August 2013
Volume 54, Issue 8
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Clinical and Epidemiologic Research  |   August 2013
Population-Based Assessment of Prevalence and Risk Factors for Pterygium in the South Indian State of Andhra Pradesh: The Andhra Pradesh Eye Disease Study
Author Affiliations & Notes
  • Srinivas Marmamula
    Allen Foster Community Eye Health Research Centre, International Centre for Advancement of Rural Eye Care, L V Prasad Eye Institute, Hyderabad, India
  • Rohit C. Khanna
    Allen Foster Community Eye Health Research Centre, International Centre for Advancement of Rural Eye Care, L V Prasad Eye Institute, Hyderabad, India
  • Gullapalli N. Rao
    Allen Foster Community Eye Health Research Centre, International Centre for Advancement of Rural Eye Care, L V Prasad Eye Institute, Hyderabad, India
    Bausch & Lomb School of Optometry, L V Prasad Eye Institute, Hyderabad, India
  • Correspondence: Srinivas Marmamula, Allen Foster Community Eye Health Research Centre, ICARE, L V Prasad Eye Institute, L V Prasad Marg, Banjara Hills, Hyderabad, India 500034; srioptom@lvpei.org
Investigative Ophthalmology & Visual Science August 2013, Vol.54, 5359-5366. doi:10.1167/iovs.13-12529
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      Srinivas Marmamula, Rohit C. Khanna, Gullapalli N. Rao; Population-Based Assessment of Prevalence and Risk Factors for Pterygium in the South Indian State of Andhra Pradesh: The Andhra Pradesh Eye Disease Study. Invest. Ophthalmol. Vis. Sci. 2013;54(8):5359-5366. doi: 10.1167/iovs.13-12529.

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Abstract

Purpose.: To describe the prevalence and risk factors for pterygium in a population-based sample of individuals aged 30 years and older in South Indian state of Andhra Pradesh.

Methods.: A cross-sectional study was conducted in one urban and three rural locations in which 10,293 subjects were examined. All the subjects underwent comprehensive eye examination and a detailed interview by trained professionals. Pterygium was defined as fleshy fibro vascular growth, crossing the limbus, and typically seen on the nasal conjunctiva in either eye.

Results.: Data were analyzed for 5586 subjects who were aged 30 years and older at the time of participation. The mean age of the participants was 47.5 years (SD 13 years; range 30–102 years). In total, 46.4% were male, 56.7% had no education, 52.2% of them were involved in outdoor occupations, and 25% belonged to urban area. The prevalence of pterygium was 11.7% (95% confidence interval [CI]: 10.9–12.6). The multiple logistic regression analysis revealed significantly higher odds of pterygium among older age groups, rural residents (odds ratio [OR]: 1.8; 95% CI: 1.4–2.4; P > 0.01), and those involved in outdoor occupations (OR: 1.8; 95% CI: 1.5–2.2, P < 0.001). Education had a protective effect (OR: 0.6; 95% CI: 0.5–0.7; P < 0.001).

Conclusions.: Pterygium is common in the South Indian state of Andhra Pradesh. Exposure to sunlight is a significant modifiable risk factor. Protecting the eyes from sunlight may decrease the risk of pterygium. However, the important public health challenge is to encourage the use of this protection as a routine in developing countries such as India.

