Yushim Kim

Community knowledge, risk perception, and preparedness for the 2009 influenza A/H1N1 pandemic.

OBJECTIVE To examine public knowledge, perceptions, and preparedness for the 2009 influenza A/H1N1 pandemic. DESIGN We conducted a telephone survey of selected households in Arizona during the month of October 2009. RESULTS Among the 727 households interviewed, one-third (34%) were not aware that the terms swine flu and H1N1 refer to the same virus. Many believed that it is more difficult to contract 2009 H1N1 (27%) than seasonal influenza (14%). About three-quarters of respondents perceived the H1N1 situation as urgent (76%), but only about one-third of those surveyed believed a family member would get sick with H1N1 within a year (35%). Approximately half (53%) of those surveyed intended to get the H1N1 influenza vaccine. Family doctors, television news, and local public health officials were the most trusted sources for H1N1 information. CONCLUSIONS The survey highlighted a number of important misconceptions about H1N1 knowledge, treatment options and transmissibility. Increased efforts should be made to understand how messages are transmitted and received in the community during a pandemic to improve risk communication plans moving forward.

A Computational Approach to Managing Performance Dynamics in Networked Governance Systems

Governance systems continue to become more networked, collaborative, and interdependent. A computational approach to understanding and capitalizing on the complexity of such systems can provide invaluable insights on managing and enhancing performance. Building upon a complex adaptive systems view, this article demonstrates the use of computer simulation modeling to understand performance in networked governance systems and inform practitioners on how benefits can be harvested from the evolution of governance structures. The article contributes to the performance management field by directing attention to ex ante conditions and dynamic tensions among multiple stakeholders, in contrast to collecting ex post performance data. It also discusses the inherent challenges of a computational approach and how they can be mitigated.

Helping those like us or harming those unlike us: Illuminating social processes leading to environmental injustice

Several theories have been proposed to explain societal environmental injustices. Studies based on standard statistical methods and empirical data are often limited in testing some of these theories. This is especially true when some potential reasons (eg, racism) for unjust environmental outcomes are invidious, and even individual-level methods (eg, surveys) are unlikely to be effective in detecting them. We use agent-based modeling to explore the circumstances under which racially defined environmental injustice occurs in a society. We test three competing theories of an environmental disamenitys location decision: cost factors alone, benign intention for the majority population, or malign intention for the minority population, along with three scenarios of residential similarity preferences. The simulation demonstrates that a purely neoclassical world—one in which firms and residents care only about costs—does not lead to environmental injustice. Nor does a similar world in which disamenity-producing firms seek to locate away from majority residents. Instead, two conditions led to societal environmental injustice: when disamenity-producing firms aim to locate near minorities or when residents prefer to live near other residents like themselves. In our model, a race-conscious society rather than just a collection of race-conscious firms produced significant levels of environmental injustice.

Local Zoning and Environmental Justice: An Agent-Based Model Analysis

This article presents an agent-based computational analysis of the effects of externality zoning on environmental justice (EJ). We experiment with two ideal types of externality zoning: proactive and reactive. In the absence of zoning, environmental injustice emerges and minority agents have lower average environmental quality than majority agents. With proactive zoning, which allows polluting firms only in designated zones, EJ problems are less severe and appear more tractable. With reactive zoning, which creates buffering zones around polluting firms, environmental injustice tends to emerge more quickly as compared with proactive zoning but tends to decline over time. This analysis examines a possible policy tool available for cities to ameliorate environmental injustice.

An Empirical Examination of Validity in Evaluation

This study addresses validity issues in evaluation that stem from Ernest R. House’s book, Evaluating With Validity. The authors examine American Journal of Evaluation articles from 1980 to 2010 that report the results of policy and program evaluations. The authors classify these evaluations according to House’s “major approaches” typology (Systems Analysis, Behavioral Objectives, Decision making, Goal-free, Professional Review, Art Criticism, Quasi-legal, and Case Study) and the types of validity (measurement, design, interpretation, use) the evaluations consider. Analyzing the intersection of evaluation type and validity type, the authors explore the status of House’s standards of Truth, Beauty, and Justice in evaluation practice.

