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Year : 2019  |  Volume : 11  |  Issue : 2  |  Page : 35-41

Relationship between dry eye syndrome and occupational categories

Faculty of Medicine, University of Medical Sciences and Technology, Khartoum, Sudan

Date of Submission24-Jul-2019
Date of Decision27-Jul-2019
Date of Acceptance06-Oct-2019
Date of Web Publication09-Mar-2020

Correspondence Address:
Dr. Amina Tarig Mohamed Ahmed Sharief
Ahmed Sharief, Faculty of Medicine, University of Medical Sciences and Technology, Khartoum
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/sjopthal.sjopthal_19_19

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Background: Dry eye syndrome is a broad clinical term that is used to describe a variety of ocular conditions that are characterized by ocular irritation and discomfort secondary to decreased tear production or increased tear evaporation. It is highly prevalent, with almost 1/3 of ophthalmic patients presenting with DES-related symptoms. Many risk factors were linked to the development of DES, including occupation-related risk factors. Aims and Objectives: This study was a cross-sectional study aimed at identifying the association between dry eye syndrome and various occupational categories to determine the occupation-specific risk factors in patients attending the corneal outpatient clinics of Khartoum Eye Teaching Hospital. Materials and Methods: Data were collected from all patients aging 25-50 years who presented with DES-related symptoms and had a positive Schirmer's test. A questionnaire was used in the data collection process. The data were analyzed using SPSS 20. Results: Among the 450 participants, 279 were females (62%). About 140 patients were white-collar workers (31.1%), 134 were blue-collar workers (67 skilled and 67 unskilled; constituting 29.8%), 112 were green-collar workers (24.9%) and the remaining 64 patients were unemployed. (14.2%) An increased risk (in comparison to the unemployed group) was observed for the white-collar (highest risk), blue-collar and green-collar (lowest risk) categories. (P-value < 0.05) Additionally, a highly significant association was noted between the female sex, computer use, and outdoor occupations. (P-value = 0.000) Conclusion: There is an increased risk of developing dry eye syndrome in the various occupational categories. Furthermore, females, computer users, and outdoor workers are also at high risk.

Keywords: Computer use, dry eye syndrome, gender, occupational categories, outdoor work

How to cite this article:
Sharief AT. Relationship between dry eye syndrome and occupational categories. Sudanese J Ophthalmol 2019;11:35-41

How to cite this URL:
Sharief AT. Relationship between dry eye syndrome and occupational categories. Sudanese J Ophthalmol [serial online] 2019 [cited 2022 Aug 15];11:35-41. Available from: https://www.sjopthal.net/text.asp?2019/11/2/35/280242

  Introduction Top

Dry eye syndrome (DES) or simply dry eyes is one of the most common conditions seen in ophthalmic clinics worldwide having a prevalence of 7%–34% in some populations. Approximately 25% of patients presenting to ophthalmic clinics complain of dry eye-related symptoms.[1]

DES is defined as a multifactorial condition of the ocular surface associated with changes in the homeostasis of the tear film which results in a variety of irritative ocular symptoms with potential damage to the ocular surface.[2] Although many clinical tests can be done to assess the tear film profile, the diagnosis of DES is made mainly on subjective symptoms perceived by the patient.[3] Patients present with ocular discomfort in the form of pain, itching, grittiness, foreign body sensation, dryness, redness, and, in severe cases, they may present with visual impairment.[1]

Various risk factors have been identified and linked to the development of DES. Consistent risk factors include age, sex, race, meibomian gland disease, connective tissue diseases, Sjogren's syndrome, androgen deficiency, computer use, contact lens wear, hormone replacement therapy, certain medications, and environmental conditions.[4] Although these risk factors have been reported by many studies, the association of DES with occupational conditions was not adequately described.[3] In spite of the fact that DES is commonly seen among elderly females, recent studies show that young active workers frequently complain of DES-related symptoms.[5] This may be attributed to occupational risk factors such as visually demanding tasks (visual display terminal [VDT]) and dry working environments with low humidity, increased airflow, and higher temperatures.[1],[5] The impact of DES could be classified into three categories: direct costs (e.g., medical fees); indirect costs such as unemployment, inability to work, or low productivity; and an overall reduction in the quality of life due to substantial impairment in daily activities and a subsequent negative impact on mental health.[6],[7],[8]

