Increasing the Accuracy of EpiPen Administration With a Brief Behavioral Skills Training Package in a School for Autism

Behavioral staff at a school for children with autism investigated the effects of a brief behavioral skills training procedure to promote the appropriate administration of the EpiPen in an emergency situation by school staff. A 10-item task analysis was created outlining the steps required to use the EpiPen effectively and safely and was validated by the school’s registered nurse. Following a pretest in which members of both groups completed a minimal number of steps, the experimental group was trained via instructions, modeling, praise, feedback, and role playing to correctly use the EpiPen whereas the control group received no such training. Posttest scores indicated that the brief intervention was an effective means of teaching appropriate administration of the EpiPen with school staff.     

Characterizing the Performance of the Conway‐Maxwell Poisson Generalized Linear Model

Count data are pervasive in many areas of risk analysis; deaths, adverse health outcomes, infrastructure system failures, and traffic accidents are all recorded as count events, for example. Risk analysts often wish to estimate the probability distribution for the number of discrete events as part of doing a risk assessment. Traditional count data regression models of the type often used in risk assessment for this problem suffer from limitations due to the assumed variance structure. A more flexible model based on the Conway‐Maxwell Poisson (COM‐Poisson) distribution was recently proposed, a model that has the potential to overcome the limitations of the traditional model. However, the statistical performance of this new model has not yet been fully characterized. This article assesses the performance of a maximum likelihood estimation method for fitting the COM‐Poisson generalized linear model (GLM). The objectives of this article are to (1) characterize the parameter estimation accuracy of the MLE implementation of the COM‐Poisson GLM, and (2) estimate the prediction accuracy of the COM‐Poisson GLM using simulated data sets. The results of the study indicate that the COM‐Poisson GLM is flexible enough to model under‐, equi‐, and overdispersed data sets with different sample mean values. The results also show that the COM‐Poisson GLM yields accurate parameter estimates. The COM‐Poisson GLM provides a promising and flexible approach for performing count data regression.     

Artificial Intelligence for Natural Hazards Risk Analysis: Potential,Challenges, and Research Needs

Artificial intelligence (AI) methods have seen increasingly widespread use in everything from consumer products and driverless cars to fraud detection and weather forecasting. The use of AI has transformed many of these application domains. There are ongoing efforts at leveraging AI for disaster risk analysis. This article takes a critical look at the use of AI for disaster risk analysis. What is the potential? How is the use of AI in this field different from its use in nondisaster fields? What challenges need to be overcome for this potential to be realized? And, what are the potential pitfalls of an AI-based approach for disaster risk analysis that we as a society must be cautious of?     

Stranger Danger: Redistricting,Incumbent Recognition,and Vote Choice*

Objectives. We take a step forward in examining the electoral effects of redistricting by: (1) demonstrating that voters with a new incumbent because of redistricting are less likely to recognize their representative, and (2) voters are less likely to vote for their representative if they fail to recognize him or her. Methods. Our data come from a survey of white respondents who resided in the redrawn Eighth District of Georgia for the 2006 U.S. House elections. We use probit regressions to first measure the effect of redistricting on incumbent recognition. Then, we assess the likelihood of voting for the incumbent depending on whether a respondent was redrawn or has the same incumbent after redistricting, and whether or not the respondent could recognize his or her representative. Results. Our analyses make it clear that redrawn voters were much less likely to recognize their incumbent and it is the inability to recognize one's incumbent, irrespective of whether the representative has changed due to redistricting, which accounts for a reduced likelihood of voting for the incumbent. Conclusions. Other scholars have examined the relationship between redistricting and incumbent recognition. Likewise, many have evaluated the effects of redistricting on vote choice. This article, however, is the first to merge these two relationships. We find that redrawn constituents are less likely to know who their representative is, and it is indeed a lack of familiarity that reduces an incumbent's vote share. Thus, we have shown empirically that the absence of a personal vote, which is exacerbated by redistricting, proves electorally harmful to the incumbent.     

