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Figure 1.
Correlations Between Multiple Outdoor Air Pollution Factors
Correlations Between Multiple Outdoor Air Pollution Factors

A, Particulate matter with aerodynamic diameter <10 µm (PM10) and humidity. B, Ozone and humidity. C, Nitrogen dioxide and humidity. D, Sulfur dioxide and humidity. E, Ozone and PM10. F, Nitrogen dioxide and PM10. G, Sulfur dioxide and PM10. H, Ozone and nitrogen dioxide. I, Ozone and sulfur dioxide. J, Nitrogen dioxide and sulfur dioxide. While nitrogen dioxide and PM10 were negatively correlated with humidity, ozone and sulfur dioxide were not. Ozone was negatively correlated with PM10, nitrogen dioxide, and sulfur dioxide and PM10 was positively correlated with nitrogen dioxide.

Figure 2.
Relationship Between Air Pollution Factors and Regional Prevalence of DED Symptoms and Diagnosis
Relationship Between Air Pollution Factors and Regional Prevalence of DED Symptoms and Diagnosis

A, Symptoms and humidity. B, Symptoms and particulate matter with aerodynamic diameter <10 µm (PM10). C, Symptoms and ozone. D, Symptoms and nitrogen dioxide. E, Symptoms and sulfur dioxide. F, Diagnosis and humidity. G, Diagnosis and PM10. H, Diagnosis and ozone. I, Diagnosis and nitrogen dioxide. J, Diagnosis and sulfur dioxide. Increased humidity was associated with decreased prevalence of symtoms and diagnosis of dry eye disease (DED); however, PM10, ozone, nitrogen dioxide, and sulfur dioxide were not associated with regional prevalence of symptoms and diagnosis of DED.

Table 1.  
Characteristics of the Study Population
Characteristics of the Study Population
Table 2.  
Multivariable Logistic Regression of Variables Associated With Symptoms of Dry Eye Disease
Multivariable Logistic Regression of Variables Associated With Symptoms of Dry Eye Disease
Table 3.  
Multivariate Logistic Regression of Variables Associated With Diagnosis of Dry Eye Disease
Multivariate Logistic Regression of Variables Associated With Diagnosis of Dry Eye Disease
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    1 Comment for this article
    EXPAND ALL
    Potential Importance of Ozone in the Association Between Outdoor Air Pollution and Dry Eye Disease in South Korea
    Parul Chawla Gupta; MS Ophthalmology, Jagat Ram; MS Ophthalmology | Department of Ophthalmology, Post Graduate Institute of Medical Education and Research, Chandigarh, India
    To the Editor,
    We commend Hwang and colleagues (1) for an excellent study investigating associations between outdoor air pollution and Dry Eye Disease (DED) in a Korean population. In their study, multivariable logistic regression demonstrated that higher ozone levels and lower humidity levels were significantly associated with symptoms and diagnosis of dry eye disease. However, we believe some issues need further discussion. Firstly, diabetes mellitus should have been added as a covariate as it can lead to severe dry eye especially in patients with diabetic peripheral neuropathy.(2) Secondly, use of preserved antiglaucoma drugs could have been considered as a covariate
    since preservatives like benzalkonium chloride are known to cause dry eye.(3) History of use of systemic drugs causing DED like antihistaminics, antidepressents, antihypertensives (beta-blockers and thiazides) should also be incldued, as they may act as potential confounders. Thirdly, why were the participants not assessed for DED on the basis of standardized questionnaires like Ocular Surface Disease Index (OSDI) or Visual Function Questionnaire (VFQ-25) or Standard Patient Evaluation of Eye Dryness (SPEED)?(4) Lastly, in accordance with previous studies, the present study revealed a higher prevalence of DED among older individuals especially women.(1) The higher prevalence of DED among women has been associated with various systemic conditions, such as complete androgen insensitivity syndrome, premature ovarian failure, and polycystic ovarian syndrome (PCOS). In addition, the tear film and ocular surface can be affected by hormonal changes in various situations, such as pregnancy, lactation, contraceptive use, hormone replacement therapy, oophorectomy, hysterectomy, menopause as well as with irregular menstruation.(5) All of these entities may act as confounders. Moreover, since high altitude exposure leads to an altered tear film resulting in an increased tear film osmolarity and a reduced tear film break up time leading to dry eye symptoms, it would also be interesting to know if altitude was accounted for before analyzing the results.(6)



