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Pterygium and Associated Risk Factors Among Ophthalmology Outpatient Attendees in South India: A Cross-Sectional Study
*Corresponding author: Ajay Mathews, Department of Ophthalmology, Govt Medical College, Guntur, Andhra Pradesh, India. ajaym59224@gmail.com
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Received: ,
Accepted: ,
How to cite this article: Mathews A. Pterygium and Associated Risk Factors Among Ophthalmology Outpatient Attendees in South India: A Cross-Sectional Study. Glob J Guntur Med Coll. 2026;1:2-7 doi: 10.25259/GJGMC_2_2025
Abstract
Objectives:
To determine the hospital-based prevalence of pterygium and associated risk factors among patients attending the ophthalmology outpatient department (OPD) of a tertiary care center in South India.
Material and Methods:
This prospective, cross-sectional observational study was conducted over 6 months (January–June 2024) in the OPD of a tertiary care center. A total of 2,475 patients were enrolled. Each underwent a detailed ocular examination, and data regarding demographics, occupational exposure, and systemic comorbidities were collected using a structured questionnaire. Statistical analysis included Chi-square tests for association and odds ratio (OR) calculations with 95% confidence intervals (CI). Multivariate binary logistic regression was performed to identify independent predictors of pterygium, and adjusted OR with 95% CI were reported.
Results:
Pterygium was identified in 271 of the 2,475 participants, yielding a prevalence of 10.95%. A higher prevalence was observed among individuals residing in rural areas (12.8%), those with outdoor occupations (12.9%), and those with systemic conditions such as hypertension (14.6%) and diabetes mellitus (14.0%). Statistically significant associations were found between pterygium and rural residence (p < 0.0001), outdoor occupation (p < 0.0001), hypertension (p < 0.0001), and diabetes (p = 0.0003). Systemic factors such as hypertension and diabetes demonstrated independent, but modest, associations, and the cross-sectional nature of the study warrants cautious interpretation of causality. Gender showed no significant association (p = 0.52). The strongest risk factors were rural residence (OR: 2.11, 95% CI: 1.53–2.92) and outdoor occupational exposure (OR: 1.88, 95% CI: 1.40–2.53). On multivariate analysis, rural residence (OR 2.05; 95% CI 1.48–2.84), outdoor occupation (OR 1.87; 95% CI 1.39–2.52), hypertension (OR 1.69; 95% CI 1.31–2.19) and diabetes mellitus (OR 1.57; 95% CI 1.21–2.03) remained independently associated with pterygium (all p < 0.001). Age (p = 0.838) and sex (p = 0.563) were not significant in the adjusted model.
Conclusion:
The study confirms a prevalence of pterygium (10.95%) in this regional hospital cohort, with rural residency and outdoor work emerging as significant contributors. Hypertension and diabetes mellitus showed a moderate association, but their clinical relevance warrants cautious interpretation.
Keywords
Epidemiology
Prevalence
Pterygium
Risk factors
INTRODUCTION
Pterygium is a frequently encountered ocular surface condition characterized by a triangular, fibrovascular overgrowth of conjunctival tissue extending onto the corneal surface. While the precise mechanisms behind its development are not fully elucidated, chronic exposure to ultraviolet (UV) radiation is widely acknowledged as a major etiological factor. Clinically, pterygium may cause visual impairment, induce astigmatism, and result in ocular irritation and aesthetic concerns, all of which can negatively impact a patient’s quality of life.
Its prevalence exhibits significant geographic variation, influenced by environmental and climatic factors. A comprehensive meta-analysis estimated the global pooled prevalence to be around 10.2%, with notably higher rates in equatorial regions.1 In the Indian context, prevalence figures differ across studies. For instance, the ICMR-EYE SEE study reported a prevalence of 11.7% among individuals aged 40 and above,2 while Marmamula et al. documented a prevalence of 11.1% in rural areas of Andhra Pradesh.3 Several investigations have also identified advancing age, outdoor work, and rural residence as key risk factors contributing to the onset of pterygium.4,5
Assessing region-specific prevalence and associated risk factors is vital for formulating effective public health interventions, particularly in tropical countries like India, where outdoor exposure is common. Despite prior research, recent data from specific regional cohorts, particularly those attending tertiary care institutions, remain sparse. As tertiary centers serve heterogeneous populations of urban and rural patients, understanding pterygium patterns in this setting provides valuable insights into broader regional trends.
