Using floating catchment area (FCA) metrics to predict health care utilization patterns.

BACKGROUND: Floating Catchment Area (FCA) metrics provide a comprehensive measure of potential spatial accessibility to health care services and are often used to identify geographic disparities in health care access. An unexplored aspect of FCA metrics is whether they can be useful in predicting where people actually seek care. This research addresses this question by examining the utility of FCA metrics for predicting patient utilization patterns, the flows of patients from their residences to facilities. METHODS: Using more than one million inpatient hospital visits in Michigan, we calculated expected utilization patterns from Zip Codes to hospitals using four FCA metrics and two traditional metrics (simple distance and a Huff model) and compared them to observed utilization patterns. Because all of the accessibility metrics rely on the specification of a distance decay function and its associated parameters, we conducted a sensitivity analysis to evaluate their effects on prediction accuracy. RESULTS: We found that the Three Step FCA (3SFCA) and Modified Two Step FCA (M2SFCA) were the most effective metrics for predicting utilization patterns, correctly predicting the destination hospital for nearly 74% of hospital visits in Michigan. These two metrics were also the least sensitive to changes to the distance decay functions and parameter settings. CONCLUSIONS: Overall, this research demonstrates that FCA metrics can provide reasonable predictions of patient utilization patterns and FCA utilization models could be considered as a substitute when utilization pattern data are unavailable.

Effects of payments for ecosystem services on wildlife habitat recovery.

Conflicts between local people's livelihoods and conservation have led to many unsuccessful conservation efforts and have stimulated debates on policies that might simultaneously promote sustainable management of protected areas and improve the living conditions of local people. Many government-sponsored payments-for-ecosystem-services (PES) schemes have been implemented around the world. However, few empirical assessments of their effectiveness have been conducted, and even fewer assessments have directly measured their effects on ecosystem services. We conducted an empirical and spatially explicit assessment of the conservation effectiveness of one of the world's largest PES programs through the use of a long-term empirical data set, a satellite-based habitat model, and spatial autoregressive analyses on direct measures of change in an ecosystem service (i.e., the provision of wildlife species habitat). Giant panda (Ailuropoda melanoleuca) habitat improved in Wolong Nature Reserve of China after the implementation of the Natural Forest Conservation Program. The improvement was more pronounced in areas monitored by local residents than those monitored by the local government, but only when a higher payment was provided. Our results suggest that the effectiveness of a PES program depends on who receives the payment and on whether the payment provides sufficient incentives. As engagement of local residents has not been incorporated in many conservation strategies elsewhere in China or around the world, our results also suggest that using an incentive-based strategy as a complement to command-and-control, community- and norm-based strategies may help achieve greater conservation effectiveness and provide a potential solution for the park versus people conflict.

Regional health care planning: a methodology to cluster facilities using community utilization patterns.

BACKGROUND: Community-based health care planning and regulation necessitates grouping facilities and areal units into regions of similar health care use. Limited research has explored the methodologies used in creating these regions. We offer a new methodology that clusters facilities based on similarities in patient utilization patterns and geographic location. Our case study focused on Hospital Groups in Michigan, the allocation units used for predicting future inpatient hospital bed demand in the state's Bed Need Methodology. The scientific, practical, and political concerns that were considered throughout the formulation and development of the methodology are detailed. METHODS: The clustering methodology employs a 2-step K-means + Ward's clustering algorithm to group hospitals. The final number of clusters is selected using a heuristic that integrates both a statistical-based measure of cluster fit and characteristics of the resulting Hospital Groups. RESULTS: Using recent hospital utilization data, the clustering methodology identified 33 Hospital Groups in Michigan. CONCLUSIONS: Despite being developed within the politically charged climate of Certificate of Need regulation, we have provided an objective, replicable, and sustainable methodology to create Hospital Groups. Because the methodology is built upon theoretically sound principles of clustering analysis and health care service utilization, it is highly transferable across applications and suitable for grouping facilities or areal units.

Do more hospital beds lead to higher hospitalization rates? a spatial examination of Roemer's Law.

BACKGROUND: Roemer's Law, a widely cited principle in health care policy, states that hospital beds that are built tend to be used. This simple but powerful expression has been invoked to justify Certificate of Need regulation of hospital beds in an effort to contain health care costs. Despite its influence, a surprisingly small body of empirical evidence supports its content. Furthermore, known geographic factors influencing health services use and the spatial structure of the relationship between hospital bed availability and hospitalization rates have not been sufficiently explored in past examinations of Roemer's Law. We pose the question, "Accounting for space in health care access and use, is there an observable association between the availability of hospital beds and hospital utilization?" METHODS: We employ an ecological research design based upon the Anderson behavioral model of health care utilization. This conceptual model is implemented in an explicitly spatial context. The effect of hospital bed availability on the utilization of hospital services is evaluated, accounting for spatial structure and controlling for other known determinants of hospital utilization. The stability of this relationship is explored by testing across numerous geographic scales of analysis. The case study comprises an entire state system of hospitals and population, evaluating over one million inpatient admissions. RESULTS: We find compelling evidence that a positive, statistically significant relationship exists between hospital bed availability and inpatient hospitalization rates. Additionally, the observed relationship is invariant with changes in the geographic scale of analysis. CONCLUSIONS: This study provides evidence for the effects of Roemer's Law, thus suggesting that variations in hospitalization rates have origins in the availability of hospital beds. This relationship is found to be robust across geographic scales of analysis. These findings suggest continued regulation of hospital bed supply to assist in controlling hospital utilization is justified.

