Human Salmonellosis: Estimation of Dose-Illness from Outbreak Data

The quantification of the relationship between the amount of microbial organisms ingested and a specific outcome such as infection, illness, or mortality is a key aspect of quantitative risk assessment. A main problem in determining such dose-response models is the availability of appropriate data. Human feeding trials have been criticized because only young healthy volunteers are selected to participate and low doses, as often occurring in real life, are typically not considered. Epidemiological outbreak data are considered to be more valuable, but are more subject to data uncertainty. In this article, we model the dose-illness relationship based on data of 20 Salmonella outbreaks, as discussed by the World Health Organization. In particular, we model the dose-illness relationship using generalized linear mixed models and fractional polynomials of dose. The fractional polynomial models are modified to satisfy the properties of different types of dose-illness models as proposed by Teunis et al . Within these models, differences in host susceptibility (susceptible versus normal population) are modeled as fixed effects whereas differences in serovar type and food matrix are modeled as random effects. In addition, two bootstrap procedures are presented. A first procedure accounts for stochastic variability whereas a second procedure accounts for both stochastic variability and data uncertainty. The analyses indicate that the susceptible population has a higher probability of illness at low dose levels when the combination pathogen-food matrix is extremely virulent and at high dose levels when the combination is less virulent. Furthermore, the analyses suggest that immunity exists in the normal population but not in the susceptible population.     

Local influence diagnostics for incomplete overdispersed longitudinal counts

We develop local influence diagnostics to detect influential subjects when generalized linear mixed models are fitted to incomplete longitudinal overdispersed count data. The focus is on the influence stemming from the dropout model specification. In particular, the effect of small perturbations around an MAR specification are examined. The method is applied to data from a longitudinal clinical trial in epileptic patients. The effect on models allowing for overdispersion is contrasted with that on models that do not.     

Before you open the doors: ten lessons from Florida's Faith and Community-Based Delinquency Treatment Initiative

In 2003, the Office of Juvenile Justice and Delinquency Prevention (OJJDP) provided Florida's Department of Juvenile Justice (DJJ) with a $3.5 million grant to design and implement a faith- and community-based program for incarcerated juveniles. Florida's experience developing a completely new program has provided valuable lessons regarding important issues to consider and address before clients are referred to the program. This article enumerates 10 key lessons from Florida's experience that can provide important guidance to other sites across the country hoping to develop faith-related services for youths in institutions.     

Integrated nested Laplace approximation for the analysis of count data via the combined model: A simulation study

The combined model accounts for different forms of extra-variability and has traditionally been applied in the likelihood framework, or in the Bayesian setting via Markov chain Monte Carlo. In this article, integrated nested Laplace approximation is investigated as an alternative estimation method for the combined model for count data, and compared with the former estimation techniques. Longitudinal, spatial, and multi-hierarchical data scenarios are investigated in three case studies as well as a simulation study. As a conclusion, integrated nested Laplace approximation provides fast and precise estimation, while avoiding convergence problems often seen when using Markov chain Monte Carlo.     

Parenting in the Context of Deportation Risk

Nearly 5.1 million children younger than age 18 live with at least one undocumented parent, about 7% of the U.S. child population. Between 2010 and 2013, an estimated 300,000 parents of U.S. citizen children were deported. Raising children in the context of deportation risk increases overall parenting stress for undocumented Latino parents. To investigate this and understand the experience of undocumented parenting, the authors interviewed 70 undocumented parents in two Southwest cities from 2012 to 2013. The authors frame their analysis using the lens of the problem of “illegality.” There are three domains of stressors associated with parenting in the context of deportation risk: trapped parenting, threat of family separation, and altered family processes. The authors discuss these findings in the context of the literature on undocumented families and parenting stress and connect these findings to the current sociopolitical context experienced by Latino families in the United States .     

Building relational capital: The contribution of episodic and relational community engagement

In this study, we provide evidence of the theorized connection between community engagement and the development of social capital, and the perceived value or worth of relationships among organizations and stakeholders. Using thematic analysis to understand the policy and practice frameworks of community engagement in Australian local government organizations, our analyses reveal two different types of community engagement—relational and episodic—each of which has the potential to contribute to relational dimension of social capital. The study introduces and develops new thinking around the ideas of episodic and relational engagement within the context of community engagement, and their respective contributions to the development of relational capital. Recognizing and identifying episodic and relational community engagement as separate phenomena allows researchers and practitioners to understand the theoretical dimensions of community engagement as a framework for practice.     

Local influence diagnostics for generalized linear mixed models with overdispersion

Since the seminal paper by Cook and Weisberg [9 R.D. Cook and S. Weisberg, Residuals and Influence in Regression, Chapman &; Hall, London, 1982. [Google Scholar]], local influence, next to case deletion, has gained popularity as a tool to detect influential subjects and measurements for a variety of statistical models. For the linear mixed model the approach leads to easily interpretable and computationally convenient expressions, not only highlighting influential subjects, but also which aspect of their profile leads to undue influence on the model's fit [17 E. Lesaffre and G. Verbeke, Local influence in linear mixed models, Biometrics 54 (1998), pp. 570–582. doi: 10.2307/3109764[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]]. Ouwens et al. [24 M.J.N.M. Ouwens, F.E.S. Tan, and M.P.F. Berger, Local influence to detect influential data structures for generalized linear mixed models, Biometrics 57 (2001), pp. 1166–1172. doi: 10.1111/j.0006-341X.2001.01166.x[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]] applied the method to the Poisson-normal generalized linear mixed model (GLMM). Given the model's nonlinear structure, these authors did not derive interpretable components but rather focused on a graphical depiction of influence. In this paper, we consider GLMMs for binary, count, and time-to-event data, with the additional feature of accommodating overdispersion whenever necessary. For each situation, three approaches are considered, based on: (1) purely numerical derivations; (2) using a closed-form expression of the marginal likelihood function; and (3) using an integral representation of this likelihood. Unlike when case deletion is used, this leads to interpretable components, allowing not only to identify influential subjects, but also to study the cause thereof. The methodology is illustrated in case studies that range over the three data types mentioned.