Introduction
Pterygium is a wedge shaped abnormal fibrovascular growth that is typically seen on the nasal conjunctiva and extends over to the cornea. Several researchers have studied both the epidemiologic associations and pathophysiology of pterygium. 13 Among the several risk factors reported, exposure to UV rays is perhaps the most common risk factor for the occurrence of pterygium. 413 The presence of pterygium results in a cosmetic blemish and it occasionally may extend on to the corneal surface resulting in irregular astigmatism and leading to visual impairment. 14  
Andhra Pradesh (AP) lies between 12°41′ and 22°N latitude and 77° and 84°40′E longitude. It is one of the largest states in India with a large proportion of its population engaged in agricultural activities and several other outdoor occupations. Nearly one third of the population reside in rural areas as per the 2011 census. 15 There are no studies on the prevalence of pterygium in Andhra Pradesh, though a survey from the neighboring state of Tamil Nadu reported a prevalence of 9.5% in the population aged 40 years and older. 16 We undertook a large population based epidemiologic cross-sectional study (Andhra Pradesh Eye Disease Study [APEDS]) to evaluate the prevalence and risk factors for ocular morbidity and visual impairment. The prevalence and causes of visual impairment have been reported from this study. 17,18 In this paper, we report on the prevalence and risk factors for pterygium in a population aged 30 years and older in the South Indian state of Andhra Pradesh. 
Methods
Informed Consent
The Institutional Review Board (Scientific and Ethics Committee) of the L V Prasad Eye Institute, Hyderabad, India, reviewed and approved the study design of APEDS. The study followed the tenets of the Declaration of Helsinki. All the participants provided written informed consent for participating in the study. The data collection was accomplished from 1996 to 2000. 
Study Area and Participants
The detailed methodology and findings of APEDS were reported previously. 19,20 Briefly, a multistage cluster random sampling procedure was used to select a study sample of 10,000 persons of all ages including 5000 individuals aged 30 years and older. One urban and three rural areas from different parts of AP were selected with an equal distribution of 2500 participants in each area, to reflect the urban–rural and socioeconomic distribution of the population of this state in the year 2000. The four areas selected were Hyderabad (urban), West Godavari district (economically well off, rural), and Adilabad and Mahabubnagar districts (poor, rural). 
Interview
All the participants underwent a comprehensive interview by trained field investigators. Detailed information on study instruments is published elsewhere. 20 In the context of the current study, the data collected included personal-, demographic-, and lifestyle-related information comprising age, sex, education, occupation, residence, smoking, alcohol consumption, use of spectacles, and systemic history including hypertension and diabetes. 20  
Ophthalmologic Examination
Two ophthalmologists and two optometrists, specially trained in the study procedures, performed the examinations. Distance and near visual acuity were assessed under standard testing conditions using logarithm of minimum angle of resolution charts. Both presenting and best corrected visual acuity after refraction were recorded. Detailed anterior segment examination was performed using slit-lamp biomicroscopy. All the participants underwent dilated posterior segment evaluation, unless contraindicated. The detailed examination protocol has been previously published. 20  
Definitions
The primary outcome was the presence of pterygium in either eye, which was defined as fleshy fibro vascular growth, crossing the limbus, and typically seen on the nasal conjunctiva. The covariates are defined as follows: the level of formal education was categorized under two groups, ‘no education' and ‘any education'; occupation was categorized as ‘indoor' and ‘outdoor' occupation, based on the working environment, as a surrogate measure of exposure to UV sunlight; smoking and alcohol status was defined as ‘ever' and ‘never' based on the history. Previous and current smokers and alcohol consumers were both classified as ‘ever' smokers, and alcoholics for the purpose of data analysis; the diagnosis of hypertension and diabetes is based on self-report by the participants; all the participants were classified as spectacle users and nonspectacle users. The use of sunglasses is uncommon among this study population, but a small proportion of those who reported use (n = 191; 3.4%) were also included in the category of spectacles users. 
Data Analysis
The dataset that included individuals aged 30 and older was used for analysis. Data were analyzed using Stata statistical package for windows version 12 software (StataCorp, College Station, TX). 21 Univariate analysis for exploring the differences between participants with and without pterygium was done using χ2 test. Simple logistic regression was done to find the relationship between pterygium (dependent variable) and each of the risk factors followed by likelihood ratio tests. Multiple logistic regression models were used to examine the strength of association between pterygium and all the risk factors including age, sex, education, occupation, alcohol intake, smoking, use of spectacles, diabetes, and hypertension. Hosmer-Lemeshow goodness of fit tests was used to assess the goodness of the model fit. Variance inflation factors (VIF) were used to test for collinearity between the covariates after fitting a multiple regression model. The final regression model reported a VIF equal to 1.4, and the goodness of fit test was not significant (P = 0.23), indicating a good fit of model. The adjusted odds ratio (OR) with 95% confidence intervals (CI) is presented. The statistical significance was assessed at the conventional level of P less than 0.05 (two-tailed); however, exact P values are reported. 
Results
Of a total of 11,786 subjects sampled for APEDS, 10,293 (87.3%) participated in the main study. Data were analyzed for 5586 subjects who were 30 years of age and older at the time of participation. The mean age of the participants was 47.5 years (SD 13 years; range 30–102 years). In total, 46.4% (n = 2594) were male, 56.7% (n = 3170) had no education, and 52.2% (n = 2915) of them were involved in outdoor occupations. A quarter (n = 1399) of the sample belonged to urban area. Nearly 20% (n = 1137) of the participants reported use of spectacles. The proportion of ever smokers and ever alcohol consumers was 33.6% (n = 1878) and 35.8% (n = 1998), respectively (Table 1). 
Table 1. 
 
Characteristics of the Study Sample Stratified by Presence and Absence of Pterygium (n = 5586)
Table 1. 
 
