Development and validation of a farm- and province-level swine flow simulation model using discrete events and Ontario swine farm and provincial input data.

Infectious disease events can cause disruptions in service-based and agricultural industries. The list of possible events is long and varies from the incursion or emergence of a reportable animal pathogen to the recently documented interruptions caused by the COVID-19 pandemic. There is a need to develop models that can determine the impact of pathogens and mitigation measures on populations that are not directly affected by the pathogen in the case of a reportable disease, particularly when the health and welfare of these populations could be affected due to resulting disruptions in trade and supply chains. The primary objective of this study was to develop a discrete-event simulation (DES) model of swine production, including pork processing, for scenarios without major disruptions, which could be scaled from the level of an individual farm to the entire province of Ontario, Canada. The secondary objective was to validate the developed simulation against observed farm- and province-level statistics. A weekly discrete-event simulation consisting of 3 connected areas (a sow farm, a pig farm, and abattoirs) was developed using AnyLogic modelling software. Using Mann-Whitney tests, model outputs representative of the standard industry statistics were compared to data from 6 individual farms separately, as well as to provincial data from Ontario. A scalable discrete-event simulation of the swine production system for typical scenarios was accomplished. The model outputs were consistent with individual farm and industry statistics. As such, the model can be used to simulate swine production at distinct levels and could be further modified to represent swine marketing in other provinces or internationally.

Les maladies infectieuses peuvent provoquer des perturbations dans les industries de services et agricoles. La liste des événements possibles est longue et varie de l'arrivée ou de l'émergence d'un agent pathogène animal à déclaration obligatoire aux interruptions récemment documentées causées par la pandémie de COVID-19. Il est nécessaire d'élaborer des modèles permettant de déterminer l'impact des agents pathogènes et des mesures d'atténuation sur les populations qui ne sont pas directement affectées par l'agent pathogène dans le cas d'une maladie à déclaration obligatoire, en particulier lorsque la santé et le bien-être de ces populations pourraient être affectés en raison des conséquences dues aux perturbations du commerce et des chaînes d'approvisionnement. L'objectif principal de cette étude était de développer un modèle de simulation à événements discrets (DES) de la production porcine, y compris la transformation du porc, pour des scénarios sans perturbations majeures, qui pourraient être étendus du niveau d'une ferme individuelle à l'ensemble de la province de l'Ontario, Canada. L'objectif secondaire était de valider la simulation développée par rapport aux statistiques observées au niveau de la ferme et de la province. Une simulation à événements discrets hebdomadaire composée de 3 zones connectées (un élevage de truies, un élevage de porcs et des abattoirs) a été développée à l'aide du logiciel de modélisation AnyLogic. À l'aide des tests de Mann-Whitney, les résultats du modèle représentatifs des statistiques standards de l'industrie ont été comparés aux données de 6 fermes individuelles séparément, ainsi qu'aux données provinciales de l'Ontario. Une simulation à événements discrets évolutive du système de production porcine pour des scénarios typiques a été réalisée. Les résultats du modèle étaient cohérents avec les statistiques individuelles des exploitations et des industries. Ainsi, le modèle peut être utilisé pour simuler la production porcine à des niveaux distincts et pourrait être modifié davantage pour représenter la commercialisation du porc dans d'autres provinces ou à l'échelle internationale.(Traduit par Docteur Serge Messier).

Etiology Beliefs About Sexual and Gender Minority Youth (EB-SGM): Validation with College Students, Community Adults, and Pediatric Healthcare Providers.

Sexual and gender minority (SGM) youth are at risk for adverse health outcomes because of stigma and discrimination exposure. Individuals' beliefs about the biological origin of SGM identity are linked to their negative attitudes and biases against SGM populations, which can also apply to pediatric healthcare providers. The current study outlines validation of the Etiology Beliefs about Sexual and Gender Minority Youth (EB-SGM) scale, a 12-item measure designed to assess adults' beliefs about youths' biological versus environmental SGM etiology. College students (N = 285; study 1), community adults (N = 258; study 2), and pediatric providers (N = 104; study 3) completed the EB-SGM and other self-report measures. Exploratory factor analysis (EFA) in study 1 revealed a three-factor structure: beliefs about gender nonconforming behavior, beliefs about gender identity, and beliefs about sexual attraction/behavior. Confirmatory factor analysis (CFA) in study 2 suggested a bi-factor structure, which was replicated in study 3. The EB-SGM demonstrated adequate concurrent and discriminant validity. We also examined bivariate correlations between etiology beliefs and sociodemographic characteristics across samples. Findings indicate that EB-SGM is a psychometrically sound instrument to measure adults' etiology beliefs. The EB-SGM has the potential to be used as a screening measure to enhance pediatric healthcare providers' SGM training.