Xenophobia and immigrant contact: French public attitudes toward immigration

How does the presence of immigrants in a local community affect xenophobic attitudes? Does contact with immigrants ameliorate or exacerbate anti-immigrant attitudes among citizens? Synthesizing public opinion, economic, and demographic data from France, we test hypotheses concerning the relationship between the presence of immigrant populations and xenophobic sentiments. Supportive of the contact theory, we find that larger immigrant populations decrease xenophobic attitudes. This finding challenges much of the country-level research on immigrant concentration and xenophobia and offers some hope for those who are concerned about the rise of xenophobia and the radical right in the midst of diverse European polities.     

Improving the Predictive Accuracy of Hurricane Power Outage Forecasts Using Generalized Additive Models

Electric power is a critical infrastructure service after hurricanes, and rapid restoration of electric power is important in order to minimize losses in the impacted areas. However, rapid restoration of electric power after a hurricane depends on obtaining the necessary resources, primarily repair crews and materials, before the hurricane makes landfall and then appropriately deploying these resources as soon as possible after the hurricane. This, in turn, depends on having sound estimates of both the overall severity of the storm and the relative risk of power outages in different areas. Past studies have developed statistical, regression-based approaches for estimating the number of power outages in advance of an approaching hurricane. However, these approaches have either not been applicable for future events or have had lower predictive accuracy than desired. This article shows that a different type of regression model, a generalized additive model (GAM), can outperform the types of models used previously. This is done by developing and validating a GAM based on power outage data during past hurricanes in the Gulf Coast region and comparing the results from this model to the previously used generalized linear models.     

Comparison of nonparametric point estimators for interquantile differences in moderate sized samples

Non-parametric estimators for interquantile differences in moderate sized samples are identified, including a simple method based on sample quantiles, an extension of the Harrell and Davis (1982) method, an extension of the Kaigh and Lachenbruch (1982) approach, and methods based on Yang (1985). For those estimators compared via a simulation study (Harrell-Davis and sample quantile), using relative bias and relative efficiency as judgment criteria, the Harrell-Davis method is judged to be preferable for interquantile estimation with data sets of moderate size.     

Individual- and Contextual-Level Factors Affecting the Use of Social Support Services among Older Adults

ABSTRACT

By 2060, the number of Americans aged 65 and older is expected to more than double, while the number of Americans aged 85 and older is expected to nearly triple. As the nation's aging population grows, older adults will need to rely on social support services, such as transportation and housing services, in order to remain active and lead independent lives. In this study we use data collected from the elderly supplement of the Southeastern Pennsylvania Household Health Survey (SPHHS) (n = 3,042) to explore the relationship between the availability of elderly specific social service providers and utilization of social support services among older adults. We find that while the number of elderly specific social service providers can increase use of social support services among older adults, its impact is relatively minimal. We find that individual factors, instead, are stronger predictors of service use. This is a finding that should be particularly encouraging for elder care providers who may not have the resources needed to undertake large structural changes (like building new facilities). Still, future research should explore how the availability of a broader range of elderly specific social services (than explored in this study) impacts use.     

Financial Reforms of Statutory Bodies in Singapore: Control and Autonomy in a Centralized State

This article explores the major financial reforms of both government-funded and self-funded statutory bodies in Singapore over the last 7 years. The reforms have been based on two models of administrative reform: the business enterprise model and the bureaucratic efficiency model. In accordance with the business enterprise model, changes have been made to place statutory bodies on a more commercial footing, requiring them to adopt the financial and management practices found in business enterprises and, in doing so, to become more financially self-reliant. The autonomy expected to flow from these reforms has been circumscribed by the retention of key decision-making responsibilities and veto powers at the center of government. The reforms thus indicate ambivalence in the thinking of political and administrative leaders in relation to the management of statutory bodies, allowing them greater freedom as business-like enterprises, but still subjecting them to centrally imposed restrictions and directives.