This study aims to evaluate the prevalence of pterygium and associated risk factors among individuals attending the ophthalmology outpatient department (OPD) of a tertiary care hospital.
MATERIAL & METHODS
Study design and setting
This prospective, cross-sectional observational study was conducted at the OPD of a tertiary healthcare center over 6 months, from January 2024 to June 2024.
Study population and sampling
Participants were recruited from the OPD on pre-specified investigator clinic days over 6 months. There were six OPD recruitment days per month (4 Mondays and 2 Fridays), totaling 36 OPD days across the study period. On each recruitment day, all eligible consecutive OPD attendees were assessed and enrolled after consent until the OPD concluded. The final denominator comprised 2,475 unique OPD attendees enrolled during these recruitment days. Prior sample size calculation was not performed because this was an OPD-based consecutive sample, and all eligible participants attending the investigator’s pre-specified OPD days during the study period were enrolled. Exclusion criteria included recurrent pterygium (history of prior pterygium excision in the same eye) and participants who did not provide consent/with incomplete data.
Data collection
Each participant underwent a comprehensive ocular examination, including slit lamp bio-microscopy. Pterygium was recorded as a binary clinical variable (present/absent) on slit-lamp examination. Grading of pterygium severity and laterality was not systematically recorded and therefore was not analyzed. Demographic variables and risk factors were recorded using a structured proforma. Family history of pterygium among first-degree relatives was not included in the pro forma and was therefore not evaluated.
Ethical approval
The study was approved by the Institutional Ethics Committee prior to commencement. Informed consent was obtained from all participants.
Statistical analysis
Statistical analysis was performed using the statistical package for the social sciences (SPSS) version 25 for descriptive statistics and regression analysis.
Continuous variables were summarized as mean ± standard deviation, and categorical variables as frequencies and percentages. Associations between categorical variables and pterygium were assessed using the Chi-square test. Unadjusted odds ratios (OR) with 95% confidence intervals (CI) were calculated for key risk factors.
Multivariate binary logistic regression was performed with pterygium status as the dependent variable and age, sex, residence (rural/urban), occupation (outdoor/indoor), hypertension, and diabetes mellitus as covariates; adjusted OR with 95% CI were reported. A two-sided p value <0.05 was considered statistically significant.
RESULTS
A total of 2,475 individuals were screened in the study, out of which 271 cases of pterygium were identified, yielding an overall prevalence of 10.95%. The mean age of participants was 58.42 ± 8.73 years. Among those diagnosed with pterygium, the mean age was slightly higher at 59.77 ± 8.91 years, compared to 58.26 ± 8.68 years among non-pterygium participants.
Age-wise distribution showed the highest number of pterygium cases in the 48-year age group, with 7 out of 80 individuals (8.75%) affected. Prevalence increased progressively with age, with the majority of cases observed between 50 and 65 years.
Prevalence was higher in males (11.38%, 132/1160) than in females (10.57%, 139/1315). Among those residing in rural areas, 223 out of 1,738 individuals (12.83%) were affected, compared with 48 out of 737 individuals (6.51%) in urban areas. A marked difference was observed based on occupational exposure, with outdoor workers having a prevalence of 12.91% (208/1611) and indoor workers at 7.29% (63/864). Among those with hypertension, 132 of 903 participants (14.62%) had pterygium, versus 139 of 1,572 (8.84%) in non-hypertensives. Similarly, diabetic individuals exhibited a prevalence of 13.95% (125/896), higher than 9.25% (146/1,579) seen in non-diabetics.
Population Chi-square analysis revealed statistically significant associations between pterygium and residence in rural areas (p < 0.0001), outdoor occupational exposure (p < 0.0001), hypertension (p < 0.0001), and diabetes mellitus (p = 0.0004). No significant association with gender was observed (p = 0.5628).
In contrast, although systemic conditions like hypertension and diabetes also showed statistical significance, the differences in absolute prevalence were modest. The prevalence of pterygium was 14.62% among hypertensives versus 8.84% among non-hypertensives, and 13.95% in people with diabetes versus 9.25% in non-diabetics. These small differences suggest that the associations with systemic comorbidities may reflect background population prevalence rather than strong causal links.
OR calculated using verified 2×2 contingency tables from the dataset. Group 1 denotes the higher-risk category for pterygium. CI = Confidence Interval at 95% level. OR > 1 implies greater odds of pterygium in Group 1.
