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MIDAS Study Shows Inequitable Access to Flu Vaccinations Could Worsen Flu Epidemic

Giving wealthier counties greater access to influenza vaccine than poorer counties could worsen a flu epidemic because poor areas have fairly high population densities with higher levels of interaction among households and communities, enabling the infection to spread faster, according to a University of Pittsburgh study.

Published in the June issue of Health Affairs, the study used a detailed computer simulation of the Washington, D.C., metropolitan area and found limiting or delaying the vaccination of residents in poorer counties could raise the total number of influenza infections. Moreover, inequitable access to vaccinations increased the number of new infections during the peak of an epidemic in both poor and wealthier counties – even though the wealthier counties had received more timely and abundant vaccine access.

“When vaccines are in short supply, distributing them quickly and equitably among populations and localities can be a difficult challenge,” said the study’s lead author, Bruce Y. Lee, M.D.,M.B.A., assistant professor of medicine, epidemiology and biomedical informatics at the University of Pittsburgh. “However, policymakers across the country, in poor and wealthy areas alike, have an incentive to ensure that poorer residents have equal access to vaccines.”

Dr. Lee is the Applied Modeling Project principal investigator for the Models of Infectious Disease Agent Study (MIDAS) National Center of Excellence, also at the University of Pittsburgh. He and his co-authors developed the flu vaccination model while working with the Department of Health and Human Services during the 2009 H1N1 pandemic. The team studied how the course of the pandemic might have been affected by vaccinating residents of various counties at different rates and times.

Computer simulation modeling suggested that equitable vaccination could reduce an epidemic’s severity because poorer counties tend to have high-density populations and more higher-risk people — such as children – per household, resulting in more interactions. This leads to increased transmission of influenza and greater risk for poorer influenza outcomes, the study said.

Even with the best intentions, inadequate infrastructure, geographical or socioeconomic barriers or cultural differences can lead to inequitable access to vaccines, Dr. Lee said. Research has shown that poorer people may have less access to medical care, including vaccination, than wealthier people.

The study was supported by the National Institute of General Medical Sciences Models of Infectious Disease Agent Study and the Vaccine Modeling Initiative, funded by the Bill & Melinda Gates Foundation. Co-authors include Shawn T. Brown, Ph.D., Rachel R. Bailey, Richard K. Zimmerman, M.D., Margaret Ann Potter, J.D., Sarah M. McGlone, John J. Grefenstette, Ph.D., Shanta M. Zimmer, M.D., and Sandra Crouse Quinn, Ph.D., all of the University of Pittsburgh; Donald S. Burke, M.D., dean of the University of Pittsburgh Graduate School of Public Health and head of the MIDAS center; Philip Cooley and William D. Wheaton, of RTI International; and Ronald Voorhees, of the Allegheny County Health Department.

MIDAS's Shawn Brown Awarded Grant

Public Health Adaptive Systems Studies (PHASYS), a five year grant from the Centers for Disease Control and Prevention located in the University of Pittsburgh Graduate School of Public Health’s Center for Public Health Practice, conducts research to develop, test, and apply criteria and metrics for measuring the effectiveness and efficiency of preparedness and emergency response to hazards with public health consequences. Each year, PHASYS seeks applications for pilot studies that expand the research capability of the University of Pittsburgh Graduate School of Public Health in the field of public health systems research, with a strong focus on preparedness.

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PHASYS is pleased to announce the 2011 Pilot Study recipient, Shawn T. Brown, PhD, Assistant Professor, Department of Biostatistics University of Pittsburgh Graduate School of Public Health and Research Fellow, Pittsburgh Supercomputing Center for his study “The Geospatial Area and Information Analyzer (GAIA), a visualization tool for understanding emergency preparedness through geospatial analysis.”