Using Model Replication to Improve the Reliability of Agent-Based Models

The basic presupposition of model replication activities for a computational model such as an agent-based model (ABM) is that, as a robust and reliable tool, it must be replicable in other computing settings. This assumption has recently gained attention in the community of artificial society and simulation due to the challenges of model verification and validation. Illustrating the replication of an ABM representing fraudulent behavior in a public service delivery system originally developed in the Java-based MASON toolkit for NetLogo by a different author, this paper exemplifies how model replication exercises provide unique opportunities for model verification and validation process. At the same time, it helps accumulate best practices and patterns of model replication and contributes to the agenda of developing a standard methodological protocol for agent-based social simulation.

Incorporating Policy Criteria in Spatial Analysis

Although spatial analysis and Geographic Information Systems (GIS) have become prevalent in the social and policy sciences, GIS and its mapmaking capability remains an underutilized tool among the decision support tools available to policy makers. Using a case study of Medicaid expenditure changes in Ohio, the authors demonstrate how spatial analysis and display can incorporate useful weights for policy makers. Through the use of dependence indices based upon the distributions of the affected recipients and service providers to weight the expenditures, the link between the effects of policy changes and the spatial distributions of these populations becomes clearer. The article argues that policy makers can be given a more appropriate picture of the potential local implications of statewide policy changes through the use of weights. Because of the power of maps to so starkly display these distributions, the article concludes with a caution that such tools should be used ethically with considered judgment.

Public risk perceptions and preventive behaviors during the 2009 H1N1 influenza pandemic

OBJECTIVE This study examines the public perception of the 2009 H1N1 influenza risk and its association with flu-related knowledge, social contexts, and preventive behaviors during the second wave of the influenza outbreak in Arizona. METHODS Statistical analyses were conducted on survey data, which were collected from a random-digit telephone survey of the general public in Arizona in October 2009. RESULTS The public perceived different levels of risk regarding the likelihood and their concern about contracting the 2009 H1N1 flu. These measures of risk perception were primarily correlated with people of Hispanic ethnicity, having children in the household, and recent seasonal flu experience in the previous year. The perceived likelihood was not strongly associated with preventive behaviors, whereas the perceived concern was significantly associated with precautionary and preparatory behaviors. The association between perceived concern and precautionary behavior persisted after controlling for demographic characteristics. CONCLUSIONS Pandemic preparedness and response efforts need to incorporate these findings to help develop effective risk communication strategies that properly induce preventive behaviors among the public.

USING BAYESIAN METHODS TO CONTROL FOR SPATIAL AUTOCORRELATION IN ENVIRONMENTAL JUSTICE RESEARCH: AN ILLUSTRATION USING TOXICS RELEASE INVENTORY DATA FOR A SUNBELT COUNTY

ABSTRACT: Many previous environmental justice (EJ) studies have argued that there is disproportionate collocation of environmental disamenities with racial and ethnic minorities, even holding constant other factors such as income and political action. However, most of the EJ studies do not account for the presence of spatial autocorrelation, especially those that also include nonnormal distributions. Using the location of new Toxics Release Inventory facilities (TRIFs) in Maricopa County, Arizona in the 1990s, we illustrate a finding of spatial autocorrelation and the use of Bayesian spatial models to accommodate the issue. The results show that the relationship between Asian minority status in a census tract and new TRIF establishments found with regression models does not remain statistically significant once spatial autocorrelation is accounted for. Instead, three variables, the percentage of American Indians in the tract, population density, and the percentage of residents aged 55–74, statistically significantly explained new TRIF establishments. This illustrates that failure to control for spatial autocorrelation can lead to incorrect policy understanding.

Modeling dynamics of an influenza pandemic with heterogeneous coping behaviors: case study of a 2009 H1N1 outbreak in Arizona

This paper aims to improve the accuracy of standard compartment models in modeling the dynamics of an influenza pandemic. Standard compartment models, which are commonly used in influenza simulations, make unrealistic assumptions about human behavioral responses during a pandemic outbreak. Existing simulation models with public avoidance also make a rigid assumption regarding the human behavioral response to influenza. This paper incorporates realistic assumptions regarding individuals’ avoidance behaviors in a standard compartment model. Both the standard and modified models are parameterized, implemented, and compared in the research context of the 2009 H1N1 influenza outbreak in Arizona. The modified model with heterogeneous coping behaviors forecasts influenza spread dynamics better than the standard model when evaluated against the empirical data, especially for the beginning of the 2009–2010 normal influenza season starting in October 2009 (i.e., the beginning of the second wave of 2009 H1N1). We end the paper with a discussion of the use of simulation models in efforts to help communities effectively prepare for and respond to influenza pandemics.