A).
RESULTS
Of 1,182 participants aged 40–69 years, 673 (56.9%) were in the myopia group, and 509 (43.1%) were in the non-myopia group (Table 1). The comparisons between the two groups showed that the myopia group was significantly younger (p < 0.001) and had a higher proportion of participants with a high school education or higher (p < 0.001). The myopia group also had a higher percentage of unmarried individuals (p = 0.022) and those engaged in near-work for more than four hours per day (p < 0.001). The sex, household income, alcohol consumption, and smoking status were similar in both groups.
An analysis of the PM2.5 concentrations over the past 1–5 years showed a slight decreasing trend in PM2.5 levels (Table 2). Table 3 lists the results of a binary logistic regression analysis examining the relationship between PM2.5 1- to 5-year moving average concentrations, classified based on Korea’s annual standard of 15 μg/m3, and the prevalence of myopia.
Model 1 represents the unadjusted OR, showing a trend of increasing odds of indicating a higher likelihood of myopia as PM2.5 concentration increases. In particular, the 4-year and 5-year moving averages of PM2.5 showed a high OR (OR: 4.038; 95% CI: 1.295–12.595), suggesting a strong association between long-term PM2.5 exposure and the risk of myopia.
In model 2, adjustments were made for potential confounding variables such as sex and age. The adjusted ORs remained significant across various time intervals, supporting the results of model 1. For the 4-year and 5-year PM2.5 moving averages, the adjusted OR was OR 3.745 (95% CI: 1.161–12.077), maintaining a strong association with a higher prevalence of myopia even after adjusting for sex and age.
Model 3 was further adjusted for additional variables, including educational level, marital status, alcohol consumption, smoking, daily hours spent near work, and residential region. The adjusted OR for the 4-year and 5-year PM2.5 moving averages being 3.474 (95% CI: 1.025–11.777). Hence, prolonged exposure to high PM2.5 levels is associated with an increased risk of myopia.
Supplementary Table 1 shows the relationship between the PM2.5 concentrations, treated as continuous variables rather than categorized by Korea's annual standard of 15 μg/m3, and myopia using binary logistic regression analysis. For the 1-year moving average PM2.5 concentration, the unadjusted OR was 1.047 (95% CI: 1.005–1.090), indicating a significant association with myopia. On the other hand, this association was no longer significant in model 2, which adjusted for sex and age, or in model 3, which adjusted for additional confounding variables. Similarly, for the 2-, 3-, 4-, and 5-year moving average PM2.5 concentrations, the unadjusted ORs indicated a weak positive association with myopia, but no significant associations were observed in the models adjusted for confounding factors.
DISCUSSION
This study examined the relationship between 1–5 year moving averages of the PM2.5 concentrations measured near participants' residences and the prevalence of myopia. Binary logistic regression analysis showed that while the 1–3 year moving averages were not significantly associated with myopia prevalence, participants living in areas where the 4–5 year moving averages of PM2.5 exceeded the Korean standard had significantly higher rates of myopia. Hence, the cumulative damage from long-term PM2.5 exposure may contribute to myopia risk.
On the other hand, when PM2.5 was treated as a continuous variable (Supplementary Table 1), no significant association was found after adjustment, suggesting a possible threshold effect rather than a simple linear relationship. Compared to the main results (Table 3), exposure to PM2.5 levels exceeding a specific threshold (15 μg/m3) for 4–5 years significantly increased myopia prevalence. This supports the notion that long-term exposure to high PM2.5 levels may have a greater impact on vision than minor incremental increases in concentration. Future studies should explore the potential non-linearity of this relationship and determine precise threshold values that may influence myopia development.
Meanwhile, the OR and 95% CI values for 4-year and 5-year exposure in Table 3 were identical across all models. This is because the same residential areas exceeded the 15 μg/m3 threshold for both exposure periods. While PM2.5 concentrations fluctuate daily and regionally, highly polluted areas tend to sustain elevated concentrations over time, whereas cleaner areas remain below the threshold. The identical OR values thus reflect consistent exposure patterns rather than an error.