Unadjusted OR are presented in [Table 3].
| Category | Ptery-gium present (n) | Ptery-gium present (%) | Pterygium absent (n) | Pterygium absent (%) | Total |
|---|---|---|---|---|---|
| Male | 132 | 11.38 | 1028 | 88.62 | 1160 |
| Female | 139 | 10.57 | 1176 | 89.43 | 1315 |
| Rural | 223 | 12.83 | 1515 | 87.17 | 1738 |
| Urban | 48 | 6.51 | 689 | 93.49 | 737 |
| Outdoor | 208 | 12.91 | 1403 | 87.09 | 1611 |
| Indoor | 63 | 7.29 | 801 | 92.71 | 864 |
| Hypertensive | 132 | 14.62 | 771 | 85.38 | 903 |
| Non-hypertensive | 139 | 8.84 | 1433 | 91.16 | 1572 |
| Diabetic | 125 | 13.95 | 771 | 86.05 | 896 |
| Non-diabetic | 146 | 9.25 | 1433 | 90.75 | 1579 |
| Risk factor | Category | Pterygium prevalence (n, %) | Chi-square | p value |
|---|---|---|---|---|
| Sex | Male | 132 (11.38%) | 0.33 | 0.563 |
| Female | 139 (10.57%) | |||
| Rural/urban | Rural | 223 (12.83%) | 20.54 | <0.001 |
| Urban | 48 (6.51%) | |||
| Outdoor/indoor | Outdoor | 208 (12.91%) | 17.64 | <0.001 |
| Indoor | 63 (7.29%) | |||
| Hypertension | Present | 132 (14.62%) | 19.03 | <0.001 |
| Absent | 139 (8.84%) | |||
| Diabetes mellitus | Present | 125 (13.95%) | 12.5 | <0.001 |
| Absent | 146 (9.25%) |
Chi-square test is used to assess the association between categorical variables and pterygium; a p value < 0.05 indicates a statistically significant association.
| Risk factor | Group 1 | Group 2 | Odds ratio | 95% CI | p value |
|---|---|---|---|---|---|
| Sex | Male | Female | 1.09 | 0.84 – 1.4 | 0.520 |
| Rural/urban | Rural | Urban | 2.11 | 1.53 – 2.92 | <0.001 |
| Outdoor/indoor | Outdoor | Indoor | 1.88 | 1.4 – 2.53 | <0.001 |
| Htn | Yes | No | 1.77 | 1.37 – 2.27 | <0.001 |
| Dm | Yes | No | 1.59 | 1.23 – 2.05 | <0.001 |
CI: confidence intervals, OR: odds ratio, p < 0.05 was considered statistically significant
| Predictor | Adjusted or (OR) | 95% CI | p < 0.05 was |
|---|---|---|---|
| Age (per 1 year increase) | 1.00 | 0.99 - 1.02 | 0.838 |
| Sex (male vs female) | 1.08 | 0.83 – 1.39 | 0.563 |
| Residence (rural vs urban) | 2.05 | 1.48 – 2.84 | < 0.001 |
| Occupation (outdoor vs indoor) | 1.87 | 1.39 – 2.52 | < 0.001 |
| Hypertension (yes vs no) | 1.69 | 1.31 – 2.19 | < 0.001 |
| Diabetes mellitus yes vs no) | 1.57 | 1.21 – 2.03 | < 0.001 |
Dependent variable: pterygium (yes/no). Reference categories: female, urban residence, indoor occupation, no hypertension, no diabetes mellitus. OR = adjusted odds ratio; CI = confidence interval. Model estimated using N = 2475. p < 0.05 was considered statistically significant
Multivariate logistic regression analysis
Multivariate binary logistic regression (N = 2475) showed that rural residence (OR) 2.05; 95% CI 1.48–2.84; p < 0.001), outdoor occupation OR 1.87; 95% CI 1.39–2.52; p < 0.001), hypertension OR 1.69; 95% CI 1.31–2.19; P < 0.001), and diabetes mellitus OR 1.57; 95% CI 1.21–2.03; p < 0.001) remained independently associated with pterygium. Age OR 1.00; 95% CI 0.99–1.02; p < 0.05 was considered statistically significant = 0.838) and sex (male vs female: OR 1.08; 95% CI 0.83–1.39; p = 0.563) were not statistically significant in the adjusted model. Model fit indices were AIC 1653 and McFadden’s R2 = 0.0416.
