Using GIS to track and prevent Cardiovascular Disease

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Introduction

Geospatial technology is one of the most effective tools in tracking and preventing diseases. Health professionals, epidemiologists, and government officials track a host of communicable diseases on a global scale using a variety of tools. Trends in applying geospatial technology (such as GIS and GPS) to non-communicable diseases has gained ample attention from public health officials in the last few years. The growing epidemic of chronic disease such as cancer, diabetes, cardiovascular disease (CVD), and obesity indicate a need for analysis and measurement to aid in the identification and treatment of chronic illness. Geospatial surveillance addresses this by integrating knowledge, and open-source data, across multiple agencies and professions. Studies highlight significant geographical variances of morbidity rates from CVD, and mapping the locations of people with CVD can aid in identifying possible risk factors (environmental/ lifestyle/ other), develop targeted health initiatives, implement preventative measures, and reduce health care costs for the population affected. The association of GIS technology solely with geographers, or cartographers, is a common misconception. Almost all data can be ‘mapped’, and the adoption of geospatial technology is prevalent in a host of areas outside of the typical geographic sciences. "Data mapping" is utilized in areas such as agricultural and environmental analysis, economic tracking, land zone decisions, redistricting, military and intelligence operations, political strategy, natural resource assessments, crime analysis, and public health. By working collaboratively, government agencies, non-profit groups, universities, and private corporations can reduce the costs associated with a GIS initiative, and work toward having a larger network of geospatial data available. Using spatial data, health care professionals and researchers can identify chronic disease incidence, risk factors, prevalence, and mortality rates. Additionally, they can analyze data at the community level and layer it onto the health data. Information such as environmental factors (pollution), neighborhood walkability scores, food (eating/buying) options, household income, and other data can be applied in order to locate regions with populations most at risk.

GIS for Communicable Disease

The World Health Organization (WHO), Center for Disease Control (CDC), and the Department of Health & Human Services (DHHS) work collectively with international organizations, researchers, and institutions to track communicable diseases such as SARS, Avian Flu, H1N1, and HIV/AIDS using geospatial technology. GIS enables successful identification, treatment initiatives, containment or elimination, and origin of communicable diseases. Through the use of GIS exchanges, travel records, and field work, researchers were able to identify “patient zero” in the SARS epidemic. In 2000, scientists identified numerous cases of West Nile Virus in the State of Pennsylvania. These were the first cases ever known in the state, and researchers didn’t hesitate to develop a tracking system for them using GIS. Known as the West Nile Virus Control Program, it was a successful inter-agency, open-source geospatial effort, which included both public and private organizations. The Pennsylvania Department of Health, Pennsylvania Department of Environmental Protection, and the Pennsylvania Department of Agriculture each contributed equal efforts to curtail the West Nile outbreak; and their efforts paid off. Geospatial technology is a powerful tool in the field of public health. Applying the same techniques as in communicable disease tracking, health officials can directly impact the mortality rates, treatment, prevention, and costs associated with cardiovascular disease.

Applications of GIS for Cardiovascular Disease

Cardiovascular disease (CVD) is one of the leading causes of death in the United States for both men and women of all races; it is the cause in 1 out of every 4 deaths. In 2010, the American Heart Association estimated the yearly costs associated with CVD to be $500 Billion. Risk factors include, lifestyle choices, genetic factors, dietary habits, smoking, excess alcohol consumption, diabetes, hypertension, lack of physical activity, obesity, congenital disorders, and stress. Almost all of the factors of CVD risk are preventable, and GIS facilitates the effort to prevent CVD. The Milan Declaration, composed from the 5th International Heart Health Conference held in Italy, states: “Risk factor monitoring and rapid health behavior monitoring, for example, with links to modern communication technology for rapid feedback, have proven their value to practical health work.” (Milan Declaration, 2004)

California Case Study

The California Department of Public Health (CDPH), has a collection of statistics and information from emergency departments, and hospital visits, along with U.S. Census data which they enter into a GIS to determine the morbidity and mortality rates from CVD. The program has been highly-successful, however, geospatial surveillance is not without issue. A few problems noted are the lack of available data on non-fatal CVD related visits, the number of out of hospital heart attacks, and HIPPA legislation. Most emergency departments only report fatalities of CVD, and not the prevalence of it, nor patient geographic residence. Also, they report number of total visits, and not individuals. This means there is a higher (false) prevalence due to the same patients returning for treatment. In response to these types of issues, the CDC developed the ‘GIS Surveillance for Heart Disease, Stroke, and Other Chronic Diseases in State and Local Health Departments Project’. The project highlighted a commitment to eliminating any reporting disparities, promoting better chronic disease management, better reporting, preventative measures, and awareness of CVD.  The program allows for an open-source chronic disease exchange where health practitioners, physicians, and the general public can share data, and develop their own maps with the goal of enhancing geospatial data in the daily operations of state and local health department initiatives. Factors such as total population, time frame, geographic location, demographics, ER visits, age adjustments, and more are calculated into the GIS. Patient confidentiality laws present a burden on surveillance efforts though. One of the most important datasets for GIS initiatives is geographic location, and HIPPA laws prevent healthcare workers from disclosing patient addresses. Only information at the county level can be shared, which limits the reporting effort due to the large sizes and populations of many counties. This is especially important when trying to determine an environmental issue as the cause.

