Spatial Health Inequalities

Adapting GIS Tools and Data Analysis

Esra Ozdenerol

• Publisher: CRC Press
• Year: 2016
• ISBN:
  9781315354590
• Edition: 1
• Topic: Medicine
• Subtopic: Public Health, Administration & Care

The neighborhoods and the biophysical, political, and cultural environments all play a key role in affecting health outcomes of individuals. Unequal spatial distribution of resources such as clinics, hospitals, public transportation, fresh food markets, and schools could make some communities as a whole more vulnerable and less resilient to adverse health effects. This somber reality suggests that it is rather the question of "who you are depends upon where you are" and the fact that health inequality is both a people and a place concern. That is why health inequality needs to be investigated in a spatial setting to deepen our understanding of why and how some geographical areas experience poorer health than others. This book introduces how spatial context shapes health inequalities.

Spatial Health Inequalities: Adapting GIS Tools and Data Analysis demonstrates the spatial health inequalities in six most important topics in environmental and public health, including food insecurity, birth health outcomes, infectious diseases, children's lead poisoning, chronic diseases, and health care access. These are the topics that the author has done extensive research on and provides a detailed description of the topic from a global perspective. Each chapter identifies relevant data and data sources, discusses key literature on appropriate techniques, and then illustrates with real data with mapping and GIS techniques. This is a unique book for students, geographers, clinicians, health and research professionals and community members interested in applying GIS and spatial analysis to the study of health inequalities.

1

New Emerging Trends in Health Information Technology

Given the current emphasis of health-care reform, in the United States and globally, the purpose of this chapter is to provide an overview of the current role of geographic information systems (GIS) in the context of strategic health planning. First, recent changes in the structure of the health-care system and the current role of GIS are described. Second, the patient-centric and population-oriented uses of GIS are reviewed. Last, disease and bioterrorism (BT) surveillance, crisis, and disaster management, as well as strategies for use of interactive maps, are discussed. Conclusions present a research agenda toward a GIS-supported health information technology (HIT) and how we can move beyond the map to view and explore health disparities.

Current Role of GIS in the Health-Care System

There are two big changes taking place in the health-care system in the United States. One of them is controlling the cost of health care by improving the clinical care delivery system through the implementation of an integrated, computer-based, national health-care infrastructure based on an interoperable electronic health record (EHR) system. EHR is the leading record in electronic health-care environment. Documents of the EHR include discharge summaries, radiological images, or lab results, and these documents are available to health-care professionals via special secure web services and/or open-source software. They are built to share information with other health-care providers, such as laboratories and specialists, so they contain information from all the clinicians involved in the patient care (Garrett and Seidman 2014). A full clinical integration (i.e., clinical, acute, and ambulatory care settings) has yet to be achieved, but progress toward integration can be assessed (ONC Data Brief 25 2015).

Second is the recognition of the importance of place in primary care research and practice. Primary care is in the front lines of providing medical care. As healthy lifestyles and chronic disease prevention take a more pivotal role in the U.S. health-care system, the opportunities for integrating the knowledge of individuals and their families’ environment into health assessments, decision-making, and treatment have become obvious (Berke 2010). Obesity, diabetes, and heart disease partly originate in busy modern schedules, in the unhealthy food choices available in the stores, and even in the way neighborhoods are designed. Additionally, depression, anxiety, and high blood pressure can arise from chronically stressful conditions at work and home. Doctors, nurses, or other clinicians know that asthma can start in the air around us or from the mold in the walls of our homes. And, just as important, caregivers want to understand how to translate this knowledge into meaningful action. With the efforts to improve outcomes of their patients, primary physicians, for the first time, are really having the chance to broadly explore patient-centered information processing using EHR system.

The Health Information Technology for Economic and Clinical Health (HITECH) Act, a provision of the American Recovery and Reinvestment Act of 2009, was created to stimulate adoption of EHR and support HIT in the United States (Blumenthal 2010). HITECH Act defines HIT as “hardware, software, integrated technologies or related licenses, intellectual property, upgrades, or packaged solutions sold as services that are designed for or support the use by health-care entities or patients for the electronic creation, maintenance, access, or exchange of health information.” HITECH Act sets meaningful use of interoperable EHR adoption in the health-care system as a critical national goal (Blumenthal and Tavenner 2010). The anticipated goal is not adoption alone but “meaningful use” of EHRs by providers to achieve significant improvements in care (Kareo 2014). Access to EHR by patients themselves is an explicit expectation in the definition of “meaningful use.” Health information exchange (HIE) has emerged as a core capability for hospitals and physicians to achieve “meaningful use” and receive stimulus funding. Health-care vendors are pushing HIE as a way to allow EHR systems to pull disparate data and function on a more interoperable level (Kareo 2014).