Characteristics of the Study Sample Stratified by Presence and Absence of Pterygium (n = 5586)
No Pterygium Pterygium Total P Value*
n % n % n %
Age group, y <0.001
 30–39 1731 35.1 131 20 1862 33.3
 40–49 1262 25.6 162 24.7 1424 25.5
 50–59 886 18 161 24.6 1047 18.7
 60–69 760 15.4 140 21.4 900 16.1
 70 and above 292 5.9 61 9.3 353 6.3
Sex 0.627
 Male 2284 46.3 310 47.3 2594 46.4
 Female 2647 53.7 345 52.7 2992 53.6
Education <0.001
 No education 2681 54.4 489 74.7 3170 56.7
 Any education 2250 45.6 166 25.3 2416 43.3
Area of residence <0.001
 Urban 1330 27 69 10.5 1399 25
 Rural 3601 73 586 89.5 4187 75
Occupation <0.001
 No outdoor 2471 50.1 200 30.5 2671 47.8
 Outdoor work 2460 49.9 455 69.5 2915 52.2
Smoking status 0.225
 Never 3287 66.7 421 64.3 3708 66.4
 Ever 1644 33.3 234 35.7 1878 33.6
Alcohol intake <0.001
 Never 3269 66.3 319 48.7 3588 64.2
 Ever 1662 33.7 336 51.3 1998 35.8
Spectacles use <0.001
 No 3880 78.7 569 86.9 4449 79.6
 Yes 1051 21.3 86 13.1 1137 20.4
Hypertension 0.002
 No 4464 90.5 617 94.2 5079 90.9
 Yes 467 9.5 38 5.8 505 9
Diabetes mellitus 0.018
 No 4743 96.2 642 98 5385 96.4
 Yes 188 3.8 13 2 201 3.6
Total 4931 100 655 100 5586 100
The overall prevalence of pterygium in either eye was 11.7% (95% CI: 10.9–12.6; n = 655). The prevalence was significantly higher among older age groups (P < 0.001), but similar in both sexes. The prevalence was significantly higher among those with no education (15.4%; 95% CI: 14.2–16.7) compared with those with any education (6.9%; 95% CI: 5.9–8.0) (P < 0.001), it was higher among rural residents (14.0%; 95% CI: 13.0–15.1) compared with 4.9% (95% CI: 3.8–6.2) (P < 0.001) in urban area, and higher among those who had a predominantly outdoor occupation (15.6%; 95% CI: 14.3–17.0) compared with indoor work (7.5%; 95% CI: 6.5–8.5) (P < 0.001). The pterygium was less frequent among those who had hypertension (P < 0.001) and diabetes. Though the prevalence of pterygium was similar among smokers and nonsmokers, it was higher among those who reported alcohol intake (P < 0.001) (Table 2). The pterygium was not a cause of visual impairment in our study population. The prevalence of bilateral pterygium was 6.9% (95% CI: 6.2–7.6). 
Table 2. 
 
Prevalence of Pterygium Stratified by Risk Factors (n = 655)
Table 2. 
 
Prevalence of Pterygium Stratified by Risk Factors (n = 655)
Prevalence, % 95% CI P Value*
Age group, y <0.001
 30–39 7.0 5.9–8.3
 40–49 11.4 9.8–13.1
 50–59 15.4 13.2–17.7
 60–69 15.6 13.2–18.1
 70 and above 17.3 13.5–21.6
Sex 0.627
 Male 12.0 10.7–13.3
 Female 11.5 10.4–12.7
Education <0.001
 No education 15.4 14.2–16.7
 Any education 6.9 5.9–8.0
Area <0.001
 Urban 4.9 3.8–6.2
 Rural 14.0 13.0–15.1
Occupation <0.001
 No outdoor 7.5 6.5–8.5
 Outdoor work 15.6 14.3–17.0
Smoking status 0.225
 No 11.4 10.3–12.4
 Yes 12.5 11.0–14.0
Alcohol intake <0.001
 No 8.9 7.8–9.9
 Yes 16.8 15.2–18.5
Spectacles use <0.001
 No 12.8 11.8–13.1
 Yes 7.6 6.1–9.2
Hypertension 0.002
 No 12.1 11.3–13.1
 Yes 7.5 5.4–10.2
Diabetes mellitus 0.018
 No 11.9 11.0–12.8
 Yes 6.5 3.5–10.8
Overall 11.7 10.9–12.6
The univariate analysis showed significantly higher odds (unadjusted) of pterygium in older age groups, rural residents (OR: 3.1, 95% CI: 2.4–4.0; P < 0.001), those involved in outdoor occupations (OR: 2.3, 95% CI: 1.9–2.7; P < 0.001), and alcohol consumption (OR: 2.1, 95% CI: 1.8–2.4; P < 0.001). Any education (OR: 0.4, 95% CI: 0.3–0.5; P < 0.001), use of spectacles (OR: 0.6, 95% CI: 0.4–0.7; P < 0.001), and having hypertension (OR: 0.6, 95% CI: 0.4–0.8; P < 0.01) had a protective effect. No statistically significant association was seen with pterygium and smoking, sex, and among those with diabetes (Table 3). 
Table 3. 
 
