Dynamic networks are used in a variety of fields to represent the structure and evolution of the relationships between entities. We present a model which embeds longitudinal network data as trajectories in a latent Euclidean space. A Markov chain Monte Carlo algorithm is proposed to estimate the model parameters and latent positions of the actors in the network. The model yields meaningful visualization of dynamic networks,giving the researcher insight into the evolution and the structure, both local and global, of the network. The model handles directed or undirected edges, easily handles missing edges, and lends itself well to predicting future edges. Further, a novel approach is given to detect and visualize an attracting influence between actors using only the edge information. We use the case-control likelihood approximation to speed up the estimation algorithm, modifying it slightly to account for missing data. We apply the latent space model to data collected from a Dutch classroom, and a cosponsorship network collected on members of the U.S. House of Representatives, illustrating the usefulness of the model by making insights into the networks.

Young children are infected by a diverse range of enteric pathogens in high disease burden settings, suggesting pathogen contamination of the environment is equally diverse. This study aimed to characterize across- and within-neighborhood diversity in enteric pathogen contamination of public domains in urban informal settlements of Kisumu, Kenya, and to assess the relationship between pathogen detection patterns and human and domestic animal sanitation conditions. Microbial contamination of soil and surface water from 166 public sites in three Kisumu neighborhoods was measured by enterococcal assays and quantitative reverse transcription polymerase chain reaction (qRT-PCR) for 19 enteric pathogens. Regression was used to assess the association between observed sanitary indicators of contamination with enterococci and pathogen presence and concentration, and pathogen diversity. Seventeen types of pathogens were detected in Kisumu public domains. Enteric pathogens were codetected in 33% of soil and 65% of surface water samples. Greater pathogen diversity was associated with the presence of domestic animal feces but not with human open defecation, deteriorating latrines, flies, or disposal of human feces. Sanitary conditions were not associated with enterococcal bacteria, specific pathogen concentrations, or "any pathogen". Young children played at 40% of observed sites. Managing domestic animal feces may be required to reduce enteric pathogen environmental contamination in high-burden settings.

Using the Nationwide Inpatient Sample and US weather data, we estimated the probability of community-acquired pneumonia (CAP) being diagnosed as Legionnaires’ disease (LD). LD risk increases when weather is warm and humid. With warm weather, we found a dose-response relationship between relative humidity and the odds for LD. When the mean temperature was 60°–80°F with high humidity (>80.0%), the odds for CAP being diagnosed with LD were 3.1 times higher than with lower levels of humidity (<50.0%). Thus, in some regions (e.g., the Southwest), LD is rarely the cause of hospitalizations. In other regions and seasons (e.g., the mid-Atlantic in summer), LD is much more common. Thus, suspicion for LD should increase when weather is warm and humid. However, when weather is cold, dry, or extremely hot, empirically treating all CAP patients for LD might contribute to excessive antimicrobial drug use at a population level.

OBJECTIVE To determine if the seasonality of surgical site infections (SSIs) may be explained by changes in temperature. DESIGN Retrospective cohort analysis. SETTING The National Inpatient Sample. PATIENTS All hospital discharges with a primary diagnosis of SSI from 1998–2011 served as cases. Discharges with a primary or secondary diagnosis of specific surgeries commonly associated with SSIs from the previous and current month served as our “at risk” cohort. METHODS We modeled the national monthly count of SSI cases both nationally and stratified by region, sex, age, and type of institution. We used data from the National Climatic Data Center to estimate the monthly average temperature for all hospital locations. We modeled the odds of having a primary diagnosis of SSI as a function of demographics, payer, location, patient severity, admission month, year and the average temperature in the month of admission. RESULTS SSI incidence is highly seasonal, with the highest SSI incidence in August and the lowest in January. Over the study period, there were 26.5% more cases in August than in January (95% CI: [23.3, 29.7]). Controlling for demographic and hospital-level characteristics, the odds of a primary SSI admission increase by roughly 2.1% per 5°F increase in the average monthly temperature. Specifically, the highest temperature group, 90°F+, was associated with an increase in the odds of an SSI admission of 28.9% (95% CI: [20.2–38.3]) compared to temperatures less than 40°F. CONCLUSIONS At population level, SSI risk is highly seasonal and associated with warmer weather.

Globally, gastrointestinal (GI) infections by enteric pathogens are the second-leading cause of morbidity and mortality in children under five years of age (≤5 years). While GI pathogen exposure in households has been rigorously examined, there is little data about young children’s exposure in public domains. Moreover, public areas in low-income settings are often used for other waste disposal practices in addition to human feces, such as trash dumping in areas near households. If young children play in public domains, they might be exposed to interrelated and highly concentrated microbial, chemical, and physical hazards. This study performed structured observations at 36 public areas in an internally displaced persons community that has transitioned into a formal settlement in Haiti. We documented how often young children played in public areas and quantified behaviors that might lead to illness and injury. Children ≤5 years played at all public sites, which included infants who played at 47% of sites. Children touched and mouthed plastic, metal and glass trash, food and other objects from the ground, ate soil (geophagia) and drank surface water. They also touched latrines, animals, animal feces and open drainage canals. Hand-to-mouth contact was one of the most common behaviors observed and the rate of contact significantly differed among developmental stages (infants: 18/h, toddlers: 11/h and young children: 9/h), providing evidence that children could ingest trace amounts of animal/human feces on hands that may contain GI pathogens. These findings demonstrate that water, sanitation and hygiene interventions could be more effective if they consider exposure risks to feces in public domains. Furthermore, this research highlights the need for waste-related interventions to address the broader set of civil conditions that create unsafe, toxic and contaminated public environments where young children play.

SSIs following TKA and THA are seasonal peaking in summer months. Payer status was also a significant risk factor for SSIs. Future studies should investigate potential factors that could relate to the associations demonstrated in this study.

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BackgroundSelf-report questionnaires are a valuable method of physical activity measurement in public health research; however, accuracy is often lacking. The purpose of this study is to improve the validity of the Global Physical Activity Questionnaire by calibrating it to 7 days of accelerometer measured physical activity and sedentary behavior.MethodsParticipants (n = 108) wore an ActiGraph GT9X Link on their non-dominant wrist for 7 days. Following the accelerometer wear period, participants completed a telephone Global Physical Activity Questionnaire with a research assistant. Data were split into training and testing samples, and multivariable linear regression models built using functions of the GPAQ self-report data to predict ActiGraph measured physical activity and sedentary behavior. Models were evaluated with the testing sample and an independent validation sample (n = 120) using Mean Squared Prediction Errors.ResultsThe prediction models utilized sedentary behavior, and moderate- and vigorous-intensity physical activity self-reported scores from the questionnaire, and participant age. Transformations of each variable, as well as break point analysis were considered. Prediction errors were reduced by 77.7–80.6% for sedentary behavior and 61.3–98.6% for physical activity by using the multivariable linear regression models over raw questionnaire scores.ConclusionsThis research demonstrates the utility of calibrating self-report questionnaire data to objective measures to improve estimates of physical activity and sedentary behavior. It provides an understanding of the divide between objective and subjective measures, and provides a means to utilize the two methods as a unified measure.