Promoting Latino Self-Management Through Use of Information Visualizations: A Case Study in New York City.

Social determinants of health, including low health literacy and limited English proficiency, and other factors place Latinos at high risk for health disparities related to chronic diseases. Such diseases require self-management skills as well as medical management. Well-designed visualizations are suitable for visualizing data related to self-management because they can help narrow the comprehension gap between individuals with low and high levels of health literacy by leveraging existing visual analysis skills while reducing the demand on literacy and numeracy competencies. Use of information visualizations also supports a common visual representation across languages to address limited English proficiency. This report illustrates the use of information visualizations for communication related to self-management through research-based case examples and summarizes key lessons from studies with Latinos in New York City.

A semi-automated approach for analyzing collages to inform the design of a family health information management system for Hispanic dementia caregivers.

Culturally- and linguistically-tailored health communication is needed for vulnerable populations to manage their health and the health of their families. This presents a significant design challenge. The use of collages is an increasingly popular technique with the flexibility to capture the needs and experiences of individuals with various cultural and language preferences. Collage analysis has typically remained qualitative in nature. We introduce a novel, objective, semi-automated approach that enhances collage analysis to elucidate pattern differences that may not be detectable by natural perception. We present a case scenario of collage analysis based on the expressed experience and self-management needs of Hispanic dementia caregivers (n = 24). We demonstrate how our innovative approach may reveal cultural differences between language groups that could have otherwise been missed using traditional techniques.