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Global Associations of Air Pollution and Conjunctivitis Diseases: A Systematic Review and Meta-Analysis

Institute for Environmental and Climate Research, Jinan University, Guangzhou 511443, China
Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht 6200 MD, The Netherlands
Department of Epidemiology, School of Medicine, Jinan University, Guangzhou 510630, China
Authors to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2019, 16(19), 3652;
Received: 25 August 2019 / Revised: 23 September 2019 / Accepted: 25 September 2019 / Published: 28 September 2019


(1) Background: As the most common eye disease diagnosed in emergency departments, conjunctivitis has caused serious health and economic burdens worldwide. However, whether air pollution may be a risk factor for conjunctivitis is still inconsistent among current evidence. (2) Methods: We searched the literature on the relationship between air pollution and conjunctivitis in multiple English databases before 18 March 2019. Meta-analysis, meta-regression, and funnel plots were used to integrate the data, identify the sources of bias, and determine the publication bias, respectively. (3) Results: A total of 2450 papers were found, 12 of which were finally included. The pooled relative risk for each 10 μg/m3 increase of air pollution on conjunctivitis was 1.0006 (95%CI: 0.9993–1.0019) for CO, 1.0287 (1.0120–1.0457) for NO2, 1.0089 (1.0030–1.0149) for O3, 1.0004 (0.9976–1.0032) for PM2.5, 1.0033 (0.9982–1.0083) for PM10, and 1.0045 (0.9908–1.0185) for SO2. In the subgroup, PM2.5 and O3 had a greater impact on conjunctivitis risk in women than in men, and people <18 years old than those ≥18 years old. Relative humidity significantly modified the risk of O3 on conjunctivitis (p = 0.023), explaining 45% of the between-study heterogeneity. (4) Conclusion: Globally, air pollution has considerable health risks for conjunctivitis. Females and the youth were more vulnerable to PM2.5, NO2, and O3. Reductions of air pollution levels are still warranted to protect the vulnerable populations.

1. Introduction

Ambient air pollution is one of the most important risk factors that affects people worldwide [1,2,3]. Numerous epidemiological investigations have revealed the short-term or long-term associations between high concentrations of air pollutants and increased health outcomes, including stroke, heart disease, lung cancer, diabetes, and chronic lung disease. Dense innervations in the ocular surface are extremely sensitive to environmental chemical substances. In addition, human eyes are only protected by a thin layer of tear film, causing them to be very susceptible to the harmful effect of air pollution [4,5,6].
Conjunctivitis disease is generally divided into two categories: infectious by pathogenic microbial factors and non-infectious by physicochemical factors. Conjunctivitis is the most common eye disease diagnosed in emergency departments and affects all ages, which has caused serious health and economic burdens around the world. For instance, in the United States, conjunctivitis accounts for almost one third of all eye-related diseases, with 4–6 million conjunctivitis visits annually and a treatment cost of nearly 800 million dollars [7]. In addition to the societal costs, conjunctivitis can directly influence the patients’ quality of life. Mild conjunctivitis can affect people’s learning and working, and severe conjunctivitis can cause irreversible damage to the eyes, such as decreased vision or even blindness [4]. Additionally, patients with conjunctivitis, particularly allergic conjunctivitis, always have coexisting symptoms, such as allergic asthma and rhinitis [8]. Therefore, identifying the environmental risk factors for conjunctivitis and then guiding the development of effective measures for reducing the incidence of conjunctivitis are important for public health in the field of ophthalmology. Recently, several studies have provided evidence that exposure to air pollution could significantly increase the risk of conjunctivitis development [9,10,11,12]. However, there is still significant controversy on which air pollutants pose the highest risk and which subpopulation of patients with conjunctivitis is particularly sensitive to air pollution. For example, Bourcier and colleagues [13] reported that NO2 is associated with a higher risk of conjunctivitis in the population than that of O3, while Larrieu et al. showed the opposite results [14]. In addition, Hong and co-authors found that the effect of O3 is greater for women with conjunctivitis than men, whereas the study of Fu et al. presented the opposite trend [5,6]. These inconsistent results indicate the necessity of quantitatively synthesizing and interpreting the current available evidence in order to provide comprehensive evidence for policymakers and protect the public’s health.
In this study, we aimed to perform a systematic review and meta-analysis to combine the global associations between air pollutants and conjunctivitis, and to identify the sensitive subgroups.

2. Materials and Methods

2.1. Data Source

We searched for articles published before 18 March 2019 in the following electronic databases: Web of Science, PubMed, Embase, and Scopus. The search terms included “conjunctivitis,” “pinkeye,” “air pollution,” “CO,” “NO2,” “SO2,” “O3,” “PM2.5,” and “PM10” (see Table A1). The reference lists of the included studies were further examined for additional studies.

2.2. Study Selection

2.2.1. Selection Criteria

The following inclusion criteria were utilized in this study:
1. Risk assessments on the relationship between air pollutants and health outcomes of conjunctivitis;
2. Studies providing quantitative effect estimates, such as the excess rate (ER), risk ratio (RR), odds ratio (OR), regression coefficient (β) or percentage and standard error (SE), and the respective 95% confidence interval (CI);
3. Literature using the following methodology: time-series, case-crossover, logistic regression, a generalized linear model (GLM), a generalized additive model (GAM), and a distributed lag model (DLM);
4. Studies that reported the link between exposures in the form of lag (day) and health outcome.
The exclusion criteria were as follows:
1. Not original research studies (e.g., commentary, communication, review, and meeting abstract);
2. Not related to outdoor air pollution (e.g., indoor, workplace, office);
3. Research related to clinical or animal experiments (e.g., drug trials, mice, and rabbits);
4. Research on conjunctivitis not caused by air pollution factors (e.g., mites and pollen);
5. The target was not conjunctivitis diseases (e.g., rhinoconjunctivitis or conjunctivitis of other organs).

2.2.2. Data Extraction

Data from all included studies that were extracted were as follows: the reference, study design, demographic data (e.g., GDP and population), average values of air pollutants, meteorological variables (e.g., temperature, relative humidity, and air pressure), and effect estimates (e.g., RR, regression coefficient, 95% confidence interval, and standard error). For articles with missing information, we contacted the corresponding authors by email to obtain the relevant data.

