Using GIS for Administrative Decision-Making in a Local Public Health Setting

Geographic information systems (GIS) include tools that capture, store, analyze, and manage data linked to geographic locations. These tools were quickly adopted by fields such as environmental management and biological sciences since the first use of GIS in the 1970s.¹ However, in recent years, GIS has been increasingly used within the realm of health care as a tool to better understand spatial relationships of health and illness.^2–6 GIS has also become a valuable tool in assuring access to hospitals,^7,8 palliative care,⁹ and primary care for vulnerable and underserved populations.^10–12 Using GIS as a tool to determine where clinics can be placed to maximize access to care is particularly relevant for primary care services associated with ongoing changes to our health-care system, especially in light of the Patient Protection and Affordable Care Act of 2010.¹³ Therefore, GIS may serve as a tool to aid decision -makers in strategizing clinic placement and managing the forthcoming demand on health-care resources.

Public health often serves as a “provider of last resort” for vulnerable and underserved populations¹⁴ and may face a heavy portion of the burden of increasing health-care demands due to expanded coverage expected as a result of health-care reform.¹⁵ Studies of GIS' use in local or county public health centers are scarce except for those conducted in international settings.^9,12,16,17 Instead, much of the literature documenting GIS use by public health within the United States has been conducted at the state¹⁸ or national^11,19–22 level, and the majority of these GIS studies are concerned with disease surveillance^23,24 and environmental health.^17,24–26 More examples of how GIS can be used at the local public health level in the U.S. are needed, particularly regarding how GIS can be leveraged for administrative decision-making. This study provides such an example.

In 2009, the Jefferson County Department of Health (JCDH) had seven primary care centers, primarily located in low-income areas of Birmingham, Alabama, and surrounding municipalities. The majority of patients were uninsured or publicly insured (i.e., insured by Medicaid and the Children's Health Insurance Program). Productivity measures from 2008 indicated that three of the seven primary care centers had declining trends in the number of patients served. The goal of this study was to use GIS analysis to objectively develop criteria to assist JCDH decision makers in consolidating any of the seven primary care centers without affecting patients' access to health services.

The purpose of this article is to describe a way in which GIS can be used for administrative decision-making at the local public health level. Specifically, we present data that guided decisions regarding the placement of county-level public health primary care centers, including center closings, consolidation, and/or the development of new center locations. This study will be of interest to public health decision makers, policy makers, and other stakeholders interested in using GIS, especially regarding decisions on primary care center locations or other administrative issues.

METHODS

To determine primary care center placement, we used a combination of the patient origin and distance measure approaches.^27,28 Using GIS software, we analyzed the proportion of the overall JCDH patient population served by each primary care center and the estimated straight-line distance patients were traveling to their health center of choice.

Patient addresses, which are routinely collected in the organization-wide electronic medical record, were extracted and geocoded to attach a latitude and -longitude for the patient's residence using MapMarker® 12 software.²⁹ The locations for the seven primary care centers for 2008 and 2009 were also geocoded using the same software. All addresses were imported into ArcView® version 9.3 for map rendering and analysis.³⁰ We used Hawth's Analysis Tools to determine the miles that individual patients traveled to their health centers.³¹ ArcView's buffer and selection tool was used to determine the percentage of patients who live within a certain mile radius of the primary care centers.

Patients were grouped by the purpose of their most recent service: adult health, child health, or family planning. All patients who had a visit from January 1 to May 31, 2009, were used to determine the distance patients would drive to their primary care centers. Hawth's Analysis Tool estimated the distance in miles that residents traveled to the primary care center of their most recent visit. Outliers, such as out-of-state patients and those living more than 10 miles beyond the county boundaries, were excluded from the dataset. We used the ArcView buffer and selection tool to determine the total number and percentage of patients who live within an optimal mile radius of the primary care center, as determined by the distance between points analysis. Lastly, we plotted census tract data on the proportion of citizens at or below the federal poverty level in relation to public health primary care center placement.

RESULTS

The seven JCDH primary care centers treated 25,349 patients from January 1 to May 31, 2009 (Table 1). Figure 1 shows the locations of primary care centers in 2009 and the size of the population eligible for Medicaid. A total of 23,967 (94.5%) of the patient addresses were geocoded and included in the analyses. The mean distance JCDH patients traveled to the primary care center of their most recent visit ranged from 4.8 to 9.0 miles, with an average of 6.5 miles traveled. A review of patient residences indicated that the majority of patients are located within a six-mile radius of their primary care center (Figure 2). Because the majority (89%) of JCDH patients lived within six miles of a primary care center, regardless of service type, six miles was determined to be a reasonable distance for JCDH patients to travel for health services and was used as a catchment measure around the primary care center (Table 2).

Table 1.

Jefferson County Department of Health patient and primary care center characteristics: Jefferson County, Alabama, 2009

^aMorris Health Center was not included in the list because it is a rural center and, unlike the other health centers, does not offer a full range of services.