The national preoccupation with the cost of clinical and acute care (reducing admissions and adverse events) evident in the lead-up to the daunting task of implementing the national EHR system is well founded, and changes in the health system’s pricing, labor, processes, and technology are essential and urgent. However, improving the clinical care delivery system’s efficiency and effectiveness will probably have only modest effects on the health of the population. An integration and building of synergy between the best evidence-based interventions at the population level and in the clinical setting are very much needed. Consequently, a GIS with more complete, useful, timely (e.g., real time), and geographically pertinent information can come as a complementary ingredient to HIT. A GIS is a powerful computer mapping and analysis technology that allows large quantities of information to be viewed and analyzed within a geographic context (Miranda et al. 2002). The delineation point between information systems and geographic information systems has been the visualization of data, particularly revealing patterns and trends that are not readily apparent in traditional databases. GIS is far more powerful than its basic mapping abilities, better described as a spatial analytical system that combines computer mapping capability with additional database management and data analysis tools (McLafferty 2003).

Currently, maintaining the integrity and accessibility of EHR on a GIS or GIS-supported HIT is unheard off. GIS has not been incorporated into centralized management of health data. The adoption of EHR is yet to be incorporated in the nationwide health-care system, and the same is true for monitoring and data analysis tools. The development and utilization of effective and real-time efficient EHRs combined with spatial databases can be used for the improvement of the health status and the follow-up of related health parameters. Extending GIS into EHR provides real-time health-care service to patients and allows health-care professionals to explore, identify, and implement preventive measures to inhibit the spread of diseases. The geographic clusters of chronic diseases (i.e., diabetes) could be identified from primary care networks. Public health departments could conduct large-scale real-time public health surveillance. The disease registry data are used for planning individual patient care looking at the different factors within the environment that can affect health-care provision and also disease causes and conducting population-based care. When conducting population-based care, GIS tools expand our understanding of disparities in health outcomes within a community.

Chronic disease management programs and pilot models continue to be tested in the United States to improve the quality of care for patients with chronic conditions. The Chronic Care Model (CCM) serves as the paradigm by which the chronic disease management programs are structured (CDC 2014). The CCM was developed to identify the essential elements of a healthcare system that encourage high-quality chronic disease care. These elements include the community, the health system, self-management support, delivery system design, decision support, and clinical information systems. If coupled with GIS capabilities, clinical information systems are likely to become important and valuable contributors to multiple regional data systems targeting improvement in community health. As data communication related to public health continues to develop, more patient-centric clinical settings will become direct contributors to public health databases through electronic data transfer. The success of the information infrastructure at the individual and population levels will enable information flow between different stakeholders in health care to maximize the utility of the information.

Patient-Centric Applications of GIS

Patient-centric GIS approaches focus on the development of information around the patient, in contrast to the approach used by the computerized medical record industry that builds information around each episode or encounter a patient has with the health-care system. The patient-centric uses of GIS are defined as studies highly utilizing patient data from an EHR and/or studies that navigate to relevant nonhealth data through realtime GIS that supports clinical mode of operations, interactive and integrative with other information technologies in health care. The patient-centric uses of GIS highly utilize EHR. EHR provides a rich source of electronically accessible patient data around demographics, race, age, patient address, vital signs, laboratory results, radiology results, and health-care provider clinical documentation. As these detailed data become electronically available, there is an opportunity to aggregate, analyze, and compare its geographic characteristics to related covariables utilizing GIS. The use of GIS in the evaluation of EHR-derived data is of major significance to research as it permits geographic and cofactor-specific targeting of epidemiologic methods and preventive and therapeutic treatment trials for patients with various conditions. This section of the review provides example projects of useful applications of GIS analyses applied to large data sets now available in EHRs. This includes studies that analyze aggregates of data generated during patient visits to clinics/hospitals, diagnostic laboratories, and pharmacies. Individual physician-conducted studies utilizing GIS with data collected at clinical settings are also reviewed. Most studies analyze outcome disparities using GIS in a population of patients.

Projects extending GIS into EHR are rapidly emerging. eHealth-PHINEX is a collaborative project with the University of Wisconsin and Wisconsin Department of Public Health (Guilbert et al. 2011). Their concept focuses on medical record and public HIE. Clinical information in the form of EHR data can be used to inform public health surveillance. In the same way, surveillance can be used to better understand what primary care physicians are seeing in the clinics. They computed economic hardship index using GIS with census indicators (i.e., crowded housing, federal poverty level, unemployment, median income, and percentage of population with less than high school education). Diabetic rates from EHR were compared with public health data and then economic hardship indices were predicted. The researchers concluded that in the Madison area, people with economic hardship are more likely to suffer from chronic diseases.