In the literature, many studies have discussed various risk factors associated with the development of DES. According to the dry eye workshop epidemiology subcommittee, the risk factors were divided into consistent, probable, and inconclusive risk factors. Consistent risk factors, as mentioned above, include age, sex, computer use, and contact lens wear. Probable risk factors include diabetes, thyroid disease, and certain viral infections. Inconclusive risk factors include smoking, alcohol, and pregnancy. However, a study that was done on the epidemiology of dry eye in Africa stressed on the importance of certain disease processes such as trachoma, HIV/AIDS, Sjogren's syndrome, systemic lupus erythematosus (SLE), and nutritional deficiencies (e.g., Vitamin A deficiency) in the development of dry eye in Africa. Low humidity and higher temperatures are said to have an association with dry eye, although not clearly studied in Africa. This study also highlighted the lack of sufficient epidemiological data in that area.[4]

According to a retrospective clinic-based cohort study by Abokyi et al. done in Ghana, 17.5% (129) of 738 patients with allergic conjunctivitis develop dry eye-related symptoms. It was noted that there was a significant association between dry eye and age (>45 years; adjusted odds ratio [aOR]: 1.03, 95% confidence interval [CI]: 1.02–1.04). Another significant association was noticed between dry eye and patients' occupation, with teachers being the most susceptible (aOR: 1.42, 95% CI: 0.54–3.74); this is thought to be due to constant reading, writing, computer use, and exposure to chalk dust. This study also established a strong association between the use of systemic antihistamines in managing allergic conjunctivitis and the development of dry eyes (23.4%; P ≤ 0.001). There was no significant association between patients' genders and the development of dry eye.[9]

In another descriptive, cross-sectional hospital-based study conducted in an outpatient clinic in Nigeria by Emina and Odjimogho, the prevalence of dry eye was found to be 19.2% with a significant association with age (>40 years; OR: 1.88, 95% CI: 1.06–3.35) as well as illiteracy (OR: 0.4; 95% CI: 0.21–74) and no association with gender (OR: 1.48, 95% CI: 0.89–2.46). This study also stressed on the impact of windy environmental conditions.[10]

A descriptive, cross-sectional campus-based study done in South Africa aimed at comparing the prevalence of dry eye among African and Indian students concluded that African students were more prone to developing symptoms of dry eye. It also stated that more females reported having symptoms of dry eye.[11]

Gillan conducted a cross-sectional survey at the University of Johannesburg aimed at investigating the occurrence and severity of dry eye symptoms. It was noted that 64% of the 112 participants had at least mild dry eye symptoms. It was also noticed that 33% (20) and 15% (9) of participants under 40 years of age were contact lens wearers and oral contraceptives consumers, respectively.[12]

According to a case–control hospital-based study conducted in Egypt, dry eyes were the most common ocular manifestation in patients with SLE. Among the 52 patients included in the study, 13.4% had manifestations of dry eye.[13]

El-Shazly et al. conducted a cross-sectional clinic-based study in Egypt aimed at investigating passive smoking as a risk factor in the development of dry eye in children. It was concluded that among the 112 children presenting with ocular discomfort, 80 of them had dry eye and passive smoking was documented in 76 of the children with dry eye.[14]

A population-based survey done in Mali screened 10,559 children for clinical signs of trachoma and Vitamin A deficiency. It concluded that the prevalence of trachoma was 39.3% (95% CI: 37.4–41.2), 1.89% (95% CI: 1.59–2.25) of which had clinical xerophthalmia, thus establishing an association between active trachoma and xerophthalmia (OR: 2.04, 95% CI: 1.52–2.74).[15]

