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Association between the first occurrence of allergic conjunctivitis, air pollution and weather changes in Taiwan
Atmospheric Environment 212 (2019) 90–95
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Association between the first occurrence of allergic conjunctivitis, air pollution and weather changes in Taiwan
T
Jia-Yu Zhonga, Yueh-Chang Leeb, Chia-Jung Hsieha, Chun-Chieh Tsenga, Lih-Ming Yiina,∗ a b
Department of Public Health, Tzu Chi University, 701, Sec. 3, Zhongyang Rd, Hualien City, 97004, Taiwan Department of Ophthalmology, Hualien Tzu Chi Hospital, 707, Sec. 3, Zhongyang Rd, Hualien City, 97002, Taiwan
ARTICLE INFO
ABSTRACT
Keywords: Air pollution Allergic conjunctivitis Nitrogen dioxide Ozone Relative humidity Temperature
Allergic conjunctivitis (AC) is one of the common allergic diseases, which may be influenced by environmental factors. This study was to examine the association between the first occurrence of AC, air pollution and weather changes, and demographic factors in Taiwan. A total of 100,636 eligible subjects were identified from the systematic sampling cohort database containing 1,000,000 insureds of the National Health Insurance of Taiwan from 2004 to 2013, and were matched with data from the environmental monitoring stations adjacent to their locations of clinics. The case-crossover design, using the same subjects experiencing exposures at the time of diagnosis for cases and that on the days one and two weeks before and after diagnosis for controls, was applied in the study. We found that the first occurrences of AC were significantly the most for youngsters by age (42.6%), for women by sex (56.2%), and in spring by season (29.6%) (P < 0.001). Multivariate conditional logistic regression analyses indicated that nitrogen dioxide (NO2), ozone (O3), and temperature were positively associated with AC (P < 0.001), while relative humidity was negatively related to AC (P < 0.001). Knowing these associations, we suggest that AC patients pay attention to air pollution and weather change in the ambient environments and manage accordingly to mitigate the problems.
1. Introduction Allergic diseases have been increasingly prevalent around the world in the recent years (Kusunoki et al., 2009; Neugut et al., 2001; Tang et al., 2009). When occurring, it causes the patients perceptible discomfort, which may affect the daily lives or even require medical treatments. One of the common allergic diseases is ocular allergy or allergic conjunctivitis (AC), which is usually found to be associated with the occurrence of allergic rhinitis (Miraldi Utz and Kaufman, 2014; Pelikan, 2010; Rosario and Bielory, 2011; Thong, 2017). A study based on the National Health and Nutrition Examination Survey III in the U.S. found that up to 40% of the population experienced ocular allergy symptoms (Singh et al., 2010); a Nigerian study reported the prevalence of AC to be 32%, and indicated higher prevalence in warmer climatic conditions (Malu, 2014). With the extensive prevalence, AC is definitely an allergic disease to which attention has to be paid. AC is a general term that includes seasonal allergic conjunctivitis (SAC), perennial allergic conjunctivitis, vernal keratoconjunctivitis, atopic keratoconjunctivitis, and giant papillary conjunctivitis (Gomes, 2014; La Rosa et al., 2013; Miraldi Utz and Kaufman, 2014; Pelikan,
2010). Unlike infectious conjunctivitis, AC usually results from an immunoglobulin E (IgE)-mediated hypersensitivity reaction and/or a Tlymphocyte-mediated hypersensitivity initiated by allergens (Irkec and Bozkurt, 2012; Ono and Abelson, 2005; Saban et al., 2013). The allergens enter the tear film, make contact with conjunctival mast cells that bear allergen-specific IgE antibodies, and result in degranulation of mast cells. The degranulated mast cells then release histamine and a variety of other inflammatory mediators that promote vasodilation, edema, and recruitment of other inflammatory cells, such as eosinophils. Thus, patients usually suffer the consequences of red, itchy and watery eyes with swelling of the conjunctiva (Bielory, 2000; Miraldi Utz and Kaufman, 2014; Ono and Abelson, 2005; Thong, 2017), which could be severe to affect their daily lives (Buchholz et al., 2003; Palmares et al., 2010). Two previous surveys using standard questionnaires for quality of life were conducted in European countries, and confirmed that SAC not only resulted in reduction of quality of life for the patients but also accounted for extra costs of health care or personal out-of-pocket expenses (Pitt et al., 2004; Smith et al., 2005). AC could be triggered by a variety of allergens, including molds, pollens, animal dander, dust mites, and even air pollutants (Miraldi Utz
Corresponding author. E-mail addresses: [email protected] (J.-Y. Zhong), [email protected] (Y.-C. Lee), [email protected] (C.-J. Hsieh), [email protected] (C.-C. Tseng), [email protected] (L.-M. Yiin). ∗
https://doi.org/10.1016/j.atmosenv.2019.05.045 Received 25 July 2018; Received in revised form 13 May 2019; Accepted 18 May 2019 Available online 20 May 2019 1352-2310/ © 2019 Elsevier Ltd. All rights reserved.
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and Kaufman, 2014). Among the various types, SAC, caused by seasonal allergens, such as tree pollens, grass/weed pollens and seeds, is the most commonly seen and known for occurring in spring, early summer and fall. The time of occurrence for the other AC types may not be as specific as is SAC, because the triggering allergens (e.g., dust mites, animal dander, air pollutants) could be present in the environment at any time. Air pollution has been recently confirmed to be related to AC by two Asian studies (Hong et al., 2016; Mimura et al., 2014). Such data, however, are relatively few as compared to that derived from other research focusing on the association between air pollution and unspecified conjunctivitis (UC) (Chang et al., 2012; Chiang et al., 2012; Fu et al., 2017; Li et al., 2016), and the data for Taiwan are yet to be procured. We would like to find out whether air pollution in Taiwan was similarly associated with AC, and thus conducted this study using data from the National Health Insurance of Taiwan combined with the environmental monitoring data. To the best of our knowledge, this is the first study to examine the association between air pollution, weather changes and AC in Taiwan. We are convinced that this outcome, combined with the previous Taiwan's data on another type of conjunctivitis (Chang et al., 2012; Chiang et al., 2012), helps raise the awareness of eye health issues associated with environmental degradation for people living in Taiwan and elsewhere with similar problems.
environmental data, which were retrieved from 73 of the available 76 environmental monitoring stations. The monitoring results of air pollutants, including carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), particulate matter with aerodynamic diameter ≤ 2.5 and 10 μm (PM2.5, PM10), and sulfur dioxide (SO2), and meteorological data, such as relative humidity (RH) and temperature, were available from the website of Taiwan Air Quality Monitoring Network (TAQMN, http://taqm.epa.gov.tw/taqm/en/default.aspx). The daily average of each pollutant or meteorological factor was used in the analysis except the 8-h average of ozone, because of O3 formation occurring during the diurnal period of a day. The environmental monitoring and meteorological data covering from 2004 to 2013 were downloaded from the website for analysis. 2.3. Data management and analysis The subject data were categorized to groups by several factors (e.g., age, sex, season) to examine whether these factors were effective in the onset of AC. Statistical tests (e.g., chi-square test, t-test) were conducted to find out whether there was any difference between the categorical data. The cohort database without personal identification provided information of the locations of subjects’ clinics at the county or district level, which was sufficient for linkage with adjacent environmental monitoring sites. As for data of air pollutants and meteorological factors, descriptive statistics and Spearman correlation analysis were performed to have a general understanding of the environmental factors. The case-crossover design, which was suitable for “brief exposure to cause a transient change in risk of a rare acute-onset disease” (Maclure, 1991), was applied to the analysis of association between the first occurrence of AC, air pollution and weather changes. This design intends to use the same subjects for cases and controls, with the former at the onset of disease and the latter at other occasions away from the disease occurrence. In this study, exposure for each case was the average of environmental data for two days prior to and the day of the first AC diagnose, because a lag effect was found from previous UC-air pollution studies (Chang et al., 2012; Fu et al., 2017; Li et al., 2016). On the other hand, exposure for control was the average of environmental data on four different days, which were one and two weeks before and after the onset of AC. This selection of control days was modified from that used in two previous studies (Chang et al., 2012; Fu et al., 2017), which selected the days of the week matching with the day of diagnosis in the same month and year for control days. Univariate conditional logistic regression analyses were conducted to learn the significance of association between each air pollutant or meteorological factor and AC, whereas multivariate analyses were to identify all significant factors that were associated with AC. For those air pollutants and meteorological factors with high correlation between one another (ρ > 0.8), only one of them was selected to enter a single multivariate analysis due to collinearity. To derive better results, data of the environmental factors with relatively large variations were converted to levels with each containing 10 units (e.g., ppb, μg/m3). Because of the case-crossover design where cases and controls were the same subjects with identical settings in age, sex and season, such adjustments for covariates were not necessary in the analysis. All the statistical analyses were performed using SAS 9.4 (SAS Institute Inc., Cary, NC USA).
