Antibiotic-resistant infections affect 2 million people and are associated with 23 000 deaths annually in the United States, according to the Centers for Disease Control and Prevention (CDC).¹ Antibiotic use is the primary driver of antibiotic resistance¹ and leads to adverse events ranging from allergic reactions to Clostridium difficile infections.² Data from other developed nations suggest that 80% or more of antibiotic use (excluding agricultural use) occurs among outpatients.³ In the United States in 2011, 262 million outpatient antibiotic prescriptions were dispensed.⁴ Declining trends in antibiotic prescribing in the United States occurred in the 1990s and early 2000s but stabilized by 2010.^5,6

Background: Sodium consumption raises blood pressure, increasing the risk for heart attack and stroke. Several countries, including the United States, are considering strategies to decrease population sodium intake. Objective: To assess the cost-effectiveness of 2 population strategies to reduce sodium intake: government collaboration with food manufacturers to voluntarily cut sodium in processed foods, modeled on the United Kingdom experience, and a sodium tax. Design: A Markov model was constructed with 4 health states: well, acute myocardial infarction (MI), acute stroke, and history of MI or stroke. Data Sources: Medical Panel Expenditure Survey (2006), Framingham Heart Study (1980 to 2003), Dietary Approaches to Stop Hypertension trial, and other published data. Target Population: U.S. adults aged 40 to 85 years. Time Horizon: Lifetime. Perspective: Societal. Outcome Measures: Incremental costs (2008 U.S. dollars), quality-adjusted life-years (QALYs), and MIs and strokes averted. Results of Base-Case Analysis: Collaboration with industry that decreases mean population sodium intake by 9.5% averts 513Â 885 strokes and 480Â 358 MIs over the lifetime of adults aged 40 to 85 years who are alive today compared with the status quo, increasing QALYs by 2.1 million and saving $32.1 billion in medical costs. A tax on sodium that decreases population sodium intake by 6% increases QALYs by 1.3 million and saves $22.4 billion over the same period. Results of Sensitivity Analysis: Results are sensitive to the assumption that consumers have no disutility with modest reductions in sodium intake. Limitation: Efforts to reduce population sodium intake could result in other dietary changes that are difficult to predict. Conclusion: Strategies to reduce sodium intake on a population level in the United States are likely to substantially reduce stroke and MI incidence, which would save billions of dollars in medical expenses. Primary Funding Source: Department of Veterans Affairs, Stanford University, and National Science Foundation.

Microsimulation models are becoming increasingly common in the field of decision modeling for health. Because microsimulation models are computationally more demanding than traditional Markov cohort models, the use of computer programming languages in their development has become more common. R is a programming language that has gained recognition within the field of decision modeling. It has the capacity to perform microsimulation models more efficiently than software commonly used for decision modeling, incorporate statistical analyses within decision models, and produce more transparent models and reproducible results. However, no clear guidance for the implementation of microsimulation models in R exists. In this tutorial, we provide a step-by-step guide to build microsimulation models in R and illustrate the use of this guide on a simple, but transferable, hypothetical decision problem. We guide the reader through the necessary steps and provide generic R code that is flexible and can be adapted for other models. We also show how this code can be extended to address more complex model structures and provide an efficient microsimulation approach that relies on vectorization solutions.

Lysine 5,6-aminomutase is an adenosylcobalamin and pyridoxal-5′-phosphate-dependent enzyme that catalyzes a 1,2 rearrangement of the terminal amino group of dl -lysine and of l -β-lysine. We have solved the x-ray structure of a substrate-free form of lysine-5,6-aminomutase from Clostridium sticklandii . In this structure, a Rossmann domain covalently binds pyridoxal-5′-phosphate by means of lysine 144 and positions it into the putative active site of a neighboring triosephosphate isomerase barrel domain, while simultaneously positioning the other cofactor, adenosylcobalamin, ≈25 Å from the active site. In this mode of pyridoxal-5′-phosphate binding, the cofactor acts as an anchor, tethering the separate polypeptide chain of the Rossmann domain to the triosephosphate isomerase barrel domain. Upon substrate binding and transaldimination of the lysine-144 linkage, the Rossmann domain would be free to rotate and bring adenosylcobalamin, pyridoxal-5′-phosphate, and substrate into proximity. Thus, the structure embodies a locking mechanism to keep the adenosylcobalamin out of the active site and prevent radical generation in the absence of substrate.

As the complexity of health decision science applications increases, high-level programming languages are increasingly adopted for statistical analyses and numerical computations. These programming languages facilitate sophisticated modeling, model documentation, and analysis reproducibility. Among the high-level programming languages, the statistical programming framework R is gaining increased recognition. R is freely available, cross-platform compatible, and open source. A large community of users who have generated an extensive collection of well-documented packages and functions supports it. These functions facilitate applications of health decision science methodology as well as the visualization and communication of results. Although R’s popularity is increasing among health decision scientists, methodological extensions of R in the field of decision analysis remain isolated. The purpose of this article is to provide an overview of existing R functionality that is applicable to the various stages of decision analysis, including model design, input parameter estimation, and analysis of model outputs.