The characteristics of this study are as follows. First, unlike previous studies focusing on short-term (≤3 years) PM2.5 exposure, this study examined the impact of exposure over a longer period (1–5 years) on myopia prevalence. Second, instead of the WHO standard, Korea’s PM2.5 standard (15 μg/m3) was used, reflecting real-world exposure conditions and regulatory feasibility. This enhances the study’s relevance to the Korean population. Third, no significant results were observed for shorter exposure periods (1–3 years), but a sharp increase in myopia prevalence was found after 4–5 years of high PM2.5 exposure. This suggests that cumulative damage over time, rather than short-term exposure, may be a key factor in myopia development.
The study participants were likely exposed to PM2.5 even before the study period, given Korea’s long-standing air pollution history. In fact, since the early 2000s, Korea’s PM2.5 levels have consistently exceeded WHO recommendations, peaking in the mid-2010s.11 Thus, the “1–5 years of exposure” analyzed here does not reflect participants’ first-ever exposure but rather the additional impact of 1–5 years on top of existing background exposure.
Despite prior exposure, PM2.5’s significant association with myopia appearing only after 4–5 years can be explained as follows: First, long-term PM2.5 effects manifest gradually through cumulative damage, including chronic inflammation and oxidative stress, including chronic inflammation and oxidative stress.12 Second, PM2.5 exposure over 4–5 years may have exceeded a critical threshold, triggering a sharper increase in myopia risk. Air pollution’s health effects may not increase linearly but escalate once surpassing a specific level. Previous studies also reported that sustained high PM2.5.
Exposure amplifies health risks after a certain time.7 Thus, while participants were already exposed to PM2.5 before the study, the additional 4–5 years of high exposure played a crucial role in increasing myopia prevalence. Future studies should incorporate longer-term exposure data to systematically analyze the cumulative effects of PM2.5 on ocular health.
Previous research on adults mainly linked air pollution to ophthalmic diseases like glaucoma and cataracts, suggesting high pollutant exposure increases disease prevalence and visual impairments.13,14 However, this study excluded participants with ophthalmic diseases and with a history of ophthalmic surgery through retinal and glaucoma examinations at data collection. This ensured that findings specifically assessed the relationship between air pollution and myopia prevalence.
The study highlights the need for air quality policies to reduce prolonged PM2.5 exposure. Myopia prevalence significantly increased in areas where long-term PM2.5 concentration exceeded 15 μg/m3, indicating that policies should go beyond short-term reductions and prevent sustained exposure above a certain threshold.
To achieve this, several policy approaches can be considered. First, areas with persistently high PM2.5 should implement stricter pollution controls and eye health programs, including air purifiers, indoor air quality improvements, and reduced outdoor activity on polluted days. Occupational groups with high PM2.5 exposure (e.g., construction, transportation workers) should receive enhanced health monitoring and protection. Second, long-term air quality management should focus on reducing chronic PM2.5 exposure through urban planning and stricter industrial emissions regulations. Third, public awareness campaigns should educate citizens on PM2.5’s effect on eye health and encourage preventive measures such as wearing protective eyewear outdoors and improving indoor air circulation.
Thus, this study suggests the need for stronger integration between air quality management and public health policies, emphasizing the necessity of addressing PM2.5 exposure as a potential risk factor for eye health. Future research should include a broader age range and regional characteristics to develop more precise policy recommendations.
This study focused on adults aged 40–69 years, a group with a high prevalence of presbyopia. As a result, the true prevalence of myopia may be underestimated in this population. In addition, individuals aged 70 years and older were excluded not solely due to the small sample size (n = 50), but also because age-related changes in the lens could cause slight alterations in refractive power, making it difficult to accurately analyze the relationship between PM2.5 exposure and myopia. This exclusion was made to maintain the reliability of the research results, minimizing potential bias due to lens aging. Future studies will require analysis including more diverse age groups.
Furthermore, the study’s generalizability is limited as it focused solely on Korean adults aged 40–69. Cultural, genetic, and environmental factors—such as air pollution levels, lifestyle habits, and genetic predisposition—may lead to differing effects in other populations. Additionally, as this study focused on middle-aged and older adults, the applicability of these findings to younger populations, where myopia typically develops and progresses more actively, remains uncertain. Future studies should aim to replicate these findings in different demographic groups and geographic regions to better understand the broader applicability of the observed associations.
