Currently, widespread assumption is that with higher socioeconomic status, you can afford to be healthier. This study aims to use the National Inpatient Sample Database, which holds approximately 10 million records across 5 years (2016-2020), in order to uncover these underlying trends and better understand the true correlation of disease across different socioeconomic groups.

Employing the K-means algorithm, we aim to explain how socioeconomic status, specifically income quartiles, influences the prevalence of various diseases. By clustering diseases according to their popularity across different income quartiles, this research seeks to uncover the delicate ways in which economic factors contribute to health disparities.

Surprisingly, the analysis reveals that high-income groups show more frequent occurrence of certain conditions: Feeding and eating disorders (odds ratio=1.747), Postoperative eye complication (odds ratio=1.79), Female reproductive system cancers (odds ratio=1.914), and breast cancer (odds ratio=2.179). These findings challenge the conventional insight that higher socioeconomic status uniformly correlates with better overall health. Instead, our results suggest a complex match between affluence and the development of specific diseases.

The differences in disease commonness across income quartiles underscore the need for healthcare practitioners and policymakers to reevaluate and redesign targeted interventions and resource allocation strategies. By recognizing that health disparities manifest differently across the socioeconomic spectrum, we can develop more effective and comprehensive approaches to address these inequities. Future research should delve deeper into the underlying mechanisms driving the observed associations between income and disease diagnosis, ultimately informing the development of evidence-based policies and practices that promote health equity for all.