The electronic band structure and optical properties of boron arsenide

We compute the electronic band structure and optical properties of boron arsenide using the relativistic quasiparticle self-consistent $GW$ approach, including electron-hole interactions through solution of the Bethe-Salpeter equation. We also calculate its electronic and optical properties using standard and hybrid density functional theory. We demonstrate that the inclusion of self-consistency and vertex corrections provides substantial improvement in the calculated band features, in particular when comparing our results to previous calculations using the single-shot $GW$ approach and various DFT methods, from which a considerable scatter in the calculated indirect and direct band gaps has been observed. We find that BAs has an indirect gap of 1.674 eV and a direct gap of 3.990 eV, consistent with experiment and other comparable computational studies. Hybrid DFT reproduces the indirect gap well, but provides less accurate values for other band features, including spin-orbit splittings. Our computed Born effective charges and dielectric constants confirm the unusually covalent bonding characteristics of this III-V system.Comment: 7 pages, 3 figure

Medical informatics in an undergraduate curriculum: a qualitative study

BACKGROUND: There is strong support for educating physicians in medical informatics, and the benefits of such education have been clearly identified. Despite this, North American medical schools do not routinely provide education in medical informatics. METHODS: We conducted a qualitative study to identify issues facing the introduction of medical informatics into an undergraduate medical curriculum. Nine key informants at the University of Toronto medical school were interviewed, and their responses were transcribed and analyzed to identify consistent themes. RESULTS: The field of medical informatics was not clearly understood by participants. There was, however, strong support for medical informatics education, and the benefits of such education were consistently identified. In the curriculum we examined, medical informatics education was delivered informally and inconsistently through mainly optional activities. Issues facing the introduction of medical informatics education included: an unclear understanding of the discipline; faculty and administrative detractors and, the dense nature of the existing undergraduate medical curriculum. CONCLUSIONS: The identified issues may present serious obstacles to the introduction of medical informatics education into an undergraduate medicine curriculum, and we present some possible strategies for addressing these issues

A qualitative study of health information technology in the Canadian public health system

Background: Although the adoption of health information technology (HIT) has advanced in Canada over the past decade, considerable challenges remain in supporting the development, broad adoption, and effective use of HIT in the public health system. Policy makers and practitioners have long recognized that improvements in HIT infrastructure are necessary to support effective and efficient public health practice. The objective of this study was to identify aspects of health information technology (HIT) policy related to public health in Canada that have succeeded, to identify remaining challenges, and to suggest future directions to improve the adoption and use of HIT in the public health system. Methods: A qualitative case study was performed with 24 key stakeholders representing national and provincial organizations responsible for establishing policy and strategic direction for health information technology. Results: Identified benefits of HIT in public health included improved communication among jurisdictions, increased awareness of the need for interoperable systems, and improvement in data standardization. Identified barriers included a lack of national vision and leadership, insufficient investment, and poor conceptualization of the priority areas for implementing HIT in public health. Conclusions: The application of HIT in public health should focus on automating core processes and identifying innovative applications of HIT to advance public health outcomes. The Public Health Agency of Canada should develop the expertise to lead public health HIT policy and should establish a mechanism for coordinating public health stakeholder input on HIT policy

A Bayesian Network Model of the Relationships between Chronic Disease Indicators

Introduction We previous developed an informatics platform to: 1) generate large numbers of indicators of chronic conditions and determinants from heterogeneous sources, 2) present indicators in context of known causal relationships. However, the causality was defined by expert-consensus and only concerning direction. Quantitative estimates of causal effects are needed to drive public health decision-making. Objectives and Approach The objective of this work is to quantify the strength of the relationships between chronic disease indicators through empirical analysis of data for a defined population. Eight chronic diseases were explored and the individual data were obtained from linked administrative data for one million randomly sampled Montréal residents. We use Bayesian networks (BN) with our causal model based on expert consensus as a prior for the structure of the BN. In addition, we compare two networks estimated separately from individual-level data and data aggregated at the regional level, the latter being most commonly available to public health agencies. Results BNs were developed using constraint-based and score-based algorithms for structure learning, and maximum likelihood for parameter estimation. We found that the BN structures and parameters learned from individual-level data differed from the one estimated from data aggregated by community health centers. Specifically, the BN structure learned from individual data contained 9 more arcs between indicators and tened to fit the data better (the Bayesian factor between two network structures was 25.55), however, the results from the aggregated data matched our prior understanding of epidemiological knowledge more closely. Conclusion/Implications Conclusion: We compared BNs built using different resolutions of data as means to describe patterns among indicators for a defined population. This strategy for interpreting indicators combines prior domain knowledge with data and represents an initial step towards an intelligent decision-support tool for public health practitioners