2.2.3. Quality Assessment

In order to distinguish between low-quality and high-quality studies, a quality assessment was performed. Due to the wide variety of study designs used in the literature, assessing the quality and their risk of bias can be difficult.
To the best of our knowledge, no validated scale has been developed to assess the quality of time-series and case-crossover studies. We selected and combined several items from the New Castle Ottawa Scale [15], the Cochrane risk of bias tool, and other tools [16], which were utilized in previous studies [17,18,19]. We created a five-point scoring system that included the following four aspects:
a. Conjunctivitis disease occurrence verification (0–1 points)
According to the International Classification of Diseases, studies on the causes of death encoded in revised version 9 (ICD-9), 10th revision, or ICPC-2 Code(s) (International Classification of Primary Care, Second Edition [20]) and official definitions of other countries are given a score of 1, but no score is given for studies that do not meet the criteria.
b. Quality of air pollutant measurements (0–1 points)
The quality of the air pollutant measurement can be judged according to the measurement frequency and the existence of missing data. If the measurement is made at least once a day and the missing data is <25%, the research score is 1; otherwise, the quality is assessed with 0 points.
c. Adjustment degree of confounders (0–3 points)
Adjustment for temperature and humidity is given 1 point. Additional adjustments, for example, seasonality, wind speed, or rainfall, acquire 2 points. If the long-term trend and days of the week are considered, 3 points are given. Zero points are given if there is no adjustment for temperature and humidity.
If the study gets full marks for all three components, the study was considered to be of a good quality. If any of the three components were zero, the study quality was considered to be low. All other studies were considered to be of a medium quality.

2.3. Data Synthesis and Statistical Analysis

The key objective of data synthesis was to unify the air pollutant concentration units, group the research population, and standardize the risk effect values. If studies used mg/m3, ppm, or ppb for the unit of measurement or unit of increment, all estimates were converted into μg/m3. Regarding population groupings, the included data were mainly divided into two groups: gender (male and female) and age group (>18 years old and <18 years old). In most studies, the risk estimates were expressed as ERs, ORs, or RRs with 95% CIs, and percent changes. The results presented as a regression coefficient and standard error were converted to RR. The summarized statistics are expressed as RRs with 95% CIs [21,22]. To pool the effect estimates, all estimates were standardized to an increment of 10 μg/m3 of air pollutant (CO, O3, SO2, NO2, PM2.5, and PM10) concentration.
The statistical analysis consisted of three steps: (1) computing the integrated estimates of each type of air pollutant using a fixed- or random-effect meta-analysis; (2) conducting a meta regression analysis based on the total population, GDP, and weather conditions; and (3) performing a sensitivity analysis. A meta-analysis was used to aggregate the risk estimates from all studies in detail. If the heterogeneity index (I2) was greater than 25%, the aggregate estimates were calculated using a random effect model; otherwise, we selected the fixed effect model [23]. The second step was to judge and test the source of heterogeneity. Heterogeneity was classified as high (I2 > 75%), medium (25 < I2 < 75%), or low (I2 < 25%) [24]. The sources of heterogeneity, such as the research design, regional GDP, geographic location (longitude and latitude, temperature, and humidity), and weather conditions, were further tested using a meta-regression analysis. Finally, we applied funnel charts and Begg’s [25] and Egger’s tests [26] to assess the potential impact of publishing bias. We conducted the sensitivity analysis by re-calculating the pooled effects by excluding each study to test whether our main findings were influenced by one study.
Statistical analysis and drawing were mainly conducted using R language software (R version 3.6.0; R Development Core Team, New Zealand, Australia).

3. Results

3.1. Search Results and Study Characteristics

In this study, 2450 records were originally obtained from Scopus (n = 723), PubMed (n = 576), Embase (n = 440), and Web of Science (n = 710). Twelve articles from 10 regions met the inclusion criteria and were included in the meta-analysis (see Figure 1), covering 30,103,982 conjunctivitis patients. Among the 12 included studies, five were case-crossover studies [4,5,9,27,28], four were time-series studies [6,14,29,30], and three were other studies (e.g., spatial analysis and multi-level regression). Table 1 and Table A2 summarize the basic characteristics of the included studies. The number of research papers including CO, NO2, O3, PM2.5, PM10, and SO2 was two, seven, nine, four, seven, and seven, respectively.

3.2. Overall Analysis

Since significant heterogeneity (I2 > 60%) was observed in the included studies, we used a random-effect meta-analysis to integrate the effect estimates of various air pollutants on conjunctivitis [31]. Figure 2 presents the pooled effect of six air pollutants on the risk of conjunctivitis among the included studies. The pooled relative risk for each 10 μg/m3 increase of air pollutants on conjunctivitis was 1.0006 (95%CI: 0.9993–1.0019) for CO, 1.0287 (95%CI: 1.0120–1.0457) for NO2, 1.0089 (95%CI: 1.0030–1.0149) for O3, 1.0004 (95%CI: 0.9976–1.0032) for PM2.5, 1.0033 (95%CI: 0.9982–1.0083) for PM10, and 1.0045 (95%CI: 0.9908–1.0185) for SO2.

3.3. Subgroup Analysis

Given the limited number of articles, we could only combine the effect estimates by subgroup for PM2.5, NO2, and O3 (Table 2). The random-effect meta-analysis was used to pool the effect risk of air pollution on conjunctivitis among subgroups as the heterogeneity was significant. Generally, the impact of air pollution was higher among females and the youth than the other groups. However, only statistically significant effects of O3 on males, with an RR value of 1.0321 (95%CI: 1.0000–1.0653), and NO2 and O3 on the youth, with corresponding RR values of 1.0472 (95%CI: 1.0249–1.0700) and 1.0357 (95%CI: 1.0156–1.0561), were found.