DISCUSSION
The present study found a prevalence of pterygium to be 10.95% in the study population, which falls within the global and regional ranges reported in the literature. A large meta-analysis by Liu et al. reported pooled prevalence estimates ranging from 7.3% to 12.0% globally, with significantly higher rates noted in tropical regions and among populations with prolonged exposure to sunlight and outdoor conditions.1 The ICMR-EYE SEE Study, a nationally representative survey from India, reported a prevalence of 13.2%, with higher occurrence in rural populations and among males.2 When compared with other Indian studies, Marmamula et al. (Andhra Pradesh Eye Disease Study) reported a prevalence of 11.7% and highlighted the association of rural residence and outdoor work with increased risk of pterygium.3 Our study’s similar prevalence with this study may reflect regional similarity in UV exposure, occupational patterns, and environmental conditions. The Tehran Eye Study by Hashemi et al. showed a 5-year incidence rate of 6.8% and identified age and outdoor work as key determinants.4 In contrast, Paula et al. found a remarkably higher prevalence of 58.8% in indigenous populations of the Brazilian Amazon, likely attributable to extreme UV exposure and lack of ocular protection.5 The Singapore Epidemiology of Eye Diseases Study also confirmed higher prevalence among males and older individuals, with significant variations across ethnicities.6 These comparative findings reinforce the multifactorial nature of pterygium and the influence of geographic, occupational, and demographic factors on its prevalence.
Association of risk factors with pterygium
In the present study, rural residence was significantly associated with pterygium, with a prevalence of 12.83% in rural areas compared to 6.51% in urban areas (p < 0.05 was considered statistically significant < 0.0001). This finding aligns with national and international studies reporting increased exposure to outdoor environments, wind, and UV radiation as contributing factors among rural populations.2,3
Similarly, outdoor occupational exposure was significantly associated with higher pterygium prevalence, 12.91% vs. 7.29% for indoor occupations (p < 0.05 was considered statistically significant < 0.0001), reinforcing the role of UV light and chronic ocular surface irritation in its pathogenesis.
A statistically significant association was found between systemic hypertension and pterygium (14.62% vs. 8.84%, p < 0.05 was considered statistically significant < 0.0001), and between diabetes mellitus and pterygium (13.95% vs. 9.25%, p = 0.0003). However, the magnitude of difference was relatively small, and these associations may reflect background comorbidity patterns in the population rather than a causal relationship. While these findings have been echoed in the ICMR-EYE SEE study, the clinical relevance of such associations remains uncertain.2
Sex was not significantly associated with pterygium in our study (11.38% in males vs. 10.57% in females, p < 0.05 was considered statistically significant = 0.5628), which contrasts with several studies that observed a male predominance.2,6
This discrepancy may arise from regional lifestyle differences, sun protection habits, or sample variation.
These findings collectively emphasize that while systemic and demographic factors may contribute to pterygium prevalence, environmental and occupational exposure remains the most consistent and strongly associated risk factor across populations.
Strength of association
The strength of association between individual risk factors and pterygium was further evaluated using odds ratios (OR) with 95% confidence intervals (CI). Rural residence was associated with more than twice the odds of having pterygium compared to urban residence (OR: 2.11; 95% CI: 1.53–2.92; p < 0.0001), and outdoor occupation showed similarly increased odds (OR: 1.88; 95% CI: 1.40–2.53; p < 0.0001). These findings are consistent with established evidence indicating that UV exposure and outdoor work are major modifiable risk factors.
Systemic comorbidities, although statistically significant, showed comparatively lower unadjusted odds ratios. Hypertensive individuals had 1.77 times the odds of developing pterygium compared to non-hypertensives (95% CI: 1.37–2.27; p < 0.0001), and diabetic individuals had 1.59 times the odds of developing pterygium compared to non-diabetics (95% CI: 1.23–2.05; p = 0.0003). These effect sizes were smaller than those observed for rural residence and outdoor occupation, indicating comparatively modest associations. Sex did not show a statistically significant association, with an OR of 1.09 for males compared to females (95% CI: 0.84–1.40; p = 0.5201).