Results of Simple and Multiple Logistic Regression Analyses for Association Between Pterygium and the Risk Factors
Table 3. 
 
Results of Simple and Multiple Logistic Regression Analyses for Association Between Pterygium and the Risk Factors
Crude OR* 95% CI P Values Adjusted OR†‡ 95% CI P Values
Age group, y
 30–39 (base) (base)
 40–49 1.7 1.3–2.2 <0.001 1.8 1.4–2.3 <0.001
 50–59 2.4 1.9–3.1 <0.001 2.6 2.0–3.3 <0.001
 60–69 2.4 1.9–3.1 <0.001 2.8 2.1–3.6 <0.001
 70 and above 2.8 2.0–3.8 <0.001 3.9 2.7–5.6 <0.001
Sex
 Male (base) (base)
 Female 1.0 0.8–1.1 0.98 0.8 0.6–1.0 0.07
Education
 No education (base) (base)
 Any education 0.4 0.3–0.5 <0.001 0.6 0.5–0.7 <0.001
Area of residence
 Urban (base) (base)
 Rural 3.1 2.4–4.0 <0.001 1.8 1.4–2.4 <0.001
Occupation
 No outdoor (base) (base)
 Outdoor work 2.3 1.9–2.7 <0.001 1.8 1.5–2.2 <0.001
Smoking status
 Never (base) (base)
 Ever 1.1 0.9–1.3 0.23 0.6 0.4–0.7 <0.001
Alcohol use
 Never (base) (base)
 Ever 2.1 1.8–2.4 <0.001 1.7 1.4–2.0 <0.001
Spectacles use
 No (base) (base)
 Yes 0.6 0.4–0.7 <0.001 0.8 0.6–1.0 0.12
Hypertension
 No (base) (base)
 Yes 0.6 0.4–0.8 <0.01 0.9 0.6–1.2 0.43
Diabetes mellitus
 No (base) (base)
 Yes 0.5 0.3–0.9 0.02 0.8 0.5–1.5 0.56
The multiple logistic regression analysis revealed significantly higher odds (adjusted) of pterygium with increasing age. Compared with those aged 30 to 39 years, the odds of pterygium increased more than 2-fold in the 40 to 49 years age group and the upward trend of increasing odds continued with odds of 3.9 (95% CI: 2.7–5.6; P < 0.001) among those aged 70 years and older. Education had a protective effect (OR: 0.8; 95% CI: 0.5–0.7; P < 0.001). In comparison to those residing in urban area, the odds of pterygium were almost twice as high as in rural residents (OR: 1.8; 95% CI: 1.4–2.4; P > 0.01). The participants involved in outdoor occupations had nearly twice the odds for pterygium (OR: 1.8; 95% CI: 1.5–2.2: P < 0.001) compared with their counterparts who had no outdoor work. There was no statistically significant association found between pterygium and sex, diabetes, and hypertension. Though the use of spectacles had a protective effect (OR: 0.8, 95% CI: 0.6–1.0; P = 0.12), it was not statistically significant. The odds of pterygium were higher among ever alcoholic (OR: 1.7, 95% CI: 1.4–2.0; P < 0.001) compared with never alcoholics, while smoking revealed a protective effect (OR: 0.6, 95% CI: 0.4–0.7; P < 0.001) (Table 3). There was no interaction between education and age. However, education had a significant interaction with outdoor occupation. On excluding education variable from the model, the overall effects attenuated, but this change in odds was not significant and there no change in direction (data not shown). 
Discussion
A wide variation in the prevalence of pterygium is reported ranging from nearly 2% in Greater Beijing, China 22 and 2.8% in Victoria, Australia, 23 to 39% in rural Dali, China, 24 and about 48% in Spain. 25 Though a lower prevalence of 1.3% was reported from Tehran, the study included all ages compared with other studies that included individuals aged 40 years or older. 26 We found a 11.7% prevalence of pterygium in our study, which is comparable to the previously reported prevalence of 9.5% from Tamil Nadu in South India. 16  
We summarized the prevalence and risk factors for pterygium from large population-based, cross-sectional studies in Table 4. A large number of risk factors were shown to be associated with pterygium pointing toward a multifactorial nature of occurrence of the condition. 1-3 Though an unequivocal evidence of association is shown between pterygium with older age and outdoor occupations (a surrogate measure of UV exposure), associations with other risk factors are differently reported. 4-13 The increasing odds of pterygium with increase in age reported from several studies can be considered as suggestive of increased cumulative lifetime exposure to sunlight. 
Table 4. 
 
Summary on Prevalence and Associations With Pterygium in Large Population-Based Cross-Sectional Studies Around the World
Table 4. 
 
