The aim of this work was to estimate and describe the Medicare beneficiaries diagnosed with hepatitis C virus (HCV) in 2009, incremental annual costs by disease stage, incremental total Medicare HCV payments in 2009 using the Surveillance, Epidemiology, and End Results (SEER)-Medicare linked data covering the years 2002 to 2009. We weighted the 2009 SEER-Medicare data to create estimates of the number of patients with an HCV diagnosis, used an inverse probability-weighted two-part, probit, and generalized linear model to estimate incremental per patient per month costs, and used simulation to estimate annual 2009 Medicare burden, presented in 2014 dollars. We summarized patient characteristics, diagnoses, and costs from SEER-Medicare files into a person-year panel data set. We estimated there were 407,786 patients with diagnosed HCV in 2009, of whom 61.4% had one or more comorbidities defined by the study. In 2009, 68% of patients were diagnosed with chronic HCV only, 9% with cirrhosis, 12% with decompensated cirrhosis (DCC), 2% with liver cancer, 2% with a history of transplant, and 8% who died. Annual costs for patients with chronic infection only and DCC were higher than the values used in many previous cost-effectiveness studies, and treatment of DCC accounted for 63.9% of total Medicare's HCV expenditures. Medicare paid $2.7 billion (credible interval: $0.7-$4.6 billion) in incremental costs for HCV in 2009. Conclusions: The costs of HCV to Medicare in 2009 were substantial and expected to increase over the next decade. Annual costs for patients with chronic infection only and DCC were higher than values used in many cost-effectiveness analyses. (HEPATOLOGY 2016;63:1135-1144 H epatitis C virus (HCV) is the most common blood-borne infection in the United States, with at least 2.7 million chronically infected Americans.(1) Current patients with HCV were primarily infected by behavioral and medical exposures between the 1960s and the early 1990s, and as a result, an estimated 70.1% of those HCV antibody positive were born during the years of 1945-1965. (2,3) In 2010, the first members of the 1945-1965 birth cohort became eligible for the U.S. Medicare program through the aged (age 65) eligibility pathway, and many more are likely to enter the program over the coming decades.To date, no paper has attempted to estimate either the per person costs or the aggregate health impact of HCV on the Medicare system, and the number of HCV patients seeking care in Medicare is unknown. This is an important knowledge gap because at least 1 million new chronically infected persons are likely to age into Medicare over the next 10-20 years. (3) Costs for HCV patients in Medicare may be different than in private insurance owing to their age, the high prevalence of other comorbidities, and differences in Medicare reimbursements compared to other payers. Finally, the need to offer treatment to patients before Medicare entry is in dispute. (4)

Achieving data and information dissemination without harming anyone is a central task of any entity in charge of collecting data. In this article, the authors examine the literature on data and statistical confidentiality. Rather than comparing the theoretical properties of specific methods, they emphasize the main themes that emerge from the ongoing discussion among scientists regarding how best to achieve the appropriate balance between data protection, data utility, and data dissemination. They cover the literature on de-identification and reidentification methods with emphasis on health care data. The authors also discuss the benefits and limitations for the most common access methods. Although there is abundant theoretical and empirical research, their review reveals lack of consensus on fundamental questions for empirical practice: How to assess disclosure risk, how to choose among disclosure methods, how to assess reidentification risk, and how to measure utility loss.Keywords public use files, disclosure avoidance, reidentification, de-identification, data utility 2 SAGE Open inferential disclosure (i.e., information that can be inferred about a record in a data set with better accuracy). There is significant literature on each of these topics, which are beyond the scope of this article.Our article is divided into six sections, of which this "Introduction" is the first. The second section presents "The Policy and Academic Context" surrounding the discussion. The third section discusses the state of the art in "De-Identification Methods," while the fourth emphasizes the state of the art in "Reidentification Methods." The fifth section presents the conclusions from the literature on the different ways in which users may "Access" public data, stressing the trade-offs between (a) confidentiality and utility and (b) confidentiality and ease of access. The last section presents the "Conclusion." The Policy and Academic Context Historic PerspectiveConcerns about privacy and confidentiality in governmental efforts to collect and disseminate information are not new. As a review by Anderson and Seltzer (2009) suggests, "the roots of the modern concept of federal statistical confidentiality can be traced directly back to the late nineteenth century" (p. 8). Notwithstanding this history, the literature on statistical disclosure methods is fairly recent by modern standards (Dalenius, 1977, is considered the seminal paper). In 1975, the U.S. Federal Committee on Statistical Methodology (FCSM) was organized by the Office of Management and Budget (OMB) to investigate issues of data quality affecting federal statistics. As part of this effort, the Subcommittee on Disclosure Limitation Methodology, created within the FCSM, published its 1994 Statistical Policy Working Paper 22 (SPWP22). This paper, which was revised in 2005 by the Confidentiality and Data Access Committee (CDAC, 2005), sets good practice guidelines and recommendations for all agencies regarding confidentiality protection. Defining Confidentiality ...

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