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In emergency departments (EDs), one of the major reasons behind long waiting times and crowding overall is the time it takes to move admitted patients from the ED to an appropriate bed in the main hospital. In "Using Hospital Admission Predictions at Triage for Improving Patient Length of Stay in Emergency Departments," Chen et al. develop a methodology that can be used to shorten these times by predicting the likelihood of admission for each patient at the time of triage and starting the process of identifying a suitable hospital bed and making preparations for the patient's eventual transfer to the bed right away if the predicted probability of admission is deemed high enough. A simulation study suggests that the proposed methodology, particularly when it takes into account ED census levels, has the potential to shorten average waiting times in the ED without leading to too many false early bed requests. Long boarding times have long been recognized as one of the main reasons behind emergency department (ED) crowding. One of the suggestions made in the literature to reduce boarding times was to predict, at the time of triage, whether a patient will eventually be admitted to the hospital and if the prediction turns out to be "admit," start preparations for the patient's transfer to the main hospital early in the ED visit. However, there has been no systematic effort in developing a method to help determine whether an estimate for the probability of admit would be considered high enough to request a bed early, whether this determination should depend on ED census, and what the potential benefits of adopting such a policy would be. This paper aims to help fill this gap. The methodology we propose estimates hospital admission probabilities using standard logistic regression techniques. To determine whether a given probability of admission is high enough to qualify a bed request early, we develop and analyze two mathematical decision models. Both models are simplified representations and thus, do not lead to directly implementable policies. However, building on the solutions to these simple models, we propose two policies that can be used in practice. Then, using data from an academic hospital ED in the southeastern United States, we develop a simulation model, investigate the potential benefits of adopting the two policies, and compare their performances with that under a simple benchmark policy. We find that both policies can bring modest to substantial benefits, with the state-dependent policy outperforming the state-independent one particularly under conditions when the ED experiences more than usual levels of patient demand. Funding: This work was supported by the National Science Foundation [Grants CMMI-1234212 and CMMI-1635574]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2022.2405. [ABSTRACT FROM AUTHOR]

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). Using a cumulative logit model, we investigated the association between a patient's acuity group (low, medium, and high) and ED census at triage time. We also used multivariate logistic regression to investigate the association between the disposition decision for a patient (admit or discharge) and the ED census at the disposition decision time. In both studies, control variables included census, age, gender, race, place of treatment, chief complaint, and certain interaction terms.Results: We found statistically significant correlation between ED census and triage/disposition decisions. For each additional patient in the ED, the odds of being assigned a high acuity versus medium or low acuity at triage is 1.011 times higher (95% confidence interval [CI] for Odds Ratio [OR] = [1.009,1.012]), and the odds of being assigned medium or high acuity versus low acuity at triage is 1.009 times higher (95% CI for OR = [1.008,1.010]). Similarly, the odds of being admitted versus discharged increases by 1.007 times (95% CI for OR = [1.006,1.008]) per additional patient in the ED at the time of disposition decision.Conclusion: Increased ED occupancy was found to be associated with more patients being classified as higher acuity as well as higher hospital admission rates. As an example, for a commonly observed patient category, our model predicts that as the ED occupancy increases from 25 to 75 patients, the probability of a patient being triaged as high acuity increases by about 50% and the probability of a patient being categorized as admit increases by around 25%. [ABSTRACT FROM AUTHOR]

The United States Department of Agriculture's National Agricultural Statistics Service (NASS) conducts the June Agricultural Survey (JAS) annually. Substantial misclassification occurs during the prescreening process and from field-estimating farm status for nonresponse and inaccessible records, resulting in a biased estimate of the number of US farms from the JAS. Here, the Annual Land Utilization Survey (ALUS) is proposed as a follow-on survey to the JAS to adjust the estimates of the number of US farms and other important variables. A three-phase survey design-based estimator is developed for the JAS-ALUS with nonresponse adjustment for the second phase (ALUS). A design-unbiased estimator of the variance is provided in explicit form. [ABSTRACT FROM AUTHOR]