Summary on Prevalence and Associations With Pterygium in Large Population-Based Cross-Sectional Studies Around the World
Place Year of Study Age Group, y Sample Size, Examined Prevalence, % Other Variables in the Multivariable Model Significant Risk Factors in the Multivariable Analysis
Shahroud, Iran (Urban)31 2012 >40 5,190 9.4% (either eye); 2.9% (bilateral) Smoking (no association) Older age, male sex, working outdoors, and the lower level of education
Bai minority population in rural Dali, China24 2012 ≥50 2,133 39.0% (either eye) Height, weight, hypertension, diabetes, smoking, alcohol use (ns)* Older age, female sex, older age, lack of formal education, outdoor work
Indigenous Australians within central Australia42 2011 ≥40 1,884 9.3% (either eye) Sex (ns)* Older age
Malay population of Singapore27 2010 40–79 3,280 12.3% (either eye) Smoking (ns),* education (ns)* Older age, male sex, higher systolic blood pressure, outdoor occupation (only for severe pterygium), total cholesterol
Kumejima Island, Japan32 2009 ≥40 3,762 30.8% (either eye); 13.1% (bilateral) Height, weight, blood pressure, use of hat, (all ns)* Older age, male sex, hyperopic refraction, lower IOP, and outdoor occupation
Beijing, China (Rural)22 2008–2009 55–89 37,067 3.8% (either eye) None Older age, male sex, UV radiation living in low latitude
Latinos in Arizona, USA33 2008 ≥40 4,774 16% (either eye) Smoking showed a protective effect, Diabetes (borderline protective), bilateral cataract surgery (protective) Male sex, lower socioeconomic status, lower levels of education
Rural Myanmar14 2007 ≥40 2,076 19.6% (either eye); 8.0% (bilateral) Smoking (ns)*; age (ns)* Outdoor occupation, female sex
Greater Beijing, China29 2007 ≥40 4,439 1.9% (either eye); 2.9% subjects Level of education (ns)* or refractive errors (ns)* Older age, male sex, outdoor work
Southern Harbin population, China43 2006 ≥50 5,057 3.7% (either eye); 2.6% (bilateral) age and education (no association) Male sex, smoking, astigmatism
Tibetans in China35 2006 ≥40 2,229 14.5% (either eye) Sea level Older age, female sex, not using sunglasses, lower level of education, lower socio economic status
Mongolian population at high altitude in Henan County, China36 2006 ≥40 2,112 17.9% (either eye) None Older age, lower levels of education, alcohol intake, lower socio economic status, non use of sunglasses or hat and cataract
North-Western Spain25 2005–2006 ≥40 619 5.9% (either eye) Alcohol intake, smoking, diabetes, iris color, pseudo exfoliation (ns)* Older age, male sex, outdoor occupation
Singapore (included studies multi ethic Asian population)44 2004–2011 ≥40 8,906 10.1% (either eye); 4.6% (Bilateral) Hypertension, total cholesterol (ns)* Older age, male sex, Malay race, poorer education, outdoor occupation
Tehran26 2002 All ages 4,564 1.3% (either eye) Race Older age, Male sex, smoking
Doumen County, southern China (rural)45 2002 ≥50 4,214 33.0% (either eye) Not reported Risk factors not reported
Sumatra, Indonesia (rural)46 2002 ≥21 1,210 10.0% (either eye); 4.1% (bilateral) Smoking protective, sex (ns)* Older age, outdoor occupations
Tamil Nadu, India16 2001–2004 ≥40 7,774 9.5% (either eye) Smoking, alcohol, nature of work, diabetes, hypertension (ns)* UV exposure, rural residence, older age, lower socio economic status
Barbados37 2001 ≥40 2,781 23.4% black, 23.7% mixed, and 10.2% white participants (either eye) Darker skin complexion, use of sunglasses/ prescription glasses were protective factors; Current smoking (protective) African ancestry, older age, lower levels of education, outdoor occupation
Australian state of Victoria 23 2000 ≥40 5,147 2.8% (either eye) None Older age, male sex, rural residence, and life ocular sun exposure
Chinese population in Singapore34 1997–1998 ≥40 1,232 6.9% (either eye); 2.9% (bilateral) None Older age, male sex, outdoor occupations, factory workers
Australia30 1992–1994 ≥49 3,564 7.3% (either eye) Iris color (ns)* Male sex, darker skin color, black hair color
Australia4 1984 (published) All ages 105,113 3.4% (Aborigines); 1.1% (Non aboriginals) (eye or person not reported) Not reported Not reported
While most studies demonstrated an increased risk of pterygium among men compared with women, 22,23,25,2734 some studies, including the present study and the report from Tamil Nadu, found a similar prevalence in both the sexes. 16 The two studies from rural Dali and Tibet, China, found a higher prevalence among women. 24,35 The studies have shown a higher prevalence of pterygium among those with lower levels of education and those belonging to lower socioeconomic status. 24,31,33,3537 We also found a significantly higher prevalence of pterygium (5.5% vs. 13.8%) among those with no education; we have not included socioeconomic status in our risk factor models. Similar to other studies, we found a significantly higher prevalence of pterygium in the rural population. 16,23,24  