Various studies have established the impact of computer use as a risk factor in the development of dry eye. Courtin et al. conducted a meta-analysis targeted at evaluating the prevalence and risk factors associated with dry eye in VDT workers. They found that the global prevalence of dry eye in VDT workers is 49.5% (95% CI: 47.5–50.6), which is greater than that of the general population (5%–33%). They also noted that the disease was found to be more frequent in females and in older people.[16]

In another cross-sectional hospital-based study, Patil et al. compared the prevalence of dry eye among computer users. A sample of 100 participants aged 20–40 years were classified into three groups according to the daily duration of computer usage (in hours). The overall prevalence of dry eye in the study population was found to be 25%, with a prevalence of 9.3%, 18.18%, and 45.71% in Group A (3–4 h/day), Group B (5–6 h/day), and Group C (7–8 h/day), respectively. There was a significant association between the development of dry eye and the duration of computer usage (P < 0.05).[17]

A Japanese study done by Kawashima et al. also emphasized on the effect of computer use on dry eye. In this study, 369 VDT workers were screened for dry eye. The mean age of the study participants was 44.4 years and the mean daily VDT exposure was estimated to be 6 h. Approximately 60% of the workers were diagnosed with dry eye. It was concluded that the duration of VDT exposure was significantly longer in patients who screened positive for dry eye (P = 0.015). In this study, the prevalence of dry eye was higher among females despite the small proportion of female participants. Contrary to other studies, the age of the participants was not associated with increased prevalence. This suggested the importance of factors such as gender and duration of VDT exposure in the development of dry eye among young and middle-aged VDT workers.[18]

Besides VDT exposure, other aspects of the work environment that may play a role in the development of dry eye were explored. Van Tilborg et al. investigated the impact of office environments on dry eye in 294 employees with a mean age of 42.5 years. The participants worked in a low relative humidity office setting with sunshine exposure and increased airflow. In addition, the participants were subjected to visually demanding tasks that involved computer, laptop, and smartphone use. About 30% of the participants reported having dry eye symptoms. Among those, 75% admitted that their symptoms interfered with their work activities.[5]

Many studies described various risk factors mentioned above; however, not many compared the risk of developing the disease in relation to occupational categories. One remarkable population-based study was done in Korea by Lee et al. regarding various occupational characteristics and their relationships to symptoms of DES. About 6023 participants were included in this study. The inclusion age was 25–65 years (the working age in Korea). Approximately 963 participants had symptoms of DES. Among those, 65.2% were female. The prevalence of DES symptoms in different occupational categories was as follows: 34.76% were white-collar workers, 30.99% were blue-collar workers, 6.4% were green-collar workers, 14.5% were skilled blue-collar workers, and 16.6% were unskilled blue-collar workers (P = 0.0002). This study concludes that office work poses a higher risk of developing DES than manufacturing work. The low prevalence in green-collar workers implied that outdoor working conditions had limited effects on DES. This study also highlighted the importance of work autonomy (e.g., self-employed) and its protective impact on controlling symptoms of DES. In addition, higher prevalence rates were observed among women and smokers.[3]

Dry eye interferes with daily activities and work productivity and is related to an overall reduction in the quality of life. In addition, patients are more likely to develop symptoms of anxiety and depression. Due to its high prevalence, it is important to understand and identify the causative factors and risk factors involved in the development of DES to prevent further disability and possible complications. Identification of high-risk occupational groups can also serve as a ground for developing efficient screening methods in those groups. Lack of data in this area calls for a need to conduct this study.

The aim of this study is to describe the risk of developing symptoms of DES from the perspective of various occupational categories (defined and classified according to the International Standard Classification of Occupations) to identify high-risk occupational groups among middle-aged patients attending the outpatient clinic of Khartoum Eye Teaching Hospital.

  Subjects and Methods Top

Study design and area

This was a descriptive, cross-sectional hospital-based study done in the outpatient corneal clinics of Khartoum Eye Teaching Hospital from June to November 2018.

Sample size

Total coverage of patients with clinical symptoms and signs of DES was done.

Data collection

All of the patients who presented with symptoms of DES, such as feeling of dryness, itching, redness, or visual defects, were diagnosed using Schirmer's test in the outpatient clinic (positive if <10 mm in 5 min). Further details about the patients were obtained (sex, occupation, VDT exposure, and outdoor work). Additional investigations were done accordingly to determine the possible cause. The data about each participant were recorded in a questionnaire.

