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Objective: To accurately assess the benefits of tobacco control interventions and to better inform decision makers, knowledge of medical expenditures by age, gender, and smoking status is essential., Method: We propose an approach to distribute smoking-attributable expenditures by age, gender, and cigarette smoking status to reflect the known risks of smoking. We distribute hospitalization days for smoking-attributable diseases according to relative risks of smoking-attributable mortality, and use the method to determine national estimates of smoking-attributable expenditures by age, sex, and cigarette smoking status. Sensitivity analyses explored assumptions of the method., Results: Both current and former smokers ages 75 and over have about 12 times the smoking-attributable expenditures of their current and former smoker counterparts 35-54years of age. Within each age group, the expenditures of formers smokers are about 70% lower than current smokers. In sensitivity analysis, these results were not robust to large changes to the relative risks of smoking-attributable mortality which were used in the calculations., Conclusion: Sex- and age-group-specific smoking expenditures reflect observed disease risk differences between current and former cigarette smokers and indicate that about 70% of current smokers' excess medical care costs is preventable by quitting., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Background: Studying rare outcomes, new interventions and diverse populations often requires collaborations across multiple health research partners. However, transferring healthcare research data from one institution to another can increase the risk of data privacy and security breaches., Methods: A working group of multi-site research programmers evaluated the need for tools to support data security and data privacy. The group determined that data privacy support tools should: 1) allow for a range of allowable Protected Health Information (PHI); 2) clearly identify what type of data should be protected under the Health Insurance Portability and Accountability Act (HIPAA); and 3) help analysts identify which protected health information data elements are allowable in a given project and how they should be protected during data transfer. Based on these requirements we developed two performance support tools to support data programmers and site analysts in exchanging research data., Results: The first tool, a workplan template, guides the lead programmer through effectively communicating the details of multi-site programming, including how to run the program, what output the program will create, and whether the output is expected to contain protected health information. The second performance support tool is a checklist that site analysts can use to ensure that multi-site program output conforms to expectations and does not contain protected health information beyond what is allowed under the multi-site research agreements., Conclusions: Together the two tools create a formal multi-site programming workflow designed to reduce the chance of accidental PHI disclosure.