This study constructed air pollution data based on the residential locations of the participants. Consequently, limitations in the exposure assessment may exist if a participant's primary activity areas differ from their place of residence. Commuting and daily mobility patterns may also contribute to exposure misclassification. Individuals who commute long distances or frequently travel through high-traffic urban areas may experience significantly higher PM2.5 exposure compared to those who remain near their residential areas. Public transportation users, cyclists, and pedestrians may be particularly vulnerable to elevated PM2.5 levels, especially in congested areas. However, due to data limitations, this study could not assess commuting-related PM2.5 exposure, which may have led to further inaccuracies in exposure classification. In addition, unlike children, adults with occupations spend significant time at their workplaces, and certain occupations may involve higher levels of PM2.5 exposure. For example, individuals working in construction, manufacturing, or transportation industries are more likely to be exposed to elevated concentrations of PM2.5, either outdoors or within enclosed work environments. However, due to limitations in the existing data, this study was unable to assess occupational PM2.5 exposure, which may have led to an underestimation of actual exposure levels for certain participants. Furthermore, indoor PM2.5 exposure from activities such as cooking, smoking, heating, and fuel combustion can be substantial. In poorly ventilated indoor environments, PM2.5 concentrations may even exceed outdoor levels, posing an additional exposure risk. However, due to data constraints, this study could not independently evaluate indoor PM2.5 exposure. The inability to account for these potential sources of exposure may have influenced the study findings. Future research should consider not only residential PM2.5 levels but also occupational, commuting-related, and indoor air pollution exposure to improve the accuracy of PM2.5 exposure assessments and provide a more comprehensive understanding of its potential effects on health.
The cross-sectional design of this study prevents the establishment of causal relationships between PM2.5 exposure and myopia. Further cohort studies will be needed to establish causality and assess the long-term effects of PM2.5 exposure on myopia. To overcome the limitations of cross-sectional analysis, future cohort studies should incorporate repeated measurements of PM2.5 exposure over extended periods, allowing for a more precise evaluation of cumulative exposure effects. In addition, detailed exposure timing analysis—such as examining the impact of early-life or long-term exposure—will be crucial in understanding the temporal relationship between air pollution and myopia development. Future studies should also include other air pollutants like PM10 (particulate matter less than 10 μm), SOx, and NOx to provide more comprehensive insights into the impact of air quality on eye health.
In 2020, during the coronavirus disease 2019 (COVID-19) pandemic, South Korea was one of the countries that implemented strict national restrictions. In addition to the outdoor PM2.5 concentrations, the actual exposure levels are likely related to the changes in the COVID-19 lockdown policies. Therefore, it is necessary to determine if similar results were observed in other years.
CONCLUSIONS
This study analyzed the relationship between long-term PM2.5 exposure and myopia prevalence in Korean adults aged 40–69 years. Short-term PM2.5 exposure (1–3 years) was not significantly associated with the prevalence of myopia. On the other hand, long-term exposure (4–5 years) to high PM2.5 concentrations was significantly associated with an increased prevalence of myopia. These findings suggest that cumulative exposure to PM2.5 high concentrations can have a negative impact on vision.
These results emphasize the importance of long-term air quality management to protect adult eye health from a public health perspective. Further studies using a broader range of age groups will be needed to confirm these findings. In-depth research on the cumulative effects of prolonged PM2.5 exposure is also required.
Abbreviations
CI
confidence interval
COVID-19
coronavirus disease 2019
OR
odds ratio
PM2.5
particulate matter less than 2.5 μm
PM10
particulate matter less than 10 μm
WHO
World Health Organization
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Competing interests
The authors declare that they have no competing interests.
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Author contributions
Conceptualization: Jung M, Kim H. Data curation: Kim H. Formal analysis: Jung M, Kim H. Methodology: Kim JH, Kim H, Kim HC. Supervision: Kim HC, Leem JH, Park SG, Kim H. Writing - original draft: Jung M. Writing - review & editing: Kim JH, Seo DJ, Kim HC, Leem JH, Park SG, Lee DW, Kim H.
NOTES
SUPPLEMENTARY MATERIAL
Supplementary Table 1.
Association between PM2.5 moving average concentration and myopia cases (continuous variable)
Table 1.
