Vanja Dukic

Interaction of prenatal exposure to cigarettes and MAOA genotype in pathways to youth antisocial behavior

Genetic susceptibility to antisocial behavior may increase fetal sensitivity to prenatal exposure to cigarette smoke. Testing putative gene × exposure mechanisms requires precise measurement of exposure and outcomes. We tested whether a functional polymorphism in the gene encoding the enzyme monoamine oxidase A (MAOA) interacts with exposure to predict pathways to adolescent antisocial behavior. We assessed both clinical and information-processing outcomes. One hundred seventy-six adolescents and their mothers participated in a follow-up of a pregnancy cohort with well-characterized exposure. A sex-specific pattern of gene × exposure interaction was detected. Exposed boys with the low-activity MAOA 5′ uVNTR (untranslated region variable number of tandem repeats) genotype were at increased risk for conduct disorder (CD) symptoms. In contrast, exposed girls with the high-activity MAOA uVNTR genotype were at increased risk for both CD symptoms and hostile attribution bias on a face-processing task. There was no evidence of a gene–environment correlation (rGE). Findings suggest that the MAOA uVNTR genotype, prenatal exposure to cigarettes and sex interact to predict antisocial behavior and related information-processing patterns. Future research to replicate and extend these findings should focus on elucidating how gene × exposure interactions may shape behavior through associated changes in brain function.

Epidemics of community-associated methicillin-resistant Staphylococcus aureus in the United States: a meta-analysis.

Staphylococcus aureus is the most frequent cause of skin and soft tissue infections in humans. Methicillin-resistant strains of S. aureus (MRSA) that emerged in the 1960s presented a relatively limited public health threat until the 1990s, when novel community-associated (CA-) MRSA strains began circulating. CA-MRSA infections are now common, resulting in serious and sometimes fatal infections in otherwise healthy people. Although some have suggested that there is an epidemic of CA-MRSA in the U.S., the origins, extent, and geographic variability of CA-MRSA infections are not known. We present a meta-analysis of published studies that included trend data from a single site or region, and derive summary epidemic curves of CA-MRSA spread over time. Our analysis reveals a dramatic increase in infections over the past two decades, with CA-MRSA strains now endemic at unprecedented levels in many US regions. This increase has not been geographically homogeneous, and appears to have occurred earlier in children than adults.

Uncertainty in predictions of disease spread and public health responses to bioterrorism and emerging diseases

Concerns over bioterrorism and emerging diseases have led to the widespread use of epidemic models for evaluating public health strategies. Partly because epidemic models often capture the dynamics of prior epidemics remarkably well, little attention has been paid to how uncertainty in parameter estimates might affect model predictions. To understand such effects, we used Bayesian statistics to rigorously estimate the uncertainty in the parameters of an epidemic model, focusing on smallpox bioterrorism. We then used a vaccination model to translate the uncertainty in the model parameters into uncertainty in which of two vaccination strategies would provide a better response to bioterrorism, mass vaccination, or vaccination of social contacts, so-called “trace vaccination.” Our results show that the uncertainty in the model parameters is remarkably high and that this uncertainty has important implications for vaccination strategies. For example, under one plausible scenario, the most likely outcome is that mass vaccination would save ≈100,000 more lives than trace vaccination. Because of the high uncertainty in the parameters, however, there is also a substantial probability that mass vaccination would save 200,000 or more lives than trace vaccination. In addition to providing the best response to the most likely outcome, mass vaccination thus has the advantage of preventing outcomes that are only slightly less likely but that are substantially more horrific. Rigorous estimates of uncertainty thus can reveal hidden advantages of public health strategies, suggesting that formal uncertainty estimation should play a key role in planning for epidemics.

Tracking Epidemics With Google Flu Trends Data and a State-Space SEIR Model

In this article, we use Google Flu Trends data together with a sequential surveillance model based on state-space methodology to track the evolution of an epidemic process over time. We embed a classical mathematical epidemiology model [a susceptible-exposed-infected-recovered (SEIR) model] within the state-space framework, thereby extending the SEIR dynamics to allow changes through time. The implementation of this model is based on a particle filtering algorithm, which learns about the epidemic process sequentially through time and provides updated estimated odds of a pandemic with each new surveillance data point. We show how our approach, in combination with sequential Bayes factors, can serve as an online diagnostic tool for influenza pandemic. We take a close look at the Google Flu Trends data describing the spread of flu in the United States during 2003–2009 and in nine separate U.S. states chosen to represent a wide range of health care and emergency system strengths and weaknesses. This article has online supplementary materials.

Analysis of repeated pregnancy outcomes

Women tend to repeat reproductive outcomes, with past history of an adverse outcome being associated with an approximate two-fold increase in subsequent risk. These observations support the need for statistical designs and analyses that address this clustering. Failure to do so may mask effects, result in inaccurate variance estimators, produce biased or inefficient estimates of exposure effects. We review and evaluate basic analytic approaches for analysing reproductive outcomes, including ignoring reproductive history, treating it as a covariate or avoiding the clustering problem by analysing only one pregnancy per woman, and contrast these to more modern approaches such as generalized estimating equations with robust standard errors and mixed models with various correlation structures. We illustrate the issues by analysing a sample from the Collaborative Perinatal Project dataset, demonstrating how the statistical model impacts summary statistics and inferences when assessing etiologic determinants of birth weight.