3.4. Meta-Regression

In order to assess the source of the between-study heterogeneity, a meta-regression was further conducted to test the influence of city-level characteristics (e.g., GDP, longitude and latitude, average temperature, relative humidity, and duration of sunshine) on the relationship between air pollution and conjunctivitis (see Table 3). Among these factors, only the relative humidity significantly modified the risk of O3 for conjunctivitis (p = 0.023), explaining 45% of the between-study heterogeneity.

3.5. Publication Bias

Funnel plot, Begg’s, and Egger’s tests were applied to determine whether there was publication bias. Figure 3 shows the funnel plots of the meta-analysis for the association between air pollution and the risk of conjunctivitis. The results of PM2.5, SO2, and NO2 presented a low probability of publication bias, reporting a p-value for both Begg’s test and Egger’s test of over 0.05. However, potential publication bias was detected for PM10 (Egger’s test: Z-value = 2.4238, p = 0.0154) and O3 (Egger’s test: Z-value = 5.4884, p < 0.001) (see Table 4). In addition, we performed the trim and fill method to validate the publication bias of PM10 and O3 (see Figure A1). The adjusted pooled relative risk of PM10 for total conjunctivitis was 1.0026 (95%CI: 0.9975, 1.0077) and 1.0041 (95%CI: 0.9957, 1.0126) for O3.

3.6. Sensitivity Analysis

Sensitivity analyses were performed to estimate the stability of the results by recalculating the pooled effect estimates after omitting one study each time [32,33,34]. We found that the effect estimate of each 10 μg/m3 increase in the six air pollutants showed no significant change by removing one single study, suggesting that the combined results were relatively stable and reliable.

4. Discussion

To the best of our knowledge, this is the first systematic review and meta-analysis to assess the association between air pollution and conjunctivitis. Twelve studies, including 30,103,982 cases of conjunctivitis from 10 countries/regions around the world, were included. Positive associations between six common air pollutants and conjunctivitis were obtained, while statistical significance was only observed for NO2 and O3. The female subgroup and those under 18 years old were most vulnerable to the risk of conjunctivitis caused by air pollution.

4.1. Risk Analysis of Air Pollution and Conjunctivitis in the Whole Population

In the past decade, the effect of air pollution on conjunctivitis has attracted increasing interest [4,35,36]. However, the evidence so far is inconsistent (Figure 2). For instance, Fu et al. [5] revealed that the risk of NO2 and conjunctivitis in the population was significant, with an RR value of 1.0403 (95%CI: 1.0228, 1.0581), while Jamaludin et al. [30] did not find any significant effects on the risk of conjunctivitis in the population, with an RR value of 0.9989 (95%CI: 0.9205, 1.0840). For PM10, Chang et al.’s [4] study revealed that PM10 was significantly associated with the conjunctivitis risk among people, with an RR value of 1.0020 (95%CI: 1.0005, 1.0036). However, in the study of Chiang et al. [29], NO2 had no significant effect on the risk of conjunctivitis in people, with an RR value of 0.9933 (95%CI: 0.9867, 1.0000). For SO2, Fu et al.’s study [5] revealed that the risk of conjunctivitis between SO2 and the population was significant, with an RR value of 1.0480 (95%CI: 1.0040, 1.0939). In the study of Jamaludin et al. [30], SO2 had a protective effect on the conjunctivitis risk among people, with an RR value of 0.8468 (95%CI: 0.7371, 0.9730). Air pollution is gradually occupying an important position in the risk factors of conjunctivitis. Our study shows that all six air pollutants have a positive correlation with conjunctivitis. Among them, NO2 had the most significant effect, followed by O3. This may be due to differences in the physical and chemical properties between pollutants, resulting in different risk outcomes. Both NO2 and O3 are highly oxidative and irritating to the eyes [37,38,39,40]. According to the chemical properties of O3 and NO2, O3 is easily removed by a reaction, so the lifetime of NO2 is longer than that of O3 [41,42]. In addition, in terms of toxicity, the toxicity of O3 may be more complex than that of NO2 [43,44], which may have a significant potential impact on eye tissue cells. From the comprehensive analysis of the toxicity degree and lifetime of pollutants, NO2 and O3 have obvious risks for conjunctivitis in the population, among which, NO2 has the highest risk value, followed by O3.

4.2. Risk Analysis of Air Pollution and Conjunctivitis in Subgroups

According to the research analysis, PM2.5, NO2, and O3 present a higher risk for conjunctivitis in women than in men; meanwhile, PM2.5 and O3 exhibit a higher risk for conjunctivitis for people under 18 years of age than people over 18 years of age, whereas NO2 had the opposite effect. Between genders, there are three possible reasons for the greater risk of conjunctivitis in women. First, women’s physical function is generally not as good as men’s [45], so their ability to resist air pollution is relatively weak. Second, women spend more time indoors than men [46,47], and indoor air circulation is not strong, so more toxic and harmful air pollutants may more easily accumulate and then be absorbed. Third, compared with men, women prefer makeup [48], especially eye shadows, eyelashes, and contact lenses. Studies have shown that these types of eye makeup can cause discomfort to the eyes, such as dryness, pain, etc. [49,50,51,52], which may increase the risk of conjunctivitis. Therefore, in combination with the above points, the risk for females of conjunctivitis is greater than that for males. In terms of the age group, for people younger than 18 years old, the development of physical function and the defense ability is still immature and they are thus vulnerable to air pollutants. The effects of NO2 on people over 18 years of age was significantly greater than that on people under 18 years of age, which may be related to people’s living and working habits. People over the age of 18 go to work, which often involves the need to travel between cities, so there is a relatively high chance of exposure to severe air pollution scenarios [53]. Exposure to more mobile sources of pollution, such as NO2 emitted by automobiles [54], increases the risk of conjunctivitis in adults.