2. Materials and methods 2.1. Inclusion of subjects The systematic sampling cohort database of 1,000,000 insureds of the National Health Insurance of Taiwan from 2004 to 2013 was used in this study. The AC diagnoses were clinically made based on patients' histories, signs, symptoms and physical examination findings, and coded by ophthalmologists following the International Classification of Diseases, the 9th Revision, Clinical Modification (ICD-9 CM) at the outpatient visits. In this study, they were defined by the ICD-9 CM codes 372.05 (acute atopic conjunctivitis), 372.13 (vernal conjunctivitis) and 372.14 (other chronic allergic conjunctivitis). Because of no corresponding ICD-9 CM codes for SAC, acute allergic conjunctivitis, or perennial allergic conjunctivitis, these symptoms were commonly registered as 372.14 in clinical practice to distinguish from 372.00 (acute conjunctivitis, unspecified) and 372.10 (chronic conjunctivitis, unspecified). To ensure no interfere from the previous symptoms or impact of chronic AC, we chose the patients’ first occurrences of AC as the study data, and excluded those identified in the first year (i.e., 2004). With such an exclusion, even the identified cases were recurrences of AC, at least one year from the last was warranted. There were 259,325 people being diagnosed as AC patients during the 10-year span, and after subtraction of the-first-year cases the patient number became 216,702. Because dry eye symptoms, which were found to be associated with air pollution and weather change (Um et al., 2014; Hwang et al., 2016; Zhong et al., 2018), might serve as confounder in the analysis, AC patients with such symptoms (ICD-9-CM 370.33, 375.15) were excluded, making the patient number down to 180,452. Additionally, those without available environmental monitoring data or sufficient information were also removed from the database, and finally the eligible study subjects were 100,636 patients, whose first diagnoses were used to link with the environmental factors. The study was granted for exempt review by the Research Ethics Committee of Tzu Chi General Hospital (No: IRB107-70-B, approved on April 11, 2018) for use of the secondary data with personal identification being removed.
3. Results The demographic results for all patients, study subjects, and excluded patients of AC are listed in Table 1. The average ages of the three groups were all around 27.5 years, and the categorical data by age, sex and season were also similar in proportion. Despite the significant differences between the subjects and excluded patients due to the largesample bias, the similarity indicated that study subjects were a good
2.2. Environmental monitoring data Environmental monitoring stations adjacent to or sharing the same district codes with the subjects’ clinics or hospitals were the sources of 91
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Table 1 Data for study subjects, excluded patients and all patients at the first occurrence of allergic conjunctivitis by age, sex and AC-occurring season during 2004–2013.
Total Age (Mean ± SD)
Age 0–18 19–39 40–64 65–105
Sex Male Female P-value AC-occurring Season Spring Summer Fall Winter P-value b
Study subjects
Excluded patients
P-value (between subjects and excluded)
180,452 27.47 ± 21.06
100,636 27.60 ± 21.06
79,816 27.32 ± 21.05
0.005a
n (%)
n (%)
n (%)
42,863 (42.6) 30,098 (29.9)* 21,164 (21.0)* 6511 (6.5)*
34,504 (43.2) 23,912 (30.0)* 16,139 (20.2)* 5261 (6.6)*
< 0.001
< 0.001
< 0.001
79,240 (43.9) 101,212 (56.1)
44,049 (43.8) 56,587 (56.2)
35,191 (44.1) 44,625 (55.9)
< 0.001
< 0.001
< 0.001
53,613 44,175 47,750 34,914
29,815 24,682 26,469 19,670
23,798 19,493 21,281 15,244
77,367 54,010 37,303 11,772
P-value
a
All patients
(42.9) (29.9)* (20.7)* (6.5)*
(29.7) (24.5)* (26.5)* (19.4)*
< 0.001
(29.6) (24.5)* (26.3)* (19.6)*
< 0.001
(29.8) (24.4)* (26.7)* (19.1)*
< 0.001b
< 0.001b
0.057b
< 0.001
Two sample t-test. Chi-square test; *significantly different from the highest percentage.
sample of all AC patients. Children and adolescents (0–18 years) appeared to be the most AC prevalent generation with more than 40% of the total patients; there was a sex difference with females possessing about 56%. As well known, seasonal variation of AC occurrence was observed with spring and fall having the first and second highest percentages. The difference within each category was confirmed to be statistically significant (P < 0.001, Table 1), suggesting that AC occurrence might have been age-, sex- and season-specific. The environmental data during the study period are shown in Table 2. Compared with Taiwan's standards for ambient air quality (TAQMN, 2018), the concentrations of CO, NO2 and SO2 were likely to be under the standards most of the time, whereas O3, PM2.5 and PM10 might have gone over the criteria up to one third of chances. Temperature and RH represented the type of sub-tropical climate over the island with means of 23.7 °C and 74.9%, respectively. It appeared that NO2, O3, PM2.5, PM10 and RH varied extensively with standard deviations higher than 8.71, and thus the concentrations of these factors were converted to levels with each containing 10 units for conditional logistic regression analyses. The most correlated air pollutants were
CO/NO2 (ρ = 0.828) and PM2.5/PM10 (ρ = 0.870) (Table 3), suggesting that they might have shared the same emission sources. All air pollutants were negatively correlated with RH, indicating that high humidity was associated with low air pollution. Each of air pollutants or meteorological factors was significantly related to the first occurrence of AC in the univariate conditional logistic regression analysis (P < 0.001, Table 4); unlike the others, the odds ratio (OR) for RH was less than 1.0, suggesting that low humidity might have favored the onset of AC. Multivariate analyses were conducted separately due to collinearity, and a combination of four models containing CO or NO2 and PM2.5 or PM10 were conducted. The results from the four separate models were quite similar, and the one with NO2 and PM10 was shown in Table 4. The significant AC-associated factors were found to be NO2, O3, RH and temperature. Every 10-ppb increase of NO2 and O3 were associated with additional 6.1% and 0.9% of AC occurrence, respectively. An increase of 1 °C in temperature accounted for 1.3% of AC occurrence, whereas every 10% increment of RH was related to 5.8% reduction in AC occurrence.
Table 2 Descriptive statistics of concentrations of air pollutants and meteorological data during 2004–2013. Air pollutant
Mean ± SD
Minimum
25th percentile (Q1)
Median (Q2)
75th percentile (Q3)
Maximum
Taiwan's ambient standards
CO (ppm) NO2 (ppb) O3 8 h average (ppb) PM2.5 (μg/m³) PM10 (μg/m³) SO2 (ppb) RH (%) Temperature (°C)
0.52 17.5 46.3 32.6 57.6 4.33 74.9 23.7
0.01 0.10 0.53 1.00 2.06 0.10 18.0 0.83
0.31 10.2 32.0 17.8 33.7 2.37 69.4 19.9
0.45 15.9 43.6 28.1 49.9 3.55 74.8 24.6
0.62 23.1 58.4 43.5 74.9 5.31 80.6 28.1
4.33 111 150 1000 1450 69.2 100 43.7
9a 250b; 50c 60a 35d; 15c 125d; 65c 250b; 100d – –
± ± ± ± ± ± ± ±
0.32 9.89 19.5 19.8 33.9 3.05 8.71 5.15
CO, carbon monoxide; NO2, nitrogen dioxide; O3, ozone; PM10, particulate matter with aerodynamic diameter ≤ 10 μm; PM2.5, particulate matter with aerodynamic diameter ≤ 2.5 μm; SO2, sulfur dioxide; RH, relative humidity; SD, standard deviation. a 8-h average. b Hourly average. c Annual average. d Daily average. 92
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Table 3 Spearman correlation among daily air pollutants and meteorological factors.
CO NO2 O3 (8 h) PM2.5 PM10 SO2 RH Temperature
CO
NO2
O3 (8 h)
PM2.5
PM10
SO2
RH
Temperature
1 0.828 0.025 0.429 0.372 0.346 −0.123 −0.186
1 0.074 0.533 0.473 0.529 −0.198 −0.216
1 0.503 0.461 0.160 −0.297 0.115
1 0.870 0.480 −0.281 −0.164
1 0.455 −0.328 −0.163
1 −0.232 0.019
1 −0.108
1
All correlations are significant, p < 0.001.
