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The authors examined the extent to which clients served by first-episode psychosis programs reflected the racial composition of the surrounding service area and, to the extent that they did not, explored possible explanatory factors.As part of a national study of coordinated specialty care (CSC) sites in the United States, 35 programs documented race for 772 clients. Programs identified a geographic service area for their clients. Using Census data, the authors identified the proportion of clients in this service area who were Black and then examined the extent of disproportionality, calculated as a risk ratio and as a relative difference in racial composition between CSC programs and their service areas.Overall, 71% of CSC programs had a disproportionately greater proportion of Black clients than Black residents within the service area. This disproportionality was still evident after conducting sensitivity analyses that included adjusting for sampling error in the service area population estimates; however, smaller study sites displayed greater fluctuations in disproportionality in the sensitivity analyses.Using data from diverse CSC programs, the authors illustrate that the odds of Blacks receiving services through a CSC program are much higher than would be expected on the basis of the population living in the area being served by the program. Multiple reasons may explain this finding, but in the absence of clear explanatory factors, this result may be ripe for discussion and further investigation.

Motivated by the United Nations’ Education Sustainable Development Goal of all young people achieving at least basic literacy and numeracy skills, the Organisation for Economic Co-operation and Development has undertaken a pilot study of the Programme for the International Student Assessment (PISA) for developing countries. In this study, PISA is administered in five developing countries to 14- to 16-year olds who are either out of school or in school below grade 7. The target population is about 1–3 percent of the overall population for the participating countries. Several options to achieve adequate representation were presented to countries. To simplify operations, reduce costs, reduce bias, and achieve sample size goals, a probability-based link-tracing approach was designed and attempted in countries as one of the several approaches administered. In this article, we provide an evaluation of the approach and describe its application toward providing proficiency estimates with adequate representation of the target population.

Slides from Privacy & Confidentiality Committee sponsored webinar presented on February6, 2019. Link to recording Description:We discuss the theory and background of useful approaches for determining the level of data disclosure risk prior to disseminating data. We focus on challenges experienced from several case studies and provide insights into the ways that the risks can be estimated. These challenges relate to risk estimate diagnostics and sensitivity analysis, longitudinal risk, when public use data already exists, variable selection, setting risk thresholds, impact of weights and missing values, query tools, geography, attribute disclosure, risk of synthetic data, and external data. Some discussion of differential privacy is provided in this context. Once the key elements that trigger the relatively high risks are understood, data protection approaches can be determined and processed while balancing the risk reduction with retention of data utility. Tom Krenzke, senior statistician and Associate Director in the Westat statistical group. He has led research on data disclosure limitation methods working toward solutions for several Federal agencies and Disclosure Review Boards. Mr. Krenzke is an ASA Fellow and President of the Washington Statistical Society, and serves on the ASA Privacy and Confidentiality Committee and Westat Institutional Review Board. Jianzhu Li, senior statistician at Westat. Dr. Li has 16 years of experience in survey research, including sample design, nonresponse adjustment, imputation, data confidentiality and disclosure protection, and small area estimation. She has worked on several statistical confidentiality projects over the past few years for Government agencies such as NSF, NCHS, NCES, Census Bureau, and USDA.

This article focuses on methods for enhancing access to survey data produced by government agencies. In particular, the National Center for Health Statistics (NCHS) is developing methods that could be used in an interactive, integrated, real-time online analytic system (OAS) to facilitate analysis by the public of both restricted and public use survey data. Data from NCHS’ National Health Interview Survey (NHIS) are being used to investigate, develop, and evaluate such methods. We assume the existence of public use microdata files, as is the case for the NHIS, so disclosure avoidance methods for such an OAS must account for that critical constraint. Of special interest is the analysis of state-level data because health care is largely administered at the state level in the U.S., and state identifiers are not on the NHIS public use files. This article describes our investigations of various possible choices of methods for statistical disclosure control and the challenges of providing such protection in a real-time OAS that uses restricted data. Full details about the specific disclosure control methods used by a working OAS could never be publicly released for confidentiality reasons. NCHS is still evaluating whether to implement an OAS that uses NHIS restricted data, and this article provides a snapshot of a research and developmental project in progress.

Slides from Privacy & Confidentiality Committee sponsored webinar presented on December 17, 2014. Description:Plans to disseminate data to the public include statistical and confidentiality controls. Learn about statistical disclosure control (SDC) methods that have been usedeffectivelyin government data production processes. From data collection to data delivery and post-delivery trainings on data usage, this webinar will review statistical methods that are usedin government data production processes to ensure the integrity of the released data while maintaining data subjects’ confidentiality. Participants will gain insights on various SDC approaches that also achieve feasible implementation.The discussion will conclude with an example from a special tabulation generated from the American Community Survey for the transportation community of over 400 transportation planning agencies across the US. The discussion illustrates a confidentiality protection perturbation technique that satisfied the Census Bureau’s Disclosure Review Board and data production staff, while at the same time provided the transportation planners with data they could use. In this webinar, statistical disclosure methodswill be discussed along with the views of the user community.

A new decentralized control method for satellite cluster flight is presented. This strategy aims to maintain passive safety between all cluster members and maintain cluster cohesion without following any specified formation. The potential reduction in fuel usage and burn frequency of this design with respect to linearized formation-keeping designs is discussed. High-fidelity simulation results are presented.

A new decentralized control method for satellite cluster flight is discussed. For a cluster of satellites flying relative to a reference orbit or leader module, the linearized relative orbital element equations are examined. A geometric argument is used to select impulsive burns that will reduce state errors between a given module and the reference trajectory. Simulation results and stability with real-world disturbances are presented.