Although the causal association between pterygium and other systemic conditions such as hypertension and diabetes is not clearly known, several authors have used these as risk factors and studied their association with pterygium. A study among the Malay population in Singapore found an increased risk of pterygium among those with higher systolic blood pressure. 27 Both the present study and the other studies from South India and China found no significant association between pterygium and hypertension. 16,24 Similar to other studies, we did not find any significant association between pterygium and diabetes after adjusting for other covariates. Due caution has to be exercised when diagnosis of hypertension and diabetes are based on self-report, as it can grossly underestimate the prevalence of these conditions, as a significant proportion of people may be unaware of the condition in developing countries such as India. 
Alcohol intake and smoking have been subject in several studies as risk factors for pterygium, though the biologic mechanism between pterygium and these factors is not clearly understood. Both alcohol intake and smoking association from epidemiologic cross-sectional studies can be occasionally confounded with other risks, and smoking itself may be confounded with alcohol intake. The study among the Bali rural population in China, Tamil Nadu in India, and Spain, showed no association between smoking and pterygium 16,24,25 ; however, a study from the United States showed a protective effect of smoking33 In our study, smoking was not significant in univariable model; it showed a protective effect when introduced in multivariable model. We found a higher prevalence of pterygium among those who reported alcohol intake with twice the odds compared with those who reported no alcohol intake. Other studies from India, China, and Spain found no such association. 16,24,25  
The use of sunglasses and other devices to protect themselves from sunlight is not common in the population studied. Though we found that the use of spectacles had a protective effect when we studied the total effect in univariable model, this effect was not statistically significant in the multivariable model. The protective effect of spectacles use is reported in other studies. 16,35,37  
Although the early researchers on pterygium reported a direct relationship between pterygium and UV exposure, in fact there are reports on the ‘pterygium belt' that extends to 37° north and south of the equator. 4,13,38 As more research work is now available that indicate a significant variation in the prevalence of pterygium in regions belonging to same geographic locations. It is now clear that though UV exposure is a key risk factor, there are several other risk factors including genetic predisposition for pterygium. However, in public health parlance, UV exposure remains is one of the important modifiable risk factors. 
We studied a randomly selected representative sample and obtained a high response rate, which makes our results externally valid and comparable with other population-based studies done elsewhere. The categories we used for alcohol intake and smoking status may have been subject to misclassification bias as we have not taken the dose or frequency, nor have we quantified the intake. We are unable to make any inferences on dose-response relationship between these risk factors and pterygium. We relied on self-report on diabetes and hypertension, hence this could have underestimated the true prevalence of hypertension and diabetes in our study population. Finally, we have not used any specific technology or measure to quantify the actual UV exposure or lifetime UV exposure as used in previous studies. 16,23,39 We used outdoor occupation as a surrogate measure of sunlight exposure and this is prone to result in bias and in imprecision in our estimates. 40,41  
In conclusion, exposures to sunlight and alcohol intake are important modifiable risk factors. The use of sunglasses or protection from sunlight for those who predominantly work outdoors may decrease the risk of prevalence. However, the public health challenge is to inculcate the use of this protection as routine, and introduce suitable lifestyle modifications in the rural populations in developing countries such as India. 
Acknowledgments
The authors thank Lalit Dandona and Rakhi Dandona, who conducted the detailed study, and Hugh R. Taylor and Catherine A. McCarty for their guidance in the study design. They also thank the participants of APEDS for their participation in the study, as well as the entire APEDS team, including Pyda Giridhar, Kovai Vilas, and Mudigonda N. Prasad for conducting the in depth interviews. 
Supported by grants from the Hyderabad Eye Research Foundation, India, and Christoffel-Blindenmission, Bensheim, Germany. 
Disclosure: S. Marmamula, None; R.C. Khanna, None; G.N. Rao, None 
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Table 1. 
 
