Using a GIS-based floating catchment method to assess areas with shortage of physicians

Abstract

This paper presents a geographic information system (GIS) based floating catchment method for identifying physician shortage areas. The traditional designation methods are primarily regional availability measures, which use administrative boundaries such as counties as the basic spatial units for calculating physician to population ratios and designate shortage based on those ratios. Such approaches have been criticized for their inability to account for either the spatial variations of population demand and physician supply within those boundaries or for population-physician interactions across them. The floating catchment method addresses the internal spatial distribution problem by deriving population data from a smaller unit, the census tract. The potential cross border patient–physician interaction is taken into consideration by using circles of reasonable radius around each census tract centroid as the basic spatial units, which can encompass areas on either side of an administrative border. By varying the radius of the catchment circle, this paper demonstrates that the physician to population ratio is scale dependent and that the greatest variability of the ratios and shortages occur at the most local scales (< 20 miles), which argues for using finer spatial resolution data in shortage designation practice.

Introduction

The two basic important factors involved in the issue of access to health care services are physicians (supply) and population (demand). Both are spatially distributed and their distributions do not necessary match. Thus access to health care is not uniform across space and access problems are especially pronounced in rural areas and impoverished urban communities (COMGE, 2000; Rosenblatt and Lishner, 1991). The US federal government spends about $1 billion a year on programs designed to alleviate health care access problems, including providing incentives or awarding financial assistance to providers serving designated shortage areas, such as the National Health Service Corps Program, Medicare Incentive Program, and J-1 visa waiver program, among others (GAO, 1995). These federal programs depend on two main systems for identifying shortage areas, conducted by the Department of Health and Human Services (DHHS) (GAO, 1995; Lee, 1991). One designates Health Professional Shortage Areas (HPSA), the other Medically Underserved Areas or Populations (MUA/MUP). Briefly, the criteria for designating HPSA are the following: (1) the geographic area involved is rational for the delivery of health services, i.e., a rational service area; (2) a specified population to full-time-equivalent (PTE) physician ratio representing shortage is exceeded within the area; and (3) resources in contiguous areas are over utilized, excessively distant, or otherwise inaccessible. For primary care HPSA, this designation ratio is 3500:1 (or 3000:1 if there is unusually high needs). In addition, the HPSA can also be designated for a population group (e.g., low income population) or facility (e.g., a correction center). MUA/MUP is designated based on four factors of health service need: (1) population to full-time-equivalent primary care physician ratio; (2) infant mortality rate; (3) percentage of the population with incomes below the poverty level; and (4) percentage of the population aged 65 and older. These four variables are applied to a rational service area to obtain a single Index of Medical Underservice (IMU) score between 0 and 100, with 0 representing the most underserved and 100 the best-served area. A rational service area with a score of 62 or less qualifies for designation as MUA/MUP. The rational service area used in both HPSA and MUA/MUP is defined as (a) a whole county (in non-metropolitan areas); (b) groups of contiguous counties, minor civil divisions, or census county divisions in non-metropolitan areas, with population centers within 30 min travel time of each other; and (c) in metropolitan areas, a group of census tracts which represent a neighborhood due to homogeneous socioeconomic and demographic characteristics. The actual designation of both HPSA and MUA/PUP is a tedious process that involves complicated rules for defining the rational service area, estimating PTE, evaluating contiguous resources, etc. The details are discussed in DHHS (1980); Lee (1991); GAO (1995) and the website of Shortage Designation Branch, National Center for Health Workforce Analysis (http://bphc.hrsa.gov/dsd/default.htm, accessed July 8, 2002). The rest of the Introduction will review the literature on health care accessibility, followed by a brief discussion of the purpose of this paper.

Health care accessibility can be classified into two broad categories: revealed accessibility and potential accessibility (Joseph and Phillips, 1984; Phillips, 1990; Thouez et al., 1988). Revealed accessibility focuses on actual use of health care services, whereas potential accessibility emphasizes the geographic patterns and aggregate supply of medical care resources. Since both spatial factors (e.g., geographic location, distance) and nonspatial factors (e.g., social class, income, age, sex, etc. (Joseph and Phillips, 1984)) and their interactions (Meade et al., 1988, p.306-311) can influence one's access to health care, each category can be further divided into spatial and nonspatial accessibility (i.e., the 2×2 matrix of Khan, 1992). This paper will only focus on potential spatial accessibility, because identifying where the truly underserved populations are located is the essential first step toward any meaningful and effective government intervention programs.

