In the United States, where one in every seven residents identifies as an immigrant, this study explores the impact of both individual and neighborhood-level factors on treatment attendance, with a particular emphasis on immigrant status. The aim is to identify the drivers and obstacles contributing to disparities in treatment attendance. Hierarchical Linear Modeling (HLM) was employed to integrate individual and neighborhood-level variables and to assess interactions between immigrant status and neighborhood characteristics. Neighborhood factors like Neighborhood Immigrant Density and Neighborhood Concentrated Disadvantage did not show a significant association with treatment attendance. However, immigrant status was significantly associated with higher attendance rates, a trend that persisted even after controlling for other individual and neighborhood factors. Age, identification as Black, and preference for the English language also emerged as significant influences on attendance. This study utilized secondary data merging publicly available census information with electronic medical records (EMRs) from an integrated care clinic. Data integration was achieved using ArcGIS software. The findings underscore the predictive role of immigrant status in treatment attendance, shedding light on the potential challenges immigrants face. This research improves our understanding of the relationship between treatment attendance and both individual and neighborhood factors, guiding healthcare providers in creating targeted interventions for equal treatment access. Although neighborhood attributes yielded no significant associations, the study underscores the need for further exploration. Ultimately, these findings equip healthcare providers with insights into diverse influences on attendance, contributing to efforts aimed at addressing health disparities.
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