General characteristics of the study participants according to myopia status
| Characteristic |
Myopia |
p-valuea | |
|---|---|---|---|
| No | Yes | ||
| Total | 509 (43.1) | 673 (56.9) | |
| Sex | 0.481 | ||
| Male | 248 (48.7) | 314 (46.7) | |
| Female | 261 (51.3) | 359 (53.3) | |
| Age (years) | <0.001 | ||
| 40–54 | 286 (56.2) | 545 (81.0) | |
| 55–69 | 223 (43.8) | 128 (19.0) | |
| Monthly household income | <0.001 | ||
| <2,072,900 KRW (<$1,756 USD) | 151 (29.7) | 179 (26.6) | |
| ≥2,072,900 KRW (≥$1,756 USD) | 357 (70.3) | 493 (73.4) | |
| Education status | <0.001 | ||
| Below middle school | 94 (19.1) | 45 (7.0) | |
| Above high school | 397 (80.9) | 598 (93.0) | |
| Marital status | 0.022 | ||
| Married | 484 (95.1) | 617 (91.7) | |
| Unmarried | 25 (4.9) | 56 (8.3) | |
| Alcohol consumption | 0.185 | ||
| Never drinking | 40 (7.9) | 37 (5.5) | |
| Even once | 467 (92.1) | 635 (94.5) | |
| Smoking status | 0.332 | ||
| Never smoking | 276 (54.3) | 390 (58.0) | |
| Even once | 232 (45.7) | 282 (42.0) | |
| Daily near-work hours | <0.001 | ||
| < 4 hours/day | 350 (68.8) | 326 (48.4) | |
| ≥ 4 hours/day | 159 (31.2) | 347 (51.6) | |
| Residential area | 0.171 | ||
| “Dong” (administrative neighborhood) | 411 (80.7) | 564 (83.8) | |
| “Eup/myeon” (town/rural township) | 98 (19.3) | 109 (16.2) | |
Table 2.
Distributions of the PM2.5 moving average concentration
All participants underwent health examinations during 2020, although the exact examination dates varied across individuals. The moving average values were calculated based on the specific health examination date of each participant.
PM2.5: particulate matter less than 2.5 μm; SD: standard deviation; IQR: interquartile range.
Table 3.
Association between PM2.5 moving average concentration classified based on Korea’s annual standard of 15 μg/m3 and myopia cases
|
PM2.5 moving average concentration (based on the year prior to the health examination date) |
|||
|---|---|---|---|
| Model 1 (unadjusted OR) | Model 2 (adjusted ORa [95% CI]) | Model 3 (adjusted ORb [95% CI]) | |
| Average over 0–365 days | 1.428 (0.891–2.287) | 1.313 (0.804–2.146) | 1.212 (0.715–2.053) |
| Average over 0–730 days | 1.629 (0.877–3.025) | 1.518 (0.797–2.889) | 1.315 (0.660–2.621) |
| Average over 0–1,095 days | 1.592 (0.810–3.128) | 1.343 (0.663–2.719) | 1.105 (0.525–2.327) |
| Average over 0–1,460 days | 4.038 (1.295–12.595) | 3.745 (1.161–12.077) | 3.474 (1.025–11.777) |
| Average over 0–1,826 days | 4.038 (1.295–12.595) | 3.745 (1.161–12.077) | 3.474 (1.025–11.777) |
All participants underwent health examinations during 2020, although the exact examination dates varied across individuals. The moving average values were calculated based on the specific health examination date of each participant.
PM2.5: particulate matter less than 2.5 μm; OR: odds ratio, CI: confidence interval.
aORs were calculated by logistic regression analysis for PM2.5 moving average concentration after adjusting for sex and age;
bORs were calculated by logistic regression analysis for PM2.5 moving average concentration after adjusting for sex, age, monthly household income, education status, marriage status, alcohol consumption, smoking, daily near-work hours, residential area.
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XML DownloadRelationship between long-term PM2.5 exposure and myopia prevalence in adults: analysis of the Korea National Health and Nutrition Examination Survey–Air Pollution Linked Data, 2020
Fig. 1. Flow chart of study population.
Fig. 1.
