In the past, disease pattern mapping depended on census tracts based on political units, such as states and counties. However, with the advent of geographic information systems (GIS), researchers can now achieve a new level of precision and flexibility in geographic locating. This emerging technology allows the mapping of many different kinds of ge

Geospatial health data are essential to inform public health and policy. These data can be used to quantify disease burden, understand geographic and temporal patterns, identify risk factors, and measure inequalities. Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny describes spatial and spatio-temporal statistical methods and visualization techniques to analyze georeferenced health data in R. The book covers the following topics: Manipulate and transform point, areal, and raster data, Bayesian hierarchical models for disease mapping using areal and geostatistical data, Fit and interpret spatial and spatio-temporal models with the Integrated Nested Laplace Approximations (INLA) and the Stochastic Partial Differential Equation (SPDE) approaches, Create interactive and static visualizations such as disease maps and time plots, Reproducible R Markdown reports, interactive dashboards, and Shiny web applications that facilitate the communication of insights to collaborators and policy makers. The book features fully reproducible examples of several disease and environmental applications using real-world data such as malaria in The Gambia, cancer in Scotland and USA, and air pollution in Spain. Examples in the book focus on health applications, but the approaches covered are also applicable to other fields that use georeferenced data including epidemiology, ecology, demography or criminology. The book provides clear descriptions of the R code for data importing, manipulation, modeling and visualization, as well as the interpretation of the results. This ensures contents are fully reproducible and accessible for students, researchers and practitioners.

Health services are often fragmented along organizational lines with limited communication among the public health–related programs or organizations, such as mental health, social services, and public health services. This can result in disjointed decision making without necessary data and knowledge, organizational fragmentation, and disparate knowledge development across the full array of public health needs. When new questions or challenges arise that require collaboration, individual public health practitioners (e.g., surveillance specialists and epidemiologists) often do not have the time and energy to spend on them. Smart Use of State Public Health Data for Health Disparity Assessment promotes data integration to aid crosscutting program collaboration. It explains how to maximize the use of various datasets from state health departments for assessing health disparity and for disease prevention. The authors offer practical advice on state public health data use, their strengths and weaknesses, data management insight, and lessons learned. They propose a bottom-up approach for building an integrated public health data warehouse that includes localized public health data. The book is divided into three sections: Section I has seven chapters devoted to knowledge and skill preparations for recognizing disparity issues and integrating and analyzing local public health data. Section II provides a systematic surveillance effort by linking census tract poverty to other health disparity dimensions. Section III provides in-depth studies related to Sections I and II. All data used in the book have been geocoded to the census tract level, making it possible to go more local, even down to the neighborhood level.

Focussing on proven techniques for most real-world data sets, this book presents an overview of the analysis of health data involving a geographic component, in a way that is accessible to any health scientist or student comfortable with large data sets and basic statistics, but not necessarily with any specialized training in geographic information systems (GIS). Providing clear, straightforward explanations with worldwide examples and solutions, the book describes applications of GIS in disaster response.

Disparities in health and health care across racial, ethnic, and socioeconomic backgrounds in the United States are well documented. The reasons for these disparities are, however, not well understood. Current data available on race, ethnicity, SEP, and accumulation and language use are severely limited. The report examines data collection and reporting systems relating to the collection of data on race, ethnicity, and socioeconomic position and offers recommendations.

This clear and accessible text helps public health students and officials gain a solid understanding of geographic information systems technology. Using examples drawn from public health practice, the author shows how to best harness the opportunities of this exciting technological development.

In this book, Dr. Richard Allen Williams has assembled the very best scholars on healthcare disparities to raise the public consciousness of this issue. Arranged into discrete categories, this volume contains comprehensive coverage, both historical and current, of the healthcare disparity crisis currently plaguing our country in hopes of leading us all to a brighter future.