4. Discussion
incomplete combustion of motor engines that also simultaneously produced NO, the precursor of NO2 (Cunningham and Cunningham, 2015). The high correlation between PM2.5 and PM10 was quiet comprehensive, because PM2.5 was part of PM10 by definition. PM2.5 and PM10 both were moderately correlated with NO2, O3 and SO2 (ρ > 0.4) (Table 3), suggesting that they might at least have come from regular burning, photochemical smog reaction, and fossil fuel combustion, respectively (Cunningham and Cunningham, 2015). The inverse relation between RH and air pollutants suggests that high humidity may usually come with a shower that washes out certain levels of ambient air pollutants. A Thai study, showing RH positively associated with rain occurrence and negatively with air pollutants, is in support of our suggestion (Wiwatanadate and Liwsrisakun, 2011). Air pollutants, O3 and NO2, and meteorological factors, RH and temperature, were found to be significantly associated with the first occurrence of AC in this study. The result of no significant association of PM2.5 or PM10 with AC was similar to that found from Hong et al. (2016), but somewhat different from that of Mimura et al. (2014), which demonstrated a positive association between PM2.5 and AC only during a non-pollen season from May to July. It is implied that PM, related to AC during a certain time of a year, might not be significantly associated with AC for a longer period across seasons, as shown by Hong et al. (2016) and this study. The associations of NO2 (a surrogate of traffic emission) and O3 with AC in this study were found to be similar to that with allergic rhinitis in a number of studies (Chung et al., 2016; Higgins and Reh, 2012; Hwang et al., 2006; Kim et al., 2011; Lee et al., 2003). The similarity is quite reasonable because AC is commonly seen as a comorbid disease with allergic rhinitis (Miraldi Utz and Kaufman, 2014; Pelikan, 2010; Rosario and Bielory, 2011; Thong, 2017). Evidence has shown that traffic emission (specifically diesel exhaust particulate) and/or O3 may induce allergic inflammation, and exacerbate allergic symptoms of allergic rhinitis when allergen exposure is present (Alexis and Carlsten, 2014; Dokic and Trajkovska-Dokic, 2013; Frew and Salvi, 1997). In contrast, the detailed mechanism for AC remains unclear, but impacts of air pollution on the ocular surface are certainly observed (Jung et al., 2018). A Korean study using a mouse model has recently indicated that
Percentages of the first AC occurrences by age were the highest for children and adolescents (0–18 years) and the lowest for the elderly (65–105 years) in this study. These data lead to a suggestion that young generations may be more susceptible to allergens than other age groups, which is in agreement with what is commonly thought that allergy occurs to children and young adults more commonly than grown-ups (Patel et al., 2017). Unlike many other studies that used the number of total outpatient visits for statistical analysis (Table 5), we used the number of subjects (i.e., selection of their first occurrences) to avoid bias caused by weighting of multiple visits. Thus, our result in percentage of occurrence by age might be different from those derived from other studies. A previous AC study (Hong et al., 2016) showing the highest percentage for people of 60 years or older was of difference, which was likely due to multiple visits contributed by the elderly. Besides, that study might not have excluded comorbid patients with dry eye symptoms, who were excluded from this study due to confounding and found to be the most in the elderly population. The prevalence of AC is much different around the world and also considered to be underestimated (La Rosa et al., 2013; Rosario and Bielory, 2011; Singh et al., 2010); it is not surprising to see different patterns for age distribution of AC in different places after all. Percentages by sex, showing females accounting for approximately 56% of AC occurrences, were consistent with that derived from the study of Hong et al. (2016) and from studies targeting on UC (Chiang et al., 2012; Fu et al., 2017). The fact that women have the higher proportion of allergic diseases (e.g., AC) than men do is commonly observed and considered to be due to sex-specific bias in IgE-mediated allergic diseases (Jensen-Jarolim and Untersmayr, 2008). As for seasonal variation, the most impact must have come from SAC, which are usually triggered by tree and weed pollens in spring and fall, respectively (Miraldi Utz and Kaufman, 2014); as expected, the percentages of AC in spring and fall were found the most in this study (Table 1). The highly correlated pollutants (i.e., CO/NO2, PM2.5/PM10) were likely to have common sources. For CO and NO2, traffic emission should have been the major source because CO was mostly produced by Table 4 Results of univariate and multivariate conditional logistic regression analyses. Univariate
Multivariate
OR (95% CI) O3 8 h (10 ppb) SO2 (ppb) CO (ppm) NO2 (10 ppb) PM10 (10 μg/m3) PM2.5 (10 μg/m3) RH (10%) Temperature (°C)
1.035 1.013 1.103 1.055 1.016 1.025 0.931 1.015
(1.029, (1.010, (1.070, (1.044, (1.014, (1.020, (0.923, (1.012,
1.042) 1.016) 1.138) 1.067) 1.019) 1.029) 0.939) 1.017)
P-value
OR (95% CI)
Standardized estimate
P-value
< 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001
1.009 0.996 – 1.061 1.002 – 0.942 1.013
0.00935 −0.00487 – 0.0337 0.00347 – −0.0289 0.0389
< 0.001 0.051 – < 0.001 0.287 – < 0.001 < 0.001
OR, odds ratio; CI, confidence interval. 93
(1.004, 1.014) (0.992, 1.000) (1.045, 1.077) (0.998, 1.006) (0.932, 0.952) (1.011, 1.016)
Atmospheric Environment 212 (2019) 90–95
Case crossover design, conditional logistic regression using a Cox proportional hazards regression model Case crossover design, conditional logistic regression analysis
Generalized least squares model with correlation structure of autoregressive–moving-average Linear regression analysis
AC This study
UC Fu et al. (2017)
Li et al. (2016)
UC
ICD-9 CM: 372.05, 372.13, 372,14 ICD-9 CM: 372.00, 372.01, 372.10, 372.11, 372.20, 372.30 ICD-10 CM: H10.301, H10.402, H10.901 ICD-9 CM: 372.05, 372.13, 372,14 AC
AC
Not available
Multiple logistic regression analysis
Generalized linear model with Poisson regression
2000–2007, OVD from National Health Insurance Database & EMD May–October 2012, OVD from eye clinic & EMD 2008–2012, OVD from Shanghai Health Insurance System & EMD June 2014–May 2015, OVD from eye centers & EMD July 2014–June 2016, OVD from eye center & EMD 2004–2013, OVD from National Health Insurance Database & EMD UC
Chiang et al. (2012) Mimura et al. (2014) Hong et al. (2016)
AC, allergic conjunctivitis; UC, unspecified conjunctivitis; ICD-# CM, International Classification of Diseases, the #th Revision, Clinical Modification; OVD, outpatient visit data; EMD, environmental monitoring data.
Jinan, Shandong, China Hangzhou, Zhejiang, China Taiwan
Shanghai, China
Urban and rural areas in Taiwan Tokyo, Japan
Ambient temperature and NOx concentration can cause greater significant risks on UC. PM2.5 was a significant predictor of AC during the non-pollen season (May–Oct). NO2, O3, and temperature increase the chances of outpatient visits for AC. Air quality index affected the probability of attending the outpatient clinic for UC. Increase in PM10, PM2.5, SO2, NO2, and CO was associated with outpatient visits for UC. NO2, O3 and temperature were positively associated with AC, while relative humidity was negatively related.
Taiwan NO2, SO2, O3, and PM10 increase the chances of outpatient visits for UC. Multicity case-crossover analysis 2007–2009, OVD from National Health Insurance Database & EMD
ICD-9 CM: 372.00, 372.01, 372.10, 372.11, 372.20, 372.30, 372.39 ICD-9 CM: 372.00, 372.10 UC Chang et al. (2012)
Design and/or Statistical analysis Study data Diagnosis codes Disease Study
Table 5 Comparison between recent studies of environmentally related conjunctivitis conducted in different places around the world.