Inclusion criteria

  • Age between 25 and 50 years
  • Positive Schirmer's test
  • Symptomatic disease.

Exclusion criteria

  • Age below 25 or above 50 years
  • Refusal to participate in the study.


Occupational categories were defined according to the International Standard Classification of Occupations which classifies occupations according to the tasks and activities involved. Four categories were consistent with the occupations of patients who participated in this study: white-collar (ordinary), skilled blue-collar, unskilled blue-collar, and green-collar. White-collar workers included technicians and associated professionals (e.g., teachers, office workers, and accountants). On the other hand, blue-collar workers included the skilled group (mechanics, cooks, carpenters, and electricians) and the unskilled group (porters, elementary workers, and housekeepers). Finally, the green-collar group of workers included mainly farmers and fishers.

Statistical analysis

The statistical analysis was performed using the Statistical Package for the Social Sciences (SPSS Inc., Chicago, IL, USA) version 20. Categorical values were represented in the form of frequency tables and charts. Chi-squared tests were used to compare the differences between variables. Differences with P < 0.05 in the two-tailed analyses were considered to be statistically significant (with 95% confidence).


Permission was sought from the Research Technical and Ethical Committee at the Faculty of Medicine, University of Medical Sciences and Technology, and informed consent was obtained from the management of Khartoum Eye Teaching Hospital. Verbal consent from the patients was obtained where their privacy and confidentiality were and will be maintained.

  Results Top

A total number of 1143 patients presented to the outpatient corneal clinic between June 2018 and November 2018. Among those, 450 were recruited for the study. There was a female predominance, with 62% of the participants being women [Figure 1].
Figure 1: Gender distribution of study participants

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Regarding occupational categories, as demonstrated by [Table 1], 31.1% of the participants were white-collar workers, 29.8% were blue-collar workers (equally divided between skilled and unskilled workers), and 24.9% were green-collar workers. The rest of the participants were unemployed (14.2%).
Table 1: Distribution of occupational categories of study participants

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The educational level of the participants had a wide range, with the majority being university graduates (39%), as demonstrated by [Figure 2]. Secondary and primary school graduates constituted 17% and 26% of the population, respectively. The remaining 17% of the participants reported having no education.
Figure 2: Distribution of participants' educational level

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As illustrated by [Table 2], the most common possible underlying etiologies diagnosed in DES patients were allergic conjunctivitis (34%; 153), blepharitis (22%; 99), spring catarrh (20.9%; 94), Vitamin A deficiency (10.2%; 46), contact lens wear (10%; 45), and, finally, LASIK surgery (2.9%; 13).
Table 2: Causes of dry eye syndrome

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The association between DES and different occupational categories was run (using the unemployed group as a reference) and was found to be highly significant (P < 0.05), as summarized in [Table 3]. Allergic conjunctivitis was noted to be the most common cause of DES in all occupational categories. Blepharitis and Vitamin A deficiency, along with allergic conjunctivitis, were mainly prominent among the green collar group. However, in the unemployed group, spring catarrh was the most common possible etiology (19 participants). It was also noted that contact lens wear and LASIK surgery were dominant in the white-collar category of workers.
Table 3: Association between dry eye syndrome and occupational categories

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Regarding the working conditions of the participants, the majority (54%) reported using computers at work [Table 4]. In addition, it was found that there was a highly significant association between DES and computer use (P < 0.05). The majority of computer users were diagnosed as having allergic conjunctivitis (42 participants). Contact lens wear was also a common etiology of DES among computer users (41 participants).
Table 4: Association between dry eye syndrome and computer use

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Regarding the participants' genders, DES was found to be highly associated with the female sex (P < 0.05). As shown in [Figure 3], certain etiologies of DES, such as LASIK surgery, were seen only in female patients (13 participants). In addition, contact lens wear was commonly found among females, in comparison to males (female:male ratio is 13:2).
Figure 3: Association between dry eye syndrome and gender

