Estimating within-household contact networks from egocentric data

Acute respiratory diseases are transmitted over networks of social contacts. Large-scale simulation models are used to predict epidemic dynamics and evaluate the impact of various interventions, but the contact behavior in these models is based on simplistic and strong assumptions which are not informed by survey data. These assumptions are also used for estimating transmission measures such as the basic reproductive number and secondary attack rates. Development of methodology to infer contact networks from survey data could improve these models and estimation methods. We contribute to this area by developing a model of within-household social contacts and using it to analyze the Belgian POLYMOD data set, which contains detailed diaries of social contacts in a 24-hour period. We model dependency in contact behavior through a latent variable indicating which household members are at home. We estimate age-specific probabilities of being at home and age-specific probabilities of contact conditional on two members being at home. Our results differ from the standard random mixing assumption. In addition, we find that the probability that all members contact each other on a given day is fairly low: 0.49 for households with two 0--5 year olds and two 19--35 year olds, and 0.36 for households with two 12--18 year olds and two 36+ year olds. We find higher contact rates in households with 2--3 members, helping explain the higher influenza secondary attack rates found in households of this size.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS474 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Estimating within-school contact networks to understand influenza transmission

Many epidemic models approximate social contact behavior by assuming random mixing within mixing groups (e.g., homes, schools and workplaces). The effect of more realistic social network structure on estimates of epidemic parameters is an open area of exploration. We develop a detailed statistical model to estimate the social contact network within a high school using friendship network data and a survey of contact behavior. Our contact network model includes classroom structure, longer durations of contacts to friends than nonfriends and more frequent contacts with friends, based on reports in the contact survey. We performed simulation studies to explore which network structures are relevant to influenza transmission. These studies yield two key findings. First, we found that the friendship network structure important to the transmission process can be adequately represented by a dyad-independent exponential random graph model (ERGM). This means that individual-level sampled data is sufficient to characterize the entire friendship network. Second, we found that contact behavior was adequately represented by a static rather than dynamic contact network.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS505 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Nutrition Knowledge, Attitudes, and Fruit and Vegetable Intake as Predictors of Head Start Teachers\u27 Classroom Mealtime Behaviors

OBJECTIVE: To examine the association between nutrition knowledge, attitudes, and fruit/vegetable intake among Head Start teachers and their classroom mealtime behaviors (self-reported and observed). DESIGN: Cross-sectional design using observation and survey. SETTING: Sixteen Head Start centers across Rhode Island between September, 2014 and May, 2015. PARTICIPANTS: Teachers were e-mailed about the study by directors and were recruited during on-site visits. A total of 85 participants enrolled through phone/e-mail (19%) or in person (81%). MAIN OUTCOME MEASURES: Independent variables were nutrition knowledge, attitudes, and fruit/vegetable intake. The dependent variable was classroom mealtime behaviors (self-reported and observed). ANALYSIS: Regression analyses conducted on teacher mealtime behavior were examined separately for observation and self-report, with knowledge, attitudes, and fruit and vegetable intake as independent variables entered into the models, controlling for covariates. RESULTS: Nutrition attitudes were positively associated with teacher self-reported classroom mealtime behavior total score. Neither teacher nutrition knowledge nor fruit/vegetable intake was associated with observed or self-reported classroom mealtime behavior total scores. CONCLUSION AND IMPLICATIONS: There was limited support for associations among teacher knowledge, attitudes, and fruit/vegetable intake, and teacher classroom mealtime behavior. Findings showed that teacher mealtime behavior was significantly associated with teacher experience

In vivo imaging of cell behaviors and F-actin reveals LIM-HD transcription factor regulation of peripheral versus central sensory axon development