Major finding
Place
J.-Y. Zhong, et al.
ozone exacerbates the detrimental effects on the integrity of the ocular surface caused by conjunctival allergic reactions (Lee et al., 2017), providing evidence of ozone effect on AC. Other than air pollutants, AC may also be affected by meteorological factors (i.e., humidity, temperature). A study testing effects of exposure to a low RH environment on tear film indicated adverse effects on the evaporation rate, lipid layer thickness, stability, and production of tears, which led to significant ocular discomfort (Abusharha and Pearce, 2013). With discomfort in the eyes caused by dry environments, the AC symptoms could be aggravated. Similar results have also been observed and reported for dry eye disease (Stapleton et al., 2017); in this study, the association with RH was still significant after exclusion of patients with dry eye symptoms, suggesting the possible effect of RH on AC. In contrast, temperature increase may not directly induce discomfort in the eyes as does low RH, but it could lead to AC exacerbation by raising the levels of pollens. Studies have shown that high temperatures increase pollen contents (Ahlholm et al., 1998) and prolong the pollen seasons (Van Vliet et al., 2002); either consequence could favor the enhancement of incidence or prevalence of AC. Our result for temperature is in support of the finding of higher prevalence in warmer climatic conditions by the abovementioned Nigerian study (Malu, 2014). There were similarities and differences between this study and others, including those on UC (Table 5). Despite the disagreement in age proportion, our result was consistent with that of Hong et al. (2016), finding AC-associated factors of NO2, O3 and temperature. Among the different patterns of disease-related air pollutants shown by these studies, the common finding of NO2 indicated its important association with AC or UC. It is suggested that traffic emissions surrogated by NO2, could cause irritation on the ocular surface with help of any other air pollutants (e.g., O3, PM, SO2) to exacerbate the eye symptoms of AC or UC. As mentioned previously, the detailed mechanism is yet to be discovered (Jung et al., 2018). There were several limitations in the study. Firstly, not all identified AC patients were used as study subjects, mainly because of the availability of environmental monitoring stations. The comparison among the characteristics of study subjects, excluded patients and all patients showed similar patterns (Table 1), suggesting that the study subjects should be sufficient to represent all patients. Secondly, there may have been certain subjects who visited hospitals but did not reside in the neighborhoods. Fortunately, access to health care in Taiwan is fairly convenient, especially in those areas with installation of environmental monitoring stations; thus, we are convinced that the pairing of health insurance data and environmental data should have not been much biased. Thirdly, use of secondary databases without identification for epidemiological studies is unable to access personal information that may be influential. To avoid such difficulties in the analysis, we applied the case-crossover design, using subjects themselves for cases and controls; thus, effects caused by personal practices or habits could be omitted. Finally, application of the case-crossover design using environmental data one and two weeks before and after the diagnoses as control may not be appropriate for patients whose latent periods of AC were longer than weeks. In accordance with previous studies (Chang et al., 2012; Fu et al., 2017; Li et al., 2016), AC or UC occurrence was almost direct after environmental exposure, with a possible lag being limited to one or two days; therefore, we believe that the majority of the subjects should have quite fit the study design. 5. Conclusions and recommendations The first occurrence of AC appeared the most for youngsters under 18 years by age, for women by sex, and in spring by season in Taiwan from 2004 to 2013. Ozone, NO2 and temperature were found to be positively associated with AC, while RH was negatively related. AC patients are advised to pay attention to air pollution and weather change in the ambient environments and manage accordingly to 94
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mitigate the problems. Additionally, as NO2 serves as a surrogate of traffic emissions, lowering traffic related air pollution may help reduce the occurrence or exacerbation of AC.
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Anaphylaxis in the United States: an investigation into its epidemiology. Arch. Intern. Med. 161, 15–21. Ono, S.J., Abelson, M.B., 2005. Allergic conjunctivitis: update on pathophysiology and prospects for future treatment. J. Allergy Clin. Immunol. 115, 118–122. Palmares, J., Delgado, L., Cidade, M., Quadrado, M.J., Filipe, H.P., 2010. Allergic conjunctivitis: a national cross-sectional study of clinical characteristics and quality of life. Eur. J. Ophthalmol. 20, 257–264. Patel, D.S., Arunakirinathan, M., Stuart, A., Angunawela, R., 2017. Allergic eye disease. BMJ 359, j4706. Pelikan, Z., 2010. Allergic conjunctivitis and nasal allergy. Curr. Allergy Asthma Rep. 10, 295–302. Pitt, A.D., Smith, A.F., Lindsell, L., Voon, L.W., Rose, P.W., Bron, A.J., 2004. Economic and quality-of-life impact of seasonal allergic conjunctivitis in Oxfordshire. Ophthalmic Epidemiol. 11, 17–33. Rosario, N., Bielory, L., 2011. Epidemiology of allergic conjunctivitis. Curr. Opin. Allergy Clin. Immunol. 11, 471–476. Saban, D.R., Calder, V., Kuo, C.-H., Reyes, N.J., Dartt, D.A., Ono, S.J., Niederkorn, J.Y., 2013. New twists to an old story: novel concepts in the pathogenesis of allergic eye disease. Curr. Eye Res. 38, 317–330. Singh, K., Axelrod, S., Bielory, L., 2010. The epidemiology of ocular and nasal allergy in the United States, 1988-1994. J. Allergy Clin. Immunol. 126, 778–783. e6. Smith, A.F., Pitt, A.D., Rodruiguez, A.E., Alio, J.L., Marti, N., Teus, M., Guillen, S., Bataille, L., Barnes, J.R., 2005. The economic and quality of life impact of seasonal allergic conjunctivitis in a Spanish setting. Ophthalmic Epidemiol. 12, 233–242. Stapleton, F., Alves, M., Bunya, V.Y., Jalbert, I., Lekhanont, K., Malet, F., Na, K.S., Schaumberg, D., Uchino, M., Vehof, J., Viso, E., Vitale, S., Jones, L., 2017. TFOS DEWS II epidemiology report. Ocul. Surf. 15, 334–365. Tang, M.L., Osborne, N., Allen, K., 2009. Epidemiology of anaphylaxis. Curr. Opin. Allergy Clin. Immunol. 9, 351–356. Taiwan Air Quality Monitoring Network (TAQMN), 2018. Air quailty standards. https:// taqm.epa.gov.tw/taqm/en/b0206.aspx. Thong, B.Y., 2017. Allergic conjunctivitis in Asia. Asia Pac. Allergy 7, 57–64. Um, S.B., Kim, N.H., Lee, H.K., Song, J.S., Kim, H.C., 2014. Spatial epidemiology of dry eye disease: findings from South Korea. Int. J. Health Geogr. 13, 31. Van Vliet, A.J., Overeem, A., De Groot, R.S., Jacobs, A.F., Spieksma, F.T., 2002. The influence of temperature and climate change on the timing of pollen release in The Netherlands. Int. J. Climatol. 22, 1757–1767. Wiwatanadate, P., Liwsrisakun, C., 2011. Acute effects of air pollution on peak expiratory flow rates and symptoms among asthmatic patients in Chiang Mai, Thailand. Int. J. Hyg Environ. Health 214, 251–257. Zhong, J.Y., Lee, Y.C., Hsieh, C.J., Tseng, C.C., Yiin, L.M., 2018. Association between dry eye disease, air pollution and weather changes in Taiwan. Int. J. Environ. Res. Public Health 15, 2269.
