Smart Use of State Public Health Data for Health Disparity Assessment

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.

Ge Lin is a professor of epidemiology in the School of Community Health Sciences, University of Nevada, Las Vegas. He is trained in spatial demography and geographic information systems. He is known for his work in spatial modeling, spatial statistics for count data, and spatial disparities in health. His most recent research focuses on the science of public health data. He uses the infrastructure approach to develop integrated data marts, data analysis utilities, and training modules for public health data specialists. He has been supported by several national and state organizations, including the National Institutes of Health. Ming Qu is administrator of the Epidemiology and Informatics Unit, Nebraska Department of Health and Human Services (NEDHHS), which provides statistical, epidemiological, and geographic information services that support public health actions and policies. He previously was an injury epidemiologist and Crash Outcome Data Evaluation System administrator for the NDHHS, where he was instrumental in the development of the Nebraska Injury Surveillance System. Dr. Qu supervises functions of professionals and disease and injury surveillance, data collection and quality assurance, data analysis and reporting, data system development and evaluation. He is the author of numerous papers and book chapters.

Enhanced Public Health Program Collaboration through Data Integration. Common Population-Based Health Disparity Dimensions. Common Public Health Data in a State Health Department. Data Linkage to Gain Additional Information. Indexing Multiple Datasets: A Bottom-Up Approach to Data Warehousing. Using GIS for Data Integration and Surveillance. Methodological Preparation for Health Disparity Assessment. SES Disparities in Hospitalization. Sex Disparities in Hospitalization. Rural-Urban Disparities in Hospitalization. Racial Disparities in Hospitalization. Using Emergency Department Data to Conduct Surveillance. Linking Cancer Registry Data to Hospital Discharge Data. Mother Index and Its Applications. Assessing and Managing Geocoding of Cancer Registry Data. Sex Difference in Stroke Mortality. Model Outcomes of Acute Myocardial Infarction (AMI) by Residence and Hospital Locations. Disparities in Motor Vehicle Crash Injuries: From Race to Neighborhood. Linking Cancer Screening and Cancer Registry Data for Outcome Assessments. Linking Environmental Variables to Parkinson's Disease.