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. Spatial variation in contact rates can influence transmission and the risk of epidemics, yet the interaction between spatial heterogeneity and movement of hosts remains relatively unexplored. Here we explore, analytically and through numerical simulations, how human mobility connects spatially heterogeneous mosquito populations, thereby influencing disease persistence (determined by the basic reproduction number R0), prevalence and their relationship. We show that, when local transmission rates are highly heterogeneous, R0 declines asymptotically as human mobility increases, but infection prevalence peaks at low to intermediate rates of movement and decreases asymptotically after this peak. Movement can reduce heterogeneity in exposure to mosquito biting. As a result, if biting intensity is high but uneven, infection prevalence increases with mobility despite reductions in R0. This increase in prevalence decreases with further increase in mobility because individuals do not spend enough time in high transmission patches, hence decreasing the number of new infections and overall prevalence. These results provide a better basis for understanding the interplay between spatial transmission heterogeneity and human mobility, and their combined influence on prevalence and R0. [ABSTRACT FROM AUTHOR]

Globalization and the expansion of transport networks has transformed migration into a major policy issue because of its effects on a range of phenomena, including resource flows in economics, urbanization, as well as the epidemiology of infectious diseases. Quantifying and modeling human migration can contribute towards a better understanding of the nature of migration and help develop evidence-based interventions for disease control policy, economic development, and resource allocation. In this study we paired census microdata from 10 countries in sub-Saharan Africa with additional spatial datasets to develop models for the internal migration flows in each country, including key drivers that reflect the changing social, demographic, economic, and environmental landscapes. We assessed how well these gravity-type spatial interaction models can both explain and predict migration. Results show that the models can explain up to 87 percent of internal migration, can predict future within-country migration with correlations of up to 0.91, and can also predict migration in other countries with correlations of up to 0.72. Findings show that such models are useful tools for understanding migration as well as predicting flows in regions where data are sparse, and can contribute towards strategic economic development, planning, and disease control targeting. [ABSTRACT FROM AUTHOR]

An experiment was conducted to test the hypothesis that the enhanced yield possible with grafted tomato ( Solanum lycopersicum L.) under field conditions will also increase the nitrogen (N) crop nutritional requirement (CNR). Determinate ‘Florida 47’ tomatoes were grafted onto interspecific hybrid rootstocks (‘Multifort’ or ‘Beaufort’) and grown in a sandy soil with six N rates (56, 112, 168, 224, 280, and 336 kg ha −1 ) under plastic mulched bed and drip-irrigation systems during the spring seasons of 2010 (March–June) and 2011 (April–July). The N-CNR for grafted and non-grafted tomatoes was assessed using five yield response functions: exponential, linear-plateau, quadratic-plateau, quadratic, and square root. Over the two seasons, the estimated N-CNR ranged from 165 kg ha −1 with the quadratic-plateau model to 324 kg ha −1 with the square root model. Confidence intervals (CI) around these N-CNR ranged from 125 to 585 kg ha −1 using the bootstrap method and from 98 to 440 kg ha −1 using the delta method. Analysis of these CIs gave N-CNR rates of 239–246 kg N ha −1 for grafted plants, and 196–197 kg N ha −1 for non-grafted plants. Predicted maximum marketable yields were similar between the models, ranging from 56 -71 Mg ha −1 for grafted plants, and 43–53 Mg ha −1 for non-grafted plants, over the two seasons. Overall, while the actual N-CNR is likely to vary with season, soil types, and management practices, the results indicated that grafted tomato plants had a greater N-CNR than non-grafted plants together with an increase in predicted marketable yield. The yield response curves also showed that at a fixed marketable yield goal within the estimated range, the N fertilization rate required was lower for the grafted tomato plants as compared with the non-grafted plants. This study demonstrated that N fertilization program for optimizing tomato production may be modified when grafted plants are used. [ABSTRACT FROM AUTHOR]