NONCONVULSIVE SEIZURES IN SUBARACHNOID HEMORRHAGE LINK INFLAMMATION AND OUTCOME

Nonconvulsive seizures (NCSz) are frequent following acute brain injury and have been implicated as a cause of secondary brain injury, but mechanisms that cause NCSz are controversial. Proinflammatory states are common after many brain injuries, and inflammation‐mediated changes in blood–brain barrier permeability have been experimentally linked to seizures.

Modeling the transmission of community-associated methicillin-resistant Staphylococcus aureus: a dynamic agent-based simulation

BackgroundMethicillin-resistant Staphylococcus aureus (MRSA) has been a deadly pathogen in healthcare settings since the 1960s, but MRSA epidemiology changed since 1990 with new genetically distinct strain types circulating among previously healthy people outside healthcare settings. Community-associated (CA) MRSA strains primarily cause skin and soft tissue infections, but may also cause life-threatening invasive infections. First seen in Australia and the U.S., it is a growing problem around the world. The U.S. has had the most widespread CA-MRSA epidemic, with strain type USA300 causing the great majority of infections. Individuals with either asymptomatic colonization or infection may transmit CA-MRSA to others, largely by skin-to-skin contact. Control measures have focused on hospital transmission. Limited public health education has focused on care for skin infections.MethodsWe developed a fine-grained agent-based model for Chicago to identify where to target interventions to reduce CA-MRSA transmission. An agent-based model allows us to represent heterogeneity in population behavior, locations and contact patterns that are highly relevant for CA-MRSA transmission and control. Drawing on nationally representative survey data, the model represents variation in sociodemographics, locations, behaviors, and physical contact patterns. Transmission probabilities are based on a comprehensive literature review.ResultsOver multiple 10-year runs with one-hour ticks, our model generates temporal and geographic trends in CA-MRSA incidence similar to Chicago from 2001 to 2010. On average, a majority of transmission events occurred in households, and colonized rather than infected agents were the source of the great majority (over 95%) of transmission events. The key findings are that infected people are not the primary source of spread. Rather, the far greater number of colonized individuals must be targeted to reduce transmission.ConclusionsOur findings suggest that current paradigms in MRSA control in the United States cannot be very effective in reducing the incidence of CA-MRSA infections. Furthermore, the control measures that have focused on hospitals are unlikely to have much population-wide impact on CA-MRSA rates. New strategies need to be developed, as the incidence of CA-MRSA is likely to continue to grow around the world.

Internet queries and methicillin-resistant Staphylococcus aureus surveillance.

The Internet is a common source of medical information and has created novel surveillance opportunities. We assessed the potential for Internet-based surveillance of methicillin-resistant Staphylococcus aureus and examined the extent to which it reflects trends in hospitalizations and news coverage. Google queries were a useful predictor of hospitalizations for methicillin-resistant S. aureus infections.

Unpacking the association: Individual differences in the relation of prenatal exposure to cigarettes and disruptive behavior phenotypes

Prenatal exposure to cigarettes has been robustly associated with disruptive behavior in diverse samples and across developmental periods. In this paper we aim to elucidate exposure related behavioral phenotypes and developmental pathways by testing: (a) differential associations of exposure and four disruptive behavior dimensional phenotypes: Aggression, Noncompliance, Temper Loss and Low Concern for Others; and (b) moderation of these pathways including sex differences and moderation by parental responsive engagement. Participants were 211 teens and their parents from the East Boston Family Study (EBFS), an adolescent follow-up of a pregnancy cohort over-sampled for exposure. A best estimate serum cotinine corrected score was used to characterize exposure. In multivariate models controlling for parental antisocial behavior, family adversity and secondhand exposure, exposure uniquely predicted Aggression and Noncompliance. Paternal responsiveness moderated exposure effects on disruptive behavior. There were no sex differences in these patterns. Phenotypic findings suggest the possibility of specific neural mechanisms. In conjunction with prior research, protective effects of parental responsiveness occurring as late as adolescence point to the potential benefit of parenting-based prevention efforts to reduce risk to exposed offspring.

Predicting Multivariate Insurance Loss Payments Under the Bayesian Copula Framework

The literature of predicting the outstanding liability for insurance companies has undergone rapid and profound changes in the past three decades, most recently focusing on Bayesian stochastic modeling and multivariate insurance loss payments. In this article, we introduce a novel Bayesian multivariate model based on the use of parametric copula to account for dependencies between various lines of insurance claims. We derive a full Bayesian stochastic simulation algorithm that can estimate parameters in this class of models. We provide an extensive discussion of this modeling framework and give examples that deal with a wide range of topics encountered in the multivariate loss prediction settings.