Comparison of pretrained transformer-based models for influenza and COVID-19 detection using social media text data in Saskatchewan, Canada.

Background: The use of social media data provides an opportunity to complement traditional influenza and COVID-19 surveillance methods for the detection and control of outbreaks and informing public health interventions. Objective: The first aim of this study is to investigate the degree to which Twitter users disclose health experiences related to influenza and COVID-19 that could be indicative of recent plausible influenza cases or symptomatic COVID-19 infections. Second, we seek to use the Twitter datasets to train and evaluate the classification performance of Bidirectional Encoder Representations from Transformers (BERT) and variant language models in the context of influenza and COVID-19 infection detection. Methods: We constructed two Twitter datasets using a keyword-based filtering approach on English-language tweets collected from December 2016 to December 2022 in Saskatchewan, Canada. The influenza-related dataset comprised tweets filtered with influenza-related keywords from December 13, 2016, to March 17, 2018, while the COVID-19 dataset comprised tweets filtered with COVID-19 symptom-related keywords from January 1, 2020, to June 22, 2021. The Twitter datasets were cleaned, and each tweet was annotated by at least two annotators as to whether it suggested recent plausible influenza cases or symptomatic COVID-19 cases. We then assessed the classification performance of pre-trained transformer-based language models, including BERT-base, BERT-large, RoBERTa-base, RoBERT-large, BERTweet-base, BERTweet-covid-base, BERTweet-large, and COVID-Twitter-BERT (CT-BERT) models, on each dataset. To address the notable class imbalance, we experimented with both oversampling and undersampling methods. Results: The influenza dataset had 1129 out of 6444 (17.5%) tweets annotated as suggesting recent plausible influenza cases. The COVID-19 dataset had 924 out of 11939 (7.7%) tweets annotated as inferring recent plausible COVID-19 cases. When compared against other language models on the COVID-19 dataset, CT-BERT performed the best, supporting the highest scores for recall (94.8%), F1(94.4%), and accuracy (94.6%). For the influenza dataset, BERTweet models exhibited better performance. Our results also showed that applying data balancing techniques such as oversampling or undersampling method did not lead to improved model performance. Conclusions: Utilizing domain-specific language models for monitoring users' health experiences related to influenza and COVID-19 on social media shows improved classification performance and has the potential to supplement real-time disease surveillance.

Evaluation of the effectiveness of maternal immunization against pertussis in Alberta using agent-based modeling: A Canadian immunization research network study.

INTRODUCTION: The re-emergence of pertussis has occurred in the past two decades in developed countries. The highest morbidity and mortality is seen among infants. Vaccination in pregnancy is recommended to reduce the pertussis burden in infants. METHODS: We developed and validated an agent-based model to characterize pertussis epidemiology in Alberta. We computed programmatic effectiveness of pertussis vaccination during pregnancy (PVE) in relation to maternal vaccine coverage and pertussis disease reporting thresholds. We estimated the population preventable fraction (PFP) of different levels of maternal vaccine coverage against counterfactual "no-vaccination" scenario. We modeled the effect of immunological blunting and measured protection through interruption of exposure pathways. RESULTS: PVE was inversely related to duration of passive immunity from maternal immunization across most simulations. In the scenario of 50% maternal vaccine coverage, PVE was 87% (95% quantiles 82-91%), with PFP of 44% (95% quantiles 41-45%). For monthly age intervals of 0-2, 2-4, 4-6 and 6-12, PVE ranged between 82 and 99%, and PFP ranged between 41 and 49%. At 75% maternal vaccine coverage, PVE and PFP were 90% (95% quantiles 86-92%) and 68% (95% quantiles 65-69%), respectively. At 50% maternal vaccine coverage and 10% blunting, PVE and PFP were 86% (95% quantiles 77-87%) and 43% (95% quantiles 39-44%), respectively, while at 50% blunting, the corresponding values of PVE and PFP were 76% (95% quantiles 70-81%) and 38% (95% quantiles 35-40%). PVE attributable to interruption of exposure pathways was 54-57%. CONCLUSIONS: Our model predicts significant reduction in future pertussis cases in infants due to maternal vaccination, with immunological blunting slightly moderating its effectiveness. The model is most sensitive to maternal vaccination coverage. The interruption of exposure pathways plays a role in the reduction of pertussis burden in infants due to maternal immunization. The effect of maternal immunization on population other than infants remains to be elucidated.