    References
    1. Hwang SH, Choi YH, Paik HJ, Wee WR, Kim MK, Kim DH. Potential Importance of Ozone in the Association Between Outdoor Air Pollution and Dry Eye Disease in South Korea. JAMA Ophthalmol. 2016;134(5):503-510.
    2. DeMill DL, Hussain M, Pop-Busui R, Shtein RM. Ocular Surface Disease in Patients with Diabetic Peripheral Neuropathy. Br J Ophthalmol. 2015 Oct 23. pii: bjophthalmol-2015-307369. doi: 10.1136/bjophthalmol-2015-307369. [Epub ahead of print]
    3. Noecker R. Effects of common ophthalmic preservatives on ocular health. Adv
    Ther 2001; 18(5): 205–215.
    4. Asiedu K, Kyei S, Mensah SN et al. Ocular Surface Disease Index (OSDI) Versus the Standard Patient Evaluation of Eye Dryness (SPEED): A Study of a Nonclinical Sample. Cornea 2016;35:175–180.
    5. Song J, Kim M, Paik S et al. Association Between Menstrual Irregularity and Dry Eye Disease: A Population-Based Study. Cornea 2016;35:193–198.
    6. Willmann G, Schatz A, Fischer MD, Schommer K, Zrenner E, Bartz-Schmidt KU et al. Exposure to high altitude alters tear film osmolarity and breakup time. High Alt Med Biol. 2014;15(2):203-7.

    __________________________

    From the article authors:

    Thank you for taking a profound interest in our study. First, we also thought that diabetes mellitus was important in dry eye disease (DED). However, diabetes mellitus was not associated with DED in KNHANES data (Am J Ophthalmol. 2014 Dec;158(6):1205-1214), and our pilot results using KNHANES data also demonstrated that diabetes mellitus was not associated with DED. Second, use of preserved antiglaucoma drugs and systemic drugs could affect DED. Unfortunately, history of use of preserved antiglaucoma drugs and systemic drugs like antihistaminics, antidepressents, antihypertensives was not available in KNHANES data. Instead, we added a history of hypertension, depression, rheumatoid arthritis, atopic dermatitis, malignancy, thyroid disease, dyslipidemia, etc. in pilot analysis. In that analyses, dyslipidemia and thyroid dysfunction were associated with DED. Therefore, we used these factors as covariates in the final models. Third, DED was not defined from physical examination findings, but rather from answers on self-reported simple questionnaires in this study. We agree that this is a limitation. However, standardized questionnaires like the Ocular Surface Disease Index (OSDI) or Visual Function Questionnaire (VFQ-25) were not available in KNHANES data. We also did not take into consideration the changes in sex hormones and the difference in altitude because accurate information about sex hormonal changes and altitude were not available. We agree that those would be important factors in DED. We have been conducting a clinical study regarding the role of air pollution and DED, and we included an ocular staining score, OSDI, Schirmer test, tear break up time as outcome measures.  Several of the aforementioned confounding factors were also included as covariates. We think that the results of this ongoing study may present more clear information about the relationship between air pollution and DED.


    References
    1. Hwang SH, Choi YH, Paik HJ, Wee WR, Kim MK, Kim DH. Potential Importance of Ozone in the Association Between Outdoor Air Pollution and Dry Eye Disease in South Korea. JAMA Ophthalmol. 2016;134(5):503-510.
    2. Ahn JM, Lee SH, Rim TH el al. Prevalence of and risk factors associated with dry eye: the Korea National Health and Nutrition Examination Survey 2010-2011.Am J Ophthalmol. 2014 Dec;158(6):1205-1214.