4.3. Source of Heterogeneity and Possible Bias

For GDP, latitude, longitude, temperature, and humidity, we observed substantial heterogeneity in the pooled effect sizes of air pollutants (NO2, O3, PM2.5, PM10, and SO2) for conjunctivitis. We found that there was a negative correlation between relative humidity and the risk of conjunctivitis for five kinds of air pollutants. There may be several explanations for this.
First, the higher the humidity in the air, the easier it is to condense and settle the solid particles in the air [55], and the easier it is to dilute the liquid or gaseous pollutants. These processes can reduce the concentration of pollutants in the air, thereby reducing the risk of conjunctivitis. Second, a high humidity will affect visibility [56], which will affect people’s travel habits; therefore, to a certain extent, it can reduce the risk of exposure to conjunctivitis. Finally, from a physiological point of view, in greater humidity, the eyes will be relatively comfortable (so it is not easy to itch the eyes, not easy to rub the eyes, etc.) and thus dry eye will not be easily caused [57]. Furthermore, it reduces the risk of conjunctivitis.

4.4. Possible Mechanisms Explaining the Relation between Conjunctivitis and Air Pollution

To date, the underlying pathophysiological mechanism of conjunctivitis caused by air pollutants is still unclear. As a human’s eyes are directly exposed to air pollution, some studies have speculated that PM2.5 [35,58,59] and PM10 [60] particles may easily cause the inadaptability of intraocular epidermal cells, leading to cell death and the inflammation of tissue cells. Second, NO2 and O3 have strong oxidative stress effects [61], which may stimulate conjunctival cell inflammation. Finally, NO2 is an acidic gas. When it enters the eyes, it easily changes the acidic and alkaline environment of the inner epidermis cells of the eyes [62], breaking the function of the eye cells and causing inflammation [63,64]. It is plausible that the association between air pollution and the risk of conjunctivitis events is a result of these important mechanistic pathways.

4.5. Limitations and Implications

Several limitations of our study should be considered. First, almost all the included references used the air pollutant data from fixed environmental monitoring stations instead of individual-level air pollutant exposures, which may have led to measurement error. Second, we included studies in the same place at different times (for example, Taiwan), which may have also had an impact on the combined value of conjunctivitis risk. Finally, few studies were available on the association between some types of air pollutants (e.g., carbon monoxide) and the risk of conjunctivitis, which led to a relatively low statistical power and limited the further stratified assessment for subgroups. Therefore, future epidemiological evidence from more countries and/or cities with a well-designed strategy is required to be able to develop more comprehensive knowledge on the effect of air pollution on the risk of conjunctivitis. Further investigations are also needed to identify the subgroups that are most vulnerable to air pollution, and the socioeconomic status should be considered. It would also be useful to explore the use of alternative exposure metrics that are more representative of individual exposure, and it would be beneficial to examine the mechanism underlying the harmful effect of air pollution on patients with conjunctivitis. Additionally, a cost-effectiveness of preventive measures for improving the air quality to reduce the incidence of conjunctivitis is also needed in future research.

5. Conclusions

This meta-analysis found that air pollution is an important factor for the risk of conjunctivitis. NO2 presented the highest impact on patients with conjunctivitis, followed by O3. For different sub-groups of patients with conjunctivitis, females and the age group under 18 years old were more sensitive to the air pollution. Notable inconsistencies in the various studies have been found for the association between air pollution and conjunctivitis, while only relative humidity significantly modified the risk of O3 for conjunctivitis, which explained 45% of the between-study heterogeneity. Our findings highlight the necessity for the reduction of air pollution levels and protection of vulnerable populations. Further research is needed to better understand the mechanisms underlying the harmful effect of air pollutants on the risk of conjunctivitis. Future well-designed epidemiological studies from more countries and/or cities are still warranted to be able to get more comprehensive knowledge and powerful evidence about the effect of air pollution on the risk of conjunctivitis and identification of the subpopulations sensitive to air pollution.

Author Contributions

Conceptualization, J.Y. and B.W; methodology, R.C. and J.Y.; validation, R.C., C.Z., and B.L.; formal analysis, R.C. and J.Y.; data curation, R.C., H.W., and B.L.; writing—original draft preparation, R.C. and J.Y.; writing—review and editing, C.Z., F.Z., S.B., H.W., and B.W.; supervision, J.Y. and B.W.; funding acquisition, J.Y. and B.W.


This research was funded by the Fundamental Research Funds for the Central Universities (No. 11618323), the National Natural Science Foundation of China (91544215, 41373116), the National Key Research and Development Program of China (No. 2018YFC0213602), and Guangdong Provincial Science and Technology Planning Project of China (No.2017B050504002).


We would like to acknowledge Ms. Jing Li from Beijing Changping District Center for Disease Control and Prevention for her helpful advice on literature screening.

Conflicts of Interest

The authors declare no conflict of interest.


COcarbon monoxide
NO2nitrogen dioxide
SO2sulfur dioxide
PM2.5particles smaller than 2.5 μm
PM10particles smaller than 10 μm
ERexcess rate
RRrelative risk
ORodds ratio
βregression coefficient
SEstandard error
CIconfidence interval
GLMgeneralized linear model
GAMgeneralized additive model
DLMdistributed lag model
ICD-9International Classification of Disease, Revision 9
ICD-10International Classification of Disease, Revision 10
ICPC-2Code(s) International Classification of Primary Care, Second Edition
GDPgross domestic product;