Characteristics of the Study Sample Stratified by Presence and Absence of Pterygium (n = 5586)
Table 1. 
 
Characteristics of the Study Sample Stratified by Presence and Absence of Pterygium (n = 5586)
No Pterygium Pterygium Total P Value*
n % n % n %
Age group, y <0.001
 30–39 1731 35.1 131 20 1862 33.3
 40–49 1262 25.6 162 24.7 1424 25.5
 50–59 886 18 161 24.6 1047 18.7
 60–69 760 15.4 140 21.4 900 16.1
 70 and above 292 5.9 61 9.3 353 6.3
Sex 0.627
 Male 2284 46.3 310 47.3 2594 46.4
 Female 2647 53.7 345 52.7 2992 53.6
Education <0.001
 No education 2681 54.4 489 74.7 3170 56.7
 Any education 2250 45.6 166 25.3 2416 43.3
Area of residence <0.001
 Urban 1330 27 69 10.5 1399 25
 Rural 3601 73 586 89.5 4187 75
Occupation <0.001
 No outdoor 2471 50.1 200 30.5 2671 47.8
 Outdoor work 2460 49.9 455 69.5 2915 52.2
Smoking status 0.225
 Never 3287 66.7 421 64.3 3708 66.4
 Ever 1644 33.3 234 35.7 1878 33.6
Alcohol intake <0.001
 Never 3269 66.3 319 48.7 3588 64.2
 Ever 1662 33.7 336 51.3 1998 35.8
Spectacles use <0.001
 No 3880 78.7 569 86.9 4449 79.6
 Yes 1051 21.3 86 13.1 1137 20.4
Hypertension 0.002
 No 4464 90.5 617 94.2 5079 90.9
 Yes 467 9.5 38 5.8 505 9
Diabetes mellitus 0.018
 No 4743 96.2 642 98 5385 96.4
 Yes 188 3.8 13 2 201 3.6
Total 4931 100 655 100 5586 100
Table 2. 
 
Prevalence of Pterygium Stratified by Risk Factors (n = 655)
Table 2. 
 
Prevalence of Pterygium Stratified by Risk Factors (n = 655)
Prevalence, % 95% CI P Value*
Age group, y <0.001
 30–39 7.0 5.9–8.3
 40–49 11.4 9.8–13.1
 50–59 15.4 13.2–17.7
 60–69 15.6 13.2–18.1
 70 and above 17.3 13.5–21.6
Sex 0.627
 Male 12.0 10.7–13.3
 Female 11.5 10.4–12.7
Education <0.001
 No education 15.4 14.2–16.7
 Any education 6.9 5.9–8.0
Area <0.001
 Urban 4.9 3.8–6.2
 Rural 14.0 13.0–15.1
Occupation <0.001
 No outdoor 7.5 6.5–8.5
 Outdoor work 15.6 14.3–17.0
Smoking status 0.225
 No 11.4 10.3–12.4
 Yes 12.5 11.0–14.0
Alcohol intake <0.001
 No 8.9 7.8–9.9
 Yes 16.8 15.2–18.5
Spectacles use <0.001
 No 12.8 11.8–13.1
 Yes 7.6 6.1–9.2
Hypertension 0.002
 No 12.1 11.3–13.1
 Yes 7.5 5.4–10.2
Diabetes mellitus 0.018
 No 11.9 11.0–12.8
 Yes 6.5 3.5–10.8
Overall 11.7 10.9–12.6
Table 3. 
 
Results of Simple and Multiple Logistic Regression Analyses for Association Between Pterygium and the Risk Factors
Table 3. 
 
Results of Simple and Multiple Logistic Regression Analyses for Association Between Pterygium and the Risk Factors
Crude OR* 95% CI P Values Adjusted OR†‡ 95% CI P Values
Age group, y
 30–39 (base) (base)
 40–49 1.7 1.3–2.2 <0.001 1.8 1.4–2.3 <0.001
 50–59 2.4 1.9–3.1 <0.001 2.6 2.0–3.3 <0.001
 60–69 2.4 1.9–3.1 <0.001 2.8 2.1–3.6 <0.001
 70 and above 2.8 2.0–3.8 <0.001 3.9 2.7–5.6 <0.001
Sex
 Male (base) (base)
 Female 1.0 0.8–1.1 0.98 0.8 0.6–1.0 0.07
Education
 No education (base) (base)
 Any education 0.4 0.3–0.5 <0.001 0.6 0.5–0.7 <0.001
Area of residence
 Urban (base) (base)
 Rural 3.1 2.4–4.0 <0.001 1.8 1.4–2.4 <0.001
Occupation
 No outdoor (base) (base)
 Outdoor work 2.3 1.9–2.7 <0.001 1.8 1.5–2.2 <0.001
Smoking status
 Never (base) (base)
 Ever 1.1 0.9–1.3 0.23 0.6 0.4–0.7 <0.001
Alcohol use
 Never (base) (base)
 Ever 2.1 1.8–2.4 <0.001 1.7 1.4–2.0 <0.001
Spectacles use
 No (base) (base)
 Yes 0.6 0.4–0.7 <0.001 0.8 0.6–1.0 0.12
Hypertension
 No (base) (base)
 Yes 0.6 0.4–0.8 <0.01 0.9 0.6–1.2 0.43
Diabetes mellitus
 No (base) (base)
 Yes 0.5 0.3–0.9 0.02 0.8 0.5–1.5 0.56
Table 4. 
 
