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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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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
 

Copyright 2016 American Medical Association. All Rights Reserved. Applicable FARS/DFARS Restrictions Apply to Government Use.

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.

References
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World Health Organization.  Air Quality Guidelines: Global Update 2005: Particulate Matter, Ozone, Nitrogen Dioxide and Sulfur Dioxide. Geneva, Switzerland: World Health Organization; 2006.
2.
Kelly  FJ, Fuller  GW, Walton  HA, Fussell  JC.  Monitoring air pollution.  Respirology. 2012;17(1):7-19.PubMedGoogle ScholarCrossref
3.
Kellogg  WW, Cadle  RD, Allen  ER, Lazrus  AL, Martell  EA.  The sulfur cycle.  Science. 1972;175(4022):587-596.PubMedGoogle ScholarCrossref
4.
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