Appendix A

Table A1. The search strategies used in the review.
Table A1. The search strategies used in the review.
Search FieldPubMed
(MeSH terms & tiab search function)
Web of Science
(TS & TI search function)
(TITLE-ABS-KEY search function)
(ti,ab,kw search function)
[1](“conjunctivitis”[MeSH Terms] OR Conjunctivitis[Title/Abstract] OR “endophthalmitis”[MeSH Terms] OR ophthalmia[Title/Abstract] OR pinkeye[Title/Abstract] OR Pink eye[Title/Abstract])(TS=(“conjunctivitis” OR “endophthalmitis” OR “ophthalmia” OR “pinkeye” OR “conjunctivitis” OR “Pink eye”) OR TI=(“conjunctivitis” OR “endophthalmitis” OR “ophthalmia” OR “pinkeye” OR “conjunctivitis” OR “Pink eye”))TITLE-ABS-KEY(“conjunctivitis” OR “endophthalmitis” OR “ophthalmia” OR “pinkeye” OR “conjunctivitis” OR “Pink eye”) AND TITLE-ABS-KEY(“air pollution” OR “ambient air pollution” OR “outdoor air pollution” OR “atmospheric pollution”)“conjunctivitis”:ti,ab,kw OR “endophthalmitis”:ti,ab,kw OR “ophthalmia”:ti,ab,kw OR “pinkeye”:ti,ab,kw OR “pink eye”:ti,ab,kw
[2](“air pollution”[MeSH Terms] OR air pollution[Title/Abstract] OR ambient air pollution[Title/Abstract] OR outdoor air pollution[Title/Abstract] OR atmospheric pollution[Title/Abstract])(TS=(“air pollution” OR “ambient air pollution” OR “outdoor air pollution” OR “atmospheric pollution”) OR TI=(“air pollution” OR “ambient air pollution” OR “outdoor air pollution” OR “atmospheric pollution”))TITLE-ABS-KEY(“conjunctivitis” OR “endophthalmitis” OR “ophthalmia” OR “pinkeye” OR “conjunctivitis” OR “Pink eye”) AND TITLE-ABS-KEY( “PM2.5” OR “Particulate Matter2.5” OR “particulate matter” OR “PM10” OR “Particulate Matter 10” OR “SO2” OR “Sulfur dioxide” OR “NO2” OR “Nitrogen dioxide” OR “NOx” OR “Nitrogen oxides” OR “O3” OR “ozone” OR “CO” OR “Carbon monoxide” OR “Smog” OR “black carbon”)“air pollution”:ti,ab,kw OR “ambient air pollution”:ti,ab,kw OR “outdoor air pollution”:ti,ab,kw OR “atmospheric pollution”:ti,ab,kw
[3]( PM2.5[Title/Abstract] OR Particulate Matter2.5[Title/Abstract] OR particulate matter[MeSH Terms] OR particulate matter[Title/Abstract] OR PM10[Title/Abstract] OR Particulate Matter10[Title/Abstract] OR SO2[Title/Abstract] OR Sulfur dioxide[MeSH Terms] OR Sulfur dioxide[Title/Abstract] OR NO2[Title/Abstract] OR Nitrogen dioxide[MeSH Terms] OR Nitrogen dioxide[Title/Abstract] OR NOx[Title/Abstract] OR Nitrogen oxides[MeSH Terms] OR Nitrogen oxides[Title/Abstract] OR O3[Title/Abstract] OR ozone[MeSH Terms] OR ozone[Title/Abstract] OR CO[Title/Abstract] OR Carbon monoxide[MeSH Terms] OR Carbon monoxide[Title/Abstract] OR Smog[MeSH Terms] OR Smog[Title/Abstract] OR black carbon[MeSH Terms] OR black carbon[Title/Abstract])(TS=( “PM2.5” OR “Particulate Matter2.5” OR “particulate matter” OR “PM10” OR “Particulate Matter 10” OR “SO2” OR “Sulfur dioxide” OR “NO2” OR “Nitrogen dioxide” OR “NOx” OR “Nitrogen oxides” OR “O3” OR “ozone” OR “CO” OR “Carbon monoxide” OR “Smog” OR “black carbon”) OR TI=( “PM2.5” OR “Particulate Matter2.5” OR “particulate matter” OR “PM10” OR “Particulate Matter 10” OR “SO2” OR “Sulfur dioxide” OR “NO2” OR “Nitrogen dioxide” OR “NOx” OR “Nitrogen oxides” OR “O3” OR “ozone” OR “CO” OR “Carbon monoxide” OR “Smog” OR “black carbon”))TITLE-ABS-KEY(“conjunctivitis” OR “endophthalmitis” OR “ophthalmia” OR “pinkeye” OR “conjunctivitis” OR “Pink eye”) AND TITLE-ABS-KEY(“air pollution” OR “ambient air pollution” OR “outdoor air pollution” OR “atmospheric pollution”) AND TITLE-ABS-KEY( “PM2.5” OR “Particulate Matter2.5” OR “particulate matter” OR “PM10” OR “Particulate Matter 10” OR “SO2” OR “Sulfur dioxide” OR “NO2” OR “Nitrogen dioxide” OR “NOx” OR “Nitrogen oxides” OR “O3” OR “ozone” OR “CO” OR “Carbon monoxide” OR “Smog” OR “black carbon”)“PM2.5”:ti,ab,kw OR “particulate matter2.5”:ti,ab,kw OR “particulate matter”:ti,ab,kw OR “PM10”:ti,ab,kw OR “particulate matter 10”:ti,ab,kw OR “SO2”:ti,ab,kw OR “sulfur dioxide”:ti,ab,kw OR “NO2”:ti,ab,kw OR “nitrogen dioxide”:ti,ab,kw OR “NOx”:ti,ab,kw OR “nitrogen oxides”:ti,ab,kw OR “O3”:ti,ab,kw OR “ozone”:ti,ab,kw OR “CO”:ti,ab,kw OR “carbon monoxide”:ti,ab,kw OR “smog”:ti,ab,kw OR “black carbon”:ti,ab,kw
Search strategy([1] AND [2]) OR ([1] AND [3])([1] AND [2]) OR ([1] AND [3])([1] AND [2]) OR ([1] AND [3])([1] AND [2]) OR ([1] AND [3])
Table A2. Supplementary information of the included literature.
Table A2. Supplementary information of the included literature.
StudyLocationPopulationGDP (billion dollars)Latitude, LongitudeTemperature (°C)Humidity (%)Duration of Sunshine (hours)
Bourcier et al. (2003) [13]Paris, France2,125,851459.2048.86, 2.359.31–16.9054.70–89.904.54
Larrieu et al. (2009) [14]Bordeaux, France600,00017.7044.84, −0.585.57
Chang et al. (2012) [4]Taiwan, China23,037,031392.9225.03, 121.5224.0975.245.26
Chiang et al. (2012) [29]Taiwan, China (four cities) a22,689,122331.0125.03, 121.5223.7877.254.95
Szyszkowicz et al. (2012) [27]Edmonton, Canada626,50028.8053.53, -113.503.9066.006.40