Figure 1.

Jefferson County Department of Health primary care center locations and percent of population below the federal poverty level: Jefferson County, Alabama, 2009^a

^aData obtained from Census 2000 for population below federal poverty level by census tract

Figure 2.

Primary care center locations and patient residences with six-mile catchment: Jefferson County, Alabama, 2009

Table 2.

Jefferson County Department of Health patient population served within six-mile radius of primary care centers: Jefferson County, Alabama, 2009

^aPlacement includes Bessemer, Central, Eastern, Morris, Northern, West End, and Western health clinics.

^bProposed placement is after consolidating the three clinics in the Western region of the county into a new clinic. This includes Central, Eastern, Morris, and the new consolidated clinic.

^cPercent represents the population served by the five clinics that offer adult health services (Bessemer, Central, Northern, West End, and Western health clinics).

Three of the public health primary care centers in the western portion of the county treated patients whose addresses overlapped within the six-mile radius with more than one health center. Given that two of these primary care centers had continuously decreasing patient visits, it was important to determine if all three centers in the western part of the county were necessary, whether one or more could be consolidated, or whether a new location would be more appropriate. Using the central latitude and longitude of the addresses of patients living in the western portion of the city, we identified a potential area for a new primary care center that would serve patients from all three primary care centers. This new location was factored into the model of the overall distances patients travel to their primary care centers.

Primary care centers located in the center of Birmingham, the east side of the city, and the one rural location (Morris Health Center) would remain open. The decision to replace the three current primary care centers in the western region with one new consolidated center (Figure 3) involved choosing a location that would maximize access for patients currently being treated by the existing centers. By downsizing from seven primary care centers to four, it was found that there would be a 4% reduction in the portion of JCDH patients served within a six-mile radius of their homes (from 89% to 85%). Specifically, among the patients currently seen in the three primary care centers in the western region, the new location would be beneficial to the patients visiting adult health services, with a 7% gain in the percentage of patients within six miles of a primary care center. On the other hand, there would be a decrease in proximity of 6% among patients using the child health or family planning services.

Figure 3.

Revised primary care center locations and patient residences with six-mile catchment: Jefferson County, Alabama, 2009

DISCUSSION

GIS has the potential to serve as a valuable tool to aid in local public health administration and decision-making. This study, which was conducted by a county health department, provides an example of how GIS can be used for managing and determining primary care center locations in such a setting. Because public health often predominantly provides primary care services to low-income populations, local public health decision makers will need to maximize the efficiency of operations when managing the upcoming primary care demands of the anticipated Medicaid expansion associated with health-care reform.¹³

Given our findings, GIS can be used as a tool in administrative decision-making on how to most efficiently assure patient access to care. Prior to the current analysis, JCDH leadership was considering closing the northern primary care center because patient volume there was declining. Leaders were also contemplating establishing a new primary care center in the north-central area of the county, where it was anecdotally believed a large number of Medicaid patients were living. However, the results of the current GIS analyses indicated that few patients in that area would likely qualify for Medicaid, suggesting that a primary care center placed there would not be justifiable. Instead, based on the empirical GIS analysis, decision makers chose to consolidate the three primary care centers in the western section of the county. Consolidation allowed JCDH to retain the same staff and capacity while reducing the fixed costs associated with the upkeep and maintenance of three facilities, with minimal or no changes in patient travel distances. Consolidating three primary care centers into one new center and closing the northern primary care center provided patients in Jefferson County with four more strategically placed care centers.

Limitations

Although GIS offers valuable information for decision-making, there were a few limitations associated with this study. First, due to software limitations, we used the straight-line method of estimating the distances from patients' homes to their primary care centers. Straight-line estimates only approximate actual travel distances. Thus, using a network analyst software to measure drive time would have improved the precision of our analysis. Furthermore, we recognize that using patients' residences may be a limitation given that some patients may travel from their workplaces or schools to the primary care center. Nevertheless, residences represented the most complete data available for this study. Lastly, we recognize that an important next step is to calculate the actual cost savings associated with the consolidation of the superfluous primary care centers. Doing so will allow for the important quantification of cost savings that stemmed from this decision.

CONCLUSIONS

Overall, using GIS to visually depict the patients served by JCDH provided valuable information that assisted in facilitating primary care center management and organizational change. This information provides a valuable example of the usefulness of GIS for local public health decision-making. GIS and other innovative analytical tools will be essential for keeping public health agencies on the cutting edge of managing populations and their health, especially given the expanding role that public health will play in our health-care system's increasing focus on efficiency and effectiveness. Future GIS studies should include examinations of the reliability or validity of GIS data, or the cost-effectiveness of using GIS in the local public health setting, especially considering that it is already being employed as a tool in myriad public health activities (e.g., environmental management, community health intervention mapping, and emergency and disaster response activities).