    CONFLICT OF INTEREST: None Reported
    READ MORE
    Original Investigation
    May 2016

    Potential Importance of Ozone in the Association Between Outdoor Air Pollution and Dry Eye Disease in South Korea

    Author Affiliations
    • 1Department of Ophthalmology, Gachon University Gil Medical Center, Incheon, Korea
    • 2Department of Preventive Medicine, Gachon University College of Medicine, Incheon, Korea
    • 3Department of Ophthalmology, Seoul National University College of Medicine, Seoul, Korea
    JAMA Ophthalmol. 2016;134(5):503-510. doi:10.1001/jamaophthalmol.2016.0139
    Abstract

    Importance  Air pollution is an important public health concern and the ocular surface is continuously exposed to pollutants in outdoor air. Ocular surface abnormalities related to air pollution are thought to be a subtype of dry eye disease (DED). However, to date, there is no large-scale study evaluating an association between air pollution and DED that includes multiple air pollutants.

    Objective  To investigate associations between outdoor air pollution and DED in a Korean population.

    Design, Setting, and Participants  A population-based cross-sectional study using data on 16 824 participants in the fifth Korea National Health and Nutrition Examination Survey was conducted from January 1, 2010, to December 31, 2012. Data analysis was conducted from September 1 to 30, 2015. Dry eye disease was defined as previously diagnosed by an ophthalmologist or the presence of frequent ocular pain and discomfort. Outdoor air pollution measurements (mean annual humidity, particulate matter with aerodynamic diameter <10 µm [PM10], ozone, and nitrogen dioxide levels) were collected from 283 national monitoring stations in South Korea.

    Main Outcomes and Measures  Associations of multiple air pollutants with DED were assessed from multivariable logistic regression analyses. Sociodemographic factors and previously known factors associated with DED were applied as covariates (model 1 controlled for sociodemographic factors and model 2 controlled for sociodemographic, behavioral, and clinical factors).

    Results  Among 16 824 participants (7104 men and 9720 women), higher ozone levels and lower humidity levels were significantly associated with symptoms and diagnosis of DED. In model 1, an increase in ozone levels of 0.003 ppm was significantly associated with symptoms and diagnosis of DED (symptoms: odds ratio [OR], 1.16; 95% CI, 1.02-1.30; P = .04; diagnosis: OR, 1.21; 95% CI, 1.05-1.40; P = .008), while a 5% increase in humidity levels was significantly associated with decreased symptoms and diagnoses of DED (symptoms: OR, 0.87; 95% CI, 0.77-0.98; P = .03; diagnosis: OR, 0.86; 95% CI, 0.76-0.97; P = .01). In model 2, an increase in ozone levels of 0.003 ppm was significantly associated with symptoms and diagnosis of DED (symptoms: OR, 1.17; 95% CI, 1.02-1.34; P = .03; diagnosis: OR, 1.27; 95 CI, 1.09-1.48; P = .002), while a 5% increase in humidity levels was significantly associated with decreased symptoms and diagnoses of DED (symptoms: OR, 0.88; 95% CI, 0.78-0.98; P = .045; diagnosis: OR, 0.86; 95% CI, 0.76-0.97; P = .02). In model 2, an increase in nitrogen dioxide of 0.003 ppm (OR, 1.12; 95% CI, 1.02-1.23 P = .02) was also associated with diagnosis of DED. Levels of sulfur dioxide and PM10 were not associated with symptoms or diagnosis of DED in model 1 or model 2 (P > .05 for each).

    Conclusions and Relevance  Higher ozone levels and lower humidity levels were associated with DED in the Korean population, while PM10 level was not associated with DED.

    Introduction

    Air pollution is an important public health concern. According to the World Health Organization, most significant constituents of air pollution include particulate matter (PM), ozone, nitrogen dioxide, and sulfur dioxide.1 The main sources of PM and nitrogen dioxide are vehicles and the burning of fuels.2 Ozone is formed in the atmosphere by photochemical reactions that occur as a result of the action of UV light on precursor pollutants, such as the oxides of nitrogen and volatile organic compounds.1 Sulfur dioxide is produced from industrial processing of sulfur-containing materials.3 Ambient levels of air pollution are known to be associated with a wide range of adverse health effects that particularly affect the respiratory and cardiovascular systems.4,5

    The ocular surface is continuously exposed to outdoor air pollutants. Only a few studies have investigated the effects of air pollution on the ocular surface.6-11 Ocular surface abnormalities related to air pollution are considered to be a subtype of dry eye disease (DED) because they are characterized by tear film abnormality, higher ocular surface disease index, and decreased density of conjunctival goblet cells.6,7,10,11 In most studies that have examined the effect of air pollution on the ocular surface, the sample sizes have been small and many air pollutants have not been considered. To our knowledge, there have been no large-scale studies evaluating the association between air pollution and DED that include multiple air pollutants. Our study investigates the associations between outdoor air pollution and DED in the Korean population, while controlling for potential confounding sociodemographic, behavioral, and clinical factors.

