Prevalence and risk factors of pterygium: a systematic review and meta-analysis

Abstract

The present study was conducted to determine the global prevalence and risk factors for pterygium. Three thousand two hundred fifty-five articles were identified, of which 68 articles with a total of 415,911 participants from 24 countries were included in the final analysis. The prevalence of pterygium in the total population was 12% (95% confidence interval [CI] 11–14%). The lowest and highest prevalence rates were, respectively, 3% (95% CI 0.0–9%) in the 10- to 20-year-age group and 19.5% (95% CI 14.3–24.8%) in those over 80 years. The prevalence was 13% (95% CI 11–15%) in men and 12% (95% CI 9–13%) in women. The odds ratio for men was 1.30 (95% CI 1.14–1.45). The lowest prevalence of pterygium was reported in a clinic-based study in Saudi Arabia (0.07%) and the highest prevalence was in China (53%). The odds were 1.24 (95% CI 1.11–1.36) for sunlight exposure over 5 hours, 0.84 (95% CI 0.74–0.94) for smoking, 1.45 (95% CI 1.33–1.57) for living in rural areas, 1.17 (95% CI 1.03–1.32) for alcohol consumption, 1.46 (95% CI 1.36–1.55) for outdoor occupations, and 0.47 (95% CI 0.19–0.57) for use of sunglasses. This is the second meta-analysis arriving at an estimate of 12% for the prevalence of pterygium. According to our results, pterygium risk factors fall in 3 categories: demographic, environmental, and lifestyle factors. Older age, male gender, outdoors occupation, and living in rural environments are the leading demographic risk factors for the development of pterygium. Exposure to sunlight is the most common environmental risk factor, and the results of this study provide a more exact and reliable value of the effect of sunlight exposure. The use of sunglasses and cigarette smoking are protective factors, and the significant effect of alcohol consumption is related to lifestyle factors.

Introduction

Pterygium, one of the most common eye disorders, is the growth of subepithelial fibrovascular tissue starting from the bulbar conjunctiva onto the cornea.⁵ Esthetic concerns, irregular astigmatism, and decreased vision are important issues associated with this disease.14, 42 Long-term exposure to ultraviolet light from the sun is the most important risk factor for pterygium4, 24, 56, 65; however, other factors such as low income, smoking,⁵⁶ region of residence (urban and rural), older age,9, 70 male gender, latitude,³⁴ and farming⁴¹ are other possible risk factors. Because of variations in the studied age groups and study settings (population-based vs clinic-based studies), the prevalence of pterygium in surveys conducted in different parts of the world ranges from 1.3% in the Tehran Eye Study¹⁵ to 53% in Taiwan.⁶³ Despite the various studies performed to determine the type and amount of the effect of each causative factor, estimates differ among studies and in some cases arrive at opposite results. The purpose of the present meta-analysis is to provide an accurate estimate of the prevalence of pterygium and to identify its determinants using a combination of all clinical and population-based studies from around the world.

All stages of this study were performed in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The criteria for including studies into the meta-analysis were: being designed as an observational epidemiological study such as cross-sectional, case-control, cohort, population based or clinic based; providing a transparent report of the prevalence and odds ratio (OR) for obtaining combined indices of prevalence and OR. Among studies conducted on the same population, the one with higher quality was included. Articles that did not meet any one of these inclusion criteria were excluded.

The outcome measures included the prevalence of pterygium and its risk factors such as alcohol consumption, smoking, outdoor occupation, region of residence, sunlight exposure, use of sunglasses and hats, age, and gender. To determine the prevalence of pterygium, we included all studies reporting the overall prevalence of pterygium or broken down by age and gender. Studies that defined exposure to alcohol and smoking as consumers and nonconsumers were included in the final analysis. In addition, given the small number of studies that defined smoking and alcohol as current users, these were allocated to the consumer group. We used indoor and outdoor categories to determine the effect of occupation environment. Region of residence included urban and rural categories. Sunlight exposure, according to extracted data, was classified into 2 groups of under and over 5 hours of exposure. Age was grouped into 10-year intervals.

To ensure the proper selection of articles in terms of their pertinence to the research subject and meeting the inclusion criteria, 2 researchers (E.H. and M.S.) independently reviewed the articles. The names of the authors, journal title, and the results were not concealed from the reviewers. If there was any dispute, a decision was made through negotiation or consultation with a third person. The Kappa percentage for the interreviewer agreement was 74.5%. The variables taken in consideration in this review included the name of the first author, year of publication, country and city of the study, the mean age of the studied sample, gender, study design, type of population studied, region of residence (village, town), occupation, alcohol consumption, smoking, level of exposure to sunlight, and wearing hats and sunglasses in the sun.

We used the STROBE checklist for cross-sectional, case-control, and cohort studies to assess the quality of the studies in terms of methodology and reporting.

Statistical heterogeneity of the studies was assessed using the chi-square test at a significance level of 5%. To quantify heterogeneity among results, we used the I-square statistic and Higgins classification, based on which, an I-square value more than 75% can be considered as heterogeneity.

The summary measures of this study included the prevalence and the OR of developing pterygium in the presence of risk factors as an index of correlation; these were calculated along with 95% confidence intervals (CIs) for a 2-tailed distribution.

Data analysis was done using the Stata (version 11, StataCorp, College Station, Texas) and applying the random effects model at a 95% confidence level.