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      Variations of pterygium prevalence by age, gender and geographic characteristics in China: A systematic review and meta-analysis

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          Abstract

          Background

          Pterygium is a common chronic ophthalmic condition, which may result in significant visual morbidity or lead to blindness in extreme cases. The prevalence of pterygium in China has not been reported at the sub-national level.

          Methods

          In this study, we conducted a systematic review and meta-analysis to estimate the prevalence of pterygium in China. China National Knowledge Infrastructure (CNKI), Wanfang, Chinese Biomedicine Literature Database (CBM-SinoMed), PubMed, Embase and Medline were searched before September 2016. We performed a multilevel mixed-effect meta-regression based on the included studies, our results showed that age, gender and latitude were significantly associated with pterygium prevalence. Based on the final model, the age and gender-specific prevalence of pterygium in 31 Chinese provinces (except Hongkong, Macau and Taiwan) and the whole country was generated.

          Results

          In 2010, the overall prevalence of pterygium in Chinese people aged 15–84 years was 9.84% (95% CI: 6.72–14.14), and the number of pterygium cases in China was 108.65 million (95% CI: 74.23–156.13).

          Conclusions

          In conclusion, the prevalence of pterygium in Chinese population in 2010 was estimated at both the national and provincial levels. The higher burden of pterygium across the country calls for efforts to advocate public health education encouraging people to take appropriate protective measures.

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          Most cited references24

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          Quantifying the impact of between-study heterogeneity in multivariate meta-analyses

          Measures that quantify the impact of heterogeneity in univariate meta-analysis, including the very popular I 2 statistic, are now well established. Multivariate meta-analysis, where studies provide multiple outcomes that are pooled in a single analysis, is also becoming more commonly used. The question of how to quantify heterogeneity in the multivariate setting is therefore raised. It is the univariate R 2 statistic, the ratio of the variance of the estimated treatment effect under the random and fixed effects models, that generalises most naturally, so this statistic provides our basis. This statistic is then used to derive a multivariate analogue of I 2, which we call . We also provide a multivariate H 2 statistic, the ratio of a generalisation of Cochran's heterogeneity statistic and its associated degrees of freedom, with an accompanying generalisation of the usual I 2 statistic, . Our proposed heterogeneity statistics can be used alongside all the usual estimates and inferential procedures used in multivariate meta-analysis. We apply our methods to some real datasets and show how our statistics are equally appropriate in the context of multivariate meta-regression, where study level covariate effects are included in the model. Our heterogeneity statistics may be used when applying any procedure for fitting the multivariate random effects model. Copyright © 2012 John Wiley & Sons, Ltd.
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            Advanced methods in meta-analysis: multivariate approach and meta-regression.

            This tutorial on advanced statistical methods for meta-analysis can be seen as a sequel to the recent Tutorial in Biostatistics on meta-analysis by Normand, which focused on elementary methods. Within the framework of the general linear mixed model using approximate likelihood, we discuss methods to analyse univariate as well as bivariate treatment effects in meta-analyses as well as meta-regression methods. Several extensions of the models are discussed, like exact likelihood, non-normal mixtures and multiple endpoints. We end with a discussion about the use of Bayesian methods in meta-analysis. All methods are illustrated by a meta-analysis concerning the efficacy of BCG vaccine against tuberculosis. All analyses that use approximate likelihood can be carried out by standard software. We demonstrate how the models can be fitted using SAS Proc Mixed. Copyright 2002 John Wiley & Sons, Ltd.
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              Geographical prevalence and risk factors for pterygium: a systematic review and meta-analysis

              Objective Pterygium is considered to be a proliferative overgrowth of bulbar conjunctiva that can induce significant astigmatism and cause visual impairment; this is the first meta-analysis to investigate the pooled prevalence and risk factors for pterygium in the global world. Design A systematic review and meta-analysis of population-based studies. Setting International. Participants A total of 20 studies with 900 545 samples were included. Primary outcome measure The pooled prevalence and risk factors for pterygium. Results 20 studies were included. The pooled prevalence of pterygium was 10.2% (95% CI 6.3% to 16.1%). The pooled prevalence among men was higher than that among women (14.5% vs 13.6%). The proportion of participants with unilateral cases of pterygium was higher than that of participants with bilateral cases of pterygium. We found a trend that the higher pooled prevalence of pterygium was associated with increasing geographical latitude and age in the world. The pooled OR was 2.32 (95% CI 1.66 to 3.23) for the male gender and 1.76 (95% CI 1.55 to 2.00) for outdoor activity, respectively. Conclusions The pooled prevalence of pterygium was relatively high, especially for low latitude regions and the elderly. There were many modifiable risk factors associated with pterygium to which healthcare providers should pay more attention.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                29 March 2017
                2017
                : 12
                : 3
                : e0174587
                Affiliations
                [1 ]Department of Child, Adolescent and Women's Health, School of Public Health, Peking University, Beijing, China
                [2 ]Centre for Population Health Sciences, University of Edinburgh, Edinburgh, Scotland, United Kingdom
                Soochow University Medical College, CHINA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                • Conceptualization: LA PS.

                • Data curation: XC MW.

                • Formal analysis: PS LA.

                • Investigation: XC MW.

                • Methodology: PS.

                • Project administration: PS LA.

                • Resources: XC MW.

                • Software: PS.

                • Supervision: PS LA.

                • Validation: LA PS.

                • Visualization: PS.

                • Writing – original draft: PS.

                • Writing – review & editing: LA XC MW.

                Author information
                http://orcid.org/0000-0003-1353-2092
                Article
                PONE-D-16-50966
                10.1371/journal.pone.0174587
                5371366
                28355301
                84bca306-f0e7-4926-bc0b-ad624ac7603b
                © 2017 Song et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 27 December 2016
                : 1 March 2017
                Page count
                Figures: 3, Tables: 3, Pages: 11
                Funding
                The authors received no specific funding for this work.
                Categories
                Research Article
                People and Places
                Geographical Locations
                Asia
                China
                Earth Sciences
                Geography
                Cartography
                Latitude
                Research and Analysis Methods
                Database and Informatics Methods
                Database Searching
                Research and Analysis Methods
                Mathematical and Statistical Techniques
                Statistical Methods
                Meta-Analysis
                Physical Sciences
                Mathematics
                Statistics (Mathematics)
                Statistical Methods
                Meta-Analysis
                Research and Analysis Methods
                Research Assessment
                Systematic Reviews
                Earth Sciences
                Atmospheric Science
                Climatology
                Insolation
                Medicine and Health Sciences
                Public and Occupational Health
                Medicine and Health Sciences
                Epidemiology
                Spatial Epidemiology
                Custom metadata
                All relevant data are within the Supporting Information file.

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