What We Know Now: An Economic Evaluation of Chickenpox Vaccination and Dose Timing Using an Agent-Based Model.

OBJECTIVES: The value of chickenpox vaccination is still debated in the literature and by jurisdictions worldwide. This uncertainty is reflected in the inconsistent uptake of the vaccine, where some countries offer routine childhood immunization programs, others have targeted programs, and in many the vaccine is only privately available. Even across the countries that have universal funding for the vaccine, there is a diversity of schedules and dosing intervals. Using an agent-based model of chickenpox and shingles, we conducted an economic evaluation of chickenpox vaccination in Alberta, Canada. METHODS: We compared the cost-effectiveness of 2 common chickenpox vaccination schedules, specifically a long dosing interval (first dose: 12 months; second dose: 4-6 years) and a short dosing interval (first dose: 12 months; second dose: 18 months). RESULTS: The economic evaluation demonstrated a shorter dosing interval may be marginally preferred, although it consistently led to higher costs from both the societal and healthcare perspectives. We found that chickenpox vaccination would be cost-saving and highly cost-effective from the societal and healthcare perspective, assuming there was no impact on shingles. CONCLUSION: Chickenpox vaccine was cost-effective when not considering shingles and remained so even if there was a minor increase in shingles following vaccination. However, if chickenpox vaccination did lead to a substantial increase in shingles, then chickenpox vaccination was not cost-effective from the healthcare perspective.

Simulation Modeling as a Novel and Promising Strategy for Improving Success Rates With Research Funding Applications: A Constructive Thought Experiment.

Writing a successful grant or other funding applications is a requirement for continued employment, promotion, and tenure among nursing faculty and researchers. Writing successful applications is a challenging task, with often uncertain results. The inability to secure funding not only threatens the ability of nurse researchers to conduct relevant health care research but may also negatively impact their career trajectories. Many individuals and organizations have offered advice for improving success with funding applications. While helpful, those recommendations are common knowledge and simply form the basis of any well-considered, well-formulated, and well-written application. For nurse researchers interested in taking advantage of innovative computational methods and leading-edge analytical techniques, we propose adding the results from computer-based simulation modeling experiments to funding applications. By first conducting a research study in a virtual space, nurse researchers can refine their study design, test various assumptions, conduct experiments, and better determine which elements, variables, and parameters are necessary to answer their research question. In short, simulation modeling is a learning tool, and the modeling process helps nurse researchers gain additional insights that can be applied in their real-world research and used to strengthen funding applications. Simulation modeling is well-suited for answering quantitative research questions. Still, the design of these models can benefit significantly from the addition of qualitative data and can be helpful when simulating the results of mixed methods studies. We believe this is a promising strategy for improving success rates with funding applications, especially among nurse researchers interested in contributing new knowledge supporting the paradigm shift in nursing resulting from advances in computational science and information technology.

Seeking the optimal schedule for chickenpox vaccination in Canada: Using an agent-based model to explore the impact of dose timing, coverage and waning of immunity on disease outcomes.