Measuring geographic access to health care: raster and network-based methods.

BACKGROUND: Inequalities in geographic access to health care result from the configuration of facilities, population distribution, and the transportation infrastructure. In recent accessibility studies, the traditional distance measure (Euclidean) has been replaced with more plausible measures such as travel distance or time. Both network and raster-based methods are often utilized for estimating travel time in a Geographic Information System. Therefore, exploring the differences in the underlying data models and associated methods and their impact on geographic accessibility estimates is warranted. METHODS: We examine the assumptions present in population-based travel time models. Conceptual and practical differences between raster and network data models are reviewed, along with methodological implications for service area estimates. Our case study investigates Limited Access Areas defined by Michigan's Certificate of Need (CON) Program. Geographic accessibility is calculated by identifying the number of people residing more than 30 minutes from an acute care hospital. Both network and raster-based methods are implemented and their results are compared. We also examine sensitivity to changes in travel speed settings and population assignment. RESULTS: In both methods, the areas identified as having limited accessibility were similar in their location, configuration, and shape. However, the number of people identified as having limited accessibility varied substantially between methods. Over all permutations, the raster-based method identified more area and people with limited accessibility. The raster-based method was more sensitive to travel speed settings, while the network-based method was more sensitive to the specific population assignment method employed in Michigan. CONCLUSIONS: Differences between the underlying data models help to explain the variation in results between raster and network-based methods. Considering that the choice of data model/method may substantially alter the outcomes of a geographic accessibility analysis, we advise researchers to use caution in model selection. For policy, we recommend that Michigan adopt the network-based method or reevaluate the travel speed assignment rule in the raster-based method. Additionally, we recommend that the state revisit the population assignment method.

A methodology for projecting hospital bed need: a Michigan case study.

Michigan's Department of Community Health (MDCH) is responsible for managing hospitals through the utilization of a Certificate of Need (CON) Commission. Regulation is achieved by limiting the number of beds a hospital can use for inpatient services. MDCH assigns hospitals to service areas and sub areas by use patterns. Hospital beds are then assigned within these Hospital Service Areas and Facility Sub Areas. The determination of the number of hospital beds a facility subarea is authorized to hold, called bed need, is defined in the Michigan Hospital Standards and published by the CON Commission and MDCH. These standards vaguely define a methodology for calculating hospital bed need for a projection year, five years ahead of the base year (defined as the most recent year for which patient data have been published by the Michigan Hospital Association). MDCH approached the authors and requested a reformulation of the process. Here we present a comprehensive guide and associated code as interpreted from the hospital standards with results from the 2011 projection year. Additionally, we discuss methodologies for other states and compare them to Michigan's Bed Need methodology.

Evaluating Michigan's community hospital access: spatial methods for decision support.

BACKGROUND: Community hospital placement is dictated by a diverse set of geographical factors and historical contingency. In the summer of 2004, a multi-organizational committee headed by the State of Michigan's Department of Community Health approached the authors of this paper with questions about how spatial analyses might be employed to develop a revised community hospital approval procedure. Three objectives were set. First, the committee needed visualizations of both the spatial pattern of Michigan's population and its 139 community hospitals. Second, the committee required a clear, defensible assessment methodology to quantify access to existing hospitals statewide, taking into account factors such as distance to nearest hospital and road network density to estimate travel time. Third, the committee wanted to contrast the spatial distribution of existing community hospitals with a theoretical configuration that best met statewide demand. This paper presents our efforts to first describe the distribution of Michigan's current community hospital pattern and its people, and second, develop two models, access-based and demand-based, to identify areas with inadequate access to existing hospitals. RESULTS: Using the product from the access-based model and contiguity and population criteria, two areas were identified as being "under-served." The lower area, located north/northeast of Detroit, contained the greater total land area and population of the two areas. The upper area was centered north of Grand Rapids. A demand-based model was applied to evaluate the existing facility arrangement by allocating daily bed demand in each ZIP code to the closest facility. We found 1,887 beds per day were demanded by ZIP centroids more than 16.1 kilometers from the nearest existing hospital. This represented 12.7% of the average statewide daily bed demand. If a 32.3 kilometer radius was employed, unmet demand dropped to 160 beds per day (1.1%). CONCLUSION: Both modeling approaches enable policymakers to identify under-served areas. Ultimately this paper is concerned with the intersection of spatial analysis and policymaking. Using the best scientific practice to identify locations of under-served populations based on many factors provides policymakers with a powerful tool for making good decisions.