Conflicts of interest The authors declare no conflict of interest. Declaration of interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments This work was funded by Ministry of Science and Technology under Grant No: MOST 105-2621-M-320-001. Dr. Lih-Ming Yiin was partly supported by Tzu Chi University Supplement Grant (610400184-08). This study is based in part on data from the National Health Insurance Research Database provided by the National Health Insurance Administration, Ministry of Health and Welfare and managed by National Health Research Institutes (Registered number NHIRD-104452). The interpretation and conclusions contained herein do not represent those of National Health Insurance Administration, Ministry of Health and Welfare or National Health Research Institutes. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.atmosenv.2019.05.045. References Abusharha, A.A., Pearce, E.I., 2013. The effect of low humidity on the human tear film. Cornea 32, 429–434. Ahlholm, J., Helander, M., Savolainen, J., 1998. Genetic and environmental factors affecting the allergenicity of birch (Betula pubescens ssp. czerepanovii [Orl.] Hämetahti) pollen. Clin. Exp. Allergy 28, 1384–1388. Alexis, N.E., Carlsten, C., 2014. Interplay of air pollution and asthma immunopathogenesis: a focused review of diesel exhaust and ozone. Int. Immunopharmacol. 23, 347–355. Bielory, L., 2000. Allergic and immunologic disorders of the eye. Part II: ocular allergy. J. Allergy Clin. Immunol. 106, 1019–1032. Buchholz, P., Walt, J., Lorenz, D., Burk, C., Lee, J., 2003. Patient impact of allergic conjunctivitis as measured by the eye allergy patient impact questionnaire (EAPIQ). Investig. Ophthalmol. Vis. Sci. 44 3735-3735. Chang, C.-J., Yang, H.-H., Chang, C.-A., Tsai, H.-Y., 2012. Relationship between air pollution and outpatient visits for nonspecific conjunctivitis. Investig. Ophthalmol. Vis. Sci. 53, 429–433. Chiang, C.-C., Liao, C.-C., Chen, P.-C., Tsai, Y.-Y., Wang, Y.-C., 2012. Population study on chronic and acute conjunctivitis associated with ambient environment in urban and rural areas. J. Expo. Sci. Environ. Epidemiol. 22, 533–538. Chung, H.-Y., Hsieh, C.-J., Tseng, C.-C., Yiin, L.-M., 2016. Association between the first occurrence of allergic rhinitis in preschool children and air pollution in Taiwan. Int. J. Environ. Res. Public Health 13, 268. Cunningham, W.P., Cunningham, M.A., 2015. Environmental Science: A Global Concern. McGraw-Hill Education, New York, NY, pp. 347–373. Dokic, D., Trajkovska-Dokic, E., 2013. Ozone exaggerates nasal allergic inflammation. Pril (Makedon Akad Nauk Umet Odd Med Nauki) 34, 131–141. Frew, A., Salvi, S., 1997. Diesel exhaust particles and respiratory allergy. Clin. Exp. Allergy 27, 237–239. Fu, Q., Mo, Z., Lyu, D., Zhang, L., Qin, Z., Tang, Q., Yin, H., Xu, P., Wu, L., Lou, X., 2017. Air pollution and outpatient visits for conjunctivitis: a case-crossover study in Hangzhou, China. Environ. Pollut. 231, 1344–1350. Gomes, P.J., 2014. Trends in prevalence and treatment of ocular allergy. Curr. Opin. Allergy Clin. Immunol. 14, 451–456. Higgins, T.S., Reh, D.D., 2012. Environmental pollutants and allergic rhinitis. Curr. Opin. Otolaryngol. Head Neck Surg. 20, 209–214. Hong, J., Zhong, T., Li, H., Xu, J., Ye, X., Mu, Z., Lu, Y., Mashaghi, A., Zhou, Y., Tan, M., Li, Q., Sun, X., Liu, Z., Xu, J., 2016. Ambient air pollution, weather changes, and
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Association between the first occurrence of allergic conjunctivitis, air pollution and weather changes in Taiwan
T
Jia-Yu Zhonga, Yueh-Chang Leeb, Chia-Jung Hsieha, Chun-Chieh Tsenga, Lih-Ming Yiina,∗ a b
Department of Public Health, Tzu Chi University, 701, Sec. 3, Zhongyang Rd, Hualien City, 97004, Taiwan Department of Ophthalmology, Hualien Tzu Chi Hospital, 707, Sec. 3, Zhongyang Rd, Hualien City, 97002, Taiwan
ARTICLE INFO
ABSTRACT
Keywords: Air pollution Allergic conjunctivitis Nitrogen dioxide Ozone Relative humidity Temperature
Allergic conjunctivitis (AC) is one of the common allergic diseases, which may be influenced by environmental factors. This study was to examine the association between the first occurrence of AC, air pollution and weather changes, and demographic factors in Taiwan. A total of 100,636 eligible subjects were identified from the systematic sampling cohort database containing 1,000,000 insureds of the National Health Insurance of Taiwan from 2004 to 2013, and were matched with data from the environmental monitoring stations adjacent to their locations of clinics. The case-crossover design, using the same subjects experiencing exposures at the time of diagnosis for cases and that on the days one and two weeks before and after diagnosis for controls, was applied in the study. We found that the first occurrences of AC were significantly the most for youngsters by age (42.6%), for women by sex (56.2%), and in spring by season (29.6%) (P < 0.001). Multivariate conditional logistic regression analyses indicated that nitrogen dioxide (NO2), ozone (O3), and temperature were positively associated with AC (P < 0.001), while relative humidity was negatively related to AC (P < 0.001). Knowing these associations, we suggest that AC patients pay attention to air pollution and weather change in the ambient environments and manage accordingly to mitigate the problems.
1. Introduction Allergic diseases have been increasingly prevalent around the world in the recent years (Kusunoki et al., 2009; Neugut et al., 2001; Tang et al., 2009). When occurring, it causes the patients perceptible discomfort, which may affect the daily lives or even require medical treatments. One of the common allergic diseases is ocular allergy or allergic conjunctivitis (AC), which is usually found to be associated with the occurrence of allergic rhinitis (Miraldi Utz and Kaufman, 2014; Pelikan, 2010; Rosario and Bielory, 2011; Thong, 2017). A study based on the National Health and Nutrition Examination Survey III in the U.S. found that up to 40% of the population experienced ocular allergy symptoms (Singh et al., 2010); a Nigerian study reported the prevalence of AC to be 32%, and indicated higher prevalence in warmer climatic conditions (Malu, 2014). With the extensive prevalence, AC is definitely an allergic disease to which attention has to be paid. AC is a general term that includes seasonal allergic conjunctivitis (SAC), perennial allergic conjunctivitis, vernal keratoconjunctivitis, atopic keratoconjunctivitis, and giant papillary conjunctivitis (Gomes, 2014; La Rosa et al., 2013; Miraldi Utz and Kaufman, 2014; Pelikan,
2010). Unlike infectious conjunctivitis, AC usually results from an immunoglobulin E (IgE)-mediated hypersensitivity reaction and/or a Tlymphocyte-mediated hypersensitivity initiated by allergens (Irkec and Bozkurt, 2012; Ono and Abelson, 2005; Saban et al., 2013). The allergens enter the tear film, make contact with conjunctival mast cells that bear allergen-specific IgE antibodies, and result in degranulation of mast cells. The degranulated mast cells then release histamine and a variety of other inflammatory mediators that promote vasodilation, edema, and recruitment of other inflammatory cells, such as eosinophils. Thus, patients usually suffer the consequences of red, itchy and watery eyes with swelling of the conjunctiva (Bielory, 2000; Miraldi Utz and Kaufman, 2014; Ono and Abelson, 2005; Thong, 2017), which could be severe to affect their daily lives (Buchholz et al., 2003; Palmares et al., 2010). Two previous surveys using standard questionnaires for quality of life were conducted in European countries, and confirmed that SAC not only resulted in reduction of quality of life for the patients but also accounted for extra costs of health care or personal out-of-pocket expenses (Pitt et al., 2004; Smith et al., 2005). AC could be triggered by a variety of allergens, including molds, pollens, animal dander, dust mites, and even air pollutants (Miraldi Utz
Corresponding author. E-mail addresses: [email protected] (J.-Y. Zhong), [email protected] (Y.-C. Lee), [email protected] (C.-J. Hsieh), [email protected] (C.-C. Tseng), [email protected] (L.-M. Yiin). ∗
https://doi.org/10.1016/j.atmosenv.2019.05.045 Received 25 July 2018; Received in revised form 13 May 2019; Accepted 18 May 2019 Available online 20 May 2019 1352-2310/ © 2019 Elsevier Ltd. All rights reserved.
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and Kaufman, 2014). Among the various types, SAC, caused by seasonal allergens, such as tree pollens, grass/weed pollens and seeds, is the most commonly seen and known for occurring in spring, early summer and fall. The time of occurrence for the other AC types may not be as specific as is SAC, because the triggering allergens (e.g., dust mites, animal dander, air pollutants) could be present in the environment at any time. Air pollution has been recently confirmed to be related to AC by two Asian studies (Hong et al., 2016; Mimura et al., 2014). Such data, however, are relatively few as compared to that derived from other research focusing on the association between air pollution and unspecified conjunctivitis (UC) (Chang et al., 2012; Chiang et al., 2012; Fu et al., 2017; Li et al., 2016), and the data for Taiwan are yet to be procured. We would like to find out whether air pollution in Taiwan was similarly associated with AC, and thus conducted this study using data from the National Health Insurance of Taiwan combined with the environmental monitoring data. To the best of our knowledge, this is the first study to examine the association between air pollution, weather changes and AC in Taiwan. We are convinced that this outcome, combined with the previous Taiwan's data on another type of conjunctivitis (Chang et al., 2012; Chiang et al., 2012), helps raise the awareness of eye health issues associated with environmental degradation for people living in Taiwan and elsewhere with similar problems.