Relationship between long-term PM2.5 exposure and myopia prevalence in adults: analysis of the Korea National Health and Nutrition Examination Survey–Air Pollution Linked Data, 2020
| Characteristic | Myopia |
p-value |
|
|---|---|---|---|
| No | Yes | ||
| Total | 509 (43.1) | 673 (56.9) | |
| Sex | 0.481 | ||
| Male | 248 (48.7) | 314 (46.7) | |
| Female | 261 (51.3) | 359 (53.3) | |
| Age (years) | <0.001 | ||
| 40–54 | 286 (56.2) | 545 (81.0) | |
| 55–69 | 223 (43.8) | 128 (19.0) | |
| Monthly household income | <0.001 | ||
| <2,072,900 KRW (<$1,756 USD) | 151 (29.7) | 179 (26.6) | |
| ≥2,072,900 KRW (≥$1,756 USD) | 357 (70.3) | 493 (73.4) | |
| Education status | <0.001 | ||
| Below middle school | 94 (19.1) | 45 (7.0) | |
| Above high school | 397 (80.9) | 598 (93.0) | |
| Marital status | 0.022 | ||
| Married | 484 (95.1) | 617 (91.7) | |
| Unmarried | 25 (4.9) | 56 (8.3) | |
| Alcohol consumption | 0.185 | ||
| Never drinking | 40 (7.9) | 37 (5.5) | |
| Even once | 467 (92.1) | 635 (94.5) | |
| Smoking status | 0.332 | ||
| Never smoking | 276 (54.3) | 390 (58.0) | |
| Even once | 232 (45.7) | 282 (42.0) | |
| Daily near-work hours | <0.001 | ||
| < 4 hours/day | 350 (68.8) | 326 (48.4) | |
| ≥ 4 hours/day | 159 (31.2) | 347 (51.6) | |
| Residential area | 0.171 | ||
| “Dong” (administrative neighborhood) | 411 (80.7) | 564 (83.8) | |
| “Eup/myeon” (town/rural township) | 98 (19.3) | 109 (16.2) | |
| PM2.5 moving average concentration (μg/m3) (based on the year prior to the health examination date) |
||||
|---|---|---|---|---|
| Mean ± SD | Median (IQR) | Minimum | Maximum | |
| Average over 0–365 days | 19.78 ± 2.89 | 20.13 (18.35) | 12.14 | 38.85 |
| Average over 0–730 days | 20.96 ± 2.82 | 21.54 (19.86) | 12.67 | 39.82 |
| Average over 0–1,095 days | 21.33 ± 2.75 | 21.33 (20.13) | 12.92 | 40.66 |
| Average over 0–1,460 days | 21.96 ± 2.79 | 21.96 (22.47) | 13.33 | 41.62 |
| Average over 0–1,826 days | 22.18 ± 2.75 | 22.18 (22.72) | 13.72 | 41.17 |
| PM2.5 moving average concentration (based on the year prior to the health examination date) |
|||
|---|---|---|---|
| Model 1 (unadjusted OR) | Model 2 (adjusted OR |
Model 3 (adjusted OR |
|
| Average over 0–365 days | 1.428 (0.891–2.287) | 1.313 (0.804–2.146) | 1.212 (0.715–2.053) |
| Average over 0–730 days | 1.629 (0.877–3.025) | 1.518 (0.797–2.889) | 1.315 (0.660–2.621) |
| Average over 0–1,095 days | 1.592 (0.810–3.128) | 1.343 (0.663–2.719) | 1.105 (0.525–2.327) |
| Average over 0–1,460 days | 4.038 (1.295–12.595) | 3.745 (1.161–12.077) | 3.474 (1.025–11.777) |
| Average over 0–1,826 days | 4.038 (1.295–12.595) | 3.745 (1.161–12.077) | 3.474 (1.025–11.777) |
Table 1. General characteristics of the study participants according to myopia status
Values are presented as number (%). Total may not add up due to missing values in alcohol consumption and smoking status. KRW to USD conversion is based on the average exchange rate during the study period: 1,180.05 KRX ≈ 1 USD.
Table 2. Distributions of the PM2.5 moving average concentration
All participants underwent health examinations during 2020, although the exact examination dates varied across individuals. The moving average values were calculated based on the specific health examination date of each participant. PM2.5: particulate matter less than 2.5 μm; SD: standard deviation; IQR: interquartile range.
Table 3. Association between PM2.5 moving average concentration classified based on Korea’s annual standard of 15 μg/m3 and myopia cases
All participants underwent health examinations during 2020, although the exact examination dates varied across individuals. The moving average values were calculated based on the specific health examination date of each participant. PM2.5: particulate matter less than 2.5 μm; OR: odds ratio, CI: confidence interval. ORs were calculated by logistic regression analysis for PM2.5 moving average concentration after adjusting for sex and age; ORs were calculated by logistic regression analysis for PM2.5 moving average concentration after adjusting for sex, age, monthly household income, education status, marriage status, alcohol consumption, smoking, daily near-work hours, residential area.