Many countries continue to consider implementing a universal chickenpox vaccine program; however, there is no consensus on the most appropriate and effective timing between vaccine doses. The chickenpox vaccine schedule debate is highlighted in Canada, where there are currently eight different vaccine schedules across the country. The objective of this study was to test the overall effectiveness of chickenpox vaccination, as well as the specific impact of two different vaccine schedules, on chickenpox disease outcomes in Alberta over 75 years. Using an agent-based model of chickenpox disease, we tested the impact of three vaccination scenarios including: baseline (no vaccination), a long dosing interval-Schedule LDI (1st dose - 12 months; 2nd dose -  4-6 years) and a short dosing interval-Schedule SDI (1st dose - 12 months; 2nd dose - 18 months) on chickenpox and shingles disease outcomes. Chickenpox vaccination led to a substantial decrease in chickenpox incidence over 75 years post-vaccine implementation. Compared to Schedule LDI, Schedule SDI resulted in a significantly lower chickenpox incidence, a higher age of chickenpox infection, a lower chickenpox breakthrough rate and a higher shingles incidence rate. Our model findings suggest that the chickenpox vaccine is effective over a long period of time and the dose timing of the vaccine may impact disease outcomes and vaccine effectiveness. However, the effectiveness of the vaccine dose timing is only one consideration for policy-makers who are implementing a chickenpox vaccine program, with others including risk of adverse events, the impact of the schedule on other antigens in a combination vaccine, parental acceptance and the cost associated with different schedules.

Agent-Based Modeling: A Method for Investigating Challenging Research Problems.

BACKGROUND: For all our successes, many urgent health problems persist, and although some of these problems may be explored with established research methods, others remain uniquely challenging to investigate-maybe even impossible to study in the real world because of practical and pragmatic obstacles inherent to the nature of the research question. OBJECTIVES: The purpose of this review article is to introduce agent-based modeling (ABM) and simulation and demonstrate its value and potential as a novel research method applied in nursing science. METHODS: An introduction to ABM and simulation is described. Examples of current research literature on the subject are provided. A case study example of community nursing and opioid dependence is presented. RESULTS: The use of ABM and simulation in human health research has increased dramatically over the past decade, and meaningful research is now commonly found published widely in respected, peer-reviewed journals. Absent from this list is innovative ABM and simulation research published by nurse researchers in nursing-specific journals. DISCUSSION: ABM and simulation is a powerful method with tremendous potential in nursing research. It is vital that nursing embrace and adopt innovative and advanced research methods if we are to remain a progressive voice in health research, practice, and policy.

Gaming National Emergency Access Target performance using Emergency Treatment Performance definitions and emergency department short stay units.

OBJECTIVE: To evaluate potential gaming of the 4 h ED length of stay metric known as the National Emergency Access Target (NEAT) in Australia and Emergency Treatment Performance (ETP) in New South Wales (NSW). METHODS: Descriptive statistical analysis was used to recalculate and compare the scores for NEAT and the NSW ETP using variations in the definitions of their measurement on 32 184 presentations during 2016. A computer simulation using a discrete event model illustrated the effect of the use of ED short stay beds on the ETP scores. RESULTS: Using the timestamp of the intent to discharge a patient, called, 'ready for departure' instead of the time of a patient physically leaving the department, resulted in an apparent 6% performance improvement. A local interpretation of the NSW state definition of the 'transferred' patient resulted in the ETP for 'admitted' patients improving by 16%. The discrete event model demonstrated that without changing patient length of stay, ETP scores can be improved by optimising the time of the admit decision or increasing the number of ED short stay beds. CONCLUSIONS: The opportunity of NEAT may be squandered unless gaming of the definitions and use of ED short stay beds is addressed. We argue that the longstanding issue of 'departure time' should be defined as 'physically leaving' the department, in accordance with the Australasian College for Emergency Medicine (ACEM) definition. Patient occupancy is a real measure of ED resource use and NSW and national recommendations should be adjusted. ACEM accreditation of EDs should include review of their application of NEAT definitions to ensure they truly reflect patient flow processes.

Motivational Interviewing Training Outcomes Among Providers in a Children's Hospital.