The proliferation of electronic health records, driven by advances in technology and legislative measures, is stimulating interest in the analysis of passively collected administrative and clinical data. Observational data present exciting challenges and opportunities to researchers interested in comparing the effectiveness of different treatment regimes and, as personalized medicine requires, estimating how effectiveness varies among subgroups. In this study, we provide new motivation for the local control approach to the analysis of large observational datasets in which patients are first clustered in pretreatment covariate space and treatment comparisons are made within subgroups of similar patients. The motivation for such an analysis is that the resulting local treatment effect estimates make inherently fair comparisons even when treatment cohorts suffer variation in balance (treatment choice fraction) across pretreatment covariate space. We use an example of Simpson's paradox to show that estimates of the overall average treatment effect, which marginalize over covariate space, can be misleading. Thus, we provide an alternative definition that uses a single, shared marginal distribution to define overall treatment comparisons that are inherently fair given the observed covariates. However, we also argue that overall treatment comparisons should no longer be the focus of comparative effectiveness research; the possibility that treatment effectiveness does vary across patient subpopulations must not be left unexplored. In the spirit of the now ubiquitous concept of personalized medicine, estimating heterogeneous treatment effects in clinically relevant subgroups will allow for, within the limits of the available data, fair treatment comparisons that are more relevant to individual patients. [ABSTRACT FROM AUTHOR]

Background Hospital-based Emergency Departments are struggling to provide timely care to a steadily increasing umber 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. We tuned the parameters of the system to mimic U.S. nationally average and average academic hospitalbased ED performance metrics and are able to assess a variety of patient flow outcomes including patient door-to-event times, propensity to leave without being seen, ED occupancy level, and dynamic staffing and resource use. Results The causes of ED crowding are variable and require site-specific solutions. For example, in a nationally average ED environment, provider availability is a surprising, but persistent bottleneck in patient flow. As a result, resources expended in reducing boarding times may not have the expected impact on patient throughput. On the other hand, reallocating resources into alternate care pathways can dramatically expedite care for lower acuity patients without delaying care for higher acuity patients. In an average academic ED environment, bed availability is the primary bottleneck in patient flow. Consequently, adjustments to provider scheduling have a limited effect on the timeliness of care delivery, while shorter boarding times significantly reduce crowding. An online version of the simulation platform is available at http://spark.rstudio.com/klopiano/EDsimulation/. Conclusion In building this robust simulation framework, we have created a novel decision-support tool that ED and hospital managers can use to quantify the impact of proposed changes to patient flow prior to implementation. [ABSTRACT FROM AUTHOR]

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. High modularity is also a property of large-scale social networks, where each node represents a population of individuals at a location, such as call flow between mobile phone towers. However, whether or not place-based attributes, including land cover and economic activity, can predict community membership for network nodes in large-scale networks remains unknown. We describe the pattern of modularity in a mobile phone communication network in the Dominican Republic, and use a linear discriminant analysis (LDA) to determine whether geographic context can explain community membership. Our results demonstrate that place-based attributes, including sugar cane production, urbanization, distance to the nearest airport, and wealth, correctly predicted community membership for over 70% of mobile phone towers. We observed a strongly positive correlation (r = 0.97) between the modularity score and the predictive ability of the LDA, suggesting that place-based attributes can accurately represent the processes driving modularity. In the absence of social network data, the methods we present can be used to predict community membership over large scales using solely place-based attributes. [ABSTRACT FROM AUTHOR]

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. However, the naive standard errors of these parameters tend to be too small. In this article, two simulation studies are used to compare methods for providing appropriate standard errors in this spatial setting. Three of the methods are extended to the change-of-support case where X is observed at points, but Y is observed for areal units, and these approaches are also compared via simulation. [ABSTRACT FROM AUTHOR]