STARTING POINT is another Wisconsin project looking at a number of strategies that are provided by the Centers for Disease Control and Prevention (CDC) for communities to use to prevent obesity through targeting physical activity and healthier nutrition (University of Wisconsin 2012). The goal of this project is to examine how well the communities are implementing these strategies and then compare those to actual obesity prevalence rates to determine if using those strategies has a significant impact. Obesity data were obtained from a clinical data warehouse. Using census data, obesity prevalence in Dean County was mapped. The study, in agreement with other obesity literature, concluded that higher-income areas had lower obesity rates, while rural communities had higher rates.

New York City has developed a pilot public health program known as NYC Macroscope (Robert Wood Johnson Foundation 2013). The population health surveillance system compiles EHRs from primary care practices to help city health officials monitor and respond to the real-time prevalence of conditions that impact public health. The EHR architecture allows real-time and low-cost data collection.

The Chicago Health Information Technology Regional Extension Center (CHITREC) builds an EHR-enabled community that directly results in collaborative research projects (CHITREC 2014). One of their research interests is geocoding and mapping health data for epidemiology. Chicago Health Atlas is a shared data resource to provide policy makers, researchers, community advocates, and public health leaders insight into the health of the Chicago community and identify opportunities to improve care. They specifically focused on developing tools that balanced the need of privacy for patients and providers, while preserving uniqueness of patients. They obtained Institutional Review Board (IRB) approvals and data extraction is underway at six large health-care institutions throughout Chicago, along with the parallel development of the data visualization platform in GIS. This program extracts diagnoses, medications, and laboratory tests for all patients seen at participating institutions for linking with publicly available citywide data.

In 2007, CDC launched the GIS Surveillance for Heart Disease, Stroke, and Other Chronic Diseases in State and Local Health Departments Project (CDC 2014). The central objective of this training project is to enhance the ability of state health department staff to integrate the use of GIS-informed surveillance into daily operations that support existing priorities for preventing heart disease, stroke, and other chronic diseases. For example, Louisiana’s Department of Health and Hospitals enhanced GIS capacity within their agency by participating in this collaborative project provided by CDC. GIS was used to identify parishes with a high burden of cardiovascular disease and the location of Federally Qualified Health Centers that receive federal reimbursement to provide primary and preventive care to medically underserved populations. Louisiana’s Chronic Disease Prevention and Control Unit plans to implement the Patient-Centered Medical Home model to deliver quality-driven, cost-effective, culturally appropriate primary care to residents across the state. This model facilitates collaborative partnerships among patients, families, physicians, and other health-care professionals that encourage active participation in care decisions.

Children’s National Medical Center has initiated a partnership with Children’s national faculty members and the George Washington University GIS team to establish the foundation to improve childhood health in the DC metropolitan region (Jacobs 2014). Patient characteristics from three regional EHRs were obtained: the Children’s National Inpatient EHR, the Goldberg Center for Primary Pediatric Care EHR, and the Children’s Pediatricians and Associates, which represents 10 independent suburban pediatric practices. Patient-specific data are aggregated and integrated with other geospatial data sets and analyzed with GIS. The first project focused on childhood immunization, the second on thermal burns, and the third one on childhood obesity. The first project examined the relationship between spatial accessibility to pediatric immunization providers and vaccination compliance in low-income, urban population of children. It was concluded that the children with greater spatial accessibility to pediatric vaccination providers were more likely to be up to date with vaccinations. The second study was designed to identify areas in the District of Columbia with an increased number of pediatric burn injuries and to determine demographic and geographic subgroups at risk for these injuries. In recognition of the frequency of burn injuries in Hispanic toddlers living in at-risk neighborhoods, a partnership was created with political and advocacy groups, including the District of Columbia mayor’s office on Latino Affairs, to better reach the Spanishspeaking community. The third study provided a unique opportunity to gather accurate and reliable data derived from regional EHRs related to the condition of obesity and compare these data to cultural and environmental factors to explore geospatial relationships. This study identified significant differences between the study populations of inpatient, suburban, and inner city areas, with a greater prevalence of underweight children in the inpatient group and a greater prevalence of overweight, obese, and severely obese children in the inner city population (Jacobs 2014).

A number of projects are taking place dealing with GIS and health, in particular EHR at clinical settings, by physicians. Geraghty et al. (2010) conducted a study to determine whether there was an association between optimal glucose and lipid control with demographic and socioeconomic status (SES) variables (median income, education attainment, unemployment, and white and black race). They used registry data derived from the University of Califo...

Table of contents

• Cover
• Half Title
• Title Page
• Copyright Page
• Dedication
• Table of Contents
• Foreword
• Preface
• Acknowledgments
• Author
• Introduction
• 1. New Emerging Trends in Health Information Technology
• 2. Chronic Diseases
• 3. Birth Health
• 4. Infectious Diseases
• 5. Children’s Lead Poisoning
• 6. GIS’s Applications in Health-Care Access
• Conclusion
• Index