Colorado Geocoding

The Colorado Department of Public Health and Environment addresses that very surveillance issue with the Colorado Health and Hospital Association. They developed a geocoding system for hospital discharge records in order to be able to assign a precise residence to 95% of patients for use in their asthma analysis in hospital records. The State of Maryland also developed a similar system to geocode patient addresses for its GIS program to assess cancer risks and prevalence in specific areas. The health coding systems for patient data are solely for insurance purposes, and therefore a GIS cannot interpret the data Colorado needed. To a GIS program, there is no difference between an insurance code, and the diagnosis given. Another issue in reporting is that only discharge dates are given, and not admit dates. This means an accurate time frame cannot be assessed. Geocoding addresses each of these issues. In order for geospatial surveillance to be the most effective though, collection methods such as patient surveys, phone interviews, and self-reporting are critical to obtaining the most up-to-date and accurate data available for the GIS.

GIS in the Neighborhood

At the local level, geospatial surveillance can explain much more than county level data. GIS helps researchers understand the phenomenon of ‘lack of utilization’ of services. To determine this, mapping the areas at the community level shows neighborhood walkability, number of fast food establishments, public parks, recreation facilities, healthcare centers, fitness gyms, and other environmental factors that have a major impact on CVD risk. Using spatial data at this level provides researchers the information needed to identify disparities in services, thereby identifying the outcomes produced. The State of Rhode Island implemented a geospatial surveillance program which studied risk factors at the neighborhood level. They found that people living in lower socio-economic areas had elevated access to smoking related products and advertisements. Specifically, “Eight cities and towns were studied to identify the number of tobacco vendors, advertisements, price incentives, illegal sales, and tobacco proximity to youth. The findings showed that children in low-income neighborhoods were exposed to a much higher rate of tobacco vendors, often operating in school zones.” (Osbourne, D. et al., 2011) This prompted law makers and policy analysts in the state to advocate for initiatives to reduce the amount of advertisements and products, and to implement CVD prevention plans in those areas.

Costs Associated With GIS

The cost cutting implications for GIS in the fight against CVD is prominent. Using spatial data to prevent CVD can significantly reduce the over $500 Billion per year in medical expenses associated with the disease, and steer lawmakers to implement changes and programs in targeted areas. Not all costs associated with geospatial surveillance is favorable. The costs associated with employee training in the use of both software and hardware is timely, and expensive. An ample amount of resources are needed in order to train healthcare workers to enter data at the user level. Employee turn-over is also an issue because it causes for repetition of training, and often makes it difficult, as there are typically only a few people on staff in healthcare systems trained in use of GIS. This can be partially offset though, because a GIS can offer a more automated approach by using standardized patient data forms, markup languages, geocoding references, and open-source data sets from healthcare facilities which share the burden of the treatment of CVD; much like GIS effort the State of Pennsylvania implemented.

Security Issues

As with most computer-based technology, security is a concern. Patient data, confidentiality, and other privacy concerns are significant and demand the integrity of the system be monitored. Security breaches, records accessibility, and third-party reviewers are the main concerns. Geocoding mitigates some of the privacy issues but, there is still the software/hardware to secure. While larger healthcare facilities have IT departments to detect anomalies, smaller healthcare providers and facilities do not. This represents a problem for an open-source GIS with many different users in remote locations. There is a difference between ‘proactive’ and ‘reactive’ in an IT security environment. The practical answer to this is for all healthcare facilities to invest the resources (time/money) in order to prevent a security breach, therefore being proactive. This is not so easily executed though. Electronic Health Records (EHR) and mHealth technology is still fairly ‘new’, meaning healthcare facilities have not experienced the level or amount of security breaches that other business sectors have over the years. Having a cloud-based, open-source, GIS which houses patient data, and is open to the public creates a dire need for the most proactive measures available in geospatial security.

GIS – ESRI & LLUMC Changing the Future of Health

A collaborative effort between ESRI and Loma Linda University (LLU Medical Center) highlights the ways GIS can improve healthcare. Ruthita Fike (former CEO at LLUM) stated: “LLUMC is the only level 1 trauma center in the region, and hosts a vast amount of earthquake-prone landscape. Their need for a geographically centered system is prominent if they are to succeed in emergency management efforts. Fike notes the centers ‘social responsibility to be prepared’”. A common problem in emergency situations is finding the populations who need resources. LLUMC sought to develop a system that could be self-sufficient, and user-friendly, so the public could access services if they needed them; versus waiting for ER personnel to locate them through search and rescue efforts. The result of their efforts in the Discovery Project made available the Advanced Emergency Geographic Information System (AEGIS). This system maps where the people are whom need help, in relation to where the ER responders are located, and links them together. LLUMC has also implemented training for staff members of the center, and into their curriculum at the university. More colleges are starting to offer Health Geoinformatics and Medical Geography programs in an effort to train people to use GIS for healthcare. LLUMC also has a number of GIS accomplishments:
  • The school’s faculty designed and taught the first graduate-level GIS course offered at an American school of public health.
  • They launched the country’s first health geographic bachelor’s degree.
  • They created an MBA in Health Administration and graduate-level certificate in Health Geoinformatics

In Closing...

Many firms believe that GIS is the missing link to providing more efficient services, cutting costs, and making better policy decisions. Spatial data for healthcare is a useful mechanism at the forefront of combatting chronic diseases like CVD. The intersection where geography and healthcare meet is highly useful for combating all chronic diseases, and having the tools available to the public is key. The implications of the effectiveness of GIS in the healthcare industry is largely unknown. With a highly-mobile, global population, it is not an easy mission to implement geospatial technology in the healthcare setting but, it is necessary.