environmental data, which were retrieved from 73 of the available 76 environmental monitoring stations. The monitoring results of air pollutants, including carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), particulate matter with aerodynamic diameter ≤ 2.5 and 10 μm (PM2.5, PM10), and sulfur dioxide (SO2), and meteorological data, such as relative humidity (RH) and temperature, were available from the website of Taiwan Air Quality Monitoring Network (TAQMN, http://taqm.epa.gov.tw/taqm/en/default.aspx). The daily average of each pollutant or meteorological factor was used in the analysis except the 8-h average of ozone, because of O3 formation occurring during the diurnal period of a day. The environmental monitoring and meteorological data covering from 2004 to 2013 were downloaded from the website for analysis. 2.3. Data management and analysis The subject data were categorized to groups by several factors (e.g., age, sex, season) to examine whether these factors were effective in the onset of AC. Statistical tests (e.g., chi-square test, t-test) were conducted to find out whether there was any difference between the categorical data. The cohort database without personal identification provided information of the locations of subjects’ clinics at the county or district level, which was sufficient for linkage with adjacent environmental monitoring sites. As for data of air pollutants and meteorological factors, descriptive statistics and Spearman correlation analysis were performed to have a general understanding of the environmental factors. The case-crossover design, which was suitable for “brief exposure to cause a transient change in risk of a rare acute-onset disease” (Maclure, 1991), was applied to the analysis of association between the first occurrence of AC, air pollution and weather changes. This design intends to use the same subjects for cases and controls, with the former at the onset of disease and the latter at other occasions away from the disease occurrence. In this study, exposure for each case was the average of environmental data for two days prior to and the day of the first AC diagnose, because a lag effect was found from previous UC-air pollution studies (Chang et al., 2012; Fu et al., 2017; Li et al., 2016). On the other hand, exposure for control was the average of environmental data on four different days, which were one and two weeks before and after the onset of AC. This selection of control days was modified from that used in two previous studies (Chang et al., 2012; Fu et al., 2017), which selected the days of the week matching with the day of diagnosis in the same month and year for control days. Univariate conditional logistic regression analyses were conducted to learn the significance of association between each air pollutant or meteorological factor and AC, whereas multivariate analyses were to identify all significant factors that were associated with AC. For those air pollutants and meteorological factors with high correlation between one another (ρ > 0.8), only one of them was selected to enter a single multivariate analysis due to collinearity. To derive better results, data of the environmental factors with relatively large variations were converted to levels with each containing 10 units (e.g., ppb, μg/m3). Because of the case-crossover design where cases and controls were the same subjects with identical settings in age, sex and season, such adjustments for covariates were not necessary in the analysis. All the statistical analyses were performed using SAS 9.4 (SAS Institute Inc., Cary, NC USA).
2. Materials and methods 2.1. Inclusion of subjects The systematic sampling cohort database of 1,000,000 insureds of the National Health Insurance of Taiwan from 2004 to 2013 was used in this study. The AC diagnoses were clinically made based on patients' histories, signs, symptoms and physical examination findings, and coded by ophthalmologists following the International Classification of Diseases, the 9th Revision, Clinical Modification (ICD-9 CM) at the outpatient visits. In this study, they were defined by the ICD-9 CM codes 372.05 (acute atopic conjunctivitis), 372.13 (vernal conjunctivitis) and 372.14 (other chronic allergic conjunctivitis). Because of no corresponding ICD-9 CM codes for SAC, acute allergic conjunctivitis, or perennial allergic conjunctivitis, these symptoms were commonly registered as 372.14 in clinical practice to distinguish from 372.00 (acute conjunctivitis, unspecified) and 372.10 (chronic conjunctivitis, unspecified). To ensure no interfere from the previous symptoms or impact of chronic AC, we chose the patients’ first occurrences of AC as the study data, and excluded those identified in the first year (i.e., 2004). With such an exclusion, even the identified cases were recurrences of AC, at least one year from the last was warranted. There were 259,325 people being diagnosed as AC patients during the 10-year span, and after subtraction of the-first-year cases the patient number became 216,702. Because dry eye symptoms, which were found to be associated with air pollution and weather change (Um et al., 2014; Hwang et al., 2016; Zhong et al., 2018), might serve as confounder in the analysis, AC patients with such symptoms (ICD-9-CM 370.33, 375.15) were excluded, making the patient number down to 180,452. Additionally, those without available environmental monitoring data or sufficient information were also removed from the database, and finally the eligible study subjects were 100,636 patients, whose first diagnoses were used to link with the environmental factors. The study was granted for exempt review by the Research Ethics Committee of Tzu Chi General Hospital (No: IRB107-70-B, approved on April 11, 2018) for use of the secondary data with personal identification being removed.
3. Results The demographic results for all patients, study subjects, and excluded patients of AC are listed in Table 1. The average ages of the three groups were all around 27.5 years, and the categorical data by age, sex and season were also similar in proportion. Despite the significant differences between the subjects and excluded patients due to the largesample bias, the similarity indicated that study subjects were a good
2.2. Environmental monitoring data Environmental monitoring stations adjacent to or sharing the same district codes with the subjects’ clinics or hospitals were the sources of 91
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Table 1 Data for study subjects, excluded patients and all patients at the first occurrence of allergic conjunctivitis by age, sex and AC-occurring season during 2004–2013.
Total Age (Mean ± SD)
Age 0–18 19–39 40–64 65–105
Sex Male Female P-value AC-occurring Season Spring Summer Fall Winter P-value b
Study subjects
Excluded patients
P-value (between subjects and excluded)
180,452 27.47 ± 21.06
100,636 27.60 ± 21.06
79,816 27.32 ± 21.05
0.005a
n (%)
n (%)
n (%)
42,863 (42.6) 30,098 (29.9)* 21,164 (21.0)* 6511 (6.5)*
34,504 (43.2) 23,912 (30.0)* 16,139 (20.2)* 5261 (6.6)*
< 0.001
< 0.001
< 0.001
79,240 (43.9) 101,212 (56.1)
44,049 (43.8) 56,587 (56.2)
35,191 (44.1) 44,625 (55.9)
< 0.001
< 0.001
< 0.001
53,613 44,175 47,750 34,914
29,815 24,682 26,469 19,670
23,798 19,493 21,281 15,244
77,367 54,010 37,303 11,772
P-value
a
All patients
(42.9) (29.9)* (20.7)* (6.5)*
(29.7) (24.5)* (26.5)* (19.4)*
< 0.001
(29.6) (24.5)* (26.3)* (19.6)*
< 0.001
(29.8) (24.4)* (26.7)* (19.1)*
< 0.001b
< 0.001b
0.057b
< 0.001
Two sample t-test. Chi-square test; *significantly different from the highest percentage.
sample of all AC patients. Children and adolescents (0–18 years) appeared to be the most AC prevalent generation with more than 40% of the total patients; there was a sex difference with females possessing about 56%. As well known, seasonal variation of AC occurrence was observed with spring and fall having the first and second highest percentages. The difference within each category was confirmed to be statistically significant (P < 0.001, Table 1), suggesting that AC occurrence might have been age-, sex- and season-specific. The environmental data during the study period are shown in Table 2. Compared with Taiwan's standards for ambient air quality (TAQMN, 2018), the concentrations of CO, NO2 and SO2 were likely to be under the standards most of the time, whereas O3, PM2.5 and PM10 might have gone over the criteria up to one third of chances. Temperature and RH represented the type of sub-tropical climate over the island with means of 23.7 °C and 74.9%, respectively. It appeared that NO2, O3, PM2.5, PM10 and RH varied extensively with standard deviations higher than 8.71, and thus the concentrations of these factors were converted to levels with each containing 10 units for conditional logistic regression analyses. The most correlated air pollutants were
CO/NO2 (ρ = 0.828) and PM2.5/PM10 (ρ = 0.870) (Table 3), suggesting that they might have shared the same emission sources. All air pollutants were negatively correlated with RH, indicating that high humidity was associated with low air pollution. Each of air pollutants or meteorological factors was significantly related to the first occurrence of AC in the univariate conditional logistic regression analysis (P < 0.001, Table 4); unlike the others, the odds ratio (OR) for RH was less than 1.0, suggesting that low humidity might have favored the onset of AC. Multivariate analyses were conducted separately due to collinearity, and a combination of four models containing CO or NO2 and PM2.5 or PM10 were conducted. The results from the four separate models were quite similar, and the one with NO2 and PM10 was shown in Table 4. The significant AC-associated factors were found to be NO2, O3, RH and temperature. Every 10-ppb increase of NO2 and O3 were associated with additional 6.1% and 0.9% of AC occurrence, respectively. An increase of 1 °C in temperature accounted for 1.3% of AC occurrence, whereas every 10% increment of RH was related to 5.8% reduction in AC occurrence.