Hong et al. (2016) [6]Shanghai, China23,030,000244.9031.27, 121.5217.2069.404.88
Szyszkowicz et al. (2016) [28]Ontario, Canada (nine cities) b12,760,000657.2050.00, -85.009.0972.205.64
Fu et al. (2017) [5]Hangzhou, China9,018,000145.9330.25, 120.1717.9074.604.69
Jamaludin et al. (2017) [30]Johor Bahru, Malaysian848,00020.061.46, 103.7625.50–27.805.75
Lee et al. (2018) [11]Daegu, Korea2,279,00045.38735.87, 128.606.20
Seo et al. (2018) [10]Seoul, South Korea10,442,426280.0037.53,127.02(7–9 month): 24.70
(1–3 month): −0.80
(7–9 month): 70.70
(1–3 month): 51.20
Szyszkowicz et al. (2019) [9]Edmonton, Canada626,50028.802553.53, -113.506.40
Note: “—“, no data; “a”, Four cities included Taipei, Kaohsiung, Yunlin, and Yilan; “b”, Nine cities include Algoma, Halton, Hamilton, London, Ottawa, Peel, Toronto, Windsor, and York.
Table A3. The quality assessment of the included literature.
Table A3. The quality assessment of the included literature.
No.StudyConjunctivitis Disease Occurrence Verification (1 point)Quality of Air Pollutant Measurement (1 point)Adjustment Degree of Confounders (3 point)Total Score (5 point)Quality Category
1Bourcier et al. (2003) [13]0134Low quality
2Larrieu et al. (2009) [14]1124Medium quality
3Chang et al. (2012) [4]1124Medium quality
4Chiang et al. (2012) [29]1135High quality
5Szyszkowicz et al. (2012) [27]1124Medium quality
6Hong et al. (2016) [6]1135High quality
7Szyszkowicz et al. (2016) [28]1124Medium quality
8Fu et al. (2017) [5]1113Medium quality
9Jamaludin et al. (2017) [30]0022Low quality
10Lee et al. (2018) [11]1001Low quality
11Seo et al. (2018) [10]1012Low quality
12Szyszkowicz et al. (2019) [9]1113Medium quality
Table A4. Sensitivity meta-analysis using the leave-one-out method.
Table A4. Sensitivity meta-analysis using the leave-one-out method.
LiteratureRR(95% CI)Z-testp-valueQ-testQ-pτ2I2H2
CO-31.0010(0.9990-1.0030)2.7470.0060.0001.0000.000000 0.0001.000
CO-81.0000(1.0000-1.0000)0.6560.5120.0001.0000.000000 0.0001.000
PM10-11.0030(0.9971-1.0089)0.8730.38216.2650.0060.000031 70.7783.422
PM10-21.0030(0.9971-1.0089)1.0520.29314.6810.0120.000020 66.9793.028
PM10-31.0040(0.9962-1.0119)1.1290.25917.340.0040.000040 62.0072.632
PM10-41.0050(1.0011-1.0090)2.2930.02210.3760.0650.000009 40.1471.671
PM10-61.0030(0.9971-1.0089)1.0060.31417.1920.0040.000033 74.5773.933
PM10-81.0020(0.9961-1.0079)0.7570.44914.7930.0110.000025 64.4912.816
PM10-101.0030(0.9971-1.0089)1.2260.22013.6950.0180.000024 70.3843.377
SO2-11.0060(0.9923-1.0199)0.7890.43047.1450.0000.000193 86.3427.322
SO2-31.0010(0.9835-1.0188)0.1550.87724.4330.0000.000287 78.0764.561
SO2-41.0131(1.0091-1.0171)5.3030.00011.3360.0450.000002 3.031.031
SO2-61.0030(0.9835-1.0229)0.3300.74248.5210.0000.000345 90.72510.782
SO2-71.0030(0.9835-1.0229)0.2680.78948.3380.0000.000348 90.07310.073
SO2-81.0020(0.9883-1.0158)0.2240.82345.1440.0000.000160 83.8386.187
SO2-101.0060(0.9923-1.0199)0.8870.37542.5620.0000.000180 85.5156.904
PM2.5-31.0050(0.9972-1.0129)1.0510.2934.8560.0880.000033 60.1872.512
PM2.5-61.0000(1.0000-1.0000)3.4590.0016.2280.0440.000000 0.0001.000
PM2.5-71.0040(0.9942-1.0139)0.9040.3665.8270.0540.000042 65.1862.872
PM2.5-81.0000(1.0000-1.0000)-0.5270.5983.1210.2100.000000 36.3561.571
NO2-11.0274(1.0094-1.0457)2.9430.00323.4450.0000.000308 77.5014.445
NO2-21.0315(1.0134-1.0498)3.5010.00024.6360.0000.000293 76.2024.202
NO2-31.0356(1.0195-1.0520)4.4630.0008.3290.1390.000135 41.1961.701
NO2-61.0222(1.0083-1.0364)3.0200.00314.4660.0130.000158 61.1832.576
NO2-71.0294(1.0094-1.0498)2.7790.00524.1100.0000.000393 76.3764.233
NO2-81.0263(1.0064-1.0467)2.5910.01017.6460.0030.000341 72.6913.662
NO2-91.0294(1.0114-1.0477)3.3920.00124.9800.0000.000298 77.5064.446
O3-11.0090(1.0031-1.0150)2.7830.00548.2110.0000.000053 94.37217.768
O3-21.0070(1.0011-1.0130)2.8020.00544.3530.0000.000032 90.82810.903
O3-31.0101(1.0022-1.0180)2.6220.00940.2140.0000.000080 95.4221.834
O3-41.0111(1.0032-1.0190)2.7930.00539.6210.0000.000074 91.51111.78
O3-51.0050(1.0011-1.0090)2.9820.00338.2580.0000.000013 80.0725.018
O3-61.0070(1.0011-1.0130)2.6480.00842.6430.0000.000033 91.01711.132
O3-71.0111(1.0032-1.0190)2.7450.00643.7410.0000.000076 94.87919.529
O3-81.0101(1.0022-1.0180)2.6890.00747.9100.0000.000077 95.90324.405
O3-111.0111(1.0051-1.0170)3.2180.00116.8620.0180.000050 83.2915.985
Note. The number in the column of “literature” denotes the number of the literature from Table A3 that was excluded, and effect estimates from the rest of the literature were then pooled using a meta-analysis. Q-p denotes the p-value for the Q test.
Figure A1. Funnel plot of PM10 and O3 on conjunctivitis using Trim-fill method. The solid circles denote effect estimates from included studies and open circles denotes estimates provided by Trim-fill method.
Figure A1. Funnel plot of PM10 and O3 on conjunctivitis using Trim-fill method. The solid circles denote effect estimates from included studies and open circles denotes estimates provided by Trim-fill method.
Ijerph 16 03652 g0a1


