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

Messina, Shortridge, Groop, Varnakovida Pariwate, Finn

Description

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.

Informations

• Publié par: biomed
• Publié le: 01 janvier 2006
• Nombre de lectures: 6
• Langue: English
• Poids de l'ouvrage: 9 Mo

Extrait

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Published: 22 September 2006 Received: 2 August 2006 International Journal of Health Geographics 2006, 5 :42 doi:10.1186/1476-072X-5-42  Accepted: 22 September 2006 This article is available from: http:// www.ij-healthgeographics.com/content/5/1/42 © 2006 Messina et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons. org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the orig inal work is properly cited.

Address: Department of Geography, Michigan State University, East Lansing, MI, USA Email: Joseph P Messina* - [e-mail]; Ashton M Shortri dge - [e-mail]; Richard E Groop - [e-mail]; Pariwate Varnakovida - [e-mail]; Mark J Finn - [e-mail] * Corresponding author

Abstract Background: Community hospital placement is dictated by a diverse set of geographical factors and historical contingency. In the summer of 2 004, a multi-organizational committee headed by the State of Michigan's Department of Community Heal th approached the authors of this paper with questions about how spatial analyses might be employed to develop a revised community hospital approval procedure. Three objective s were set. First, the committ ee needed visualizations of both the spatial pattern of Michi gan's population and its 139 co mmunity hospitals. Second, the committee required a clear, defens ible assessment methodology to quantify access to existing hospitals statewide, taking into account factors such as distan ce to nearest hospital and road network density to estima te travel time. Third, the committ ee wanted to contrast the spatial distribution of existing communi ty hospitals with a theoretica l 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 tw o models, access-based and demand-based, to identify areas with inadequate access to existing hospitals. Results: Using the product from the access-based mo del and contiguity and population criteria, two areas were identified as being "under-ser ved." The lower area, located north/northeast of Detroit, contained the gr eater 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 be d demand in each ZIP code to th e closest facility. We found 1,887 beds per day were demanded by ZIP centroids more than 16.1 kilo meters from the nearest existing hospital. This represented 12.7% of the average statewide daily bed dema nd. 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 inters ection of spatial analysis and policymaking. Using the best scientific practice to identify location s of under-served populations based on many factors provides policymakers with a powerful tool for making good decisions.

Research Open Access Evaluating Michigan's community ho spital access: spatial methods for decision support Joseph P Messina*, Ashton M Shortridge, Richard E Groop, Pariwate Varnakovida and Mark J Finn