<p>Abstract</p> <p>Background</p> <p>Development of specific neuronal morphology requires precise control over cell motility processes, including axon formation, outgrowth and branching. Dynamic remodeling of the filamentous actin (F-actin) cytoskeleton is critical for these processes; however, little is known about the mechanisms controlling motile axon behaviors and F-actin dynamics <it>in vivo</it>. Neuronal structure is specified in part by intrinsic transcription factor activity, yet the molecular and cellular steps between transcription and axon behavior are not well understood. Zebrafish Rohon-Beard (RB) sensory neurons have a unique morphology, with central axons that extend in the spinal cord and a peripheral axon that innervates the skin. LIM homeodomain (LIM-HD) transcription factor activity is required for formation of peripheral RB axons. To understand how neuronal morphogenesis is controlled <it>in vivo </it>and how LIM-HD transcription factor activity differentially regulates peripheral versus central axons, we used live imaging of axon behavior and F-actin distribution <it>in vivo</it>.</p> <p>Results</p> <p>We used an F-actin biosensor containing the actin-binding domain of utrophin to characterize actin rearrangements during specific developmental processes <it>in vivo</it>, including axon initiation, consolidation and branching. We found that peripheral axons initiate from a specific cellular compartment and that F-actin accumulation and protrusive activity precede peripheral axon initiation. Moreover, disruption of LIM-HD transcriptional activity has different effects on the motility of peripheral versus central axons; it inhibits peripheral axon initiation, growth and branching, while increasing the growth rate of central axons. Our imaging revealed that LIM-HD transcription factor activity is not required for F-actin based protrusive activity or F-actin accumulation during peripheral axon initiation, but can affect positioning of F-actin accumulation and axon formation.</p> <p>Conclusion</p> <p>Our ability to image the dynamics of F-actin distribution during neuronal morphogenesis <it>in vivo </it>is unprecedented, and our experiments provide insight into the regulation of cell motility as neurons develop in the intact embryo. We identify specific motile cell behaviors affected by LIM-HD transcription factor activity and reveal how transcription factors differentially control the formation and growth of two axons from the same neuron.</p

Using Case Description Information to Reduce Sensitivity to Bias for the Attributable Fraction Among the Exposed

The attributable fraction among the exposed (\textbf{AF}$_e$), also known as the attributable risk or excess fraction among the exposed, is the proportion of disease cases among the exposed that could be avoided by eliminating the exposure. Understanding the \textbf{AF}$_e$ for different exposures helps guide public health interventions. The conventional approach to inference for the \textbf{AF}$_e$ assumes no unmeasured confounding and could be sensitive to hidden bias from unobserved covariates. In this paper, we propose a new approach to reduce sensitivity to hidden bias for conducting statistical inference on the \textbf{AF}$_e$ by leveraging case description information. Case description information is information that describes the case, e.g., the subtype of cancer. The exposure may have more of an effect on some types of cases than other types. We explore how leveraging case description information can reduce sensitivity to bias from unmeasured confounding through an asymptotic tool, design sensitivity, and simulation studies. We allow for the possibility that leveraging case definition information may introduce additional selection bias through an additional sensitivity parameter. The proposed methodology is illustrated by re-examining alcohol consumption and the risk of postmenopausal invasive breast cancer using case description information on the subtype of cancer (hormone-sensitive or insensitive) using data from the Women's Health Initiative (WHI) Observational Study (OS).Comment: 30 pages, 8 tables, 1 figur

Case Definition and Design Sensitivity

In a case-referent study, cases of disease are compared to noncases with respect to their antecedent exposure to a treatment in an effort to determine whether exposure causes some cases of the disease. Because exposure is not randomly assigned in the population, as it would be if the population were a vast randomized trial, exposed and unexposed subjects may differ prior to exposure with respect to covariates that may or may not have been measured. After controlling for measured preexposure differences, for instance by matching, a sensitivity analysis asks about the magnitude of bias from unmeasured covariates that would need to be present to alter the conclusions of a study that presumed matching for observed covariates removes all bias. The definition of a case of disease affects sensitivity to unmeasured bias. We explore this issue using: (i) an asymptotic tool, the design sensitivity, (ii) a simulation for finite samples, and (iii) an example. Under favorable circumstances, a narrower case definition can yield an increase in the design sensitivity, and hence an increase in the power of a sensitivity analysis. Also, we discuss an adaptive method that seeks to discover the best case definition from the data at hand while controlling for multiple testing. An implementation in R is available as SensitivityCaseControl

Increased uptake and improved outcomes of bowel cancer screening with a faecal immunochemical test: results from a pilot study within the national screening programme in England

The funding for the evaluation of the pilot was provided by the National Office of the NHS Cancer Screening Programmes (now part of Public Health England)