Importantly, multivariate binary logistic regression confirmed that several factors remained independently associated with pterygium after adjustment for age and sex. Rural residence OR 2.05; 95% CI 1.48–2.84; p < 0.001) and outdoor occupation OR 1.87; 95% CI 1.39–2.52; p < 0.001) retained the strongest associations. Hypertension OR 1.69; 95% CI 1.31–2.19; p < 0.001) and diabetes mellitus OR 1.57; 95% CI 1.21–2.03; p < 0.001) also remained significant in the adjusted model, while age (p = 0.838) and sex (p = 0.563) were not significant predictors. The McFadden’s R2 (0.0416) suggests that additional unmeasured environmental and behavioral factors (e.g., cumulative UV exposure, protective measures, ocular surface irritation) likely contribute to disease risk beyond the variables included in the model.
In summary, rural residence and outdoor occupation emerged as the most consistent and strongest predictors in this cohort, both in unadjusted and adjusted analyses. Systemic factors such as hypertension and diabetes demonstrated independent, but modest, associations, and the cross-sectional nature of the study warrants cautious interpretation of causality.
Interpretation and clinical relevance
The clinical implications of these findings are significant, particularly for community-based eye health interventions. The robust associations observed between rural residence and outdoor occupations highlight the continued need for preventive measures, such as protective eyewear, health education on UV exposure, and policy-level emphasis on occupational ocular safety. These measures could be especially beneficial in regions where agriculture and outdoor labor are prevalent.
Compared with global data, these study findings align with those from tropical and high-UV-index countries, further substantiating the environmental etiology of pterygium. The stark contrast with populations in temperate regions or urban industrialized settings, where pterygium is considerably less common, underlines the modifiable nature of this condition.
Although systemic comorbidities such as hypertension and diabetes showed statistically significant associations, the modest differences in prevalence and odds suggest a need for cautious interpretation. These variables likely represent background systemic health patterns rather than true etiologic contributors to pterygium formation. Further longitudinal and mechanistic studies would be essential to clarify any underlying inflammatory or microvascular pathways. Since pterygium grading, laterality, and family history were not captured, severity-wise and laterality-wise associations could not be examined.
Overall, the study adds to the growing evidence supporting the role of environmental and occupational factors in the development of pterygium, especially in rural, tropical regions of the Indian subcontinent.
LIMITATIONS
The study has certain limitations. As a hospital based cross-sectional study conducted at a tertiary care center, the findings may not be fully generalizable to the wider community. The cross-sectional design limits causal interference between risk factors and pterygium. Exposure variables such as occupation and residence were self reported, introducing the possibility of recall bias. Grading and morphological characteristics of pterygium were not analyzed in detail which limits dose - response relationships between risk factors and disease progression.
Despite these limitations, the study provides valuable epidemiological data and identifies independent predictors of pterygium in the studied population.
CONCLUSION
Pterygium remains a prevalent ocular surface disorder with significant implications for visual health and quality of life in tropical, rural populations. This study confirms a prevalence of 10.95% and identifies rural residence and outdoor occupation as key contributors, each showing strong and statistically significant associations with disease occurrence. These findings emphasize the modifiable nature of pterygium and offer clear guidance for public health interventions focused on environmental protection and education.
While hypertension and diabetes mellitus also showed statistically significant associations with pterygium, these associations were modest in magnitude compared to rural residence and outdoor occupation and should be interpreted cautiously. Notably, both conditions remained independently associated with pterygium in multivariate analysis, after adjustment for age, sex, residence, and occupation, though the study design does not allow for causal inference. Gender was not associated with significant differences in prevalence in this cohort.
The study highlights the importance of tailored preventive strategies in vulnerable populations and underscores the role of early detection and behavioral modification in reducing the burden of pterygium. Integration of these insights into community eye care programs can lead to more effective disease control and improved ocular health outcomes.
Acknowledgement
I would like to thank the Department of Ophthalmology and all the patients who participated in this study. I thank the statistician for assistance with data analysis.
Ethical approval:
The research/study was approved by the Institutional Review Board at Guntur Medical College and Govt General Hospital, number GMC/IEC/28/2023, dated 06/01/2024.
Declaration of patient consent:
The authors certify that they have obtained all appropriate patient consent forms. In the form, the patient has given consent for clinical information to be reported in the journal. The patient understand that the patient’s names and initials will not be published and due efforts will be made to conceal their identity, but anonymity cannot be guaranteed.
Conflicts of interest:
There are no conflicts of interest.
Use of artificial intelligence (AI)-assisted technology for manuscript preparation:
The authors confirm that there was no use of artificial intelligence (AI)-assisted technology for assisting in the writing or editing of the manuscript, and no images were manipulated using AI.
Financial support and sponsorship: Nil.
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