Table 2 Descriptive statistics of concentrations of air pollutants and meteorological data during 2004–2013. Air pollutant
Mean ± SD
Minimum
25th percentile (Q1)
Median (Q2)
75th percentile (Q3)
Maximum
Taiwan's ambient standards
CO (ppm) NO2 (ppb) O3 8 h average (ppb) PM2.5 (μg/m³) PM10 (μg/m³) SO2 (ppb) RH (%) Temperature (°C)
0.52 17.5 46.3 32.6 57.6 4.33 74.9 23.7
0.01 0.10 0.53 1.00 2.06 0.10 18.0 0.83
0.31 10.2 32.0 17.8 33.7 2.37 69.4 19.9
0.45 15.9 43.6 28.1 49.9 3.55 74.8 24.6
0.62 23.1 58.4 43.5 74.9 5.31 80.6 28.1
4.33 111 150 1000 1450 69.2 100 43.7
9a 250b; 50c 60a 35d; 15c 125d; 65c 250b; 100d – –
± ± ± ± ± ± ± ±
0.32 9.89 19.5 19.8 33.9 3.05 8.71 5.15
CO, carbon monoxide; NO2, nitrogen dioxide; O3, ozone; PM10, particulate matter with aerodynamic diameter ≤ 10 μm; PM2.5, particulate matter with aerodynamic diameter ≤ 2.5 μm; SO2, sulfur dioxide; RH, relative humidity; SD, standard deviation. a 8-h average. b Hourly average. c Annual average. d Daily average. 92
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Table 3 Spearman correlation among daily air pollutants and meteorological factors.
CO NO2 O3 (8 h) PM2.5 PM10 SO2 RH Temperature
CO
NO2
O3 (8 h)
PM2.5
PM10
SO2
RH
Temperature
1 0.828 0.025 0.429 0.372 0.346 −0.123 −0.186
1 0.074 0.533 0.473 0.529 −0.198 −0.216
1 0.503 0.461 0.160 −0.297 0.115
1 0.870 0.480 −0.281 −0.164
1 0.455 −0.328 −0.163
1 −0.232 0.019
1 −0.108
1
All correlations are significant, p < 0.001.
4. Discussion
incomplete combustion of motor engines that also simultaneously produced NO, the precursor of NO2 (Cunningham and Cunningham, 2015). The high correlation between PM2.5 and PM10 was quiet comprehensive, because PM2.5 was part of PM10 by definition. PM2.5 and PM10 both were moderately correlated with NO2, O3 and SO2 (ρ > 0.4) (Table 3), suggesting that they might at least have come from regular burning, photochemical smog reaction, and fossil fuel combustion, respectively (Cunningham and Cunningham, 2015). The inverse relation between RH and air pollutants suggests that high humidity may usually come with a shower that washes out certain levels of ambient air pollutants. A Thai study, showing RH positively associated with rain occurrence and negatively with air pollutants, is in support of our suggestion (Wiwatanadate and Liwsrisakun, 2011). Air pollutants, O3 and NO2, and meteorological factors, RH and temperature, were found to be significantly associated with the first occurrence of AC in this study. The result of no significant association of PM2.5 or PM10 with AC was similar to that found from Hong et al. (2016), but somewhat different from that of Mimura et al. (2014), which demonstrated a positive association between PM2.5 and AC only during a non-pollen season from May to July. It is implied that PM, related to AC during a certain time of a year, might not be significantly associated with AC for a longer period across seasons, as shown by Hong et al. (2016) and this study. The associations of NO2 (a surrogate of traffic emission) and O3 with AC in this study were found to be similar to that with allergic rhinitis in a number of studies (Chung et al., 2016; Higgins and Reh, 2012; Hwang et al., 2006; Kim et al., 2011; Lee et al., 2003). The similarity is quite reasonable because AC is commonly seen as a comorbid disease with allergic rhinitis (Miraldi Utz and Kaufman, 2014; Pelikan, 2010; Rosario and Bielory, 2011; Thong, 2017). Evidence has shown that traffic emission (specifically diesel exhaust particulate) and/or O3 may induce allergic inflammation, and exacerbate allergic symptoms of allergic rhinitis when allergen exposure is present (Alexis and Carlsten, 2014; Dokic and Trajkovska-Dokic, 2013; Frew and Salvi, 1997). In contrast, the detailed mechanism for AC remains unclear, but impacts of air pollution on the ocular surface are certainly observed (Jung et al., 2018). A Korean study using a mouse model has recently indicated that
Percentages of the first AC occurrences by age were the highest for children and adolescents (0–18 years) and the lowest for the elderly (65–105 years) in this study. These data lead to a suggestion that young generations may be more susceptible to allergens than other age groups, which is in agreement with what is commonly thought that allergy occurs to children and young adults more commonly than grown-ups (Patel et al., 2017). Unlike many other studies that used the number of total outpatient visits for statistical analysis (Table 5), we used the number of subjects (i.e., selection of their first occurrences) to avoid bias caused by weighting of multiple visits. Thus, our result in percentage of occurrence by age might be different from those derived from other studies. A previous AC study (Hong et al., 2016) showing the highest percentage for people of 60 years or older was of difference, which was likely due to multiple visits contributed by the elderly. Besides, that study might not have excluded comorbid patients with dry eye symptoms, who were excluded from this study due to confounding and found to be the most in the elderly population. The prevalence of AC is much different around the world and also considered to be underestimated (La Rosa et al., 2013; Rosario and Bielory, 2011; Singh et al., 2010); it is not surprising to see different patterns for age distribution of AC in different places after all. Percentages by sex, showing females accounting for approximately 56% of AC occurrences, were consistent with that derived from the study of Hong et al. (2016) and from studies targeting on UC (Chiang et al., 2012; Fu et al., 2017). The fact that women have the higher proportion of allergic diseases (e.g., AC) than men do is commonly observed and considered to be due to sex-specific bias in IgE-mediated allergic diseases (Jensen-Jarolim and Untersmayr, 2008). As for seasonal variation, the most impact must have come from SAC, which are usually triggered by tree and weed pollens in spring and fall, respectively (Miraldi Utz and Kaufman, 2014); as expected, the percentages of AC in spring and fall were found the most in this study (Table 1). The highly correlated pollutants (i.e., CO/NO2, PM2.5/PM10) were likely to have common sources. For CO and NO2, traffic emission should have been the major source because CO was mostly produced by Table 4 Results of univariate and multivariate conditional logistic regression analyses. Univariate
Multivariate
OR (95% CI) O3 8 h (10 ppb) SO2 (ppb) CO (ppm) NO2 (10 ppb) PM10 (10 μg/m3) PM2.5 (10 μg/m3) RH (10%) Temperature (°C)
1.035 1.013 1.103 1.055 1.016 1.025 0.931 1.015
(1.029, (1.010, (1.070, (1.044, (1.014, (1.020, (0.923, (1.012,
1.042) 1.016) 1.138) 1.067) 1.019) 1.029) 0.939) 1.017)
P-value
OR (95% CI)
Standardized estimate
P-value
< 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001
1.009 0.996 – 1.061 1.002 – 0.942 1.013
0.00935 −0.00487 – 0.0337 0.00347 – −0.0289 0.0389
< 0.001 0.051 – < 0.001 0.287 – < 0.001 < 0.001
OR, odds ratio; CI, confidence interval. 93
(1.004, 1.014) (0.992, 1.000) (1.045, 1.077) (0.998, 1.006) (0.932, 0.952) (1.011, 1.016)
Atmospheric Environment 212 (2019) 90–95
Case crossover design, conditional logistic regression using a Cox proportional hazards regression model Case crossover design, conditional logistic regression analysis
Generalized least squares model with correlation structure of autoregressive–moving-average Linear regression analysis
AC This study
UC Fu et al. (2017)
Li et al. (2016)
UC
ICD-9 CM: 372.05, 372.13, 372,14 ICD-9 CM: 372.00, 372.01, 372.10, 372.11, 372.20, 372.30 ICD-10 CM: H10.301, H10.402, H10.901 ICD-9 CM: 372.05, 372.13, 372,14 AC
AC
Not available
Multiple logistic regression analysis
Generalized linear model with Poisson regression
2000–2007, OVD from National Health Insurance Database & EMD May–October 2012, OVD from eye clinic & EMD 2008–2012, OVD from Shanghai Health Insurance System & EMD June 2014–May 2015, OVD from eye centers & EMD July 2014–June 2016, OVD from eye center & EMD 2004–2013, OVD from National Health Insurance Database & EMD UC
Chiang et al. (2012) Mimura et al. (2014) Hong et al. (2016)
AC, allergic conjunctivitis; UC, unspecified conjunctivitis; ICD-# CM, International Classification of Diseases, the #th Revision, Clinical Modification; OVD, outpatient visit data; EMD, environmental monitoring data.
