PREDICTING AND MAPPING THE GEOGRAPHIC DISTRIBUTION OF GLAUCOMA IN THE UNITED STATES: THE ROLE OF SOCIAL DETERMINANTS USING THE ALL OF US DATASET

Abstract

Vision impairment and eye diseases are significant public health concerns in the United States and globally. Glaucoma, a chronic and progressive disease, is one of the leading causes of irreversible blindness worldwide. In the U.S., more than three million individuals are estimated to be affected, with projections indicating a rise as the population ages. While clinical and genetic factors influencing glaucoma onset and progression have been extensively studied, growing evidence suggests that environmental exposures, socioeconomic status, and lifestyle factors also play a crucial role. With disparities in healthcare access and outcomes based on socioeconomic factors, it is crucial to explore how these factors, alongside genetic predispositions, affect glaucoma onset and progression. Addressing these gaps could lead to more targeted interventions, improving outcomes for vulnerable populations. This study aims to bridge this gap by leveraging machine learning techniques to build predictive models for glaucoma risk. By utilizing demographic information and Social Determinant of Health (SDOH) from the All of Us dataset, this research develops a comprehensive framework for glaucoma prediction. These models allow for an improved understanding of how SDOH influences glaucoma risk, helping to inform early detection strategies. The optimized Decision Tree model, tuned with GridSearchCV, was the best-performing model for this prediction task, achieving an accuracy of 67.87%. For class 0 (Non-Glaucoma), it yielded a precision of 0.71, recall of 0.52, and an F1 score of 0.60. For class 1 (Glaucoma), the model achieved a precision of 0.66, recall of 0.81, and an F1 score of 0.73. Feature importance analysis identified age as the most significant predictor, followed by race and the affordability of seeing an eye doctor. In contrast, factors such as affordability of specialist care and copay affordability had minimal impact. The findings from this study have broader implications for enhancing glaucoma risk assessments and healthcare interventions. Additionally, the methodological approach can be applied to other complex diseases, contributing to a more equitable and informed public health approach. By emphasizing social determinants, this research takes a promising step toward reducing the burden of glaucoma and advancing the goals of precision medicine.