Publications by authors named "Kenneth Lopiano"

Background: Emergency department (ED) crowding is a recognized issue and it has been suggested that it can affect clinician decision-making.

Objectives: Our objective was to determine whether ED census was associated with changes in triage or disposition decisions made by ED nurses and physicians.

Methods: We performed a retrospective study using one year of data obtained from a US academic center ED (65,065 patient encounters after cleaning).

View Article and Find Full Text PDF

Objective: This study profiles an innovative approach to capture patient satisfaction data from emergency department (ED) patients by implementing an electronic survey method. This study compares responders to nonresponders.

Background: Our hypothesis is that the cohort of survey respondents will be similar to nonresponders in terms of the key characteristics of age, gender, race, ethnicity, ED disposition, and payor status.

View Article and Find Full Text PDF

Mosquito-borne diseases are a global health priority disproportionately affecting low-income populations in tropical and sub-tropical countries. These pathogens live in mosquitoes and hosts that interact in spatially heterogeneous environments where hosts move between regions of varying transmission intensity. Although there is increasing interest in the implications of spatial processes for mosquito-borne disease dynamics, most of our understanding derives from models that assume spatially homogeneous transmission.

View Article and Find Full Text PDF

Background: Hospital-based Emergency Departments are struggling to provide timely care to a steadily increasing number of unscheduled ED visits. Dwindling compensation and rising ED closures dictate that meeting this challenge demands greater operational efficiency.

Methods: Using techniques from operations research theory, as well as a novel event-driven algorithm for processing priority queues, we developed a flexible simulation platform for hospital-based EDs.

View Article and Find Full Text PDF

Spatially referenced datasets arising from multiple sources are routinely combined to assess relationships among various outcomes and covariates. The geographical units associated with the data, such as the geographical coordinates or areal-level administrative units, are often spatially misaligned, that is, observed at different locations or aggregated over different geographical units. As a result, the covariate is often predicted at the locations where the response is observed.

View Article and Find Full Text PDF

In environmental studies, relationships among variables that are misaligned in space are routinely assessed. Because the data are misaligned, kriging is often used to predict the covariate at the locations where the response is observed. Using kriging predictions to estimate regression parameters in linear regression models introduces a Berkson error, which induces a covariance structure that is challenging to estimate.

View Article and Find Full Text PDF

Social networks can be organized into communities of closely connected nodes, a property known as modularity. Because diseases, information, and behaviors spread faster within communities than between communities, understanding modularity has broad implications for public policy, epidemiology and the social sciences. Explanations for community formation in social networks often incorporate the attributes of individual people, such as gender, ethnicity or shared activities.

View Article and Find Full Text PDF

Background: RNA-seq is revolutionizing the way we study transcriptomes. mRNA can be surveyed without prior knowledge of gene transcripts. Alternative splicing of transcript isoforms and the identification of previously unknown exons are being reported.

View Article and Find Full Text PDF

When a response variable Y is measured on one set of points and a spatially varying predictor variable X is measured on a different set of points, X and Y have different supports and thus are spatially misaligned. To draw inference about the association between X and Y , X is commonly predicted at the points for which Y is observed, and Y is regressed on the predicted X. If X is predicted using kriging or some other smoothing approach, use of the predicted instead of the true (unobserved) X values in the regression results in unbiased estimates of the regression parameters.

View Article and Find Full Text PDF