As one of the missions of the PHASYS project is to provide a deeper understanding and quantification of emergency preparedness capability, having the ability to map and model such capability geospatially is critical. As part of the National Institute of General Medical Sciences Modeling Infectious Disease Agent Study, Dr. Brown’s group has been developing the Geospatial Area and Information Analyzer to provide the ability to create information based visualization for public health. As part of a PHASYS Arm 1’s Public Health Systems Indicators Project, Luis Duran has augmented the National Association of County and City Health Officials 2008 Survey of Local Health Departments and done preliminary mapping of the data, creating the PHASYS Arm 1 Local Health Departments Preparedness dataset. In this Pilot Study, an interactive, web-based application of Duran’s dataset will be created to provide a dynamic information-based presentation for public health officials to explore this resource. The application will give public health officials, researchers, and the general public the capability to explore this important dataset visually and geographically, providing an intuitive way to interact with the information.

MIDAS researchers play key role in ISSH 2011

MIDAS team members John Grefenstette, Shawn Brown, and Bruce Lee will play a critical role in this year's Institute on Systems Science and Health, held this year at the University of Pittsburgh. 

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The Public Health Dynamics Laboratory at the University of Pittsburgh's Graduate School of Public Health is proud to host the 3rd Annual NIH-CDC Institute on Systems Science and Health (ISSH) at the University of Pittsburgh, May 22-27, 2011. ISSH 2011 is a week-long course providing a thorough introduction to selected systems science methodologies that may be used to study behavioral and social dimensions of public health challenges. After a highly competitive applications process, 45 participants have been selected for one of the following three tracks:

  • Agent-Based Modeling (led by John Grefenstette, Shawn Brown, and Bruce Lee, University of Pittsburgh)

  • System Dynamics Modeling (led by Hazhir Rahmandad, Virginia Tech)

  • Network Analysis (led by Steve Borgatti, University of Kentucky)

The participants will spend six days in Pittsburgh, participating in intensive small-group training sessions and attending lectures from leading researchers on a variety of topics pertaining to systems science. The objective of ISSH is to provide training for investigators who have had little or no formal training in systems science gain a base of knowledge that will enable them to develop proposals to the NIH and CDC for research projects to improve population health and health equity.

ISSH is sponsored by the NIH Office of Behavioral and Social Sciences Research in partnership with the CDC Syndemics Prevention Network. For further information, see the ISSH web site or contact: Patty Mabry, NIH Office of Behavioral and Social Science Research (This email address is being protected from spambots. You need JavaScript enabled to view it.; 301-402-1753).

Supercomputers Alter Science

A recent NYTimes article explores the impact supercomputing is having on science. Says author John Markoff, "Computing is reshaping scientific research...[it] has made it easier to share research findings, and that in turn has led to an explosion of collaborative efforts. It has also accelerated the range of cross-disciplinary projects as it has become easier to repurpose and combine software-based techniques ranging from analytical tools to utilities for exporting and importing data." Markoff further states what MIDAS research teams have long known: "Computer power not only aids research, it defines the nature of that research: what can be studied, what new questions can be asked, and answered."

For MIDAS, simulating large-scale epidemics requires the computational power of supercomputers, provided in large part by the Pittsburgh Supercomputing Center, a joint venture between the University of Pittsburgh and Carnegie Mellon University. Increasingly complex problems require complex models, and a popular tool for MIDAS researchers is what is known as Agent-Based Modeling (ABM), where individual persons are represented as autonomous "agents" who move within a given population. Agent populations are constructed using real data (for example, the U.S. Census) and individuals may be assigned to households, their children to schools, and may go to workplaces with a specific commuting distance, and other demographic factors.

Because Agent Based Models (ABM) represent an entire population inside the computer,they require large amounts of memory. For a simulation of H1N1 spread in the DC metropolitan area, MIDAS’s ABM included 7.4 million people, requiring seven gigabytes of memory. “This is a shared-memory problem,” notes Shawn Brown, head of MIDAS's Computational Team, referring to massively parallel systems that allow each processor to access all the memory without message passing. Brown and the MIDAS team are now working on scaling up their ABM model to cover the entire United States, incorporating a population of 300-million agents and requiring from 74 to 300 gigabytes of memory. 

Read the full story from the NYTimes here, and visit the Pittsburgh Supercomputing Center's homepage here.

 
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