The measures of potential spatial accessibility include regional availability and regional accessibility (Joseph and Phillips, 1984). The regional availability approach is simpler and measures distribution of supply versus demand within a region, often expressed as a ratio of population to practioner (or its variation). The region used is usually a predefined area such as a county, a minor civil division, or a census county division. Although the DHHS shortage area designation methods also take into account some non-spatial factors such as age and socio-economic status, they are primarily regional availability measures using predefined administrative boundaries. The advantage of such a regional availability approach is that it is simple and thus straightforward to implement as the data for physician and population are readily available and such boundaries can be easily located in the real world (Florin et al., 1994). In addition, it is also practical to administer federal funding programs because the government infrastructure is already in place (Florin et al., 1994). However, this approach carries the following assumptions that draw sharp criticisms (e.g., Kleinman and Makuc, 1983; Wing and Reynolds, 1988): (1) people within the region have equal access to the physicians within the same region (i.e., the subregion variation of supply and demand and “distance decay” of utilization behavior are ignored), and (2) people within the region do not go beyond that region to seek care (i.e., the boundary of the region is impermeable or self-contained). The first assumption is obviously not true in most cases, especially if a large areal unit such as county is used. Overcoming or ameliorating the problems of the first assumption requires spatially disaggregated data (e.g., census tract or even smaller areal units) as described by Bullen et al. (1996), Curtis and Taket (1989), and Kivell et al. (1990). However, the smaller the areal unit that is adopted, the more likely it is that people will go beyond that areal unit to seek care, thus violating the second assumption. While the second assumption may be true for delivery systems in some countries (such as the provincially based health insurance programs in Canada), it is not true in US, because people often seek care in adjacent or nearby administrative units (Kleinman and Makuc, 1983; GMENAC, 1980; Wing and Reynolds 1988; GAO, 1995; COGME, 1998). Overcoming or lessening the permeability problem related to the second assumption requires spatially aggregated data to higher levels (e.g., groups of counties, Makuc et al., 1991). The two problems can not be easily reconciled. Although step (3) of the HPSA method is intended to consider adjacent areas, the physician to population ratios are still calculated within their respective boundaries and the actual interaction across boundaries is not accounted for. The fact that the whole county or group of contiguous counties can still be defined as rational service areas in the current DHHS systems suggests that the existing methods can easily lead to overestimation in some areas and underestimation in others and thus funding for programs aimed at alleviating access problems based on such designation may not be channeled to where it is most needed (GAO, 1995).

The problems of using a predefined administrative boundary as the basic unit to determine the adequacy of supply of a service or resource relative to its demand have long been recognized in geography (e.g., Openshaw and Taylor, 1981) but are still not well resolved. This is partially due to the complexity of the problem, i.e., both the supplies and demands are spatially distributed and are likely overlapping, and competition exists among the supplies and the demands (e.g., Huff (1964), Huff (2000)). The regional accessibility approach considers such potential for complex interaction between supply and demand located in different regions using a gravity model formulation and thus addresses the problems of the regional availability approach. However, the regional accessibility approach is more complex and requires more data input: the location of supply and demand (Joseph and Phillips, 1984), traffic network and travel time analysis between supply and demand, and the frictional coefficient in distance decay function (exponent in the gravity model), which has to be determined by physiciain–patient interaction data and may be region specific (Huff, 2000). The physician–patient interaction data can only be obtained through surveys, which are tedious and time consuming, or from physician records directly, which are hard to get due to confidentiality concerns, or from insurance payment sources, which are very expensive. An extensive literature about gravity-based models exists in the areas of retail location, trade area, and location-allocation of resources (e.g., Huff (1964), Huff (2000); Berry, 1967; Berry and Parr, 1988; Rushton et al., 1977; Rushton, 1979; Beaumont, 1981; Eiselt, 1992; Church, 1999). These models, which deal with supply and demand of resources or services, could be applied to refine the process of determining health service supply and demand and thus define physician shortage areas (e.g., Knox, 1978; Joseph and Bantock, 1982; Thouez et al., 1988; Khan, 1992; Rushton et al., 1977; Huff (1964), Huff (2000); Shen, 1998). Despite the superiority of the regional accessibility measure and the advancement and refinement made in past three decades, current DHHS shortage designation methods are still primarily based on regional availability measures. This is perhaps because of the ready availability of data and practicality of administering funding for the regional availability measures on the one hand and the difficulty in obtaining patient visit data to determine friction coefficients (confidentiality concerns among other things) and the difficulty in administering the final results for accessibility measure (continuous across space) on the other hand. Even the most recent proposed revision of the shortage area designation is still primarily a regional availability measure, although the smaller census tract areal units are favored in the proposed guideline (DHHS, 1998).

If the government intervention programs are to achieve their goals of alleviating health care access problems, the shortage designation method needs to more accurately reflect demographic and behavioral realities (COGME, 1998), i.e., both physicians and population are spatially distributed and the availability of services depends, not only upon the supply of resources in a community, but also the supply of such resources in neighboring communities and the distance and ease of travel among them (Kleinman and Makuc, 1983, p. 543). Given the appealing simplicity and practicality of regional availability measures and the fact the DHHS still uses such measures, this paper presents a GIS based method that can reveal more spatial variations by taking advantage of fine resolution spatial data and at the same time addresses the permeability problem associated with regional availability method. The purposes of this paper are (1) to demonstrate the principle of the floating catchment methodology with a simple case study in northern Illinois, and (2) to show that the greatest variability in physician to population ratios occurs at the most local scales, suggesting that finer spatial resolution data should be used in shortage area designation. The following section describes the floating catchment method and its implementation in GIS. This method will then be applied to counties surrounding DeKalb in northern Illinois region after a brief discussion of the study area. The results will be discussed and conclusions drawn, followed by a brief discussion of on-going research and future directions.