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Figure 1. Flow chart for the study selection process.
Figure 1. Flow chart for the study selection process.
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Figure 2. Forest plot of the association between conjunctivitis and exposure to air pollution: (a) CO, NO2, O3; and (b) PM2.5, PM10, SO2. Risk ratio was calculated by considering a 10 μg/m3 increase of air pollution.
Figure 2. Forest plot of the association between conjunctivitis and exposure to air pollution: (a) CO, NO2, O3; and (b) PM2.5, PM10, SO2. Risk ratio was calculated by considering a 10 μg/m3 increase of air pollution.
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Figure 3. Funnel plot showing the risk of publication bias in the meta-analysis on the risk of conjunctivitis with per 10 μg/m3 increase of air pollutants. Horizontal axis represents the log RR and vertical axis represents standard errors.
Figure 3. Funnel plot showing the risk of publication bias in the meta-analysis on the risk of conjunctivitis with per 10 μg/m3 increase of air pollutants. Horizontal axis represents the log RR and vertical axis represents standard errors.
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Table 1. The characteristics of studies included in the meta-analysis.
Table 1. The characteristics of studies included in the meta-analysis.
StudyLocationStudy Design Time-spanStudy PopulationPollutantControlled VariablesTotal EventsLag (d/w)Main Findings
Bourcier et al. (2003) [13]Paris, FranceLogistic regression
AllNO, NO2, O3, SO2, PM10Temperature, pressure,
humidity, wind speed,
day of the week
1272d: 0–2A strong relation between NO, NO2, and conjunctivitis was observed. Atmospheric pressure, minimal humidity, and wind speed may increase the incidence of ocular surface complaints.
Larrieu et al. (2009) [14]Bordeaux, FranceTime series Poisson regression model 2000–2006AllNO2, PM10, O3Long-term trends, seasonality, days of the week, holidays, temperature, influenza epidemics179,142d: 0–3There was a much higher effect of nitrogen dioxide on visits for conjunctivitis when delayed effects were considered. Conjunctivitis was also significantly associated with PM10 and ozone levels.
Chang et al. (2012) [4]Taiwan, ChinaCase-crossover
Meta-analysis 2007–2009
AllCO, NO2, SO2, O3, PM10, PM2.5Temperature,
rainfall, humidity
26,314,960d: 0,
0–1 to 0–5
The effects on outpatient visits for nonspecific conjunctivitis were strongest for O3 and NO2. In winter, PM10 and SO2 had a more prominent impact on the risk of conjunctivitis.
Chiang et al. (2012) [29]Taiwan, China (four cities)Time series Generalized linear model 2000–2007AllPM10, SO2, NOx, O3Relative humidity, wind speed,
rainfall, public holiday, calendar months and years.
234,366d: 0There were higher risks of conjunctivitis in rural areas, but higher sensitization to air pollutants in urban cities. Children, females, and the older population were at higher risks for both types of conjunctivitis.
Szyszkowicz et al. (2012) [27]Edmonton, CanadaCase-crossover Logistic regression Time-stratification
All, Sex: male, femaleO3Long-term trends, seasonal effects, day-of-week and month-of-year effects7526d: 3–8For conjunctivitis, associations of these conditions with ozone exposure were observed only in females.
Hong et al. (2016) [6]Shanghai, ChinaTime series Generalized least squares 2008–2012All,
Sex: male, female
Age: <18, 19–40, 41–60, >60 years
SO2, NO2, PM10, PM2.5, O3Periodic trends3,211,820w: 1, 3Research revealed that higher levels of ambient NO2, O3, and temperature increased the chances of outpatient visits for allergic conjunctivitis. Meanwhile, those older than 40 years were only affected by NO2 levels.
Szyszkowicz et al. (2016) [28]Ontario, Canada (nine cities)Case-crossover Time-stratified
Apr 2004–Dec 2011
Sex: male, female
Age: ≤17, ≥18 years
NO2, O3, SO2, PM2.5Temperature, humidity77,439d: 0–8There were positive associations between air pollution and ED visits for conjunctivitis, with different temporal trends and strength of association by age, sex, and season. Children and young adults were more vulnerable to conjunctivitis infections.
Fu et al. (2017) [5]Hangzhou, ChinaTime-stratified
Case-crossover Logistic regression
Sex: male, female
Age: 0–1, 2–5, 6–18, 19–64, >65 years
PM10, PM2.5, SO2, NO2, O3, COTemperature,
humidity, atmospheric pressure
9737d: 0, 0–1PM10, PM2.5, SO2, NO2, and CO were associated with the risk of conjunctivitis. SO2 was significantly associated with conjunctivitis patients between 2 and 5 years old and male. PM10 and NO2 were significantly associated with female conjunctivitis patients.
Jamaludin et al. (2017) [30]Johor Bahru, MalaysianTime series Poisson generalized linear model, negative binomial model
AllNO2, PM10, SO2Rainfall,
temperature, humidity
1396w: 14,19,20SO2 was the most abundant source that contributed to the eye diseases.
Lee et al. (2018) [11]Daegu, KoreaSpatial analysis