Summary on Prevalence and Associations With Pterygium in Large Population-Based Cross-Sectional Studies Around the World
Table 4. 
 
Summary on Prevalence and Associations With Pterygium in Large Population-Based Cross-Sectional Studies Around the World
Place Year of Study Age Group, y Sample Size, Examined Prevalence, % Other Variables in the Multivariable Model Significant Risk Factors in the Multivariable Analysis
Shahroud, Iran (Urban)31 2012 >40 5,190 9.4% (either eye); 2.9% (bilateral) Smoking (no association) Older age, male sex, working outdoors, and the lower level of education
Bai minority population in rural Dali, China24 2012 ≥50 2,133 39.0% (either eye) Height, weight, hypertension, diabetes, smoking, alcohol use (ns)* Older age, female sex, older age, lack of formal education, outdoor work
Indigenous Australians within central Australia42 2011 ≥40 1,884 9.3% (either eye) Sex (ns)* Older age
Malay population of Singapore27 2010 40–79 3,280 12.3% (either eye) Smoking (ns),* education (ns)* Older age, male sex, higher systolic blood pressure, outdoor occupation (only for severe pterygium), total cholesterol
Kumejima Island, Japan32 2009 ≥40 3,762 30.8% (either eye); 13.1% (bilateral) Height, weight, blood pressure, use of hat, (all ns)* Older age, male sex, hyperopic refraction, lower IOP, and outdoor occupation
Beijing, China (Rural)22 2008–2009 55–89 37,067 3.8% (either eye) None Older age, male sex, UV radiation living in low latitude
Latinos in Arizona, USA33 2008 ≥40 4,774 16% (either eye) Smoking showed a protective effect, Diabetes (borderline protective), bilateral cataract surgery (protective) Male sex, lower socioeconomic status, lower levels of education
Rural Myanmar14 2007 ≥40 2,076 19.6% (either eye); 8.0% (bilateral) Smoking (ns)*; age (ns)* Outdoor occupation, female sex
Greater Beijing, China29 2007 ≥40 4,439 1.9% (either eye); 2.9% subjects Level of education (ns)* or refractive errors (ns)* Older age, male sex, outdoor work
Southern Harbin population, China43 2006 ≥50 5,057 3.7% (either eye); 2.6% (bilateral) age and education (no association) Male sex, smoking, astigmatism
Tibetans in China35 2006 ≥40 2,229 14.5% (either eye) Sea level Older age, female sex, not using sunglasses, lower level of education, lower socio economic status
Mongolian population at high altitude in Henan County, China36 2006 ≥40 2,112 17.9% (either eye) None Older age, lower levels of education, alcohol intake, lower socio economic status, non use of sunglasses or hat and cataract
North-Western Spain25 2005–2006 ≥40 619 5.9% (either eye) Alcohol intake, smoking, diabetes, iris color, pseudo exfoliation (ns)* Older age, male sex, outdoor occupation
Singapore (included studies multi ethic Asian population)44 2004–2011 ≥40 8,906 10.1% (either eye); 4.6% (Bilateral) Hypertension, total cholesterol (ns)* Older age, male sex, Malay race, poorer education, outdoor occupation
Tehran26 2002 All ages 4,564 1.3% (either eye) Race Older age, Male sex, smoking
Doumen County, southern China (rural)45 2002 ≥50 4,214 33.0% (either eye) Not reported Risk factors not reported
Sumatra, Indonesia (rural)46 2002 ≥21 1,210 10.0% (either eye); 4.1% (bilateral) Smoking protective, sex (ns)* Older age, outdoor occupations
Tamil Nadu, India16 2001–2004 ≥40 7,774 9.5% (either eye) Smoking, alcohol, nature of work, diabetes, hypertension (ns)* UV exposure, rural residence, older age, lower socio economic status
Barbados37 2001 ≥40 2,781 23.4% black, 23.7% mixed, and 10.2% white participants (either eye) Darker skin complexion, use of sunglasses/ prescription glasses were protective factors; Current smoking (protective) African ancestry, older age, lower levels of education, outdoor occupation
Australian state of Victoria 23 2000 ≥40 5,147 2.8% (either eye) None Older age, male sex, rural residence, and life ocular sun exposure
Chinese population in Singapore34 1997–1998 ≥40 1,232 6.9% (either eye); 2.9% (bilateral) None Older age, male sex, outdoor occupations, factory workers
Australia30 1992–1994 ≥49 3,564 7.3% (either eye) Iris color (ns)* Male sex, darker skin color, black hair color
Australia4 1984 (published) All ages 105,113 3.4% (Aborigines); 1.1% (Non aboriginals) (eye or person not reported) Not reported Not reported
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