    Box Section Ref ID

    Key Points

    • Question Are multiple air pollutants associated with dry eye disease when potential confounding factors are controlled for in a large Korean population-based cross-sectional study?

    • Findings Multivariable logistic regression demonstrates that higher ozone levels and lower humidity levels were significantly associated with symptoms and diagnosis of dry eye disease.

    • Meaning While these results do not show a causal link, they suggest a relatively strong association between ozone air pollutants and dry eye disease.

    Methods
    Study Population and Data Collection

    The Korea National Health and Nutrition Examination Survey (KNHANES) is an ongoing, nationwide, population-based, cross-sectional epidemiologic survey consisting of 3 parts: a health interview survey, health examination survey, and nutritional survey. A field survey team that included an ophthalmologist and nurse examiners for health assessments composed a mobile examination unit and performed interviews and physical examinations. From January 1, 2010, to December 31, 2012, a total of 11 400 households in 576 survey districts were involved in the fifth KNHANES using the stratified, multistage clustered sampling method based on national resident demographics from 2009. A sample population included 31 596 individuals, 25 533 (80.8%) of whom participated in this survey. After excluding participants who did not provide relevant responses, 16 824 adults were included for statistical analysis (eFigure in the Supplement). This study was approved by the Korea Centers for Disease Control and Prevention Institutional Review Board and complied with the tenets of the Declaration of Helsinki.28 All participants provided written informed consent.

    Dry Eye Disease

    We identified DED using the following 2 questions: (1) Do your eyes frequently feel dry or irritated? and (2) Have you ever been diagnosed by an ophthalmologist as having dry eye syndrome? Possible answers to both questions were yes or no. Based on answers to the questions, we defined 2 different outcome measures for DED: symptoms or diagnosis.

    Outdoor Air Pollutants and Humidity Data

    Data on air pollutants gathered at 283 atmospheric monitoring stations in South Korea from January 1, 2010, to December 31, 2012, were obtained from the Korea Ministry of Environment in August 2015. Air pollutants and meteorologic data in the current study include ambient PM with an aerodynamic diameter less than 10 μm (PM10), ozone, nitrogen dioxide, and sulfur dioxide levels, as well as relative humidity. We calculated the annual average levels of these pollutants for the 16 administrative divisions of South Korea (7 metropolitan cities and 9 provinces). Values from the monitoring site located in participants’ residential division were used as proxies of exposure to ambient air pollutants and humidity. Each air pollutant was measured using β-ray absorption (PM10), UV photometry (ozone), chemiluminescence (nitrogen dioxide), and pulse UV fluorescence (sulfur dioxide). Outdoor humidity was considered an air pollution factor in the analyses because levels of air pollutants could be affected by the humidity level.12

    Covariates

    Because DED is a multifactorial disease, we considered traditional risk factors as covariates, including sociodemographic factors, alcohol consumption (current drinker or nondrinker), smoking (current smoker or nonsmoker), and obesity (body mass index [calculated as weight in kilograms divided by height in meters squared] ≥25). Sociodemographic factors included age, sex, region of residence (urban or rural), educational level (high school or less or university or higher), and income level (high [first or second quartile] or low [third or fourth quartile]). A previous study13 and our pilot analysis (eTable 1 in the Supplement) using data from the KNHANES showed that symptoms and diagnosis of DED were significantly associated with thyroid disease, dyslipidemia, subjective health awareness (what participants think about their own health), and previous ocular surgery. Therefore, we added thyroid disease (presence or absence), dyslipidemia (presence or absence ), subjective health awareness (poor or good), and previous ocular surgery (presence or absence) as covariates.