Clinic accessibility and clinic-level predictors of the geographic variation in 2009 pandemic influenza vaccine coverage in Montreal, Canada

Background: Nineteen mass vaccination clinics were established in Montreal, Canada, as part of the 2009 influenza A/H1N1p vaccination campaign. Although approximately 50% of the population was vaccinated, there was a considerable variation in clinic performance and community vaccine coverage. Objective: To identify community- and clinic-level predictors of vaccine uptake, while accounting for the accessibility of clinics from the community of residence. Methods: All records of influenza A/H1N1p vaccinations administered in Montreal were obtained from a vaccine registry. Multivariable regression models, specifically Bayesian gravity models, were used to assess the relationship between vaccination rates and clinic accessibility, clinic-level factors, and community-level factors. Results: Relative risks compare the vaccination rates at the variable's upper quartile to the lower quartile. All else being equal, clinics in areas with high violent crime rates, high residential density, and high levels of material deprivation tended to perform poorly (adjusted relative risk [ARR]: 0·917, 95% CI [credible interval]: 0·915, 0·918; ARR: 0·663, 95% CI: 0·660, 0·666, ARR: 0·649, 95% CI: 0·645, 0·654, respectively). Even after controlling for accessibility and clinic-level predictors, communities with a greater proportion of new immigrants and families living below the poverty level tended to have lower rates (ARR: 0·936, 95% CI: 0·913, 0·959; ARR: 0·918, 95% CI: 0·893, 0·946, respectively), while communities with a higher proportion speaking English or French tended to have higher rates (ARR: 1·034, 95% CI: 1·012, 1·059). Conclusion: In planning future mass vaccination campaigns, the gravity model could be used to compare expected vaccine uptake for different clinic location strategies

Towards probabilistic decision support in public health practice: Predicting recent transmission of tuberculosis from patient attributes

AbstractObjectiveInvestigating the contacts of a newly diagnosed tuberculosis (TB) case to prevent TB transmission is a core public health activity. In the context of limited resources, it is often necessary to prioritize investigation when multiple cases are reported. Public health personnel currently prioritize contact investigation intuitively based on past experience. Decision-support software using patient attributes to predict the probability of a TB case being involved in recent transmission could aid in this prioritization, but a prediction model is needed to drive such software.MethodsWe developed a logistic regression model using the clinical and demographic information of TB cases reported to Montreal Public Health between 1997 and 2007. The reference standard for transmission was DNA fingerprint analysis. We measured the predictive performance, in terms of sensitivity, specificity, negative predictive value, positive predictive value, the Receiver Operating Characteristic (ROC) curve and the Area Under the ROC (AUC).ResultsAmong 1552 TB cases enrolled in the study, 314 (20.2%) were involved in recent transmission. The AUC of the model was 0.65 (95% confidence interval: 0.61–0.68), which is significantly better than random prediction. The maximized values of sensitivity and specificity on the ROC were 0.53 and 0.67, respectively.ConclusionsThe characteristics of a TB patient reported to public health can be used to predict whether the newly diagnosed case is associated with recent transmission as opposed to reactivation of latent infection

BAND: Biomedical Alert News Dataset

Infectious disease outbreaks continue to pose a significant threat to human health and well-being. To improve disease surveillance and understanding of disease spread, several surveillance systems have been developed to monitor daily news alerts and social media. However, existing systems lack thorough epidemiological analysis in relation to corresponding alerts or news, largely due to the scarcity of well-annotated reports data. To address this gap, we introduce the Biomedical Alert News Dataset (BAND), which includes 1,508 samples from existing reported news articles, open emails, and alerts, as well as 30 epidemiology-related questions. These questions necessitate the model's expert reasoning abilities, thereby offering valuable insights into the outbreak of the disease. The BAND dataset brings new challenges to the NLP world, requiring better disguise capability of the content and the ability to infer important information. We provide several benchmark tasks, including Named Entity Recognition (NER), Question Answering (QA), and Event Extraction (EE), to show how existing models are capable of handling these tasks in the epidemiology domain. To the best of our knowledge, the BAND corpus is the largest corpus of well-annotated biomedical outbreak alert news with elaborately designed questions, making it a valuable resource for epidemiologists and NLP researchers alike