AllPM10SO2, NO2, O3, CO769d: 0Incidence of conjunctivitis and keratitis varied from region to region.
Seo et al. (2018) [10]Seoul, South KoreaMulti-level regression model
AllO3Temperature, humidity sex, age48,344d: 0The outpatient incidence of conjunctivitis was increased by O3.
Szyszkowicz et al. (2019) [9]Edmonton, CanadaCase-crossover Time-stratified Logistic regression
Apr 1992–Mar 2002
Sex: male, femaleO3Temperature, humidity17,211d: 0–9Significant association was observed for air pollution at lag 5 day for males, and lag 1 day and lag 3 day for females.
Note: d, day; w, week; CO, carbon monoxide; NO2, nitrogen dioxide; SO2, sulfur dioxide; O3, ozone; PM2.5, particles smaller than 2.5 μm; PM10, particles smaller than 10 μm.
Table 2. Risk analysis of air pollutants on patients with conjunctivitis, stratified by gender and age group.
Table 2. Risk analysis of air pollutants on patients with conjunctivitis, stratified by gender and age group.
PollutantGroupsNo. of the StudiesHeterogeneity, τ2Heterogeneity, p-valueHeterogeneity, I2 (%)Summary RR (95%CI)p-Value
Note: RR—relative risk; CI—confidence interval.
Table 3. Meta-regression analysis of study level predictors on the association between air pollution and risk of conjunctivitis.
Table 3. Meta-regression analysis of study level predictors on the association between air pollution and risk of conjunctivitis.
Air pollutantsCovariantIQREstimatep-Valueτ2I2R2
NO2GDP343.070.24 (−2.69, 3.26)0.8730.00038578.5869810.00
Latitude19.210.57 (−2.25, 3.47)0.6950.00035072.0857960.00
Longitude119.960.44 (−2.19, 3.14)0.7450.00038375.2421530.00
Temperature4.28−0.43 (−2.25, 1.42)0.6440.00049785.3368190.00
Duration of sunshine
−2.02 (−4.35, 0.37)
−2.77 (−5.60, 0.16)
O3GDP246.99−0.65 (−1.46, 0.17)0.1200.00005492.1018030.00
Latitude18.610.70 (−0.51, 1.91)0.2590.00006893.5913510.00
Longitude122.10−0.55 (−1.37, 0.28)0.1930.00005693.0629460.00
Temperature11.19−0.70 (−1.83, 0.44)0.2270.00005684.4725650.00
Duration of sunshine
−0.76 (−1.42, −0.10)
0.42 (−0.67, 1.52)
PM2.5GDP238.83−0.40 (−1.06, 0.27)0.2380.00001866.2053200.00
Latitude7.01−0.07 (−0.58, 0.44)0.7860.00004763.7146830.00
Longitude52.650.11 (−0.29, 0.51)0.6000.00004265.1529110.00
Temperature4.280.00 (−0.55, 0.55)0.9950.00004962.4647570.00
Duration of sunshine
−0.17 (−1.32, 0.99)
−0.52 (−1.25, 0.21)
PM10GDP266.31−0.71 (−2.00, 0.60)0.2840.00003369.7976370.00
Latitude12.710.51 (−0.21, 1.22)0.1650.00002159.0176238.35
Longitude60.26-0.38 (−1.05, 0.30)0.2780.00002767.9650360.00
Temperature6.13−0.73 (−1.77, 0.32)0.1710.00002067.21305023.38
Duration of sunshine
−0.32 (−0.85, 0.21)
0.04 (−1.34, 1.44)
SO2GDP230.650.20 (−2.13, 2.59)0.8650.00031489.6921120.00
Latitude15.030.99 (−1.43, 3.47)0.4250.00031989.0379670.00
Longitude68.460.01 (−1.43, 1.47)0.9940.00035890.3071760.00
Temperature6.58−0.47 (−2.26, 1.35)0.6080.00026891.6177620.00
Humidity3.04−0.52 (−2.10, 1.08)0.5230.00022190.5586270.00
Duration of sunshine0.66−0.71 (−4.23, 2.93)0.6980.00038089.8404830.00
Note: CO—carbon monoxide; NO2—nitrogen dioxide; SO2—sulfur dioxide; O3—ozone; PM2.5—particles smaller than 2.5μm; PM10—particles smaller than 10μm; GDP—gross domestic product; IQR—interquartile range. The descriptive information of city-level predictors is provided in Table A2.
Table 4. Begg’s test, Egger’s test, and trim-fill test on the effect of air pollutants on conjunctivitis.
Table 4. Begg’s test, Egger’s test, and trim-fill test on the effect of air pollutants on conjunctivitis.
Air PollutantsBegg’s TestEgger’s TestTrim-Fill-Begg’s TestTrim-Fill-Egger’s Test
O3-0.05560.91955.4884< 0.0001−0.13160.5388−0.02080.9834
Note: Egger’s test was unavailable for the CO because of the limited number of studies on the association between CO and the risk of conjunctivitis. The trim-fill test was only performed for PM10 and O3, which showed significant publication bias. CO—carbon monoxide; NO2—nitrogen dioxide; SO2—sulfur dioxide; O3—ozone; PM2.5—particles smaller than 2.5 μm; PM10—particles smaller than 10 μm.

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MDPI and ACS Style

Chen, R.; Yang, J.; Zhang, C.; Li, B.; Bergmann, S.; Zeng, F.; Wang, H.; Wang, B. Global Associations of Air Pollution and Conjunctivitis Diseases: A Systematic Review and Meta-Analysis. Int. J. Environ. Res. Public Health 2019, 16, 3652.

AMA Style

Chen R, Yang J, Zhang C, Li B, Bergmann S, Zeng F, Wang H, Wang B. Global Associations of Air Pollution and Conjunctivitis Diseases: A Systematic Review and Meta-Analysis. International Journal of Environmental Research and Public Health. 2019; 16(19):3652.

Chicago/Turabian Style

Chen, Renchao, Jun Yang, Chunlin Zhang, Bixia Li, Stéphanie Bergmann, Fangfang Zeng, Hao Wang, and Boguang Wang. 2019. "Global Associations of Air Pollution and Conjunctivitis Diseases: A Systematic Review and Meta-Analysis" International Journal of Environmental Research and Public Health 16, no. 19: 3652.

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