    Statistical Analysis

    Data analysis was conducted from September 1 to 30, 2015. To account for the complex survey design of stratified, random, and cluster sampling, all statistical analyses were performed using SPSS Complex Samples procedures (SPSS Statistics, version 18, IBM Inc) according to the SPSS manual from the Korea Centers for Disease Control and Prevention.29 Pearson correlation analysis was used to evaluate the associations between humidity, PM10, ozone, nitrogen dioxide, and sulfur dioxide levels because multiple air pollutants may be interrelated. Linear regression analyses were used to evaluate the associations between each air pollutant’s level and regional prevalence of DED. To determine which air pollution factors were independently associated with symptoms and diagnosis of DED, multivariable logistic regression analyses were performed. We developed sequential models controlling for covariates: model 1 controlled for sociodemographic factors, and model 2 controlled for sociodemographic, behavioral, and clinical factors. P < .05 was considered significant for variables in both models.

    Results

    The baseline characteristics of the study participants are shown in Table 1. The mean (SD) age of the participants was 50.9 (16.7) years. There were 7104 men and 9720 women. The mean (SD) levels of humidity, PM10, ozone, nitrogen dioxide, and sulfur dioxide during the study period were 66.3% (4.5%), 46.87 (5.62) µg/m3, 0.025 (0.004) ppm, 0.021 (0.006) ppm, and 0.005 (0.001) ppm, respectively. Correlations between multiple air pollutants are presented in Figure 1. While nitrogen dioxide and PM10 were negatively correlated with humidity (nitrogen dioxide and humidity, R = –0.4115; 95% CI, –0.6229 to –0.1442, P = .004; PM10 and humidity, R = –0.7059; 95% CI, –0.8397 to –0.4912, P < .001), ozone and sulfur dioxide were not (ozone and humidity, R = 0.2699; 95% CI, –0.0154 to 0.5146, P = .06; sulfur dioxide and humidity, R = –0.1672; 95% CI, –0.4309 to 0.1228, P = .26). Ozone was negatively correlated with PM10, nitrogen dioxide, and sulfur dioxide (ozone and PM10, R = –0.4864; 95% CI, –0.6770 to –0.2346, P < .001; ozone and nitrogen dioxide, R = –0.8040; 95% CI, –0.8858 to –0.6737, P < .001; ozone and sulfur dioxide, R = –0.3861; 95% CI, –0.6040 to –0.1145, P = .007), and PM10 was positively correlated with nitrogen dioxide (nitrogen dioxide and PM10, R = 0.4431; 95% CI, 0.1819 to 0.6460, P = .002). Multicollinearity between all air pollution variables was checked by ensuring that variance inflation factors did not exceed 10.

    In the linear regression models to evaluate associations between levels of each air pollutant and regional prevalence of DED, increased humidity was associated with decreased prevalence of symptoms and diagnosis of DED (symptoms, R = –0.4109; 95% CI, –0.6224 to –0.1435, P = .004; diagnosis, R = –0.4254; 95% CI, –0.6330 to –0.1606, P = .003) (Figure 2). Other air pollution factors (PM10, ozone, nitrogen dioxide, and sulfur dioxide) were not associated with regional prevalence of symptoms and diagnosis of DED (each P > .05) (Figure 2).

