GIS Automated Delineation of Hospital Service Areas

Fahui Wang, Changzhen Wang

• Publisher: CRC Press
• Year: 2021
• ISBN:
  9780429535901
• Edition: 1
• Topic: Technology & Engineering
• Subtopic: Transportation & Navigation
• Index: Technology & Engineering

Hospital service areas (HSAs) and hospital referral regions (HRRs) are considered more appropriate units than geopolitical units for analyzing the performance of health care markets and policy implementation. GIS Automated Delineation of Hospital Service Areas represents the state-of-the-art approach in delineating HSAs and HRRs by using GIS-automated processes. It provides the best practices for defining such areas scientifically, in a geographically accurate manner, and without a steep learning curve.

This book is intended to mainly serve professionals in geography, urban and regional planning, public health, and related fields. It is also useful for scholars in the above fields who have research interests related to GIS and spatial analysis applications in health care. It can be used as a supplemental text for upper-level undergraduate and graduate students in courses related to GIS and public health.

Features:

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• Introduces innovative state-of-the-art methods for delineation of HSAs (Dartmouth method, Huff model, network community detection methods)

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• Provides best practices and one-stop solution for related data processing tasks (e.g., distance and travel time estimation, identifying the best-fitting distance decay function)

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• Automates the methods in ArcGIS Pro toolkits

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• Includes free ready-to-download GIS tools and sample data available on authors' website

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• Presents a methodology that is applicable to delineation of other service areas, catchment areas or functional regions for business analysis, planning, and public policy studies

1

Why Hospital Service Areas?

DOI: 10.1201/9780429260285-1

When a rural hospital is closed (seemingly a national trend in the USA), what will be the impacts on its local residents as well as other hospitals in the region? Has a National Cancer Institute (NCI) Cancer Center been serving the population representative of various demographic groups in its projected catchment area, as required by the federal guidelines? How should Medicare/Medicaid adjust the reimbursement prices for procedures that pertain to local health care markets and over time? Answering all these questions relies on a data-driven, evidence-based, and timely delineation of hospital service areas (HSAs).

1.1 Hospital Service Area (HSA) as a Functional Region

Hospital service areas (HSAs) were pioneered by the Dartmouth Atlas of Health Care Project (http://www.dartmouthatlas.org/). An HSA is an area within which patients receive most of their hospital care and thus captures a local pattern of hospitalization. HSAs are more meaningful analysis units for studies of health care than geopolitical units (e.g., county, state), administrative units (e.g., township, city), or census units (e.g., metropolitan statistical area) because they represent health care markets.

Hospital referral regions (HRRs) are other units developed by the Dartmouth Atlas of Health Care Project and represent regional health care markets anchored by at least one hospital that provides more specialized care such as major cardiovascular surgery procedures and neurosurgery. An HRR is aggregated from HSAs and is thus a larger unit at a coarser geographic scale than HSA for studies of health care utilization, outcomes, and cost. Currently, there are 3,436 Dartmouth HSAs and only 306 Dartmouth HRRs in the USA. According to the central place theory (Christaller, 1966), an HRR is a higher-level tertiary hospital market area characterized by higher-order medical services that are available in their anchoring hospital(s) but absent in those anchoring HSAs.

The Dartmouth Atlas of Health Care Project has also developed other health care service areas such as pediatric surgical areas (PSAs) (Dartmouth Atlas of Health Care, 2013) and primary care service areas (PCSAs) (Goodman et al., 2003) for their respective medical services. Conceivably, for analysis of any health care service, one needs to define a service area pertaining to that type of service in order to capture its unique market structure. For example, cancer service areas (CSAs) are defined for cancer care (Wang et al., 2020). In this book, the term HSA, unless specified or implied otherwise in a context, refers to broadly defined hospital (or health care in general) service area regardless of the service types provided by hospitals or other medical facilities.

In geography, HSA is a type of functional region. A functional region is an area around a node, facility, or hub connected by a certain function (e.g., retail distribution, advertisement for a media company, and telecom coverage). Delineation of functional regions occurs by defining regions that are coherent in terms of connections between supply and demand for a service. By defining a functional region, a distinctive market area has a geographic boundary that encompasses many smaller areas more closely connected with the central node(s) than beyond.

The literature in various fields uses other terms that possess the same or similar properties as a functional region. For example, catchment area (CA) refers to an area around a facility where most of its clients, patients, or patrons reside. Similarly, trade area is “the geographic area from which a store draws most of its customers and within which market penetration is highest” (Ghosh and McLafferty, 1987, p.62). In urban and regional studies, hinterland (Wang, 2001) or urban sphere of influence (Berry and Lamb, 1974) includes the rural area that maintains the highest volume of commerce, service, and other connections with and around a city.

Defining functional regions is a classic task in geography. It can be as straightforward as assigning areas to their nearest facility to form a proximal region around it, or as complex as untangling a massive interconnected network of interactions. The development of geographic information systems (GIS) has helped advance related methodology, especially in automating the delineation process, spatializing network methods, and visualizing the results. The book focuses on the methods for defining HSAs. While the methods introduced in this book are illustrated in case studies of medical services, they can be applied to delineation of any functional regions.