Jinan, Shandong, China Hangzhou, Zhejiang, China Taiwan
Shanghai, China
Urban and rural areas in Taiwan Tokyo, Japan
Ambient temperature and NOx concentration can cause greater significant risks on UC. PM2.5 was a significant predictor of AC during the non-pollen season (May–Oct). NO2, O3, and temperature increase the chances of outpatient visits for AC. Air quality index affected the probability of attending the outpatient clinic for UC. Increase in PM10, PM2.5, SO2, NO2, and CO was associated with outpatient visits for UC. NO2, O3 and temperature were positively associated with AC, while relative humidity was negatively related.
Taiwan NO2, SO2, O3, and PM10 increase the chances of outpatient visits for UC. Multicity case-crossover analysis 2007–2009, OVD from National Health Insurance Database & EMD
ICD-9 CM: 372.00, 372.01, 372.10, 372.11, 372.20, 372.30, 372.39 ICD-9 CM: 372.00, 372.10 UC Chang et al. (2012)
Design and/or Statistical analysis Study data Diagnosis codes Disease Study
Table 5 Comparison between recent studies of environmentally related conjunctivitis conducted in different places around the world.
Major finding
Place
J.-Y. Zhong, et al.
ozone exacerbates the detrimental effects on the integrity of the ocular surface caused by conjunctival allergic reactions (Lee et al., 2017), providing evidence of ozone effect on AC. Other than air pollutants, AC may also be affected by meteorological factors (i.e., humidity, temperature). A study testing effects of exposure to a low RH environment on tear film indicated adverse effects on the evaporation rate, lipid layer thickness, stability, and production of tears, which led to significant ocular discomfort (Abusharha and Pearce, 2013). With discomfort in the eyes caused by dry environments, the AC symptoms could be aggravated. Similar results have also been observed and reported for dry eye disease (Stapleton et al., 2017); in this study, the association with RH was still significant after exclusion of patients with dry eye symptoms, suggesting the possible effect of RH on AC. In contrast, temperature increase may not directly induce discomfort in the eyes as does low RH, but it could lead to AC exacerbation by raising the levels of pollens. Studies have shown that high temperatures increase pollen contents (Ahlholm et al., 1998) and prolong the pollen seasons (Van Vliet et al., 2002); either consequence could favor the enhancement of incidence or prevalence of AC. Our result for temperature is in support of the finding of higher prevalence in warmer climatic conditions by the abovementioned Nigerian study (Malu, 2014). There were similarities and differences between this study and others, including those on UC (Table 5). Despite the disagreement in age proportion, our result was consistent with that of Hong et al. (2016), finding AC-associated factors of NO2, O3 and temperature. Among the different patterns of disease-related air pollutants shown by these studies, the common finding of NO2 indicated its important association with AC or UC. It is suggested that traffic emissions surrogated by NO2, could cause irritation on the ocular surface with help of any other air pollutants (e.g., O3, PM, SO2) to exacerbate the eye symptoms of AC or UC. As mentioned previously, the detailed mechanism is yet to be discovered (Jung et al., 2018). There were several limitations in the study. Firstly, not all identified AC patients were used as study subjects, mainly because of the availability of environmental monitoring stations. The comparison among the characteristics of study subjects, excluded patients and all patients showed similar patterns (Table 1), suggesting that the study subjects should be sufficient to represent all patients. Secondly, there may have been certain subjects who visited hospitals but did not reside in the neighborhoods. Fortunately, access to health care in Taiwan is fairly convenient, especially in those areas with installation of environmental monitoring stations; thus, we are convinced that the pairing of health insurance data and environmental data should have not been much biased. Thirdly, use of secondary databases without identification for epidemiological studies is unable to access personal information that may be influential. To avoid such difficulties in the analysis, we applied the case-crossover design, using subjects themselves for cases and controls; thus, effects caused by personal practices or habits could be omitted. Finally, application of the case-crossover design using environmental data one and two weeks before and after the diagnoses as control may not be appropriate for patients whose latent periods of AC were longer than weeks. In accordance with previous studies (Chang et al., 2012; Fu et al., 2017; Li et al., 2016), AC or UC occurrence was almost direct after environmental exposure, with a possible lag being limited to one or two days; therefore, we believe that the majority of the subjects should have quite fit the study design. 5. Conclusions and recommendations The first occurrence of AC appeared the most for youngsters under 18 years by age, for women by sex, and in spring by season in Taiwan from 2004 to 2013. Ozone, NO2 and temperature were found to be positively associated with AC, while RH was negatively related. AC patients are advised to pay attention to air pollution and weather change in the ambient environments and manage accordingly to 94
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mitigate the problems. Additionally, as NO2 serves as a surrogate of traffic emissions, lowering traffic related air pollution may help reduce the occurrence or exacerbation of AC.
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Conflicts of interest The authors declare no conflict of interest. Declaration of interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments This work was funded by Ministry of Science and Technology under Grant No: MOST 105-2621-M-320-001. Dr. Lih-Ming Yiin was partly supported by Tzu Chi University Supplement Grant (610400184-08). This study is based in part on data from the National Health Insurance Research Database provided by the National Health Insurance Administration, Ministry of Health and Welfare and managed by National Health Research Institutes (Registered number NHIRD-104452). The interpretation and conclusions contained herein do not represent those of National Health Insurance Administration, Ministry of Health and Welfare or National Health Research Institutes. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.atmosenv.2019.05.045. References Abusharha, A.A., Pearce, E.I., 2013. The effect of low humidity on the human tear film. Cornea 32, 429–434. Ahlholm, J., Helander, M., Savolainen, J., 1998. Genetic and environmental factors affecting the allergenicity of birch (Betula pubescens ssp. czerepanovii [Orl.] Hämetahti) pollen. Clin. Exp. Allergy 28, 1384–1388. Alexis, N.E., Carlsten, C., 2014. Interplay of air pollution and asthma immunopathogenesis: a focused review of diesel exhaust and ozone. Int. Immunopharmacol. 23, 347–355. Bielory, L., 2000. Allergic and immunologic disorders of the eye. Part II: ocular allergy. J. Allergy Clin. Immunol. 106, 1019–1032. Buchholz, P., Walt, J., Lorenz, D., Burk, C., Lee, J., 2003. Patient impact of allergic conjunctivitis as measured by the eye allergy patient impact questionnaire (EAPIQ). Investig. Ophthalmol. Vis. Sci. 44 3735-3735. Chang, C.-J., Yang, H.-H., Chang, C.-A., Tsai, H.-Y., 2012. Relationship between air pollution and outpatient visits for nonspecific conjunctivitis. Investig. Ophthalmol. Vis. Sci. 53, 429–433. Chiang, C.-C., Liao, C.-C., Chen, P.-C., Tsai, Y.-Y., Wang, Y.-C., 2012. Population study on chronic and acute conjunctivitis associated with ambient environment in urban and rural areas. J. Expo. Sci. Environ. Epidemiol. 22, 533–538. Chung, H.-Y., Hsieh, C.-J., Tseng, C.-C., Yiin, L.-M., 2016. Association between the first occurrence of allergic rhinitis in preschool children and air pollution in Taiwan. Int. J. Environ. Res. Public Health 13, 268. Cunningham, W.P., Cunningham, M.A., 2015. Environmental Science: A Global Concern. McGraw-Hill Education, New York, NY, pp. 347–373. Dokic, D., Trajkovska-Dokic, E., 2013. Ozone exaggerates nasal allergic inflammation. Pril (Makedon Akad Nauk Umet Odd Med Nauki) 34, 131–141. Frew, A., Salvi, S., 1997. Diesel exhaust particles and respiratory allergy. Clin. Exp. Allergy 27, 237–239. Fu, Q., Mo, Z., Lyu, D., Zhang, L., Qin, Z., Tang, Q., Yin, H., Xu, P., Wu, L., Lou, X., 2017. Air pollution and outpatient visits for conjunctivitis: a case-crossover study in Hangzhou, China. Environ. Pollut. 231, 1344–1350. Gomes, P.J., 2014. Trends in prevalence and treatment of ocular allergy. Curr. Opin. Allergy Clin. Immunol. 14, 451–456. Higgins, T.S., Reh, D.D., 2012. Environmental pollutants and allergic rhinitis. Curr. Opin. Otolaryngol. Head Neck Surg. 20, 209–214. Hong, J., Zhong, T., Li, H., Xu, J., Ye, X., Mu, Z., Lu, Y., Mashaghi, A., Zhou, Y., Tan, M., Li, Q., Sun, X., Liu, Z., Xu, J., 2016. Ambient air pollution, weather changes, and
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