Motivational interviewing (MI) has proven a well-established psychotherapeutic intervention designed to enhance motivation for behavior change. While the benefits of MI have been established, little research has systematically evaluated dissemination of MI efforts to healthcare providers, especially among pediatric providers. The present pilot study evaluated whether healthcare providers gained valuable knowledge, confidence and desire to utilize MI, and skills in MI techniques and if these outcomes varied based on provider characteristics or duration and intensity of MI training. Twenty pediatric healthcare professionals in a large academic pediatric hospital completed an advanced 20-h MI training and 103 pediatric healthcare professionals completed a basic 4-h MI workshop. The study demonstrated no significant differences in post-workshop MI knowledge, confidence, or desire based on trainee demographics. We also found no significant change from post-basic workshop to post-advanced workshop for advanced MI trainees. However, the advanced training workshop participants evidenced significant growth in utilizing MI skills (via MITI coding) and self-reported confidence in using MI skills. We therefore conclude that while the basic workshop allows participants to gain valuable MI knowledge and confidence and desire to utilize MI, it is through the advanced training that providers have the opportunity to practice these skills, receive feedback, and ultimately gain the expertise necessary to be effective MI providers. Overall, results from this pilot study suggest MI training in pediatric hospitals represents an important area of opportunity for multidisciplinary training, dissemination, and practice.

Evaluation of the effect of chickenpox vaccination on shingles epidemiology using agent-based modeling.

BACKGROUND: Biological interactions between varicella (chickenpox) and herpes zoster (shingles), two diseases caused by the varicella zoster virus (VZV), continue to be debated including the potential effect on shingles cases following the introduction of universal childhood chickenpox vaccination programs. We investigated how chickenpox vaccination in Alberta impacts the incidence and age-distribution of shingles over 75 years post-vaccination, taking into consideration a variety of plausible theories of waning and boosting of immunity. METHODS: We developed an agent-based model representing VZV disease, transmission, vaccination states and coverage, waning and boosting of immunity in a stylized geographic area, utilizing a distance-based network. We derived parameters from literature, including modeling, epidemiological, and immunology studies. We calibrated our model to the age-specific incidence of shingles and chickenpox prior to vaccination to derive optimal combinations of duration of boosting (DoB) and waning of immunity. We conducted paired simulations with and without implementing chickenpox vaccination. We computed the count and cumulative incidence rate of shingles cases at 10, 25, 50, and 75 years intervals, following introduction of vaccination, and compared the difference between runs with vaccination and without vaccination using the Mann-Whitney U-test to determine statistical significance. We carried out sensitivity analyses by increasing and lowering vaccination coverage and removing biological effect of boosting. RESULTS: Chickenpox vaccination led to a decrease in chickenpox cases. The cumulative incidence of chickenpox had dropped from 1,254 cases per 100,000 person-years pre chickenpox vaccination to 193 cases per 100,000 person-years 10 years after the vaccine implementation. We observed an increase in the all-ages shingles cumulative incidence at 10 and 25 years post chickenpox vaccination and mixed cumulative incidence change at 50 and 75 years post-vaccination. The magnitude of change was sensitive to DoB and ranged from an increase of 22-100 per 100,000 person-years at 10 years post-vaccination for two and seven years of boosting respectively (p < 0.001). At 75 years post-vaccination, cumulative incidence ranged from a decline of 70 to an increase of 71 per 100,000 person-years for two and seven years of boosting respectively (p < 0.001). Sensitivity analyses had a minimal impact on our inferences except for removing the effect of boosting. DISCUSSION: Our model demonstrates that over the longer time period, there will be a reduction in shingles incidence driven by the depletion of the source of shingles reactivation; however in the short to medium term some age cohorts may experience an increase in shingles incidence. Our model offers a platform to further explore the relationship between chickenpox and shingles, including analyzing the impact of different chickenpox vaccination schedules and cost-effectiveness studies.

Ovarian suppression in the management of premenopausal breast cancer: methods and efficacy in adjuvant and metastatic settings.

Ovarian suppression has been used to treat hormone-responsive metastatic breast cancer in premenopausal women for over 100 years and is currently under continued evaluation for treatment in the adjuvant setting. In this article, ovarian suppression by surgery, radiation, and pharmacological therapy is discussed, including the risks, benefits, and efficacy of each strategy. The role of ovarian suppression in premenopausal women with early and advanced stages of breast cancer will be reviewed. It is hoped that this review will assist clinicians and their patients in selecting the appropriate therapy if ovarian suppression is indicated.