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About 46% of the participants (203 out of 450) reported being involved in outdoor occupations. The association between outdoor occupations and DES was run and was found to be highly significant (P < 0.05). [Table 5] demonstrates this relationship. The majority of patients with outdoor occupations were diagnosed as having allergic conjunctivitis (53 patients). It was noticed that etiologies such as contact lens wear and LASIK surgery were not seen in this group of patients.
Table 5: Association between dry eye syndrome and outdoor work

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  Discussion Top

Due to its high prevalence, many studies were centered around identifying the causative factors and associated risk factors of DES. In the literature, the prevalence of risk factors varied widely in accordance with geographical areas. Occupational risk factors such as computer use (or VDT use) and outdoor environmental factors were studied and evaluated for their impact on the evolution of DES. However, only a few studies grouped occupations into categories and described the risk of DES in association with every occupational category.

According to the findings of this study, white-collar workers had a significantly higher prevalence of DES symptoms than blue-collar and green-collar workers. This finding is similar to other studies[3] and is attributed to the prolonged and extensive use of the eyes, which is common in this category. Most of the white-collar workers in this study reported using computers at work and it was concluded that computer use is significantly associated with DES. Furthermore, contact lens wear and LASIK surgeries were commonly documented in white-collar workers. This might be attributed to the fact that white-collar workers are generally more paid and can afford to undergo refractive error surgeries. The high prevalence of DES among white-collar workers implies that office work, which is common in this category, poses a higher risk for the development of symptoms. Besides computer use, office environments were reported to be dry with increased airflow (air conditioning, fans). Such environmental office factors contribute to the worsening of DES symptoms.

In this study, it was found that occupations with outdoor work were significantly associated with DES. Outdoor work is common among all green-collar workers; consequently, they are exposed to atmospheric conditions such as higher temperatures and low relative humidity, which were documented as unfavorable conditions for DES.[1] Despite the significant number of green-collar participants, green-collar workers had a lower prevalence of DES in comparison to white-collar and blue-collar (combined skilled and unskilled) workers. This finding is consistent with previous studies.[3]

Blue-collar workers had the second highest prevalence of DES. Contrary to other studies, it was found that the prevalence of dry eye is equal in both skilled and unskilled blue-collar categories.

The unemployed group of participants had the lowest prevalence of DES. This suggests that occupational risk factors play a major role in the aggravation of DES symptoms which often require medical attention.

Computer use is a documented risk factor in the development of DES.[4] As previously mentioned, a highly significant association was established between DES and computer use in this study. Several studies documented similar findings regarding the impact of VDT exposure on the development of DES symptoms.[16],[17],[18]

Regarding the possible etiologies of DES in this study, allergic conjunctivitis comes first. This finding corresponds with one study in which allergic conjunctivitis was the main etiological factor involved in the development of DES symptoms.[9] Other causes included blepharitis, spring catarrh, Vitamin A deficiency, contact lens wear, and LASIK surgery. All of the reported etiologies were established causes of DES.[19]

The majority of the participants were female. In this study, there was a highly significant association between DES and female sex. This finding is consistent with the majority of studies done, in which there was a high prevalence of DES among the female participants.[3],[11],[16],[18] However, most of the studies done in African countries documented no significant association between DES and gender.[9],[10]

The impact of climatic changes on DES was reflected on the significant association between outdoor occupations and DES. Although there is a lack of significant evidence in the region, most studies suggest that sunlight exposure, windy environments, and low humidity play a role in the progression of symptoms.[1],[5],[10] In spite of that, other studies still suggest that atmospheric conditions have little impact on DES.[3]

The strengths of this study include the use of occupational categories to stratify participants. This can be used to determine an individual's risk of DES. Furthermore, occupational categories provide a clear classification in which certain characteristics are attributed to each category, allowing the identification of occupation-specific risk factors. This study sheds light on the impact of patient occupation on DES and suggests that controlling occupational risk factors is of importance when managing patients, instead of treating the underlying etiology alone.

