A novel method of adverse event detection can accurately identify venous thromboembolisms (VTEs) from narrative electronic health record data.

BACKGROUND: Venous thromboembolisms (VTEs), which include deep vein thrombosis (DVT) and pulmonary embolism (PE), are associated with significant mortality, morbidity, and cost in hospitalized patients. To evaluate the success of preventive measures, accurate and efficient methods for monitoring VTE rates are needed. Therefore, we sought to determine the accuracy of statistical natural language processing (NLP) for identifying DVT and PE from electronic health record data. METHODS: We randomly sampled 2000 narrative radiology reports from patients with a suspected DVT/PE in Montreal (Canada) between 2008 and 2012. We manually identified DVT/PE within each report, which served as our reference standard. Using a bag-of-words approach, we trained 10 alternative support vector machine (SVM) models predicting DVT, and 10 predicting PE. SVM training and testing was performed with nested 10-fold cross-validation, and the average accuracy of each model was measured and compared. RESULTS: On manual review, 324 (16.2%) reports were DVT-positive and 154 (7.7%) were PE-positive. The best DVT model achieved an average sensitivity of 0.80 (95% CI 0.76 to 0.85), specificity of 0.98 (98% CI 0.97 to 0.99), positive predictive value (PPV) of 0.89 (95% CI 0.85 to 0.93), and an area under the curve (AUC) of 0.98 (95% CI 0.97 to 0.99). The best PE model achieved sensitivity of 0.79 (95% CI 0.73 to 0.85), specificity of 0.99 (95% CI 0.98 to 0.99), PPV of 0.84 (95% CI 0.75 to 0.92), and AUC of 0.99 (95% CI 0.98 to 1.00). CONCLUSIONS: Statistical NLP can accurately identify VTE from narrative radiology reports.

A scoping review of malaria forecasting: past work and future directions.

OBJECTIVES: There is a growing body of literature on malaria forecasting methods and the objective of our review is to identify and assess methods, including predictors, used to forecast malaria. DESIGN: Scoping review. Two independent reviewers searched information sources, assessed studies for inclusion and extracted data from each study. INFORMATION SOURCES: Search strategies were developed and the following databases were searched: CAB Abstracts, EMBASE, Global Health, MEDLINE, ProQuest Dissertations & Theses and Web of Science. Key journals and websites were also manually searched. ELIGIBILITY CRITERIA FOR INCLUDED STUDIES: We included studies that forecasted incidence, prevalence or epidemics of malaria over time. A description of the forecasting model and an assessment of the forecast accuracy of the model were requirements for inclusion. Studies were restricted to human populations and to autochthonous transmission settings. RESULTS: We identified 29 different studies that met our inclusion criteria for this review. The forecasting approaches included statistical modelling, mathematical modelling and machine learning methods. Climate-related predictors were used consistently in forecasting models, with the most common predictors being rainfall, relative humidity, temperature and the normalised difference vegetation index. Model evaluation was typically based on a reserved portion of data and accuracy was measured in a variety of ways including mean-squared error and correlation coefficients. We could not compare the forecast accuracy of models from the different studies as the evaluation measures differed across the studies. CONCLUSIONS: Applying different forecasting methods to the same data, exploring the predictive ability of non-environmental variables, including transmission reducing interventions and using common forecast accuracy measures will allow malaria researchers to compare and improve models and methods, which should improve the quality of malaria forecasting.

Neighborhood determinants of 2009 pandemic A/H1N1 influenza vaccination in Montreal, Quebec, Canada.

Neighborhood-level analyses of influenza vaccination can identify the characteristics of vulnerable neighborhoods, which can inform public health strategy for future pandemics. In this study, the authors analyzed rates of 2009 pandemic A/H1N1 influenza vaccination in Montreal, Quebec, Canada, using individual-level vaccination records from a vaccination registry with census, survey, and administrative data to estimate the population at risk. The neighborhood socioeconomic and demographic determinants of vaccination were identified using Bayesian ecologic logistic regression, with random effects to account for spatial autocorrelation. A total of 918,773 (49.9%) Montreal residents were vaccinated against pandemic A/H1N1 influenza from October 22, 2009, through April 8, 2010. Coverage was greatest among females, children under age 5 years, and health-care workers. Neighborhood vaccine coverage ranged from 33.6% to 71.0%. Neighborhoods with high percentages of immigrants (per 5% increase, odds ratio = 0.90, 95% credible interval: 0.86, 0.95) and material deprivation (per 1-unit increase in deprivation score, odds ratio = 0.93, 95% credible interval: 0.88, 0.98) had lower vaccine coverage. Half of the Montreal population was vaccinated; however, considerable heterogeneity in coverage was observed between neighborhoods and subgroups. In future vaccination campaigns, neighborhoods that are materially deprived or have high percentages of immigrants may benefit from focused interventions.

Electronic event-based surveillance for monitoring dengue, Latin America.

The current dengue epidemic in Latin America represents a major threat to health. However, surveillance of affected regions lacks timeliness and precision. We investigated real-time electronic sources for monitoring spread of dengue into new regions. This approach could provide timely estimates of changes in distribution of dengue, a critical component of prevention and control efforts.

Simulation Analysis Platform (SnAP): a tool for evaluation of public health surveillance and disease control strategies.

Increasingly, researchers use simulation to generate realistic population health data to evaluate surveillance and disease control methods. This evaluation approach is attractive because real data are often not available to describe the full range of population health trajectories that may occur. Simulation models, especially agent-based models, tend to have many parameters and it is often difficult for researchers to evaluate the effect of the multiple parameter values on model outcomes. In this paper, we describe Simulation Analysis Platform (SnAP) - a software infrastructure for automatically deploying and analyzing multiple runs of a simulation model in a manner that efficiently explores the influence of parameter uncertainty and random error on model outcomes. SnAP is designed to be efficient, scalable, extensible, and portable. We describe the design decisions taken to meet these requirements, present the design of the platform, and describe results from an example application of SnAP.

A simulation model of waterborne gastro-intestinal disease outbreaks: description and initial evaluation.

We present an agent-based simulation model for generating realistic multivariable outbreak signals. The model defines a synthetic population and simulates the dissemination of pathogenic organisms through a municipal water distribution system, the mobility of individuals between geographic locations, their exposure to pathogens through water consumption, and disease progression in infected individuals. We present the results of an initial evaluation of the model - a simulation study replicating the historical outbreak of cryptosporidiosis in Milwaukee in 1993.