Quantifying and explaining accessibility with application to the 2009 H1N1 vaccination campaign

Jessica L. Heier Stamm (), Nicoleta Serban, Julie Swann and Pascale Wortley
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Jessica L. Heier Stamm: Kansas State University
Nicoleta Serban: Georgia Institute of Technology
Julie Swann: Georgia Institute of Technology
Pascale Wortley: Centers for Disease Control and Prevention

Health Care Management Science, 2017, vol. 20, issue 1, No 5, 76-93

Abstract: Abstract Accessibility and equity across populations are important measures in public health. This paper is specifically concerned with potential spatial accessibility, or the opportunity to receive care as moderated by geographic factors, and with horizontal equity, or fairness across populations regardless of need. Both accessibility and equity were goals of the 2009 vaccination campaign for the novel H1N1a influenza virus, including during the period when demand for vaccine exceeded supply. Distribution system design can influence equity and accessibility at the local level. We develop a general methodology that integrates optimization, game theory, and spatial statistics to measure potential spatial accessibility across a network, where we quantify spatial accessibility by travel distance and scarcity. We estimate and make inference on local (census-tract level) associations between accessibility and geographic, socioeconomic, and health care infrastructure factors to identify potential inequities in vaccine accessibility during the 2009 H1N1 vaccination campaign in the U.S. We find that there were inequities in access to vaccine at the local level and that these were associated with factors including population density and health care infrastructure. Our methodology for measuring and explaining accessibility leads to policy recommendations for federal, state, and local public health officials. The spatial-specific results inform the development of equitable distribution plans for future public health efforts.

Keywords: Public health; Potential spatial accessibility; Game theory; Network optimization; Statistics (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10729-015-9338-y

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