1.2 Value of HSAs

The purpose of delineating HSAs is to define a reliable unit of analysis for examining the geographic variation of the health care system (Kilaru et al., 2015). Obviously, the definition and choice of an areal unit of analysis affects the validity of findings based on data aggregated in such a unit, commonly known as the modifiable areal unit problem (MAUP) (Fotheringham and Wong, 1991). If the unit does not capture the actual health care market structure, corresponding policy and planning strategy would be ill-informed. Here some exemplary studies are cited to illustrate the values of HSAs for designing research, informing health policy, and planning resources.

Glover (1938) is believed to be the first to study geographic variation in health care that helped explain the variation of tonsillectomy rates in Great Britain (Onega et al., 2014). In the USA, Wennberg and Gittelsohn (1973) were among the first to examine the study of geographic variations in health care resource, utilization, and expenditures across small areas in Vermont. However, researchers have long debated about the magnitude and underlying causes of variation. A report by Goodman et al. (2010) revealed that the quality of end-of-life cancer care for Medicare beneficiaries varied significantly across hospitals and the aforementioned Dartmouth HRRs. It helped identify hospitals and geographic regions that aggressively treated those patients with likely unwarranted curative attempts and incurred excessive costs without improving the quality of their last weeks and months. The report has been instrumental in guiding improvements in the development and delivery of symptom control and other palliative care for terminally ill cancer patients.

An influential study reported by the Institute of Medicine (IOM, 2013) also found substantial geographic variations in health care spending in the USA, and such variations were not necessarily associated with quality of care, by largely relying on the same analysis unit Dartmouth HRRs. While the subtitle of the report, “Target Decision-makers, Not Geography”, might appear to cast doubt on the promise of geographic and regional efforts to improve US health care services, one major policy recommendation made by the report was to promote the transition to value-based payment models by providing financial incentives for health care providers to reduce costs and improve quality. However, the variability of cost efficiency in individual providers is embedded in the geographic variation across regions. Regional initiatives are just as important as providers-targeted incentives to improve both health and health care (Fisher and Skinner, 2013).

Health care markets also evolve over time as people move, some hospitals close and others open, and transportation networks connecting people and hospitals change. Units such as the Dartmouth HSAs and HRRs defined in the early 1990s have become outdated (Jia et al., 2015, 2020). Hospital closures have left some Dartmouth HSAs without any hospitals, and these units would no longer be suitable for analysis decades later. But many studies still used the same units “in order to preserve the continuity of the database” (Dartmouth Atlas of Health Care, 2011, p.4)¹. These units need to be updated with more recent data and defined with the methods that better reflect the scientific advancement in the field of deriving functional regions.

¹ Per conversations with Dr. Anna N. A. Tosteson and Dr. Jonathan Skinner of the Dartmouth Institute for Health Policy & Clinical Practice on March 23, 2021, the Dartmouth HSAs and HRRs are updated to the current ZIP code areas every year, but their basic geographic boundaries have not changed in order to facilitate longitude studies.

Most recently, the National Cancer Institute (NCI) mandated its designated cancer centers to identify and describe their CAs and document work that specifically addresses the cancer burden, risk factors, incidence, morbidity, mortality, and inequities, within the CAs (Paskett and Hiatt, 2018). While the mandate came without specific guidelines for defining the boundary of CA, its description suggests that CA should capture where a center’s patients come from, its marketing area, and where its research participants live. All are important elements for defining an HSA. Defining the CAs for the more than 70 NCI-designated cancer centers consistently is critical to assess whether these hospitals funded by federal resources serve their corresponding population base. This is especially challenging for hospitals without the personnel with highly specialized expertise in GIS and spatial analysis. Delineation of CAs is also important for accurately assessing epidemiological disease burdens and planning health care delivery accordingly (Alegana et al., 2020).

In short, HSAs have increasingly been adopted as a basic geographic unit for health care delivery assessment, management, and planning. The unit needs to pertain to the specific medical service being investigated and be defined in a timely fashion and at a scale suitable for the purpose of research and public policy relevance. Given the increasing volume of data required and complexity of algorithms, it is critical to develop methods that are computationally efficient, adaptable for various scales (from a local region to as large as a nationwide market), and automated without a steep learning curve for public health professionals.

This book sets out to meet these challenges by developing GIS-automated methods that cater to data needs (or lack of some data) and are efficient and easy to use in customized tools.

1.3 Study Area and Data

Unless specified otherwise, the same dataset for one study area is used throughout the book to illustrate various methods.

The study area is the state of Florida. Florida is only contiguously connected with two states—Alabama and Georgia—to the north, and bordered by Gulf of Mexico to the west, Atlantic Ocean to the east, and Straits of Florida to the south. This unique geography makes Florida an ideal study area as the edge effect is limited. Edge effect refers to cross-border interactions (e.g., residents in Florida seek health ca...

Table of contents

• Cover
• Half Title
• Title Page
• Copyright Page
• Table of Contents
• Foreword
• Preface
• Authors
• List of Major GIS Datasets and Program Files
• 1. Why Hospital Service Areas?
• 2. Estimating Distance and Travel Time Matrices in GIS
• 3. Analysis of Spatial Behavior of Health Care Utilization in Distance Decay
• 4. Delineating Hospital Service Areas by the Dartmouth Method
• 5. Delineating Hospital Service Areas by the Huff Model
• 6. Delineating Hospital Service Areas by Network Community Detection Methods
• 7. Delineating Cancer Service Areas in the Northeast Region of the USA
• Appendix A: User Guide: Estimating a Large OD Drive Time Matrix
• Appendix B: User Guide: How to Create Curved-Line and Straight-Line Network Flow Maps
• References
• Index