    Because the levels of air pollutants were highly correlated with each other, single-pollutant models may provide misleading results; therefore, models with multiple pollutants were used in our multivariable logistic regression. In model 1, a 5% increase in the humidity level was associated with a decrease in the prevalence of symptoms of DED (odds ratio [OR], 0.87; 95% CI, 0.77-0.98; P = .03) and a 0.003-ppm increase in the ozone level was associated with symptoms of DED (OR, 1.16; 95% CI, 1.02-1.30; P = .04). In model 2, a 5% increase in the humidity level was associated with a decrease in the prevalence of symptoms of DED (OR, 0.88; 95% CI, 0.78-0.98; P = .045) and a 0.003-ppm increase in the ozone level was associated with symptoms of DED (OR, 1.17; 95% CI, 1.02-1.34; P = .03) (Table 2). In model 1, a 5% increase in the humidity level was associated with a decrease in the prevalence of diagnoses of DED (OR, 0.86; 95% CI, 0.76-0.97; P = .01) and a 0.003-ppm increase in the ozone level was associated with diagnosis of DED (OR, 1.21; 95% CI, 1.05-1.40; P = .008). In model 2, a 5% increase in the humidity level was associated with a decrease in the prevalence of diagnoses of DED (OR, 0.86; 95% CI, 0.76-0.97; P = .02) and a 0.003-ppm increase in the ozone level was associated with diagnosis of DED (OR, 1.27; 95% CI, 1.09-1.48; P = .002) (Table 3). In model 1, a 5% increase in the humidity level was associated with a decrease in the prevalence of both symptoms and diagnosis of DED (double positive) (OR, 0.87; 95% CI, 0.76-0.98; P = .02) and a 0.003-ppm increase in the ozone level was associated with both symptoms and diagnosis of DED (OR, 1.25; 95% CI, 1.08-1.45; P = .002). In model 2, a 5% increase in the humidity level was associated with a decrease in the prevalence of both symptoms and diagnosis of DED (OR, 0.87; 95% CI, 0.77-0.98; P = .03) and a 0.003-ppm increase in the ozone level was associated with both symptoms and diagnosis of DED (OR, 1.31; 95% CI, 1.12-1.53; P = .002) (eTable 2 in the Supplement). In model 2, a 0.003-ppm increase in the nitrogen dioxide level was associated with diagnosis of DED (OR, 1.12; 95% CI, 1.02-1.23; P = .02) (Table 3). In both models, PM10 and sulfur dioxide were not associated with symptoms or diagnosis of DED (Tables 2 and 3 and eTable 2 in the Supplement). Sex, dyslipidemia, thyroid disease, subjective health awareness, and previous ocular surgery were significantly associated with DED symptoms and diagnosis as covariates (Tables 2 and 3 and eTable 2 in the Supplement).

    Discussion

    This large-scale population-based study demonstrated that decreased humidity levels and increased ozone levels were associated with DED, after controlling for known risk factors such as sex, dyslipidemia, thyroid disease, subjective health awareness, and previous ocular surgery. Nitrogen dioxide was associated with a diagnosis of DED in one model. However, PM10, one of the leading public health issues, was not associated with DED.

    Low humidity is a well-known risk factor for DED.14 Several human studies have suggested that evaporation rate, tear lipid layer thickness, corneal epithelial integrity, tear stability, and visual quality are adversely affected by a low-humidity environment. Human and animal studies have demonstrated that exposure to a dry environment can result in a significant decrease in tear production, an increase in fluorescein staining, and a decrease in goblet cell density.15-17 Our study also demonstrated an association between low humidity and DED, which is consistent with the findings of previous studies.15-17 Previous clinical studies of DED have focused mainly on dryness in specific indoor environments such as an aircraft or office.17-19 Our study expands the evidence of the association of low humidity with DED from indoor humidity (ie, extreme occupational settings) to outdoor humidity (ie, environmental and community levels of humidity). We suggest that outdoor humidity may also be important in DED.

    Ozone is known to be one of the most toxic air pollutants and to be strongly associated with various adverse health effects. A meta-analysis of articles from the United States showed that an ozone increase of 10 parts per billion was associated with a 0.52% increase in total mortality and a 0.64% increase in cardiovascular and respiratory mortality.20 Ozone can cause oxidative damage to various biomolecules and is rapidly converted into several reactive oxidant species.21,22 A previous study of ozone exposure in mice showed a dose-dependent increase in damage to corneal integrity and conjunctival goblet cells and a dose-dependent increase in inflammatory cytokine production in tears.23 To our knowledge, our study is the first epidemiologic investigation to observe that increased ozone levels are associated with DED, and we suggest that a potential mechanism that induces DED is subclinical ocular inflammation as a consequence of exposure to ozone. Further clinical studies to examine the association between ozone level and ocular surface changes are needed.

