Current Projects at the UGHL
Smartphone-based mobility research
Aging is a global challenge that faces not only the United States but also a lot of other countries. How to maintain the health status of the aging population and how to efficiently utilize healthcare resources have become important tasks for health professionals. Smartphones, which come with GPS, accelerometer, and other inertial sensors, provide an innovative way of tracking the health status of older adults while minimizing their burdens of data collection.
This project adopts the concept of “life-space” to measure older adults’ functional status and mobility. Specifically, it estimates the location, duration, and physical intensity information of activities from the phone collected GPS, Bluetooth, and accelerometer data. Such information is then integrated to generate life-space indicators that are potentially useful in clinical settings. The template of smart-phone data collection and processing will not only directly benefit geriatric studies, but will also facilitate the understanding of individuals’ spatial behaviors in various environmental health studies such as air pollution exposure.
Health consequences of exposure to agricultural pesticides
The extensive application of pesticides in modern agriculture has led to growing concerns about their adverse consequences to the environment and population health. Exposure to agricultural pesticides has been linked with various health problems such as cancer, neuro-degenerative diseases, and human reproductive problems. However, such studies are generally limited by inadequate measurement of pesticide exposure and small patient cohorts.
This project utilizes GIS, remote sensing-derived land use information, and grid-based population data to examine population exposure to agricultural pesticides at high spatial resolutions in Nebraska. Such exposure information is then linked with state level disease registries to explore health consequences of the exposure. The long-term goal of this project is to build an environmental data infrastructure that could facilitate environment-health studies in agricultural states.
Access to Healthcare
Access to healthcare refers to a person’s ease of obtaining healthcare services that can bring about the best possible health outcome. Access to health services can be classified into spatial and non-spatial access based on how the accessibility is influenced by spatial factors (for example, spatial location and travel distance) and non-spatial factors (for example, Socioeconomic Status (SES), health insurance status, and cultural background). This project focuses on utilizing GIS to model access to medical services such as primary care, oncologist care, and emergency general surgery, and to understand the geographic and socio-demographic patterns of the access. The study sites include Texas, Nebraska, California, Utah, and the whole United States.
Health disparity refers to an inequality in which disadvantaged people systematically suffer worse health and less access to health care than advantaged people. Disadvantaged social groups can be characterized by race/ethnicity, SES (e.g., income, education, poverty status), geographic location, age, disability, and sexual orientation. Understanding how health outcomes vary among different social groups is an important prerequisite for reducing these disparities. This project uses GIS and spatial statistical methods to investigate geographic, socioeconomic, and racial/ethnic disparities in cancer outcomes such as late-stage diagnosis, survival, and mortality. It also explores how these disparities are related with access to cancer prevention and treatment.
Neighborhood Contexts of Health Behaviors
Various theories and models, including social cognitive theory and social ecological models, have been adopted to model the individuals’ health behaviors. Generally, these models posit that an individual’s health behavior is influenced by multilevel factors including intrapersonal, interpersonal, neighborhood environmental (both built and social), and policy variables and thus individuals cannot be adequately studied without a consideration of the multiple ecological systems in which they live. My research on the influence of neighborhood environments on health behaviors has focused on studying how an individual’s dynamic exposure to neighborhood factors may influence subsequent behaviors such as smoking. This differs from traditional, static definition of neighborhood contexts by using individual trajectory data and high-resolution GIS data to model individuals’ exposures. In addition, taking advantage of mobile health technologies, I also study how intrapersonal and self-regulatory factors (e.g., stress, negative affect) may mediate or moderate the relationship between neighborhood context and health behaviors.