The Development of Impulse Control and Sensation‐Seeking in Adolescence: Independent or Interdependent Processes?

This study examines whether changes in impulse control and sensation‐seeking across adolescence and early adulthood reflect independent or interdependent developmental processes. Data are drawn from a national longitudinal study (N = 8,270; 49% female; 33% Black, 22% Hispanic, 45% non‐Black, non‐Hispanic). An autoregressive latent trajectory model is used to test whether development in one trait influences development in the other. Although levels of these traits are inversely correlated, we do not find evidence that change over time in either trait is influenced by the prior level of the other. This failure to reject the null hypothesis is consistent with the view that sensation‐seeking and impulse control are the products of distinct neuropsychological systems that develop independently of one another.     

Deriving Behavior Model Parameters from Survey Data: Self‐Protective Behavior Adoption During the 2009–2010 Influenza A(H1N1) Pandemic

In this paper, we demonstrate how public opinion surveys can be designed to collect information pertinent to computational behavior modeling, and we present the results of a public opinion and behavior survey conducted during the 2009–2010 H1N1 influenza pandemic. The results are used to parameterize the Health Belief Model of individual health‐protective decision making. Survey subjects were asked questions about their perceptions of the then‐circulating influenza and attitudes towards two personal protective behaviors: vaccination and avoidance of crowds. We empirically address two important issues in applying the Health Belief Model of behavior to computational infectious disease simulation: (1) the factors dynamically influencing the states of the Health Belief Model variables and (2) the appropriateness of the Health Belief Model in describing self‐protective behavior in the context of pandemic influenza.     

Shared frailty model based on reversed hazard rate for left censored data

Frailty models are used in the survival analysis to account for the unobserved heterogeneity in the individual risks to disease and death. To analyze the bivariate data on related survival times (e.g., matched pairs experiments, twin or family data), the shared frailty models were suggested. In this article, we introduce the shared gamma frailty models with the reversed hazard rate. We develop the Bayesian estimation procedure using the Markov chain Monte Carlo (MCMC) technique to estimate the parameters involved in the model. We present a simulation study to compare the true values of the parameters with the estimated values. We apply the model to a real life bivariate survival dataset.     

Feeding Patterns and Emotional Care in Breastfed Infants

A number of studies have suggested breastfed infants have improved bonding and attachment or cognitive development outcomes. However, mechanisms by which these differences might develop are poorly understood. We used maternal time use data to examine whether exclusively breastfeeding mothers spend more time in close interactive behaviors with their infants than mothers who have commenced or completed weaning. Mothers (188) participating in a time use survey recorded infant feeding activities for 24 h over a 7 day period using an electronic device. Tracking was conducted at 3, 6, and 9 months postpartum. Data was collected for maternal activities including infant feeding and time spent in emotional care. The mothers of exclusively breastfed infants aged 3–6 months fed them frequently and total time spent in breastfeeding averaged around 17 h a week. Maternal time spent in emotional care was also substantial, and found to correlate positively with time spent breastfeeding. Exclusively breastfed infants received greatest amounts of emotional care from their mother, and exclusively formula fed infants the least. Mixed fed infants received more emotional care time than formula fed infants, but less than fully breastfed infants. These findings may help explain the differential cognitive developmental outcomes reported in the medical literature for breastfed and non breastfed infants.     

An overlook on statistical inference issues for stochastic damping hamiltonian systems under the fluctuation-dissipation condition

ABSTRACT

In this paper, we are interested in nonparametric inference issues for stochastic damping hamiltonian systems under the fluctuation-dissipation condition. This condition relates the magnitude of the dissipative term and the magnitude of the random term. The precise balance between the drift term which removes energy in average and the stochastic term provided by the fluctuation-dissipation relation insures that the canonical measure is preserved by the dynamics. In this framework, it is possible to give an explicit construction of a Lyapunov function and thus to prove exponential ergodicity. Then, we consider various estimation procedures and provide also a numerical section, where simulations are conducted.     

The Medical Assessment of Migrants: Current Limitations and Future Potential

Attempts to control the importation of infectious diseases through the medical screening and evaluation of immigrants and refugees represent the modern application of some of the earliest recorded public health interventions.
States with long-standing immigration programmes continue to require the medical examination and screening of migrants for certain diseases. In some instances, the public health effectiveness of these immigration medical assessments is of questionable value when considered from a population health basis.
This article reviews current practices and describes recent studies where more modern and epidemiologically based immigration medical interventions have been undertaken. A more effective immigration medical assessment process is proposed through the use of results of this more empirical approach to immigration medical screening.     

New Data,Strategies, and Insights for Listeria monocytogenes Dose‐Response Models: Summary of an Interagency Workshop, 2011

Listeria monocytogenes is a leading cause of hospitalization, fetal loss, and death due to foodborne illnesses in the United States. A quantitative assessment of the relative risk of listeriosis associated with the consumption of 23 selected categories of ready‐to‐eat foods, published by the U.S. Department of Health and Human Services and the U.S. Department of Agriculture in 2003, has been instrumental in identifying the food products and practices that pose the greatest listeriosis risk and has guided the evaluation of potential intervention strategies. Dose‐response models, which quantify the relationship between an exposure dose and the probability of adverse health outcomes, were essential components of the risk assessment. However, because of data gaps and limitations in the available data and modeling approaches, considerable uncertainty existed. Since publication of the risk assessment, new data have become available for modeling L. monocytogenes dose‐response. At the same time, recent advances in the understanding of L. monocytogenes pathophysiology and strain diversity have warranted a critical reevaluation of the published dose‐response models. To discuss strategies for modeling L. monocytogenes dose‐response, the Interagency Risk Assessment Consortium (IRAC) and the Joint Institute for Food Safety and Applied Nutrition (JIFSAN) held a scientific workshop in 2011 (details available at http://foodrisk.org/irac/events/ ). The main findings of the workshop and the most current and relevant data identified during the workshop are summarized and presented in the context of L. monocytogenes dose‐response. This article also discusses new insights on dose‐response modeling for L. monocytogenes and research opportunities to meet future needs.