One limitation of this study is its cross-sectional design, which provides limited information about the direction of the relationship between DES and occupational categories. Another limitation was the wide heterogenicity in the diagnostic criteria of DES. In addition, most of the participants in this study had underlying etiologies that may mask the true impact of occupational risk factors on DES. Finally, it was challenging to determine all possible causes of DES in the participants due to the lack of advanced diagnostic methods and the limited budget for this study.

  Conclusions Top

This study assessed the risk of developing DES from the perspective of occupational categories. White-collar workers had the highest risk of DES in comparison to blue-collar and green-collar workers. Blue-collar workers had a higher risk than green-collar workers. However, skilled and unskilled blue-collar workers were similar in terms of developing DES. Furthermore, female workers had an increased risk in comparison to male workers. In addition, occupational computer use and outdoor exposure also played a role and were significantly associated with DES.


  • Education of high-risk workers is an essential approach to limiting DES-related visual impairments. Workers must be aware of their symptoms and must know when to seek medical advice
  • Improving occupational risks (e.g., humidifying office environments, limiting computer use, and wearing protective sunglasses when exposed to sunlight) can significantly reduce the severity of symptoms
  • It is important to implement screening programs (e.g., annual Schirmer's tests and self-questionnaires) for high-risk workers to limit vision-related disability and improve work productivity
  • Future studies should cover a wider population area in which other occupational categories (e.g., executive white-collar and pink-collar) can be represented.


I would like to thank Dr. Ibtihal M. El Rasheed for her guidance, advice, and support.

Financial support and sponsorship

Faculty of Medicine, University of Medical Sciences and Technology.

Conflicts of interest

There are no conflicts of interest.

  References Top

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Lee JH, Lee W, Yoon JH, Seok H, Roh J, Won JU. Relationship between symptoms of dry eye syndrome and occupational characteristics: The Korean national health and nutrition examination survey 2010-2012. BMC Ophthalmol 2015;15:147.  Back to cited text no. 3
Stapleton F, Alves M, Bunya VY, Jalbert I, Lekhanont K, Malet F, et al. TFOS DEWS II epidemiology report. Ocul Surf 2017;15:334-65.  Back to cited text no. 4
van Tilborg M, Kort H, Murphy P, Evans K. The influence of dry eye and office environment on visual functioning. Stud Health Technol Inform 2015;217:427-31.  Back to cited text no. 5
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Wan KH, Chen LJ, Young AL. Depression and anxiety in dry eye disease: A systematic review and meta-analysis. Eye (Lond) 2016;30:1558-67.  Back to cited text no. 8
Abokyi S, Koffuor G, Abu E, Abraham C. Dry eye: An adverse effect of systemic antihistamine use in allergic conjunctivitis management. Res J Pharmacol 2012;6:71-7.  Back to cited text no. 9
Emina MO, Odjimogho SE. Ocular problems in HIV and AIDS patients in Nigeria. Optom Vis Sci 2010;87:979-84.  Back to cited text no. 10
Castelyn B, Majola S, Motilal R, Naidu M, Ndebele S, Vally T, et al. Prevalence of dry eye amongst black and Indian university students aged 18–30 years. Afr Vis Eye Health 2015;74:6.  Back to cited text no. 11
Gillan WA. Small-sample survey of dry eye symptoms using the ocular surface disease index. Afr Vis Eye Health 2009;68:e188-91.  Back to cited text no. 12
El-Shereef RR, Mohamed AS, Hamdy L. Ocular manifestation of systemic lupus erythematosus. Rheumatol Int 2013;33:1637-42.  Back to cited text no. 13
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Schémann J, Malvy D, Sacko D, Traore L. Trachoma and Vitamin A deficiency. Lancet 2001;357:1676.  Back to cited text no. 15
Courtin R, Pereira B, Naughton G, Chamoux A, Chiambaretta F, Lanhers C, et al. Prevalence of dry eye disease in visual display terminal workers: A systematic review and meta-analysis. BMJ Open 2016;6:e009675.  Back to cited text no. 16
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  [Figure 1], [Figure 2], [Figure 3]

  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5]

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