    Particulate matter with an aerodynamic diameter less than 10 μm is a major concern in Asia and nitrogen dioxide is recognized as a marker for combustion-related pollutants, particularly those from road traffic or indoor sources of combustion.1 An increase of 10 μg/m3 in the PM10 level has been shown to be associated with a 0.49% increase in all-cause mortality in Asia. In European studies, a 1.3% increase in daily deaths per 50-μg/m3 increase of nitrogen dioxide has been found.24,25 High levels of PM10 and nitrogen dioxide in large metropolitan areas were associated with decreased tear breakup time and density of conjunctival goblet cells.6,7 We were not able to demonstrate an association between PM10 levels and DED. One possible explanation of these negative findings is that reflex tearing might help flush PM from the ocular surface. Although the annual PM10 levels in Korea between 2010 and 2012 were higher than those recommended by the World Health Organization (20 µg/m3), the levels are similar to those in Europe and North America.1,26 Therefore, another possible explanation for our negative findings is that the environmental PM10 levels currently in Korea are not high enough to induce adverse effects on the ocular surface. To confirm an association between PM10 levels and DED, further studies are needed in populations exposed to higher PM10 levels (eg, China, with an annual PM10 level in 2010 of 90 µg/m3).27 Epidemiologic studies have failed to demonstrate any harmful effect of nitrogen dioxide on human health, and it is regarded as a copollutant with PM.1 Previous studies examining the effects of PM2.5 and nitrogen dioxide on the ocular surface showed no significant correlation between nitrogen dioxide and tear film abnormality, ocular surface disease index score, tear osmolarity, or conjunctival goblet cell density.6,7 However, we demonstrated an association between nitrogen dioxide and diagnosis of DED. Further clinical and experimental studies examining nitrogen dioxide exposure and damage to the ocular surface are needed. Sulfur dioxide levels have been decreasing worldwide as a result of the use of fuel with a low sulfur content, and there is uncertainty as to the pollutant responsible for the adverse health effects.1

    There were some limitations in this study. First, the design was cross-sectional; thus, the results do not definitively indicate a cause-and-effect relationship between DED and outdoor air pollution factors. Second, fine PM (PM2.5) was not investigated as air pollutants because data on PM2.5 were not publicly available. Third, seasonal and daily variations of air pollutants were not considered. Fourth, DED was not defined from physical examination findings, but rather from answers on self-reported questionnaires, which may result in underreporting or overreporting. Fifth, repeated-measures analysis about air pollutants and meteorologic data were not applied according to time progression. Despite these limitations, we believe that this study is important because it is the first study, to our knowledge, of a large-scale population with a well-defined data set for the effects of outdoor air pollution on DED. Consistency between models 1 and 2 for air pollution factors and DED also yields supportive and valuable results.

    Conclusions

    Even after adjustment for sociodemographic, health behavioral, and clinically important factors, lower humidity and higher ozone levels were associated with DED, while no association was found between PM10 levels and DED in a Korean population. These results, however, are just associations and do not definitively indicate a cause-and-effect relationship between DED and outdoor air pollution.

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    Article Information

    Submitted for Publication: October 30, 2015; final revision received December 28, 2015; accepted January 17, 2016.

    Corresponding Author: Dong Hyun Kim, MD, Department of Ophthalmology, Gachon University Gil Medical Center, 1198, Guwol-dong, Namdong-Gu, Incheon 405-835, Korea (amidfree@gmail.com).

    Published Online: March 10, 2016. doi:10.1001/jamaophthalmol.2016.0139.

    Author Contributions: Drs Hwang and D. H. Kim had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

    Study concept and design: Paik, Wee, M. K. Kim, D. H. Kim.

    Acquisition, analysis, or interpretation of data: Hwang, Choi, D. H. Kim.

    Drafting of the manuscript: Hwang, D. H. Kim.

    Critical revision of the manuscript for important intellectual content: Choi, Paik, Wee, M. K. Kim, D. H. Kim.

    Statistical analysis: Hwang, Choi, D. H. Kim.

    Administrative, technical, or material support: Paik, Wee.

    Study supervision: Paik, Wee, M. K. Kim, D. H. Kim.

    Conflict of Interest Disclosures: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest and none were reported.

    Additional Contributions: The Epidemiologic Survey Committee of the Korean Ophthalmologic Society participated in making and processing data from the Korea National Health and Nutrition Examination Survey about the ophthalmologic questionnaire and examinations, and helped us access data from the Korea National Health and Nutrition Examination Survey. The members of the committee were not compensated for their contribution.

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