Influence of the Local Food Environment on Hispanics' Perceptions of Healthy Food Access in New York City.

INTRODUCTION: Studies have characterized food environments and documented its impact on access and consumption of healthy foods as well as diet-related health conditions. This study aims to characterize the local food environment in New York City's Washington Heights and Inwood community and to examine its influence on Hispanics' perceptions of healthy food access. METHODS: Person-level local food environments were created by spatially modeling food retailers selling fresh fruits and vegetables or low-fat products within a participant's 400- and 800-m residential radius buffers. Data were analyzed using multivariate binary logistic regression. RESULTS: Fruit/vegetable markets significantly increased participants' odds of perceiving the availability of a large selection as well as the high quality of fresh fruits and vegetables in their neighborhood. Medium-/large-size supermarkets/groceries within 400-m radius significantly increased participants' odds of perceiving the high quality of fresh fruits and vegetables in their neighborhood, whereas meat markets significantly lowered the odds. Fruit/vegetable markets and medium-/large-size supermarkets/groceries significantly increased participants' odds of perceiving the availability of a large selection of low-fat products in their neighborhood. CONCLUSION: Study findings advance our understanding of the relationships between local food environment and perceived healthy food access among urban Hispanics.

A Data Mining Approach for Exploring Correlates of Self-Reported Comparative Physical Activity Levels of Urban Latinos.

We applied data mining techniques to a community-based behavioral dataset to build prediction models to gain insights about physical activity levels as the foundation for future interventions for urban Latinos. Our application of data mining strategies identified environment factors including having a convenient location for physical activity and psychological factors including depression as the strongest correlates of self-reported comparative physical activity among hundreds of variables. The data mining methods were useful to build prediction models to gain insights about perceptions of physical activity behavior as compared to peers.

Network Visualization of Dementia Tweets.

We applied topic modeling techniques to 123,229 Tweets to gain insights about dementia caregiving as the foundation for future interventions. Network visualization elucidated the cultural similarities and differences of topics.

Method for the development of data visualizations for community members with varying levels of health literacy.

Many Americans are challenged by the tasks of understanding and acting upon their own health data. Low levels of health literacy contribute to poor comprehension and undermine the confidence necessary for health self-management. Visualizations are useful for minimizing comprehension gaps when communicating complex quantitative information. The process of developing visualizations that accommodate the needs of individuals with varying levels of health literacy remains undefined. In this paper we provide detailed descriptions of a) an iterative methodological approach to the development of visualizations, b) the resulting types of visualizations and examples thereof, and c) the types of data the visualizations will be used to convey. We briefly describe subsequent phases in which the visualizations will be tested and refined. Web deployment of the final visualizations will support the ethical obligation to return the data to the research participants and community that contributed it.

Using the LOINC Semantic Structure to Integrate Community-based Survey Items into a Concept-based Enterprise Data Dictionary to Support Comparative Effectiveness Research.

In designing informatics infrastructure to support comparative effectiveness research (CER), it is necessary to implement approaches for integrating heterogeneous data sources such as clinical data typically stored in clinical data warehouses and those that are normally stored in separate research databases. One strategy to support this integration is the use of a concept-oriented data dictionary with a set of semantic terminology models. The aim of this paper is to illustrate the use of the semantic structure of Clinical LOINC (Logical Observation Identifiers, Names, and Codes) in integrating community-based survey items into the Medical Entities Dictionary (MED) to support the integration of survey data with clinical data for CER studies.